forecasting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "forecasting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/michal-filipiak/IML-bearing-failure-forecast/blob/master/forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GgeHtgecSILc"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Ay4X20lz2aUI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Downloading and unpacking Dataset"
      ],
      "metadata": {
        "id": "8MuEqCyg4pCw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "n17OHFz436-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rwL6RPGJzDtG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e329f06-adb1-4890-c9b6-74ab96ad5637"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-21 08:56:43--  https://ti.arc.nasa.gov/c/3/\n",
            "Resolving ti.arc.nasa.gov (ti.arc.nasa.gov)... 128.102.105.66, 2001:4d0:6311:2227:14b6:372b:2078:2a94\n",
            "Connecting to ti.arc.nasa.gov (ti.arc.nasa.gov)|128.102.105.66|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: https://ti.arc.nasa.gov/m/project/prognostic-repository/IMS.7z [following]\n",
            "--2022-01-21 08:56:43--  https://ti.arc.nasa.gov/m/project/prognostic-repository/IMS.7z\n",
            "Reusing existing connection to ti.arc.nasa.gov:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1075320408 (1.0G)\n",
            "Saving to: ‘data.7z’\n",
            "\n",
            "data.7z             100%[===================>]   1.00G  7.05MB/s    in 2m 56s  \n",
            "\n",
            "2022-01-21 08:59:39 (5.83 MB/s) - ‘data.7z’ saved [1075320408/1075320408]\n",
            "\n",
            "\n",
            "7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21\n",
            "p7zip Version 16.02 (locale=en_US.UTF-8,Utf16=on,HugeFiles=on,64 bits,2 CPUs Intel(R) Xeon(R) CPU @ 2.20GHz (406F0),ASM,AES-NI)\n",
            "\n",
            "Scanning the drive for archives:\n",
            "  0M Scan\b\b\b\b\b\b\b\b\b         \b\b\b\b\b\b\b\b\b1 file, 1075320408 bytes (1026 MiB)\n",
            "\n",
            "Extracting archive: data.7z\n",
            "--\n",
            "Path = data.7z\n",
            "Type = 7z\n",
            "Physical Size = 1075320408\n",
            "Headers Size = 279\n",
            "Method = LZMA:24\n",
            "Solid = +\n",
            "Blocks = 1\n",
            "\n",
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3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 67% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 68% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 69% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 70% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 71% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 72% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 73% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 74% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 75% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 76% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 77% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 78% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 79% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 80% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 81% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 82% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 83% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 84% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 85% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 86% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 87% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 88% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 89% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 90% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 91% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 92% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 93% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 94% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 95% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 96% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 97% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 98% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 99% 2 - 3rd_test.rar\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b 99% 3 - Readme Document for IMS Bearing Data.pdf\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                                 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEverything is Ok\n",
            "\n",
            "Files: 4\n",
            "Size:       1061595979\n",
            "Compressed: 1075320408\n",
            "\n",
            "UNRAR 5.50 freeware      Copyright (c) 1993-2017 Alexander Roshal\n",
            "\n",
            "\n",
            "Extracting from 1st_test.rar\n",
            "\n",
            "Creating    1st_test                                                  OK\n",
            "Extracting  1st_test/2003.10.22.12.06.24                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.09.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.14.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.19.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.24.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.29.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.34.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.39.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.44.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.49.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.54.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.12.59.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.04.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.09.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.14.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.19.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.24.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.29.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.34.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.39.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.44.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.49.13                                 \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.54.13                                 \b\b\b\b  0%\b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.13.59.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.04.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.09.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.14.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.19.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.24.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.29.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.34.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.39.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.44.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.49.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.54.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.14.59.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.04.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.09.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.14.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.19.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.24.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.29.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.37.48                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.44.13                                 \b\b\b\b  1%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.15.54.13                                 \b\b\b\b  1%\b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.04.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.14.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.24.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.34.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.44.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.16.54.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.04.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.14.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.19.38                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.24.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.34.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.44.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.17.54.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.04.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.14.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.24.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.34.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.44.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.18.54.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.04.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.14.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.24.13                                 \b\b\b\b  2%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.34.13                                 \b\b\b\b  2%\b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.44.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.19.54.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.04.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.14.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.24.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.34.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.44.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.20.54.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.04.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.14.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.24.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.34.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.44.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.21.54.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.04.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.14.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.24.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.34.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.44.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.22.54.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.04.13                                 \b\b\b\b  3%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.14.13                                 \b\b\b\b  3%\b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.24.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.34.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.44.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.22.23.54.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.04.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.14.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.24.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.34.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.44.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.00.54.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.04.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.14.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.24.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.34.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.44.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.01.54.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.04.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.14.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.24.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.34.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.44.13                                 \b\b\b\b  4%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.02.54.13                                 \b\b\b\b  4%\b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.04.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.14.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.24.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.34.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.44.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.03.54.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.04.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.14.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.24.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.34.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.44.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.04.54.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.04.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.14.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.24.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.34.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.44.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.05.54.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.04.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.14.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.24.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.34.13                                 \b\b\b\b  5%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.44.13                                 \b\b\b\b  5%\b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.06.54.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.04.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.14.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.24.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.34.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.44.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.07.54.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.04.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.14.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.24.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.34.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.44.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.08.54.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.04.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.14.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.24.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.34.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.44.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.09.54.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.10.04.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.23.10.14.13                                 \b\b\b\b  6%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.14.39.46                                 \b\b\b\b  6%\b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.14.49.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.14.59.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.09.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.19.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.29.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.39.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.49.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.15.59.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.09.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.19.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.29.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.39.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.49.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.16.59.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.09.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.19.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.29.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.39.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.49.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.17.59.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.09.46                                 \b\b\b\b  7%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.19.46                                 \b\b\b\b  7%\b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.29.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.39.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.49.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.18.59.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.09.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.19.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.29.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.39.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.49.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.19.59.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.09.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.19.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.29.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.39.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.49.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.20.59.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.09.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.19.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.29.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.39.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.49.46                                 \b\b\b\b  8%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.21.59.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.09.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.19.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.29.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.39.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.49.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.22.59.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.09.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.19.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.29.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.39.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.49.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.29.23.59.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.09.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.19.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.29.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.39.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.49.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.00.59.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.09.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.19.46                                 \b\b\b\b  9%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.29.46                                 \b\b\b\b  9%\b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.39.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.49.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.01.59.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.09.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.19.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.29.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.39.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.49.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.02.59.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.03.09.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.03.19.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.03.29.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.30.03.39.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.09.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.19.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.29.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.39.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.49.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.00.59.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.09.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.19.46                                 \b\b\b\b 10%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.29.46                                 \b\b\b\b 10%\b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.39.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.49.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.01.59.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.09.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.19.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.29.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.39.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.49.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.02.59.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.09.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.19.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.29.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.39.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.49.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.03.59.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.09.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.19.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.29.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.39.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.49.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.04.59.46                                 \b\b\b\b 11%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.09.46                                 \b\b\b\b 11%\b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.19.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.29.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.39.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.49.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.05.59.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.09.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.19.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.29.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.39.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.49.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.06.59.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.09.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.19.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.29.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.39.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.49.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.07.59.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.09.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.19.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.29.46                                 \b\b\b\b 12%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.39.46                                 \b\b\b\b 12%\b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.49.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.08.59.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.09.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.19.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.29.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.39.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.49.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.09.59.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.10.09.46                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.05.51                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.11.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.21.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.31.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.41.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.12.51.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.01.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.11.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.21.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.31.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.41.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.13.51.44                                 \b\b\b\b 13%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.01.44                                 \b\b\b\b 13%\b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.11.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.21.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.31.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.41.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.14.51.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.01.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.11.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.21.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.31.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.41.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.15.51.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.01.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.11.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.21.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.31.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.41.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.16.51.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.01.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.11.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.21.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.31.44                                 \b\b\b\b 14%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.41.44                                 \b\b\b\b 14%\b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.17.51.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.01.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.11.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.21.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.31.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.41.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.18.51.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.01.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.11.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.21.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.31.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.41.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.19.51.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.01.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.11.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.21.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.31.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.41.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.20.51.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.01.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.11.44                                 \b\b\b\b 15%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.21.44                                 \b\b\b\b 15%\b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.31.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.41.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.21.51.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.01.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.11.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.21.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.31.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.41.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.22.51.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.01.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.11.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.21.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.31.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.41.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.10.31.23.51.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.01.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.11.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.21.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.31.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.41.44                                 \b\b\b\b 16%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.00.51.44                                 \b\b\b\b 16%\b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.01.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.11.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.21.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.31.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.41.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.01.51.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.01.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.11.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.21.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.31.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.41.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.02.51.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.01.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.11.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.21.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.31.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.41.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.03.51.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.01.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.11.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.21.44                                 \b\b\b\b 17%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.31.44                                 \b\b\b\b 17%\b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.41.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.04.51.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.01.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.11.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.21.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.31.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.41.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.05.51.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.01.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.11.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.21.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.31.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.41.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.06.51.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.01.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.11.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.21.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.31.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.41.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.07.51.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.01.44                                 \b\b\b\b 18%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.11.44                                 \b\b\b\b 18%\b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.21.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.31.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.41.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.08.51.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.01.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.11.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.21.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.31.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.41.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.09.51.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.01.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.11.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.21.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.31.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.41.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.10.51.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.01.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.11.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.21.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.31.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.41.44                                 \b\b\b\b 19%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.11.51.44                                 \b\b\b\b 19%\b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.01.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.11.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.21.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.31.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.41.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.12.51.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.01.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.11.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.21.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.31.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.41.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.13.51.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.01.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.11.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.21.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.31.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.41.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.14.51.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.01.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.11.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.21.44                                 \b\b\b\b 20%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.31.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.41.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.15.51.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.01.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.11.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.21.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.31.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.41.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.16.51.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.01.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.11.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.21.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.31.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.41.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.17.51.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.01.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.11.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.21.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.31.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.41.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.18.51.44                                 \b\b\b\b 21%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.01.44                                 \b\b\b\b 21%\b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.11.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.21.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.31.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.41.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.19.51.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.01.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.11.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.21.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.31.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.41.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.20.51.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.01.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.11.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.21.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.31.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.41.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.21.51.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.01.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.11.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.21.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.31.44                                 \b\b\b\b 22%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.41.44                                 \b\b\b\b 22%\b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.22.51.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.01.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.11.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.21.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.31.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.41.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.01.23.51.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.09.53.55                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.01.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.11.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.21.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.31.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.41.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.10.51.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.01.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.11.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.21.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.31.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.41.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.11.51.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.01.44                                 \b\b\b\b 23%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.11.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.21.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.31.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.41.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.12.51.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.01.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.11.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.21.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.31.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.41.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.13.51.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.14.01.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.14.11.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.03.14.21.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.14.51.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.01.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.11.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.21.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.31.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.41.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.15.51.44                                 \b\b\b\b 24%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.01.44                                 \b\b\b\b 24%\b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.11.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.21.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.31.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.41.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.16.51.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.01.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.11.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.21.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.31.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.41.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.17.51.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.01.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.11.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.21.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.31.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.41.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.18.51.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.01.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.11.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.21.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.31.44                                 \b\b\b\b 25%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.41.44                                 \b\b\b\b 25%\b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.19.51.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.01.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.11.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.21.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.31.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.41.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.20.51.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.01.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.11.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.21.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.31.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.41.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.21.51.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.22.01.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.22.11.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.22.21.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.22.31.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.07.22.41.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.10.22.19                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.10.31.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.10.41.44                                 \b\b\b\b 26%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.10.51.44                                 \b\b\b\b 26%\b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.01.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.11.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.21.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.31.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.41.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.11.51.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.01.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.11.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.21.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.31.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.41.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.12.51.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.01.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.11.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.21.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.31.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.41.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.13.51.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.01.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.11.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.21.44                                 \b\b\b\b 27%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.31.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.41.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.14.51.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.01.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.11.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.21.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.31.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.41.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.15.51.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.01.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.11.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.21.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.31.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.41.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.16.51.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.01.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.11.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.21.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.31.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.41.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.17.51.44                                 \b\b\b\b 28%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.01.44                                 \b\b\b\b 28%\b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.11.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.21.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.31.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.41.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.18.51.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.01.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.11.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.21.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.31.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.41.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.19.51.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.01.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.11.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.21.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.31.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.41.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.20.51.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.01.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.11.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.21.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.31.44                                 \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.41.44                                 \b\b\b\b 29%\b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.21.51.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.01.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.11.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.21.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.31.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.41.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.22.51.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.01.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.11.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.21.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.31.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.41.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.08.23.51.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.01.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.11.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.21.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.31.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.41.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.00.51.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.01.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.11.44                                 \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.21.44                                 \b\b\b\b 30%\b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.31.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.41.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.01.51.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.01.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.11.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.21.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.31.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.41.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.02.51.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.01.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.11.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.21.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.31.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.41.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.03.51.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.01.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.11.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.21.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.31.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.41.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.04.51.44                                 \b\b\b\b 31%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.01.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.11.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.21.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.31.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.41.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.05.51.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.01.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.11.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.21.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.31.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.41.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.06.51.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.01.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.11.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.21.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.31.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.41.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.07.51.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.01.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.11.44                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.20.39                                 \b\b\b\b 32%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.25.58                                 \b\b\b\b 32%\b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.35.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.45.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.08.55.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.05.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.15.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.25.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.35.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.45.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.09.55.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.05.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.15.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.25.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.35.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.45.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.10.55.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.05.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.15.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.25.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.35.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.45.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.11.55.58                                 \b\b\b\b 33%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.05.58                                 \b\b\b\b 33%\b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.15.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.25.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.35.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.45.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.12.55.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.05.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.15.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.25.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.35.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.45.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.13.55.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.05.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.15.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.25.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.35.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.45.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.14.55.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.05.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.15.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.25.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.35.58                                 \b\b\b\b 34%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.45.58                                 \b\b\b\b 34%\b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.15.55.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.05.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.15.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.25.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.35.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.45.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.16.55.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.05.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.15.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.25.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.35.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.45.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.17.55.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.18.05.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.18.15.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.02.15                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.05.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.15.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.25.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.35.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.45.58                                 \b\b\b\b 35%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.22.55.58                                 \b\b\b\b 35%\b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.05.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.15.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.25.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.35.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.45.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.09.23.55.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.05.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.15.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.25.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.35.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.45.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.00.55.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.05.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.15.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.25.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.35.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.45.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.01.55.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.05.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.15.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.25.58                                 \b\b\b\b 36%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.35.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.45.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.02.55.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.05.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.15.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.25.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.35.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.45.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.03.55.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.05.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.15.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.25.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.35.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.45.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.04.55.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.05.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.15.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.25.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.35.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.45.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.05.55.58                                 \b\b\b\b 37%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.05.58                                 \b\b\b\b 37%\b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.15.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.25.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.35.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.45.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.06.55.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.05.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.15.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.25.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.35.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.45.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.07.55.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.05.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.15.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.25.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.35.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.45.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.08.55.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.05.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.15.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.25.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.35.58                                 \b\b\b\b 38%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.45.58                                 \b\b\b\b 38%\b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.09.55.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.05.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.15.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.25.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.35.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.45.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.10.55.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.05.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.15.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.25.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.35.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.45.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.11.55.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.05.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.15.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.25.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.35.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.45.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.12.55.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.05.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.15.58                                 \b\b\b\b 39%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.25.58                                 \b\b\b\b 39%\b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.35.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.45.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.13.55.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.05.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.15.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.25.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.35.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.45.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.14.55.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.05.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.15.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.25.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.35.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.45.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.15.55.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.16.05.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.16.15.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.10.16.25.58                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.02.17                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.12.17                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.22.17                                 \b\b\b\b 40%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.32.17                                 \b\b\b\b 40%\b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.42.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.11.52.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.02.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.12.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.22.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.32.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.42.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.12.52.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.02.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.12.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.22.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.32.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.42.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.13.52.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.02.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.12.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.22.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.32.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.42.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.14.52.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.02.17                                 \b\b\b\b 41%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.12.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.22.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.32.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.42.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.15.52.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.02.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.12.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.22.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.32.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.42.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.16.52.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.17.02.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.17.12.17                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.17.52.14                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.17.58.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.08.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.18.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.28.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.38.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.48.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.18.58.46                                 \b\b\b\b 42%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.08.46                                 \b\b\b\b 42%\b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.18.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.28.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.38.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.48.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.19.58.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.08.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.18.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.28.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.38.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.48.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.20.58.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.08.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.18.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.28.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.38.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.48.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.21.58.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.08.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.18.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.28.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.38.46                                 \b\b\b\b 43%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.48.46                                 \b\b\b\b 43%\b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.22.58.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.08.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.18.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.28.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.38.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.48.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.14.23.58.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.08.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.18.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.28.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.38.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.48.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.00.58.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.08.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.18.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.28.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.38.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.48.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.01.58.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.08.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.18.46                                 \b\b\b\b 44%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.28.46                                 \b\b\b\b 44%\b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.38.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.48.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.02.58.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.08.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.18.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.28.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.38.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.48.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.03.58.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.08.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.18.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.28.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.38.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.48.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.04.58.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.08.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.18.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.28.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.38.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.48.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.05.58.46                                 \b\b\b\b 45%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.08.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.18.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.28.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.38.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.48.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.06.58.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.08.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.18.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.28.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.38.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.48.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.07.58.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.08.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.18.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.28.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.38.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.48.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.08.58.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.08.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.18.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.28.46                                 \b\b\b\b 46%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.38.46                                 \b\b\b\b 46%\b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.48.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.09.58.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.08.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.18.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.28.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.38.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.48.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.10.58.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.08.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.18.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.28.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.38.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.48.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.11.58.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.08.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.18.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.28.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.38.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.48.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.12.58.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.08.46                                 \b\b\b\b 47%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.18.46                                 \b\b\b\b 47%\b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.28.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.38.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.48.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.13.58.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.08.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.18.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.28.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.38.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.48.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.14.58.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.08.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.18.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.28.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.38.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.48.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.15.58.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.08.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.18.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.28.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.38.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.48.46                                 \b\b\b\b 48%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.16.58.46                                 \b\b\b\b 48%\b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.08.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.18.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.28.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.38.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.48.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.17.58.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.08.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.18.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.28.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.38.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.48.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.18.58.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.08.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.18.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.28.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.38.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.48.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.19.58.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.08.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.18.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.28.46                                 \b\b\b\b 49%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.38.46                                 \b\b\b\b 49%\b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.48.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.20.58.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.08.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.18.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.28.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.38.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.48.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.21.58.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.08.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.18.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.28.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.38.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.48.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.22.58.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.08.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.18.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.28.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.38.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.48.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.15.23.58.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.08.46                                 \b\b\b\b 50%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.18.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.28.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.38.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.48.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.00.58.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.08.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.18.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.28.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.38.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.48.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.01.58.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.08.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.18.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.28.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.38.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.48.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.02.58.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.08.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.18.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.28.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.38.46                                 \b\b\b\b 51%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.48.46                                 \b\b\b\b 51%\b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.03.58.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.08.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.18.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.28.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.38.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.48.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.04.58.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.08.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.18.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.28.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.38.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.48.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.05.58.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.08.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.18.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.28.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.38.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.48.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.06.58.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.08.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.18.46                                 \b\b\b\b 52%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.28.46                                 \b\b\b\b 52%\b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.38.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.48.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.07.58.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.08.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.18.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.28.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.38.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.48.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.08.58.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.08.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.18.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.28.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.38.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.48.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.09.58.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.08.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.18.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.28.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.38.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.48.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.10.58.46                                 \b\b\b\b 53%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.08.46                                 \b\b\b\b 53%\b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.18.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.28.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.38.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.48.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.11.58.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.08.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.18.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.28.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.38.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.48.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.12.58.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.08.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.18.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.28.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.38.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.48.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.13.58.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.08.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.18.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.28.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.38.46                                 \b\b\b\b 54%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.48.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.14.58.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.08.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.18.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.28.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.38.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.48.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.15.58.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.08.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.18.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.28.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.38.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.48.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.16.58.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.08.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.18.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.28.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.38.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.48.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.17.58.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.08.46                                 \b\b\b\b 55%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.18.46                                 \b\b\b\b 55%\b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.28.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.38.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.48.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.18.58.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.08.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.18.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.28.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.38.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.48.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.19.58.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.20.08.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.20.18.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.16.20.28.46                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.10.18.56                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.10.22.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.10.32.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.10.42.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.10.52.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.02.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.12.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.22.30                                 \b\b\b\b 56%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.32.30                                 \b\b\b\b 56%\b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.42.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.11.52.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.02.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.12.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.22.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.32.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.42.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.12.52.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.02.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.12.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.22.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.32.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.42.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.13.52.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.02.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.12.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.22.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.32.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.42.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.14.52.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.02.30                                 \b\b\b\b 57%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.12.30                                 \b\b\b\b 57%\b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.22.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.32.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.42.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.15.52.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.02.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.12.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.22.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.32.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.42.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.16.52.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.02.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.12.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.22.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.32.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.42.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.17.52.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.02.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.12.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.22.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.32.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.42.30                                 \b\b\b\b 58%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.18.52.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.02.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.12.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.22.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.32.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.42.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.19.52.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.02.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.12.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.22.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.32.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.42.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.20.52.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.02.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.12.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.22.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.32.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.42.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.21.52.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.02.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.12.30                                 \b\b\b\b 59%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.22.30                                 \b\b\b\b 59%\b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.32.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.42.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.22.52.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.02.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.12.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.22.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.32.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.42.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.17.23.52.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.02.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.12.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.22.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.32.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.42.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.00.52.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.02.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.12.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.22.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.32.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.42.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.01.52.30                                 \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.02.30                                 \b\b\b\b 60%\b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.12.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.22.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.32.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.42.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.02.52.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.02.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.12.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.22.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.32.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.42.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.03.52.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.02.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.12.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.22.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.32.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.42.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.04.52.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.02.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.12.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.22.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.32.30                                 \b\b\b\b 61%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.42.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.05.52.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.02.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.12.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.22.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.32.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.42.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.06.52.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.02.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.12.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.22.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.32.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.42.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.07.52.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.02.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.12.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.22.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.32.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.42.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.08.52.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.02.30                                 \b\b\b\b 62%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.12.30                                 \b\b\b\b 62%\b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.22.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.32.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.42.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.09.52.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.02.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.12.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.22.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.32.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.42.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.10.52.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.02.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.12.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.22.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.32.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.42.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.11.52.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.02.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.12.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.22.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.32.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.42.30                                 \b\b\b\b 63%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.12.52.30                                 \b\b\b\b 63%\b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.02.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.12.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.22.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.32.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.42.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.13.52.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.02.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.12.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.22.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.32.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.42.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.14.52.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.02.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.12.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.22.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.32.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.42.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.15.52.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.02.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.12.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.22.30                                 \b\b\b\b 64%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.32.30                                 \b\b\b\b 64%\b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.42.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.16.52.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.02.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.12.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.22.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.32.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.42.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.17.52.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.02.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.12.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.22.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.32.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.42.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.18.52.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.19.02.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.18.19.12.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.09.06.09                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.09.12.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.09.22.30                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.04.17                                 \b\b\b\b 65%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.06.07                                 \b\b\b\b 65%\b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.16.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.26.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.36.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.46.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.11.56.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.06.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.16.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.26.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.36.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.46.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.12.56.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.06.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.16.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.26.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.36.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.46.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.13.56.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.06.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.16.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.26.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.36.07                                 \b\b\b\b 66%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.46.07                                 \b\b\b\b 66%\b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.14.56.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.06.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.16.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.26.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.36.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.46.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.15.56.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.06.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.16.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.26.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.36.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.46.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.16.56.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.06.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.16.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.26.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.36.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.46.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.17.56.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.06.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.16.07                                 \b\b\b\b 67%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.26.07                                 \b\b\b\b 67%\b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.36.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.46.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.18.56.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.06.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.16.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.26.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.36.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.46.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.19.56.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.06.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.16.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.26.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.36.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.46.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.20.56.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.06.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.16.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.26.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.36.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.46.07                                 \b\b\b\b 68%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.21.56.07                                 \b\b\b\b 68%\b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.22.06.07                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.19.22.16.07                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.11.50.35                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.11.54.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.11.59.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.04.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.09.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.14.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.19.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.24.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.29.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.34.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.39.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.44.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.49.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.54.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.12.59.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.04.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.09.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.14.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.19.03                                 \b\b\b\b 69%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.24.03                                 \b\b\b\b 69%\b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.29.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.34.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.39.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.44.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.49.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.54.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.13.59.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.04.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.09.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.14.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.19.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.24.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.29.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.34.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.39.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.44.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.49.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.54.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.14.59.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.04.03                                 \b\b\b\b 70%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.09.03                                 \b\b\b\b 70%\b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.14.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.19.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.24.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.29.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.34.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.39.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.44.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.49.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.54.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.15.59.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.04.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.09.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.14.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.19.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.24.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.29.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.34.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.39.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.44.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.49.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.54.03                                 \b\b\b\b 71%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.16.59.03                                 \b\b\b\b 71%\b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.04.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.09.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.14.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.19.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.24.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.29.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.34.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.39.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.44.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.49.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.54.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.17.59.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.04.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.09.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.14.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.19.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.24.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.29.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.34.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.39.03                                 \b\b\b\b 72%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.44.03                                 \b\b\b\b 72%\b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.49.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.54.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.18.59.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.04.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.09.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.14.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.19.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.24.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.29.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.34.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.39.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.44.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.49.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.54.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.19.59.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.04.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.09.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.14.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.19.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.24.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.29.03                                 \b\b\b\b 73%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.34.03                                 \b\b\b\b 73%\b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.20.39.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.23.40.19                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.23.44.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.20.23.54.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.04.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.14.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.24.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.34.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.44.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.00.54.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.04.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.14.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.24.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.34.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.44.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.01.54.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.04.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.14.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.24.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.34.03                                 \b\b\b\b 74%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.44.03                                 \b\b\b\b 74%\b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.02.54.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.04.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.14.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.24.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.34.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.44.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.03.54.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.04.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.14.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.24.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.34.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.44.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.04.54.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.04.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.14.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.24.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.34.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.44.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.05.54.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.04.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.14.03                                 \b\b\b\b 75%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.24.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.34.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.44.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.06.54.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.04.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.14.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.24.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.34.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.44.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.07.54.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.08.04.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.08.14.03                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.10.29.21                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.10.38.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.10.48.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.10.58.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.08.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.18.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.28.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.38.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.48.36                                 \b\b\b\b 76%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.11.58.36                                 \b\b\b\b 76%\b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.08.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.18.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.28.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.38.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.48.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.12.58.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.13.08.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.13.18.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.14.58.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.15.08.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.15.18.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.15.28.36                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.16.40.54                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.16.46.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.16.56.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.06.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.16.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.26.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.36.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.46.56                                 \b\b\b\b 77%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.17.56.56                                 \b\b\b\b 77%\b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.06.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.16.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.26.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.36.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.46.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.18.56.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.06.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.16.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.26.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.36.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.46.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.19.56.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.06.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.16.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.26.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.36.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.46.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.20.56.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.06.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.16.56                                 \b\b\b\b 78%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.26.56                                 \b\b\b\b 78%\b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.36.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.46.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.21.56.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.06.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.16.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.26.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.36.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.46.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.22.56.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.06.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.16.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.26.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.36.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.46.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.21.23.56.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.06.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.16.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.26.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.36.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.46.56                                 \b\b\b\b 79%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.00.56.56                                 \b\b\b\b 79%\b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.06.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.16.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.26.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.36.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.46.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.01.56.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.06.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.16.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.26.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.36.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.46.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.02.56.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.06.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.16.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.26.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.36.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.46.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.03.56.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.06.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.16.56                                 \b\b\b\b 80%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.26.56                                 \b\b\b\b 80%\b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.36.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.46.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.04.56.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.06.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.16.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.26.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.36.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.46.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.05.56.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.06.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.16.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.26.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.36.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.46.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.06.56.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.06.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.16.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.26.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.36.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.46.56                                 \b\b\b\b 81%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.07.56.56                                 \b\b\b\b 81%\b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.06.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.16.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.26.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.36.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.46.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.08.56.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.06.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.16.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.26.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.36.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.46.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.09.56.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.06.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.16.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.26.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.36.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.46.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.10.56.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.06.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.16.56                                 \b\b\b\b 82%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.26.56                                 \b\b\b\b 82%\b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.36.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.46.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.11.56.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.06.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.16.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.26.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.36.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.46.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.12.56.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.06.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.16.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.26.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.36.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.46.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.13.56.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.06.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.16.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.26.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.36.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.46.56                                 \b\b\b\b 83%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.14.56.56                                 \b\b\b\b 83%\b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.06.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.16.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.26.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.36.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.46.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.15.56.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.06.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.16.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.26.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.36.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.46.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.16.56.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.06.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.16.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.26.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.36.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.46.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.17.56.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.06.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.16.56                                 \b\b\b\b 84%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.26.56                                 \b\b\b\b 84%\b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.36.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.46.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.18.56.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.06.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.16.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.26.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.36.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.46.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.19.56.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.20.06.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.20.16.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.14.58                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.16.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.26.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.36.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.46.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.22.23.56.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.06.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.16.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.26.56                                 \b\b\b\b 85%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.36.56                                 \b\b\b\b 85%\b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.46.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.00.56.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.06.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.16.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.26.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.36.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.46.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.01.56.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.06.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.16.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.26.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.36.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.46.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.02.56.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.06.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.16.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.26.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.36.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.46.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.03.56.56                                 \b\b\b\b 86%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.06.56                                 \b\b\b\b 86%\b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.16.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.26.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.36.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.46.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.04.56.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.06.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.16.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.26.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.36.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.46.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.05.56.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.06.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.16.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.26.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.36.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.46.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.06.56.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.06.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.16.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.26.56                                 \b\b\b\b 87%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.36.56                                 \b\b\b\b 87%\b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.46.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.07.56.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.06.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.16.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.26.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.36.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.46.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.08.56.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.06.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.16.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.26.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.36.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.46.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.09.56.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.06.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.16.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.26.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.36.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.46.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.10.56.56                                 \b\b\b\b 88%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.06.56                                 \b\b\b\b 88%\b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.16.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.26.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.36.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.46.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.11.56.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.06.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.16.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.26.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.36.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.46.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.12.56.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.06.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.16.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.26.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.36.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.46.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.13.56.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.06.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.16.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.26.56                                 \b\b\b\b 89%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.36.56                                 \b\b\b\b 89%\b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.46.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.14.56.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.06.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.16.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.26.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.36.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.46.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.15.56.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.06.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.16.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.26.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.36.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.46.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.16.56.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.17.06.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.17.16.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.17.26.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.17.36.56                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.01.11                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.01.24                                 \b\b\b\b 90%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.11.24                                 \b\b\b\b 90%\b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.21.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.31.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.41.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.20.51.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.01.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.11.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.21.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.31.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.41.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.21.51.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.01.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.11.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.21.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.31.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.41.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.22.51.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.01.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.11.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.21.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.31.24                                 \b\b\b\b 91%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.41.24                                 \b\b\b\b 91%\b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.23.23.51.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.01.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.11.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.21.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.31.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.41.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.00.51.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.01.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.11.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.21.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.31.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.41.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.01.51.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.01.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.11.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.21.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.31.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.41.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.02.51.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.01.24                                 \b\b\b\b 92%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.11.24                                 \b\b\b\b 92%\b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.21.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.31.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.41.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.03.51.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.01.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.11.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.21.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.31.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.41.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.04.51.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.01.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.11.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.21.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.31.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.41.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.05.51.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.01.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.11.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.21.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.31.24                                 \b\b\b\b 93%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.41.24                                 \b\b\b\b 93%\b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.06.51.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.07.01.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.01.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.11.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.21.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.31.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.41.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.08.51.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.09.01.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.09.11.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.09.21.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.02.30                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.11.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.21.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.31.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.41.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.10.51.24                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.12.53.55                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.12.57.32                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.07.32                                 \b\b\b\b 94%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.17.32                                 \b\b\b\b 94%\b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.27.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.37.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.47.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.13.57.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.07.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.17.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.27.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.37.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.47.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.14.57.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.07.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.17.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.27.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.37.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.47.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.15.57.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.07.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.17.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.27.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.37.32                                 \b\b\b\b 95%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.47.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.16.57.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.07.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.17.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.27.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.37.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.47.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.17.57.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.07.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.17.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.27.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.37.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.47.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.18.57.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.07.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.17.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.27.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.37.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.47.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.19.57.32                                 \b\b\b\b 96%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.07.32                                 \b\b\b\b 96%\b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.17.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.27.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.37.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.47.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.20.57.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.07.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.17.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.27.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.37.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.47.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.21.57.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.07.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.17.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.27.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.37.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.47.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.22.57.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.07.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.17.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.27.32                                 \b\b\b\b 97%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.37.32                                 \b\b\b\b 97%\b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.47.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.24.23.57.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.10.47.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.10.57.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.07.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.17.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.27.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.37.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.47.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.11.57.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.07.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.17.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.27.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.37.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.47.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.12.57.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.07.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.17.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.27.32                                 \b\b\b\b 98%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.37.32                                 \b\b\b\b 98%\b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.47.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.13.57.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.07.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.17.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.27.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.37.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.47.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.14.57.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.07.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.17.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.27.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.37.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.47.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.15.57.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.16.07.32                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.23.13.21                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.23.19.56                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.23.29.56                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  1st_test/2003.11.25.23.39.56                                 \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "All OK\n"
          ]
        }
      ],
      "source": [
        "!wget -O data.7z https://ti.arc.nasa.gov/c/3/\n",
        "!7z e data.7z\n",
        "!unrar x -Y 1st_test.rar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BWh6_q2O4vvf"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import datetime\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy.stats as sts\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing\n",
        "\n",
        "- Load dataset to pandas DataFrame\n",
        "- Compute basic column-wise summary statistics\n",
        "\n",
        "  - Absolute mean\n",
        "  - Standard deviation\n",
        "  - Kurtosis: Length of the tails of a signal distribution, or equivalently, how outlier prone the signal is. Developing faults can increase the number of outliers, and therefore increase the value of the kurtosis metric.\n",
        "  - Skewness: Asymmetry of a signal distribution. Faults can impact distribution symmetry and therefore increase the level of skewness.\n",
        "  - Entropy\n",
        "  - Root mean square "
      ],
      "metadata": {
        "id": "KDXU9gjw-E0K"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y18Yf4RSzNwB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "58199208-8b89-46de-e557-d9a70a827e53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-67afaae1-7591-45b9-86b1-f318b8823651\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>b1_x_abs_mean</th>\n",
              "      <th>b1_y_abs_mean</th>\n",
              "      <th>b2_x_abs_mean</th>\n",
              "      <th>b2_y_abs_mean</th>\n",
              "      <th>b3_x_abs_mean</th>\n",
              "      <th>b3_y_abs_mean</th>\n",
              "      <th>b4_x_abs_mean</th>\n",
              "      <th>b4_y_abs_mean</th>\n",
              "      <th>b1_x_std</th>\n",
              "      <th>b1_y_std</th>\n",
              "      <th>b2_x_std</th>\n",
              "      <th>b2_y_std</th>\n",
              "      <th>b3_x_std</th>\n",
              "      <th>b3_y_std</th>\n",
              "      <th>b4_x_std</th>\n",
              "      <th>b4_y_std</th>\n",
              "      <th>b1_x_skwe</th>\n",
              "      <th>b1_y_skwe</th>\n",
              "      <th>b2_x_skwe</th>\n",
              "      <th>b2_y_skwe</th>\n",
              "      <th>b3_x_skwe</th>\n",
              "      <th>b3_y_skwe</th>\n",
              "      <th>b4_x_skwe</th>\n",
              "      <th>b4_y_skwe</th>\n",
              "      <th>b1_x_kurt</th>\n",
              "      <th>b1_y_kurt</th>\n",
              "      <th>b2_x_kurt</th>\n",
              "      <th>b2_y_kurt</th>\n",
              "      <th>b3_x_kurt</th>\n",
              "      <th>b3_y_kurt</th>\n",
              "      <th>b4_x_kurt</th>\n",
              "      <th>b4_y_kurt</th>\n",
              "      <th>b1_x_entropy</th>\n",
              "      <th>b1_y_entropy</th>\n",
              "      <th>b2_x_entropy</th>\n",
              "      <th>b2_y_entropy</th>\n",
              "      <th>b3_x_entropy</th>\n",
              "      <th>b3_y_entropy</th>\n",
              "      <th>b4_x_entropy</th>\n",
              "      <th>b4_y_entropy</th>\n",
              "      <th>b1_x_rms</th>\n",
              "      <th>b1_y_rms</th>\n",
              "      <th>b2_x_rms</th>\n",
              "      <th>b2_y_rms</th>\n",
              "      <th>b3_x_rms</th>\n",
              "      <th>b3_y_rms</th>\n",
              "      <th>b4_x_rms</th>\n",
              "      <th>b4_y_rms</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-11-16 05:58:46</th>\n",
              "      <td>0.133482</td>\n",
              "      <td>0.129230</td>\n",
              "      <td>0.125831</td>\n",
              "      <td>0.124860</td>\n",
              "      <td>0.142518</td>\n",
              "      <td>0.132230</td>\n",
              "      <td>0.122594</td>\n",
              "      <td>0.114919</td>\n",
              "      <td>0.097133</td>\n",
              "      <td>0.089162</td>\n",
              "      <td>0.088216</td>\n",
              "      <td>0.073739</td>\n",
              "      <td>0.116597</td>\n",
              "      <td>0.109872</td>\n",
              "      <td>0.078853</td>\n",
              "      <td>0.073108</td>\n",
              "      <td>-0.363632</td>\n",
              "      <td>0.088817</td>\n",
              "      <td>-0.048111</td>\n",
              "      <td>-0.163085</td>\n",
              "      <td>0.515082</td>\n",
              "      <td>-0.109746</td>\n",
              "      <td>-0.152477</td>\n",
              "      <td>0.221514</td>\n",
              "      <td>1.150462</td>\n",
              "      <td>0.807463</td>\n",
              "      <td>0.184608</td>\n",
              "      <td>0.064818</td>\n",
              "      <td>0.949110</td>\n",
              "      <td>0.406540</td>\n",
              "      <td>1.148736</td>\n",
              "      <td>0.731819</td>\n",
              "      <td>1.675930</td>\n",
              "      <td>1.701513</td>\n",
              "      <td>1.932410</td>\n",
              "      <td>1.882726</td>\n",
              "      <td>1.709478</td>\n",
              "      <td>1.835625</td>\n",
              "      <td>1.668722</td>\n",
              "      <td>1.746503</td>\n",
              "      <td>0.158934</td>\n",
              "      <td>0.150368</td>\n",
              "      <td>0.147353</td>\n",
              "      <td>0.142835</td>\n",
              "      <td>0.169170</td>\n",
              "      <td>0.159366</td>\n",
              "      <td>0.141170</td>\n",
              "      <td>0.131708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-22 12:26:56</th>\n",
              "      <td>0.123724</td>\n",
              "      <td>0.125719</td>\n",
              "      <td>0.131439</td>\n",
              "      <td>0.124289</td>\n",
              "      <td>0.131787</td>\n",
              "      <td>0.137629</td>\n",
              "      <td>0.148932</td>\n",
              "      <td>0.143259</td>\n",
              "      <td>0.102368</td>\n",
              "      <td>0.090672</td>\n",
              "      <td>0.103949</td>\n",
              "      <td>0.084227</td>\n",
              "      <td>0.111275</td>\n",
              "      <td>0.120448</td>\n",
              "      <td>0.154891</td>\n",
              "      <td>0.141925</td>\n",
              "      <td>-0.272257</td>\n",
              "      <td>0.128432</td>\n",
              "      <td>-0.051002</td>\n",
              "      <td>-0.067150</td>\n",
              "      <td>-0.073404</td>\n",
              "      <td>0.088467</td>\n",
              "      <td>0.101909</td>\n",
              "      <td>0.654351</td>\n",
              "      <td>1.173617</td>\n",
              "      <td>0.282246</td>\n",
              "      <td>0.065103</td>\n",
              "      <td>-0.110735</td>\n",
              "      <td>-0.095649</td>\n",
              "      <td>0.175356</td>\n",
              "      <td>2.172298</td>\n",
              "      <td>2.400539</td>\n",
              "      <td>1.675229</td>\n",
              "      <td>1.780296</td>\n",
              "      <td>2.023557</td>\n",
              "      <td>1.980405</td>\n",
              "      <td>1.942806</td>\n",
              "      <td>1.864729</td>\n",
              "      <td>1.574016</td>\n",
              "      <td>1.647957</td>\n",
              "      <td>0.151582</td>\n",
              "      <td>0.147810</td>\n",
              "      <td>0.157098</td>\n",
              "      <td>0.145255</td>\n",
              "      <td>0.160314</td>\n",
              "      <td>0.166279</td>\n",
              "      <td>0.189654</td>\n",
              "      <td>0.175760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-09 09:15:58</th>\n",
              "      <td>0.131496</td>\n",
              "      <td>0.122875</td>\n",
              "      <td>0.125066</td>\n",
              "      <td>0.128912</td>\n",
              "      <td>0.127693</td>\n",
              "      <td>0.135075</td>\n",
              "      <td>0.116349</td>\n",
              "      <td>0.122715</td>\n",
              "      <td>0.098555</td>\n",
              "      <td>0.093860</td>\n",
              "      <td>0.093604</td>\n",
              "      <td>0.076067</td>\n",
              "      <td>0.109940</td>\n",
              "      <td>0.112419</td>\n",
              "      <td>0.074701</td>\n",
              "      <td>0.078828</td>\n",
              "      <td>-0.085304</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>-0.080557</td>\n",
              "      <td>-0.202580</td>\n",
              "      <td>-0.003832</td>\n",
              "      <td>0.257444</td>\n",
              "      <td>0.250508</td>\n",
              "      <td>-0.198230</td>\n",
              "      <td>0.412454</td>\n",
              "      <td>0.832404</td>\n",
              "      <td>0.111609</td>\n",
              "      <td>-0.047601</td>\n",
              "      <td>0.251163</td>\n",
              "      <td>0.108899</td>\n",
              "      <td>1.343453</td>\n",
              "      <td>0.929627</td>\n",
              "      <td>1.822648</td>\n",
              "      <td>1.715681</td>\n",
              "      <td>1.924050</td>\n",
              "      <td>1.919501</td>\n",
              "      <td>1.808025</td>\n",
              "      <td>1.861719</td>\n",
              "      <td>1.631173</td>\n",
              "      <td>1.761746</td>\n",
              "      <td>0.155800</td>\n",
              "      <td>0.146016</td>\n",
              "      <td>0.149213</td>\n",
              "      <td>0.148124</td>\n",
              "      <td>0.155707</td>\n",
              "      <td>0.160886</td>\n",
              "      <td>0.132911</td>\n",
              "      <td>0.141390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-14 19:28:46</th>\n",
              "      <td>0.127686</td>\n",
              "      <td>0.127713</td>\n",
              "      <td>0.131158</td>\n",
              "      <td>0.120763</td>\n",
              "      <td>0.130134</td>\n",
              "      <td>0.134553</td>\n",
              "      <td>0.116851</td>\n",
              "      <td>0.121671</td>\n",
              "      <td>0.104213</td>\n",
              "      <td>0.084039</td>\n",
              "      <td>0.094671</td>\n",
              "      <td>0.075304</td>\n",
              "      <td>0.105485</td>\n",
              "      <td>0.112640</td>\n",
              "      <td>0.072241</td>\n",
              "      <td>0.082719</td>\n",
              "      <td>-0.322271</td>\n",
              "      <td>-0.050267</td>\n",
              "      <td>0.079860</td>\n",
              "      <td>0.044323</td>\n",
              "      <td>-0.090241</td>\n",
              "      <td>-0.072683</td>\n",
              "      <td>-0.264844</td>\n",
              "      <td>-0.279910</td>\n",
              "      <td>0.445161</td>\n",
              "      <td>0.438284</td>\n",
              "      <td>0.040884</td>\n",
              "      <td>0.331266</td>\n",
              "      <td>0.217808</td>\n",
              "      <td>0.676228</td>\n",
              "      <td>0.279270</td>\n",
              "      <td>1.072475</td>\n",
              "      <td>1.901099</td>\n",
              "      <td>1.815236</td>\n",
              "      <td>1.969048</td>\n",
              "      <td>1.901578</td>\n",
              "      <td>1.889860</td>\n",
              "      <td>1.718225</td>\n",
              "      <td>1.907665</td>\n",
              "      <td>1.756565</td>\n",
              "      <td>0.156164</td>\n",
              "      <td>0.148547</td>\n",
              "      <td>0.153390</td>\n",
              "      <td>0.137939</td>\n",
              "      <td>0.157608</td>\n",
              "      <td>0.160949</td>\n",
              "      <td>0.135244</td>\n",
              "      <td>0.142601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-10-22 22:24:13</th>\n",
              "      <td>0.109213</td>\n",
              "      <td>0.102761</td>\n",
              "      <td>0.112542</td>\n",
              "      <td>0.102020</td>\n",
              "      <td>0.109338</td>\n",
              "      <td>0.113428</td>\n",
              "      <td>0.097629</td>\n",
              "      <td>0.098542</td>\n",
              "      <td>0.087680</td>\n",
              "      <td>0.076663</td>\n",
              "      <td>0.094031</td>\n",
              "      <td>0.074949</td>\n",
              "      <td>0.093231</td>\n",
              "      <td>0.095972</td>\n",
              "      <td>0.071915</td>\n",
              "      <td>0.065963</td>\n",
              "      <td>0.253187</td>\n",
              "      <td>0.439239</td>\n",
              "      <td>-0.212718</td>\n",
              "      <td>-0.251921</td>\n",
              "      <td>0.144751</td>\n",
              "      <td>0.276032</td>\n",
              "      <td>-0.275270</td>\n",
              "      <td>-0.088272</td>\n",
              "      <td>1.342844</td>\n",
              "      <td>1.975988</td>\n",
              "      <td>0.845097</td>\n",
              "      <td>0.147655</td>\n",
              "      <td>-0.057515</td>\n",
              "      <td>1.944025</td>\n",
              "      <td>2.048675</td>\n",
              "      <td>0.472597</td>\n",
              "      <td>1.665661</td>\n",
              "      <td>1.599541</td>\n",
              "      <td>1.673121</td>\n",
              "      <td>1.940411</td>\n",
              "      <td>2.039540</td>\n",
              "      <td>1.595535</td>\n",
              "      <td>1.570912</td>\n",
              "      <td>1.790171</td>\n",
              "      <td>0.130272</td>\n",
              "      <td>0.121147</td>\n",
              "      <td>0.136763</td>\n",
              "      <td>0.122247</td>\n",
              "      <td>0.131092</td>\n",
              "      <td>0.138171</td>\n",
              "      <td>0.115434</td>\n",
              "      <td>0.114823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-22 08:06:56</th>\n",
              "      <td>0.131340</td>\n",
              "      <td>0.121270</td>\n",
              "      <td>0.123759</td>\n",
              "      <td>0.120575</td>\n",
              "      <td>0.133805</td>\n",
              "      <td>0.129860</td>\n",
              "      <td>0.147101</td>\n",
              "      <td>0.149781</td>\n",
              "      <td>0.101926</td>\n",
              "      <td>0.099089</td>\n",
              "      <td>0.101592</td>\n",
              "      <td>0.078019</td>\n",
              "      <td>0.113010</td>\n",
              "      <td>0.116954</td>\n",
              "      <td>0.142148</td>\n",
              "      <td>0.150519</td>\n",
              "      <td>-0.105490</td>\n",
              "      <td>0.630696</td>\n",
              "      <td>-0.171129</td>\n",
              "      <td>0.114279</td>\n",
              "      <td>-0.164952</td>\n",
              "      <td>-0.010104</td>\n",
              "      <td>-0.482727</td>\n",
              "      <td>0.775404</td>\n",
              "      <td>0.932048</td>\n",
              "      <td>6.625688</td>\n",
              "      <td>0.202461</td>\n",
              "      <td>0.056849</td>\n",
              "      <td>0.017018</td>\n",
              "      <td>0.337771</td>\n",
              "      <td>4.600107</td>\n",
              "      <td>4.524487</td>\n",
              "      <td>1.752359</td>\n",
              "      <td>1.275479</td>\n",
              "      <td>1.818095</td>\n",
              "      <td>1.948512</td>\n",
              "      <td>1.864848</td>\n",
              "      <td>1.824276</td>\n",
              "      <td>1.334751</td>\n",
              "      <td>1.506609</td>\n",
              "      <td>0.157346</td>\n",
              "      <td>0.147663</td>\n",
              "      <td>0.150793</td>\n",
              "      <td>0.139073</td>\n",
              "      <td>0.164211</td>\n",
              "      <td>0.158859</td>\n",
              "      <td>0.187509</td>\n",
              "      <td>0.187457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-18 08:42:30</th>\n",
              "      <td>0.123825</td>\n",
              "      <td>0.122965</td>\n",
              "      <td>0.129618</td>\n",
              "      <td>0.120390</td>\n",
              "      <td>0.133610</td>\n",
              "      <td>0.131342</td>\n",
              "      <td>0.080862</td>\n",
              "      <td>0.121581</td>\n",
              "      <td>0.094378</td>\n",
              "      <td>0.087066</td>\n",
              "      <td>0.093180</td>\n",
              "      <td>0.076338</td>\n",
              "      <td>0.115637</td>\n",
              "      <td>0.118492</td>\n",
              "      <td>0.083119</td>\n",
              "      <td>0.082074</td>\n",
              "      <td>-0.148327</td>\n",
              "      <td>-0.130263</td>\n",
              "      <td>-0.026948</td>\n",
              "      <td>0.099508</td>\n",
              "      <td>0.292048</td>\n",
              "      <td>0.030236</td>\n",
              "      <td>0.283842</td>\n",
              "      <td>-0.166707</td>\n",
              "      <td>0.208375</td>\n",
              "      <td>0.655445</td>\n",
              "      <td>0.611528</td>\n",
              "      <td>0.167579</td>\n",
              "      <td>0.369879</td>\n",
              "      <td>-0.021468</td>\n",
              "      <td>0.209311</td>\n",
              "      <td>0.563871</td>\n",
              "      <td>1.801309</td>\n",
              "      <td>1.864670</td>\n",
              "      <td>1.804226</td>\n",
              "      <td>1.918499</td>\n",
              "      <td>1.912173</td>\n",
              "      <td>1.995476</td>\n",
              "      <td>1.849568</td>\n",
              "      <td>1.816634</td>\n",
              "      <td>0.148653</td>\n",
              "      <td>0.144651</td>\n",
              "      <td>0.152665</td>\n",
              "      <td>0.138224</td>\n",
              "      <td>0.160293</td>\n",
              "      <td>0.159868</td>\n",
              "      <td>0.098968</td>\n",
              "      <td>0.142255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-18 01:52:30</th>\n",
              "      <td>0.129366</td>\n",
              "      <td>0.125127</td>\n",
              "      <td>0.123524</td>\n",
              "      <td>0.117061</td>\n",
              "      <td>0.129002</td>\n",
              "      <td>0.138018</td>\n",
              "      <td>0.120555</td>\n",
              "      <td>0.122037</td>\n",
              "      <td>0.104951</td>\n",
              "      <td>0.086012</td>\n",
              "      <td>0.093429</td>\n",
              "      <td>0.073550</td>\n",
              "      <td>0.108448</td>\n",
              "      <td>0.114341</td>\n",
              "      <td>0.079066</td>\n",
              "      <td>0.081897</td>\n",
              "      <td>-0.291254</td>\n",
              "      <td>-0.050986</td>\n",
              "      <td>-0.109432</td>\n",
              "      <td>0.090062</td>\n",
              "      <td>-0.417327</td>\n",
              "      <td>0.001602</td>\n",
              "      <td>0.100358</td>\n",
              "      <td>-0.148554</td>\n",
              "      <td>0.919002</td>\n",
              "      <td>0.245268</td>\n",
              "      <td>0.118795</td>\n",
              "      <td>-0.000292</td>\n",
              "      <td>1.318832</td>\n",
              "      <td>0.238031</td>\n",
              "      <td>1.278475</td>\n",
              "      <td>1.868364</td>\n",
              "      <td>1.777362</td>\n",
              "      <td>1.844297</td>\n",
              "      <td>1.870802</td>\n",
              "      <td>2.029746</td>\n",
              "      <td>1.630005</td>\n",
              "      <td>1.827020</td>\n",
              "      <td>1.602449</td>\n",
              "      <td>1.522781</td>\n",
              "      <td>0.156056</td>\n",
              "      <td>0.146674</td>\n",
              "      <td>0.147844</td>\n",
              "      <td>0.134332</td>\n",
              "      <td>0.158646</td>\n",
              "      <td>0.165740</td>\n",
              "      <td>0.138604</td>\n",
              "      <td>0.142178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-19 09:22:30</th>\n",
              "      <td>0.123158</td>\n",
              "      <td>0.123252</td>\n",
              "      <td>0.136246</td>\n",
              "      <td>0.120954</td>\n",
              "      <td>0.129125</td>\n",
              "      <td>0.136417</td>\n",
              "      <td>0.124893</td>\n",
              "      <td>0.121399</td>\n",
              "      <td>0.097229</td>\n",
              "      <td>0.086171</td>\n",
              "      <td>0.099402</td>\n",
              "      <td>0.072563</td>\n",
              "      <td>0.110838</td>\n",
              "      <td>0.113122</td>\n",
              "      <td>0.084327</td>\n",
              "      <td>0.083749</td>\n",
              "      <td>0.063881</td>\n",
              "      <td>0.017914</td>\n",
              "      <td>-0.050954</td>\n",
              "      <td>0.112470</td>\n",
              "      <td>-0.125150</td>\n",
              "      <td>-0.107754</td>\n",
              "      <td>0.101594</td>\n",
              "      <td>0.168281</td>\n",
              "      <td>0.108283</td>\n",
              "      <td>-0.100531</td>\n",
              "      <td>0.206858</td>\n",
              "      <td>-0.003109</td>\n",
              "      <td>1.166612</td>\n",
              "      <td>0.365292</td>\n",
              "      <td>0.557437</td>\n",
              "      <td>0.892683</td>\n",
              "      <td>1.816578</td>\n",
              "      <td>2.003624</td>\n",
              "      <td>1.926497</td>\n",
              "      <td>1.842407</td>\n",
              "      <td>1.672575</td>\n",
              "      <td>1.761459</td>\n",
              "      <td>1.732209</td>\n",
              "      <td>1.714063</td>\n",
              "      <td>0.146391</td>\n",
              "      <td>0.143968</td>\n",
              "      <td>0.161078</td>\n",
              "      <td>0.138315</td>\n",
              "      <td>0.157696</td>\n",
              "      <td>0.166159</td>\n",
              "      <td>0.145554</td>\n",
              "      <td>0.141463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-11-01 18:21:44</th>\n",
              "      <td>0.131542</td>\n",
              "      <td>0.123529</td>\n",
              "      <td>0.132024</td>\n",
              "      <td>0.120281</td>\n",
              "      <td>0.133851</td>\n",
              "      <td>0.136675</td>\n",
              "      <td>0.117680</td>\n",
              "      <td>0.122182</td>\n",
              "      <td>0.100961</td>\n",
              "      <td>0.088124</td>\n",
              "      <td>0.097506</td>\n",
              "      <td>0.081992</td>\n",
              "      <td>0.116042</td>\n",
              "      <td>0.113689</td>\n",
              "      <td>0.082339</td>\n",
              "      <td>0.078706</td>\n",
              "      <td>0.171531</td>\n",
              "      <td>-0.117791</td>\n",
              "      <td>-0.218983</td>\n",
              "      <td>-0.011697</td>\n",
              "      <td>0.101762</td>\n",
              "      <td>0.081747</td>\n",
              "      <td>-0.119242</td>\n",
              "      <td>0.246855</td>\n",
              "      <td>0.557723</td>\n",
              "      <td>0.706040</td>\n",
              "      <td>0.133364</td>\n",
              "      <td>0.666954</td>\n",
              "      <td>0.030151</td>\n",
              "      <td>0.806809</td>\n",
              "      <td>1.894710</td>\n",
              "      <td>0.594828</td>\n",
              "      <td>1.846215</td>\n",
              "      <td>1.816020</td>\n",
              "      <td>1.936897</td>\n",
              "      <td>1.733884</td>\n",
              "      <td>1.958030</td>\n",
              "      <td>1.836720</td>\n",
              "      <td>1.655348</td>\n",
              "      <td>1.777471</td>\n",
              "      <td>0.156351</td>\n",
              "      <td>0.145100</td>\n",
              "      <td>0.158209</td>\n",
              "      <td>0.140616</td>\n",
              "      <td>0.162616</td>\n",
              "      <td>0.163962</td>\n",
              "      <td>0.137249</td>\n",
              "      <td>0.140023</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2156 rows × 48 columns</p>\n",
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            ],
            "text/plain": [
              "                     b1_x_abs_mean  b1_y_abs_mean  ...  b4_x_rms  b4_y_rms\n",
              "datetime                                           ...                    \n",
              "2003-11-16 05:58:46       0.133482       0.129230  ...  0.141170  0.131708\n",
              "2003-11-22 12:26:56       0.123724       0.125719  ...  0.189654  0.175760\n",
              "2003-11-09 09:15:58       0.131496       0.122875  ...  0.132911  0.141390\n",
              "2003-11-14 19:28:46       0.127686       0.127713  ...  0.135244  0.142601\n",
              "2003-10-22 22:24:13       0.109213       0.102761  ...  0.115434  0.114823\n",
              "...                            ...            ...  ...       ...       ...\n",
              "2003-11-22 08:06:56       0.131340       0.121270  ...  0.187509  0.187457\n",
              "2003-11-18 08:42:30       0.123825       0.122965  ...  0.098968  0.142255\n",
              "2003-11-18 01:52:30       0.129366       0.125127  ...  0.138604  0.142178\n",
              "2003-11-19 09:22:30       0.123158       0.123252  ...  0.145554  0.141463\n",
              "2003-11-01 18:21:44       0.131542       0.123529  ...  0.137249  0.140023\n",
              "\n",
              "[2156 rows x 48 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "DATASET_DIR_PATH = r'/content/1st_test'\n",
        "\n",
        "COLUMNS = ['b1_x','b1_y','b2_x', 'b2_y','b3_x','b3_y','b4_x', 'b4_y']\n",
        "\n",
        "# First 43 files were taken every 5 minutes\n",
        "SKIP_FIRST_NTH_FILES = 43\n",
        "SAMPLE_EVERY_NTH_ROW = 45\n",
        "import numpy as np\n",
        "import scipy.io\n",
        "\n",
        "def signaltonoise(a, axis=0, ddof=0):\n",
        "    a = np.asanyarray(a)\n",
        "    m = a.mean(axis)\n",
        "    sd = a.std(axis=axis, ddof=ddof)\n",
        "    return np.where(sd == 0, 0, m/sd)\n",
        "\n",
        "FEATURES = {\n",
        "  'abs_mean': lambda x: np.abs(x).mean(),\n",
        "  'std': np.std,\n",
        "  'skwe': sts.skew,\n",
        "  'kurt': sts.kurtosis,\n",
        "  'entropy': lambda x: sts.entropy(pd.cut(x, 10).value_counts())\n",
        "  ,\n",
        "  'rms': lambda x: np.sqrt(np.mean(np.square(x)))\n",
        "#  , 'str':signaltonoise\n",
        "}\n",
        "\n",
        "# We are taking every nth row of each file for performance reasons\n",
        "def skip_row(x, n):\n",
        "  if x % n == 0:\n",
        "    return False\n",
        "  return True\n",
        "\n",
        "def extract_datetime_from_path(path):\n",
        "  segments = path.split('/')\n",
        "  filename = segments[-1]\n",
        "  return datetime.datetime.strptime(filename, \"%Y.%m.%d.%H.%M.%S\")\n",
        "\n",
        "def extract_col_stats_features(df, columns=COLUMNS):\n",
        "  dfs_reshaped = []\n",
        "  for name, func in FEATURES.items():\n",
        "    applied = np.array(df.apply(func))\n",
        "    columns = [c+'_'+name for c in COLUMNS]\n",
        "    df_reshaped = pd.DataFrame(applied.reshape(1,8), columns=columns)\n",
        "    dfs_reshaped.append(df_reshaped)\n",
        "\n",
        "  return pd.concat(dfs_reshaped, axis=1)\n",
        "\n",
        "def read_file(path, n):\n",
        "  dfs = []\n",
        "  time = 0\n",
        "  files = glob.glob(DATASET_DIR_PATH + \"/*\")\n",
        "  for filename in files:\n",
        "    df_raw = pd.read_csv(filename, index_col=None, header=None, sep='\\t', skiprows=lambda x:skip_row(x, n))\n",
        "    df = extract_col_stats_features(df_raw)\n",
        "    df['datetime'] = extract_datetime_from_path(filename)\n",
        "    dfs.append(df)\n",
        "  return dfs\n",
        "\n",
        "data = pd.concat(read_file(\"\", SAMPLE_EVERY_NTH_ROW), ignore_index=True)\n",
        "data.set_index('datetime')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Comparing first and last signal"
      ],
      "metadata": {
        "id": "pPqJaa4246D0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('/content/1st_test/2003.10.22.12.06.24', sep='\\t')\n",
        "ax = dataset.plot(figsize = (24,6), title= \"Bearing Vibration\" , legend = True)\n",
        "ax.set(xlabel=\"cycle(n)\", ylabel=\"vibration/acceleration(g)\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "hMif_7y8EtBQ",
        "outputId": "cd25adbb-c042-478d-b293-09b745fb1b13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1728x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('/content/1st_test/2003.11.15.05.08.46', sep='\\t')\n",
        "ax = dataset.plot(figsize = (24,6), title= \"Bearing Vibration\" , legend = True)\n",
        "ax.set(xlabel=\"cycle(n)\", ylabel=\"vibration/acceleration(g)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "FznDFS2VEuiI",
        "outputId": "04375a44-3619-4910-9844-a87ec24a6337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Scoping and transforming datetime"
      ],
      "metadata": {
        "id": "hv2PYzxKE8QQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime as dt\n",
        "\n",
        "def timediff(x):\n",
        "  first=dt.datetime(2003,10,22,13,59,13)\n",
        "  timesince = (x - first).total_seconds()\n",
        "  return  timesince/60\n",
        "def timeleft(x):\n",
        "  last=dt.datetime(2003,11,25,23,39,56)\n",
        "  timesince = (last - x).total_seconds()\n",
        "  return  abs(timesince/60)"
      ],
      "metadata": {
        "id": "j-bMPVbpXgZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKW_lsgL3Pb0"
      },
      "outputs": [],
      "source": [
        "bearing_columns = data.columns[pd.Series(data.columns).str.startswith('b4_')]\n",
        "columns = bearing_columns.tolist() + ['datetime']\n",
        "data = data[columns]\n",
        "data.head()\n",
        "\n",
        "data['datetime']=data['datetime'].apply(timeleft)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using SVR algorithm \n",
        "  - dataset dividing\n",
        "  - data fitting \n",
        "  "
      ],
      "metadata": {
        "id": "8QpcrdUm6G5A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n=len(data)\n",
        "data=data.sort_values(by='datetime')\n",
        "data.head()\n",
        "train_df_svm =  data [int(0.2*n):]\n",
        "train_df_svm.head()\n",
        "test_df_svm =data[0:int(n*0.2)] \n",
        "\n",
        "# test_df_svm['datetime']=test_df_svm['datetime'].apply(timeleft)\n",
        "# train_df_svm['datetime']=train_df_svm['datetime'].apply(timeleft)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVR\n",
        "Y=train_df_svm[['datetime']].copy()\n",
        "X=train_df_svm.copy()\n",
        "\n",
        "del X['datetime']\n",
        "\n",
        "Y_test=test_df_svm[['datetime']]\n",
        "X_test=test_df_svm.copy()\n",
        "del X_test['datetime']\n",
        "sc_X=StandardScaler()\n",
        "sc_Y=StandardScaler()\n",
        "sc_X_test=StandardScaler()\n",
        "# sc_Y_test=StandardScaler()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "X = sc_X.fit_transform(X)\n",
        "\n",
        "# Y = sc_Y.fit_transform(Y)\n",
        "X_test=sc_X_test.fit_transform(X_test)\n",
        "# Y_test=sc_Y_test.fit_transform(Y_test)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Y5uin5zwAHeQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aUW2XsHM6Zts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "regressor=SVR(kernel='sigmoid')\n",
        "\n",
        "regressor.fit(X,Y)\n",
        "svm_predict=regressor.predict(X_test)\n",
        "# Y_test=Y_test.to_numpy()\n",
        "regressor.score(X_test,Y_test)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "TLiMvjNkAHqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaf7a72b-bb6d-4200-fa0b-76f45235a514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-154.32468206876845"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Scoring "
      ],
      "metadata": {
        "id": "G1jToHvk6n8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error\n",
        "print(r2_score(Y_test,svm_predict),\n",
        "mean_absolute_error(Y_test,svm_predict),\n",
        "mean_squared_error(Y_test,svm_predict)**0.5,len(svm_predict))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vviFAp5lVNg",
        "outputId": "52ffb1ca-ca4d-4720-fa6a-ede1951c6b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-154.32468206876845 18884.961035914097 18948.520968869874 431\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt =  sns.scatterplot(x=test_df_svm['datetime'],y=svm_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WJaTuGuA_HjI",
        "outputId": "62ea5b81-1949-4410-961d-3dfe59e62c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1gIVPrbWffOlknJDvokp4KGxTkpfO6lsrdbOLlnOg5RSsnF/BuLx0UpJcFOWmMX5U5E2EonV0ulyCpAShazX3Xl+u+1C0844Ls7oPp800tnaH1Jo+ecUYANZsPcQDCybx4O/2+OWU2PXRhOrrrbjYGEr7LjU/UEdvP2te3jfialcNZb6LhhIUClt4PJLXDjSxZushPcls8ths3S9w17WlZKcl8U1fkprW2Gd0ZkpEjrgxWdGr2caV+M/eqOOua0v50W1XkpLkonxUpn6svPRkbplZjBBezWNstvfY4ZyGhTlp+qpOyxM539vH5WMymTEuj2MtnboQ/aCpXfflGE1IcyeM4uqy/JA/cidMDE6mIDtV1xi13JW12w/zlbnlIzaaaqirESvTk8IW2iR6vOU8T75ey9pttRxv6dTzJs5f6NdDU8FrCvjw7HkOm6KgILgjzuORHG3pYOX8iqjUbKOK3tjazRPbDuNyCeZPKuDyMZmMH5XBujtncNe1pax/q45NuxqQwHsn2jhyqiOs03BqYTaP31apZ5p//be7+dff7+dkaw8QqA1p16CZkP6w5wSpSS52Hm0Ja4IbiGlmpFPmzmB6cY7+nBiTIFXtqvigNAqFLawS7NKSEnho4WT+9/PvBqzmbplZzOnOXkRnr20N4VhLJ8uf201eerK+AncJmFKYZWuiDFVNVnt/vDtTP4c5bPUXS6qCjtVbQLCLwpxUvvW7vX5ayQdNbUy9LMvP6Wg2T5W60/j6vAoWV+8Yklj4YL3Fh2MZDZdLMO+KAiaMzmRmSR790sMv33TFNRT7UkcJCoUtzJEXt8ws5j+2HORrH5/gt5ozlrrwSOs8ih/cPN3yx6sJI20FriWqHTzpjWIyT2TBJr/3G9uD+hlOtXf7mSuMZqYnth3iP26ezsMv7fP7bEleuu67+MrcckshU+rO4LOVl+m+Bc2p/+svXY1LCPo8HpY+VTMk9nMr38u6O2fQ2yeHbRkNl0tQNiqTslFe35J235/Z4Q3ZnliQxeSx2YwfNTyEn9NRgkJhC7ODVcs+7u33rpYzkhP4zqIpuvkpMzmB8xf6OdvV62ejz05JYMLoDHYebfGb3OvPdJLoCsz2DpaoFszxPHFMZkg/gybwzK1NC3NSmTdpLD/e9kFAn+v6s11+x7y9KlDIPPLSPq4al+vnWxibncr7je2s3nKAr30sMOFvsOznVr6XvQ2tVL9RF3Cfpqyci0cyrLQM5dOJP0pQKGxh/jGmJSVS/UYdbxw6xb3XT+Df/7ifvPRkll1fzpTCbJBwqr1bjzx68vVa3fzyOYP55edLZnK+18PR050kCCyzvSFwwrea/FZvOcADC64IOSFrAq++pTNAQ9LOZ8zU3rxirp/ZbdOuBu6/YWLAOfLSk2lu79En2Fllbo61dLJ6ywEWV5XwYev5IYuFt/K9mPNMtGt4p/4cj5g0quGgZQy1s3ekowSFwjbGH6Om7h882aaX6G5s7WbttlpWzJ9A9Rt1eonxx2+7Eik9FOak8c3f7dG1i8yUBM73eni/sY3qN+r4ytxyNu1qYOmcckry0kJO+FaT36LKIj48az0hj85M1a9hweQC/nKwyS981lyfyXg+o9mtsbWbU+3dfufQor7u/vXf/SbYvPQkvUBgNGGyscIqYcsYNaRxe1WxLiS06x8J4aWKgaOinhRRoWkYV43LDbpabWztZvWWQ/xg8wEaznazu/6sX7Ofrt5+3m9s81vdnu3q5cnXa/nnufMhI1isurMluLxlh81RUyvnV5Bg2LX+bBff2PguT799XO9QN7EgK+j5zAlPz9f8k+/fNE3/25hPAhcn2PTkRF0AGcNkl8+bwFP3zBq0lbpVwtb04pyAbRPHZAUVlor4M9QVYkOhNApFVGiO5IzkxICVaWZygqVZ5/HbruRbvjwL8AoUuLi6NTq+jRVbrVbgVklpV5fm84/jZ3ihpsGv9MXTbx9nRkkuZb5cCrPTHLxawQ9unh5gdtFs3WYbeEleOjNL8jjV3k1Xb7/lBHuhv5+rS/MtczcKslNslzQZaGRSMBs+4LdNxrHSryI0TqgQGwolKBQRE6yKrBYGWpSXZmnWOXo6sJf2H/ac4MvXjeeRf5lE94V+Skdl8l93zqS338PEMZksmDKW5o5AB2WwyRug+o06v6ip26uKSUtKxOORuFzC0hRztquXmSW5bA7iELWygWs+j30nWi0n2PyMFEqK0/nx4qs4errTT+hdMTabkvzQk34sJ49gNnzjNmP0kAovHVycUCE2FMLXRmLYUVVVJWtqaoZ6GJckdc0dfGrtm342+turipkxLpf8jGQWV+/wW0FPGpvFN1/Yw1fmlvPLN+v8PnfXtaVsO3CSL8wu46QvSW4gk2Jfn4c/vdfImq2HArrMaceDyMqKBEObyDWHdbAIrSOnOvj0T94MECSbw0wC5vts93MDQdNgVPTQ4PL2kdN8/hfmxqGwYdk1zC4fFdNzCSF2SSmrIvmM0igUEeHxSI63+GsGmhP7t1+9RjfDWJl1ntj2gZ9D92xXLxUFmVx3+VS2HTwVk3DNxEQXn6m8jDJ3up7cZjyetkKz0kYiNfEYV4HG/tnzJ43xq5ar5W4YsRMaOxQNfmIZPRTvvth2jz8c+nM7oUJsKJSgUAQQ7IelraA90rqpTnpyAlmpSQERQbdXFTM6K5m1d8ygt9/DxmWz6ertpyA7VU9mi2W4psslgvoNzJOslN7s79cONEWsYTS1dftpTgAv1DRw3eVuv89FOwk4ffIIRbxs7h6PpP5MJy0dvRxt6eLRl0M/G063/Ws4vRCkMj0p/Aj1wzrW0smn1r7JNz5ZgceDn6llxbwKZo3PY2ZJvp//QquLFOxHWtfcwSvvejvi/tygUQB6mG00ppdQZpsyd4bfNUZ7nmOnO3j1vZN+17dyfgX/Mm2s7jjXJjZN4OWlJ3N7VTETx2QxuTB05rBmRjN20nPiJGdFPMxmxj4U5y/02/rOhsJ8Fy2DZfaLxvSkwmMVfoTqB6GZQjp6+tlYU6+Hei6dU87GmnryM1J0J/PmFXP58eKrLMNGjb0lmtq6eb6mgXyfUzxW4ZpWIaHr7pyBlPCPY2cC+mdHc55+DwHX98S2w/T7DqUJ3YVPvMnjfz7E/Z+s4P4bJlL9Rh3Lf7ubT/8keL8Ec7XeFfMnUL2kigWTCwImD6uwyqEOtYxHVzZjHwq735kTusPZxcmFIJXpSeFHqB+WZgrZtKvBsryGpiZrD7wdG3tBdipnu3r1suBa6e5JY7PISEmK2vRijorSyml8+idv+hUwLMxJ5QpfDkWk5wnme2ju6ObyMf7Z442t3bR297PmL+/5masOnWxjSmGWroFoGD9rzhQ3roTD1XGKRIOJJVZms1J3GmlJCbx95HRUvgJzHwo739lwNt85CaVRKPywSmQzJ55p9ZuWXV/Oujtn8KevW/fBDnUsDeMxV285xLd+t4fM1CSmFeUyftTAOnsZV2geiZ8WkZrk0utJ/ei1g379qbUWqU1t3SFX4+GuzywohUAvKKglHf78jTreqT8XcI5wK2FNYzBrR90XvHWcNCGxZHapLQ3GLtH0Mwf8qudG233O2IdCy7kJ92w4oTvcSCCsj0IIMQ54GijAm/haLaV8QgjxOPAZoBc4AtwjpTxn+FwJ8D7wXSnlj3zbFgJPAAnAL6WUj/m2jwc2AG5gF7BEStkbalzKRxEfwjn/IrGj2nUkhjpmrOy2xvBDTUB09120c2s5FzmpCYzOStUryJa60/jejdMt+2qHuz6zfXz5vAm4BAO2rRt9LF+ZW8667bV+17p83gTWba/lvk9MYP1b0fl4rIjUMWz87tKSEvyi0KIZi7lXtqYtTSvKoSAzha4L/Zaaigr59Sde4bF9wANSyneEEFnALiHEVmAr8LCUsk8IsRp4GFhl+Nwa4FXD4BKAJ4EbgAbgH0KI30sp3wdWA/8ppdwghPgZsBT4aSQXoogN4SpxRhI+abeqp3bMMncGx1o6/SrLxipc01yv6Zkdx/nGJyv0iUsL573vExNY85d9uvBYXFXCsmdqLCfGcNdnjmT5w54T3H9D6KKFGqGiYMx+pGB1nMwVcoOdyy6RJIWZI+es+pncMrOYD5ra9esNN3mb+1B09fZR5s7gwMl2Fv8ieJ8PVTBw4IQVFFLKRqDR97pdCHEAKJJSvmbYbQdwm/aHEOIm4CjQadhnFlArpazz7bMBuNF3vHnAnb79ngK+ixIUQ0Ysf1h2j2XO9o61Xd088Z7t6uUyX2tTv0nWUBwwXAVb7fo0M0ZTW7d+rmCCRHqsQ4vNNvNQQshczdZcbFCr43ToZFtM7fN28zqsNA9jU6hwJeRDYexDAV7Ny6qKcFFuqh6CfalrELEgIme2EKIMmAGYUwi/DGz07ZOJV7O4AfimYZ8i4J+GvxuAa/Cam85JKfsM24uCnH8ZsAygpKQkkqErHECoxCdttWpsCpSXnsw915UyqTCbvn5JqTsjaqERrOSHUXiUutO4ttytm4bsrMjDmWPMgjKSMhnBhKyVdrTs+nJmjMul1J2hH2tKYRal7gzL+lXRYNcxbKV5PPrKPn68+Creb2yjYkyWX82vUJpJMLRnSetNrqFpgUPVSXCkYltQ+ATAJuAbUso2w/Zv4zVPPevb9F28ZqQOIWL7xUgpq4Fq8PooYnpwRVwJN6Fqq1VtFZ+Xnsy915fTdaGfZc/sismP3mr1v2ByAZtXzOVMZw8nznXz0It79RU6hI+siaZGz9TLsnj6nlmc7uyhKDedqYXZEV2PlXY0aWw2H5s4xs+P1NTWzcySXP709bmW9bIixW5SmJXm0dsn6ert18vJD8QkZnyWvjK33LIA5UCEkCIQW4JCCJGEV0g8K6V80bD9S8AiYL686BW/BrhNCPFDIBfwCCG68TqpxxkOWwycAFqAXCFEok+r0LYrRhDhJlRz57lbZhbT0tVrWdYj2h99KGEF8MX13n4SWjmO7JQEy9aoxokxkjIbZmdstMIvlFnKWH9qUWURCS64ujSfa8vdJCYGD3K0U+bCrs/JSvMw97oYiEnM+CyZTW+h+oooQRE9YQWF8KoF64EDUso1hu0LgQeBj0kpu7TtUsq5hn2+C3RIKdcJIRKBCl+E0wngDuBOKaUUQryO18exAbgbeCUWF6dwDuEmVG21qtnVtZ7bwT6jOXWjrc2kHUsTPMbxGetU/e7e2UErykJkcfpawlgshF8ws5Sxq57RB7D61ko+U3lZ0Ogku9FMdnxOVpqHMXnSyq8SiUnM/F1pgr2yKJuxOWmWUWUqb2Jg2NEoPgosAfYJId71bXsEWAukAFt9JqYdUsp7gx3EFx21HPgz3vDYX0kp9/veXgVsEEJ8H9iNVzApRhDGCVWLeElw4Vf+e+HUsbpd/bgve9tqEh6bnRpV/R47yYTmc+VnpIScGM2TohZOa3Zsayv2UMIvFiveprZuvaueURit2rSX6UU5lueIdYlrSye+oddFML+KXY0q2LM0NieNqYXZjq6ZNFyxE/X0FmD1DW628dnvmv7ebPU5XyTUrHDHUwwuRnNEYU4q/R70STVSW7c2oWqr3Y019SyqLGLbwSbazl/QTSNlozIpyc+g/kwn+z9ss2xe1O8hqokt1Oo/2qJsxklR83OYw2kXTC7gtQNNHDrZZtmCNJYr3oLs1IjNL/GoUhvOiW/2q1gRzBxmfpY0oVj9Rp1+v0NpgcORoa6Aq4oCKiwxh6sai/uFSkILd8x9J86xYsNuW6YRraBeU1sPXb19lOR7o552Hm2JqnZ/LJMJrQiWJLdx2Wy9R4fmoB9o341Q1/g/taf5qk9YGccRLLltsArnxSJZc8HkAurPdtHS2YNA8MX1O+M+7qEm1hVwVT8KRcwwmiNumXmxJ3SwJDTtBxzOGdrV26+bRow1j+qaO6g/0+lX88gcM68Rbf2eWCYTWhFsZd7YerH1qrGmVXKCiwljMmNaf8nlElxb7mb1rZUBVWc17ci8OjWHCcfLXBPJ/bUyh63ecoAL/R79ulbMnzCiHdfa99Tc3jPk3e+UoFBYYpz0jPkEVuGH5h9wqBWPZhox5ktonyl1Z4RtDwoDq90fzyzdYAKs0JDY19jazUHMFkAAACAASURBVOoth+K68tWaN00vyrEdFfXJK8Y4ylxjJXQXVRbpzxh4gx1GasE/cwiw0R+jZR2c6exRgkIxtJgnPe21VRKa+QdsteLRVkctnT18pCQPIEDgPPLSPq4alxv24bcbpjnYBBNgmoPVPDlrPb7jQbRRUU5ZiWvPn2b2LM5Lx+ORfs9eNNFTQ23rt4tZoyp1p3HnrFL+8y8fGCLJMvVAkHijBIXCEuOkt2lXg+5UBovaQmGcp2Yba1VpDkuuHT8gs8FQ1O8JN8mEEmALJhfY1rriPf5Io6LMnx+MSbbMncG6O2fw4dnzdPb2863f7QlIrmts7Wb7wZP8Zuk1nO7oodAX9TTQIpVOwKhRvXHoFN/97FT+12/e8fvOHty0l6mX5XD5mPj/BpSgUFhi1c9hwZSxnO3qoWJMpt+Ed3VpfkgTgHl1VHO8lU9OPh/yM05b+dmdZIIJsPqzXWG1LrvjiOa+aOM/dLItqqS0wZ5kXS7BeHemX96JWYModadxx6xS3aEdbkyxDgOOJ0aNauG0Qlo6ei2/s/oznYMiKFQ/CkVQtElvVpkbj/SGxuZnpPDpaYVsXjGXDcuuYfOKuVxb7g5Z89+qcqgEvn/TNMvPaJPSp9a+GXXvglgTqvOfHWLRaW0g90Ub//M1DUwemx22T0iwz0d7/dFwqt2/UZExue7nX5zJ2jtm8OjL79ke03Dqdqdp9LdXeX2CKYkJlt9ZevLgrPWVRqEISaiVpHEVFspnYE6QWjK7VO8nsOz6ciYWZDF57MUqsVYVQYd65TfQXINoI7WMGkR6ckLU90Ubf2NrNz/9ay3fWTSF7/3x/QDbfjCNZaDXH40mZGxUZBQW69+qY7Mpm97OmOLZ7S7WGrCm0WvjPXGuKyCnaOX8CgqyUwY8djsoQaEIiV11PZTPwOjvMEZNNbZ280JNA7dXFZOenIAQ3n0jnQAGw0w10Ekmmkgts5CONBzUX8gk6uPfe6KN5u21lhVngy0KBnL9Wo2rvQ2teKS3X8b04hzmXRHY/9t8z6YX51gmXWrjDZa5X9fcETRRL5pouVDPWLzMctqYU5NcPPW349x7fTnLri/HI8EloKLAm5w6GChBoQhJLLJ2jf4OY1noYH0JIulhPVi284FMMhBdpJZZSKckuqK+L6XuNL5/0zTdVGOVGR1Kk7Nz/cEm0+MtnRxu6tB9DdpqeMLowBwZ8z0zNyrSki6tJv5SdxqP3VLJO/XnAkqra89DNNFy4Z6xePo+jNf4szfquL2qmElj/TXwwUBlZitCYszaNdbVmT+pgOlFORE/qMbjBWvVuWXlXN5vbLc1+Q9WVjEMfktNc/vWSLK6re5LqTuNtXfM4PyFfsvxG89nRMt4D9eyNlgm9d/qTuul4jVSk1w8dc8sril3274fVoIIvAJVK59ypLnDVqvZSNDupTFBNEHAzTOKKBuVGfa+DZRYP3cqM1sRc8LV1Yl05W5cIQVrDHSyrdv2yi8edYqCMdghuUZzzy0zi/mPLQfJS09m6ZxyhPCaH6YUZtm+L8dbznP+Qr/l5OXxSPr6Q3ffC3X9wVbVG5fN5mSr9XfU1dsXcJxg2PGVfXH93wfc68KKprbukAmi8fR9gDNauaqoJ0VINHV97R0zLBvCRBr1oh1v84q5zK0YFTT6RvtxzC4fRfnozKDCSPuRWh1juKMJVa3suubXefL1WtZtr2XttlpOtllH7IS7Lx6PpK65g7ePnKauuYOjpzt59JV9rJhX4ReJtvrWyotO7tMd7KxrYfvBJo6c6vCLtgpVvqS5o8dyLJHY18NFXRnPb+d5MF9/qMixguxUPfrIeP5HXtrHsZZOv+9JO18sSqBEMsZ4ozQKRVi0Gk1WE8GZzh6AiBzJmhAYqN0fBu47cDJGm3pzRw+/fNN+n4VQ98Vqdf6j267keMt5PfxUCJASinK9xw/XcClU+ZL/ePUA//uTE/nPv3yg90OvGHMxY9+ORhpOc9TObydbO1K/Vpk7w6+fhtX5Y10pwGnJgcpHobBFMJv3/TdcMaBs41jYXwfbdzAUhKumGiwax3xfAPadOKf3lNZYOX8CPw9i2wd4+d0TIW3/ocb32oEmVm85wB1Xl5CdlhQQlmvneQnnizJXO769qjgg7Nrusaw4cqqDT/9kcHxh0Y7RLtH4KJSgUNjCaiKoXlKlV5HViOeP51LHPPGX5KXz2oEm26tO7Ts8eLKNtdtq9eCErNQExo/KpL37gh4VZTzWzqMt/M+RFtZtrw04ptFhG0xgG6ug3v3rv0f1vNhZYdtdMETjfB7sFX48HeTKma2IG1ahhYPpSFYEOjUjTUzU7PxfmVtOqTtNbyC1uKqElRt2B02AtEp8g0DTVzCnq7Z9IM+LndBW4/lD5T1E43wezEKUHo/0y3uxO8Z4opzZCtuYHcyROkyH0hk3Eom0JIW2/6ZdDaxaOJm12w/7FQhsbO1m7bZaHv/zQTp6LrDzaAt1zR2U5KXriW92HbZW3/1AAw/sBjiEK3USrfPZ7vkHgjb27/3xPb6zaErMHeTRElajEEKMA54GCgAJVEspnxBCPA58BugFjgD3SCnPCSFmAdXax4HvSilf8h1rIfAE3p7Zv5RSPubbPh7YALiBXcASKWVv7C5TEQ8idZg6tVKn07CbaR7pyljbv7G1m9pTHXRfCCwbrzWm0nwYRl9DsMQ3q/EH81cMRuBBuAQ4p5apB/8y8NVvHGHpnHISXDCzJI85l48asjGG9VEIIQqBQinlO0KILLwT+U1AMbBdStknhFgNIKVcJYRIB3p92wuBPcBleIXMB8ANQAPwD+DzUsr3hRDPAy9KKTcIIX4G7JFS/jTUuJSPwhkEswsPZiLcSCISAWuVfR2qRa25Gc4v36zT/9e+J2MSZLQJlqG++zJ3RtwDD+KdABctdhYAbx85zVu1LZaJqLH67cTFRyGlbAQafa/bhRAHgCIp5WuG3XYAt/n26TJsT8UrIABmAbVSyjrfYDcAN/qONw+407ffU8B3gZCCQuEMgtmlw9mjnVZG3CkEawFalJtKV2+/370yroy1zOTvvLLPrznSteVuEhO95gvz/hVjMlmz9ZBfOKlWgtxcXsVOgqX2nRrLtGgYv/t4J4/FOwEuGuwuALQOkE7z/UXkzBZClAEzALO4/jKw0bDfNcCvgFK8ZqQ+IUQR8E/DZxqAa/Cam85JKfsM24uCnH8ZsAygpKQkkqErBplQP1ZllgqOlgWslYrITElAIAJMQdq90gQ1wAMv7AnauU67r0bBPtMjmV6Uw5nOHjYum01Xbz/pyYlUv1Fn2fI2lKPcrK0M5kQdrAd4pB0F47l4sVsPqsydEba/y1BgW1AIITKBTcA3pJRthu3fBvqAZ7VtUsqdwFQhxGTgKSHEq7EYrJSyGp//o6qqSnlGHUwo/8VwaCAzVBpPYU4qd11bqie2rZg/geo36vyEx6GTbUwpzPIrqNfU1h1x5zorbdDjkaz53FUcPNkW0arW+J1G06I0EozfTWFOqmVdsE9eMSaijoLxXrxEEvFV4k7jBzdPDyhsOJRJpLYEhRAiCa+QeFZK+aJh+5eARcB8aeHskFIeEEJ0ANOAE8A4w9vFvm0tQK4QItGnVWjbFcOYUA5Dp4fVDqXG0+9BFxIAHknIOkPGkM9YmCy0760oN80ywS7Yqtb4nRobDFUWZVNRkBUzQWtVet04Tm3RsXHZ7Ig6CsZ78WLHHGZOGrQKVR4qwobHCiEEsB44IKVcY9i+EHgQ+KzRLyGEGC+ESPS9LgUmAcfwOq8rfO8nA3cAv/cJmNfx+TiAu4FXYnBtiiEmWDih0+szDUU3N41T7YFCVKszlJeezH2fmMBX5pZzvKWT+jMXx2M0WRiJ5r66XILpRTkRhZCav1OtwVBFQVZMQ0nN342xA56GFuqr+Vru+8QEls/z3jet5IwZo6DTPvOVueU0d/TEJKzbTkiu8dq0UOVvvrDHWwByiE2ydjSKjwJLgH1CiHd92x4B1gIpwFavLGGHlPJeYA7wkBDiAuABvialPA0ghFgO/BlveOyvpJT7fcdbBWwQQnwf2I1XMClGKE6vzzSUGo955blpVwMPLJgYVqtwuQTXlrtZfWtlgLklmvsaaQip1Xe67s4ZSAn/ONZCcoIrwBkfCaEc5cFqTGlJhcZ7VjHG65sxn9/Yo9p4n3/5ZnRVks3YuZ9O1rRVCQ/FkODk+kxDGdprZfZaf3cVO4+eCfBVGHsiGD8/VPfVeO6x2V7fgbk8vZ0aVaHuiTmc1+zT8e+D0WK7xIyxvEms+1nYZbCeO1XCQzHk2HUCO6HGfjCGUuOxWnmW5KVztvOCpVYxsSCLfo/XZKXd78G8r1bfd/noTL28yNI55QEO9tVbDkTkaA7lKD/b1UtFQSZ/+vpcmjv8hWNSgrC9Qtfuu1k7CfWZWONkTVsJCkXMGClhr0OduWslRCcXZgf0RMhLT6b+TBcPvLBnSO53qO9bM6NYNadaVFkUkaPZrqP88jH+n400n0JrrzpUoalD/dyFHNtQD0AxcgjnBB5OtZ8Go65PJIwfFdgT4ZaZxbrJpTAnlaVzyjl4so19J1rxeGTc73eo79vo3DY72ENFZ1kRraM8mppO8WpCZBenPXcaSqNQBMUcr242cZgf4lDOuDJ3xojQNgYLK5PO5MJsv9Wutlq3yqJed+cMevtkzO+3cVznLwQ2s8pLT6a5vQeXC1bfWhmQ+Z2a5Io4oSxak0w0K3Qnr+qHEiUoFJYYzQoTx2SyeFZJ2IYzoVT94ZBk5xTsFtXTSn9bZVHvbWi1zC8YyP02j2vl/Al+37fmWNZ6Tmi1p9KSXXrmtzFz2u7EP5DJWzMngXchA9gSFk71nw0VSlAoLNEm9rz0ZO79+AS/ST4vPZmDJ9tITXJR5s7Qf3ihVn47j7Y4NvTPaQQTqptXzPWbMMdmp3LF2GzLLOpg+QXR3G9j4yHjuJ6vaWDl/Ard/HV7VbFfsuDxlvMse6bGMmonmpV+NJN3X5+HP73XOKAujANhpNQ0U4JCYYlmRrplZrHfRGQ2c5h/eMEmACcWanMq4eLpjRNmSX6GZRa1nUZDdjCHphqP19jazdNvH+epe2YhkUH7qhuFk3ninFXmjtvE6fFI/lbXEpHjPNhxopnsR0pwByhntiII2sSekujCIy86JIMVi9Mc1sGccZE6CYeT4zvW2Mlc1+7PzqMt5KYnsvrWSr97O704sszqYJi1G/O4znb1Mjorhdnlo/SIoWDjDtdQKNYca+mk5viZiBznZgYy5qHM8I81SqNQWFKSl86PF1+FBB579YDukLQKd7Rj0ojEzjySVmLREM55a64JdNe1pWz4R73e5KaqNJ/ryr0rdfP9Bm9il93VsVG7CVfsL9y4B9tP1dTWrS9yotWsBjJmJ2daR4oSFIoAPB7JaweaON7SyYZ/1Ou9lZfOKWfS2Kyof3h27cyXuuM7nFA13h9jiOyTr9cC/tm8xvsdTgBbmViMJkMth2HZ9eXMGJdLqcE/ZWfcgz1xFmSn8oc9J3ThlpeezO1VxUwYk4mU3vsRbuERbMxazahQAnckmVuV6UkRgDYRdfb2c7zlPM/sOM6iyiKEgH+e6eIHN0+Pa5x5pL2gRyJGE55Wml0zw508d/H+hNLwzIQyhQQzsWgRStr3fbarl0ljs/nYxDGWcf6h8gAGWgwynDnS/H5JXjqrFk5mY009K+dXsHyet9Lsit++y6d/Ys+EZDXmUncaJ851hzVHDXVORixRGoUiAONEnZrk7bFsXK1uWTmXzXGMMx9JK7GBYtYCqkpz+NonKvzuj917FU4Aa+fQWqAePNlGUW4aCyYXxOT7HkiJCjvaULCQ4kljs2hu79HDdrXrtqOlWo35ezdO96shFexYIyknQwkKRQDaRB3MJq1VLI2XGcjJNW8GG7MWcNd15fz7H/br38umXf4hqqHulbFCqrGw4NjsVL+y3MFaoA70+x7IxBnOHBkqpLh8dGbUJiSrMUdiQgvWHGq4hcwqQaEIwDhRazbpwWygMpJWYgPFPCmd7+nTzYFL55QjfLdk/d1VJLhEyHtV5s5g3Z0zONzU4SdYrhibzRUFWX7Je+E66pmJdzHIcJNzuPettFTNhPTAC3uC9hkPNuZINV7t/rSe7+XY6S4eNnWvc3qghhIUigCcMFGr7Fgv5gkuPSVRNwe++E4Dt8ws5vyFflITE5hRkhc2aW28O5Plz+0OWHlvWTlXb4Fqp6OeEbPZR8vITkoQMVsxj8kK3YO9r1+GnLyDmZC+88o+vzLope40/v2z00hJdPmN3VzOJhKNV7s/q7ccYNXCybqQMN5/pwdqKEHhAJyoiqqJ2hmYJ7in/lbH/3vjNJ58/bDfBKfVdxrvzgxZj8uqg173BQ8n27r1FqhAQK7MIy/t46pxuWHbiBbmpLK4qkS34cdixezxSI62dAQ1sR1r6eTRV/YFmElX31qpT97BTEjGPuPa2O/9za4AP8drB5oCmjJZlTa3Qrs/WtHG4RgyqwTFEHOp5wwoQmM1wRXnpFE+KoMvrt+pTzp56ckcburQtYVo6nFpLVD/eaYrosnMaPYJlpA5kBXzsZZOlj+3m7z0ZN3c5hIwpTBL78FuNsdJCUW5qQH+BvPix1jJNtjYNy6bHeD/WP7cbjavmMvs8lFhx28suT7QvI6hQoXHDjFOzd68lDOjnYY55DQ5OYEL/R6/ycaYTwHBn6NwIZsul9Cr1BoJNZkZQ0gjCde1izbRatF367bXsnZbLSd9Rf608xvfX/9WHfkZKSGPa+4zHmzsmqMfouunbbw/Wl6H8f4bNR+nElZQCCHGCSFeF0K8L4TYL4RY6dv+uBDioBBirxDiJSFErm/7DUKIXUKIfb7/5xmO9RHf9lohxFrha7YthMgXQmwVQhz2/Z8Xrwt2GvFu6h4Ng11qQRE55vh+uxO0pqFsXjGXDcuu0QsNGlfe40dFFv9vJXyMDHTFHC7/Itp8BWOf8VBjL8xJ8/2fyr3Xe7PfAf5We5rth5rC/i608f1hzwm/5NUV8ydQvaSKT08rDNkG1gkLtrA9s4UQhUChlPIdIUQWsAu4CSgGtksp+4QQqwGklKuEEDOAJinlh0KIacCfpZRFvmP9HVgB7AQ2A2ullK8KIX4InJFSPiaEeAjIk1KuCjWukdIzW+uTa+VAHCoT1FD2jFbYw6rk989j2Os50t7b2v5nOns4ca47ptVa7ZhnB9IrPNzYNR9Fw5lO+iV+fpKV8yv4l2ljQ0aEmc+RlODSS67b7RUeyzkhmp7ZYQWFxUleAdZJKbcatt0M3Cal/IJpXwG0AIVAPvC6lHKS773PAx+XUv4/QohDvteNPsH0VynlFaHGMVIEhROaupt5+8hpPv+LnQHbNyy7xpZNVjE4GCfHsdmpvN/YPmi+rr4+D/sbW2ls7aYwJ42phdl6SOlAJu1gRCu4Ig0QCXZdHo/k7brTLH2qJuA3+tQ9s7im3D2g67MiXgu2aARFRM5sIUQZMAOvRmDky8BGi4/cCrwjpewRQhQBDYb3GoAi3+sCKWWj7/VJoCDI+ZcBywBKSkoiGbpjcUJTdzMqM3p4YHbOluRnDEpIc29vP6/s/ZDvvPKeLpS+f9M0brqyyC//IMI1aEgiicKLdiWu1ThbveUAiyqLeL+xjbbzF/S8ip4+j+VvtKu3b8DXp53fKNycVFTQtqAQQmQCm4BvSCnbDNu/DfQBz5r2nwqsBhZEMiAppRRCWD5iUspqoBq8GkUkx3UyLpegNH/omrqbUZnRw5PBCGn2eCS7/nlWFxLgnbx+sv0wZe4MJJIPY2x6ipRoi0oea+lk9ZYDfmHHmrP5M5WXBf2NluQP7Hfh8Ujqz3TyTv05HjEk4v1iSZVj5gRbUU9CiCS8QuJZKeWLhu1fAhYBX5AGG5YQohh4CbhLSnnEt/kEXr+GRrFvG0CTz+Sk+URORXU1wxRjnLgTCojZcXgqLk2OtXRSbwqf1fIPvrh+J389dNqyUdBgRvFFW1TSmFeRl56sB5bUNXdQf6YzqJN//Kjof6Oa9vPi7hO6kNDG++gr+wIc7UM1J4TVKHx+hvXAASnlGsP2hcCDwMeklF2G7bnAn4CHpJT/o233+R/ahBCz8Zqu7gJ+4nv798DdwGO+/18Z6IUNJ8LFiQ8FKuFOAdbmkPTkRL+VrjH/wE6/kngnmAYr15GWlMDbR06HLAue4CJkZnqsKxZo2o+5eyB4W8kW5abGtQCnXeyYnj4KLAH2CSHe9W17BFgLpABbfVGuO6SU9wLLgQnAvwoh/tW3/wIp5Snga8B/A2nAq75/4BUQzwshlgLHgc8N8LqGFeY4cY3rLneHjaZQKOKFla3/F0uqeOpvdfzboqn8+x/3k5eeTEl+WoB5JJi5JBr/QaSCxWw6LXWn8fV5FSyu3hEyasolYGaJNzI/VGZ6LBdQ5krN5vuWn5GiaxBNvryRoRAWYQWFlPItwGpUm4Ps/33g+0HeqwGmWWxvAeaHG8tIRTmPFeGI1yo81HGtbP2PvrKP+2+4gmfePsoTd8ygtauXE+fO68+vueKwVvdJm+SkJCL/QTSCxZzNnpaUoAsJq3MazzFxTCb3zBk/aE7ksJWa89IdUblBlfBwAMp5rAhFvOLpzS1Vb68qZuKYLCYXeqsEW9n6NXPID2+7iub2HlZu8JpMtUmusbWbjTX1VC+pIi3ZxYfnuv3qPv3otisjmoSjdUwbTadvHzkd8pzGc+w90caHBsGnEa+FW7hKzU7p9qgEhQNwQrVWhXOJZLKIRPPQjhss2VMrPW5lDjGW9tZapGr+tbkTRnF1WT7HWjpZst6/WdDhU+0RTcKxCBENp7Fr59AaNiW4BN9ZNIXv/fH9AS3c7HwXTmsfGwwlKByCch4rgmF3soi0J7Z23GDF8P78jbmsvrUyINRVmzDN/bSffL3W29NiRpFerM887udrGvjBzdP9wkBDTcKxMMuG09gLslMpdacFlBv/2Rc/ElBu3C6RaIGhfvtOMUsrQaFQOBy7k4VRQzA3HSrJz7B0TKcmuSwjlfLSk9l1/BxPbPuApXO89Y2qSvO5rtytT3R2JmDzuM929TKzJNd2JE8szLLhVu1l7oyA9qbHW85z7292Bc2CDqctxMpk5BSztBIUDsKJfSkUQ4/dyaKprTtoaGe/J9CJrMXp1zV3BEzot1cV66t+Y79048RpZwK2GnckrXRjZZYNtWp3uQRJCcK2iceOthArk5FTzNJKUDgE1ZdCEQy7k0VBdiq3VwWakR55aR//9YWZQR3TVxbnUOrO8DMHTRyTZWuiCzcBL5hcwMZls321k1KZWpgT00k+GqwWZJGYeOxoC8bjXfR9QFpSojcUN4J74ASztBIUDsEp0Q0KZ2JnsihzZwSd4DNSEi0nwvyMFMpGZVKSn8FV43J1QSRj0GBHq53kpMVPsAXZgskFtk08drQFTZv61VtHuHnmON0xXv1G3ZDfg2hQjYscQrRlBxQKjWBNh0rdaWQkJ/CDm6eHbFhkbI6klasodadx3ycmsGL+BH6xpIqSvHTb43FiU65gY6o/22W7bE24/hiaxlKYk8LyeRN1IWE831A3JosUpVE4BKdENyiGJ3q/g64ev0glLSv5tp+9TV56ckCcfqiktQWTC7jQ74m6wJ9TQjsjGZMdE08on5FRY9GCAJx2D6JBCQqH4JToBsXww2xOKXWnUb2kiqQEQXryxazkxtZu1m6r1Z3S4Sb7+rNdlgX+7JpDnbj4icWYQvmM6po79O9hOPfINqNMTw5BVWxVRIvZnHK85TzLnqmhIDuVrt7+qE2aAzWHRtuiNJ7EakxmU12waKfh2iPbjNIoHIQTohsUw49QE/pAVtDBPjs2O5W65o6wYdxOCe2Ei6a5ls4eSvLTeOqeWXT19lGSnxHSBBcpxnu2aVcDS2aX6j2yrXJRhgtKUCgUw5xQwmAgJk2rz667c0ZELVeHcvFjFA4fnutmzdZDAU2JgvWTCJXTFOo94z3T6l5978bpJCUIv32HW85UxD2znYLTe2bbeRCG28OicCZ2S3dEs6o3f1ZK+PRPYt/HOdaYncrr36rT/w839lD3Ewib7xTufocrxhjvOSDuPbMV9rCTPKcS7BSxIpyJZyCrevNnw1Vidcrix+i30UqUGEuVaElwQkBzR49leXWrUigeG2XSw93vcMUYnTgHKEERB+wkz6kEO0UsGSwTTygzl5MWP2a/jdGZbJ6gf/mmfxJcqFIohTmpAw53DVWMcfWWAxTlptLb7yE5wUVXb78jrA0q6ikO2IkWUQl2isHA45HUNXfw9pHT1DV34PEMzNQcKmrISQl2xqQ4rSmQFoFkVebEOE5jKRRj7+zjLZ16hrsRY5tVO/dYG5u5GKPWe3zFht384+hZFlfv4PO/2Mmn1r7Jlv0nB/zdDQSlUcQBO5EmTowxV4ws4rHCD2XmclKCXTCnclqyi3NdF0KOUyuFYqVVTCzICtlmVevol5bsCqoRaGM7dLLNsvf40jnlloJsKK0NYTUKIcQ4IcTrQoj3hRD7hRArfdsfF0IcFELsFUK8JITI9W13+/bvEEKsMx3rI0KIfUKIWiHEWuFrti2EyBdCbBVCHPb9nxePix0s7MRqOzHGXDGyiNcKP1gOQbjSFoOJOS/p11+axZwJo7i6zM3lozNDjlMrhWKleTzwwh6mFGbpx117xwweffk9vfjf4qoS1m47xAdNHUE1Am1sN88o8iuromVxW5V9H2prgx2Nog94QEr5jhAiC9glhNgKbAUellL2CSFWAw8Dq4Bu4Dt4e2Ob+2P/FPgqsBNvz+2FwKvAQ8A2KeVjQoiHfH+vGvDVDRF248eTEwXLri/HI8ElvH8rFLEikoZHsXBAO626QDC/jZ1xjh+VwRUF1gUWmzt6cGekICW0dPb6aQQba+pZtXCyLYe3ygrDnAAAGiRJREFUuRhjWlIi1W/UAc7L5g4rKKSUjUCj73W7EOIAUCSlfM2w2w7gNt8+ncBbQogJxuMIIQqBbCnlDt/fTwM34RUUNwIf9+36FPBXhrGgAHuRD8uf2+34MEPF8MWOeTOW5iknJdiFws44XS5BcV56wP0rdafRcOa83uJ15fwJ+j4piS4WVRZx8GSbbROccZ7weCRrPncVq7cc0HuQO0HgQoTObCFEGTADr0Zg5Mt4J/xQFAENhr8bfNsACnwCCeAkUBDk/MuEEDVCiJrm5uYIRu48lDNbEW/smDdjbZ4KZpZyGi6XoMydwZgsb0vYYy2dAc7i3v7+gPIbqxZO5mFf3w7wtnZdOb+CUncaFWMySXBdrO9kxJjRHszprQmwX39pFrPG57Fx2Wx++1VnlPOx7cwWQmQCm4BvSCnbDNu/jdc89WwsBiSllEIIS/e+lLIaqAZvwl0szjdUKGe2It7YWTk7yQE9mNjRpPLTU/TyG0KAlFDX3OF3vxpbu3n67eM8sfgqvrVpDw8tnMxjFhrBjxdfZSuj3allfGxpFEKIJLxC4lkp5YuG7V8CFgFfkOFTvE8AxYa/i33bAJp8pinNRHXK1uiHMcqZrRgMwq3wneSAHkzCaVIej+RoSwd3XF3C+rfqWLe9lvVv1XFlcW7A/Trb1UtHbx/HW87z07/Wsuz6y3UBs2L+BKqXVDFhdKZjQoejIaxG4YtMWg8ckFKuMWxfCDwIfExK2RXuOFLKRiFEmxBiNl7T1V3AT3xv/x64G3jM9/8rkV7IcMDsNFwwucB2k3mFIh4MxAFtrKXkpOQwO4TTpDQfYl56sq5RuASUuNMs71epz5+x90Qbzdtr9dan8yeNYXpRLjuPtkSluTkl092O6emjwBJgnxDiXd+2R4C1QAqw1RflukNKeS+AEOIYkA0kCyFuAhZIKd8Hvgb8N5CG16eh+TUeA54XQiwFjgOfG/CVOYxQqq7T1EzFpUO0DmjteV695YBlob2htqmHI5zpVxMkja3dPPl6rb7PdZe7A+5XSV46fz18ipXzK3hi22EaW7tZ/5Y323t6US4ul4jK1OykTHdVFHCQqGvu4FNrnV9MTaGwg/Y82y205wSMq/PCnNSQPgO7v1ePR7LvxDkWV+/wqw3lEnDLjCLKRmXq+0U66cdrzlBFAR1MKFVXK38w1OqlYmQSD/OF9jyHSg5zkqCwmqjX3TmDP319Ls0dgZqUHZOcdkwtHNZK+9AEhVFzO9PZQ5LPVHespdOyuuyxlk4+aGp3zL1VgmKQCNUExinqpWLkES/zhdEJPhyi96yc18uf283mFXOZXT4qYH87Jrmjp73H/Mrcclv3QAvJPXgyuCZj/L7sHncwUEUBB4lgUU79HuuyxcMlGkLhbOJVxkN7nq1afToxei+avKVQEWMej+RAo1eT0IoO2rkH4b4P4/uRHDfeKI1ikAi2Qok2GkKhsEO88iT053lsFmc6e9i4bLajo56MGr3WiyLBBWlJiXg8MuLxHmvp5PCpdlKTXDS2dvPMjuN6u9P5k8YwtTDH0twX7vswvm88bmVRNhUFWY6OelLECKtkGu0BNjrCEgSMzXaW6q4YnsQzsdOpyWFWaBqQOUqr+o26qExxTW3dPF/ToCfWaZFOP7h5OlMLc3jtQJNfhVmtHWp6cmLI78P8fWnHHergABX1NASYoy9qmzs43NTBE9uGV4ihwvk4KcRyqDFGKA00kkiLSLKKdPJI9GilwpxUlswuZWNNPYsqi8hJTcCdlcq3fWVANKf6eHcmp9rDR2PFAhX15DCsok0gsOfuL++u0oUEOKP+vGJkEKtCfU5J/BoILpegq7c/oFnQLTOL+aCpHcD2dRmjop58vVaf0Evy/c3JWkVZoxZT6k7jZ1/8CCmJLl0waH3Iw0VjDRVKUMSJYCu5KwqyApxZfz96RvkpFHFjoCaikaSVmH0V0fasDiWAjecQAhZVFvn1tTjecp57f7OLzSvm6j24jZrJvoZWJvic6E5BRT3FiWDRDcfPdAYIhWDVJp0WYqi4NAn2LNef6Yxpm9XBwBh9aNWzOpKIME0AzypzA7DzaAt1zR2U5KX7RThqDYnAq8ForVWbO3o42XqxP7dWU+rnb9TxTv05R91PpVHECS16QVNthW+BkpUS6Mz6w54TrL61klWb9jqm/rxCoWEVqZOXnsw79ed4xGBrHw5ahlET0BLazL/RM509trWvYNqWVsftTGcPnT39esCKUYPZcaSZ5fMmWnbSe+SlfVw1LtcxFgUlKOJEQXYqpe60ANvkteX5/ODm6X4/sFULJ7NgcgHTi3JUgUCF47CKnLq9qlh/hmF4+dU0TQAI+I2mJrmoGJPJTEPIbDD/jOYc14SEJnAOnmyjKDeN6UU5lI/OpK/Pw+pbKznS3KGfpzAnlS/PuZx/+/17LP9EheNNz0pQxImSvHS++5mp/K9n39EfjMVVJSx9qoa89GSWXV/OxIIsphZm45Hwj+NnKMhOZVaZWwkIhaMoyUsP0HgnjrFuE+qkyS0cZe4MvnfjdJY9U+PnI6hr7qD+TCdlozJDagyvHWjSy3eY/R3GsNvERBefqbyMv35wys/JffBkG8dbznOqvdsxGdjBUIIiTtSf7WL/h21+D4b2EDW2drN2Wy2l7jTuv+GKAJOT09V3xaWDxyN57UATa7Ye0hPKqkrzKcoZ/o23XC5BUoIIMAmlJrkodWdQkp8R1D+zcdlsvzIbwfwdmoalle8wtk3t7fee6zc76h3X+tSMEhRxoqmtm+4+j1/0g3kFtqiySP8BavbR1VsOMGls1rBZlSlGNsaJUit4l5rkYsvKuVH3sXASBdmpIX0EwTKpG1u7/cpsdPf1h9WwjEl/FWMy/TrhPbPjOMuuL6diTCZTCnMYP8pZpmclKOJEQXaqXgdn7fbDQGDxtJzUhAD76Ip5FRE50xSKeBJsojzZ1h2T/IyhpsydEdKMFiyzvTAnTY9qEgKqSvPCaliaI70oN5UVG3azuKpE74SX4IKZJXnMuXwUiYnOC0Z13ohGCGXuDFYtnKw/CGOzkvnBzdP9CnxNL84NWMms3X6YpAT1tSicQahWqeHarA4HXC7B5MLsoNcYrJjn1MJs1t05g7uuLeWJbYdZtWkfK+eHL+CnJf0dbznPMzuOs6iyCCGg3wOZKQmOFBKgNIq4YS6aduJcN2u2HmLl/ArG5aWT4BJ091qrq129/UM0aoXCn4G0Sh0ujB8V/BpDJdaNd2ey/Lnduinq6be95qMZ43IpdWcE1bA04WvsX5Ga5OLWmUWDfem2UYIijhjD8L64/u/kpScjJXzzd3vovuBh5fwJlupqgSoIqHAIsSoB4mTCXWOwzPZT7f5mucbWbl6oaeCjl4+iqc1bvtzqXg1H4RtWUAghxgFPAwWABKqllE8IIR4HPgP0AkeAe6SU53yfeRhYCvQDK6SUf/ZtXwg8ASQAv5RSPubbPh7YALiBXcASKWVvLC90KNHsvObIiOdrGvQ+u8PlgVFcegynKrHREs01mv0XhTmp3HVtKXf/+u8hoxiHo/C1YxDrAx6QUk4BZgP3CSGmAFuBaVLKSuAD4GEA33t3AFOBhcB/CSEShBAJwJPAvwBTgM/79gVYDfynlHICcBavkBkReDySvn5JapIrIPJJU1efumcWG5Zdw+YVc1VorEIxTDD7L26vKrYs7mlVEmS4+XfCahRSykag0fe6XQhxACiSUr5m2G0HcJvv9Y3ABillD3BUCFELzPK9VyulrAMQQmwAbvQdbx5wp2+fp4DvAj8dyIU5hWMtnTz6yj5WzKugp6/fsnlKalIC04tyHP+wKBSKi5g1A3NlWhh+SYjBiMjFLoQoA2YAO01vfRl41fe6CPin4b0G37Zg293AOSlln2m71fmXCSFqhBA1zc3NkQx9yGhq69YjHFKTEvjOoimUutP0ImBrt9WyuPpttuw/6agiYAqFIjxGzUBLqDMy3JIQg2FbUAghMoFNwDeklG2G7d/Ga556NvbD80dKWS2lrJJSVo0ePTrep4sKj0f6VdQck3UxwmH1lkOs217LtxZMGlDVSoVC4TyChdKOBJ+jragnIUQSXiHxrJTyRcP2LwGLgPnyYqu8E8A4w8eLfdsIsr0FyBVCJPq0CuP+wwqrujDr7pzhF+Fwtsvrox+pKqpCcakyHJ3UdrET9SSA9cABKeUaw/aFwIPAx6SUXYaP/B54TgixBrgMqAD+DgigwhfhdAKvw/tOKaUUQryO18exAbgbeCUWFzfYWNWFWf7cbrasnMtmw8MjpbdqpZZsA95S4yNBRVUoLmVGaoSYHY3io8ASYJ8Q4l3ftkeAtUAKsNUrS9ghpbxXSrlfCPE88D5ek9R9Usp+ACHEcuDPeMNjfyWl3O873ipggxDi+8BuvIJp2BGq3IEW3QDQ1+fh6/MqePTl93TN4/s3TaMkL30ohq1QKBQhsRP19BZebcDM5hCf+T/A/7HYvtnqc75IqFnm7cONYHVhzJpC/dkuXUiAV5g8+vJ7zCzJG3ErEYVCMfxRmdkxpMydwbo7Z7C3oRWPhAQB04tzApxZwTQP5aNQKC4dgjVEciJKUMSY3j5J9Rt1fpmZZuxqHgqFYmQSrCGSUxNunVmqcJhitwm9ufn6SAqjUygU4Qk2Vzg1RF5pFDEkkib0WvP1kRZGp1AowjPczM9Ko4ghVrX7gzWhrz/bNaxqvSgUitgRqs+HE1GCIoZomZml7jTu+8QEVsyfwJTC7KArB4VCcWky3LK4lekphrhcggWTC+jr9/Dgpr3kpSfzr5+ZEuC4LnWnkZaUwNtHTjs+2kGhUMSe4ZbFrQRFjDl+posHN+3V+0889urFBurdFzyUutNY/okKFlfvGBbRDgqFIj4MpyxuJShizPEznXoZ8ZK8NL1y7NI55QgBEwuyeNDX4Q4u+iwmrZg7LB4YhUJx6aEERYzJSE6k1J3G4qoSPmw9H9Abd8X8CcMq2kGhUCiUMzvGFGSn8NDCyazdfpjnaxpYMa/Cz2F1dWn+sIp2UCgUCqVRxBCt8VCfR9J9wUNja7ef2WnuhFHMKM5l9a2VrPL5MZwe7aBQKBRKUMQIY0r+1+dN0COdNLNTapKL22YW8ZdDp1iz9RBL55ST4IKq0nyuK3crR7ZCoXAsSlDECGNK/m921PtFOmlaQ78HfR/NZ5Ga5GKzcmQrFAoHowRFjDCm5BtNTpVF2VQUZFHmzmDn0RblyFYoFMMO5cyOEeaU/MbWbta/VUdFQZZeomO4pe0rFAoFKEERM+yk5A+3tH2FQqEAZXqKKcmJgmXXl+OR4BLev40Mt7R9hUKhABuCQggxDngaKAAkUC2lfEIIcTvwXWAyMEtKWePbPxn4OVAFeICVUsq/+t77CPDfQBrelqgrpZRSCJEPbATKgGPA56SUZ2N1kYPBsZZOlj+3O6AZkdlRPZzS9hUKhQLsmZ76gAeklFOA2cB9QogpwHvALcAbpv2/CiClnA7cAPx/QgjtPD/1vV/h+7fQt/0hYJuUsgLY5vt7WBGqvrxCoVAMZ8IKCillo5TyHd/rduAAUCSlPCClPGTxkSnAdt/+p4BzQJUQohDIllLukFJKvFrKTb7P3Ag85Xv9lGH7sEE5qhUKxUglIme2EKIMmAHsDLHbHuCzQohEIcR44CPAOKAIaDDs1+DbBlAgpWz0vf6/7d17jFxlGcfx729butAu0JbLpqEstKQBG4OlrtxtDEgRgthENBACVaoN4h8gEUIlEjUmiCaoEJWLIP6BcitIg4FyTUAhhS30BqUXSrml0CJyT4hlH/84z5bpsjvMbrc7O3N+n+Rk3vOed868D5ztM/Oec97zOsUwV0PxiWoza1Y1n8yW1AYsBC6IiHerNL2R4rxFF/AS8Djwca2fk+csop8+zAfmA3R0dNS6y2HhE9Vm1qxqShSSdqFIEjdHxJ3V2kbEVuBHFe99HFgL/BeYXNF0MvBalt+QNCkiNuUQ1eZ+9n0dcB1AZ2dnn8mknnyi2sya0WcOPUkScAOwOiKurKH9WEnjsnwCsDUinsuhpXclHZn7PBu4O9+2CJib5bkV9UOuuzvYsOV9nnjhTTZseX/bRH5mZta3Wn5RHAOcBayUtCzrfgK0AlcD+wD/lLQsIk4E9gUWS+qm+MVwVsW+zuOTy2PvzQXgV8BtkuZRDFd9e0eC6k/lxH1+upyZWW1UXIDUeDo7O6Orq2tA79mw5X1Ovuqxz7zXwcysWUlaGhGdA3lPqabw8L0OZmYDV6pE4XsdzMwGrlSJomPCWK745qG+18HMbABKMylgd3dw/+o3/HQ5M7MBKk2iqHwCnZ8uZ2ZWu9IMPflEtpnZ4JQmUfhEtpnZ4JRm6Kln0r4r7lvNKYfux6gW+NIBE+mYMLbeXTMzG9FKkyhaWsTsz7Wz9eNuLl64wndmm5nVqDRDTwAvvfXhtiQBxTmKC29bxotvflDnnpmZjVwlSxQf9HlC++W3nCjMzPpTqkQxbszoPk9ojx1TmhE4M7MBK1WiaN+jlfOPn7bdndnnHz+N9j1a69wzM7ORq1RfpTsmjmNaexvzZ02lO6BFMK29jY6JnsLDzKw/pUoULS3iuIPbmbp3mx9XamZWo1IlCvDjSs3MBqpU5yjMzGzgnCjMzKwqJwozM6vKicLMzKpyojAzs6oUEfXuw6BI2gK8NMi37w28OYTdqbdmiweaLybHM7I1WzzQf0wHRMQ+A9lRwyaKHSGpKyI6692PodJs8UDzxeR4RrZmiweGNiYPPZmZWVVOFGZmVlVZE8V19e7AEGu2eKD5YnI8I1uzxQNDGFMpz1GYmVntyvqLwszMauREYWZmVZUuUUj6mqQ1ktZLuqTe/emPpBslbZa0qqJuoqQHJK3L1wlZL0lXZUwrJM2seM/cbL9O0tx6xJL92F/SI5Kek/SspPMbOSZJu0p6UtLyjOfnWT9F0pLs962SxmR9a66vz+0HVuxrQdavkXRiPeKp6MsoSc9IuifXGz2ejZJWSlomqSvrGvKYy36Ml3SHpOclrZZ01LDEExGlWYBRwAvAVGAMsByYXu9+9dPXWcBMYFVF3a+BS7J8CXBFlk8G7gUEHAksyfqJwIZ8nZDlCXWKZxIwM8u7A2uB6Y0aU/arLcu7AEuyn7cBp2f9NcAPsnwecE2WTwduzfL0PA5bgSl5fI6q43F3IfA34J5cb/R4NgJ796pryGMu+/JX4HtZHgOMH4546vI/r44HzVHA4or1BcCCeverSn8PZPtEsQaYlOVJwJosXwuc0bsdcAZwbUX9du3qHNvdwAnNEBMwFngaOILiTtjRvY83YDFwVJZHZzv1PgYr29UhjsnAQ8BxwD3Zv4aNJz9/I59OFA15zAF7Ai+SFyENZzxlG3raD3ilYv3VrGsU7RGxKcuvA+1Z7i+uERlvDlMcRvEtvGFjymGaZcBm4AGKb89vR8TWPvq2rd+5/R1gL0ZQPMDvgIuB7lzfi8aOByCA+yUtlTQ/6xr1mJsCbAH+ksODf5Y0jmGIp2yJomlE8VWg4a5tltQGLAQuiIh3K7c1WkwR8XFEzKD4Jn44cEiduzRokk4BNkfE0nr3ZYgdGxEzgZOAH0qaVbmxwY650RTD0X+KiMOADyiGmrbZWfGULVG8BuxfsT456xrFG5ImAeTr5qzvL64RFa+kXSiSxM0RcWdWN3RMABHxNvAIxdDMeEk9jxiu7Nu2fuf2PYH/MHLiOQY4VdJG4BaK4aff07jxABARr+XrZuAuioTeqMfcq8CrEbEk1++gSBw7PZ6yJYqngGl5JccYipNwi+rcp4FYBPRcoTCXYpy/p/7svMrhSOCd/Cm6GJgtaUJeCTE764adJAE3AKsj4sqKTQ0Zk6R9JI3P8m4U51tWUySM07JZ73h64jwNeDi//S0CTs+riKYA04AnhyeKT0TEgoiYHBEHUvxdPBwRZ9Kg8QBIGidp954yxbGyigY95iLideAVSQdn1fHAcwxHPPU6yVSvheJKgLUU48mX1rs/Vfr5d2AT8D+KbxLzKMaAHwLWAQ8CE7OtgD9kTCuBzor9nAOsz+W7dYznWIqfxCuAZbmc3KgxAYcCz2Q8q4DLsn4qxT+M64Hbgdas3zXX1+f2qRX7ujTjXAOcNAKOva/wyVVPDRtP9n15Ls/2/L036jGX/ZgBdOVx9w+Kq5Z2ejyewsPMzKoq29CTmZkNkBOFmZlV5URhZmZVOVGYmVlVThRmZlaVE4VZkvQzST+usn2OpOk17Ge7dpJ+IemrQ9VPs+HmRGFWuzkUs6MOqF1EXBYRD+60XpntZE4UVmqSLpW0VtK/gIOz7vuSnlLxrImFksZKOho4FfhNPtvgoFzuywnnHpN0SD/tbpJ0Wu57o6TLc1uXpJmSFkt6QdK5Ff26KPuwQvmsC7N6Gf3ZTcyak6QvUkxXMYPib+FpYClwZ0Rcn21+CcyLiKslLaK4Y/mO3PYQcG5ErJN0BPDHiDiuj3a9P/rliJgh6bfATRTzLO1KcYf3NZJmU0x9cTjF3bWLJM2KiEd32n8MsyqcKKzMvgzcFREfAuQ/8ACfzwQxHmijj3lwchbco4HbKxJBa42f2/M5KykefvQe8J6kj3L+qNm5PJPt2igShxOF1YUThdmn3QTMiYjlkr5DMfdRby0Uz2qYMYj9f5Sv3RXlnvXRFL8iLo+Iawexb7Mh53MUVmaPAnMk7ZazjH4963cHNuW06GdWtH8vtxHFszRelPQt2PZ84i/0bjdIi4Fz8lcLkvaTtO8O7M9shzhRWGlFxNPArRSzi95LMQ09wE8pnr73b+D5irfcAlyk4uliB1EkkXmSemYn/UY/7Qbar/spnlv9hKSVFM8d2JHEY7ZDPHusmZlV5V8UZmZWlROFmZlV5URhZmZVOVGYmVlVThRmZlaVE4WZmVXlRGFmZlX9H1FwFcbn+p+VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt =  sns.scatterplot(x=test_df_svm['b4_x_rms'],y=svm_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "mO9t9rJWpon0",
        "outputId": "dafeaca4-965e-47d9-acfc-611886bec20b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dE7f96TNDmC802ELmgAjCrBYOldehUdQ3t7JkY4HfNRbdMJqESCtz/+or7DcsKYqn39vvN9N6Xm42Iy6KZltRJU4Na7YVU1rtwBZiYcWcy4gPD+XoyXq++LqK17cWGx6NJx8iOQVBEKB7BkJyEB1g1njW2urk44ITrNtdSnVDCwu+O4zKhmbe2F5MSJDiwb/tZM4r26hrbuOZ9w/g1OY5hZKqRqq8zn/+gwJe2lyIBr+Z1nHhVqLDQvjPFa7S15c2u/IQnnLWw8frUQpsIUEs2Vjg12/RvtFN9JYEQegKkoMIgFnj2dKbx9PQ1MZDXvmE+64YwrzcbNLiwvmZVyjJU43U2YS6uZdnMWdyJkqBRUFqrH+z2o05acbcCfCvVLJZgymvdXSp0U30lgRB6CpiIAJgpku0q7g6YI/CV2X+qq+2EAul1Q5WbCni6Vlj/UpaARytTqNLG1zlsO37L+zx4QErlR6bMZJX/1XIU7PGGRVWi9bvMybdXZoejz0uvMPvda7E/wRB6N2IgQiAmS5RoB4Fj2SG99P7mm3FzMvN5tmNBymtdlBQXstLmwv9nu7bd1NHhoYweXAsq/ImUVrtID7Cyu7ialPP4JL0OJ7beICbJ2YQZIHDJ+pJiQnl3twhPOLVlOftIcj8akEQuorkIAJgJrURqEdBa3hnZwm/uXaU8XllQzPhIUHMvTyL/NwsRl4Uw+PXnfrcNVxoFGt3uRTP0xPCWDx7HHVNzfzjy1JuWraFe/6yndv+9BmxESE8ds1In3MXXjuKfaU13PHNTP78f4dZt/sY33tuM+/vO24YB/BXZA0kISJ6S4IgtEeqmAIQKAfR3Kp91n43czRp8WEkRISSFhPG/vIaDp9o4NDxOl53dzw/NmMk//tZEdWOFpb8YDyNLW2EhQSxcO0eJmYOJMoWRJQthIVr9xp5hfbewv1XZJMWH0Hh8ToyB0by4ocFHCivY87kTADjnLnTsli6yb9qamXeRCZlDpAchCD0c87ZyNH+RCCpDaBDraJoWwj/rmokNSaMBVcPp7iygTXbj3L31MEcKKvF0epk3EWx7DlWw+3fHIRSiuM1DiMJrZR5GKumqY37V+/gD7PHUXC8ju8MS+T//04WL3xYwNShiX4Gpf37sJAgnE4tekuCIHQZMRAdEEiXyLPWfla0PS6c7UereHbjqYqlR6YP48oRKUaz3EubC/nN90fx/IcHKapoxBZi4bczRzMkMZJdJa5JrmYbvHbnP1qcTpZ+UOBTERVutZCfm4VTQ2RoEPddMYRn3j/gc0z+yi+YP3244SmI3pIgCJ0hIabTxCxUs+iGMSze8JVPH0N+bpZP5RO4Nvw5kzON6iVbiIU/3pbD0+v3c7yu2a8z27ss9v4rsql2tBmJ8S2HjnPDJXZ+/c6p7uoF3x1GdnIUnxyqoM0Jb2w/1VQnTXOC0L+RRrnzgFm56Pw1u4wpcR46q3zyvP/8yEluuswOwKqtR3lm9jgWzx5L3tRMwzj8141j0SiWf1zImm3FWBT8+PJswzh4rvXEu/tpbGpjycYCHyHA9k1z0jAnCEJHSIjpNAlULhrUzuR6Kp86K29tc8LCtXt5etZYFPCbtXtJjQ3lwauGkZMeR1Ork/gIK0/9cz9x4VbDy7h7Sqbpfdislg6b5iRZLQhCZ4gHcZoEKhfNSY/3KUcdkhTFUzeMaVfeOtIob/UMD9p8oBxHi5OiinqiwoJ54Koh5OcOYVdJNXkrtvGjV7dxy0ufclOOndu+4TIOceFWhiZFmd7Hl8XVfmW13vLfgRrmujOBThCEvk2nHoRS6mLgVSAJ0MAyrfWzSqmngWuAZuAQcKfWusrrPDuwF/iV1vr37rXpwLNAEPCS1vpJ9/ogYCWQAGwDbtVa+87o7GEEmjj3zcwE1plUPo1KjeFAWS3NbZpX/1XIjDGpKAVaw4sfFfCDS+18Z1gigwZEsL2oktVbi7ntG+lGwhtOSWw8PWus4UX8/r39plIeL/+rCGuwYlXeJBpb2vyqlaRhThCEzuhKiKkVeEBrvV0pFQVsU0ptADYAj2itW5VSi4BHgPle5y0G3vW8UUoFAc8DVwLFwOdKqbe11nuBRcAzWuuVSqkXgTnAC2fh+50zOioXDVTllJ0YxcZ9x5iYOdDIQXgGDUWHhfhUP/1yxgjiI6zcPSXTUG4F1ybe2NzKjTlphlFYsaWIOZMzCbJAVmIUv1u3zzi+saWNSZkD/O6/K7pNgiD0bzo1EFrrUqDU/bpWKbUPSNVav+d12BZglueNUuo64DDgHa+4DCjQWhe6j1kJXOu+3jTgZvdxrwC/oocbCOh4PKdZjP9Pd1xCQqSNxe+fksF4ZPowEqNt7DtW42MMfuPVNOc9Y9oWYmFYchShIUHG5l5a7TAqouZOy/KR+g604QfygGQCnSAIHrqVpFZKZQDjgU/bfXQXsMp9TCQuT+JK4EGvY1KBr73eFwMTcYWVqrTWrV7rvqVAp35+HpAHYLfbu3Pr5532Mf64cCvVDW38/O+7fdYaWtq4z2uT9jYG9rgw7p6SSVNrmxFu+q8bxxIRGkK4NYh5uVmsbjf7weKlC+XZ8Nt7Mh5PRxrmBEHoiC4bCPfGvwa4V2td47X+c1xhqNfcS7/CFS6qU+rsbjZa62XAMnD1QZzVi59lvGP8KTE2Hrl6ODWOFp+QzvUT0kxzDB7PoaS6kaWbCozE9i+uHkabU/O95zabTo97YuZoxqXF8M3BCT75j01flbGruBqndlVVjU6LYdrQJGmYEwShQ7pkIJRSIbiMw2ta6ze81u8AZgC5+lTH3URgllLqKSAWcCqlHLiSzxd7XTYNKAEqgFilVLDbi/Cs92o8MX5PMvmhv+3k7imZPnH/QLIaQRaMjd+z9su39/DyHZdy558/9zEoz248yH/fPIGG5jZGp0WTnhBJZmKUcb0jJ+o4WFZnNOt5Gum+LKmivrnNx6MQBEHwpitVTApYDuzTWi/2Wp8OPAR8W2vd4FnXWk/xOuZXQJ3WeqlSKhjIdlcslQA/AG7WWmul1Ae4chgrgduBt87Gl7uQeGL8+4/VGMnk9sODAvVITBqUwAOv7/SbDFfd2GJqUI7XNfF1ZSPJMaGkJ/h6A2U1TT5eSly4lfrmNmYv2yL9D4IgdEhXPIhvAbcCu5VSO9xrC4AlQCiwwR1K2qK1vifQRdzVTnOBf+Iqc/2T1nqP++P5wEql1OPAF7gMUq/GE+OPsgXR5swkNNjCoAERVDY0Gc1wX1c28Ng1I31kMvKnZVNQXktlg2+Vry3EQkqMeeXR0ZONLP+4kOzESCa4Bfk81De3dhrWun/1DhLuvIyBUaHiTQiCYNCVKqaPAbMdY10Xzv1Vu/frzM5zVzZd1tn1ehtOp+ZEbYshxe0xAMs/3s9vvj+KZzce5N4rso2Ro55pcwCPzhhhKLzaQiw8MXM0seEhfpVHnqS2R+ojOdrms9Gnx0eQnhDGjDGpRNmCGJ4STWhwFs1tTqNiytHiZHPBCV7aXCjehCAIBiLWd45wOjUfF5wgb8VWU6G+tbtKuDd3CEcq6vkfEzE/jyhfkAWGJ0fzgnv+w9KbxzMoIZKD5bXsLqkxhPg8zJ2W5bPRO52av+8s4blNB7kpxx5QBNAjHiiCfoLQt5F5ED2AIxX1bC06GVCor6iikeaWVr45eACDB0Zy6Hgdq90DhhbPHseIlCgOn6jni6+r+M3avZRWO0iJsbGruBqLUqTEhPG7d/f5GAePxlNcuJX9x2qwhVhIiAjluU0HmTEm1TAOnvtYsukgeVMzsQUHGZ6LdFMLguBBDMQ5oqzGgVMHFupLTwgjKDiI21/+zCeMNMEeiz3eFR4qrXawZGMBKTE2HrxqCEnRNoorG/j5m19iDVb86pqR7Pl3DY5WJ+/sLOGmHDvrvyw1lQtvdWpTY5UWG8Yz7x/sUnOdIAj9CzEQ54ikaBvv7Cwx1UlatfUo86cP9xPLW/Dmbv4yZyLH65qwBlmob2plwXeHolE+A4B+OWMEkbZgdnxdxQf7y/nOsETuvWII9Y5WfjItm3krvzDVbzIzVskxYUZCXLqpBUHwRnIQ5wiP1Mai9fuYMSaVzAHhhAQH8e+qBmodbYQGW1i84YDfeQuuHkZEaDDLPjrEjDGpDE+O4kH3NDoPthALcy/PwhqkiA0P5Zdvf2kYj0dnjGDppgKf0BPAgu8OpdWJj7FaPHscVw1P4mhlg3RTC0I/QXIQ54lAEhbgJeaXHEV5rYOwkGBuWvaJsdHPnZZl+kQ/LDmaR9/abSSUA817SI62cbSygcXvf+njLSxcu5e8qZks2Vjgc92JmQm0tDlZlTeJhnYNctJNLQiCGTIP4jTxeAhXL9nMD//4KVcv2cz6Pcd8prJ5Nt9JmQMYnRrD4tnjjPkM7+wsYeG1vvMaHp0xgv2l1X4JZbN5D0crGwJOq7PHhfvNgRiZEkN8RKifcRAEQQiEeBCnSaCBO8MClIiaiePZ48IZkhTJxv3lDEuOovhkAzVNrtJWz3Xbd197h5FuuCTN1AupamzmqVljcTS3khxjwx4fznv7ys7q9LiOvCdBEPoGYiBOk9MZuGOxKCMBXFbjyhGMTImhqrGZ1jbN4MQofrN2Dw9PH25s/KXVDlZsKSJvaiZDk6KICA3mwLEaKhuaTY3H/VcOIcRi4SF33sIWYuHx60bz3KYDnRoz700/McpGkMUlJd7eAMi4UkHoH4iBOE1OZ+BOoI3VouDeVTsYkhhJ3tTBLP/4kE8ndWVDM7bgIJ76535umZiOLSTI+NxjPAYPjMQeH8bx2mby21Ux/eLvu3lq1lgOlNUCGB3U3sbM7N68lWK9DUB3vSdBEHonkoM4TTxifIFmPpsRaGPdW1qDo8XJrpIalm4q4KoRydjjwnj5jkvJz81izuRMVmwpoqiikcUbDlBW28zSTQXMmZzJDZekMf7iWJZvLuTeVTtobGkz9WwKymtZs60Yi4L7rxzCw9OHkhxt6/Dent14kOsnpPnMq/Z4GYG8J0EQ+g7iQZwmpzNwJ1BYytmu0rjVCT9asY27p2SydFOB3/FKnZoiZwuxMGTWWHaVuEZ0lFY1mno21iCLXwOdPSHCaMoLdG+ekR6OFicn65vYf6yWr9xd2jKuVBD6NmIgzoDulogGCkt525TrJ6T5VTB5H5+eEMawpCjmTssiSEF8uJV/VzWQEmPj+glpoOCJmaNZ8Oapsab507JRClZ+ftQQBgRYtH4fw5KjyBwYGfDePG0ythALIUEW7l+9g7hwq0/uIz0hjIXXjjbyKpKwFoS+gRiI80igOdDWYGVszh1VMKUnhPHj72QZjXOepHRoO+8gJz2G/75lAju+riI7MYrfv7efH15mNxXrO1nfRObASNN78+QgPPfZ0NzmkzifMzmTKFsQiVE2Q5RQEtaC0HeQTurzjCeG7x2WAoy19g11KTE2bsxJIy02DJs12KhO8mALsfCH2eO41yt/8JPLswyJ8ZQYG7dOSsceH27akf2XORNpaXOSFO0qu/V0VQ+MdFUxHas5dZ9HKuq5eslmn2vk52YZ0+q8ryuKsILQM5FO6h5MoLCUZ83p1D5P8pUNzYSFBPHM+we54ZI00zyBVjD38iwGRoYSHhpMU+upRLXnaf+Bq4aYnrv9aCVPrNvv8+TvfW8ZA7xem3gZQxKjul3uKwhC70AMRA/DO/l9sr4JpxO2HD7JDZekMSAixDRPEB4SxNIPCoxN+5mbxvkcV1rt4OuTDQHF+qBrpapmiXkdQLFWEtaC0PuRMtceiKehrry2mdte/ozFGw7w0uZCbCHBLPjuMJ/S2ievH2OI9YFro3/y3X08ds1I0hPC+MnlWeTnZnHZoHjmT/c9974rhvDvKmOceJdKVb3lQzIHRjJoQPfLfQVB6B106kEopS4GXgWSAA0s01o/q5R6GrgGaAYOAXdqrauUUpcByzynA7/SWr/pvtZ04FlcM6lf0lo/6V4fBKwEEoBtwK1aa9+hzP0Ms76EX72zh7mXZxmVSBYF4dYgiioafc5tbtVE2YLImzrYaLZLTwjj3iuGkDc1E6d2nRsWYqG+uc04z+zJvzNJjdMp9xUEoXfQlRBTK/CA1nq7UioK2KaU2gBsAOAUQpYAACAASURBVB7RWrcqpRYBjwDzgS+BHPd6CrBTKfUOLuPyPHAlUAx8rpR6W2u9F1gEPKO1XqmUehGYA7xwlr9rryJQX4Kj1cnzH5zqjVh0w2i/EM+NOWkcKKvzSR7PGJPKI2/s9gsFzcvNNl57P/k7nZqiinq+rmzgWLWD43VNrN76NfOnD/epUBJNJkHou3RqILTWpUCp+3WtUmofkKq1fs/rsC3ALPcxDV7rNlyGAeAyoEBrXQiglFoJXOu+3jTgZvdxrwC/op8biM76Ejzvj9c2cd8VQ3wGCtnjwzlS0eBzrlLmyq8ZAyJY/Z+TqG9qxR5/yjhs+qqMg2V1PLvxVFnsfVcM8emdEE0mQejbdCtJrZTKAMYDn7b76C5glddxE4E/Aem4wkWtSqlU4Guvc4qBibjCSlVa61av9dQAPz8PyAOw2+3dufUeQ1efuM0qhh6/bhQn65qMJrkBkaH894eHsAYrVudNoqK+mXBrMGEhQQGT0mYG57Y/feazwY9IiWJXcbWPB+JocfLM+weYMznTqFASTSZB6Nt0OUmtlIoE1gD3aq1rvNZ/jisM9ZpnTWv9qdZ6JHAp8IhS6qyUtGitl2mtc7TWOQMHDjwblzyvdGWGhAdPbH9d/hRW5k3knbmTiQkLoc4rZxBlCyY1NpT504czKjWWbw9JZGBUKBX1TYy3x7Lgu8OMRHV0aBC/vW603/yJRev3+W3wZTVNAWdNBFkw8hQdKdoKgtD76ZIHoZQKwWUcXtNav+G1fgcwA8jVJh13Wut9Sqk6YBRQAlzs9XGae60CiFVKBbu9CM96n+N0Zkh4upw/KTxBQfmpvIInf/CL741kTFosgF+453fXj+an07L5xd+/NBLVz8weR31zK4dPNFDraPFLcDtanDQ0txKkzD2OnPR4I09xOoq2giD0Hjr1IJRSClgO7NNaL/Zanw48BHzfO++glBqklAp2v04HhgFHgM+BbPfnVuAHwNtuw/IB7hwGcDvw1ln4bj2O033iPlJRT2V9i5EP8Jz37MaD1Da1+khwx4Vb+cnlWdw9JZOQIAvPeek6FVU0ct/qHYQGB7H840LqmtoMjyIlxmaUxMZHhDIhPZZ5udk+HsdTN4zhm5kJRkjMTNH2iZmjsShMvSJBEHoXXfEgvgXcCuxWSu1wry0AlgChwAaXDWGL1voeYDLwsFKqBXACP9ZanwBQSs0F/omrzPVPWus97uvNB1YqpR4HvsBlkPocp/vEXVbjCCjj7WhpM46JC7f6KbbmT8tmxZYiSqsdxjmHT9STPy2bVVuPkj8tm037jzFzwsVGSeyyjwpZPHscV49OZoI9joZmVwJ70ADzEtehP53CvmM1HCir5el/fuU3P0IQhN6JaDGdR0636ufIiToKyuuY+79f+BmXV++6jAGRoWgNb+0sMdVFmjM50yiNtYVYyJuayYf7y8mbOhiLBWLDrNz1yuenradUeLzOT6NJ9JgEoWciWkw9lNNtKmtzwsJ/7PUrZ33smpHMW7mDyoZmlt48nqzEyICJZTgVAhqe4spreMT78nOzzkhP6XTGrwqC0PMRA3Ge6e4MCYDyWgdFFY38+V9HjC5qraG6sdkIHc396xe8fOelpiGsyYMHMCQpyggB3ZiT5uNpOM9QT0mS1YLQNxEtph6I06kpPF7HJ4dOUHi8jsQo1wbsmSK3dFMByz8upNZxquTV0eLEFmTxSyzPy80mPDSIB1/fyZKNBZRWO/xKWD1zJ7zP+93M0djjwrt0v6czflUQhJ6PeBA9DLM8xdKbxwcc5uMhJz2GhpY2EqNDffSWshIjTRPc7dVeV209ylOzxlJQXsuYtFiOVTXwSWEF38hMIDi44+cI0WMShL6JGIgexuET/r0Sc//6Be/mT2FV3iQ27i/HGuSS+K5scOkZpieE8cOJGcx5ZStx4VZuzEkjIz6c43VNRIYGEx8eSnpCGDPGpKLcIn1PzBzFgje/NAxO3tTBVNQ6GJMWy8K1eyiqcM22XnTDGK4Zc1Gnm/3phM4EQejZiIHoYRSdrDdN+B6pqOfbQxIpqXIY/Q55UzMZkhTFxbFh3PTHLcY40CUbCwwvo7G5jYPltfzqmpH86p1TG/9vZ47muR+Op6nVydcnG1j20SHmTx/O3L9u9zFO89fsYnRqjGz8gtAPEQPRw4iwBpsPBbIGBwzlfHq4ws+oxIVbiQ4L4cfuDb99T8TP39zNnMmZLP+4kMevG8XzN0/gw6+O+1wnJcbG9RPSOFBWCxAwbCSKroLQN5EkdQ8jMSrUNNGcFB0KnBomlBhlo6zGwZGKeiOJ7c2NOWlG4xu4vIElmw5y/YQ0471H4fUXf/+SsJBgxqbF+nRW3zopneUfF3LPX7YH1I3qjr6U9zneSXjpuhaEnol4ED0Ip1Nz6EQdEdYgn0RzdlKkjxR3V5LYgwea90Qo94O9t3S4o8XJ8ToH38hMYNENY5i/ZhfXT0gzOrI9x3jrRnm8huO1Td3SlxKJcEHoPYiB6EEcqahn7l+/IC7cyvUT0ozNPGtgpLF5mgn+zf3rF6yfN4V1+VMoq3EQbg2iuc1pGqoakhRFfm4Ww5OjeeHDAlJibNyYk0ZDcxtHKxv43qgURqfGcKCsNmDzW0ZChLHJ3z0ls1tNciIRLgi9BzEQ55HOYvWejmRPv4OHbw5OIGOA66n9eG0Td0/JBFz9C6XVrnOO1TiMOdEAra1OFl47ikffOlWp9Ovvj+S/3ttvJKrvu2KIq6Lp3f1+8yDiwq0Bm9/ab/LdaZKTrmtB6D2IgThPdCW00lFHstn5nqRzZUOz34YcHGxhaHIkf7w1h+rGFqLDgnn0rS8NeW/PAKC8qZl+T/N5UzN5fWsx83KzfSbKeZrfvJPiniY7b4HAjprkpOtaEHoPYiDOE10JrZhNkfNstmbnL9l0kPuvyGZYSgxlNQ7jGhaLwunUHD7RwPw1uwy9JbPZD+3zw44WJ+HWICPE9ftZY4kIDSI9IcK4tvcmX1rtYMWWIublZpOVGEm0LcRIqJvR0XcUBKFnIQbiPNGV0EpHHclm58eFW4mPtJG3YqtfiKjwRD1l1Y3MvTwLR6uT7MQo0yf39nnh9IQwomwh/OH9Ux7BEzNH8+0hpwyP1i7DcbC8ltVbi7EGKwZGhfL4P/YyY0wqQRa4ND2eiRnxFFc3+oXUpOtaEHoHYiDOE10NrQTqSDY7/8acNH7x991+XolHhuO2b6Tz+ravmTEmldKqBh6/bpTPdLmHpw+nsaWNeblZrN5aTGVDMw9PH8597TyVBW/uZtzFsdjjwtlypIKSykbCrcFEWIP4xfeGk54Qzk/+up2bcuw+oaaF145i6QcHjZyHd0hNuq4FoecjBuI8caahFbPzhyRGmXolAyNDuWWinZWfH/XZtNMTwvjvWybQ5tTUOVoNQ2ALsfDLGSMYnBjBidpm02uerG/iy5JqHnKHrDz9GRV1TQyIsjJjTKpfWeyjb31pzKKQaiVB6H2IgThPnGloxex8HUCm+2hlA6kxYX6bdlFFIz9+bTvP3jSOR9709Tx+s3Yva+dOZkCEuacTEmQxjIPnnGc3HiRvaiZpjjaCLJgaFqV830u1kiD0HqST+jziCa14ylG7G3dvf/6gARE8MXO0T9d1/rRsXt9aTHhocMBNu7HFabr+dWUDgwb4S3cvumEMlQ1NzJmcydxpWcydlkVKjM1IclfUNzM8Jdqvm9u7Gc/zXqqVBKH30KmBUEpdrJT6QCm1Vym1Ryk1z73+tFJqv1Jql1LqTaVUrHv9SqXUNqXUbvff07yudYl7vUAptUS5h1krpeKVUhuUUgfdf8edqy/cl7BYFBPsseRNdW/cl2ehlCs3ERykGHtxjOmmHR8RYroebnU5lCNSonjlzst4+Y4clt2awz92FdPqVMZkund2lnDrpHTSE8KwKFcC/oUPCvyM1ePXjWLtrhLjvVQrCULvotOZ1EqpFCBFa71dKRUFbAOuA9KATVrrVqXUIgCt9Xyl1HigTGv9b6XUKOCfWutU97U+A/KBT4F1wBKt9btKqaeAk1rrJ5VSDwNxWuv5Hd1Xb5xJfS7w9EcsWr/PL0n81A1jsAZbuHeVb+9EpC2I+qY2nx6HebnZTB+ZzL5jtUaeIz83i7d2lJA3dbCh6+S5xqqtR3l4+nBO1DXx3x8eorKhmfXzpuDUGCEwe1w4RysbpFpJEHoQ3ZlJ3amBMLn4W8BSrfUGr7WZwCyt9S3tjlVABZACxAMfaK2HuT/7IfAdrfV/KqW+cr8udRukD7XWQzu6DzEQp3A6NbtLqrhp2Ra/3ME/fjoFpeBYtYMgi+JYTSPWIAstbW2UVDURH24lIjSYAZFWUmJt/McfNuNocZISY2PB1cMB+Jl7drX3dedMziRzQDi/f+8AlQ3NoqckCL2E7hiIbiWplVIZwHhcHoA3dwGrTE65AdiutW5SSqUCxV6fFQOp7tdJWutS9+tjQFKAn58H5AHY7fbu3HqvpyOZDotF0dDsPzXOI8KXY49n+9FKo8TVU4I6NDmSH7/2hU+/wzcGxXPbtzLQTthRXEVqTJjpdYMsrml1z9w0VrwDQeijdNlAKKUigTXAvVrrGq/1nwOtwGvtjh8JLAKu6s4Naa21UsrUrdFaLwOWgcuD6M51ezNnKtOxp7TaMA7g2uCXfnCQhdeO8tF1enbjAR68aij/rnIYIaV5uVmm181Jj2d0aqwYBUHow3SpikkpFYLLOLymtX7Da/0OYAZwi/aKVSml0oA3gdu01ofcyyW48hYe0txrAGXu0JIn51F+Wt+mjxJIpuNIRb1xjKdPwjtJ7EkKewT9PKTE2Lgpx07eim0s3VTAS5sLuXVSOj+41M7B8jqfORKr3ZpM7auavpmZIMZBEPo4nXoQ7jzCcmCf1nqx1/p04CHg21rrBq/1WOAfwMNa6//zrLvzCzVKqUm4QlS3Ac+5P34buB140v33W2f6xfoSXVVAtQYrnzkSFuXyPlJiwny8ALNZD0s2HeT3s8ayv53Md2m1g1c/KeL3s8ZS39RKSXUjY9NiCA7u/NnCpQdVT9HJeiKswSRFh2KPl1CUIPQWuhJi+hZwK7BbKbXDvbYAWAKEAhvc1apbtNb3AHOBLOCXSqlfuo+/SmtdDvwY+DMQBrzr/gMuw7BaKTUHKAJmn+H36lN0RabDM0ui/THLbr2ElJgwnp41hp/9bZeRPzAzOBYLBCn/5rvKhmZsVgtHKppwaiirbep0ozcLi83LzSY7KZJpQ5PESAhCL6DbVUw9hf5UxdRZDsLp1Hx4oJy7/uz/7zF3WhYvbS7kv24cR3pCGCWVjQyIDOWW5Z/6GZMnZ47CYlGUVDl8SmCfmDmKk/Ut/P69r4y1RTeM4XujUgJ6EoXH67h6yWa/n5E3NZPrxqVKN7UgXCDOWRWTcGHoSKbDYzyOVtSbehlau7yDB17fwbr8KfzHqBScTu2n67To+jEMiLKy4M3d/OBSO0/PGoujpY3YsBAKjtcZBgNc15u/Zhdx4VYmZw0w9QYChcU8fRJiIASh5yMGopcQSAHVk8Cel5vNvNxsVn5+1JDcHpESTXmNg7nTsgA4Wd9kSHx4DE5ZjYOWNs2jb+3mmrGpFFU0smj9V8b1U2JsPHbNCNPNfmvRSdLiwkw3+0BhMYtC5DYEoZcgBqKX43lSr29u46Ovyv26nuflZrNmm0vKOzsxkglOjcWiDIMD+ISCzPIP7ZPcnuPanIG9ATP1WU8OQuQ2BKF3IGJ9vZxwa7BRgvqdYYk+JaoexdXrJ6QZYSHv0lhwGZi4cCs/uTwLa5CFP9zkylUAhlzHyJRoFt0wxk8UcO2uEgZGmnsDHi/lHz+dwst35PDKnZfx3VHJkqAWhF6EeBC9nOa2NkMbae7l2R1KbjtanBwoqwVOjSaNsgVz2zfSfZLSj84YQa2jhYbmNsJDg7BYFN8blUJcuJWtRSdpc8KqrUf5waV2DlfUMWiAeUWTxaIYnBjJ4ETJNwhCb0Q8iB6K06kpPF7HJ4dOUHi8Dmf74dFuEiJCWbXVlXeIsp3yJjx4S27bQizsLqnhzj9/xscFJ/jk0Akamtv8EtAL1+6l1tHGko0FzP3rFxypqCc42II93uVZKAUzxqTy6idFzP3rFxw9Wd+lexUEoXchHkQPpCvSGh4yEiKYP30496/eQVy4lXm52X4qra9+UmSEhdZ/WcpNOXYefWs3M8akYo8P4+4pmazZVkxptQPw9zo8eYbSagdLNhb4/PyUGBvbj1axwD2AqKN7FQShdyEGogcSSFrDbFxn+xLY5GgbV41I5nidg4GRNoIsMGhABLtLalixpYjrJ6SxautRP2nw/GnZrNhSRGm1A1uIhaFJUcydlsU7O0sICwnC6dQB52IveHM3ceFWrp+QhlLw1bEaRqREkTFAQkuC0JsRA9ED6aq0hgezEljvuL9TY8yE8ISHzKQ25kzOZPnHhczLzeaJdfuobGjmsRkjWbh2D3dNHsxVw5NM52LHhVu5dVK6j8FJT4gQWQ1B6OVIDqIH4nlS9+ZMxnW2F/ILJLUxJDGSvKmZvPpJkSHw9+u1e5iYOZD7V+/gaGUD00cmsy5/CivzJrIufwrDU6K5Mcdf22nBm7v9KqYEQehdiAfRAzHrITiTcZ3eYaiT9U3UN7WZN7FZlF+OweN1eHsw3t6K06kZkhjVLY9HEITegRiIHkhH0hpdIdBwIc/G3trqZNENY5i/ZpdPDqKkqiGgXIfHg/Fcu6K+CWuQhYbmNi6OD+9UTFAQhN6HGIgeSiBpjc4wq4BaevN4BiVEUl57ymBcM+YiRqfGUFRRzxdfV7FiSxEA+dOy/ZLXq7YeZfHscaTFhPGvwhMcq3aQEGHlYFktL/+rCGuw4vHrRvlMrDsTj0cQhJ6BqLn2MdqrqKbE2Pwa4RbPHsdVw5MoOtnA0ZP1KKV47O0vKapoJD0hjIXXjsbpdBJuDaa5rY2UmHDS48NZ92UpD3l5HfNyswkPCeLFjwqxBiuW/GA8jS1tJEfbDBmO9uNRBUG4sIiaaz+mfQXU9RPS/Brh7l+9g5fvuJRPCitwanhnZwl5UwczMCqUzIQIjpw8NVvCY1AAwzh4rvPsxoPkTc3k+glpPP9BASfrm5mSNZD39pV1qYfDjI5mbwuCcH6RKqY+gqfzurGljXm5WaTEuOL/ngSzN44WJ58UVvD61mIsCuZenk1tYwtHjtdR39zqM3jIY1CKTtYHlO9W7iFDX3xdxb8KKzodj9rRd1i/5xhXL9nMD//4KVcv2cz6PcekM1sQLhDiQfQBAk1ve/WTItMJcbYQC2EhQX69C4/OGEGNo5k5kzONTmpPh3VEaLDpdYYlRfHvqgaj0Q7MDVJXKpq60yAoCMK5RzyIPoDZxvrsxoP84aZxzByf6tMD4Uk8tzm1X+/CwrV7aXMqln9cyNJNBby0uZBbJ6WTnhBGSkwov5052u86T7+3n6gwK+u/LKW02oHTXfHkTVcrmtqHx1JibMyZnMmBslrReBKEC0CnHoRS6mLgVSAJ0MAyrfWzSqmngWuAZuAQcKfWukoplQD8DbgU+LPWeq7XtS7h1EzqdcA8rbVWSsUDq4AM4AgwW2tdeba+ZF8nUOe1RpMxIBJ7fIRRMhsWEkT+yi+4Zmyq6Tnbj1b6dVgvvz2HL45Ws2TjAeZMziTIAsOSo3nxwwKKKhr59Tt7mDM5k10lNbyzs8SvhNa7oqmjHIO3lEdKjM3PwxGNJ0E4v3QlxNQKPKC13q6UigK2KaU2ABuAR7TWrUqpRcAjwHzAATwKjHL/8eYF4EfAp7gMxHTgXeBhYKPW+kml1MPu9/PP+Nv1EwJNb/M8tXuXzDqdmvnTh/PVsZqAQ4C8cbQ4qXW0Ghv+8x8UGMd6jIKjxYk9Loz83CwuTY9nYkY8o1NjAo5HDZTA9m4QvH6Cf3e2hJsE4fzSaYhJa12qtd7ufl0L7ANStdbvaa1b3YdtAdLcx9RrrT/GZSgMlFIpQLTWeot21da+Clzn/vha4BX361e81oUu0F5Ko6M+BE8T3szxqTzRLmS06IYxrN1V4nO8LcRCS5vucM6ELcRCSXUjSzYW8KMVW3n/q3IyEiKYlDnAGHEKgXMMh0/U+9zbuvwpjE6NDpjLEATh/NCtJLVSKgMYj8sD8OYuXCGijkgFir3eF7vXAJK01qXu18dwhbPMfn4ekAdgt9u7ett9nu52XlssiowBkaTFhjN4YASl1Q5SYmwMT4omJMji84SfPy2bkCDVYYe1JyEOHT/pBwqF7TtWYwwd8h6Fmp4QxowxqYYhemdniXRnC8J5pMsGQikVCawB7tVa13it/xxXGOq1s3FD7pyEaTZSa70MWAauRrmz8fP6Ct3tvHY6tWm/wlXDk1iVN4mN+8tpc8KKLUXc/s10vzkTj84YwUUxNoYlj+WJdfuMWRJgXrXkdGpjPGp7Q3OgrJYRKdE+x9vjwvnptGyf7uzHrxuFPS78LPxrCYLQFbpUxaSUCsFlHF7TWr/htX4HMAO4RXfekl2COwzlJs29BlDmDkF5QlHlXbp74bQJFO45WtnA6NRYhiVHs/zjQkqrHfzvZ0dJiwsjb2omc6dlkTc1k6ToUAYNiKCgvJbKhmafa7evWvLkHvJXbufRGSP8KqFe31rsFzo6WtlgGAfP/f3i719ytLLhXP6zCILgRVeqmBSwHNintV7stT4deAj4tta6099arXWpUqpGKTUJV4jqNuA598dvA7cDT7r/fqu7X0ToHoHCPUUV9WQkRPiFrOxx4Yy8qMEnhAUwOi3Gz7tYevN4tIZPDp0gKdqGRWEYo1pHC3lTM3Fq0NrloVQ2NBsGxVPldKCsttv9FNKFLQhnl66EmL4F3ArsVkrtcK8tAJYAocAGlw1hi9b6HgCl1BEgGrAqpa4DrtJa7wV+zKky13fdf8BlGFYrpeYARcDsM/5mQocEqnz64usqGlucTB+Z7BeyMgthTRuaRNbASCbY42hobiUjIYJ9x2r53nObDYPxxMzRxIVbKa128Mq/ikzLVzMSInyqnO6ektkthdjujGkVBKFriFhfP6X9hpqeEMb86cMpKK+jzelk5vjU0xoZ2l4sEFwbe97UTGPWREqMjRtz0hh/cSzpCRHGk773ud3tgwj0c9dJWawg+CBifQLQccjFYlGMSIni97PGUlzVQJQtxOfp+3RHhgYKXdnjTs2MqGxoZlhyNN8ekuhzfe9zS6sdrNhSxNzLs8hOjCI2PISk6NBu/1wZWiT0FS5ECFUMRB+lKyGX0moH+8tqsSj4w/v+I0PHXRzb7c01UOgq3BrEr68ZSYQtmIyEcEakxPj9525/7sBIK4MHRrKntNpQnZ0/fbipF9FZs6Ag9GYuVAhVtJj6KIGqlLxVVZOibbyzs4SL48LPWlOaWdPeYzNGsuif+5n/xm4efH0nB8rqOj03JcbGTZfZuW/1DpZsdOlC3ZRjZ9H6fabKsN1pFhSE3kZXfp/PBeJB9FHKahzEhVu5fkKajzKrd8glIyGC+dOHc7Si/qw9fXs37RVV1FPf1MbT7+2nqKKRlBgb109I40hFPbtLqhmd6utFeJ97vLaJ21/+zE8Xas7kTNOw0ZmOaRWEnsyFCqGKgeijmE2Sm5ebTXL0qU3fs6kePVlPYrSNBW/uPu2RoWbx0YyECNbtLjWMg3fSedlHhaYusqfhL9AvRJCFgIbrdMe0ns53E8MjnE8uVAhVDEQfpc2J3yS5Zzce5KoRyT7HeWQ37PERTLDHUlbTRH1zK+nx3TMO7eOjT8wczQR7LMOTo7CFWLotvhfoFyInPf68ho2kfFboCXgLWZ7Pue9iIPoo5bXmT+DH6xwMTjR/wt5bWntaG6H5PIoD/OyqYaBg2a057C+tNr2fshoHGQkRpt5H+1+IRTeM4ZuZCed1Y5YhRkJP4EKFUMVA9FG665Ieqahn0fp9PtPkFq3fx7DkqE43QrNBPzfl2HnwbzuNzf13M8eQnhBGUUWjz/20tGk2fVXmNwN7+sjkHpFTkPJZoadwrkKoHSEGoo/SXZe0or6Jm3LsPo1p+dOyOVnf1Ol/yJQYG/m5WXgGvtmCLX7hpEfe3MWL/98l3POXbT7Xf/St3Vw7LjXgE/r5/oVoj5TPdh/J2fQdxED0UbrrklqD/Df1JZsO8pc5Ew1NpfbnO52awyfq2Vdag8JVJVXZ0Mzj140yfepubnUaHopHh8kzprT9sT3lCf1CxX7b01s2XcnZ9C3EQPRhuuOSNjS3mW7qWwor+P17B/x+0c02gvuuGMKf/3WEr082mD51J0WFsvzjQr/19vuG5wndsylW1DdhDbLQ0NzW7c3xTDfWnlA+25s2XcnZ9C2kUU4AToVSvLGFWGgM0Jhz+IT/RvDM+we4ZaKd1VuLWXjtKJ+mtf+6cRxVjmY/ue/7rhjCgAirX4ObPS6c9XuOceefP+Pzw5XctGwLP/zjp1y9ZDPr9xzD6XY7nE5N4fE6Pjl0gsLjdca657P1e45x9ZLNpud2FY+hbT8h73xxoZqkToeOcjZC70M8CAEwD6V4PAIP3qGfopP1phvBwMhQKhuaqaxvYs7kTIIskJMeT3pCGP/xh83ce0U2T80aS0F5LW1OjOvnTc30Ee/zbIpzJmcGLI/NSIjo8Mn66Ml69h+r4e4pmYArBNYbn2Z7U6JccjZ9CzEQgoE1WBmzGiwKwkw8Cs8vekSA6XARocHMy83m5X8VGVPmbCEWXrnzMhwtTuqa2igorzWUXT0s2VjAyryJxobn2RSVosMn0kCVVxkJEWw/WsWyjwp9kuIrthT1yI21I3rTpttTcjbC2UEMhIDTqdldUsWuYpco3pptxZRWO3xkutv/oidFh/oNCpqXm81FMTZ+azKCtKG5FVuIhTXbivmlO8zU0YbnHfIKdGxHlVeA0RnuuYclmw6SNzWzR26sHdGb8kxK4AAAELVJREFUNt2ekLMRzh5iIPo5ZglQz5N2abWD8RfHsjJvot8vuj0+guykSB+PIzspkvhIq+kIUnv8qU3uhQ8LeHTGCBau3Rtww/NsiovW7yN/WrbpgKFaR4tp5dWqH00KGJYZkhTVIzfWjuhtm+6FqNcXzg1iIPo5ZglQjyje8o8LscdHoJQr5AMYG5PFopg2NInMAZF+Y0jNnnYHDYhg0IAIY5NLjrbxj59O4Xid+YZnbIrJUZysb2JV3iS/KqZAlVfF1Y0MS4o29TyGJ0f32I21I2TTFS4EYiD6OR2J4i2ePY7DFXWmXc4eI2G2aXX0tNv++ECyH9D5phgoNv/VsVpGpkQHNFSCIHSNTstclVIXK6U+UErtVUrtUUrNc68/rZTar5TapZR6UykV63XOI0qpAqXUV0qp//Ban+5eK1BKPey1Pkgp9al7fZVSynq2v6hgTqDy1txhiYxIiTKMA5yqINpdUmVaVurBuyzUU5HU0fGnS0ZCBE/MHO2Tq8ifls3rW4s5VuNg+shk1uVPYWXeRNblT+mRfQOC0JPpigfRCjygtd6ulIoCtimlNgAbgEe01q1KqUXAI8B8pdQI4AfASOAi4H2l1BD3tZ4HrgSKgc+VUm9rrfcCi4BntNYrlVIvAnOAF87i9xQCECgBOjo1lk8PV5h6Fxv3l/skrgNtvOe6wctiUUywxxp5EE93dmVDM4lRNgnLCMIZ0qkHobUu1Vpvd7+uBfYBqVrr97TWre7DtgBp7tfXAiu11k1a68NAAXCZ+0+B1rpQa90MrASuVUopYBrwN/f5rwDXnZ2vJ3SGJ9Zv9qTd3rvwaC6lxoQxd1oWceHWDhu2zkeDlz0+gmHJ0by0uZDnPyigsqG5x1b4CEJvo1s5CKVUBjAe+LTdR3cBq9yvU3EZDA/F7jWAr9utTwQSgCovY+N9vHAeCPSk7e1dxIVb/QYQddZXcD4avHpbhY8g9Ca6bCCUUpHAGuBerXWN1/rPcYWhXjv7t+d3D3lAHoDdbj/XP67f05URoB31FXTW4HW2BOgklCQI54YuaTEppUJwGYfXtNZveK3fAcwAbtFae7KPJcDFXqenudcCrVcAsUqp4Hbrfmitl2mtc7TWOQMHDuzKrQtniGfzdWrd7b4CjwfSXmcpIyHirOkkCYJw7ujUg3DnCJYD+7TWi73WpwMPAd/WWjd4nfI28Fel1GJcSeps4DNAAdlKqUG4DMAPgJu11lop9QEwC1de4nbgrbPx5YSzRyBvoKO+go7CP4XH60T1UxB6OF3xIL4F3ApMU0rtcP+5GlgKRAEb3GsvAmit9wCrgb3AeuAnWus2d45hLvBPXInu1e5jAeYD9yulCnDlJJafva8onC7eSqlaw9Kbx/t5A531FQRSQhXVT0Ho+XTqQWitP8b19N+edR2c81vgtybr68zO01oX4qpyEnoIgUpU18+bwrGaM08G9yYBOkHor8g8CMGUQCWqTk235yKYzWzoKD8h9B86muchXHhEakMw5WyVqHbULCflqf2b3jQpr78iHoRgSiAJju6GgDpqlrvQk9qEC0tvmpTXXxEDIZhytkJAp5OMlrBD/0AKFXo+EmISTDlbHcrdTUZL2KH/IIUKPR/xIISAnI0QUCBPxB4XzpETdXxaWMGm/WUcKq8zOqsl7NA/kEKFno94EMI5w7Phx4WHsCrvG7S0tREfEYo9LpwPD5ZzsKzOR9tp8exxxIWHdCs5frbkOoTzj+ho9XzEQAjnhEChogn2eI5U1LOruJplHxX6eQqr8r7R5bCDhKN6P6Kj1bOREJNwTugoVFRW48CpMfUUWtrauhx2OHxCwlGCcC4RD0I4J3RUoZIUbSNIYeopxEeEMsEe32nYwenU7Cv9f+3deZAcZR3G8e+ThEQTDCZyE8IGE5BglGOhQMRCkOJQLoujtLgpUUEJCqUoXqVoIadEKIpQcgkWeKBQ3IehuNEVAgFiIAlBAiEJV4UkBSHszz/6XRiGnp3ZnZndnszzqZpKT08fz06m5zfdb/fby5renbhZO/MehDVFb9dRdHxiFFPGrcPUPSbl7inU0ji+4NUVPLvkzYZcq2Fm+bwHYU1R6VamPQVg9y03YOJ6a7Pd+DGsXLWa8WNHMWHd2hsoFy97ixn/XcJPvzKZX9309Hvr+M1BU3wWjFmDuEBYU1Q7Q2XIENGx7tp0rNv3Q0Hd3YGAfaZsxPR753Hc5zdn6BDYasPRTN74Y26gNmsQFwhrmmadobLg1RU89sIb750ie9GMuUB2eOnm7+7a0HWZtTO3QVjLWbzsLVaseje3gXrpcnfTYNYoLhDWckrPgirlBmqzxnKBsJZT6Syocw9xNw1mjeQ2CGs5Q4aI3Satz6jhwzjn4M+y4u3VvLZyFSPWcuO0WSNVLRCSNgWuAjYAApgeERdIOgT4BbAVsGNEdKXphwOXAJ1ANzA1Iu5Jr20PXAF8lOzWo1MjIiSNBa4DOoAFwKER8Xqj/khb8/zv9ZUcc8W/P3Sh3S0n7eqL5MwapJZDTKuBUyJiMrATcKKkycCTwFeBe8um/wZAREwB9gTOldSznovT65PSY+80/jTg7oiYBNydnptV5HsJmDVf1QIREYsi4tE0/CYwG9gkImZHxJycWSYD/0zTLwHeADolbQSMjoiHIyLI9koOTPMcAFyZhq8sGW+Wq1F3vDOzyvrUSC2pA9gWeKSXyR4H9pc0TNIEYHtgU2ATYGHJdAvTOIANImJRGn6Z7HCWWUW+l4BZ89XcSC1pbeBvwMkRsayXSS8ja5foAp4HHgTerXU9qU0i9x6Tko4HjgcYP358rYu0NZDvJWDWfDUVCElrkRWHayLi+t6mjYjVwPdK5n0QeAZ4HRhXMuk44MU0vFjSRhGxKB2KWlJh2dOB6QCdnZ2+UXGb870EzJqr6iEmSQL+AMyOiPNqmH6kpFFpeE9gdUQ8nQ4hLZO0U1rmkcANabYbgaPS8FEl483MbJDUsgexC3AEMEvSzDTux8AI4PfAesDNkmZGxF7A+sDtkrrJ9hCOKFnWCbx/muut6QFwJvBnSceRHZY6tJ4/yqxRfEtTa2dVC0RE3A9U2iL+njP9AmDLCsvqAj6dM/5VYI9qWcwGkm9pau3OXW2YVdDbbVPN2oELhFkFvhjP2p37YjKroOdivPLuPPp6MZ7bMaxVeQ/CrIJGXIzX046x77T7+Nqlj7DvtPu47amX6e72WdpWfMp6vWg9nZ2d0dXVNdgxbA3X8+u/vxfjzV+6nH2n3edOBa0wJP0nIjprmdaHmMx6Ue/FeL21Y7hAWNH5EJNZE7lTQWtlLhBmTdRbO0Z3dzB/6XIemvcK85cud7uEFY4PMZk1QKUzlSp1Kgj4IjwrPBcIszpVu+I6rx1j/tLluRfhfcqN11YgPsRkVqf+XHHti/CsFbhAmNWpP1/2bry2VuACYVan/nzZ+4541grcBmFWp54v+/I2iN6+7H1HPGsFvpLarAHqveLabKD4SmqzAebbn9qayG0QZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrla9jRXSUuB55u8mnWBV5q8jkZppazQWnmdtXlaKe+aknWziFivloW0bIEYCJK6aj1feLC1UlZorbzO2jytlLcds/oQk5mZ5XKBMDOzXC4QvZs+2AH6oJWyQmvlddbmaaW8bZfVbRBmZpbLexBmZpbLBcLMzHK1bYGQtLekOZLmSjot5/UvSHpU0mpJB5e9Nl7SHZJmS3paUkeBs54l6amUdZqkpvZBXUPW76f37AlJd0varOS1oyQ9mx5HNTNnPVklbSPpofS+PiHpsGZnrSdvyeujJS2UdGGRsxZw++ot64BuXzXm/ZakWZJmSrpf0uSS136U5psjaa+qK4uItnsAQ4F5wObAcOBxYHLZNB3AZ4CrgIPLXrsH2DMNrw2MLGJW4HPAA2kZQ4GHgN0GOesXe94v4NvAdWl4LDA//TsmDY8paNYtgElpeGNgEfDxAnxmc/OWvH4B8CfgwiJnLeD2VelzMKDbVx/yji4Z3h+4LQ1PTtOPACak5QztbX3tugexIzA3IuZHxCrgWuCA0gkiYkFEPAF84GbDqRoPi4g703TLI2JlEbMCAXyE7IM0AlgLWDzIWWeUvF8PA+PS8F7AnRHxWkS8DtwJ7F3ErBHxTEQ8m4ZfApYANV2ZOhh5ASRtD2wA3NHknHVlLej2Vel9Hejtq9a8y0qejko5SdNdGxFvR8RzwNy0vIratUBsArxQ8nxhGleLLYA3JF0v6TFJZ0sa2vCE7+t31oh4CJhB9gt3EXB7RMxueML39TXrccCt/Zy3XvVkfY+kHcm+IOY1NN2H9TuvpCHAucCpTUv3QfW8t0Xfvt7LOgjbF9SYV9KJkuYBZwEn9WXeUu1aIOoxDNiVbGPbgWxX7+jBDFSJpInAVmS/eDYBdpe06+Cmykg6HOgEzh7sLNVUyippI+CPwDERUb73Nmhy8p4A3BIRCwcvVb6crIXdvsqzFnn7ioiLIuKTwA+Bn/R3Oe1aIF4ENi15Pi6Nq8VCYGbaxVsN/APYrsH5StWT9SDg4bSbvpzsl8/ODc5Xqqaskr4EnA7sHxFv92XeBqonK5JGAzcDp0fEw03M2aOevDsD35G0ADgHOFLSmQXNWsjtq0LWgd6+as5b4lrgwH7O27aN1MPIGkEn8H5Dz9YVpr2CDzb8Dk3Tr5eeXw6cWNCshwF3pWWsBdwN7DeYWYFtyQ7HTCobPxZ4jqyBekwaHlvQrMPTe3lykT6zlfKWTXM0zW+krue9Ldz21UvWAd2++pB3UsnwfkBXGt6aDzZSz6dKI/WAfLiL+AD2BZ5J//Gnp3G/JPuFANnu7UJgBfAq8FTJvHsCTwCzyL6Uhxcxa9rYLgFmA08D5xXgfb2LrCFvZnrcWDLvsWQNZ3PJDtsUMitwOPBOyfiZwDZFzVu2jKNpcoFowOegaNtXpc/BgG9fNea9AHgqZZ1BSQEh2wuaB8wB9qm2Lne1YWZmudq1DcLMzKpwgTAzs1wuEGZmlssFwszMcrlAmJlZLhcIa0uSOiQ92cvr0yQtH8hMZkXjAmFWRlIn2cV6zVr+sGYt26yRXCCsnQ2TdE3qy/+vkkamjuHOBn5QbWZJN0g6Mg1/U9I1vUx7j6TfSeoCpqbn50vqSuvfIXVQ96ykM9I8oyTdLOlxSU8O1H0nzHr4l4y1sy2B4yLiAUmXkXVq9w7ZlbKLarj3y/HAA5KeA04Bdqoy/fCI6ASQtB+wKiI6JU0FbgC2B14D5kk6H9gNeCkivpzmWac/f6RZf7lAWDt7ISIeSMNXA6cBI8m+mKuKiMWSfkbWncFBEfFalVmuK3t+Y/p3Fln3KIsAJM0n61RtFnCupN8CN0XEfbXkMmsUH2Kydlbez8wOwERgbur5dKSkuVWWMYWs/6uNa1jfirLnPb2CdpcM9zwfFhHPkPVkOgs4IxUjswHjAmHtbLyknu6Zvw6cEREbRkRHRHQAKyNiYqWZ082C9iHr7fNUSRMaGU7SxinD1WTtIs3s9trsQ1wgrJ3NAU6UNJvsrKWLa51R0gjgUuDYyG47egpwWYNvWj8F+JekmcDPgTMauGyzqtybq5mZ5fIehJmZ5fJZTGZVSDodOKRs9F8i4tc5014E7FI2+oKIuLxZ+cyaxYeYzMwslw8xmZlZLhcIMzPL5QJhZma5XCDMzCyXC4SZmeX6P9KhuqAyivmcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt =  sns.scatterplot(x=test_df_svm['b4_x_rms'],y=test_df_svm['datetime'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "eooZu9sWR0sU",
        "outputId": "67dc9999-c26e-4da4-9f5c-e77c80e87a7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "at =  sns.scatterplot(y=data['b4_x_kurt'],x=data['datetime'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "j-GWCNftUaK8",
        "outputId": "b5c11bfb-8575-4d20-fc1d-744b3d55a0f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "at =  sns.scatterplot(y=data['b4_x_entropy'],x=data['datetime'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Js3qlthre2EQ",
        "outputId": "54503412-233a-4906-825c-d571c5c4cc53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}