convert_original_datasets/Convert for Device 3.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Convert for Device 3.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyMEbJLnAwoy8K+WCXiIkIvi"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vhML_9mQeosl","executionInfo":{"status":"ok","timestamp":1625205017822,"user_tz":-600,"elapsed":20099,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"84ab00e9-39c5-4404-9379-d026314f82d9"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QKdnoUL3lGgk","executionInfo":{"status":"ok","timestamp":1625205018336,"user_tz":-600,"elapsed":525,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","from scipy import stats"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Caxxoby4oxbP"},"source":["# Load Datasets"]},{"cell_type":"code","metadata":{"id":"zDZ1WaK0lGZb","executionInfo":{"status":"ok","timestamp":1625205033381,"user_tz":-600,"elapsed":15048,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# IoT Device #2 --> device_2_Ecobee_Thermostat\n","benign = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.benign.csv') \n","benign.shape\n","\n","# Botnet --> Mirai\n","# Attack --> Scan volnerable devices / Ack flooding / Syn flooding / UDP flooding / UDPplain\n","\n","# m_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.scan.csv')\n","# m_ack = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.ack.csv')\n","# m_syn = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.syn.csv')\n","# m_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.udp.csv')\n","# m_udpplain = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.udpplain.csv')\n","\n","#Botnet --> Gafgyt (BASHLITE)\n","#Attack --> Scan vulnerable devices / Sending spam / UDP flooding / TCP flooding / Sending spam & opening connection\n","g_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.scan.csv')\n","g_junk = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.junk.csv')\n","g_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.udp.csv')\n","g_tcp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.tcp.csv')\n","g_combo = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.combo.csv')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"QxlE1MfhorlX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033388,"user_tz":-600,"elapsed":28,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"0670465a-d0cd-44ec-ea04-acfb5f43030d"},"source":["# print(m_scan.shape)\n","# print(m_ack.shape)\n","# print(m_syn.shape)\n","# print(m_udp.shape)\n","# print(m_udpplain.shape)\n","print(g_combo.shape)\n","print(g_junk.shape)\n","print(g_scan.shape)\n","print(g_tcp.shape)\n","print(g_udp.shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["(53014, 115)\n","(29797, 115)\n","(28120, 115)\n","(101536, 115)\n","(103933, 115)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IL0f3rd_o-DL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033388,"user_tz":-600,"elapsed":22,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"67093ee1-4755-4bf3-8262-264631bc6eb6"},"source":["benign.info()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 39100 entries, 0 to 39099\n","Columns: 115 entries, MI_dir_L5_weight to HpHp_L0.01_pcc\n","dtypes: float64(115)\n","memory usage: 34.3 MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lh9-vCzLo510","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033389,"user_tz":-600,"elapsed":19,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"a302e481-e33f-43e1-c079-90cc568dc149"},"source":["#missing value \n","benign.iloc[:,:60].isna().any(),benign.iloc[:,60:].isna().any()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(MI_dir_L5_weight         False\n"," MI_dir_L5_mean           False\n"," MI_dir_L5_variance       False\n"," MI_dir_L3_weight         False\n"," MI_dir_L3_mean           False\n"," MI_dir_L3_variance       False\n"," MI_dir_L1_weight         False\n"," MI_dir_L1_mean           False\n"," MI_dir_L1_variance       False\n"," MI_dir_L0.1_weight       False\n"," MI_dir_L0.1_mean         False\n"," MI_dir_L0.1_variance     False\n"," MI_dir_L0.01_weight      False\n"," MI_dir_L0.01_mean        False\n"," MI_dir_L0.01_variance    False\n"," H_L5_weight              False\n"," H_L5_mean                False\n"," H_L5_variance            False\n"," H_L3_weight              False\n"," H_L3_mean                False\n"," H_L3_variance            False\n"," H_L1_weight              False\n"," H_L1_mean                False\n"," H_L1_variance            False\n"," H_L0.1_weight            False\n"," H_L0.1_mean              False\n"," H_L0.1_variance          False\n"," H_L0.01_weight           False\n"," H_L0.01_mean             False\n"," H_L0.01_variance         False\n"," HH_L5_weight             False\n"," HH_L5_mean               False\n"," HH_L5_std                False\n"," HH_L5_magnitude          False\n"," HH_L5_radius             False\n"," HH_L5_covariance         False\n"," HH_L5_pcc                False\n"," HH_L3_weight             False\n"," HH_L3_mean               False\n"," HH_L3_std                False\n"," HH_L3_magnitude          False\n"," HH_L3_radius             False\n"," HH_L3_covariance         False\n"," HH_L3_pcc                False\n"," HH_L1_weight             False\n"," HH_L1_mean               False\n"," HH_L1_std                False\n"," HH_L1_magnitude          False\n"," HH_L1_radius             False\n"," HH_L1_covariance         False\n"," HH_L1_pcc                False\n"," HH_L0.1_weight           False\n"," HH_L0.1_mean             False\n"," HH_L0.1_std              False\n"," HH_L0.1_magnitude        False\n"," HH_L0.1_radius           False\n"," HH_L0.1_covariance       False\n"," HH_L0.1_pcc              False\n"," HH_L0.01_weight          False\n"," HH_L0.01_mean            False\n"," dtype: bool, HH_L0.01_std             False\n"," HH_L0.01_magnitude       False\n"," HH_L0.01_radius          False\n"," HH_L0.01_covariance      False\n"," HH_L0.01_pcc             False\n"," HH_jit_L5_weight         False\n"," HH_jit_L5_mean           False\n"," HH_jit_L5_variance       False\n"," HH_jit_L3_weight         False\n"," HH_jit_L3_mean           False\n"," HH_jit_L3_variance       False\n"," HH_jit_L1_weight         False\n"," HH_jit_L1_mean           False\n"," HH_jit_L1_variance       False\n"," HH_jit_L0.1_weight       False\n"," HH_jit_L0.1_mean         False\n"," HH_jit_L0.1_variance     False\n"," HH_jit_L0.01_weight      False\n"," HH_jit_L0.01_mean        False\n"," HH_jit_L0.01_variance    False\n"," HpHp_L5_weight           False\n"," HpHp_L5_mean             False\n"," HpHp_L5_std              False\n"," HpHp_L5_magnitude        False\n"," HpHp_L5_radius           False\n"," HpHp_L5_covariance       False\n"," HpHp_L5_pcc              False\n"," HpHp_L3_weight           False\n"," HpHp_L3_mean             False\n"," HpHp_L3_std              False\n"," HpHp_L3_magnitude        False\n"," HpHp_L3_radius           False\n"," HpHp_L3_covariance       False\n"," HpHp_L3_pcc              False\n"," HpHp_L1_weight           False\n"," HpHp_L1_mean             False\n"," HpHp_L1_std              False\n"," HpHp_L1_magnitude        False\n"," HpHp_L1_radius           False\n"," HpHp_L1_covariance       False\n"," HpHp_L1_pcc              False\n"," HpHp_L0.1_weight         False\n"," HpHp_L0.1_mean           False\n"," HpHp_L0.1_std            False\n"," HpHp_L0.1_magnitude      False\n"," HpHp_L0.1_radius         False\n"," HpHp_L0.1_covariance     False\n"," HpHp_L0.1_pcc            False\n"," HpHp_L0.01_weight        False\n"," HpHp_L0.01_mean          False\n"," HpHp_L0.01_std           False\n"," HpHp_L0.01_magnitude     False\n"," HpHp_L0.01_radius        False\n"," HpHp_L0.01_covariance    False\n"," HpHp_L0.01_pcc           False\n"," dtype: bool)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"9cm3cXb1o_Tr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033390,"user_tz":-600,"elapsed":16,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"6d42b217-4820-4531-8662-6804eea9d66f"},"source":["#checking if all the files have the same features \n","print(g_combo.columns == benign.columns)\n","# print(g_combo.columns == m_ack.columns)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IeRbXZ4SpeY2"},"source":["# Convert into one dataset"]},{"cell_type":"code","metadata":{"id":"xyhQoJRJpIvV","executionInfo":{"status":"ok","timestamp":1625205033390,"user_tz":-600,"elapsed":11,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# add new column\n","benign['type'] = 'benign'\n","# m_scan['type'] = 'm_scan'\n","# m_ack['type'] = 'm_ack'\n","# m_syn['type']  = 'm_syn'\n","# m_udp['type'] = 'm_udp'\n","# m_udpplain['type'] = 'm_udpplain'\n","g_combo['type'] = 'g_combo'\n","g_junk['type'] = 'g_junk'\n","g_scan['type'] = 'g_scan'\n","g_tcp['type'] = 'g_tcp'\n","g_udp['type'] = 'g_udp'"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"vDIuhHj3pS7f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205034099,"user_tz":-600,"elapsed":719,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"efb44c19-d8ff-42a7-b0fb-0a573b14e1f9"},"source":["d = pd.concat([benign,g_combo,g_junk,g_scan,g_tcp,g_udp], axis=0, sort=False, ignore_index=True)\n","d.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(355500, 116)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"8hYBsMGvpZl4","colab":{"base_uri":"https://localhost:8080/","height":232},"executionInfo":{"status":"ok","timestamp":1625205034101,"user_tz":-600,"elapsed":15,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"37b23a1e-771e-45d6-888b-81bbd221df00"},"source":["#value counts\n","labels = d['type'].value_counts()\n","labels\n","label_count = pd.DataFrame({'labels': labels.index,'counts': labels.values})\n","label_count"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>labels</th>\n","      <th>counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>g_udp</td>\n","      <td>103933</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>g_tcp</td>\n","      <td>101536</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>g_combo</td>\n","      <td>53014</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>benign</td>\n","      <td>39100</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>g_junk</td>\n","      <td>29797</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>g_scan</td>\n","      <td>28120</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    labels  counts\n","0    g_udp  103933\n","1    g_tcp  101536\n","2  g_combo   53014\n","3   benign   39100\n","4   g_junk   29797\n","5   g_scan   28120"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"kecoeAtgppQl","colab":{"base_uri":"https://localhost:8080/","height":441},"executionInfo":{"status":"ok","timestamp":1625205206931,"user_tz":-600,"elapsed":439,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"e9861be2-12b8-42ea-85fc-05b10719d58f"},"source":["labels = label_count['labels']\n","sizes = label_count['counts']\n","plt.figure(figsize=(8,6))\n","colors = ['red', 'orange', 'yellow', 'green', 'olive', 'darkgrey']\n","# colors = [1,2,3,4,5,6,7,8,9,10,11]\n","explode = (0, 0, 0, 0.1, 0, 0)\n","patches, texts = plt.pie(sizes, explode = explode, colors=colors, shadow=True, startangle=100, pctdistance=100, labeldistance=10)\n","plt.legend(patches, labels, loc=\"best\")\n","plt.axis('equal')\n","plt.tight_layout()\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjgAAAGoCAYAAABL+58oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXydVYH/8c/JnjRtkqZJk3QFCmWXXUGKioAbhAGlgKig85vFZWZ+zoA441Kvzjiio+OKK47zE3FcGLXoDG3pRqGsYW2hT9J9SZM0+57c5fn9cVNI0ps2y733PM9zv29feZWEJ/d+DenNN+c85xzjui4iIiIiQZJlO4CIiIhIsqngiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjgqOCIiIhI4KjgiIiISOCo4IiIiEjg5NgOICLe9+tf/zobmD3yVjzqn2cBLhCd5Fs/0Al0rly5Mpze/xcikkmM67q2M4hImo0Ulhpg0ai3hSN/VvB6gTn6VpiCGH3Ey04H0Aa0jLwdGfmzGdgD7Fy5cmVPCp5fRAJMBUckoH7961/XAGcCZwBLGFtmqoFse+mm7AiwK9HbypUrm2wGExFvUsER8TtjSoDzBkpLz3zohz+8gHipORMotRssbfqA3cA24FmgDnhOoz4imU0FR8RPjCkC3gi8GbgQOA9YCuCC+5v/+q9ejJltL6BnxIAGXi88zwLPr1y5stdqKhFJGxUcES8zZh5w+cjbCuB8IHeiy//7pz/dFpk16+w0pfObGOAQLzxPAI+sXLmy3m4kEUkVFRwRLzFmKXAFrxea5YCZ7Kf/4u67m3MvuGB+SrIF035gPfAI8cLTYjmPiCSJCo6ITcbkEC8z1wHXAqfN5OHW3nzzvs4bb1ySjGgZyCV+H88jI2+bV65c2Wc3kohMlwqOSLoZMxd4N/FC8w6SeDPwyxdc0PTq3XdXJevxMlwYeBJ4GPjvlStX7rCcR0SmQAVHJB2MOYP4KM11wKWkaIl2d3Hx4MP33VeQiscWXgUeBB5cuXLlC7bDiMjxqeCIpIoxi4DbgA8AZ6Xrae//2c968woLi9P1fJkoHG5ds3Pnx58HfrlqlfuS7Twiciwd1SCSTPE9ad5HvNS8hSncIJw0ra3dLFqkgpNCXV1bioBPA58OhcwrwH8RLzs77SYTkaM0giMyU8bkAu8iXmquA6xOET34d3+3N3rZZUttZggy13WjDQ1/2RmNdpcn+NfPAD8CHli1yu1PczQRGUWniYtMlzHnYsx3gUbgD8BNWC43APN27vTTEQy+E412vThBuQG4GPgx0BgKme+EQubMNEYTkVE0giMyFcZkAzcAf0N8vxrP2XXaaW11X/rSRD+AZYZ2775/99DQQydP4VMeBX4APLhqlTucolgiMo4KjshkGFMO/CXwUeKHVXpWOCcn+uDPf26ysrI0QptksVgs9uqrfzmcldUznZG6FuCnwA9XrXL3JjeZiIyngiNyPMacR3y05v14YPppsn7+4x935M+ZU2Y7R9C0t+893Nx8d/UMHyYGrAG+D/xp1So3NvNkIjKefsMTGc+YLIx5H8ZsAZ4HPoKPyg1AdlOTDpVMgfb2R8NJeJgs4jelrwZeDoXMbaGQ0X1TIkmmgiNyVLzYvJ/4dv2/IX6Egi/N3rcvajtD0MRisdjw8KPzkvywZwL3A6+GQubDoZDR1h0iSaKCI2JMNsZ8AHgF+AVwhuVEM1bZ0DDhieMyPZ2d+5qN6SlK0cOfSvz+nIZQyPxVKGTyUvQ8IhlDBUcyV7zYfJB4sfk58ZO7A2Fxff1s2xmCpr19SzKmp05kKfEVV7tCIfM3oZDx1dSoiJfoJmPJPPGl3rcBnyX+m3PgxIBf/eIXw9k5ORoJSIJYLBbbseMvh4zpKUzzUzcB/wb8YNUqVyebi0yBRnAkcxhjMOZWYAfwnwS03ED8L3ako6PTdo6g6OjY22yh3ABUES84e0Ih83HdjCwyeSo4khmMeSOwFXgAWGY5TVrkHTo0aDtDUHR0pGV66ngqgO8CL4ZC5mrLWUR8QQVHgs2YhRhzvwtPAG+yHSedSvfs0fxzEsRXT22psJ1jxFnA2lDIPBQKmdNshxHxMhUcCSZjijDmCy44wG3GxqnellU3NOTbzhAEFqenjudaYFsoZL4RCplS22FEvEgFR4Ilfp/NB0aKzSoDqVrW63lL6+tLbGcIAg9MT00kF/gk8aXlH9X9OSJjqeBIcBhzychU1M8NLLQdx7Y5PT2F4cFB7Wg8AyOb+3llemoi84B7gedDIfN222FEvEIFR/zPmFkY800XnjDwRttxvMQ9cqTHdgY/6+jY22RMr9empyZyDvBIKGT+OxQyNbbDiNimgiP+ZsxVsfjRCn9n9P18jMIDB4ZsZ/Czjo4tEdsZpuEG4JVQyPyF7SAiNukHgviTMaVRY34KrMuK7/4qCczbuVP3ZUyTT6anJlIC/CgUMutDIXOK7TAiNqjgiP8Yc30UnGz4sO0oXregocEv0yueE1895ZvpqYlcSfzE8r8PhYxe7yWj6Bte/MOYyqgxvwF+nw2VtuP4waLdu8tisVjMdg4/6uh41Kurp6aqEPg6sFmjOZJJVHDEH4y5dWTU5n22o/hJfiSSHe7t7bKdw29isaiXNvdLlsuJ74T8sVDIZNy+UJJ5VHDE24yZHTbmAeCBbNCGZtOQ1dysQxqnyGerp6ZiFvA9YE0oZBbZDiOSSio44llRYy4Zhu25cKvtLH42e+/eqO0MfuPT1VNTcTXwUihkam0HEUkVFRzxHmNMnzFfMPBEHui3zBma39CQYzuDn8Snpx7NhHu8SoHfh0Lmq6GQ0feIBI4KjniLMRV9sGUWrMrS92dSLK6vn207g5+0t+9tMqavwHaONDHAXcAGbQ4oQaMfIOIZPcZcOQTOLHiz7SxBMv/w4TnRSGTYdg6/6Ox8NOjTU4msQEc9SMCo4Ih9xph2Y/51FjySD2W24wRNFhDp6Oi0ncMPRlZPZcL0VCKVwNpQyHxOq6wkCFRwxKpuY4o7YNNc+HRWfLhcUiCvsXHAdgY/6OjYk0nTU4lkAV8E/icUMuW2w4jMhAqOWLPHmNOAV8rgCttZgq50927bEXyhvT0jp6cSeSfxKas32Q4iMl0qOGLFS8b8WRU8P0erpNKiuqEhk0clJiXDp6cSWQQ8GgqZj9oOIjIdKjiSdtuMCZ0FDxZCke0smWJpff0c2xm8rr19T1NWVr+K4Fi5wL2hkPma7ssRv1HBkbRZY0zOLmN+fzZ8Plvfe2k1p6enMDw42Gs7h5d1dj6qDREndifwq1DIqACKb+iHjKTFRmPmnwMvnALX286SqdwjR7ptZ/CqWCwaHRoK3NlTyXYT8IhuPha/UMGRlHvcmAvOh201cJbtLJms8ODBoJyOnXTt7XuaNT01KW8GntCp5OIHKjiSUuuMefd5sKUU5tnOkunKd+7UPRQTyNDN/abrVOIlRyusxNNUcCQlao0xq435ixXwu1m6mdgTFjQ06L9DAiPTU1o9NTUVxI93uNF2EJGJqOBI0tUak30HhN4F9xZAnu08Erd4164y13Vd2zm8pr19t1ZPTU8h8JtQyPxf20FEElHBkaSqNSbvI/CDWvhMLuiEYg/Jj0Syh3t6dGTDOJ2dW7R6avqygH8PhczXbQcRGU8FR5Km1pjCj8L9tfDnOfre8qSs5uY+2xm8JBaLRoeHt8y3nSMA/j4UMt+yHUJkNP0QkqSoNWb238Pv3gU36Uwp75q9d69uph2lvX13kzH9+bZzBMTfhkLm27ZDiBylgiMzVmtM2T/Bw2+Fd9jOIsc3f+fOXNsZvKSj49GY7QwB8zehkPmO7RAioIIjM1RrTOU/wZo3wWW2s8iJLaqvL7adwStisWg0HH5Mq6eS7xOhkPmu7RAiKjgybbXGLPwkPPQmuNh2FpmcqsbGkmgkMmw7hxdoeiqlPh4Kme/p/CqxSQVHpqXWmFM+Br95G1xiO4tMXhYQ6ezssp3DCzo6Nmt6KrU+BqS05JiQmZWqxxb/U8GRKas15rQPw8/fCdrJ1IfyDh3qt53Btmg0Gg2HH9f0VOp9lPhp5EkvOSZkbgF2mZA5I9mPLcGggiNTUmvMybfBj/8MLrWdRaandPdu2xGs6+jYpemp9PlrklxyTMh8CngAmA/8yYSMDkqVY6jgyKTVGrP4JvjhTbBCE+v+Vd3QkPE/2Ds7tXoqzf4amPFmgCZkskzIfA+4h9e3ozgJ+IMJGe1GLWOo4Mik1Bqz4Hr4wW1wpfa58bclDQ0ltjPYFI1Go8PDWj1lwSdDIXPndD/ZXGOq6eQl4vf2jHcp8DOjm5plFBUcOaFaY+a/E757B7wjS98zvlfS3V0YHhzM2B2N49NTAxk/imXJV0Mh84GpfpK53pzNudRRylnHuexm4EvTjyZBox9Wcly1xsy7AL76f+A92fp+CYxYa2u37Qy2dHRs1tlT9hjgp6GQmfSmoGaluYozeIzZVE/i8s+YkLl9+vEkSPQDSyZUa8zcRRD6B7gxD7QDboAUHjgwZDuDDSOrp3T2lF25wC9DIXPCqVLzfnMHp/EQhUxlWvWHJmTeMP14EhQqOJJQrTElxfDpz8FNs0G73wbMvF27MvLvfnv7Tk1P2dcD3LxqlTvhfkxmuTHmQ+YLLOMn5DLVm4fzgV9pjxzJyBc5Ob5aYwqy4O9WwU1VoOWXAbSgvr7IdgYbOju1uZ9ljcCKVavcdRNdYJabbC7np5zM58kme5rPsxz43jQ/VwJCBUfGqDUmG/jIP8BNy2Gp7TySGot37y51Xde1nSOdotFIRNNT9oTD7AXetGqV++JE15jzTQFX8jCLuQMz49Wat5uQ+eAMH0N8TAVHXlNrjAH+7APwwRVwtu08kjr54XDOcE9Pp+0c6dTevqvZmME82zkyUWcnh//0J36xapV7YKJrzFWmghU8TRVXJfGp7zUhc1oSH098RAVHRltxJfz1+3S+VEbIam7OqKXimp6yo7mZ3WvX8sP+fr420TWm1pzGG6ijnHOS/PTFxO/H0X1XGUgFRwCoNeaMM+GTH4MV2usmM8zety9iO0O6aHrKjj17eHn9er4ZiXCP4yS+qdi811zOGTzBHBalKMZ5wL+l6LHFw/SDTKg1ZsFsuPNuuCIvvgJBMkDlzp05tjOki6an0st1cbdv56knnuAe4PuO4w4mus7calZyOmsoYm6KI33ChMwNKX4O8RgVnAxXa0wp8Ml/ghVlpPxFRjxkcX39bNsZ0kXTU+kTjRJ+5hk2vvgiXwAecBz3mJFCs9wY8wFzF8u4nzzStaLvPhMyi9P0XOIBKjgZrNaYfOATH4I3ngWn2s4j6VV16FBJNBIZtp0j1TQ9lT7hMAOPPcbDO3fyj47jPuw4x67UM8tNFpfxXU7hHnLSuoFoGfBLEzIZM3KZ6VRwMtTIiqnbLoSLb4gfVCcZJguIdHYGfiVVe/tOTU+lwcAAnevX8/tDh7jLcdynE11jlps83sofWMrHyLJyaO9lwBctPK9YoIKTud5cClf/PVyRzbQ30xKfy21sTHhvRJB0dj6q6akU6+6med06Hmhv5y7HcZ1E15gVpoSreZwark13vnE+bULmassZJA1UcDJQrTGLgQ9/Hi6cDaW284g9pXv2BHqzP01PpV5rK/vWruW+3l4+5zjuoUTXmPeYJVzEc1RwUbrzJWCAn5uQqbQdRFJLBSfD1BozC/jEX8Npy3TfTcarrq8P9Ko5TU+l1oEDvLpuHd8eHuZfHMdtT3SNudFcxFk8QyknpzvfccwH/t12CEktFZwMUmtMFvChS+H0d8bnoiXDLWlomGM7Qyp1dGj1VCq4LuzYQd2WLXzVdfmO47j9ia4zN5vrOJ2NzPLkmXbvNyFzpe0QkjoqOJnlrXNgxd/AFdrMTwBKu7qKwoODgdzROBqNRCKRrZqeSrJYjOhzz/Hoc8/xReD/OY4bTnSduc18nNP4LfkUpzniVNxrQkYjfAGlH3IZotaYk4EP3g1nFkOJ7TziHbHW1m7bGVKhvb1B01NJFokwuHUrax2HzwIPOY57zAiZWW6yzB3mqyzj2+Tg9a//cuAu2yEkNVRwMkCtMXOAT7wbys6BN9jOI95SeODAkO0MqaDN/ZJraIiejRt5aP9+PuU47pYJ9rjJ5Qp+yVLuIss3P18+Y0LmJNshJPn88g0o0zRy382Hy6D8Q6D5ZjnGvF27Avc6EF899YSmp5Kkt5fWdev41ZEj3OU47rZE15hLzCyuYgMLWWllh5vpKwS+YzuEJF/gXtjkGJcBF3waziuCjNmaXyZvQX19urbKT5u2toYmTU8lR3s7B9es4Wfd3fyT47j7El1jrjHVXMqzVHJ5uvMlyXt0VlXwqOAEWK0xFcCHboDiM+Bc23nEmxbv3l3qusdON/hZV9dm2xECobGRhrVruXdoiC86jnsk0TXmenM251LHXE5Pd74k+5YJmVm2Q0jyqOAE1MjU1B0VkHsLvMN2HvGu/HA4Z7i3NzBHNkSjkcjwsFZPzdTOnbywaRNfj8X4uuO4PYmuMSvNVZzBY8ymOt35UmARsMp2CEkeFZzgWgGc/Wm4uBD0W4kcV1Zzc2CWire3NzRlZQ2l8xDHQInFiL34Iluffpp/AX7iOG7CA1nN+80dnMZDFAZqVeYnTcicZTuEJIcKTgDVGlMJfOAmmHMq6C+rnNDsvXsjtjMkS2fnpkBNt6VTNMrwU0+xfvt2Pgc86DhudPw1Zrkx5kPmCyzjJ+RSYCFmKuUA3zch46/bpCUhFZyAqTUmG/hICWS9F66ynUf8oXLnzhzbGZIhPj31RJXtHH40PEzf5s38z549fMpx3A0TLAPP5nJ+ysl8nuzAHtK7ArjddgiZORWc4HkLcMbfwRlFeHoHUfGQRfX1gVhh19ZWr+mpaejvp/2RR3iwqYk7Hcd9IdE15nxTwJU8zGLuwPhsIfjUfdWEzFzbIWRmVHACpNaYKuD958HQBfBG23nEP6oPHSqJRiIJt9z3k66uzZqemqLOThrXrOH+zk7udhx3V6JrzNvMPFbwFFUZMypcAXzRdgiZGRWcgDi6oR8Q/ku4SmdNyVRkAZHOTl+vpNL01NQ1N7N77Vp+NDDA5x3HbUp0jbnOnMoFPEd5xm018RcmZBbZDiHTpx+CwXExcPotUL4QtO24TFluY+OA7Qwzoempqdmzh23r1/PNSIR7HMftSnSNea+5nDN5kjlk4g/6POAfbYeQ6VPBCYBaY2YBHyyG9lrteSPTVLpnj6+ndzo7NT01Ga6Lu307Tz3xBF8Bvu847mCi68ytZiWns4YiMvlelD83IbPQdgiZHhWcYHg3UPg3cGExzLEdRvypur4+33aG6YpGI+FwWNNTJxKNEn7mGTa++CJfAB5wHPeY7QHMcmPMB8xdLON+8gjcMR5TpFEcH1PB8blaYxYA7z4DBi6JnzslMi1LGhp8W47b2uqbNT11fOEwA489xsM7d/KPjuM+PMEy8Cwu4zucwj3koK9nnEZxfEoFx8dqjTHA+4HBj8I12QR2XwpJg9KurqLw4KAvdzTu7NxkO4KnDQzQuWEDvzt0iLscx3060TVmucnjrfyBpXycrMAvA5+KfDSK40sqOP52PnD2e2DWUjjVdhjxv1hbW7ftDFMVjUbCkcgTOntqAt3dNK9dyy/b2viU47hOomvMClPC1TxGDdemO59P/LkJmQW2Q8jUqOD4VK0xhcAHDRy5UTsWS5IUHjiQ8NwhL2trc5qNGdZ0SgKtrexbu5b7+vr4rOO4hxJdY95jlnARz1HBxenO5yMaxfEhFRz/egdQcjMsriAQJ/mKB5Tv2uW7qYmurs22I3jSgQPsWLeO7wwP82XHcdsTXWNuNBdxFs9QysnpzudD/0ejOP6iguNDtcbMB67Lhab3wJW280hwLKyv99WqmZHVU5qeGsV1wXGo27KFr7ou33YcN+F9VeZmcx3L2cgsKtKd0afygU/bDiGTp4LjT9cD0dvh7BIotx1GgmPxrl2lrnvs6hqv0vTUWLEY0eef59G6Or4I/KfjuAmP3zC3mY9zGr+lQOfVTdFfmJCpsR1CJkcFx2dqjVkMXFoALVfCFbbzSLDkh8M5w729vjmyQaunXheJMLh1K+t27OCzwEOO48bGX2OWmyxzu7mHZXybHPIsxPQ73YvjIyo4PjKyLPx9wODtcF4xlNjOJMGT1dzsi6XikUg4HIk8qekpYGiIno0beWj/fu5yHHfLBHvc5HIFD3ASnyJLr/0zoFEcn9A3ub+cCryhENreCitsh5FgKt6375jdbb1I01Nxvb20rlvHr48c4S7HcbclusZcYmZxFRtYyM3a4WbG8oG7bYeQE1PB8YmR0ZubgJ474IJZOpJBUmR+Q0OO7QyTodVT0N7OwTVr+Fl3N//oOO6+RNeYa0w1l/IslVye7nwB9hETMrNth5DjU8HxjzOAUwug4y3ohUpSZ1F9vedvPB2Znsros6caG2lYu5Z7h4b4ouO4RxJdY643Z3Mudczl9HTnC7hi4IO2Q8jxqeD4wMjozXuBnlvhnCLQbw6SMlWNjSXRSCTh6huvGJme8sVIUyrs3MmLmzbx9ViMbziO25PoGrPSXMUZPMZs7ZOVIh+1HUCOTwXHH84ETgHa3gKX2g4jwZbtuibS2enplVRdXZtsR7DCdYm99BJbn36afwF+4jjuUKLrzPvNHZzGQxRqIUIKnW1CRvdCepgKjsfVGpNFfOVU97VwylyotJ1Jgi+3sXHAdoaJxKennsq46alolOEnn2T9tm18Dvit47jR8deY5caYD5lVLOPH5FJgIWam0SiOh6ngeN9y4CSg/Z0avZE0Kd2zx7Ob/WXi9NTwMH2bN/M/e/bwKcdxN0ywDDyby7mPk1lFNhn19bHovSZk9EunR6ngeN+7gb4LoWJxfJpKJOWqGxo8uwlcV9dG2xHSqr+f9kce4cGmJu50HPeFRNeY800Bb+NhFvNhjBaCp1Ee8Oe2Q0hiKjgeVmvMQuAcoPV9Gr2RNFrc0ODJezcikfBwJPJ0xkxPdXZyeM0a7u/s5G7HcXclusZcacpZwVNUc1W68wkQ3/hPpdKDVHC87e3A8CIoOh3OtR1GMkdZZ2dReGio33aO8dradmTM9FRzM7vXruWHAwN83nHcpkTXmOvMqZzP85Tr9cGik4C32A4hx1LB8ahaY0qJnzXV/H64JBuybWeSzBJrbe2ynWG8rq5NGfGatXcv29av55uRCPc4jpvwv4N5r7mcM3mSOSxKdz45xodtB5BjZcSLhU9dDlAM5kK4yHYYyTyFBw4M284wWnx66qlAnz3lurjbt/PU1q18Bfi+47iDia4zt5qVnM4aipib5oiS2Pu0s7H3qOB4UK0xBcRvLm5ZCecUQJHtTJJ5ynft8tR9BfHpqXBgp6diMSLPPsvmF18kBDzgOO4xZ4KZ5caYD5i7WMb95Ol1wUOKgJW2Q8hYgX2x8LkLgUKg5U1wvu0wkpkW1NcXJdz/35LOzk2eKlzJFA4zsHUr6w8d4kuO4z6d6Bqz3GRxGd9mMR8jSyulPOjDwH22Q8jrNILjMbXGZAPXA+1vgPIqNL8udizes6fUdY/db8WGSCQ8HI0Gc3O/wUG61q/n94cOcedxyk0eb+UPLOXjKjee9WYTMqfaDiGvU8HxnjOI71bc8x44z3YYyVwFQ0M5w729nrjRuK3t1UBOT3V307xmDQ+0t/Mpx3GdRNeYFaaEq3mMGq5Ndz6ZstttB5DXqeB4z1VAXw6Yc+ENtsNIZstqaem1nQGgq2tz4EYtWlvZt3Yt9/X18VnHcQ8musa8yyzhIuqo4OJ055NpudF2AHmdCo6H1BpTRny/m7b3wDKdGi62Fe/bd8yNruk2snoqUNNTBw6wY906vjs8zJcdx21PdI250VzEOTxDqXYw95EzTMicbDuExKngeMsFI3/G3qLpKfGA+Q0NubYzBGl6ynXBcajbsoWvuS7fchy3L9F15hZzHcvZyCwq0p1RZkxTiR6hguMRtcYY4B1AexUUnhQ/ZFPEqkWOM8t2hq6uYKyeisWIPv88W+rq+CLwM8dxw4muM7eZj3Mqv6WA4jRHlOS4znYAiVPB8Y6TgAqg90Y4RzsXixdUNTaWRKPRhD+I0yEoZ09FIgxu3cq6HTv4DPCQ47ix8deY5caY2809LOPb5ODZw07lhK7Qpn/eoILjHW8GwgAXa+8b8Yhs1zWRzs5OW88fhOmpoSF6Nm3iof37uctx3C2Oc+zSe7Pc5HIFv+QkPkWWXpd9Lg+4xnYIUcHxhJGdiy8HjlwIFeXg+99YJThyGxsHbD13V9dGX79G9fbSum4dv25p4S7HcbclusZcYmZxFRtYyM3a4SYwNE3lAb5+8QiQs4m3/vDb4vvgiHhGyZ49x0ynpIPfp6fa2zm4Zg0/6+7mHx3H3ZfoGnONqeZSnqUyfvacBMa7TMjo56tl+g/gDVcBPQBnquCIx1Q3NOTbeN7W1leajYn48l60xkYa1q7l3qEhvug4bsITL8z15mzOpY65nJ7ufJJylcAltkNkOhUcy2qNqSC+YqrjNCiZp+kp8Zgl9fUlNp63u3uTL1+fdu7kxU2b+EYsxjccx+1JdI25ybydM3iM2VSnO5+kjaapLPPlC0jAnAO4gHs1+k1OvKess7MoPDTUn87njESGh/w2PeW6xF56ia1PP82/AD92HHco0XXm/eZ2lvNHCrFSHCVttB+OZSo49l0OdAGco+kp8ahYa2taz6RqbX21xU/TU9Eow089xYZt2/gc8FvHcaPjrzHLjTEfMqtYxk/IpcBCTEmvc03ILLYdIpOp4Fg0cjTDUqC7GoqqQH8ZxJMKDx5MOBqRKn5aPTU8TN/mzfzP7t3c5TjuhgmWgWdzOfdxMqvIxtfL3mVKNIpjkW9eRALqdMAA7jvhtCy0SFS8qXzXrrS9VkQiw15+SQkAACAASURBVEPR6DO+mJ7q76f9kUd4sKmJOx3HfSHRNeZ8U8CV/C+L+TBGf8czjAqORSo4dr2ZkdVT52t6SjxsQX19Ybqeq7X1FV9MT3V2cnjNGu7v7ORux3F3JbrGXGnKWcFTVHF1uvOJJ7zNhEyR7RCZSgXHklpjZhEvNZ0lkLcQdAKteNbi3bvLXPfYqZdU6Ory/uqp5mZ2r13LjwYG+LzjuE2JrjHXmVM5n+cp59x05xPPKAAutR0iU3n+hSTAlhP/+seugZNz0Ly8eFfB0FDOcG9vym809sP01N69bFu/nm9GItzjOG7Cr4l5r7mcM3mSOSxKdz7xnIttB8hUKjj2vBEYADg7ftCmiKdltbT0pvo5vDw95bq427fz1NatfAX4vuO4CY+wMLealZzOGoqYm+aI4k0X2Q6QqVRwLKg1Jp/4gZptAEtVcMQHivfti6T6Obq7vbl6KhYj8uyzbH7xRULAA47jHvO1MMuNMR80d7KM+8lD913IURrBscSTLyYZYBnxKanoQphVBhW2A4mcSGVDQ0qnUcPh4aFI5FnPTU+Fwwxs2cKahgbudhz3fydYBp7FZXybk/kqOeTayCmetdiETKXtEJlIBceOc4AowIr4PjginreooaE4lY/f1ua96anBQbo2bOB3hw7xD47jPp3oGrPc5PFWfsdSPkGWloFLQhrFsUAFx47zgQ6As1RwxCeqDx4siUaj4VQ9vtc29+vpoWXtWn7Z1sbdjuM6ia4xK0wJV/MYNdSmO5/4igqOBZ56QckEI7sXVwD9AItVcMQnsl3XRDo7O1Px2OHw8FA06p3pqdZW9q9Zw096e/mM47gHE11j3mWWcBF1VOiHl5yQbjS2QAUn/ZYe/YclUFwK8yxmEZmS3MbGhCuHZqqtbXuzV6anDhxgx7p1fGd4mC87jtue6Bpzo7mIc3iGUk5Jdz7xJZVgC1Rw0u9MIAxwuUZvxGdK9uyJpeJxu7o2Wd8HynXBcajbsoWvuS7fchy3L9F15hZzHcvZyCwtDpBJq9TBm+mngpNGtcYY4vffdAKcqeXh4jPVDQ35yX7Mkemp+cl+3KmIxYg+/zxb6ur4Z+BnjuMmvNfI3GY+zqn8lgJSesO1BJJGcdJMBSe9yoByRjb40/034jdL6utLkv2YtqenIhGGtm5l3Y4dfAb4g+O4x4xSmeXGmNvNPSzj2+SQZyGm+J/uw0kz68PCGeYkwAWYBwUlaKdT8Zeyzs6i8NBQf25+ftI2suvq2mjtdWhoiJ4tW1jT0kLIcdxtia4xy00OV/BzFnCLFoHLDGgEJ800gpNeZzFy/81F4JkVIyJTEWtrS9qZVOHw0FA0Wmdleqqvj9Z16/h1Swt3TlhuLjGzeDsbWKhyIzN2kQkZfRelkQpOmozcf3MeI/vfnAbVdhOJTE/hgQPDyXqs1lY701MdHRxcs4b/7O7mHx3H3ZfoGnONqeYynmU+K9KdTwKpBDjVdohMooKTPmVAKTAIsEgFR3xq7u7dSfsttLs7/aunGhtpWLOGewcHCTmOeyTRNeZ6cxbnUkcZp6c7nwSa7sNJI92Dkz7VjNx/AzBfBUd8aqHjFLYm4XFGpqeq0jlov2sXLz71FD8A/sNx3KFE15ibzNs5gwcpJOk3VEvG075JaaQRnPRZePQfZkPunPhqKhHfWbx7d5nrHnvg5FS1tW1vMSaSltcg1yX20ktsfeop/gX48YTl5v3mdpbzR5UbSRHthZNGGsFJn9MYOZ7hQpifhW5ZFH8qGBrKGe7r68wvLi6dyeN0dW1My7030SjDzzzDo7t386/AxglOAze8kc+zjM+SrddFSRkVnDTSX+Q0GLnBeBnQC3C6pqfE57Kam3uZQcEJh4cGI5G6qqwUj98MD9P3+OOsO3yYLzqO+3yia8xyk83l/JhF3IHRLx6SUktsB8gkmqJKj9kjb0MAi7VEXHyueP/+yEw+v7V1e0tWVjSlrz/9/bSvX8+Dhw9z54Tl5nxTwJX8L4v5sMqNpMEi2wEyiQpOeoy5wbhKIzjicxUNDTMa/e3u3pDS0ePOTg6vWcP9HR3c7TjurkTXmCtNOSt4iiquTmUWkVGKTMjogOU00RRVelQzUiYNUIoO6RN/W9zQUHxwmp8bn556LmXTUy0t7Nm0if8XifDvjuMm3JTQXGdO5XweYY7uiZC0WwwkYyGinIBGcNJjOSM3GC+F2TkqluJz1QcOlESj0YQHUp5Ia+u2lE1P7d3L9kce4d8jEe6ZsNy811zOmTypciOW6PsuTVRw0uO1G4yX6fwpCYBs1zWRzs7O6Xxud3fyz55yXdzt23l661b+Ffi+47gDia4zt5ibWM4aivT3UKxRwUkTjSSkWK0xRcT3vNkPsDC+o7GI7+UePjxA+dS2c0rF9FQsRqSujscaGvgq8PCEy8Av4R84lS+TQ27ynl1kylRw0kQFJ/XKgdjRdypVcCQgSvbscXvOPntKnzMyPZW0F/hwmIGtW9lw6BBfchz3qUTXmOUmi8v4Fov5OFlaKSXWqeCkiQpO6pUxalO/eSo4EhDV9fV5PVP8nGROTw0O0rV5M//T1kbIcVwn0TVmucnjrfyGGmqT9bwiM6SCkyYqOKlXxqh7nUpVcCQgFjU0lNRP4fpkTk/19NCycSO/6+3lnx3HTbigy6wwJVzNOiq4eObPKJI02uwvTXSTceotBIaPvjNHBUcCoryjoyg8NNQ/2etbW19Oyuqp1lb2r1nDfb29/NOE5eZdZgkXUadyIx4034RMnu0QmUAFJ/UWAAMAJZBXCLMs5xFJmlhbW8Kl2Il0d2+c8c29Bw+yY906vjM8zL84jtue6Bpzg7mQc3iGUp3cLJ5k0I7GaaGCk3pVjBSc5Rq9kYApPHBg+MRXxaenotHn5k/3eVwXHIe6Rx/la67LtxzH7Ut0nbnZXMvpbGKWNtMUT1toO0AmUMFJoVpjcoFSRqaoFsX/WSQw5u7ePalVSa2tL7cYE5vW600sRvSFF9hSV8c/Az9zHDfhBoPmNvNRTuVBCiiezvOIpFGJ7QCZQDcZp1Yp8TOoXIC56IVXgmVBfX3hZPacn+7qqUiEoSefZOP+/XwZeGzCPW7exFdYxp1k6Zc28QX9LEgDFZzUKmPUIZuzochiFpGkW7xrV+kLrusaYyYcyRkeHhyIRp+rmviKxIaG6NmyhTUtLYQcx92W6Bqz3ORwBT9nAbdohxvxEd2LmQb6bSe15jLqa6yCI0FTODSUG+7r6z7eNW1tLx+Z6vRUXx+t69bx65YW7pyw3FxiZvF2NrBQ5UZ8RyM4aaARnNQqZ9QIThEUWswikhKmpaWX4uIJ7ymY6vRURwcHN27k14ODfMVx3CMJn/MaU81lbKCM06eaV8QDNIKTBhrBSa0y4LUbIos0giMBVLxv34Snisenp56vmuxjHT7MzjVr+P7gIF+YsNxcb87iXOpUbsTHNIKTBio4qVXKqIJTqIIjAVTR0DDhCE1r6+Snp3bt4sWNG/l6LMbXHcdNeAqEWWmu5AweZzbV080r4gEqOGmgKarUmsOoglOggiMBtLihYVbC7YSBnp4Tb+7nusRefpknt23jW8CDjuNGE11n3m9u5zR+QC4FM8krklTxdbIRYkSO+TNGFJcIYbKZzavk0g4MAk9bTp0RVHBSq4RRBSdf9+BIAFUfOFAajUYj2dnZY15PRqan5h9v9VQ0SviZZ9i8ezdfATZMuAz8jXyOZXyObL1myTjxEhGdoGAc+350VPkY/X50grfR/4sSJTzyz+GRtwgJC/k4C4Gvuo47lePbZIb0YpFas4F2gFzIykO/eUrwZLuuiXR1dWbPnTtv9MdHpqcmPDl5eJi+xx9n3eHDfMlx3OcSXWOWm2wu58cs4g6M1kp5jos7pjwkGsEYXTTGvyUqF7GRWpGoYBz939FyMUzE9pdgCmZ8VIlMjQpOitQak018xCYCUAWFenWWoMppbOxn7twxH+vu3jDhC3p/P+2bN/PHjg6+6DjurkTXmPNNAW/jD1RzTZLjBsPRqZHxoxeJRi2OfYtOUC5eLxUnGr0YJkKMmO0vgwdlJXjLG3mTNFLBSZ1CRi0RL9PojQRYyZ49sd6zz37t/eHhwYFY7IWE01NdXRzeuJHf9Pfzr47jNiV6PHOlKWcFGyjn3JSFnqkYsZGCkbhUuAlGL6IJ3h8/ejFRwRg9cjH5qZFMY0hcMCb7Znh99/nXdqE/znON/9MQP5onPO5tJ9CShP9/MgUqOKlTBK//dlOor7UEWHVDQ37DqPdbW19qMSa2ZPx1LS3s2bSJn0cifMNx3IQnkZvrzKmczyPMYcLprVFTI9HjFIyJikV0TLkYXzSON3ox+s097g+/THS0XMykZCQqFicqGaMLRozXS8XRojEw8s+j38Kj/nlo3J+RCd6ix/l3r13jJriPTOzQD93UGbNiKl9fawmwxfX1JaMLTnf3xmOG4/fuZfvWrfwQ+InjuAOJHscsNydTzud4mTpc1kw4ehHV1EgCkx2hmOzoBUxcLsaPWowvF6MLxOiyMcTYojE06u1ouZhMkUhYPlzH1feFvEY/dFNnzIqpPMi2FUQk1co7OooG+/uiBUWzsuPTU8+/dvaU6+K++irPvPAC3wZ+5ThuwhtDzXJzLvC3tNFOG7vTlz4pkjE1cvSH83SnRsZPi4wfqRhfLMa/H2aSRWKCt5h3Ry9euyeyYNxbfoKPnejjvwJ3a5r/D8g0qOCkTv7od/L0tZaAG25pGihYekpxa+tLR4xxFwPEYkTq6nisoYGvAg9PuAwc3gLcARwB+qb41Ed/wM+kYCQatZjM1MjRtyjHnxpJNIpxoqmRqYxkeHhqxBimViKmUzxO9DnJ/AXzFUAFxwf0Qzd1cnj9NyxyNYIjAVfYeLCfpacU9/RsyAMIhxnYupUNhw7xz47jPnmcT70K+CjQT/yA2rLjXDuZqZFEUyTHmxqZzMjFcYuHt6dGTB7pKRETfTxoq4e03NsnVHBSJ5tRBSdHx2JIwFXu3TvYfNEl/dHoC/OHhujavJn/bWsj5DjujhN86g7g35j+jZ1enxqxPXqhHSqSK2iFLbBUcFJnzAhOll5kJOAWNuwq2N76Umtvr1uwcSO/6+3lnx3HnegUh9e4jnsAOJD8RMYwvRGHZBYPjdwGj0ZwfEIFJ3XGvLCp4EjQLdm1t2zf3s0Nj6zngeFhvuY49ICZjb3RC/2mLamgguMTKjipM6bgGBUcCbC2k2jvvHUo8rd/Vpf1jX/n74zhU2haVoJJGyz6hApO6oz52moER4Jo77l5g5H3RiPLlkXnlgPuTkrMeewilzNtZxNJkSHbAWRyVHBS5+imWQBE0cZkEhxHLuZQ5EZTvHTxcMnojxuXfP6HEmppw1BuK59ICqng+IQKTuqMGZ4fiC9FFfGtmMFteQsHC2+gqGIeCybcJqaPBS1/omXeu3GzsjRyKYGjguMTKjipc3TzMAAGVXDEp6I5RFvewcHZ11JaNYdFk/mcym4qDz3DvgVv5JjzqER8TgXHJ1RwUkcjOOJrwwUMt9ZyuOxq5lUXTb2o1OxkSct8DlUuZUEq8olYooLjEyo4qTPMqBuLB+Ibkol43uAc+tvfS8u8FVTX5E9/BMYYKH2SeT1ldM4uoTSZGUUsGrQdQCZHBSd1hhhVcPo0giMe11dBd/fNdFRczIKaHJYm4zHzYuQPrqcvfB3h3FztHyKBoBEcn1DBSZ0wo+7BUcERr+peRHv/LfRWnsvCWVnMSfbjzxlkbvNmDsy/anL374h4nAqOT6jgpM4wowpOrwqOeEzHaRwJ38pwxaksmGOYm8rnmt/CosMvsa/6XN10LL6nguMTKjipM2YEp0cFRzziyPkczlqJKV9MVTqfd/42FrVV0Fxezfx0Pq9IkukeHJ9QwUmd8Lh3YlGIZWv7erEgZnCPXM6h/BspqKik2kaGLMgqepTZA9fRW1hEsY0MIknQbjuATI4KTuocM2ITgeHs+CGAImkRzSbacjWHiq9j9vxSFtrOUxilqG09zXnvpig7W2VffKnFdgCZHBWc1Dmm4AxCX74KjqRBOJ/wkWtpLH0H5dWzWGw7z2jlPcxvfJJ9NW/W/TjiO73gDtgOIZOjgpM6xxScPugtQefzSOoMFTPQdiPN5W+laiZ72KRazT6WNFVyoOpUrawSX2m2HUAmTwUndY4pOL3QYyOIBF9/OT1dK2mf90ZqanKTs4dNqs17lqquctpK5qr0i29oespHVHBSZ8xOxgA90GspiwRUTw0dvbfQU3keC6qzmW07z1TkuORmbyRn+FqG8vLJt51HZBJUcHxEBSd1+hi3YqpLBUeSpPMUWoduZbDidBbMNpTZzjNdxUOUtGziUMU1LDA6d1y8T1NUPqKCkyKrXTdca0w/8a9xBKBdU1QyQ63n0sTNuPOW2lnqnQqVbSxofI69NRf6Y2pNMppGcHxEBSe1OoB8RgpOq0ZwZBpcoOVSDua9j7x5VendnC9dqnaw5EgljRWLqLGdReQ4VHB8RAUntdqApcSnq2jWCI5MQSyLWMuVHCy6ntnz59rfwyaVsgxmzuOU9b2H7lmzk38elkiSaIrKR1RwUqsdOO3oO4c0giOTEMkl0vIeDpW8i7lVxd7awyaV8mMUDqynL3ItkZwcvTaJJzXZDiCTpxeR1GoF8o6+0wQDUYhmQ7bFTOJRQ0UMtt1A89wrqagp8O4eNqlU2s+8w1vYX/22zCl24iu7bAeQyVPBSa0Oxi0V74PuOfh31YskX38pfZ03caTizdTU5GZmsRmt+jCLD29nX/VZ+lqIp/QBjbZDyOSp4KRWL6NOFAfogDYVHAHoraKr52Y6Ky9kYU22VhCNVvkiCzsqaCmrpNJ2FpERO8F1T3yZeIUKTmr1MK7gtELrElhmKY94QOdS2oZupb/iTBYWZ1FiO48XZUN2/mYKB6+lv6CQItt5RIAG2wFkalRwUquXcVNUjdB6oaUwYlfbWTTHbiZacQo16EyyEyoKM7t1A01576IwKwttAyi2qeD4jApOanUR383YMDKSszd+47FkkJZLaMx5H9nlC5hvO4vfzOuiqvFp9ta8SVN4Yl297QAyNSo4KbTadYdrjWklvtnfIMAOFZyMEDO4LW/lYOENFFWWa/O6majZzdLm+Rycf1Kw9wISz9MIjs+o4KTeAeL33AyOvNM3BIP5UGA3lqRCJJdIyzs5VPJuyqrmsMh2nqAoe5LKnjI6ZpfqBn2xRiM4PpN14ktkhvbC2JskOzWKEzjDhQw13sze8L0M1dzCkllztBtvMuW55LkbIBxm2HYWyUhd4B6xHUKmRgUn9Q4z7uvcFj/CQQJgYA79jR9hH/dCTS1LC4uYZTtTUM0ZpKx9k84CEis0PeVDKjip18q4peLNGsHxvb4Kuhs/wb7c75BX83aW5OWRbztTJph/hIWNL7LPdg7JOK/YDiBTp3twUq+NcUvF94OGOn2qexHt/bfSV3kOC2ZlaRrKhqrtLG6roKm8Jpgnq4snPWM7gEydCk7q9QBDxL/WEYAX4PDtViPJVLUvpyVyC+GKU1kwxzDXdp5MlgWmaAtz+q+lt2gWxbbzSEZQwfEhFZwUW+26bq0xh4C5QDfALujuh54imG03nZzIkQtozFpJVvkijRZ4SWGUoo71HMl/D4XZ2Tq8VlIqDLxoO4RMne7BSY99jFtJ1QSHLGWRE4gZ3OYrOND5TVor/oEalRtvKuulomUrB23nkMDbBu6g7RAydSo46bEXxt6Euk8Fx3Oi2UQPv4t9fd+ja/5fsai0gnm2M8nxVR9gSZPDfts5JNA0PeVTmqJKj0YgNvoDr8LBt1kKI2OF8xk+ch2HS6+hvHoWS2znkamZV0dN5zxaS8tVSCUlVHB8SiM46XGI+Eqq11ZTPQWNsXHLxyW9BosZaLydvbHvE6u5gSW6YdWfciAnZyN5Q0NoGkFSQQXHp1Rw0mC16w4SH8V57T6cDhju0n44VvSV09P4MfblfI+cmmtYmp+vYzP8rniYOd0baI/F9EuDJNUAsN12CJkeFZz0eRXG7pvSiG6QTKfuBXQ03cn+gm8yq+bNLMnJIdd2Jkmeig5qmuq0CaAk1fPgRmyHkOlRwUmfehj7A3W3bjROi45lHGn+HAeL76G06nwWZ2fp+z6oqutZemS//l5J0mh6ysd0k3H6HGLcPTcvwcHrLIXJBK1v4DA3Y+Yt0TLvTGEMzNlKeW8pXcVzKLGdR3zvUdsBZPpUcNKnGYgC2SN/UgctQzCYj+4BSRYXOHIZB3PfS/68Kqpt55H0y49RMLiBvsi1RHJy9Bon0xYFNtgOIdOnofo0We26UWAXo3YvjoB7EPbYSxUc0Wxih69hf+936aj8OAvLqqiwnUnsKemnvPVRGm3nEF97FtxO2yFk+lRw0usVxh3P8Eq89Mg0RfIIN97AvsF76au+ncWzyyiznUm8oaqJxYe36aZjmbZHbAeQmdHwbXodM1qzGXbqPpypG5rFYOsNNJW/jfk1BdqcTxKrfJFF7RW0zJ1Ppe0s4jvrbAeQmVHBSa99vL7hnwtQD11d0FYC5VaT+cRAGb0dN9FWcRnVC3JZajuPeFu2IavwUYoGr6W/oHDseXAix9EHPGE7hMyMpqjSaLXr9gD7GTdNtVvTVCfUW0Xn4f/LvrxvUVjzFpbk5pJnO5P4Q2GY4r71dMdiY49LETmOR8Edth1CZkYFJ/2ehrHLV19QwZlQ50m0Nv8TB4v+jZLqi1mSnU227UziP+XdVDU9yQHbOcQ3ND0VAJqiSj9n/AfWw54PQTQb/fA+qu1smmI3E6s4mRrbWSQYavaypHk+B+afwiLbWcTzVHACQAUn/fYBEeJf+whAN4Sb4UANmX1PiQsceSOHct5HbnmNNueT5Jv7NFXdc+mYo9V2MrEmcLfZDiEzpymqNFvtuhHgZaB09MfrM3iaKpZFrOntHOj5Nu2Vf8uCuTVa8SKpkeuSazbA8DBDtrOIZz1sO4AkhwqOHXUwdkXH49BgKYs1kVwijdexb+B79FZ9hEVzyplrO5ME3+whyjo2ccTVueOS2G9tB5Dk0BSVHceM1jwFzZmyXHy4kKHWP+Pw3LdTWVOoPWwk/ea3srDxBfbWnJ/Z08JyjC50/01gaATHjiNAO1A4+oPbYbudOOkxUEJ/45+zl3uh5lqWal8SsanqVZa0HuKw7RziKX/Q8vDgUMGxYLXrusAzMHZK5pGAFpzeSroP/y37cr9NXs2VLM3LI992JpEsMMWPUdLXS4/tLOIZv7EdQJJHBceelxm3LPxZaOmIj+4EQtdi2pru5kDR1ymufiNLdLKzeE1BlKLh9QxGo0RtZxHruoC1tkNI8qjg2NMADAG5oz8YhGmqtjNobglxaM6XKa86l0VZWfo+E+8q66Oi5XEO2c4h1q3W9FSw6AePJatdNwxsBSpGf3ydjwvOkYtobLuH5vLPMr9yGQuMsZ1IZHKqD7L48A72284hVml6KmA0ZWDXM8CVoz/wPLS2Q8tc/LEXTMzgHrmCg/k3UFRRoV2Hxb8qnqOmcx6tpfOYZzuLpF03mp4KHI3g2LUTGICxB0du88EoTjSH6OF3s6//e3TP/0sWlVYEf3m7BFsO5ORuIm9okAHbWSTtVoOrzR8DRgXHopFdjbfC2N8YHwbPbhM+XMBw40r2Dn+fwerbWFJcMvbgUBE/mzXMnO6NdMZiaBvAzPJr2wEk+VRw7HuGcVOF26D9CDRaypPQ4Gz6G+9gr3svsZrrWVpYxCzbmURSoaKD6qZn2Wc7h6TNYeB/bYeQ5FPBsW8X0A9j94Z5Ep6zE2esvnJ6Gj/GvpzvkltzNUvz8ymwnUkk1aobWNqyTyurMsR/gBuxHUKSz7g6kMW6WmNuAd4Or7+glkDeffAPeePuz0mX7oV09N9KT8W5LMzWMm/JQMNZDA29i4HZJWMPxpVAcYGTwd1rO4gkn35weUMd4zb964LhHfHNANOq4zRaWj7PodlfoazqPBar3EimyouRH9tANBwmbDuLpMw6lZvg0jJxb9hNfBfNQnh9Bcef4Nlz4cJ0BGg9n8PchJm3hKp0PJ+IH5QMUN70KAeq3s4i21kkJX5kO4CkjqaoPKLWmHcCNwEHRn/8PviLClKzv0zM4B55M4fybiS/bP7YDQdF5HWN57Cv5hydfB8wTcBicDVCF1CafvCOpwHDuP8mW+HZZD9RNJvY4Xewv++7dM7/KAtVbkSOr+plFrU10Ww7hyTVz1Rugk0jOB5Sa8wngDOBlqMfmwO5P43fbDzjE7jD+YSPvIdDpe9kXtEsimf6eCKZpD+HPnMtaIuEQHCBZeDuth1EUkcjON6ygfh9OK/phvAr8NJMHnSomIHGD7I3di+RmveyVOVGZOqKIswaWE9vNErMdhaZsfUqN8GnguMtDtABFI3+4B/jq6ymrH8uvY1/xb6s75Jd806W5heMLU8iMjVze5jf/OTY++TEl35oO4CknqaoPKbWmGuAW2DsycY/hA9Xw+LJPEZPNZ29t9JdeR4LsrPHLj8XkZlrupgDVadqZZVP7QNOATdqO4iklkZwvOdp4vPDY/7brI2fWXVcnafQ2vwZDs76GiXVF7JY5UYkNeY9S1VXO222c8i0/LvKTWbQCI4H1RrzMeAceH3VhgH+Ez5eOu5gToC2c2iK3YxbcRLVaYwpktF68+nMu47CvLyZLwCQtOkAFoHbZzuIpJ5GcLxpA4w988kFNo8axXGBlks51P5vtJR/miqVG5H0Kh6itHMjrfod0Vd+oHKTOTSC40G1xmQBXyZ+DlX30Y8XQPZPs/ibnrfRPevPmDVnLnOthRQRABqXs7fmQpbaziEnNAQsBbfJdhBJD43geNBq140BvwfKRn98EKKPf4gnqj/CIpUbEW+o2sGSIwdptJ1DTug/VG4yiwqOdz0H9DJ6X5xsCn62DnoHiERyjQAAIABJREFU0A1yIh6RZTBzHqOsr+f10VbxnAhwj+0Qkl4qOB612nWHgdVABTkUM49LWUJt3xwKntqZ/OMbRGT68mMUhjcwHIkQsZ1FEnpAp4ZnHhUcb9tKGWUs4d1UksVsHqaQzb94gk3DEYZshxOR15X2Me/IY5qq8hrXJQb8q+0ckn4qOB622nX7qOL3zOZZCnic7PgQeGsPg3V7eNp2PhEZq7qRxYdfHbtJp9hlDA+Cu8N2Dkk/FRyvK+QPZDEE5Iz+8E8388RQhEFLqURkApXPs6CjhSO2cwi4LhHgc7ZziB0qOB63us7tBB6BsfvcNHcx8LjDY3ZSichEsiE7bzMFg4P0286S6YzhPnAd2znEDhUcf3hk5M8xRy/8aANP9Q5q5YaI18wKM7t3PV2xGNpozBLXpQ/4gu0cYo8Kjg+srnPbgI1A1eiP9w8TWfcyG+2kEpHjmddFddMz7LOdI1MZwze0701mU8Hxj/8lfiRV7ugP/ucWXmzvpcVOJBE5nppdLG3ew0HbOTKN63IE+JrtHGKXCo5PjIzi/Ilx9+LEXNzfPfvaFJaIeEzZk1T2dNFhO0cmMYYvgdtjO4fYpYLjL+uIn6cy5iDOP9TR0NjBXiuJROS48lzy3PUQDjNsO0smcF12AT+wnUPsU8HxkdV1bi/wW8bdiwPwi8dZl/5EIjIZcwYpa9+sqeR0MIbPghu2nUPsU8Hxn8eAdqB49Ae3ODTubOIVO5FE5ETmt7Cw8UWNtKbYs8CvbIcQb1DB8ZnVde4Q8F/AvPH/7gfrWRuJot9cRDyqajtL2hrRyp4UcF1c4JPgamm+ACo4flUHHADKRn+wvomuLQ6P2okkIieSBaZoC3P6++i1nSVojOGn4GrzU3mNCo4Pra5zo8AvgZLx/+5769ja0adt4kW8qjBK0eB6+qNRorazBIXr0gp8ynYO8RYVHP96FdgOVI7+4HCE2M8f448apBXxrrm9VDZv1f44yWIMd4LbbjuHeIsKjk+trnNd4qM4hYw7iPORbex/5RAvWAkmIpNSc4AlTfU6eXymYjE2g/uftnOI96jg+NjqOvcg8EegZvy/+9bDrB0MM5D+VCIyWfPqqOlso812Dr9yXcJZWfyV7RziTSo4/vcnoAOYM/qDTV0MPPyi9sYR8bIcl5zsjeQMDTFoO4sfGcNXdFq4TEQFx+dW17mDwM+AcuJnVb3mPzbz/OFODYGLeNnsYUq6N9Km++amJhZjN/Bl2znEu1RwgmEb8DTjzqlygR9t4I/RGDErqURkUiraWXC4TiePT0VWFn8Nrka+ZEIqOAEwcsPxf428mz/639Xt4chjDpvSHkpEpqTaYcmR/TTazuEHsRj3g6speDku405xXLSurq4yJyfnJ8DZqCBNVQzYFolE/s+FF16Y9HNpai80bwc+CGO3g8/Jwtz7ET5cVcKiZD+niCTPUBaD4XczVDzn2D2uJC4a5WB2Nmfz/9u797Ao72vR4993uA8gAqKICmgEBG+hk+j2EklMtNoIbbfGpona2qRp7CXutNpjL8c2xt2YtE08prvVnUQbjU00adKMidGGREGjiTheUFABlYtyERSGgRkuM/OeP14wChhBgWFgfZ5nHpV5GdYgD7Nm/X6/tVDNro5F9GyeN7+kxSd4er4aHh4eHxYWVqnT6WTVuAOcTqdSXl6eUFpa+iqQ0gVfIg24F20/ztWTGXYn6ks7eW/1fJ708sC7C76uEKIT+DjxtX1KjX0Odk/Pjv9+7u1UFaeHB49IciPa41YqMGPCwsKqJbnpOJ1Op4aFhZnRql+dzmhS7WgbjgNokbyeKqZy5zF2dcXXFUJ0nv5WBpTvo8TVcfREdjt/BnWfq+MQ7uFWEhydJDe3rul712VLe0aTehatN86Qlve9tpej5y8hRyqF6OEGlzCsJEs2HV+rro5sLy9+4+o4hPuQPTS90w7gAhDW8o7nP8Boa6C2+0MSQnTEwGMMu1JGp+/Vc0cOBw2+vswFtdHVsQj3cftrvIpi6IQ4vqSqpk59vD7IaFIbUgzKBmAV4A00NN9XXIn1rYMYFyfxXZcFKIS4KQ8FnW8a+rpkrL5+6F0djys1NrLcw0M97eo4hHuRCk6TCRMmxKWnp/eaXyJNYxz+QRtLVe8dJudEEZJICtHD6e0E1H5CtbMP97Ky2djj66uuc3Ucwv1IgtO77UFrAhje8o7nd7C7qpaK7g9JCNERodWEl35BkavjcIXGRsx+flJtFrfGbROc5cuXD46Ojh5jMBjikpOTh69cuXJQW9ddW5kpKSnxHDJkyFiAmpoaZc6cOSNGjBgxesaMGXfU1dVdHXOg1+sTH3vssWEjR44cPWnSpNji4mK3PK5pNKkOYFPTP6+rTlXbaHzxI7Y12L9cvhJC9EwR54kqO8sFV8fRnVQVVVV5FNQyV8ci3JNbJjhpaWn6HTt2BGdnZ2elpqbmZmZm+nf0Mf70pz8N9PPzc547dy5r9erVxdnZ2Vcfw2az6e66667avLy8rClTplhWrFjRalq3uzCa1ApgI1oV57pZVccKqHj7C/7lksCEEB0ScoiB1ZVUujqO7mKxsMbbW/3Q1XEI9+WuCU7A7Nmzq/R6vRocHOycMWNGVUcfY//+/QELFy68DDBx4kRbbGystfk+nU7H448/fgXgBz/4weVDhw4FdF70LpEBfEYb+3G2fc6pQ2c50P0hCSE6wkvFm09RGhqod3UsXa2ykr39+smRcHF73DLB6QhPT0/V4XAAYLValZtc3iZFuaVP6zGaZlVtBaqAkJb3rzGSevEK57s9MCFEh/Srp39VGuWujqMrWSyUeHqSAjJfXdye209wVNXUqbd2SEpKqtm9e3eQ1WpVzGazLjU1tf+Nrh02bFj9oUOH/AG2bt0a3PzxqVOn1mzdujUEICMjwzcnJ+fqHhWn08mmTZuCAf7+97+HTpgwwXKr356ewmhSa4CXAX/A99r77E7UZ//FOzV1VLskOCFEuw0sZ2jx0evnzfUWDQ00lJTwjcBA1e1/5wrXc8sKTlJSknXWrFnmhISE0dOnT4+Ji4uzBQUFOdq6dsWKFWWvvfZaWHx8fEJFRcXVzcLLli27VFtb6zFixIjRv/nNb4YkJCRcbX7n5+fnPHTokH9MTMzo9PT0wOeee65XtE03mtQC4DUgghb/98WVWNd/wnaHkza/j0KIniP8FFEVF3vXOAdVhcJCnoyNVY+5OhbRO3R4mvjx48fzx48f7/LjxWazWRcUFOS0WCy6SZMmxa1fv75g6tSp1pt/5s3p9fpEq9V6tDMeqy3Hjx8fMH78+OiuevyvkmJQFGABMB1at4J/4j4Mc77GnG4PTAjRIXWeWJ3fwKEPINDVsXSGwkJeiYxUn3B1HKL3cMsKDsCCBQuiRo0alTBu3Lj45OTkys5Kbnq7pv0424CztNEf53/3YDpZxJFuD0wI0SG+dvT1n1DncLh/1fXSJQ5HRvIjV8chehe3reC0tHDhwsiMjIzrTjstWbKkbOnSpZddFdONuLKC0yzFoIQCvwca4fq9N14e6NYu5JFhodzhitiEEO1XMpSCwdOIcnUct6q6mnKzmbhhw9Q+cwRedI9ek+C4k56Q4ACkGJRRwArgIlqic1WwP94vPsri0MDWVR4hRM9S8jUKB48i0tVxdJTVijUvj4njxqknXR2L6H3cdolK3D6jST0NvAkMo0UTwMpaGp55j3/IySoher6wI0RUVbjX6JX6ehqPHmWeJDeiq0iCIz4GDqAlOdfJL8fy5528UW+nrvvDEkK0lyd4eu3Fu74Om6tjaQ+HA+dnn/H0lCnqR66ORfRekuD0cUaT6kSbV5VLG52OTecpf3UP2+T4uBA9m38D/ao/pdLppEc3yFNV2LOH/1m7lr+6OhbRu93+EMl/KIZOiONLj7Sv2V97rVu3LjQlJaU6Ojq68eZX901Gk1qfYlD+AvwaGAhcuvb+3ZnkhwXy/kMT+U83b+osRK8WVkVE8WEKIib03E3HBw7w7rp1PG00Sqdi0bV6fQXnjTfeGFBYWOjl6jh6OqNJtQAvAQ6gVWfoNz7jxN5TfNLtgQkhOmRwLlGXCrjo6jjacuwY+59/nkeMRlUqwqLLuW2Cs3z58sHR0dFjDAZDXHJy8vCVK1cOannNpk2bgk+ePKlftGjRiFGjRiXU1NQoaWlp+sTExFFxcXEJY8eOja+srNStW7cu9P77779jwoQJcVFRUWN+8YtfDHbFc3I1o0m9BLyINs6h1YT2lz5i/5F8vuj2wIQQ7aYo0P8gA2qqMbs6lmvl5JD9xz8y22hUe/2wUNEzuGWCk5aWpt+xY0dwdnZ2Vmpqam5mZmarF2OAxYsXV44ZM8a6efPmc6dPn8729PTk0UcfvWPt2rWFZ86cyU5LSzsTEBDgBMjMzPQ3Go15WVlZWUajMSQ9PV3f1mP2dkaTeh5Yh7ZU5dPy/t//k13HCzjc7YEJIdrN24mP4xMcdjs9Ymm+sJCiTZu4f+tWtcbVsYi+w10TnIDZs2dX6fV6NTg42Dljxoyq9nxeZmam78CBAxuTkpKsACEhIU4vL231aurUqdXh4eGOgIAA9cEHH6zcu3dvwFc+WC9mNKkngI1om449Wt6/8h0+PCHdjoXo0YJshFSkUerqOAoLKX79daY/95zq8lhE3+KWCU5XUFrsnm357z4oHXgPiKLFz4kK/HY7O7IuIEPxhOjBwssYVnyi9cy57pKfT8nLL/P1//t/1TxXxSD6LrdMcJKSkmp2794dZLVaFbPZrEtNTW21KbZZQECAw2w2ewCMGzeu7tKlS15paWl6gMrKSl1jo1bB3b9/f7+ysjKPmpoaZefOnf2TkpL6dCm1aWbV+8CnaEnOdRmfCvxmO8ZTFznugvCEEO00KJNhV0op6+6ve/48JS+8wLf/+Edp5Cdc4/aPiXfyse72SEpKss6aNcuckJAwOjQ0tDEuLs4WFBTU5q78RYsWVfzsZz+LWr58ufPw4cOntm7devapp56KrKur0/n6+jrT09NzAMaNG1ebkpJyR2lpqfe8efMuT5s2rc8P7zSaVGeKQdmC9nNyD5APX/bYcKqov9rG+89/F13cYMa6KEwhxFfwUND5pRNgm0Otn7714YGucO4cJX/4A99/9VVVDiUIl3HbWVRms1kXFBTktFgsukmTJsWtX7++4FYniq9bty708OHD/ps3by7s7Djb0lNmUbVXikHxBH4ATKFFkgPgqUN5/rvMjQlntAvCE0K0w+V+lAV/gzCdrmsr92fPUrx6NY9v2iRdioVrueUSFcCCBQuiRo0alTBu3Lj45OTkyltNbsTNGU2qHW3T8UHaWK6yO1FXvMW7eWVkuyI+IcTNhVYzqPQgRV35NfLyKF69msckuRE9gdtWcFpauHBhZEZGxnUnn5YsWVK2dOnSy66K6UbcrYLTLMWgeAE/BCYABbRRyVk1jzljhvE1V8QnhLi50gkUhY9sPXvuduXmcnH1ah57/XV1d2c/thC3otckOO7EXRMcuJrk/Ai4C225qpVfpTB9Ugz3dGdcQoj2aVRotM3C0i+YkM56zNOnKXruOR5//XX13531mELcLrddohKuYTSpjcD/AkeA6Lauec7Ip7uO85FMmhGi5/FS8VI+RdfQQKd0FP78c3JWruT7ktyInkYSHNFhRpPaAGwAjtHGnhyAv6ZyaNvnvCNTyIXoeQLr6V+1h4rbeROiqvDhhxz5wx94Yvt29dPOi06IziEJjrglRpNaD/wVbeNxNG38LP3jAFmv7OEfjQ4aujk8IcRNDLzMkJKjbS8z34zDgeP119m/YQNPG41qWieHJkSnkARH3LKmSs5rwC60JKdVX6Wdxzj34k7+XteAnHIToocJP0VU+QVKOvI59fXUr13LJ+++y6+MRjW9q2IT4nbdfqM/FMPtP8a1ur9xoLh1RpPqSDEobwHVwHeAi3D92v5nOZSYbbz2q2QWBvpxw67TQojupVNQAj+jf+2DVPsH0O9m19fUULNmDf/OzOT3RqN6ojtiFOJWSQXnFsydOzd606ZNwa6Oo6cwmlTVaFI/BF4BIoBWk9hPFnFl2T94paTSdXNxhBCt+Trwa/iEBrsd+1ddV1HBlV/9irczM1kmyY1wB5LgiE5jNKn7gBeBUGj9brCkCuvPNrNZJpEL0bME1zKgfD/FN7o/P5+Ly5axuaCAFUajer47YxPiVrltgrN8+fLB0dHRYwwGQ1xycvLwlStXDmrrupMnT/pMnjw5Ni4uLiEhISE+KyvLx+l08qMf/WhoTEzM6NjY2IRXXnklGOCDDz4IvPvuu+Puv//+O4YOHTr2xz/+8ZC//e1vIWPHjo2PjY1NyMrK8ml+3I8//jhwzJgx8dHR0WPefPPNIACr1arMmzcvOjY2NiE+Pj5hx44dgd3z3eg5jCb1OLAGrYrTqs9Ggx3nb7azY1cmu5wqcpBciB5icDGRJadaV1j37yfr5z/nb1eu8DujUb3kitiEuBWdsAen+6Wlpel37NgRnJ2dnVVfX6/ceeedCYmJiW1uYn3kkUeGL1u2rHTRokVVVqtVcTgcyubNm/ufOHHC79SpU1klJSWeEyZMiJ85c2YNwOnTp/1OnjyZNXDgQHtUVNRYHx+fihMnTpx69tlnB/75z38euHHjxiKAoqIin+PHj5/Kzs72eeCBB+K++c1vnnj++ecHKopCTk5O9tGjR32/8Y1vxJw9e/akXq/vUy/kRpOam2JQ/hv4BRAOlLa85q8f88X5S1xanMRDvl74dXuQQohWBh5laOUAyoPDCLPbsW/dysF//pO3gI1Go1rn6viE6Ai3rOCkpaUFzJ49u0qv16vBwcHOGTNmVLV1XWVlpa6srMx70aJFVQB6vV4NDAx07tu3L3D+/PlXPD09GTZsmH3ixIk1+/fv1wOMHTu2NioqqtHPz0+NjIysnz17thlg/PjxtsLCQu/mx547d+4VDw8Pxo4dWz9s2LD6Y8eO+R44cCBg4cKFlwESExPrIiIiGk6cOOHb9d+RnsdoUouAVUAxN+iV89Fxzv92OxsqLK0TICFE9/MAD++9+FSUU7ZqFbv++U/WAxskuRHuyC0TnK7k4+Nztdqi0+nw9fVVm//ucDiuvkgryvWv1y3/LcBoUq8AL6D1yhlOGxXDnFLMT73Oa6eKyezu+IQQrZWXc3n5T9lz7Bh/Ad40GlVp1incUickOKqpc283l5SUVLN79+4gq9WqmM1mXWpqaptHj4ODg53h4eENW7Zs6Q9gs9kUi8WimzZtmuWdd94JsdvtFBcXex46dCjgnnvuqe3Is3733XeDHQ4HWVlZPkVFRT7jx4+vmzJlSs0bb7wRApCZmelTUlLiPW7cuD79zsdoUuuAV4FtwDDAv+U1NfXY/8+bvPfhUT60O776JIcQoms4VdTUcxxauot/X7bx30ajuttolIErwn255R6cpKQk66xZs8wJCQmjQ0NDG+Pi4mxBQUFtvst44403zv/whz+MevbZZyO8vLzUt99+++zChQurDhw4EBAfHz9aURT1mWeeuRAZGWnPzGx/EWHIkCEN48ePj6+pqfFYu3ZtgV6vV3/5y19eWrRoUVRsbGyCh4cHGzZsyPfz8+vzvyCMJtUJfJhiUC4AP0HbgFze8roNn3LYdJ7Cn32decH+hHV3nEL0VbUN1L9yhD2f5nMEeNloVGXZWLg9t50mbjabdUFBQU6LxaKbNGlS3Pr16wumTp3qFt1y3Xma+O1KMSgRwM+AgcAFaH2SSu+N54oUZt4Zxd3dHZ8QfU1+BcWr92C5ZOdlFDYZjapb/B4V4mbcdg/OggULokaNGpUwbty4+OTk5Ep3SW76OqNJLQZWA0fR9uV4tbzG2oB95TvsfH0fb9U1YuvuGIXoC+wO7B8eY+/S1/n40kV+SR1/k+RG9CZuW8FpaeHChZEZGRkB135syZIlZUuXLr3sqphupC9XcJqlGBQd8HVgPtqYh8q2rhseRuAv5/CfQ0KI7sbwhOjVLlso/fNO9p/U5lD9xWhSc1wdkxCdrdckOO5EEpwvpRiUEcCPgf5oc6xa/UDqFJSls5iSNIr7dDr3rToK4WpOFfWLPA7+eSfnGuycAF4xmtQ231wI4e7kxUK4lNGkngN+B3yBtmTVqumfU0V96SP2v/gRGytrW29OFkLcnMVG1Ys7+edzRnIb7LwDvCjJjejN3PIUlehdjCa1NsWgvAqcAH4ABAKtWsKnn+biobNseOrrTJ0Uwz0eOjy6O1Yh3FHWBY49Z+REtY1SYIPRpJ51dUxCdDVJcESPYDSpKvB5ikE5D/wIGAEUAdcd/69rxPHCB6R9LZrsJx8gOTyIYS4IVwi3YKmj6q2D7NlxBAvwCfC20aTKxn3RJ9z2HhzlGcXQmQGpv7t5s78zZ854z5kzJyY3Nzfrdr5Wenq6fuPGjaF///vfi27ncTpK9uB8tRSD4gUkA98ELqNtQm5FAX50P3c/MIYHvD3xbusaIfoihxPn4XMcXLuLnNp6aoH/BU42vZEQok/o0xWcadOmWadNmybHInsYo0ltBN5NMSjZwBNAJNoG5OuqOSqw/hMyPj7BmaWzeDA6jNjuj1aInqWkisL1qXx8tABv4AiwxWhSza6OS4ju5rabjO12OykpKcNHjBgxetasWSMsFotu3759+rvvvjtu9OjR8VOnTo0pKCjwApgwYULckiVLhowdOzY+Ojp6zK5duwIAPvjgg8D77rtvJEBxcbHn5MmTY0aOHDn6O9/5TlRERMTYkpISzzNnzniPGDFi9MMPPxw1cuTI0VOmTImpqamRwVPdwGhSTwO/AXajjXkIbeu6s5eofmozb755gHdsDXRo5IYQvUVdI9b3DvP+k6/x0dECnMAG4H8kuRF9ldsmOPn5+b4//elPL507dy4rMDDQ+cILL4Q99dRTke+///7ZrKysU9/73vcqli1bNqT5ervdrpw4ceLU888/X7Rq1aqIlo+3YsWKiKSkJEteXl7WQw89VFlSUnJ1yaOwsND3qaeeupSXl5cVFBTk2Lx5c3B3Pc++zmhSbUaTug1tMnk1EE0bzQEB3jxI1o838XLGWQ44nMiAQNEnqCqcvMDRn2xi06Y0zCrkAL81mtTPZElK9GVuu0QVHh7eMHPmzFqAhQsXXl6zZs3g3Nxcv+nTp8cCOJ1OwsLCGpuvf+ihhyoBJk+eXLt8+fJW+zUOHToU8K9//SsPYN68edX9+vW7+gI5ZMiQ+smTJ9sAEhMTrfn5+T5d++xES0aTei7FoPwemAHMBRqAspbXXa6h/tl/8XF8BIefmM6MOwYR382hCtFtLlso3byfXXuycaIt4f4/4KgkNkK4cYKjKNevEvn7+ztGjhxpO3bs2Om2rvf19VUBPD09cTgcHVpi8vb2vvrLwsPDQ7XZbG5b+XJnTXtzdqYYlKPA94B4oARoNbH9VDGVT7/B9gfGEPndSXw9rB+tqnZCuCtLHVW7jvPpG/spVcEf+BjYYTSpskQrRBO3faEuKSnxTk1N9QfYunVryIQJE2qvXLni2fyx+vp65fDhw77tfby77767ZsuWLSEA7777br/q6mrpsdJDGU1qCfAC8CpaB+Qh3OBnOfUkhY+/wivvHOK92vq2T2MJ4S7qGrH++wS7f7CBV7bsp0bVqpi/M5rUtyS5EeJ6t13Bac+x7q4QHR1d9/LLLw984okn9DExMXUrVqwoevDBB81PPfVUpMVi8XA4HMqSJUvK7rrrrlbv7tuyZs2a4nnz5o2IiYkJNRgMNQMGDGjs37+/o7q62m2TwN7MaFKdwL4Ug3ISSAHuRavktFq2UoHN+8h8L4NTT97P5P+IYYqXR9v7eIToiewOGjPO8fmGTzhwpZZQoB+wEdhvNKmy30yINsgsqiY2m03x9PRUvby8SE1N9f/pT38adfr06eyu+FrSB6fzpRiUSOA7wBjgCnDDkyORoQQsTmLK+EgMnpLoiB7MqaJmXeDoK3vYm1+OH6AH9gPvGE1qlYvDE6JHc9s9OJ0tLy/Pe/78+Xc4nU68vLzUDRs25Ls6JtF+RpNamGJQ/oSW4CwAotCqOa0qeIWXqXnmXXYPDWH/96cxKTGau708pFGg6DmcKuq5S5zaso89RwtwAAOALGC70aTmuzY6IdyDVHBcQCo4XaupE/JU4CHAB20jsv1G14cH4bc4iUmG4Uzw9kROyAmXcThxnCom860DfJZZRD0Qhjay5E0gW05HCdF+UsERvU7Taas9KQYlA5jddLMDpYCz5fWlZmzPGfl0YD8OLE7iP+4awUQfT9q9QV2I29XooOF4AaYt+zl4vpxGYBDQCPwPYJJ9NkJ0nCQ4otcymtQa4O0Ug5IOPIhW1bGjLV21esG4VE3d8zvYGxrAwcVJTLx7BBP8vPHv3qhFX1LXiC3jLF9s3s+hMjN2YDDasuoWtA3EDa6NUAj3JQmO6PWMJrUM2JhiUD4EZgFJaAlOKW0kOpdrqP/Th6T7efPZQxMZnTSKidJHR3SmmjqqP8vhwOZ9HLHUoUOr2NgBI5DalJwLIW6D7MFxAdmD41opBiUM+DpwH9op8lK+Yo8OwLRRDJmTyISYcEZ76JAeSaLDVBUuXOFs2ilM7x3mTKMDX7Q9NjbgA2Cf0aRaXBulEL3HbSc4zzyjGDozoN+5qK9Od5IEp2dIMSihwEzg/qYP3TTRGRqC/8OTMBiGc5e/D4FdHaNwf7YGajMLOfbeYUzZF6lE62ETAlQB7wOfG02qzaVBCtEL9folqv/6r/+KuPfeey3f+ta3OvzO6MyZM95z5syJyc3NzeqK2IRrGU3qZeDNFIOyG23G1QOAB3AZ2p5KfuEKtX/6kHQvD/bPm0B8Ujx3D+5PlCLz5UULxZXkp5/m8LsZnK5rxAEEow2LLQX+Bhxp2hAvhOgCvT7BWbt2bbGrYxA9m9GkXgHfE7Y8AAAMfUlEQVS2Ne3RmYC2ITkKsAIVaMtY12l04HzzIFlvHiQrNpygOV9j3J2RjOvvz4BuDV70KHWN2LIucPxfhzl8vJDLaAnzQMAbyEfrPnxSTkUJ0fXcNsFZvnz54Lfffjs0NDS0MSIioiExMdG6atWqVm36586dGz1nzhzz4sWLK4cMGTL28OHDpwYPHmxPT0/XL1u2bNihQ4fO/PznP48oKiryLigo8CkuLvZ+8skny377299euvZxsrOzvefOnTty/fr1+UlJSdbue6aiuzRt7Pw0xaCkoQ3y/Dpa40AH2smrNt9t55RifnEn+4B9k2MYPGMs4+KHMEbvTUB3xS5cp8FOfV4Zpw/kcvKjY5xrdOAEAoBItLYEXwB7gTzpYyNE93HLBCctLU2/Y8eO4Ozs7Kz6+nrlzjvvTEhMTLytpCMvL8/3wIEDZ6qqqjzi4+PHLF++vLz5vuPHj/s8/PDDd2zcuPH8pEmTZK28l2t6d30SOJliUAYD04DpaO/CrwA3XO48kEvJgVxKPHX8e9Z4RtwzinEjBzFKOiX3Lo0OGs5d4sznuWTtPE6erQEH2sDXgWjNJSuArUCG0aTKkFchXMBdE5yA2bNnV+n1elWv16szZsy47ZksM2fOrPLz81P9/PzsISEhjRcuXPAEuHLliue3vvWtke+8885Zg8HQrsGdovdomly+LcWg7AAMaMtXkWhVnQqgvq3PsztRPzjK2Q+OcjbQF68HE4lJjCZueBgjfb3Qd9sTEJ3G7sCeX0HOF3lkfXiUnJr6qxvS9XB1aTID+BTIbRoIK4RwEbdMcG6Vh4eH6nRqv3NsNtt1U8J9fHzUa67DbrcrAIGBgY6IiIiGPXv2BEiC03cZTaoVbXr5frT9ORPQKjuDgAagnDZ66gBY6mh86yDZbx0kW6egJMUzdFIMsbHhxIYEMLC7noPouJo6qvPLyTteyNldmeSZrTQ33vNB+7/3QDsN9SZatUYGYArRQ9x2guOKY91JSUk1S5YsibJarSWNjY1Kampq/0WLFpXf7POGDh3a8Nlnn+nnz59fvX379uD2fC0vLy/1o48+OnvffffFBAQEOJ988skrt/8MhLtq2kORD+SnGJR3gVhgClrC4wnUoC1jtbnXwqmi7smmaE82RcAnseEETR9N7JihxA4JYbj02HEtuwN7SRUFp4vJ+yyHs0fyufb3ijcQgfb/bAU+AUzAOdk0LETP45YVnKSkJOusWbPMCQkJo0NDQxvj4uJsQUFBN/wFoyiKCrBy5criJ598MnrVqlWOyZMnt/vYeL9+/Zy7d+/Ou/fee2MDAwMdjz76qLkznodwb0aTageygewUg7IVGA3ci7ZBWQGqATNtzL9qllOKOaeUDCCjnx9e00cTPXYo0dFhRIcGMlinIAfQu1hVLRVnL5F35Dx5e7IpuGbpCcALbfnJC22Ewj7gEHC26f9fCNFDuW0nY7PZrAsKCnJaLBbdpEmT4tavX18wderUVhuNp0+fPvLpp58uS05O7jEdQqXRX++WYlCCgbHAZLQKD2jLWFea/myXYH+874kjcswwoqJCGRbWjyGeHu75pqSncDhxXq6hpOgyRaeLKfo8j8KCClqORfBFa8Tnhfb/9TnaSahc6VsjhPtw21+WCxYsiMrNzfWrr69XHn744cttJTcPPfRQtM1m082cOVPmuohuYzSplUA6kJ5iUPyBGOBrwF1oezdUtH0bX/lzWVlLg/EIecYj5AF4e6KbeAfh4yIZNjyMoQP7Ed5PT6hUeW7MYqOq1MzF/HIunirm4ue5FLeo0IC2jyYIrh7rr0b7/zsG5MjASyHck9tWcFpauHBhZEZGxnV9R5YsWVK2dOnSy66K6UakgtM3pRgUD7QNyqPRqjuD0JaybGgJT4dfSP288bgzkrC4CAYNC2VQeBCDBgQyqK9NQa9rxFpZS0WFhfKSKiryyyk3naekpIobtY/wR+ssrKAlnNnAYSAXKJV+NUK4v16T4LgTSXBEikFRgFC06s54tCUtX7QX3Dq0vTttHkFvj4hg9OMjGTRiIAPD+hEcrKd/Pz+CAnzp7+OFbyc8hW7nVFGt9VRX1lJRXk1FcSXl58upOHmB8q9IZED7nvqjzYBqrlpfQttLkw2cN5rUW/5eCyF6JrddohLCnTVVCCqabgdTDIoOraIThdY9eUzTv9WmmxltPla7eqsUV2ItruQ8cL7lfcH+eN8xkP5DQwkKD6L/gECCgv3p7++Dv48Xfj6e+Hp74uvlgU93zdiqb6TO2oClpo7qahuWahuWKzVUV1iwlJmxFF2huugytU617dNpLXgDgWhJjYqW4BSj9ajJBQqAK1KlEaJ3kwRHiB6gqSlcSdPt86YKTzAwDBiJVuEZClf32yhoCU8tWsWn3SpraTh8nkuHz3Ppq67z1KGEBuIb4o9vSAC+/fzw7eeHn94Hbw8FnaKgKAqKrul2zd91StO+oAYH9roGGmwNNFgbaLDW01BbT0NNHQ1mG/XVNhrMVhrbmbi0pAB+aI32/K75mA3IQavOFAJFTX2MhBB9iCQ4QvRATdWFK02348A/m/bwDECr7ESgJT7D0cYDNFcq7Ggv8HVoS1y33E3X7kQtM2MrM+Pq8SQ6tCRGj7aM15wMKWgzwrKBc2hVmotIdUYIQSckONu3bzd0RiDN5s+f3+2NA4VwB03N5MqabpnNH08xKHq0JGcQEI1W6WlOgpo30SpoiUIjWuLTnAC5upeLDu1kmQ/a0pIPX8bcHHdzdesUWpPFMrSlvctybFsIcSNSwRHCzTUtv+Q33b5o/njTvp5AtCPQ/Zv+HAQMbvozBK5uOG5OKJop19zs19yvXnN9W38qaMeum28tP+9auqbHrkSrvJQ33SrRjmpbmm5VMtdJCNFRkuAI0Us1JQXmplthW9c0JUE+aImOX9Ofvtf8W4+WJHnyZRVId83fr/1Yc8JiveZWj3b8vaGNv1uAOllOEkJ0BbdNcJYvXz747bffDg0NDW2MiIhoSExMtK5ataqs5XWrV68euGnTpjAPDw81Nja27oMPPjhnNpt1jz32WGRmZqYe4Ne//nXx97///apHH3008vjx4/51dXW65OTkypdeeqkYYMiQIWPnz59/effu3UF2u13Ztm3bucTERBm8KdxeUxJka7pVujgcIYToNG6Z4KSlpel37NgRnJ2dnVVfX6/ceeedCYmJiW2ekli3bl14QUHBCT8/P7WiosIDYMWKFYP79evnyMnJyQYoLy/3AHjxxRcvDho0yGG325k8eXLcF1984Tdx4kQbwIABA+zZ2dmn1qxZE7ZmzZpB27ZtK+iu5yuEEEKIjtG5OoBbkZaWFjB79uwqvV6vBgcHO2fMmFF1o2vj4uJs3/72t4f/9a9/DfHy8lIB0tPT+z399NNXj8iGhYU5AF5//fWQhISE+ISEhITc3Fzf48ePX22I9sgjj1QCTJgwwVpUVOTTdc9OCCGEELfLLROcjtizZ0/uT37yk/IjR47oExMT4xsb2z50cfr0ae+//OUvg9LS0nJycnKyp0+fbq6rq7v6/fH19VUBPD09VbvdLrN/hBBCiB7stpeoXHGsOykpqWbJkiVRVqu1pLGxUUlNTe2/aNGi8pbXORwOzp49652cnGyZOXNmzbBhw0LMZrNHUlJS9UsvvTRw48aNRaAtUVVWVnr4+fk5Q0JCHEVFRZ579+4NSkpK6jETyIUQQgjRfm65BycpKck6a9Ysc0JCwujQ0NDGuLg4W1BQkKPldXa7XXnkkUeGWywWD1VVlccff/zSgAEDHM8991zJ4sWLI2NiYkbrdDr117/+dfH3vve9qjFjxljvuOOOMYMHD24wGAwygVwIIYRwU247bNNsNuuCgoKcFotFN2nSpLj169cXTJ061S3ascuwTSGEEKJruWUFB2DBggVRubm5fvX19crDDz982V2SGyGEEEJ0PbdNcHbs2HHdlOSFCxdGZmRkBFz7sSVLlpQtXbr0cvdGJoQQQghXu5UEx+l0OhWdTtejuo9u2bKlzU6tPY3T6WyerSOEEEKILnIrx8RPlpeXBzW9UIsOcDqdSnl5eRBw0tWxCCGEEL1Zhys4drv98dLS0ldLS0vH0Af66HQyJ3DSbrc/7upAhBBCiN6sw6eohBBCCCF6OqnACCGEEKLXkQRHCCGEEL2OJDhCCCGE6HUkwRFCCCFEryMJjhBCCCF6HUlwhBBCCNHrSIIjhBBCiF5HEhwhhBBC9DqS4AghhBCi15EERwghhBC9zv8HUMj79HrevgQAAAAASUVORK5CYII=\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"qcuz3bkap0-r"},"source":["# Save dataset"]},{"cell_type":"code","metadata":{"id":"JkF4Keo5pwsz","executionInfo":{"status":"ok","timestamp":1625205080872,"user_tz":-600,"elapsed":46781,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["d.to_csv('/content/drive/MyDrive/5703 CP/Device #3.csv')"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"rnrNSzdrsFH8","executionInfo":{"status":"ok","timestamp":1625205080873,"user_tz":-600,"elapsed":31,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":[""],"execution_count":12,"outputs":[]}]}