<?xml version='1.0' encoding='utf-8' ?>

<!-- build 20212.21.1214.2055                               -->
<workbook original-version='18.1' source-build='2021.2.6 (20212.21.1214.2055)' source-platform='win' version='18.1' xmlns:user='http://www.tableausoftware.com/xml/user'>
  <document-format-change-manifest>
    <_.fcp.AnimationOnByDefault.true...AnimationOnByDefault />
    <_.fcp.MarkAnimation.true...MarkAnimation />
    <_.fcp.ObjectModelEncapsulateLegacy.true...ObjectModelEncapsulateLegacy />
    <_.fcp.ObjectModelTableType.true...ObjectModelTableType />
    <_.fcp.SchemaViewerObjectModel.true...SchemaViewerObjectModel />
    <SheetIdentifierTracking />
    <WindowsPersistSimpleIdentifiers />
  </document-format-change-manifest>
  <preferences>
    <preference name='ui.encoding.shelf.height' value='24' />
    <preference name='ui.shelf.height' value='26' />
  </preferences>
  <_.fcp.AnimationOnByDefault.false...style>
    <_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule element='animation'>
      <_.fcp.AnimationOnByDefault.false...format attr='animation-on' value='ao-on' />
    </_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule>
  </_.fcp.AnimationOnByDefault.false...style>
  <datasources>
    <datasource caption='mr_0104' inline='true' name='federated.1x98umr1jje5vr1bnte480mwzzo2' version='18.1'>
      <connection class='federated'>
        <named-connections>
          <named-connection caption='mr_0104' name='textscan.1rram1907ms0b41awvwvb149gd6t'>
            <connection class='textscan' directory='C:/Users/User/Desktop/ssd-work/homeiot/preproc/data' filename='mr_0104.csv' password='' server='' />
          </named-connection>
        </named-connections>
        <_.fcp.ObjectModelEncapsulateLegacy.false...relation connection='textscan.1rram1907ms0b41awvwvb149gd6t' name='mr_0104.csv' table='[mr_0104#csv]' type='table'>
          <columns character-set='UTF-8' header='yes' locale='ko_KR' separator=','>
            <column datatype='datetime' name='timestamp' ordinal='0' />
            <column datatype='real' name='hum' ordinal='1' />
            <column datatype='real' name='temp' ordinal='2' />
            <column datatype='real' name='door' ordinal='3' />
            <column datatype='real' name='motion' ordinal='4' />
            <column datatype='real' name='illum' ordinal='5' />
          </columns>
        </_.fcp.ObjectModelEncapsulateLegacy.false...relation>
        <_.fcp.ObjectModelEncapsulateLegacy.true...relation connection='textscan.1rram1907ms0b41awvwvb149gd6t' name='mr_0104.csv' table='[mr_0104#csv]' type='table'>
          <columns character-set='UTF-8' header='yes' locale='ko_KR' separator=','>
            <column datatype='datetime' name='timestamp' ordinal='0' />
            <column datatype='real' name='hum' ordinal='1' />
            <column datatype='real' name='temp' ordinal='2' />
            <column datatype='real' name='door' ordinal='3' />
            <column datatype='real' name='motion' ordinal='4' />
            <column datatype='real' name='illum' ordinal='5' />
          </columns>
        </_.fcp.ObjectModelEncapsulateLegacy.true...relation>
        <metadata-records>
          <metadata-record class='capability'>
            <remote-name />
            <remote-type>0</remote-type>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias />
            <aggregation>Count</aggregation>
            <contains-null>true</contains-null>
            <attributes>
              <attribute datatype='string' name='character-set'>&quot;UTF-8&quot;</attribute>
              <attribute datatype='string' name='collation'>&quot;ko&quot;</attribute>
              <attribute datatype='string' name='currency'>&quot;₩&quot;</attribute>
              <attribute datatype='string' name='debit-close-char'>&quot;&quot;</attribute>
              <attribute datatype='string' name='debit-open-char'>&quot;&quot;</attribute>
              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
              <attribute datatype='string' name='locale'>&quot;ko_KR&quot;</attribute>
              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
            </attributes>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>timestamp</remote-name>
            <remote-type>135</remote-type>
            <local-name>[timestamp]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>timestamp</remote-alias>
            <ordinal>0</ordinal>
            <local-type>datetime</local-type>
            <aggregation>Year</aggregation>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>hum</remote-name>
            <remote-type>5</remote-type>
            <local-name>[hum]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>hum</remote-alias>
            <ordinal>1</ordinal>
            <local-type>real</local-type>
            <aggregation>Sum</aggregation>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>temp</remote-name>
            <remote-type>5</remote-type>
            <local-name>[temp]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>temp</remote-alias>
            <ordinal>2</ordinal>
            <local-type>real</local-type>
            <aggregation>Sum</aggregation>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>door</remote-name>
            <remote-type>5</remote-type>
            <local-name>[door]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>door</remote-alias>
            <ordinal>3</ordinal>
            <local-type>real</local-type>
            <aggregation>Sum</aggregation>
            <approx-count>1</approx-count>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>motion</remote-name>
            <remote-type>5</remote-type>
            <local-name>[motion]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>motion</remote-alias>
            <ordinal>4</ordinal>
            <local-type>real</local-type>
            <aggregation>Sum</aggregation>
            <approx-count>1</approx-count>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
          <metadata-record class='column'>
            <remote-name>illum</remote-name>
            <remote-type>5</remote-type>
            <local-name>[illum]</local-name>
            <parent-name>[mr_0104.csv]</parent-name>
            <remote-alias>illum</remote-alias>
            <ordinal>5</ordinal>
            <local-type>real</local-type>
            <aggregation>Sum</aggregation>
            <approx-count>1</approx-count>
            <contains-null>true</contains-null>
            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
          </metadata-record>
        </metadata-records>
      </connection>
      <aliases enabled='yes' />
      <_.fcp.ObjectModelTableType.true...column caption='mr_0104.csv' datatype='table' name='[__tableau_internal_object_id__].[mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A]' role='measure' type='quantitative' />
      <column caption='Door' datatype='real' name='[door]' role='measure' type='quantitative' />
      <column caption='Hum' datatype='real' name='[hum]' role='measure' type='quantitative' />
      <column caption='Illum' datatype='real' name='[illum]' role='measure' type='quantitative' />
      <column caption='Motion' datatype='real' name='[motion]' role='measure' type='quantitative' />
      <column caption='Temp' datatype='real' name='[temp]' role='measure' type='quantitative' />
      <column caption='Timestamp' datatype='datetime' name='[timestamp]' role='dimension' type='ordinal' />
      <layout _.fcp.SchemaViewerObjectModel.false...dim-percentage='0.5' _.fcp.SchemaViewerObjectModel.false...measure-percentage='0.4' dim-ordering='alphabetic' measure-ordering='alphabetic' show-structure='true' />
      <semantic-values>
        <semantic-value key='[Country].[Name]' value='&quot;대한민국&quot;' />
      </semantic-values>
      <_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
        <objects>
          <object caption='mr_0104.csv' id='mr_0104.csv_DED8B6AA8EA34FF18F1FEDB79A546E0A'>
            <properties context=''>
              <relation connection='textscan.1rram1907ms0b41awvwvb149gd6t' name='mr_0104.csv' table='[mr_0104#csv]' type='table'>
                <columns character-set='UTF-8' header='yes' locale='ko_KR' separator=','>
                  <column datatype='datetime' name='timestamp' ordinal='0' />
                  <column datatype='real' name='hum' ordinal='1' />
                  <column datatype='real' name='temp' ordinal='2' />
                  <column datatype='real' name='door' ordinal='3' />
                  <column datatype='real' name='motion' ordinal='4' />
                  <column datatype='real' name='illum' ordinal='5' />
                </columns>
              </relation>
            </properties>
          </object>
        </objects>
      </_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
    </datasource>
  </datasources>
  <worksheets>
    <worksheet name='시트 1'>
      <table>
        <view>
          <datasources>
            <datasource caption='mr_0104' name='federated.1x98umr1jje5vr1bnte480mwzzo2' />
          </datasources>
          <datasource-dependencies datasource='federated.1x98umr1jje5vr1bnte480mwzzo2'>
            <column-instance column='[hum]' derivation='Avg' name='[avg:hum:qk]' pivot='key' type='quantitative' />
            <column-instance column='[illum]' derivation='Avg' name='[avg:illum:qk]' pivot='key' type='quantitative' />
            <column-instance column='[temp]' derivation='Avg' name='[avg:temp:qk]' pivot='key' type='quantitative' />
            <column caption='Door' datatype='real' name='[door]' role='measure' type='quantitative' />
            <column-instance column='[timestamp]' derivation='Day' name='[dy:timestamp:ok]' pivot='key' type='ordinal' />
            <column-instance column='[timestamp]' derivation='Hour' name='[hr:timestamp:ok]' pivot='key' type='ordinal' />
            <column caption='Hum' datatype='real' name='[hum]' role='measure' type='quantitative' />
            <column caption='Illum' datatype='real' name='[illum]' role='measure' type='quantitative' />
            <column-instance column='[timestamp]' derivation='Month' name='[mn:timestamp:ok]' pivot='key' type='ordinal' />
            <column caption='Motion' datatype='real' name='[motion]' role='measure' type='quantitative' />
            <column-instance column='[timestamp]' derivation='Quarter' name='[qr:timestamp:ok]' pivot='key' type='ordinal' />
            <column-instance column='[door]' derivation='Sum' name='[sum:door:qk]' pivot='key' type='quantitative' />
            <column-instance column='[motion]' derivation='Sum' name='[sum:motion:qk]' pivot='key' type='quantitative' />
            <column caption='Temp' datatype='real' name='[temp]' role='measure' type='quantitative' />
            <column caption='Timestamp' datatype='datetime' name='[timestamp]' role='dimension' type='ordinal' />
            <column-instance column='[timestamp]' derivation='Year' name='[yr:timestamp:ok]' pivot='key' type='ordinal' />
          </datasource-dependencies>
          <filter class='quantitative' column='[federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:motion:qk]' included-values='non-null' />
          <slices>
            <column>[federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:motion:qk]</column>
          </slices>
          <aggregation value='true' />
        </view>
        <style />
        <panes>
          <pane id='5' selection-relaxation-option='selection-relaxation-allow'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
          <pane id='6' selection-relaxation-option='selection-relaxation-allow' y-axis-name='[federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:temp:qk]'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
          <pane id='7' selection-relaxation-option='selection-relaxation-allow' y-axis-name='[federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:door:qk]'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
          <pane id='8' selection-relaxation-option='selection-relaxation-allow' y-axis-name='[federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:hum:qk]'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
          <pane id='9' selection-relaxation-option='selection-relaxation-allow' y-axis-name='[federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:illum:qk]'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
          <pane id='10' selection-relaxation-option='selection-relaxation-allow' y-axis-name='[federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:motion:qk]'>
            <view>
              <breakdown value='auto' />
            </view>
            <mark class='Automatic' />
          </pane>
        </panes>
        <rows>([federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:hum:qk] + ([federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:temp:qk] + ([federated.1x98umr1jje5vr1bnte480mwzzo2].[avg:illum:qk] + ([federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:door:qk] + [federated.1x98umr1jje5vr1bnte480mwzzo2].[sum:motion:qk]))))</rows>
        <cols>([federated.1x98umr1jje5vr1bnte480mwzzo2].[yr:timestamp:ok] / ([federated.1x98umr1jje5vr1bnte480mwzzo2].[qr:timestamp:ok] / ([federated.1x98umr1jje5vr1bnte480mwzzo2].[mn:timestamp:ok] / ([federated.1x98umr1jje5vr1bnte480mwzzo2].[dy:timestamp:ok] / [federated.1x98umr1jje5vr1bnte480mwzzo2].[hr:timestamp:ok]))))</cols>
      </table>
      <simple-id uuid='{8CDE6A9A-C9C7-4329-BF14-FC12000FAFB8}' />
    </worksheet>
  </worksheets>
  <windows source-height='30'>
    <window class='worksheet' maximized='true' name='시트 1'>
      <cards>
        <edge name='left'>
          <strip size='160'>
            <card type='pages' />
            <card type='filters' />
            <card type='marks' />
          </strip>
        </edge>
        <edge name='top'>
          <strip size='2147483647'>
            <card type='columns' />
          </strip>
          <strip size='2147483647'>
            <card type='rows' />
          </strip>
          <strip size='31'>
            <card type='title' />
          </strip>
        </edge>
      </cards>
      <viewpoint>
        <zoom type='entire-view' />
        <highlight>
          <color-one-way>
            <field>[federated.1x98umr1jje5vr1bnte480mwzzo2].[:Measure Names]</field>
            <field>[federated.1x98umr1jje5vr1bnte480mwzzo2].[none:timestamp:qk]</field>
            <field>[federated.1x98umr1jje5vr1bnte480mwzzo2].[yr:timestamp:ok]</field>
          </color-one-way>
        </highlight>
        <floating-toolbar-visibility value='1' />
        <default-map-tool-selection tool='2' />
      </viewpoint>
      <simple-id uuid='{EDDD92D6-E154-4D7C-9FF8-6079B9220B8C}' />
    </window>
  </windows>
  <thumbnails>
    <thumbnail height='192' name='시트 1' width='192'>
      iVBORw0KGgoAAAANSUhEUgAAAMAAAADACAYAAABS3GwHAAAACXBIWXMAAA7DAAAOwwHHb6hk
      AAAgAElEQVR4nOy9eYxdV57f97nb26perSzulESKopbW0upWqz1Lz/RsPQvGE9vxILBhx8jE
      cRzASGIECAJMEjTgJHYWBIinHdsxPJPY7tl6pmdz791qtaRudUsURYkUSXErsva93nrXs+SP
      u7xXVe8VayOLpN4XKNxX95x77u8sv9/5/X7n3N8xtNaaHnr4iMLcbwJ66GE/sacM0Gw297K4
      Hnq469hTBoiiaC+L66GHuw57vwno4cFG6Fb53S/+IR//zE8zNjjM8EAJDAPLdtAywnLyRIEP
      qk7gDJOXEU6+QBQEYBjYjoMIQ+xcgZxj3XP6ewzQw66QKw3y+OOneerUGN///gcsTVxF5kfI
      ORGzE7d49JkXmb9xhU/+2IvkBg/y1je/wciR4/hao1yXodFharMug8cP8Ms//9l7Tn+PAXrY
      NUbHDmCaOcbGRjk08gm0VUaGKwwPDHD85GMMKA974ChBvcqxU08zNJCjb+wYuA20cnn7+iJn
      Dr+wL7Qbe+kGrVQqDA0N7VVxPXwkELG64jM8Ut6Xt/dmgB72GQ7DI86+vb23DtDDXYCmXq0Q
      RJJmvUYoJG6jjpAK1/X2m7g16M0APew9RMBypUIwM4UvbApFh5JpELBCo9ng4899bL8pzNCb
      AXrYe1g2SMUjR0Ypjx3FNsB0TB577BFGhwb2m7o16DFAD3sOFTRZrjZYCRx0ZZKB4SFWlios
      LiywvLjA9PzKfpOYoecFus+gtcYwjP0m4yOD3gxwn6Fare43CR8pPNQM0Kwuc+XaOJEIcd2A
      KAzxfJ/J8avcmp7H8zx6e8E/gtCa5cV5IikebgawnDyGV2dxeZbf+Zf/nH/+L/4lH04sceLk
      GUwVsrS0tN8k9rAPEEGT69c+pO7WH243qNaaXM5EW/38B3/lV3GNEkfGhgndGk1f0F94qKvf
      QxdMTdwmCDxqDfFwM4AIfQKrjxND/VyfmyHSPmJ0mFqlypnTj1GrVPabxB72AY+d+RhHTjyG
      mSvsnxdICIFtP9T8tyP0PGmbY6/Hzb7ZAI1GY79e3cMDjL0eN3dNBE/fvoGr8hzot5iaX2Jk
      9DCHj4xx7z956IxmdZnxqQVOnz7JrevXyfcPMjI6ymB/ab9J21coGXJrfIpHTj7K+NVLDB05
      ib86S8XTnDh2iKHBwf0mcU9x12aAarVKo15ncW6Kt986y83xSeTdetkOUFlZQUYVrl2f5t1z
      Z7ly9RqVhrvfZO07DNOmmLcIvGXyQ49SXZ7l5uULnDv/HjMLq/tN3p7jrjGAVKBlQClvMzN5
      iz/5yre4n9Y3Dxw6TF+uyMEjYzSqy7zytT9nZuX+2qm4H9BSsLS4SLUpqCxMoo08BoLrH7zD
      62cv7Td5e467ZgQHXhOBRd4yqDddDMNiYGgg47j9NvZkFNIMIsp9Req1GlJpin1lCrn9Ncz3
      u120ElSqdXKFIsiIfLGPyHfxwwg7V6DcV9w32mDv22fPZwAhBEII8sU+bNPAzuUplUoMDQ0g
      owgp1ypCQRAAEIYhKS+m99Kr1nrDvfSqlCIMw45pUsosUsWGMg2TUiGPYZjkC0WGh4cxdExb
      FEUopTY8142+vaRzfbsIIbI2a2+rTvTtBZ0ak/7+fvqKBWwnh22ZmLbD8PAwpUIOIcSa/FFb
      n64va7M+3Sm969unW3uup7MbvXeNAQA8z1tzbe+4FGlaEARZ2vrnAHzf75jW3jjr09oH1vq0
      9oboROf6tPT9nfJvhc52BtiMzvXviKJoQ3u2D5r1ZW2Fzs3aTCnVNU0IsaE92/t0ff7N+nQr
      dWlv1/bf3dI2o7Nbv99XjviZiZusNiXHjx/eb1LuKyxM32alEXL0+PH9JuU+gub2javUI5Pj
      hw/suJT7ai/QQLlM2AuutQHlgQHCMOK+8iLsOwwOHjpEf9/u3NbW5z//+c/vDUHx9Ov7PjaC
      kBxKhERRhNaaKIowDAMtfCJyyChYkwawsrRAwwsYHBpGiThNKwGWQ+g1EdpESZE9p2SEYdoI
      3yXCRskoSxMiwjRtZNgk6kCLaZro0O1Kp2kovEChVOt9hhJII36fUKCUzNK0EmjTIfI30ill
      hGk5REGTSHemU21C59LCLH4QUBwYwVCxOoQMEYaDCD0iqdBKtejUAmXYRL6LUMYGOjEdQq/R
      hU4bEbgb6IyiCMuy0DrC9xVayy31aUpnGAZg55CRTyRkRi8AKkIaTvxeBbqNXpQAK9ehXSVX
      PvgAM99HuVTYQKcMXSKczv0eeURGHhndBRugv78fM1eiLx8veZXL5TVXO99HKWd2TDtx6gwv
      v/QJHLN1r1jqxzYNDMuhv1RYk7/UV8YyDQw7R38xtyatv7+MYYDpFOlLNr2tf5+Z7+tKp2E6
      9Pfl19zLFfvI2yaYViZ52ul0rM509vWVMQ0w7XxXOo1N6Dz6yON88qVP0ecY2T2n0EfBMcEw
      Kff3raWz0Ecuo7O4gU7bBNPOdaHTwOhAZ3oFm3J5bZmb9WlKp2lA0TEBg3J//wZ687YJhkV5
      XbsWEnox7XX0mnzqx36Sp08/1rlvnSJ9+c7tabXRm9kAzWZz17E9XdfNyvB9H9d1s/sQGzy2
      bWNZ1oY0z/MQQmAYxoa09LfWekNaalxKKTekCSGy9PVpKZ2O42yZztRYW59/O3QKIbZFZxiG
      mKaJ53nZPd/3u7bVduhUSu2IziAIdt2n7dfUUL4TvVtN60Tn+va0bRvXdVsM0NfXx24hpEIZ
      Vqyq2nm0GYFdyK4IjbYcsO2NaZZEW3kM09yYpjWsu5elSYk2dec0HaGRHdO0NOJPD+3cxrRI
      wzo6MaOYPsPYmD+9ag2W6EonFt3pNDrTiTTQ5no6JdrKYZhWV1qAHdGpN6FT6xAMtSd92soj
      7tium9WlY5oOwdAdx6CWgGmB7aDNaG+9QFcnl/n2uQk00AgURwZtZqsiuw6XLLxQ4Qu9IW2s
      bLPSFEjFhjSAw4M2c2330qtjGQwWLZYaG9OKjkHeNql4ckNauWCiNDS70OmGiqCNzsMDNnO1
      mJb1+dOrARwcsJmvdaZzoGiy3NhISzFnkLNMqh3oHCiYSAXNsEXngX6biisQHdqqvc26pZkG
      jJU705mzDMoFk+XmRlpKORPbgpqn1rTVZn262tyczkMDNot1gdI7q0untL6ciWVCzd/Yt4NF
      i0hq3KQ90XuI1dVV7Xme9jwv+7/92mg0dBiGHdNqtZoWQnRMU0rpSqXSMU0IoWu1Wse0IAh0
      s9nsmOa6rvZ9f8t0VioVrZTqmH8rdEZRpOv1+p7QuVlbbYVOKaWuVqvbptP3fe267h3bait0
      ptdqtaqllHds125pW6UzvTabTR0EQXZvR0awEgHXb94iCn1u3Z6id8hSDw8qdsQAN69fo9Rf
      ZmpykoIZsdjwM0NGax27KJXqeu2Wttlz2y1rK2Vul5adlnU36rDTsnbbD92e22lZu82z2zrs
      iAG01lRnb7PYjLAtK/M6pC9O/9J77Wnr7231uXtd5vq0zfLv9n2b1WU3tOw0bTttvdOy7mZd
      tlOHHTHAYydPYZWGeOHMo6x6mrFyAdM04wUk08Q0TSzLwjCMNde7kdbp3lbL7PZ8p+c2K6ud
      lntR952W9aDR0p5nszbfTR26eoHO/eBVFmqSH/vsZxgs5NakOYU+zjxxCoAnHt+9+7SHHvYL
      XWeARtNlYKCM1QvT18NDjK4MYFkmvh+gex6eHh5idGWA48dPICMf0WOAHh5ibGoER4GPH/S2
      J/fw8KIjA0xe/4C3z76PzJUoFTae36REyJs/eJO56du8/fY5GoG464T20MPdQEcvUHnoAGU7
      x4GTj5C3NvLI5PgNLMdh1ZM8f/ooS15I0TZQSmWfm6U78tqv6beahmF0TJNSZgsV7Wla6+zZ
      bs/tJE0phWVZXZ9rp3Mz2ndLZ3v5nZ7TWndsx05tlb4H2BGdd6Jlr/p0fZ50capTns3q0imt
      E53taQCmaaKU6swAlmVx5lOfAEw6OYEGRkapNDzKeYsL43M88+yBNT5wANu2s22n6dWyLGzb
      7prW7ktvT9NaZ8+uT0v9u53S0tW+bu9bf289LZ1oT9c6Oj23GZ1pu3ajc7M269aO3dJSH3c3
      OlPG70ZnGn5wfVoqFPaiT9fnSf3z3dq1W1o6mO9EZ1d6OzFAoa9MwVccHBsBcyMHDI8eZHj0
      IADHTzzaqYgeengg0JEBzr/5Q65Pz3Lq2BEOPvVxTh56uMLh9dBDio5GsGnlGR0ZwrFMjN6X
      2D08xOg4A5x57hlWq1UMFTE20tvq0MPDi44zQG1hig+uXGN5ZZmVWi9gbA8PLzoywNz0HJX5
      G1wcn2Xy6uSGdClCVlarKCmoVGu97RI9PLDoyACG7fD8Sy9C3WfskUMb0hu1GvOTE1y5cZ35
      qUlW3aBDKT30cP+jow3w4k/8JABPP/lJzA5uUNM0KI8dIvIbHBjO04haixm9hbDeQtgDvxCW
      nlTebSd0vVZltepx/Phhbs8t89SZo1iW0VsI6y2EPRwLYXfC0UdOcTT5PTw8upMieujhvsB9
      FRy3hx7uNXbFACJ0uX7jNqrnBurhAcWuGGBqaoahIizW/Ttn7qGH+xC7U4E0KNULW9/Dg4td
      McCJR47TECZj5f09OK2HHnaKXYdHHxkqU61WgF549K3QKUQvPPr6a+oGfuDDowMUCoU11/RI
      y6GhIZrNJrlcDsdxNqRZlkWpVMrWEdrTtNYYhsFgckp5e1o68NPDD9rT0o4slUob0jzPwzRN
      8vn8lug0DIOBgYFsjaQ9/1boFELg+z79yeEQu6GzXq93baut0KmUwjRNBgYGtkVneuhdsVjc
      dZ+mV9OMT6U0TXPTdu2WluJOdKZX13WxbZtcLo511XOD9vCRRo8BevhIo8cAPXyksacnxIzP
      Vnjn2gIa8AUMFaDit659OQgFRGpj2kAemiFIvTHNAAbX3UuvlgGlHNSDjWk5CxwTmtHGtKJN
      Vzr7cxB0oLPqx8+sz78VOm0zfmc93JiWt8Aywe1Cp9IQyK21VXplEzpNoJzUZ7d09jkQSQi3
      2afpdbAAdR/UJvRuVpdOaXkr/pTd69C3JQekarWnofXeLeNOzMyz2kg8SZaDjjwMp9i6ihDD
      tMC0NqZFPoadA8PckIbWaOGvy5+mKbSMMOz8xjQl0Upi2LmN75NR/LmnZW+dTrsAhtGB9nY6
      AwynsE06BVorDGuLdIoAw3I6tlVWJuyMTqXQajM6NUZ73+6wT9fmyW/erpvVpUMaUqBZR2fW
      niGGYYIZtye6C2ZuXdVv/OgdHUrVLcsG9I5I6h2RtFU67/sjkq5eG8dsLHLh2tReTRA99HDf
      oSsDlGzJxRszlAotM2FxZoL3L12lujzP+5c+pFFZ4vyFS0RS3RNie+hhr9HVCPYFPPPMMxw/
      fCC7Nzx2iKo3zVK1ySNDJT6cXebpo0Ms1n0OlfO9L8J6X4Q9HF+EQbxk3F+2shVFgOtXLkJh
      lLwRcGuuzsBAgeuTC5w40wuN2Psi7CH7Iuypp86w0NCx3y/BqTMfIxKKYiFPGAnyOQc/jCjk
      c92K6aGH+xpdGeDq1QuM3wwYO/4offk4Wy5fIBdvSaGQSPpiIX/3qeyhh7uEjgwwOzHFyz/1
      S5x8ps7R4dK9pqmHHu4ZOjLAjQ/O4+VLWDiMHjxIucMhGT308DCgIwM8+/KPMzA60tso1MND
      j45j3HerfO/VN5ibnqbmhveaph56uGfoyADTt+eRocfKzDTLde9e09RDD/cMHRnAyTloLZmc
      mcG2rey+koIoEigpcF0frSRN1+sFx+3hgUVHG+D5lz+J23Qp9a31AFVX51mumziqRqXmU+wv
      ELg+x594iuFSzx3aw4OHjgxw9e0f8K2zH3D61FGOnXqRZ5+IAyEOHzhAw6sihMMjB0vcrLo8
      dWyMaigZyPeC4/a2Qjx4WyE6qkDLlSbPnjnAzakqzdVadn9+eob5+TkGB/qYqvk8dewAN+Zr
      jJULa7ZCdFtm38qyeae09me3k7aXtGyWf7e0bPe5nbbjg0bLVrat7LrfOzHA4RPHUHmLXK7G
      kx87md0/dOwkh47Fv0dG43MDnh/sBcft4cFFRwY4+dQzADx+slNqDz08POitdfXwkUaPAXr4
      SGNXDKBEwOTUbG8doIcHFrtigMmJSUzhstTohUfv4cHErhhAY1DMO70DMnp4YLErBjh29BBL
      TcmB/sJe0dNDD/cUuw6PfvDAMPVaFeiFR98KnUL0wqOvv6bfRD8w4dG11mjiUIBaawzDzI5U
      3W149E5hsvVdCo9uGMZ9FR69UzjvexkefXBwkCgS+IFPX18fWkN5YCC7hmGIkopCsZDds10X
      x8lhO/aG/IZhUuqL+7Q9bWBgMHufYRiUy+WM3rTt2tswZei9DI9uJ327o9ig9dUlphZrDBVg
      en6VJ557nnLO2nQv0Mxilb5cvAej276R967Ncn22ws8+d4TLtxY4OGBvaY9N0wv45jsT/Mzz
      R7ruvwkjwdkrU5w6WMrSvv3OLcaGyzx9vLzrvUBf++FVPvHEYRxjZ3uB6q7P19++xWefO7zl
      vUCd9s9sdy9QJCST8xX6HMWHE4t84ctvcXCwwFI94thwjunVMLv25S0cy6DiiuzeSL+NGyr8
      UG3If2jQYbkhEFKvSRtf9Piv/+oLvHtzhUajwbOnj/Fn37/Gy0+M8O3z7/Ebn3uKf/GVN/g7
      P/8UE9dnOXlkqGNd3r48xaOHBrHZ2l6gKIqQUjK5UON3X7nMP/jLz+2MAQZGxhho+gRS8Nzp
      Y6wEgqFibtOwKP/sy+/wd3/pWY4fKnXdozE+V+XbZ8f5S0+O8VtfPsvn/+O/lIXFwDC5eGuJ
      xw4Usue+f3GaWtMjCgN+/9WrfPrJMd4+d5uXTo8wsVDH9XyOjfbx5gfTHB50+P1XLvHf/82X
      War5zC1VmF9tEkiDxw6WePvcbV4+M0q1GYLSFLcZFuWtK7Os1H1eOj3Kn/9ogr/86Uc3hO9o
      35+idKscpRQfTq7wrbPjfObZw0RSb9gHEwi15t77Nxd5/f3JeJAs1Ak8l+vzHofKNsPDWw+L
      UvdC/o8vneM3/8bLLNd8Xjh9mL/+mTOU+krUazXKAwPZNQpDpFIUCoXsnud6OI6D7dgb8jcb
      DYrJrN6e9ruvXmN+1eXdGysUTcmRg6N8OLHEqYNF3r0+h/czT/DOh3P8/MeP88ffv8l/+NNP
      c2Ikz2ojwDIMvvS9K/zCx4/y+vuTVB4P+dTpkQ1hUZTWvHdjkccPFWn4AtM0ef3CFAVbY1s2
      16ZX8KMum+HuhJWFGWZnZ+krFrk8tZIZwUppas2g4zOuHzExH9sKTT9c4zmquyGRkLhBxEKl
      iR8I3CAiEq2Ic2c/nOELf/wWSmsWVpsEkeTrb13nyu0lvCAijCRnr8zwL//8LDNLdc5dneU7
      58ZZrLj88WuXY0m3UGOh4vLvf3CVr755DYBrU8tMLtT4N994D9eP+KPvXeKNCxNorQkjuaYO
      QSSQam0UvMu3FvHDWKpOzFd5/8Y8f/L6lTXtcPn2Ej+6PE3DC/nOOzepuQH/0799bU05t+Yq
      LNdc6m7Ab/6rV1iqunhBhJAKLxD8N1/45poIfG+8P8GF8QWkUvzFD67y/o15fuer57ma1Gc9
      7Rv6I4hYqroopZldbvDh5BIXbi5Qyjs4tknesbGttVfHtsjZ1rp7JjnH2nJ+2zLpKzj4geDN
      D6YIhKTpR0RS4YcSpTSuFxFJyVuXp6m7ATenV/n+hQn+y//r6zTckG++dYN3rs4ilebrP7q+
      pl4iaaPlmscX/uQtpNL8ky++wdRijbmVBrPLDTQaPxD4odgZA4wcPMqnXn6Zg4cO8+LzHyNn
      x8W8f3Oe/+Xfvd7xGSEVfiSoNHz++Z+eZaXW+tLsH37h63zlh9cIhWKwr8AHtxapNQNmlur4
      YUzoSs3DDSJqzYD/6p9+nd//zkUA/EhknS21RmmNF0R4oSAIBaGQ1JoBkVREQlKpe4zPVvDC
      CKkUM8v12Gj1I967MU8QSZpexKvnb/Nnb1wBYHa5TqXh84ffvcTvffvimnp98dsXuDmzSigk
      kVBZB4SiNQA/nFji3WtzXL69xKvv3sb1IxZWm+vaJxYIWkPNDWh4If/+zWt8991bLFWbNPyQ
      IBSt/Cp+l9KaUMQSr+HFguT3vnOR69Mrm/bhhRvzfOnVS6ikzUKhGJ+tZH15N1HMObgJc0up
      ePXdW0RCoZMgVG4QoTVMLlSxLRM/EjS8CDeIqDYD3CDi8q1FhFRUm2vXoP7Zn7zNUtXF9SNk
      0udNL8IPBa4foZQmjFqCZE9rG0YSr62TNqQLxW9/9V1+eGkKraHphdyer9JwQ6YWagShYKRc
      xAsipNJML9b4ypvX+OoPrxEJiR8KhFQEQmRSP4pkFrtLK43WUG0G3JheIQglsysN/DDOr7Sm
      6UdUmz5+KJFSE4SSIBIorVmquISRpO4FcSMGcV2+8844r78/wexyg3NX45XvdgnrJ3WeXKgi
      VUxN+2C9OL6AEJK55TpCxYO20vCZWoxnnt/+6ruIZBB7QUQQxXV99d1bVJsB56/PI5XOygaQ
      UmfGoUhmyiAS+FFsM6SDez3+6NVLTC7UCCLJctXNntVaI6Qk51gbntlrWJaBVBqV1MkNIjSt
      GdcNIvqLOV584gh/6xeeZ3qxTpQIlHQMuEF8TZ+JhGR6qc6tuQrVhs/VyWWUjvu34YcopXnr
      8vSGNrlne4EiEQ9WLxCZajOzXOf9G/NESUWEVJTaQrCsNnwmF6o0vHiKzBpNtirhJ1LeMMhU
      BNePWK37CKWYWqihdTwDaQ0LlWYmeSCW1EG4TtVJ/r8xvUIoJO9cncULIiCWllIpPv87r+L6
      sZoWRBKtYbHqZh0l2tSVhhcSConUOnm3xgsEN2dWuTVX4e0rM1nHzC03kvwKIWOmiT8oif+y
      9pQtmoWM398+qC7cnF+TP8Xl20u8d2OOSEjOfjhDw4/dwWEkkUpjmfdmSESiu4ompOLYgTKf
      +9Qpjh4oM7lYXdOuOp312oTC9FKdr//oOq4f0fBi4aiU5tZ8hdWaRyRiwZM+rwGN7s4Ab373
      G3z969+h6m8/KkSl4fP6+xNr7mnNhg7xEgmrtEZqTSQVOcfK1AeldDLABFHK6TKVdjKbutHg
      2FYmib0wIhIy09c1OmO6IIwZLRLxzCGlohm01j9CIbP3v39znmojYKniZg2dqgwrdY+6F6K1
      zqSSkdQzLacdUmmihMnTdghF6/+UYYSKJXua1297pt3+SJtSKc1yzQPiMtqZW2m9YaCFQlJ3
      YwbTGhpukN1XSt+TGQDIZq9UgguhslnTDwSmGbtFh8sFbNPET/I1k/Fo0Go/iMeSTNrxjQsT
      mUARQqGJ2yIVslFSV6U2YYBISAYGyljG9s6Bl1JzbWqFb7x1fUOaaJNat+cq/N9/+nbcALrF
      2XnHygZR2qGx4RJjqeJimkbsykv04FBIcrbVUj8SNUBKTZRIx0go8o5FwwvjwZjMOvGM0mbY
      6pZ00jpu8CASWYfFeqVq6bBJo9umSbmUJxIS0zTWGPBpHpWofamaKKXOBr9M1Ld0USg2uGNG
      SAd/uwokks6XST0zBk2ej4Ti6uQy/+SLbyCk4trUctamkZDZwGt4IeVSjttzVVTS/vcCaftk
      6mvb70gqHCsemnnHpph3qNR9Hjk0yBsXJsnnbEzDyGwGpXWsxqpYOF2+vUiYqMap0MgYTSrm
      2uyv7ucDFAv4frDtnZ5SKRYrLg0v5tQ/ee0yNTeIjdk2vXm55jG73Gg1SBQbkYVcyzMrpKLh
      BTS9WIfLOxbTy3Xstmm6XSqmAzkIJUrHhqJOkiMp6S/mCCKBbZmtQU6LOVKGazdmpdSJJFVZ
      p3mJMS6ESowqiSamT2ko5R2CMFZxtI7pUiqetudXm1yZWMrql0Klkj+RzKFoSXFIZlCt+c47
      41yZWMrur9Y8ppdqa8qKktmo7oZcn16l7oZ84ctvZ44AP2zNcpduL/HooSG8IMIyTY6NDWyr
      v3cCx7YIopaNpDWYXQStaRr8D3/np/jPf+2T/KP/9Gd48sQow/2FbIZIBZ2UsRGt2wasUjrT
      MlKHQSQkS5UtMMDYwUMEXo2gTZJtFX4Y0fRjS/67795ierFOmBpd6VTfds3nrHiwap00Tkvd
      aa+AY1topTFNA6XJJF8YyXjmSCueqCmibRCFkSSfa1/2iCVIOjBzTrKQp3SiPqUDr6U+xbOO
      znT+2KBVNLwQrcG2TISU9BUdam7A//ivv5t5KdIZDloGcijkmtkupRXiWUgnwblTL49Smtvz
      FW7NVbLnqs0gm2EA3EBgGCSzkIyn+8R17Cdt6fohUioeOzyEAZw4OIBGY5kGh0f6t93f24Vh
      GBsEq2HEbZDSTRtD9BUcinmbof4CtmViWfHOgzCS2WbMdhexVJogm8XXvii12WzL3FwFAjCN
      eMBtF2EUN3y16TO73KDWDGIvS+J3BjK1xTQNHLslfW3LJAhFVsHUcAHI2RZNP6KUd7JtGOlz
      eccmSsoMwtZ0GAqZqDWKYs7O3uHYJk0/wrZNwkiQS+IfpQMmhR+1GDBt3HQ69QNBpRFk6wCm
      acR6tBXbMc1ETUoHbyQUowPFdR3S2RiUiUCI2mbNlk0Uq0W2ZWaSNG3X1L3s2BahUPihoJa4
      DqtNn0gobsysIqTir/3UUzx2ZIjjB+MtCoYR69b3AlHiuGhXP2WiSgah2NQdW8zZpJRapoGU
      as26S2oHqURYQcsmS8eTY5vUvbAzA0xe/4DLNyfRpkMut32dMPN0hCLTzyzTiKW5AYdH+hFS
      ceRALG1sy8y8Dwax5M/ZViZ9lYp1ecc213B0yxbQOI6Z3WtJ72TfUkJDPmejlM462Q8EfQUH
      LxCZ8Te9VF/rbRHpwG/NAinSgZ3OOHYy8It5J/PIpJ4KkUzR+ZxNKGSihq2VWjDST4QAACAA
      SURBVO0OgPVpJO2ik5ktFZDp+kFKs5Cxe9exzWxGW6rGG8EaXpjpzSl++oVH+eSZI4lENtZI
      3rsJDTiWlXieDFTimTEMuJPITdWf9He6BpL2BZAY+XqNmzRVV5XSFHMOzW4MsFuEQiG1wvOj
      eJEpEPQXc4RCYhkGn37mGEppnjxxAClj/TtWHxR2wvmxBGt5cpTSWJaJVArLiittmWbsQUok
      Rzz44gGWTaUJUsmvtMa2TWwryZ9zCCKJZZpEMvZEpNLeMo1sapVSI2RLAkNsqEZSZjOVY5uZ
      dAnXSR6AKFL0F+LDREzDWDPTyETFS2mWycyR0p5Kuij1HGlimlP3oIrziDYX8fpFuXZmiZL3
      FXJ21o7GvRv/sSBK3qW1xsBI7Dw7tkes7kPTtlqC0LGt2NmRDPhiLtYOspm3gxGb1tXb2kpw
      d35cnrjO2+9fwt/g8oulth8KNLFfvpBI37gzW4NVaxgdKHHyyFAsyW0LIRTFvE0kYgkmE07u
      L+ayPJBOf7GHxDJNdCJFhZCZlE+9AV4QZYaubcUb8sJIUsjbBIkKFAmJaRnZAksx72Su1dgL
      k0plnV1SyYWO6QlFvJaRSmSZGrcy9uvbCXOUCk7m/WnvWNHuHUntAxGreFIplqpe1r6maWTM
      KIRKPGExncV8i4Z0XSNllnQmSQegY5vZzGDcAyUoneXTfhRKx+0v4373Q7npekQnV23KEMW8
      Hc+kkaCQszNBlNmHKlWBLLwg6rwZ7sTpj3Hi9MfuWJEIE0eF+KHESVQbyzQyHdwLItCaphfE
      Ko00kUpjGnGHmUasY+ccM+l8Sc4ysgGdnvElhMQPoeDkk442s+kyNZ4c28YNIvryDpGIKxiG
      YXyv4NDwQvoKJVYrLrZpERFLzULOIhIBOSe2DxwrprHS8OK6BFEiWRWRiBnBT9YNhFJEkcoW
      qgzDIIoExWJLMl+4MY8fCiwz7thiLk8oEmYUMllgi2eTQs7OZhyRrFMIpZAqVv8iIZlfbVCw
      ikRCUszZuH5IKe9gWQYDffl41rVMnGSGAwhTOyaxh0j0YIM4YpththbZlLpzNL3dRoazktm5
      kDk/DGw7vldMBJKTz2XPxHTFvy3ToL+Qw3PjNRjbNAiSXZ6qjanrbsih4b7MRkodD5GUmbFd
      bwadZ4Cbly/wpT/4Eu+dO89itXt06MHhYfLFfvry8Q7F42MDWKbZ0lkTl17dixjoyzMyUCSM
      JOVSnqobMFwu0vQj+go5ck4sAfI5JxvYGhJ/bywx+oo56l5IfzGPlArDMCjkbKSKdetIKPpL
      eTSxNM7sA9vCDQSlQi7jftsyUVJTKuSIhMquuWTnpusn7tJkxpJKU3NjPTPVMpp+lHmYNLH7
      M5KKg8P9mcRp92K5QcTwQJGGFzJULrDa8HGDKNZpDYODQ31tToKYGWPDV1PMO9kWjKWah0ja
      Y3qpTqng8KmnjvGJM0dQxOsgfcVcRkPKCOlKNIZBrRkw0FfENE36S3nyjoVpGDj3IDKcZZnU
      mgGPHh7CsUzcZAw0vJCDw/24frwVolNkuJNHhvn4E4cTo91IPEImUpGphYN9cdv+0qdPM5O4
      2pWObUIDAz8UnDg4wELF7cwAq8tNRg8cYCBn0NhkJbhYHubpJ09nixYpZGKMRDJehfNDgWNb
      9BVyuGFcOdePMlXBtkwMjMyTEglFXyFHGMnMsyJlPK03vJCRgWKm21mJxE6NytSbQ7t+aaQ2
      gJGpQLGxHA+y1A+tkve3Iz4UsN17ZLXcmOn0mhjbaZmffPJI26KcyD72EFIxNlhCKY1jxR6t
      EwcHKZdy/Hd/8yc4PNKXecd0ex3QicEfl5HaKaMDRaqNANMwOH1shL/2U09jJ7bMcLnA9GJt
      DRNK1TKB/TB2AECsRx87MJBsob43RkAQSR45OMg/+Gsv84//3s/Rlwig504dxPUjhsudP7M9
      eWSITz99LOv/QuJUaMfhkX6kVPzkc49w+tgwkH7EFavLDS/k5z95itW635kBBkeGcRybucVl
      +grbOAHSIPPUtLvoUg+OaRoIoTJjMbULcraFY7cGstI6WxhJpa9Umr5irDfn2laL84nemxq/
      pYKDVCpbGQ4T/TmMZLyO0DZNCqUo5hxCEdMnpY7VECFROtHltcZOjESI9fR0J2eqd2paHgwp
      FcPlIgeHSplNYJkGOllfyLXZISrxgKR6t2NbCZ1ruyUWCDGdqbGbtlFq8BtG4k2z4rJ+41de
      zDbzpWh3OaZqYopPP3McexPD826gr+BQyNkM9OUz4VHMOximQWmTcWcksztAMe/Eu3XX7QqN
      RNzfqTHdWgnW2cJnKGRnBjj2yFHOPPUkz3zikwz3by/suWEYmSTODEEZd5aZER53uWNZ8dAx
      SHTItUvxmpar0zDg6GgZwyCZcdZ6AVKjKV1JNo2WO9RKJH9sSLbyWmbrU87UE2IlklZrODTc
      T5DMQu1IB50XiMywTmdB04ztmdgeSiIQp24+3aKrmJy8mapjpHUlNvJiX7iVrQekqmDs4rSQ
      srVxMKUudScLoTg03Jf4y9fun7KSgQZ6zeprX8HJmOdu4/jYAI8fG+YTTx7J7qWOiWMHynzq
      qaO88Pihrs8XnBbT/MyLjzE+W8H1I37hk6cwzXic2ZYJBplG0O7KiVW95MOtTi+4dfF9fnTl
      GmOHxjh89Gk++cKpbVVQqtidGSTSLNW7oyhqSZ1ktoCYyJxtrlFBTNNAyVSFMTCN2MgDEiYx
      EimqEEqtKSvVD9e79NI8pmngWCaWaeAkDJd3LNZbO6krteDY8T4mHZepNdhmq15KaYyE7nZX
      omUZCBUPNKEhb5vZanQ6+NKZMf5tEUUy8/jYtomQsRTPORauH66pQ/q+z738+BrpbRgt/Tj2
      prVcq+kqbHww9doGytn3Zh/Q848f4pHRPENDQ9RqsZpmJn1cLuX4xU89ztDQIJVKpePzphmr
      fMJo8FMvPMrzjw4yNDTE+OQcRjIuDKPlz2qvVygklmW0hE6nF6zW6jxyYpib4/M7WgmWyUJW
      7GlodVTetjCMlhsr1f3TAaOSwZK6Jw3DSFSgWII5Sf6cY2MYsdTO2bHxls4c/aXcmm0HSiks
      oyXxIR50xjruKLStEkM6hSaNlOxfb9+4ZSXeqsyVaLVoMY14/aCQrE7bppm5LdO3lpMDRdoH
      bryXSG/YF5PSJaXO9kHZyaenhmHw4hOH4/pZBlbboHaSGWH9JkEroWO9gLjXKlA70rZe3y/d
      8JPPP8I//PW/1DU9HfTFXPzVWroHTCfrR48dHuLAYKnzDPDEc89DzuSxR0OOPXZsexWxTESi
      ZqS6FsTGqtIxYWkVCzk76bR4a0I8A8TPlROJbEDiYzez2ePwSB/HP/0E3zo/jWnEnX70QJkP
      rsPxsTIjxZN8/dxUtsWiWMjjewF2MsWnAyydeYDYa5LMQGkfmIZBECY7TdtCxkRCUso7uIHI
      jLecnTK6kalzjhPbIbHUTcuO8506Osz0/CKO1ZJO6eJf7AqMZ4JmGHuebCd2a9rJjGGaqd9+
      46BJ28k0jTXM1L4waCWOANTG5/YDlmV2ZMpuMNjIsLpdeCSXl585RlOYLK2sZvlsK/6MM2eb
      nWeAsSNHGRs9zKnTj5DfRqOknZEaoVq39pfbpoFpxJJ3bLjEySPDDJeLrconU/aJsQGOHihz
      5nh87oBCZ6vDOdsCI85/8shQbDwnzw4l3yXnbIvHjgzFnp60LbJWbakaOdvKBivEG8KG+gvJ
      lob21WixZjpdM9gS26R9tTHVQYFs/SBd5U7vAZQKNs+dPMiTj7TOV3j86HC2UJhJ/XT2s03c
      ICLvxFuBHcvqKrFTEu1EBfKDlku3GyzL4MkT+3fWg5UIh/advtuF0hrDjDUMx4r7t5R34tk2
      Eb6p/Zbirsx5UbqSK9d6GtIOe/zoCH/rF56jv9iy9POOlQ3e3/iVj/Pjz56IO1K39ORyXx7H
      NjP3HaRGpEG5lMeyYu/BgYEi/9mvfoL1W7v6im0Hfq+TNKMDRX7x5cc5OhrHF4qyrRO03KZS
      Uco7mZcr3UUKZN4Gg9jEj0ScN/U+pCpb2h4GBr/48uN87lOPZzQ8//ghPvfS42uYIqPFNhPP
      TavLWp4yIyuznT+fPXmQ42MDmRcu/SoudXemqiHAQCnPr/74GfYLTiLc9movxvpi2sdiu+C4
      Kwygtc5cVVabgZfu/MxenkzROdvkpaeO8hu/8vEsLZbgqbsv9tYMlHL8/V97iY+fTnVeMzFo
      LB47PMR/8Wsv8fSjB7AskzMnRjOXamsWSiR7slKaS/YEpfQdHxvgP/nlmIYwigd3usMUyIz0
      UCgKeRuRuE1NM1bDUrebZSV1T94fG/zWOgO5c9O/cPoQZ06MZjNAWkYh3SOT2QAtnTkNJmYm
      s2ya9qmnjnLySBwUKu/YyYq00doQeK+2fm4Bqe1k78ITlTI4JHZecr+Ys6k2/cwF3W4U7ygu
      UAolI1YqDUZHhpOONbCTbcYDpTyRCGNfeiQz92U7Z6beCjDI2RbPnjy4Jg1arj3TiKeuwb78
      mr1Av/DSKRZXqhTzNiMDxTXcXchZ+EHIUH+BajXOYxhtaophbDA4bTt+XyA1/cUcdTfkwGgf
      brPlWUqN9XjLhpOp0Z/9+GMsLK1QyqcLTCZBpLGM2Otgmy3ju91YXb+3Jf3foCXlDSNe0Crm
      bWQkun5AkrZZipTRcskaQ+pWTpl28+Ap9w4/8+JjjE8VGCjlaTZ2djh7MefwSy+fplwweeLY
      cKbqfPyJwyytrHLiyEEWVioMl+NIgS8+cWR3DDA9cZsgiDAKJUYTF6VpGASR4PSxYd6+OM7j
      xw9x7dZM5sfupLd2Wn3sK+QY7C9QUDZD/XkCz433+reFLzUNg+dOHcI9XO64eer42ADzK3VO
      HRlidmGJof4C5WIOyzSyHagp7LaBcmyszNyqx6OHBnnnksGpo8NMEpHzNMfGBpieW+Qnnn2U
      s5fGOXFkhNvTLrZl8sLpQ9QOFrOZZ7i/SIOI/nIJx4KBopX5/zcbwAAkqkpfwSEI41XT16bm
      OXXiCNPzYbx/JjEcrXXt125YW4kH6tBIH34YAAZHRvsZKMbqmNzZWNtzPPPYGEeHnF0Z4oP9
      eX76+KEsNGIqbMaG+vjlT59OQiMeyAKVffLJXTKAwGRspIwr0mMu4w1OecfiV3/sNJ999hBT
      KwHXbs1gmbHv2TINoiiK96oLgWmAbZKFCEzD/eVsg7/7Ky8wODiI1ppGo4FtQjPJJ4TAtsjC
      3aXXNC2KIv7+r30ii7n5408dYGCgzC9/+nEuTdYp5h0s06CYtxJbQyGEBq34e7/6IrlcDtu2
      eeroTzAwMEC1eiSLYVmv1ymXy/zMc4cItcNvfWkR01hLS9MLePzoED/1sTMMDg4ipcT3feZr
      YWIP6IxO0zDW1EGr+JPFkbLNf/sffQbbssjlcvzE02PY+SL/+xdfw7FS2g20kggl0Cre7mHr
      VhtrFYeqefHJo/ziJ45RLg9gGHEwZMvQWXultKSb2Nrr0p6WhhhMr+lGOGDNvfTa3qfrn2v/
      SzfTrU9LgxhvNW09nZ3qAGTv2xUDHD04yo3JRc6cKZKutf3tz71AzQ0y/ev0sRH++k8/zelH
      DhNFIR9/4lj2kYptmfzqj5/hyMERtAyz+1v5K+Ztfv2zz9wxH7Dm/0PDfTxy9GAySCOOjA1y
      dCjPkdF+GvXatssbHSzy6599huGhwTXpz546yLFDBxCBu+bZRw4O8jd+7jnOHB/FbcYhT/72
      555fU+Zzpw6CfJpjh0azL9/Sv4Jj8euffYZCIc/IQB9njpYZLheo10Jsy+CvfOYp7Fwxy/+z
      nzjJsZECT586io68ZCGve33u9P9W2mSrz201/53SUmzlmfX3rc9//vOfh1giuK6L7/tb/hMK
      yv0lwiAgCAJ836eYsxgoxvEnfd/HMg36cvGuTS0jykU7k4YAthF/z5v+n0ZRhjj0drd7URjS
      X8wRRRFhmIQnWZcnDMNM6qRpSsafVJpISnkTy4CiHev06XPpFNpOp+/7yXbnaM17DCBvaQZK
      OYQQeJ6X1Cs2tjJ6oyjLX7B1stU6TivYrKHTMk36csQf8QdBtq04fW/6PstQFKx4nSEty9SC
      wb58RmchZ1POxypfe3umdG5o14TOrbZnp7ZK83fq0055OrXrepo2S0vrcic602t7e+4oPPpm
      2G149E4hv9PtEHsdHn192PHN6LwX4dG3SufdCo9+Jzo7hR3faZ+mV9M06e/v7xgWv71du6Wl
      2G549Fwu+TKPHh5IfO1H15ldru83GQ88egzwgOLq5DJzK407Z+xhU/QY4AHGOhuwhx1gRwwQ
      eE0Wl1dRMmJ5ZbXXET08sNiRGzSKIuYmbrG0XMJWCvKthbAeeniQsCMGsE2N1AYog2MjZZpC
      ZhEDup0RttMIAls5I2ynaelRQd2e284ZYbuhc/25X1s5Iyy+1zlKw3bOCNsuLXvVp+vzdDrv
      rFNUiK2kdTsjLE2DeFF2xwthzaaLUyjx6NExbs8s8eSZIpZpbHpG2FYjCLRHAEiv8cayzpEK
      4m3UndPSsCrd3tftDK3Noh9sdvbWZnQCm9K5WZt1umcYBjL5sqsT7Vs9I6wTnUKIjmmpUNiL
      Pl2fJ/5CrXu7dktLB/Od6OxK704YYPTQMUaTTzY/NjC8kyJ66OG+QM8L9IBi/WF9HyXcnq/S
      9Ld/qHsndGUAuckRNj3sP9qPidordNpfcz/iaz+8xqVbi3tSVlcG+OGr3+Ds+Q/wItEtSw/7
      jL2eBW5Mr/IXP7i6p2XeDaTxVvcCXRng2PGj3Lo9QafPhlYWZzh//iLLS3Oce+9idppJD/cW
      cpNvfHeClbrH7bnqnpZ5NyCl2rMx19UIvj29yKMjBZbqPidG1m6UGxo5SL3us1ht8syJAyw1
      fA6V8z036D12g8oOrsNduUGlJLxP3KDXJpeo1+s8n2zKW0/nXrlBu84Ap06McWm6yqHB0oa0
      m1cusFhtkENy4fpkFhw3dYNuFlx1u4FU0/w7SdtLWjbLv1tatvtc6ua0OrgON3vPnfIoFYcO
      vNvBcbeS5/LEMhduLnR0BafnHOzkvVt2g96cXOK5EyPMV1xOjK6dAR5/pvXx+slTcF99Xb1P
      aCYnud/LyDqbhTnZCTSt46juB8guQdnSaOF7gY4zQOTVadaWuTTb4MjwxhkgjafT+tsTWh5o
      XLi5wJ+98eE9fWeng7B3gzBqnTF8tyBlfI7vHWm5i15IrTVvfjAFdGGA82+d5cWf/iWeP15m
      btW9a4Q8TNC6u8R6kHC361D3Qr782uU75tvMySOVQuzCCFZK8ztfezc+m6FThgMHR7l0/h3m
      qyGlwq4+G95TTC5091AstB1+vB9Iz+/aLpTSLFW2L2RiI3hvB6vWGnGXF9iU0nc+BY/N1yS8
      QHQ8+2vLNGhNFMVH8HZkgJNPP8/P/ezP8gu/+HOM7NEuz/dvzO9KvxRS8T//29eZXmp9BSWV
      4vz1OSKh+Ef/5rW9IHPH2IpPfnqxtuErrqtTy/y7b72/7feFUu35OkAk5Z6rVTtFesbxelSb
      AZdvL24YS6+9d5tq21Gpm2Gx4saHK97pnOBuqK0scn18ktX5Kc6+cx43vPNi2e99+2J2hu1O
      Oi490DpqOzlyte7zB698gFQ7k77tZf/+Kxd3/PxW8faHM/zo0jTQaoPLtxazg+7uB2xHSK3U
      PL599iawvT7tNnM1/eiOdlQ8M8BiZe2M/7vfvsDldavDv/+dix1niisTS8jkLOEdMcDAyBgF
      G6q+4LlTh1l1wzXboTv5kf0wIogEfhDyv37x+0RCslJrErX5wTf1k6v4gDc/iPg//+BNgjCJ
      9SJkVpntrgOkZTf9kNfO3970ufb8nXzMqU9+U3++iv3vi5UmX/jyWyiluDi+gNaauuvT9IIt
      1WEr26G70blZHpWEld/qWsb0UpV3rs7iByH/2+9+H9GBzvVlRUIyvVhjZqmW5YmEYLXWZKXm
      8qNLU0ne+HitTn590NSawZr+QMczWBCEVBseUkq+++4t/CDaQNPcch0Nm68DbIaVhRlu3bqF
      oTXv35hhsJjDsixqbsjUYr2jDzY5nJCJhTrjs6uAwW9/9T0mF+pb8pNLlcR+xOD69CpCgRcq
      IqGwkpDsu/EHq7YttztZB2h6EbMrzU3fF0lFpRkSSc3cSjN5b+xV++bZcX54aXpb6wBqk+3Q
      d/K9Yxh8OLWaXU3TpOFH8ZFOm9RhtREwX/Eyf7yQCsO0uD1fJT7tcXNahIzPjqu5YZZnftXl
      C3/yTlJmHLFC6jh+6fp1AMOIz0kg2e7cPr5Mw+Ts1Xn+7PtXsSwrK2s9TXUvPtxQ6h3uBh05
      eJSf/MxnePSxk3zqpU/Qn4T7uzq5zOvvd3ZxBVF8POVXfngNoeJVzMmF2popN4wkf/H9zlPg
      tallqs0AIRV+KJhcqPIXP/gwOzN3O3tD3rw4yexy64NyKdWu/d/vXJ0lEpK5lQZvfjDZNd93
      3x1npea1zuxNDr6OxPaX97vp6wuVJt+/0J0GgIYb8q+/cg7Xj/h//vwdpNK8e20OgJob8K1E
      tVmPc1dnefXdW0C8IS9WMfSGzXkXbi5wdXK5I20AM0ut9k9P0ARYrLrZ2cyd2kPr1kEll24t
      8sH4AhC3Y0tF1rzz4WxXV2p67JSQXc4I2w26DSSdnBzpJqfHS6WZSQxa14+ouwHnrs3yyrnx
      ji6wiYX4KB0/OSHRCwRXbi8BZOdv3Ql1NzaSLowvcHOmdWDCasPftfHnBhGRUFydXOa3v3K+
      o+4plabuhkzMV9bYLPH0LGm4WzPiUkQJ4/7WH79FJBRffu0y16aWuT61yrvXZjd99tLtJSoN
      H6n0mrN0lVKs1n3eeP92x+fSbQ0QM4oQ8aHi8cBs1fnKxBIfdmCA7MhWpfjT169wbWoZkdQj
      Vos9VmpeNk42w42ZVT64tchS1aXhxcHRlqouYSS5Mpno+R36td3mumcMIHVLSkRCMb1UTyx9
      xW9/9V2+8OW3Wa56a443WkN0etBxwuVp46YNqrSmtskAavoRv/mvXiGIWt6FIBLcmosPWJNq
      56ugWscHfyutCSLJQqWJ22G/elq+kJqpxTp+2HLnKa358mtXWK37G57r+l6lmV6q86PLUwSR
      4PLtJW7PV3nr8vQd67Kw2mC15tPwwtitmJzEKRLvUjfDPBQKkQwqL4hVCSEVUuvsOK2GlzB5
      hzJSQSOkYmKhyvRinfmVJn4gMppX6n4iCDeOg3bBEoSCW7MVqo0gozcVcumCXqcyZBJGXatd
      hkfXSuL5IcVisXXOVxeuzc7WFZIgEswmhkgoJK+cG+eR0QJRssuvUxlRMh0uVl2CSDKVqE9a
      w+RCDdePuD1X5fZSQNGSjHrQcD0ePxJHabs+tcLsSgMpFcs1j9pwkW+9fZPbiw1eenxkl9tr
      dbZFN2VIL+juGZMqlngXxxcRQmHZJEe6ykwad8OV20v4ngvJu1w/ylSoc1dnOTl2iroXZGeQ
      dcP0Uh2lNU0vpOGFTC7WsnafnK91nRFTia+05q3LM0RC8qPL01n6u9dm+fDmNK+9N8Ff/fGN
      hyu2jitVXJtcAdHHQl1Q94LsnbfnK9kRt+vRrroGkaDhh/hRa2zNr/qgJLbV/ZhVIeMxprTe
      HQPMTIyzUm1y/ImnGE4avJPkSQ/NjkTsyYmEYjZRf5p+lBxRCstVl8Vqk4vji3x2dGRNGenA
      aHqteN5pF30wvoCQilBI3rx4i2PDOUZWA2qNFgOEQmYD9NZshcD3KeZzTC83OXOkj7obcvbK
      DL/0E2vfuxVoTeaeTSVPJwmaCoF0Wj5/fS4+aV7EUjR19W6G89fnCHyXyYUasytN+pyj+GE8
      gFI1IorkBunrr3NVp0G1vESlvHBjHqk0XiDioFurjS59Gc8OXiC4MrHEUMni7JUZGm7ISt3n
      z9+4QRTGC3ud6pIutDW9kImFKocGbaZWEtrTGSBxl3d6fm6lkYWWrzQCLt9aYig5U3t2ucHV
      qSrDJYdiPj5wsJ2RvcTOiITCACpNf3cMEGqLU8fGqIaSoWL8osWKy/fO30ZikjMEoV7EUiGR
      kJy7NketGTDYl2elHlfy1uwqo4NFbEsn/lnN5duLuKL1fFq5uHGSIKuhYLnqUshZvH8zNoS+
      ffYmV2YaWE+MMlOJEFHEGxcmENpianYVlRh6bhAxuywwTIPVRsSN6VWU1ly4uYCyruEYEqlN
      FEZGQ3yN37P23iIOsYu36UdcuLmAYcD7N+aYWPazPCY6q/NSNR4g8ysNbs67HB0psVq3sCyD
      D8YXmVoJs+dsYndg2p4Xxxfoz0G16RMKzY2ZOC7T6+/Fzof51SZLVZe6F3L2w1lCvYgIXL53
      cZ5ffukEoV7Mti5apsGVidiOmlmqY5nxOW3jcxXqbsgPP5jCzC23+hHFxEKNmRWPd68VEwax
      WKl7hELyb775Hldnmjx6oJCMhSbffPsGuq0dU6N1fC4+ArXuhqw045ns2tQKAFPJCfcNL+Sr
      P7y2ps3nVhocPzhAGAleOTeOkIqpxViYrtZ9hFDUvZDlWqxOn78xz8XJGjlD8Mp7M7xwcpTp
      pRqFnM2HE8sYehdzv1db5urkIs88dQYTzfjUPDfn6rHHRxkULIkvLQqWRFt5kBGmZXFsbJCZ
      +WXsfJHQdxkdHiIIPOqBppjPEXgNPGFlzwMMFKBY6qder5ErlLBUSD00MA0wkSjDwZA+2sxj
      qhBXmNgWGFoRKZOCJbFzRUToYeeKRFEIGChMLB1SLPXjJ+91TIXUBkqvrUNKy/p7BUuSK5QY
      KOVZrVY5MDLE/OLKmjyGEUd17uvro9mo09fXj+c2iXCwTY1WimOHhpmeW1rznG0qtDaQCS2B
      silYCsNy0Bho4dNfLtNo1BksD9BoNFBmLj4wTgZZWYG2yRsiq0N/TjM2oII7/QAAEdZJREFU
      OsTswgr5Yh9R4FIolCjmbVaqdbThQNvzvrSwDI1jGRi2g458pJmjnDeoe4JCPo8Mvew92HlU
      FBAoE61bbVZyFKZTRIYepb5+ZOjRCA2Gy0Xq9RrSyJEzIrAL2DqkmphEWVvbkkePHqReq1EL
      482YInCxnCKWDskVikghcf2QgXIftVptTRvYSHKOzchgP4vLqy0GaDab2YEDO4XrulmUYMdx
      cF2XUqmUXYMgyHyx69M8z6NQKGAYxoa0tOz2e+lVKUUYhhQKhQ1p6QJILpfbkJbWdbt0bkbL
      3aAzDMPMh53e832ffD7fsa3uNZ077dO9ordT2nbasxcevRcevRcevYce2jGzXOeVc+P7TcY9
      QY8BetiA+ZUm79+Y328y7gl6DNDDBnjB3gSdehCwIwZQUtB0PZSSuJ7XC4/+kOFh+LJtq9jR
      OkCtssztWzPkB4qEzYATZ55muNR95a2HHu5X7IgBHNth5MgRosDlkeMDVELBQN7qxQW6h3GB
      0vftaVygtv31qkvZ9youUPt1s7p0Srvr4dErlRUqtYBjRw9ybXaZZ54+jGWZWVwguDvh0c0O
      aR/V8Oibpe02PLppxnvu16c9jOHRu9oA3Q4jBjj26Gmee+5jjIyO8cKzT+FY98aW/uI338+2
      EzwsmF2u80ffu7TfZKyBUrrrpsaHDV1H7jvff5VXX32Dxia7Gu8lKg2f7757i4YX8sffu/zQ
      GGordT/efak1X/ruB/tNDhB/WvhRQVcGUFrdV96dxYrLcs1DSs13zt1kufbwxCtKv4t49Xzn
      j1AeJjS9iB8mQanuB3RlgFKpiOc1uF8i5aWqmNIaIRTjM5V9pmhv0e1DoHuJMJJrvpS7G5iv
      NPnOfbTK3JUBokhgGvffSSTp11z3UwzL+w0NL1zz3cRWMbFQ5YvfunAXKGrhfuu3jgzQWF5C
      WQ4aG9u+t4vFtS7BjdYbZUHyaeRu4gHtJbTWWzq2x/WjNXWJIrmVQGnbwjfeusHrW4i/2Ql3
      +iJtt0i/KLtf0HF0F8r9RJHBqeMHs6+Y9gqbzSg1N+A3/9UrHaXEhnsa3ro8zZ93iSJxrzG/
      2uSf/tGP7pjvD175gA8nllvfrLal7VVAWDeIdvSRf/snnelHO3fKv12tTekHYAaQIqS6usR8
      I2B4jw/A/tKrl7qe76SVpumHW3LBhYnh6N4nXiopFXXvzlEd/DBifrXJP/7iG3eNlq1Emuvk
      4k6/LQa2NLNOzFf5/75xPitvK7jfThPqyAD50gDFnE1UWWShtjFKQW1lkeu3JlldmObsO+fx
      wq1Lrkrd31J47G7wQ0HDC7PwwbuJErwfEFIRRiL7JjeF1lsLGrsVbGUwnrs6yw8urvXGnL0y
      uy338vxqk6WKixdE/L9fe2+rxG25/HuBjgygteaxkycw+4YY7S9sSB8YGaNgQdWLeO7UYVbc
      4I6hEdNl6DQiRMcldRnrw0puXJ7PokpEEj+Ml/P9MELpzcMRbiU04lbDEXbLr5QiiKIkROPm
      tAShIBKxpFVKcWNqGSHi+922H2yHFiklYSjWHCPUKc/CapPbc6tr0maWavHquYrDu9yJliCM
      CCNJvelz4eb8mvx/+N2LXbZ4aLRWW+qjrWxt2G2/d2SAd1/7Gn/4F99hbnaBQGxUMVYWZhi/
      dYv/v70r65HjKqOnqruqNztjbMfIDgkJUiAJCBSCIqSIByTgB/DG70C8W4CExAsSUvLACwoQ
      IoGQkdgSJcbZmdgeL4mX2exZumeme3rvrvWuPNy6t7fqns2IIPJJo9L0rbp17r59dQ6EwEer
      25grOPuWSGJczRvTwqrtABYAOzNJjai/cJJSnVQqYQ57pivENCxXl6rg4mASO3tRI2bsDISc
      7u6g40QiKKJp+zYTOpJMxk51XziMLJFlK7mjWe4HOv9s28at9QZ6ATFEW5qdYS8sXFFywk5x
      vXjz6hpIitwSlxKUiT3LaL9uKwcp99S8S2sAT339m2DuQ6hU0/eET545h2+dOQcAePyJ1Fum
      mpSDY/YwpqjUe+aTtevLO4brZ9z0IpgwNUocZK46bn+bX8Gp48/gsc+eSDApmr1zJ5wDx5WG
      cZYpCr+BkNIwf5CQ0pBO7WUSCvPZuUnM+11Dbbe7kPgcXvtwFc8/eSp5dvZz/YCg2onwmYI1
      smgfX8BzIVOnp58U+nVtqSPA+vJdVBsdFAtFHET/qOfHe0rs6IwRUuLmvRqur1Sx3egrvkZM
      nyIaLs0kfgEJbx+LzjQjlGGrPuDp90KCFy9cOVRc2va7vy3l6Bx9ZFdsVgaMWaMT4Fd/WUhn
      oNtHpyAl8P6tMnbbPm6v1U2nstezy+WmUXhp9kJTocfXHYp//5O/PksdAZ557gU889zBI1sq
      N/GwT/HUY6cnwjQxk64olXofL11YwHeffQQv/fkKvv/CF2buD2tlQJ3hQki8dWMDX3r0JCgT
      +xJH8CNinh2usHc36hP78we1YVzTtjMV8xsHF4Nsp4dcxP/h0m1U6r0RPs5+QPbdw+oKSygH
      EwO2t72MMI6eP5qPw895AYGUigSMpajZT2sU48RdsyymbKLDSesI9mMP9JRLSkytxD9++W00
      E8YvxUdJh6YxIll0TS+AelexCmvXYeXTrpjQ3rqxjh+++Pqe+H7y8jsj+DjXLMECrX6IhaXZ
      hLJ7mRCKXOu3r6crvly6to6b96pKy0CO9pxCjJLL7mWajHe4zv781feTRrG36RGVJPnOhTRk
      uRpPmg0r3DDz7UfCtSkVho/u1RARlkrQy7kEH2toYczw09+8M7IDpRbL6dj/Mb86wkJea/v4
      5Z8mz2CmEjXLASHwAz7mlYb+etgoF9iq980pb3m3h+1GfwLgrGmEbiz6nuHM8kKCfjD76H+n
      6Rk6dppUwN9fvDWijO4dwn3AYE96wzCmuLhw39B9D1tIqClUnQ79/+JmExFh++7Jxtc+MsEf
      UwZCOTre/kh2uRRGbENTtbe9aGpZ6B24cdMu1H5E0QtiSGAqWfH42oBxgUY3GGl0b99Yx6WE
      hn3YYsrx2uVV3E2YwQGVF2nl/7PfvZeajhurVfzopTdwd6Px4D+Kv3RtfeI3mpC+CinVFmhC
      j04Yx3q1g9WtlhlKNXf7hCU/6aGykuyesEQdRq89Xnnj49SKrHpMVcBeSECZwG7LQ8c/OjU6
      MGAlZkLAj2gqOa4ujOEzAF2Ru34EQvlMhmsAmL9dwcLyjpleSCnx679fRxjTZERU+bhXA9Aj
      UNeL1e7PMIfmjEa4UmmBJPl4ZXEbhPERLQEuBJbKTQihhOimvXfYgogiThrtK4leWrMXpp5G
      U8bR9WMzzXxz4T7Wdjom3sXNBi4u3Mc/r61hOaFeH7dmL0S9E6DdDx98A5j2wYqmENctVS+2
      2n3V29xeq5ueOa3+6wTrk9+YMPQDxWxMeSKnwwXubtRTMeiKERGGeOjgjrHBtt9RfFQiwhAR
      Zka5NJ8a3dDG1yuUCcNlv9cp7kati616z0w/hJS4sVpDGDHDUM1YOi/++DsB9Z2FEDI5w0nW
      BYyDUIb5O1sTvlla0ZIwYcTm9FrEDwkanWDonsk8UEIgCvsHt8pYLjdx6fpaQvBLcHutbtKZ
      lofju0vrOx30gxjVloeQMHx4ZwtrOx1sN/tTp1H1tm+o7I/UAAZfjc2+L83VtzYma7pcbqLR
      8VXPnCJ5qr0bdU9LmOrJd9u+2o2QShVkq96fUCsBkGiIqZ0nyjlWKk1ViF3fLI6P4qlImer5
      y4mQRxrPf31IDjWKKW6sVtHqhQhjhvs7yr17r1PcmKqDNN2RXF3aRscLcW1lB81eoHpnIVSl
      nBFVP8lP3cuWd3sIier5+wFBSBhWKs3U7y6a3RA3VnYScUKBfhCDc4nLd7fQD4mp4GnTEl1+
      QkpceHcRi5sN3LxXA09OyLcbfSxXWlivdlL90IalXK8t72BlqwXCBFq9EEsbDSyVG+BCJuuj
      9J2ozd2eoag/UgOolu/jyuWr6EWjCV2ptEY9Ho14xCBBG7VRzV9VKdVUaL066etfTwqqPqap
      K6WSHxVC4vUr99Dqh/BCgkY3MAo0gJqPcqEUwsOY4aN7NaUYfquCWks1uKN4Quppnl4kps3l
      N4fSTJjAX/+1gno3QKsfYnWrta/3RISBC2He88GtCjpebNLlRxScS9zfbs/U/NXP30v8/7ca
      fVNhm93AUL6njUi7HR9vLqyZUWSn6YEwbli6tW2PScLqe/sBwdpOB8vlwbqHMI7NWg9tT50q
      9/w4dWeo68ewoBrChXcXsbjRQJDUv+ur1SQPCLwZa0I/ImCJTsSR6NEjYeOrTz6KVswwV3BR
      yitqlF/8cR4/+M7XUHIlRLmHaq0BypSmr21b+Pazj2Nze3dqvPVugPc+3oQtCES5h1knEX5E
      MFfIjPy2sLSNa/fbaHc9fO8bT0DYWUPLPWxdP0al6eOLZxUPZseLk/dSSCsDadkGgy0IpN1V
      J59Dv+nrwvJgxyPnZFBteaNpmLJwHLfNWg+R2ByKm0FaFqSVgS0IyrUejs/wT9yodkyvvbjR
      wNK2N4FX5+ephwojGOZKOejPu7Wc1c3VKlqn8kkapk/POl6EU6XJ/nT+dgXSssy7CVNz/Vcv
      KlnajVrXjFbzd5Rv0u21OjpehH6YNX5j+vlby2U8fvYECGW4U1HsdatbqhF3/Ri1tod210PG
      Vh3v4mYD641oJD+3G32cnitiuXxEenSvvYvlzTq+8uWnkbEkavUGLi/VEVGGiALHXYk+sXDc
      leC2CwiKbCaL559+BNcWNxGwDHI2UxThYOBQx9SShujFlnnesoCTRRvFkqJHt50CXIui4Qlk
      MxZyGQFhubB4BJFR9Oi9CCg4NqTkiJiFh3KA5eQAFgOZHChVXqcCijq7UCghCn10I6DgSFBu
      gYlBGo65Ej61IOVouvQ14Bk8PFdAFAY4fXIO9WYH3Whwb8YGSq4FN18ACX2Dk0oHWVsAUioW
      Y68/EncuKyGlBcJVXD61UXIlrIyDnOOAxAEYHGRBkS+UEIY+hOXAtixIFk/g1Pk5l7dw7sxJ
      VKoNWE4BtojhuHnk3Sw6PQ/SdmALMpIGJwPkHRtZx4WgEUKewYlCBr2IwclmYQkCK5sHWIRC
      sYQ4CtENBcRQns3lAWHnYIkYuXwRYDF6MTB3LI8o9BX9u83ALRf5DEPDU6OMSUNO4vPnzsDr
      99EM1ParLWLki8fA4gAUDjhjsG2gVCgg8L2RtOezEo6TxdnTc6jWW4MGEIah4Uw5dIPwPMO6
      67ouPM/DsWPHzDWKIuOHMR4WBAHy+byS6xwLk1LC9/2R3/SVc444jlEsFifCKKXgnCOfz0+E
      xXEMy7JScYZhCMdxJnCWSiVYljVx/zDOIAhQKpVScRKi5KTScAohkMvlUnHatg3HcfaVV/qq
      y+M/jfOwZaqvvu+jWCzOzNdZaUkLO0h+mimQ67pH/iY1DEMTxzCtylH/tP03w2bd/0n60xj/
      F7Cm4T5MWR3lvaYBaEKro5jjOGYEyOVyYIwZwQLNfe+6LrLZ7ESYEALFYlGJJac8N/6bvmoS
      pLQwSpVKeKFQmAjTHpGu6x4I5ywsAEw60nDatn1gnJowahjnrLzaD04p5aFwEkIghEA+nz9y
      mQ6/T48Ah0lLWlgaTn3VhFmO44AxdrRFcJppoQNdwYavujAPGialnBoGYOI3fdWFmRam3Xf3
      i0X/ptyI098npZwZNu19s3DOwjLr/lk4tUDGQXGmle1hy3T8Ho35oPmaVv4HqYOf0qN/av/X
      ljl//vz5BxVZFEUIowgOGAhcCEZAqVL8ppTCspSoG4ULTuORMADIWBIxleCcmjApGJBxQEIf
      TNoQnJkwwSksOwsWBaDIQgw9xxiFbWfBiQ+agsW2bUgSTMVpWwJhLCDE4H2WYOCWeh8TgBB8
      BKe0HdBoEifnFHbGAY19UJmOU8zAaVkcYcghJR/ouHECZjlgJFQn4UIMcEoGYWVBowBMWBM4
      YTsgoTcFZ1btpozhpJQmvTFFFAmDZa8y1TgJiYGsC04jUMYNXgCAoOCWo94rlP60jgeCARk3
      NV8twcGtDBghEzg5UTtCqeVOQ1ArB05j/Bs8zHPjpkMrXwAAAABJRU5ErkJggg==
    </thumbnail>
  </thumbnails>
</workbook>