OEM Spectrometer Modules from
Carl Zeiss
Carl Zeiss 스펙트로미터
모듈의 특징은 Align없이 영구적으로 사용할 수 있다는 것입니다. 이동
파트가 없어 빠른 속도로 측정하며 온도변화에 둔감하여 공정관리를 포함한 산업용으로 사용됩니다. 여러가지
타입의 센서로 190 –2200nm 파장에서 사용합니다.
타입
|
특징
|
파장
|
MMS
|
Compact
|
UV-VIS
|
MCS
FLEX
|
Higher
resolution
|
UV-VIS
|
PGS
|
Compact
|
NIR
|
CGS
|
Compact
and Sensitive
|
UV-VIS
|
![](data:image/png;base64,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)
|
MMS
– Monolithic Miniature
Spectrometer
MMS Spectral Sensor는 견고하고 간결한 구성으로, 넓은 시그널 영역에서 안정적인 데이터를 얻을 수 있습니다. 일반적인 Spectral Resolution으로 dynamic한 시그널을 얻고자 하는 용도로 사용됩니다.
|
특징 및 주요기능
Compact, robust, permanently
aligned High Optical throughput NMOS photodiode array (256 pixels)
with high signal dynamics Optical Input : 600um diameter
fiber bundle, FSMA connector
Specification
|
Wavelength
Range
|
Pixel
count
|
Optical
Resolution
(Raleigh
criterion)
|
MMS1
|
310-1100nm
|
256
|
10nm
|
MMS
UV
|
195-390nm
|
3nm
|
MMS
UV-VIS
|
195-720nm
or
250-780nm
|
7nm
|
![](data:image/png;base64,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|
CGS
– Compact Grating
Spectrometer
Compact, High
Sensitivity and Resolution
컴팩트하고
온도 변화에도 안정적인 spectral Sensor 제품입니다. 또한
낮은 stray light level을 가지고 있기 때문에 다양한 측정 환경에서 신뢰할 수 있는
결과를 얻을 수 있습니다.
|
특징 및 주요기능
Specification
|
Wavelength
Range
|
Pixel
count
|
Optical
Resolution
(Raleigh
criterion)
|
CGS
|
190-1000nm
|
2068
|
UV-VIS
: < 2.5nm
NIR
: < 4nm
|
![](data:image/png;base64,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)
|
MCS FLEX
– Multi Channel Spectrometer
High Resolution and
Stability, Low Stray light
MCS 시리즈는 높은 재현성과 Spectral Resolution을 제공합니다. 또한 낮은 stray light level을 가지고 있기 때문에
다양한 측정 환경에서 신뢰할 수 있는 결과를 얻을 수 있습니다.
|
특징 및 주요기능
Specification
|
Wavelength Range
|
Pixel count
|
Optical Resolution
(Raleigh criterion)
|
MCS FLEX PDA
|
190 - 1015nm
|
1024(total)
|
3 - 4nm
|
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAARoAAACpCAYAAAD0mvLVAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAGa4SURBVHhe7b0HmF5Hefb/rLZIu+q9S5bkKssNWy6AbZrpxjghlBgS6gcBQvnHGAxfMGBKgJCPEkIJycUVSEIJhBASqu24QcANd1uybMtW7221fff//J557/edPXpXlqW1Qdpzr0bTnnlmzpyZ+0w7520YcFiJEiVKPIEYVbFLlChR4glDSTQlSpR4wlESTYkSJZ5wlERTokSJJxwl0ZQoUeIJR0k0JUqUeMJREk2JEiWecJREU6JEiSccJdGUKFHiCUdJNENiwAYG+sPOTS2sRIkSB4qSaIZAejNDpqFqGrDCPRjIl29zlChRH+W7TkOAkQuk0tnZaevWrbOmpiZrbm62trY2a2xsdPfo8I8aVXJ1iRKPhZJoHgPf/va/2qZNm2zu3LlBOmPHjg3SaWxstpaWlqqBdMaMGWOtra1VP0bxpClRYqSiJJoh0NXVad///vds8+bNNnp0qxPLKCeSVieMRh/pjIqRDFWXam/Aw5hWNXh4Q4yGRo1qDFkIBpPIB9IZHWQEKYmEMKNHpxESsoc6SqJclCW5/SYXZnq648XwEiWeKJREMwTuv/9++/jHP26LFy+x3bt3BQEsWLDQenshEQhlVHTUxsZEDBiIgrCGhoEIV2cHcmOp8ydyqqVNhMRIKBFPaytklAgIYiIsEVZzTN+Qz/M4UOiWH0zaEiUOBiXRDIFrrrnGvvWtb9miRUt8VLPFxo8fZ5MmTrb+/gE3/dbX15M6anRWH0HwB+G4f5STABABQQrIMsKBpBJBQFL4kUGeUUgayeiWEIc7ERumsUowkE1xNISbNST501pSkk15lCjxu0FJNEPg29/+jl133fU2e/Yc27plm82cOdM78Bgnmb6I7+/r99oLZ5BBGp0oIP4FOUBMCu5na7yBEZGPhvirpIEMNLIRCYlUMGhDF9M3wqv5RC7J3dXVFQRIHHogG9aTSCNCgnAIHz0achrj/hpBKR75mv4SJYYHJdFkEGGAz33u87Zq1YM2bdp027Ftp82aPSs6vYhD9r7VxxqN/+9xGpHIn6KdbNwP6UReEVcjKWxPEURU4ZBIm4gmkQxGpDTKiaHR3RAQMpHedRIn0sp1D0bSIUJL07SmIKOWlmRrdCQywihNEOY+OoeG6rdYZwp7PLpKHF4oiSZD3hE+8IH/a729fdHRdu3cZfPmz3eBwR22WHXJy390eMmpYyVZ3Mon71iDiaUWTjKIJ+LQ43HKN9fB/+GHdNxOJODTs6Y0UoIYgpiClJI/kVOarsmu5Z1GXqncSZ8MenLioY5Y4C4SkvIkvMTIRkk0dbBnzx67/PLLbdKkyUE2vb29NmfOHOvp6aXLedfzTugdMgiALg5xeAdMp4bTtCgnlKCK4IhEDIyGAPHq4AlJTh0cN+pDA27/j3zqoUYQlUQORjoDDa4nRl8pjFEP8fhZtknBWjdKhJJIKK0hiZyIS9O7lBak8qeRFOVL/hopQTAiGhazsTE5MeUykJN0lziyUBJNHTz00EOx4zR//kLbvWuPhzTEOZru7u6Ij85Af4iaS9VX696pI1KtrJkk0khIHXGU9bG+4ykUJ/mhMDge/ejCncgsQgmsIrkTAdT0esqIIgh5ssedyiXZVH7Woly7k5VP11wwyMPDIZ9ERCkMdxCEZEYxJcROZaiVHX8qS8q7du1NTZBN0s30jZ03yKi2sJ2mb8mdyIn0pD0YpPKIfA9OR4nHh5Jo6uBXv/qlfe1r/2hLlhxtW7dutRZv6NOnz4iRTY5aJ0ooNnwtzhZBmjxcejDqgIL0F/MC0oGlqKQ7uT2mYicMlR7UdGFDNIkMBYVj4SYedfgTwVQ6fmWxO+2wQURpRJQWsjG1UZL0JpLr9xFjT6xdMQLr6+urbu+rXkiXyCgdAdAoCAIaMwaCyhe6066bzIFA11Ni+FESTR384Ac/sJ/+9GdxboZTwWxrjx07Pp7yOVLnSxiqGpF5rCqWHsnhz3WD/enI44r5SY/C5c9JMM8X5HJAfhlB4YPlGQe5H2cmKzAqykdIHAUgbSISyKk2WmryuIh3TmqsTMkYLdXKWys7qOlJRKbRj8goEVI6j4QhHCMC0zWgsuIsMUwoiaYO/vZv/9YeeGBV7DhxMnjmjFnxtEydk8Zea+A0zvT0r4Ew4nOZoTBUHGmJU3w9XQqTDYr69leGPEzEgzzmseQFwkibSKBGckVZgqtBiBRU5WkH6/ApVVMaNeWjFHbbmn3KxWhIW/LIqBzJDL5XQEcEMOhjBDRp0iQ7+uijg4QGlbPEsKEkmjr4yEc+Ynv2tPsoZpxt2bLF5s9bGA04EUpqxOmly1rnyKHwYpw6gDom2F/1Eyc5IFl05BBJKBxbssU0uR+3wgXFC8V0oJ4uoahPGMVIJBgmoV7e+Iv5Jzm3GSW5gzUjSCrC+XOiCT2eDPIhn5qdSIgRS4xycAdRpXNLibiwm+zUU0+1KVOmVHItMdwoiaYAXpx83/veZxMmTHRfg23fvt0WLjjKG3aKF/LOXa8KFY6dCCqF5bbS4YdQ8Kcw7Iiq6gG5jHSAPB7kaXI7zzdPDySXo14euQ7ZuRzueroVhp3ntT/ZIhKxpFHKYHj9oTfqDd0IqHyp7qkb9JI+zh05CeFnFMOU7cwzl9sxxxyz3/xLHDxqj8sSAdZk9u5tj7k7i5MxTPenaN45gHZV6iFvqHnDzXUovBiX7MG6kal2lMyNrAwo+oH0g3rxoOhXPrmsyLKYXrIycSCxweMz44OHZOM34iXT73TQF27pVb45GH34/zgiTscDQPL3pUXkSnrujepo1CjemmenDzmXd8P7al2d3X6f99qOHTviYcKRBsAluFS4SwwfSqIpgF0mzstAMOx8QDipgySoQ9EXaNhq3DkIy6F4yefx8tORsYu6FJ4bdQSVBUN4LgvwKwzIRj4PL+aZ6wC46bh5eJ630kfcgJfFO3MU0Y3Hpr8KWURyDj5W7eTO9WDneSUbf+36IJtcplhPunbS1UgaXchhKI1fk4t0dnaFH+Q6SwwfSqIpYO3atUEyNDa2syEa7/4Rp46QXj+oPTUPFEXZvEEP1cCLeeBGLO9YoCin8HrlI6woq7Bap9zXVBdiXUZyuTzuWpNCf2a8OBSpOjrJTKWodZGuMScREYWIJEHxPBwwikuE0+dlS2WUTJJP95L7zGdBFI5cieFFSTQFQDQsEtLovGkaZzI418EaAFCDzpH71Uhp4HmDxS0zlD911MH6gGSUt+LryWGGkkG/4nKdeRqMwmWkF8gGRXcyg8NzPY8XNZ3JUP6c2DACbuUhOydj3DWCkT/Fk5Yp1KOPPhq7jK4t0pcYPpREUwDz9XHjxllfEMWoOKxHY3SP9dNAow3TWGuNuB7yTvBYUKfJdeVh6jDFzgJkSyaH0uThebnkzuORx+Ry0pHrk1yeFuTpitD11IP0y9TTo7jcjalXjqJcXu4aanFtba02b958mzx5ciWuxHCi3HXKwCsG73//+6MzNzW1WHPLGJsxY4Y3xNTwg3xcLrqAh1FxiXAIqN/Q60GdCJlih8rj6umgU0gGWzLSpVERyHXkoxmQ54tOjRTUIeUH+PO8VAbpxC1ZkOchN5D8wUD6lV89PcXwvEw5chnuH4vRc+bMsqVLl/qDZlu8h9Xa2mbz58+3qVOnViRLHApKosnA8PnVr361PfLII8Y3gefOW2hHH73EY9JLh3SUOCTmIx22RJliEUZ8OjjG6dLBDVzVS+cUCMPknQfgL3YOyclGjzo6UJzcCgcKz+Nx50QBpFuQPFAaIKKQ4Xpz5DqUTmHSAerllcsOBekcCsX43D90Wg9zopk+faotXrw4dhznzptnc+fOo1DlCGeYUBJNhg0bNtgrX/nKOKTnzd4WLlzsw+l5xhvcVQzQ2fqt0fsEHa+xQkA0ZMgIOx0C40XBtHDKezlaSAXIYNTRlX4AMsL2W5Ib4oD8/l9lNJXiIswhdy4vFMOUdy6TuyODyjUSTlRFRQCyUrmBbJC7c0h/Hj8oT0c9GSB/UT4HMvuLHwrdPZ12zjln2TOf+czw79ixjUeHtYxuiZFNiUNHSTQZ+E7wa17zGuvq7PRp0oAtPvpYmzVztqVXnKimwZ06oRZei0vx+Gvv9kAs6X0aCKeJ06mcXPUwCKq5pSne52luaal2YsJdifG+TzqQ5t6YsrmLvDyML/0NDCQizMvm/1LRKstwxKlMtbInKB2oxnHGpSqblIU/3JySVhpk0vVhD6WbYOnL8wP1wupBug9EtgjSKp9i+gG/1rZWnybPnG6jW5ptql/HqhX32bP+8A9s3lFHV6T2j5iCOdKuWokiSqLJ8Otf/9re+MY3RidnFHPMscfbtGms0aTupcYKEdSqjbAUp/B40rtxT4QjQ0cExKcwRgU0+tRASROn6z1OJojGITcEhVwQlfsxIeudPT7n4O5qh8eOsqXOpbLlhrgauJ7MnxENtsDx/QGvD8qdp095J6JRXtgiIenI9cmd++shT3OwQEcxPwGSX7tujW3fttV27tppUzx+3JjRtvTpT7W5i46JehzlYf0Dva4okWxXV0+MWhm9jhvbZsvPPCPeh3OBpLTEIJREk+EnP/mJvetd74oGRKWccMIymzhxsnGqlHUZQVWWN17ZdC46HMCvzoepB+KlL8ni2pcggGxkksvT0JEbBpx00nqRpmhBTD51a6pM49CneJWlVibsSl6MVNBfzasmm5tBQLYSHunckBo300Hc5J2jqD9kC2FCVe/jRDFd7s/zbWxie3u711OTdXR0WLOT6Gif7nZ6RfQ3NNpAT2/UY29/jzV6XfZ098b1BNl7fba1jrbXve5PbfKUaaGzxL4oiSbDt7/97dh14hMCTGGWLTvF5+hjY+pEw6zX4PMGm8dJXvG5Oz3pa+EJxEuu/u5MHpY+WeEJyNf/SJdGR1kZQ1+Kw4hkgojcBClVpm7x7WEfrdTebnZiQpY4Lw8jpCCLSn7V661kpZLF1aRihRsZXS9ut0JAZSRcSPH71vHBoqgr9ytf/NQD6zJ7O/baxAnjbZSTSa8/XNiFbGnkZDhEyX9cFMTOVNbT+j3goN/kSRPtDW98fXxKpER9lEST4atf/apdeeWV8WU3fizu5FNOsxZ++YARs7e24lNZUAOmQwnIFhs6DRrkYWrwyU554E66UnqisJNBNk27KkkDuU4hqUzp0BnrPJmeQKZDREQZEuFUntr8ZEt8BS8REkmCiCCkIKtU5njhMamKzGNxO0OMlip5a+qVyo0uXXN91GQPDMiDodJEed0kEhxlPT3dduedd1hnZ4fNnD/fiabf2ljE97juvh6X63Ny5gBna1x3V3entXi9cB1j28baG9/0Jps6ZUocjRBU10DvYvEnVMvmVvVdLf8LqUocdcaVRNqKSRFJUz+vcIQ3H/km+eSupUOGjYl58+b6PSO2ereecJREk+ETn/iEfelLX4qpxvjxE+yUU0/3zuXTqP50Q9R4semEVF3+tM6rUmGSzdPksoTXk8WAXB6Td0bJMbVzyYjfF7lez9/TI4X6WMtBJGUVcspHhOI+tyWQrJAJonO9FULSyChGSW4gp3B7ODYyTD9UB6QF2MldKz+2rjMPU5ocCpOcMFS4oHzJh/K1t7fb/1zzPzZ9xnQbP2Wij2L7bBQjWSeXWfPnxsNnrHfS7dt22IOrH3JSmexk0+XX02Rz5s23O+6401r8+l1j6OU6dQ3UM+s7/bFoP7itiJjy8qpNYbhf1D/hClMaVMDdyV2Lj3QV/QqnPLwkPH78ePvwh66wBQvmR/yThZJoMrznPe+x7373u3FTJk+e6kRzmofS4WojEeKAbiiNQlWoBgJkAxqyoPBaI6zJAfTnOiQn6GSwyoEbOWyVpagTP4Y0Si8ZxQmSU3jaXao1EcnWrhv5SsOOf0k20vsf/9JuG/XALlthHSnswf48fyHlVd+WO4fS1osrgjwhGs5RrVixwu659y77/Oc+a81e7kvf91476rijbdq0adZqzXbHbbfZ1Fkz7G//9gu2dt06+/N3vMuWnXKqx8+y66652skm5cm1CHEtfrt6enuq9wBbZSNeboUPincLN1N6ZPVVgfTJjNS2uB+EIUfeGGS5f9QvQCc35L3vvcxOf8qpEfZkYUQTja6cNskW8Zt8+PuLX1wdN2z6jNl24oknxY2LDlPZJo5GQ4KKWx1ORvFyE88NVni62bUOIFlB6bAlm0P6sEGetqgLfw75SSt3LpPnpzLn1wcIIwU+wqrpsYnLwzIgm7bopYv6qISTr/cm4glPeSdCSuSTdt3UeQhnoRsCcwURFiOwpDBRXaUa0FivrmplTwvpe/bsjlPBP/nxf9v0qdPtH//pH+LnkC//wGX2xc9/ydrGjXNd/bZ75zZ7y1vfZe9817ttwvhW++RHr7Rv/egX9jdf/Ir1b9vio5w9PppjYZ7fAKOeyY21LieY3vRVANVPtb7dz8gFAmJNTGmijF4+6gJZrp8wiCZ9VYBrrhEI/7huRpOxSeDh1AZupmaM1Hu6e2z2nNm2ZPFiMhkE1Y/KN5wY8SMarp567erqsD/+41fZbbfdEWGLFx9jixYf7Y0lDVNHNaQnFNXFDYyb6G5Vnzok0I0q2gJ+TF71uS5QTAOGSqOw1OBqUD5yF/UrLenkz+08TS6PRt77AtGIac4eH+s2tSwijZC7U9KaXh0FUEcBtfqsBTa4clyMigDl1gI2HRh/IqYKQRFfMfqZYuLQiy0CYt1iw4b19oN//zdbu/ZRW3LcMXbmmWdY794O27Zzp/3i59fY6DFtNuBNoHtvu53uU+pJU6fYmDENtnnNJutoGGNf/Md/sEU+0mkbXRvJ/L6CkdvEiROHvL/FdjQcKEc0DvrArl077GUve5k9+OBDcYZmyZLj7KhFS1xGUxyenuEcdCN0c+pVIzev3k2LzlW4yRg1fCCdhBeR55XL5enJV3qJx49NBwPSIRtIHqArT6/wsN3Pe1/h978BeAK3E01808rdum6lVXqgsMF6U7nQzV/SmFCTGWyr7MmOEkQc0RCh51B5sjPicSLyiLSL5iMkf+qz/tI0ii1qtrX32g9+8D3b277Lmtuabeb0GbZ9/Q57dON6G2j2UcKoZhvT3OaT6Ea3B2zchBbr6Oq1LU5EJyw51j7/d1+1aXNm2ewpE+OzE/WRyl0EZda1HCjyustBmO6dkOvH3r1795DvcA2l91Ax/NR1mGKnN5hdu3Z7Racbk4bptQ43eFV/cKPAn0ztGym6WbpxRSMduAXcIggBfdIvN/FyA+kjrZ7sQOnkVrp8nUZpCc/zyoFM0SaPKKtnJT309fiOFTtIbve56fd43LGe7qYqW9EjJLeXJXkdLldx5UBuwDMZcHaDnGLnK8rCSMZJhDLF9nOy/Spjq7qnp8/2dnR5J2u37Tt22abNW2zjhk32yKNr7OHVq+P8zGQfpTT46AjSuXflA7Z223ZP3WjNfb3WNMDCb581NDdY10CPbdi5y3Z39MVhvzHWaRsffNR8VuIXz/3nKpJJdVm7qnoo1veBINXD4HQKU70qPpejfliPevTRR+IkPIdUb7nl5rAfeujBatrhRkk0FWzfviMaGOC+jGkdE26gG8VNkJFfdnLX4kDRjR4ZQMcGigN5PCAuN4qnwQiKA8Tl7lxXDsUV40U2qcPWyp/LYtfiajIAnwwldAqIEQT+eH0i05G7E/ml+vAQN/2x7tLc0hzvHLEmQ3hKI5PSktu+5Ul2nkd+TZTIadnL5noJctPS3OzXP2BdO/Zax94u29Xb4eXqsd7OPuvudSLu6bDO7p3W3tdt/X3N5nxjnXv22NhxY213e7dn4g8nJ7w4ZxPEx5EATg9j0pQOo+ld0VA27Hz6V5TN41hzyf1cX1Emj8PG9Pb22E03/cZ+/vOf2ooV99nq1Q/7SP4Bu/XWW5yE0idNhxvlrlMF119/bSwG8xlP+v/ppy+Pk54aBtN4WHwT1NGpPhFGsSrVcIhHvhiPn3gBP4Z00llDsUPV8lM6QLxk8jDpzdMLkgOSkZziBqXHnXVap6ZKt/d6cUcs6lZkq2WgJyfxKvJ8QyL0oYv6Sp/t4EVX7gGfbOAFxz7v8MgykkkjGx9lpOxCH/cIO9ednMmvXwl1ySTj5Ea+EyaOtV//743e0XZbl496Otdv8hFZv+2dOtls7FSb0D3Kxpu3heZe63UCHHAzpaXNpraMs2OWnmpnXPgSe8ryZTbNn0/pOvx6fGRHtqkeGDGm7W2gMtLx5dc9V3vBHlSHoTfJypYOiFrI25x0SA/XP3HieE/THOtRpCOuq6sr9MycOTMOqQ43SqKp4L/+6z/tHe94h9+MNG067SlnxlmadPO5Ud6xKo0E6MblN18mD+Nm48/D5M7DZMudN5AcIqxcTyrj4DIpTvpkK1w2KMYJNFjiZBQfFAsTOAhhqoQGjNNxrZYqej1hsutAeqt5s8jjmrgH9957j91ww3V+fQO27MST7Kyzz4nwuNxQTd2iI9WVEJ0MwnHCSnBXpSycjyFRn99n9DSSzNNPnDTebrvtJlu8eKGdcsbp9uv/+A/ruu9Ba5qy0GzpWTblmOOsf88m69++3rq2bLSJ3btt7pzZ1jT1BGuffpT1NO+xcR2bzPknvsoIOXb39ERHZkdpTCs/XtcS0xauQT+vzKIsoB4IIx43n6eg8/OrHFFEN9Rsn5MVcuw8MaJBjryQZfubtOSJDPXC+1fUDeH84gP6Fi5c4IQyPeJYryEtOvh9+eXLl/t9b44yDSdKoqngX/7lm/H6AUPeMWPGxoimpXLjohGz4unQTcsbNm4adzFcICwPr0cW2PiBiEP+YnpB8ULoDDHJO9G5O8ILssV8i/qjs1ZIMkeeLuzk8ZzQ7yONTF4jHYVxHkTp9i0P9Yt8YpGZM2fYd7/7Hbv7rtsjfsGCxXbBc59r48aNd7JABnlMkidvQfWXwlNekR/lzOo2jgihwws2adIEny7ttFNPOck27dpmK++/xzbfdpdN391orWOm2sDcuWZTJ1nLzu02pqPDrM0JYvYc2z1+vK3fscV2PnSntVmP9Te32ICXL84WeV4d3rGZ9rW2tTqpNcRrDv0+HWO0AzFMmDjOO3+PdXZ0BTmkcy9pigN6eru9Hfo0zcsKGXBCu31ve5AJZDq6pfZrHdQtOjBp63uUj07GhM2UCDeYO3dOfFFQoxkMuvkkyqWXXup1PCHkhhMjfNep1sE++9m/sc985jN+g1ts4oTJdsqpT4kdChAy8R2a2tpInha3jG4ckA2K7qHkFQ5yGSDZPF4gnA5WOcRcTaPt5lxWIAwSIKaoW5AexUlOYbJx5SkJq06pKvH51jdQ+gTpd6IY6PWRxSJ74IEH7J+/+c3YITrbRzN8mIohP7INfj8SUfBfUow+dMgIcud2lNdtjvDDcTNmTLXFi+bZnNkzfQp1k61YtdpHPd226cGVNttHDm2NY6y7aay1jRsb06aN/Z6/TzH6+js9rs/6uhkV9MfUrsnbCETDQbmuru5YZ4IUIAPWR3qdPIifOJEOncrQ080H1Qd8lJLOyvDLDNxPFpubRzf5SKk2uoxw1pMqZMNordnbqggHmTS6oc2mRX70cgyAups7d3Z8snb9+nWxCE4aRjXHH3+8XXHFFf6gHf5v8JQjmgo++MH/a1//+te90ptt6tQZdtLJpwapUD3RISpEE52q0qABdu4Gkol0Dt18brhGCUXZHMgByeQQ0eXI5fpweyP2wKQ79cZAWoz1aYbI0k0QDWk8STRazxt/vXIW7XpuQJqk09NXgomHaNBWk3SQLuTRwfWRpw/5vXPxM7U7drAbuNOfvv3W2cU0gj8tDCdN6XKSW3kXDfnnbpDqGfeAzZ83y55x3jn+kGmzX1/7S89zr3V7Gbb1tJtPgqypp8/G9zVbX9Mo62lptJZRPhVq8JGKFwU9fd7xGT96K4l6zEExGVVQzjTSqDwUyD8qxEmpkSlhqmvqnriQ87pAhnILuha5AX61VyHFAF13Oll+xhln2LnnnuvTJup2V0iQltcsJk6c5L5ayuFCSTQVvPOdf24//OEP/WawIDbLlsap4BSXbqw7vMWkG5a2d3GrQagadeOB5IqQTL00IBpgBbluIU+fp6UL9qf5SYrzsHiRMeJobq6b4TJl8ni2ntWI83LiZ61FYfXyw5YRcjfw5u3l8bBKun3Km7u9nFpEhmg8ZTy1WcPgSV39gTfvylRPEk3p8/Ljzo3C6t2nWli/j2bm2yv/6CIb0zLKPv3RD9qWjRtt/PT51jZlmrW0+ZQDfb0N5lSTpjXN47x3+hSngRGM62lyPZEZxwcw5MlOmgd52ZhGkU+vT1HSIrZ3enay0OtyPb2MYGpkgR2XFw84XVv8H26Qrp97QFwKr12jG88PPWkROm08kP8555xjr3jFK0K+iLS4XtugGC6UU6fKjXnta//Err/++qjkuXPn27HHnRANJt1MbqDfsHh5Md1MTE4IguSB3NxgQD55dROPv9hJBDW6PAzk4Xl+IYeuWlA1bdGOTpb5pQeba2U9IUeeD8j9g9PWwr2EaQXF84i4CE0dQ6ieCvZ/WXDIk5pyioBiK9pR1YdMpfw58jDcRTOojCHab4ucaP70kpfbuLGj7cuf/7Td+ttbbfLshTZ39nybEB+06rG9Db3W3+vk0esk4WExDer2qQcavC93dHZbx96OOMzY7XIQUktLq8eOire9+/p6rdcN5B9vXVcOF+3du8d27d4R19jW2hYvPkKyIM4GNTD1oj4SKH8a+YmQaA+01URO6Zo8Ltxcb6h2k9Kcd9559vKXvzx0PVkoRzQOPg3wqle90u688y6/PQ22cP4iH7Yfaz3eKEA0am9OIpbBDTVVX7qhgzua/EUiyatcMsUw+XGDoh/kaXATw7dUPId94jAqf8TQgT2F0rniqm5syozJyTTPOwfhMgCdcc3u7c3SM0oCuR7cKutgN8n5L8lGOeukU1g9HQC3jOLCHbqJx/iIZvECe92fXmKNPjr5yt99yR5Zu97GTZ9mW7ass4k+oJk1bbwtPnqBtY6dbC0+mmnxeWD3rm3Wt7fd2tu7bcuObtu0a49t2bXDNm7eYp3dvdbU3OLk4vrTRlfkB9FwA9I2eypAV3e38QoM5WIniEVbdoggjnQfnMpIH+kYnaSkweIeGNflLmTRgU7SAtUPcqzRkB6SKYnmdwDec3n1qy+JXz9gyHvM0cfZgoWLvFFUbiqorNEANdi84SpcYfJHhxsCSlttIA7S5CA8DyvGV8lDciz4SQad7mbYnpNk1a1pFTKV/FPDrjXSPA5EHhUoT8lgD7penrpuVXVX1EiHbOKVPtlpHSPFUaaa/oSauxaWIF2C/EqPzfWTDLIZ8FGS144tOsqJ5rWXeIX22PpNm6xtzDjb8uha++gnPmh7unfY1KkTbNnRR9usGTOtlV2Zngbr2rXbGnxa19nbabvc3t7eZTt377EdHt7R0+sjlS7b62FMuXnfqrevx7o6u2KLOr8flEk7QHkYu2us0TCRBKRBrno9/lddYwtg5/WR6jGZNHVC9pOf/GR8hP/JREk0jpUr00fJWXjs7um3E5cus1mz58R8FqQG4IYbXPH7/a8ibnp2w5N8suUWaCx5Z1Qawoo6cEv3Y0GyLlyRj5Li9fAaIRGX3C6fEY3yw87Ll4eDemWvF6cwd4SV1l2SvxJTk3GwtDRYHwY/5Ul+wpFBF1CdAaVN8YPrTNeuOBCLrC4T0w7/W7xogb3+T19jTc6GG1avNutid6jfPnjlh23Ntg3W3ddpo31kMWXiRJswmfeEGq1zb6d35T5rHtMUb2wzRmzfyxkXJxkfJXd0OKk4WaieuR6VRWXPy1m7vtrDJ4z/UWthhzzp4v9qOumRO5l0P3BThxANeiGas846K+KeLJRE47jttlvsta99rQ9he627u89OOeVUmz59RpVoEryauHnhZEqRbroag9wAt5A3GMnk8QC/jHQonZCnPxDk+eCmgSttbhfzlluQO7cll4PyCsQV05HGn8XhrobF/8nf6J6i3uQnnk6ZbFJ5cFUWk6fDzbWqPPJLDgOiu4dO0vXZooUL7A1ONGNamuy7//A1W++j26XLz7QZCxZaN2sqseibdodYN+FgYRCeM2RzY7On86nOqCbXSz4op05qZcvLIzfh9QxARu7q/+kfCfl/nzSyBRG06gyglxcqdU7nyUJJNI5rr73G3vyWt/iQ1j1OIqeddrpNmJBOBathqLFy0/x2xYiGcOJzI6gx5TKkz915A5HJ5RQP8NdDLpfrzeVxY3QN2EB+pc/dIA8Hqgcg2WKe2Lr2PAyw9olfyNORwn1VWZDrlr8IpZctmcfyY+tFT3Z9lixaaG//szdbS/Mo++KnP2YPrbjPTjnrafbGd1wa8iUODalFjHBs27rNen2OzROKBTMOWtGh1KlolHQeNXggt2w1YJDLSQeQDEPYXAbk6QHxMrm/HnIZgK5ieSi/rkFy+TVJXmkxqoN6YawVaL0AHbkMkF7Alrr05JAMdvxhZ+lArrOYXnEYRp95Hnm5gdw14/kS5aMV1jn27t1rV11ztd1yy612zjOeYa9+y1vtGc99XqQV1q5baytWrrQ97bsrIcDz8f/ZkKzd6QTl/XiQl3E4IX1PhO4DQUk0jk2bN1U6jTnJNMfw2G9JNHo9nYXBC5WDG0W9jgvyDiR9xOcdg/jcLX+uB+TpMUU5pcXkULzsHPX0CAoHksuBP78ObPyDwirhMqCoVyjWiUCY0siAXB43ULpcVnIyUSq3/Y7Fqdr29r129dXX2I033mBnnn2unXXuBXb08SfZqlUP2tbt20LfNdf81D7zN5+0f/nXf7Yf//hHtmP7Vg9Fg/8/uFoCefkPFKSRGU5I3xOh+0BQTp0cn/rUX9mXv/Jlr41GGzduoj3ltDMG3RhANeH2Zuot2m8WHO1RCs+RV6ncuT6RhRo/YTKA+FyHoHjiZHLkOuqlV5q8cwpKJxT15PH13MUwGSAdvHwJRMj59bMjhS2/ILds4nNCkW4hT0uc4iWrtIAwTF9/r5199nJ75ctfZg2j+mxs2ziPbLSNGzfZB6/4kLW08ImGgfge8PSZs21Mq8f39dqcmbPi2D7reVNnzLDGeD1i8IOpREJZKw4aS3rAeWNqTr/jA9QwMWqU/DHFil2LDGrIecMuGjqYSARDGNOofCqFrTh1SMljVB4M/nogTiimxc51ywCVLw9XHMj9yruou4pKeAxnCpC89ElHPZNv52KEol/Iw3PdefkiX+zwgTQ1Ztt57NiJsQ0NOMB5x+232y0332I/+N5/2Mb1m235GWfanNmzbNqUKdbsc6/VK++3//z+d23nlo2eouxOQ6GsGcfs2XOM78ayljB6NETD07bWiFODpQMn413Vn3zEeyNmjh7EQ4fA7EsE0bDdFKFwdQbJA9xFHUU5wuggcksG5DIAf+4GSoORnjy90uTpFJYTkhDx2G5i4ddtr5F41weT6xGqYZiK7kFwv9dCrH9IT4598i/o1tkRjOqz6ncDeMfnN7/5jV1yyavtox/7sN1+x29jlHPV1T+zrdu22vbtu31q1GirH34ofiGBD13NmD7VJk8YaxPGNPlorDdeTtT0W/pL1DCiiUYNgvMzb33r24wfjQPaRqVhVhGtHNvjsTxd+p0eAlJXiGR1OkKx8ygMozLIkKcWWtUxsOM7Kg78OfDT6TGKU36Dyu9QuPLOcSBxACf5VMtVyKPPiRZyoRPjdklqKBLm16Prq6avlB3k+uOwYdSudCa35JEDqgPKmpcNP+FgUN4VN9cjuZ07dthNN91kv/rV/9q6tY/a9TfcGG2DN50hHr7hwi98rrj3flv/yMN2843X2X133mpzZ061O++609ZvWBf6SuyLEU00NK5t27bZypUr0wEm96cPBaU4NVz+qqj1hyqirUd46qS5UacpmjwON3nx9A0t9dJ7OB2iiMi6IkM8Rjrz9EDp5c+hzqZrrl67G4AfwzUWoTwwnqKORLomQfpBNW34Bpct8mddx4MUzg5RKte+IyohD++Nlxhr9aHwCPM/DtRJhs+3Tp0y1X7y45/aFVd81LZt2WHtu9utfc9Oax3THJ+uaB091kY3tljPzh320F232j03/8r+94ZrPb7JNm3cELpL7IsRTTSAn5648cYb7eqrr46nZ/qOx+DGOsA+aHz5zeFBiss7I6baGYqdqBKOjKBOr7QCbukNpMKkMx+8XuCm+vFv74FJc0K+1kOeQ+nFqEygWE4gObkFuXNZUE0vUXfjlLyITHIyhEW4i8VIyO3c8DMuMZj09KHfkXTWdAARLFC4/Hm+8gMPqYR73j7K2du+N9rCww8/HB/rnj1rls2ZPd0aBrps4cJZtmzZ8bans9OaRzfbzs3rbVTXLpvsBNTY12M97e3x/WBeY9E1lahhxBNNa2trfD7x1ttujU8vco6GRssajRpm2MkZwMliMOH7a1CpEacFTXUEGZCnxU24bMl4QLIduMJ4GO/OeHN2uZpeQTqUZzGf3Chd7s/DgeIA4XlcMX3sHnE2hdK5e6DXp3w+asjfq8pNPX3hd0OOyV0nrkLmQ6EqW0GRiMgbEM5b1Xx+c87cuXbRRRfZsccf4+2ixeVYs2uw8ROa7dzzlvuIp9m6erqsp7/bNm56xJor7zq272y3b/3jN2zVfffbf/7oR0FW/KqGwMOMfHLkZcnLeaSi8UOOintEgtfxv/3tb9vdd90db9YumL8wRjWg2ji8HcRPd1QbxeCGQZgabtiFdqNOBfbXsIpxSgPiUwmVrBPFKCzlLdk8HyHXAyQvk4cV3UC68pFaPQwVXh2huYmPQoWzph93EBTuzES9u0FrLg/4XCbYJ7xOGQjDICs3iHy9bKMaG+Jrfjt2bLMXvvD58VW79vYOH0VxkG+3LT9zmb3kohfb+g2bvSxN1rlji217+D5rbWq0jv7G+PLeBS9+qU2cNt1WPviQ3XvvvbZu3Tpbu3ZttKFf/vKXtmrVqjgUyC9MsvhcLHfRf6RhxI9o+MIYH1XiqcPUg4an0QymOhrhiVxtw95ga55A3pAF6cgbOG7+Qke2QxV5IFOxhZAvNELKUtUXulJe0lEPQ4XnUF4YkUOeTm7JyE04RmH7QDqwXSRPW3TnJofnkOzIyx2V+LzuqnUYAjUU/aAa5mr4YPfq1avj/t9yyy22Zv16ax070ebNX2QzZsy2ceMn2Nr1G61voNkmjG6z9o3rrddJg9+LGjdjjj3nD/7InvKM8615bFvooM3w3V7W/r7zne/Y1q3bbOPGjXbzzTfbP/3TP9lnP/tZu+GGG2LUU+9aj0SMSKLJG97dd98duwrjxo3z6Qg/GufjYY+mM+edjR2myn6K+30qkHXwiPdGTgNLn2SodTwWLfmiGt8Uid0sf0STBa/t61OUIBY93fA1No9M6xJEE1ZxFw3P9PiiXgaVNzciyxy5XLGhEwYUjl/XJ0hmH1k3XkNpVydqK6299PJpBA9Dvq6J1DXkZaKRxognDORaQ65DyP3FcNWD35EobLOPStht6vfRLFmud0KxvlGxu9TZuccmTppqPX3jbMvWHuvpabTmhm7bvt6nTaOabU9nl3W4nhWrNzoxjbcJk8ZaZ1e7j5Kb4lvB6OMXUGkvHJ9gpNzb2xs7W1/5yleMycSXvvSlmGqxKVFEfh2HO0bk1ClvxH/3d39nP/nxj+NDQ61jxvo8fZ6TQlOswUAG0SSj8yNNpyQ9jSA9TWkIeYeMZlFJl4CdTIhV3volvdLJVjdOOjzMDe7wY6dEQTLhJx+Ckjf05A1TZcsNiOtxEC8ylZ0jrieTEXK38iMMEyQYIRU7sto3rdIJBb6sIvR6XKTLypMj8vVw6Sa+qF/+Wrjr9f95w/nRR/mlyj3xcyP8/Mi6Nett25YtPsVptlmz5ti8BUc7YTZZW0uz9bZvtjX33WktnsdeJ41712yxabMX2YlLj7fW0Y32re9813bvavdp0zonrTXW2d1hc2bPiSkTeUPWW7dujZEPIykWnm+99dYgnwcffDA+Wzp27NhYO9T1yD6cMfiOjUBs8QYVP6vi7jR1ogmyuZN2cDDqaLTRdJBv8I1X403yEeJy6bONfK81GbZQ/dnusomgIkmkAeFXoAO5MBW/EGEVgotITx7uCqQPFDucnuaE4U7lHXwtQp6uCMUV00sv5asi6qsGZHLd0lE0OZDO88x1VOUrSRSv8DyvPDzus5NMdze/h5TWTSAZ1lF4yIwe02JTJ0+xmTOmecdvsfvu/q11+Qhnx6YtNn3SRB+VDtjWXT224sE1dv2vr7MbfnmD6+PQp4+K1m3yadMqu+2239qGdRtjFKOyQDi0MwiFMEY5kAp5QzZ///d/bx/72MdipHPXXXeFzJGAEU00dArmznybg9s5yofR8dnECnRWQx2Ujps/TdVw5R7UkBvVmAkLkapMIq5aGMAfO0mVCNmARV/t5rCDU5tGpDDpAEO5BcIw0l8kn2J6kZXiZDSNSuRcK3eT11mjG8oY5fSwqju7ptwNKpPSMLjD7yKYNKWsGYVjJMs2uPwRh6z/AcoLyFMGQP7jx4+zmbNm2vLlZ9iSJUviB9m2bvWHj09/xo1ttYcefMCuveYqn2I12OSJ4+zhlSt9WuXX7YSxYdtee3jdZlv10P22YeNa19tkT3/6ebGhsDt+xz39ztK2bVuri8D6DgxliKm2lw0iAtQlxAPpsJ7zb//2b3Hi+EjAiCYabqIWgelIvMVLA1DnE9S5csivDpgaLyb9Ng8Nix2taNjRy1I3UiN3R7IFF/HuWuuUTiAYPggVM6RKGUJfyEdgNX9suSWHXQ8KV1nkz+XrhQHlAbnk+QnhroRhKG01vrLN7WPFsONaK9cZ9cG1ibRwV+xIXdXnQWFSfO0AX3LHrlZFLqQ8LFwVGRBl83tBB3/ucy+wU04+qfqLkdu2bbe5Pn2e7iMZRjrnnneuXfzSl9qLXvQi6+3pti3bt9uG7bttW2evrdu63Uc+Tdbj0yN6EudrOEG8cfPG+MG2vXvb43vU3//+92OKfu2118bmA3kxVacOKQP1KEKXoU0yuuLoxZGAEb29zao/i3LMlcHUqdNs2rTptNPUmlMTj0Yg0Ahy0HgVJvdmb2jMt/lKPh/Qis85enje2KMTqDtUw2r6c9nB6QaXIQ+vB6XVNeTlkMmhsDyPoruYRvHYGDoOkByxuHK9eZ2CXCdu+ZGLkRvuSo1VjYdrtBeEQyjl8zhGVbiBypODaewEH81c8ppL/P702r333hfh/JDaeeefaxe99EJbeuJSW7BgoU2cPMlHN2N9pLPZ7+0GH1U12tbd3faUs860/++977ajjlpoCxceFS9b/uZ//9dmzJhlr3v9a70tTfWRznqfSm2wDRs22h133B5b34xYuD42IFiPwS3CAZAM9cT06rTTTosv4h3uGJFEw03k5m7atMm+9rWvVW/y9BkzbZLPy7WV/VhvaNMJ8g6Dmzn/jTdc53P0+2zH9p02adLkGJ6jP6VP+ScOq3Wo3JZbUHkjlPzdyt3JKVfSoXJJH/H1OlwRkg93/F8Lw191V0yOvJzVtBUDEXhkxBfLhhnwaIw7K0hh+KsEU4sMkF+YzJ1D16vwSO9OLKY0J554gnfkU+N4Ax1+0qRJtnTpUlt+9hk2e85Mmz9vvk3w0ccNN/zK1q5bZ3v8wdTR2W6NY8ZbQ3ObLTx6to+InmHPftbzbKePVJpbGoNIzj33PLvwwpfYc57zHHt49YP2q1/9xlpb23wUMzpG0bQ7ztlgGOHERkRra0ytIBlNqSjjsmXL4qdqD3cMfqyMMLA+w9A0EYbPj1tGB8nQPmm+0QEqDRijhqvOIRDHqIXGMmnSxBjRAH5hkaEzcJHQG7rdjcmBDkG6B+XhCZEhrDpd8HIjkacFxfTES2aQzixscDgky+TC3W4854pJbkBd0CFy8pKe6vQHRZUwUpEfdT04r6Qv0sXVCH6t/od+8hQRSQ8m0lRMESme5j04L9KzlkKS+Qvm2d133+WjjQ0+Cplhxx57rC1adJTt3L7LHnrgYXvk0UfsUTd8vH7lihV22+1328z5J9i0OUfbgkXH2KNrNthffeoz9vOrr/L702u9nbvswuef752q29aseTh2slY//LCxLMPP/NLGqDPIBhJhZMOu03XXXWc/+9nP7LbbbrP169cH2TASZj0n3/ZWXR2OGNHb23feeWcsuLGewpY2n4to46NHlfuZtrcrnaDQoHHrxkcD9tEPDeTY4471IW93POEWLznG5s9fEF/tA04RoVs/hAakI8+DsDyvHIqXEfL0GI0aBHVwGSF3A/x0xyAU4ogmLMId5FnIt1iWKipxOSRPefK4cJGXO5BJfxW4A1FkSJOKtK9u+bFlUqoE0tDZCW5pGWXnnLPcO/uemKIwfabzQ2x8k4iPjUMI9957t61dsyYeJF1d/bZw0bF+n5tcT78tO/kUJ5xjbcv2HTZ1ymS767brrLFvpz38yAb71rf+w37xs2vt9tvvCNLmR+V4iPEwolzkif7eykIw+W7evDmm3BwehGCYxjGiOfHEE6vXwzUcjhjxIxpudGqAbiqf8DRvRKmB1r+pEAppeDphAB2HhrFh/QZ78Ytfam9+89vsaU87N4iLGdioeIqmN7QhJZA6Qq3TyC93bnKosSlcfmyZev56QAeNPM9DzkH5uhstEE5RH3IyIM83N0B5qd5ApPU/l4p6zOXDdrXSHR6H4kGed26n80pKpzjy7ol1D97U556Rp4gPP52c7wOzhrdmDbtJo2zrlq12zTXX2L9+65/s7ntv9UZgdstvb3dCbrSFRy2ynbt32fYtu+2UZWfaa15zib3oRef6qKTfJk8aY73dPhXqHxWbA+TB2sy0adNsypQpMV2CdFj4pU6YRiHHsQtGOJxUPhIw4olGDZ5hKrtF6Rszjko7ViOkgwCIAhQbMIbGz+cf77vvfuM7JpwOhVM0hNd2OaaYXh1H+TwWkK/XKXN/jmK43HneVVTKlodKRmF5+hz4iyYHftJW9VXc4XfR4vUPiq8AHdy3PA8ZERkmh7ycaert67ZlJ50YsvXyS7uGzTHi2LiBaXCDjzIesd0+Sr37jt/a9ddeYxc85zl2yav+2GUbrbtjt21c+4CNHd1ou7dvt173X3TRc+2vPvY+++d/+oJ99m/+0hYfNTNGT0yXMBALhMLaEITD+hCjHdoXZAcBffnLX7bLLrssFcyR18HhhhFNNNxsbipPrnii9PJLCHSEfW8qnRoQTyNXA1WD1hORdJ0dXbZnN/pqh+KkTvKE4cbknUP+3OSQHzmVETdQnAgoR54Ot9KrHILCqzs5brMdrW13jKC00oMRchLMw0FeFhn/z3mmdv0Kl7umL9mKx+QYFJdWd1xHilPY6NHNNm/u3LjnxbIB5BqbGm2dj2o6OzpDbu3aNdbi6VpHt1pD34CtWrHSjlo4zxbMnW6tzX3W2LvLpk8ZY9/85t/bP/7D5+y7//JVu+6aH1uTP7guevFLbMbU6T596ov2xjSNRWAIhzVCwiAdRjP87jbrM1wvtnac0rXvW9bDBSNujYZGpBu2aNGi2D48+eST7VS3W/0psn37tniSIdPYyNrK4IYv4FaHyTs25ziSX8SEDHnWOkEKT3beIXP90peHDQWlz43Ci8jjgfQrPI8njvLVK5cwVHoZ0gPkMAqXG4Q/HOGti1oZ3M7S6h7UB3li18rBouy0aVPs+OOPjXfcimVJ+eAfiJO5/ETy5k2bbYUTCz/D0ziq2Xq6euyqq39uq1Y/YJOmTLTp0ybZglnT7Bc/vc4eXbvLduzuiK/zrXrgASerDfabX99s//WTq5xoGmKkwsiF6RNlh8R0Spi8eYgxnWe9hnZ55plnRrkOd4zIX0HQJatxCXv96bVy5QN288032a+9caxfvyEahjpb1aZRVNIoHF0yQA0HmwaVwj19f0ZYbsXLlN4RKslw8V9FhsBavtIvd8grYQHFcOWJLX0Kq6cjjyvKKW1uVAe4sZUHULj8xAHCqzpDvkLcfODLRatxGNJX6lGjqlyn7Dwvzsp4iIelg5io40XJp517tp207ERbvy7t8CCrvOjoDZXDlT//2S9iIZ93kVasuD/WdEY50XgCG2jssTPOfoq9/o1vsEcfXGU3/uSntmHdJjv32S+yLdu22b//+/estaXBZkyfYt19vdbR3WBbtu6JKRmbDxim6zzUNA2knTCimTVrlj372c+2F7/4xXb66adHuQ53pDs+wpAaXa3TCG2tY+yUk5fZG17/Ovt/f/Npe9MbX+83fmwMdXmbF1Jg6zZ2YKIhpw6WQ/qw1ejV6VgfoAF7bLKjx7jh631xKi2FV79F7O5UzKQToEd5gKHcQlEe7C8sD1f58/oSclnJ5bKKyyEdilOaACTiltOV93YPI87DsKuniD19vQarfOUWGFXmr5RAPKy/LFl8VBw7UP50cBmXsmYngb0dfAaiz+W6KlvOnPL2++hl6fIRyOJjltjzXvTCWOzfvGmn3XXfo2Ytk2zmvAV294oHrLOn3/Z0+RTr4S22dbvZjp3dTip7Y5rOlAlbIyrIZfr06TFN4sG2cOFCe9Ob3hQkk8p0+GNEEk0ONdAixo5ts5e85EX2sY9+xM4++4xaw/Q4TN6wccsANVrFCXBS2t3iiU+4a3JyQSQ9zdOTNHZLKm6Rj4DOHLn+3J0jL5vcucmBX3lga6RSlFO4IBlMHpeH1dNFHpEfxjtx2G4qvBthfBoikYynq4wIVcaavlpd13TWygAg+nHj2oLQWCPxmKoe0sebUl73fDt446ZNntWAj0K2OTGwlkNXGUi/v+2mq7snXrwd6Om17Vt32AknnWrPuOC5scGwxdPGFnijj1rGtPrd4wt+jXHwbsaMmbGNzsOL9RnIBj/lYcqEzWdE77nnnijVkYIRTzT7A41w5swZ9p73XGoXvfQl1rG3wwN58tYaem7LTcOWfzDhpAYv1DqAiCm8gWL6HApXXDFeyMPlzsuWIyeC3C3Z3ADiZIpApl6c0soW8vCiO0zFH/oyvZJN/n2vi5T40+J9XyzGciiPFxe7u7pQFUjpkfV6cEJp37PHHn3kkVig5fQu6akPSKi7t8cmTp5or3jFK232zNm2acMmH9Fs8qnVinghk8N/vD+nlyd5oLAG0+3EhI0fOYxeP2ArmzTEs+3N6WRthetaD3eURLMfcKPpzDxNX/PqP7aLL77IOlkozuJBsdMXG4j01DOanwP8uQ6FY7OI2dffE7Z3HTe10U7EV9IKcuflEHnICHmYwvO8iwaQH8jTDIV6OjCC/HneuYxskOeHncsOJe8h7iBswBYuWBCdvvgVRezQ7USgVwN6nFQ2bNwQ6zhcL/Ede/fa6WecbqefdlqcJF/98CO2bu16mzJ1WpDF/fffHyMUZKlv1mLYVWLkAqEwZSI/wJSJ6RJb27ghQDYovvrVr9r5558fMqn8hz9KotkP9GQXXvOaP7anPvWsyrZoCs8bdbGzC3lY3rBlSMewmfBiHH7iFVeLJy+ZwbowgsJpsIqTHHYReZzii4091w+K8bk/1yOI8IDiivkVdRZRzCNHfs+Iw8snIXiJcsLECdVj/cTpWlTPkAef3mRtZ5vbe3bvjt0mQJ5NLU12xhlnkIl17u2KczacnVq8ZEnsFG10YkIOXSIotqkxQNMlypCfDJYsRMSHsQB6itd2uKIkmv0hv8fupmm/7nWvjYU7nlpDIW8c9RqKOgJxMkU/jQ4jd724IvI4pSnaMvXkZRReJDelUSfHxhQJOdcrGaUHuHOZHAqXvGTz8KK7nlzKt1ImLy5vaM+eMztGgLwiwtRG5ZLNNfBLGJs2bwp3fFzcwwFTHl5DmDt3ri1cyKio24lli49+1sd5KRZxH1692olkb5AGBqgsjFjy08CEiXAgH+qYBxhTsDVr1kRaynSkoCSax0LlXuvnVab5UPcPLn5pNFwaSw7ii6aIPI3iCSI8JyD8g4w3dHa8aMDIEYZc/h1eGnUCemv5Kx5IH1Aa7CKh5HJAOvI02DJKJxmgMsoPinpzd45iOvnzvHIU9XoKN/gpQyLNY449Jn7xM5W5Vl/YpGXqwpSJUQzyTHVYKyEuSMjJ5fjjj7Nx48Z6fH+s4zCqWbp0mcu1xO4Ui7z8ZE8yjdURCnpJTxhEw8OKdRoIiHM1EN+5555rP/zhD2PaNNR1Hq4oieYAkTfiZz7zfDtq4fxotHGmptIgcpk8LG8wuPNGhM2X2PB6f/D/UucANG4aYJALYaQL40REPB3AXeQhk9IyDEe8lgcgPs8b8ZQmQe5UploapcMoLMUnGUHp8vSg6MZIbz0oLo8fquMVw5SOYEVBGrxVP2vmjBhFpBcro7IjHjlGPy2jW3w0syHitvhoBcJpbkqLsuhwITvz7DOpXuvwUcj6teudKCbYCScsjXehIJXYAvd7CBFBUhzMozwQDVMr8mfKhOG+siAM8WjxeMGCBbGuo+vAHAkoiWZ/4B5X7nN+0/ny2tOednbsXHhgNVydSI1fbo1UBOQJ42BajE78r5Ik5eM2jZ1v29Ag+73Rjhs7zqZPmx5mqj8JR/sTlOPxLE6yaMksgW1znqQM81XefJSi8ogwdHH482sogrA8XjJY0icdcT0VWVBMK+RuUExLfG7ycJFOMVymBo/zTt/b22fz5s2xMaObB62vxbkml4+yO/l0+5Rq3YZ1sSYD0XR39Vp75e1uplRz58+zRUcvssamBtuwbk2cGF67fkOk37Jxo+3ZtcfrH8JIp8p5141REqMXPkPB6AXw/RumTCwe670n3Hwugl+6BFyXru1IwIg8GTwcePDBh+yKD37Yn4n+tK9UYV6VaiQ5yRSrGj8mdVJkGYmkI+izZ8+yo48+2p+Wx8dTjs8QtLWNjXTE7/KnJ2c9+LmYu++5rzJs552ZJmtp5vfDBz/VQQqrBRTLWyxfDpUVuaFk82tFRpA7Ty8dXDuoR1BF1AsDuX7ZKYhO328dnbvtWc86zxYdNT/WVBobWyIcmXSgr8HGtLU6Aey0q676hXX59GflilU+GplgfT29ttVJ4eFHVtuLLnyB/enr/8Trd5Q9unqdfftb33Wi77Jzzn6qXfWzn9tDq1fHqCjVw0CQB8TP1AjCYdSia2aKlba7R8XOE4fzXvWqV/lo+ZlVQjqSUBLNQYJFv7/8yyts7boN8a1hKlFVqU6kDkBjwi+jeMDIhYaHTFdXRzRIjp6/8AXPizn8gYARzUMPrbY77rjD7rzzLn8qPupPyfTBLY7NN/nTNfXnWt4yoNhR8SsMEC5CyMMF4vNwpVdYUS+GMKXL45lOKA5gF+svDwOkA/gHu524+3qMV9YuvvhC6+nudDJhsZb6oM5rZRw7vi2+jnf33XfGLtLPf3qVTZo40RbMX2ALjloYRwue98Ln21GL5luzl3H2rHle13fbv33n+zZhwkS74fobrH1ve7X8vGrAR84hFMA0CsJhqgThMD1CjhEnYc9//vPtr//6r0M2v44jBSP6m8GHAhrS7b+9M36/p9FHEYDGkXcK/DIK82YUsrhp6HJzPP30p5xm73znO+z888+NRpmgtYShGx4nVKdOnWJLl55gzzj/PH86PsWnCnMj3z3t7bZr987YaSE/ygeIK0JlrYdU9gTJKKxeXFFPLoNbBrncBpAAbpGbkKeRX8jd0gWJ85M3M2dOt2UnLY3pUEqbRhzSw0wKguA0bodPRx966GEfMe6OVxDWr1tnK1autDGt/gC48EUhP3fOXGtuGWO/+dVN7vPRzaOPxpf4GL24ttDL9Iu3sXlwoBtCgXSYNnGuBj/3Alm9jvCCF7yg+pF0le1IQWp1JQ4KnI1gN4pFWYEGTgNShwb5aID1AeycZLr9SXvRSy60D3zgcluyZPGgTnNgSGREOnTPnz/Pnve8C+y9773UrvzIFfb2t73FnnrOmf6EnmCdHe0+3N9jfb3pUFk9A2SDocqTy0oGm+sVWSh8KH1aQ1KY3KozuRU/FNCvPJCVm18lmDV7ZkxTmHImMoBY0ofIvOTx0NixY3vsDsW29aYtMcrgIB0/KgfpH3/88XFPmeY8/PBqe+97LrMf/3f64UHWW1QHFJMdKEa8EAvxbGmzTsP2dr5Ow9SK0Q1fEHje854Xi8J52Y8klERzCOBpFYONSidQY1djSe68k+CvNMhKCNOll73sD+31r39tNGR2KxCvpeEWHdhtUt469QpmzJhu5513rr373e+0j37sQ3bpe95tz3nOM+PVCn6lQZ8pqOVXA/oeC3EtlbQ5oQLFsUsGCC+SxlB5EC6jJ3+eroZaesVXdXJy2sM4+8LPn7hE+IlGNMrrMo2NDbZ586YY9W3fnhZoIaWe7h7r4gfmfJqz/IzlQT78fC5vdd97z71xSI8PjbMNno4Z+J2KUWNDLOJDXMQzWkEf0yXIi4VhDFNj2hDb2vxonL49cySiJJpDQK1BJ5tGHI3dycQD470ZjWBANOwK8XAifq9Pa848c7ld8sevinigkVBV9wGBNDX5lFZ+jrunEQ+N/IwzTrf/8+Y32pUf/ZC97/JL7cKXvMAWHjXPxfu8M+72zlU5iFg5sk+nQV8yKR+/yvgLsUo5UwdObtkgRm7R4WsEU62nDArLiSgPw+R6JSPdLKLnaSlnn3duTgNPmjg+RhBMMZOMDLKQ30AspjO6YKrU19sX0x0+7coP+c+eO8cWLFxgrT46uf/eFbbivpU+NV0QufBBLN70DoLx0RJlRO/kyZOqa2wQF4SDYZrEqEnlxM+UjbAjGSXRHAIgBRqMOkGY+PNqjZELJski44HVDsoC7vRpU+18H20kpHD6UnTOg0QqR9KTwK7KYCICbW2tdtKypfanf3KJffTKD9pf/t/L7ZWveLkdd9zR1tLU6E/kduvq6YypR0K6lkQ2lDXv1AD9qTmJEKLThSMZ5GWoD5lcTx4vv/QpbrDx9BCpE45IOo3ofLTY2eWjmdnxsfGeLu/IoUfEn+SbfJSxazfTmO1xXGHr5s3W3MT9Y9pl1unpnrL89PhBOZQ+59nPtWOOPT5IhPu0YcN6f3Y4MbnONIXmK3odHmdB7HxbhpEKoyHyhfAY6XAamPzRQzgjHsA1HYlId6bEQYNGQoPBDuNunY+JRhOdYXBHEpb7aIbdCI04nnREWQb8Sd5sxx57jL3sj15qV3zoA/aXV7w/pnKnnXqKxW8Rdbb7U5upR69fm0Y5+m1y6oDrlT+RQrLdT8eOj4GmI/kglxNEOkB1hI0cttxKV7Xj/5QTSGmRZ7HV4ogAnVsgDKQsfNrkARyk48wLv1K5ew8E4GV1/YxqmO5wGhjC7ejoitcSmCpBHIxCmHoGwVXkmfqib9OmzUEorNOw9gLZQDpMmfBz39FxySWX2De+8Y1B6zPpGo4slERzCPDmnP4fsmHwpE2u6BguRsdjAXTBgnn+tJ3jDZFtaHQkuScSxXJ6s458k6ldx8KF8+35LCZf9hd25ZUfsj/7s/9jT3/62TZuwtgY6cS7OTokGISTtA1WTyD6a3kUUSSO3Ah5meXO42OEEs14cFPmFZEZs6bHQT2mLmxpswCcTu2yAJ1IDV2bNm0IN78myWgoHhQ+Qurp6fIp01ybN39uEMH3vv89e89ll7n8xjjxy3QLsoFoRJRMv1jwxaaemC7xjRoWfvVtYHaWWGAWUVGG/DoHX9+RgZJoDgGc2C3umtBG1GY8NBpfNb7aoAZiBNHb2+U6GDLTuZ74W1FswHgJq9nE0wFlLI7tP+uZ59u73/UO+8THPmLvfvc77IILnmNTpk5x0tlre9r5nALfdmFRNV2nrlFG+WLH078C5Kgf5S05UNOT3Bo1gqK81sGqYS7PaeCZPnpg7YYOzWjDJas6KRc7ULt277LNPkLpchlGIYzu0MrIlHTHn3C8TZ8+ze9zT2xhM5phJwoigUB0fwFuwJSJXaaZM2cGqVAmrdMw2mGUQxjk89vf/rbqr5b/CERJNIcIdYiq8Sei2oqmC3kcZzNoiDNnzYi5vJr97weKpRnsZz3hnLPPsre8+Q326U99wt5/+WV24YtfZAvmzbVef/q3Qzo9nZ6qz1uWp22ARNCR6iTvRLghj0QAtU4KqCtBcuqEuFWfipcf4o+RiuftYk7mR3sH50t66CRv9NTSY/isAx8027Z1u48udOCOvEYZr5qweE75mUK9/e1vj61oj62us6hcpGPEApGw3Q15MHrhCASkw7QJ0kEOAmPb+61vfat98YtfjJ0nynIkoySaQwGNrLJwq4bLNifDb+bq2C4UnQPDo5In7OzZM70RNlkfC5kN6SNIvxtoR0oG0CRkasQAdI105tbW0d7pTrHXvu7V9vFPfNT+8oPvt5e97GI75tglMaXa287BNCcdl/VqClXqTFEXDnVShctGnnB4jiDWTOi0EVZBrSxJJtzxxysaPTbRyZxDi2lbO+mr6Uc33wDqCaLxO2brWdStxAN232bNmhlnkviWDZ+OYATCtjWf+uTjWPm0B1uH8whntMPUil0lSIdrZkrFQwbywc/IaP78+ZE2ynQEI93xEgeFaJiVRi5/Mmy1MiUavCUr8MPxhPIE7u+tPcmffFCK3AwNXaOuJ61xpDBOwXIq+VWveqVd+ZEP20ev/LD9yZ9cYiefvMzjGnx6tdP6mF5Fa6vk5XqClDFRT5CEpiFp1NHto6N777vDbrrlV7Zh41oPh8RrUxX8ONPUqWLcz/kXyJxtZ6ZETY1NWbo06mFqxAiE08JdHd22eeNmL1XKl3Un4k5cdry1jR9tLU4gn/qrT9vn/ubz1t2ZfgeMlx81CoM0cDNK4VAeC74c0oN0WIhmugXxkA45ptv85va1114b6UcCSqI5JNBwax2URkojjl8xoMVXpg3qGPRROmVba2u1kWINDPyubgP5Vjp+1dRH/tRVp64nj8jCBfPtJRe+2D7w/vfaxz72YXv5H11s48aOtj27+YyC1mx8JOD/QzIqBiHammb7fe26R+zmm/7X7rrjNrvzzt86cTBVgQwYRThhkJbPmka9U5eq6wFbesKx8XIkn/Jg1AkZsFumeHLmM52dTjJbt2z36RPfkUlvXg8M+AiqZYyddOrSIJpVqx6wDes2256d7UFivHLQ29sbxII+rgfyYMoEQTHN0s/eMmqBdAhjysTohinU61//+jA1AjyyURLNISM17qJRp8Sd2yxA0uhcKt7DSTiyGlp+zbNnz7aXv/zlduVHr7TnPu+C2Cpn8biRdZQK4aQDjqoFpp28nzTT06fwAMTmAnToIGmPoo5TXqq/ftfdHS9DchqYFyj5HSamsaRLp65ZT2m27t4+e2TdWuvp64kPiqMnyMsfEh1dHTZj5nQ76qiF1uzkc9utt8UodMbMGbFuA2kdddRRsV4Tp8Md6OebMxq96DQwZWQbG7LBQECMdJ71rGfFbzcB1deRjJJoniBoxJKj2gH9j10RulFzy+C1hyMBNQJI14xhQfT/vOkN9ra3vtk7msXRfraRG4NA0ulcbNJiePq/8IUvtNOecoadfMqpdvZZ58QnGNIIgLqjzlK9xSiy34nE47DpzHT09D0YXk5Nrz5IlpFLh48sWNDd6/aWbVusqTm9dY3c3r177MQTT4gtbfPR5rhxE4NQ+F7NFk/DyIW1GqZCSWf64X5NmQDrNOwyQWI6T4McIx6mTTfdxAuZ3gGdtI60+18PJdEcAlLDrI1eBBoPSJ2itq6QZBWXnsxzfWh9JEKEIZMwEO9dXfoX77YW79g93fyihNeJc0w+FePH+tatX+/xvfa6173BXvayV9ikSek4P1VL/Xp1Vus1Ga3zmI0dN9Zuve0W++Y3v2m/+c1votO3to5x4hhTWVQ227xpo4+qGmzL5s3x4/1pxyqRI0VhV7BxVJP1dvXZqgce9FHKZnv7n789DCPShx56yB544IEgHEYz5EE4oxemTNraJj9Ihu1tyJO4s846Kw4SAvIstp8jEanVlzgopE5E90kNRlAHKCK1pxSODIe2Zs8+MommHqgS6uXkk0+KQ4DUASbqsLK+EoY68n8PPLDKbr/9Tlu9+hHvqO1OBjUdQQw+SOG3mmr1PRC7eWy1j/HRD6OJz33u/9mHP3yFfeELn7Wf/OTHtn79Wvvxj//b/v3737OV991n69esicViPg/Bwi3TH96R2rNnl7WOHmNr16yzXTt22bHHHBujMr7nyy9JMjKBQCAXXkplykR+OicDGUI67DJBLhwNYE0H+UsvvTTWZ0YSSqI5BPDyHedieCqrkwh6khMmm6G9vvnLbwvNnTfXG+P4SooRgApJYHiqX3zxS6y9Y0+FgGtQPTLK6e7qiQVb6kyEEsb/8KO0YZSHW3+MLo455mi74kNX2Bvf+Eb37w09rJnceOOv7Jv//A37uy//rd1w47XW56OL9Y+usQEnlVYnpRe/+AX2qU990s4+50ybPnu6TZsxzdrGtsUUZ92a9V7es2NUdNfdd8aP//OQSCOrgXCzda0zMkypIB2dp1G5sSEitr2BrlP2kYySaA4BNHank3iyerdI7kpnErkANbToT955COarfEsWL4n4itgIQKovbMA3eI5ZssRHB+lrgPU6XNpJSkxEPHWp+gx5jxvoTzrZYTr1lFNj5HDzzbfEbpHeqG5rbbFnP2u5zZg61ab6CIPPvTd6Wn7e1kV8Ktds73nPZXb9dTfY3//9l+yEE471fCwO8kGERx+9KHaQ7vARFus/kAqjE5VLZ2QY9TB64eQwox7O3jBSogxaCGaalYO4Ix0l0RwCRnmjSYu5jUE2NExvddVOgKERycTuiht2m1g0nDF9Fgk8LtQd8WDto2FUqhPAAutLL7ooCKK4KKr6S/VTq8c8LuqXJszxAJ9W8QH3U3xaBu64406P93sSay/mBDPJpkzg5c6068T9Gu1kEL+l7Uxz9TX/48S0ysmh00ccj9hMH9Hccftdtmd3h5199lk2ecpEJ5htTmA3RzkoL+svjKIYpaTF567IGzKBbLjHWqdhHYewL33pS/bmN7855PJrOtJREs0hQE/X6pmZWIxMT10aVuooCeH2NsUZG9YA5s2f742Vcxs1mZGDWudavvwptnjx4vhsRj0ikZ/61EhmMJDrix0hftht3vy05nWRE9jHP/5xO+2UM0Jm0mR+i2m0jWsdZ2N9xDN+TJONa2mw8a2jbeL4CdbT1Wt3332XrVh5j91zz91BCqy9rPRRESORWTNn24oVq2zlylVBkJSV8kAijF40ZWKHiYVf7TIxrWKtBvKJU8WeNyQ10lASzUFATZ3Gz1fZIJj06YQUTiNUh0hkpCcyIQPR2Pjo9chEjWRAU3OTnX76adbT3eWdMf16JKiRTM2AVI/plY60U5W+kAfZLFt2YkxnSMuuzuWXX24//OF/2AXPOsvmL5zkZNDkxOBkM6bFRjd02bjmARs7utkWLTjKTjzhRB8NnRynmI877hi7774V9l//9d9xluaCCy5wshlv11//yzhJzKFLkQVEAylhmDppygTpYJg20QYIX7lypT344IORbqShJJqDgLoKjZufPdm1faft3rXTenmh0MNEOmy50hH4JjAv4PHSHp9Z4B2c1jH6+PhIugVcq4gkrMAJS0+IEcnuPTt9OtpgLd75Mc0tTfEbShzw6+3jg949bnfHi5u8Nd6+d7en2eUdekeQ/imnnlzRWMO8BXPsGc88xzhO093rJN822ppH+X3p67TRrp9jBmN9lPOey95nC49aFIcI+e2spz7t6XbSSSfFqwY33nijXXXVNXbvvfc54UyIskN4kIjef4J4GOlANkyZ+P4M53D0rWLeafra175mF198safPLn6EoPy5lYMAVQaR3H33vXbrLb+NpxoP16uu+rnddttt3ujSuzU0fobMz3nOc2L4TYNjl+oFL3y+PfXssyvaSvDTMFdffY1td8L+6le+Gjs2dFzWsib4tOa888/1KadPmyojAzo5vw45bdrUuBfU8/jx4+K0LSd3ExGwD+VwIvni5z9hN99+va1b7/raJljn9g22e+Oj1ucjzenzFtkb3/YX9uKL/yDKcs+9t9mDDz1gz3v+hfHawee/8AW75ebfWGdHl5PHrFhP+u0dt3hZBoJoWNilDNxn2gFkQxmxuQamVITzcfMvuK7jjjsuyptGZHpkHfkoieYgkKoMshk8Gvnrv/60ffvb3/JGx68Udrrpjg9bv/8DH/Cp0nzr9OkBP3/CzsSZZyyvpCohMMU899zzvGPfDFPEaICj/pdf/l4bO7bNO++YsBkdPt1HHDNmzqykLCLdH95Nb3CCGtU/yrZsX2+33H2P3f6bO+zuX11rK++51bbt2msf+8xn7A9f9TonskjoRNIZ2+KtYydYU6yhmW3ctN4u/YtL7Zprrrf58+bbmrWPBIFAFBAM7vgYmBMIDxNIhOmxvqTHQwdC4kt6F154YfiRGUkYWVc7TEhPon2fRj09vd6wWuK8BY1s3Lg2axvb6o2Pl+k6wvS6TP7zLCMZELYM2LNnt9dZa/w8Cr/FNHnyxPhMA18i5Gdo+P4vv6Edsu27w1Z6Oq/0JKLhDlHP3sT9gTBt2lx73vkX2GXv+Qv70te/YZ//2jfsz971HjvnvGckWRdt8HRNjaN9dDTF75GPSiv6+OYwv0y6fdtWe2DVyiAUSJC1GBELu0sczMNAMpQF8mFaxfteb3rTm2I0M1JREs1BYqhhL99O4dch+fnaMWN4oo32J3FrNHY+P8Dxen4doUSqQxmBuuNMitY5IBY6Lr+9lEYBjCIYaaQ6VPrBUxHivL5dNuq6UN1tEybaGU9/pr3zssttztxFEUbKSF/RMcotPRBuueU2u+mmm+NMDms6yEE2TJ20ywTpQD6McCAdvjmDYccJsuGjWccee2zoG2mjGTDyrvgJBnNzhstsa6YOkg51CTx5MSXqg7qj3rQtDOkwQmSEoM88PJkdlfxYFP7Upz5lz3jGM2KkypkZyqiv5kE8kA6vL7CtzVRK9xhCAqQZySiJZhhBo1RHoPGxKKjFS4EGmPtLDAb1RScWSePXQivjDuzkfvLAC5Lvete77Ic//KH95Cc/ifM5y5cvr45qKCeEw4I/958RDPcZomS3iYXgkX7PS6IZRujpJaKhg2gXQuEl9o+8Q2p0k+qvoUpA1OuTPSqEQCjbiSeeGKTzox/9yH7xi1/EL0w+9alPDVJhvYZyMRrjviM/b948+/rXv26nnHJKRdPIRNnyhxEardAxaGg86TSVIlwjnie7kxwuoI4gEeoPUsamvkAa1aQRIu4nC5RJtu6fyrR06dJ4E/s///M/g3iuvPJKe+YznxkjG6ZRTJd4PYGzNiMdJdEME2h8NEaeaoxs+EocoGOosdJBcGvkU2Iw1ImpI0haZJNIJpE15nc1MlT+GEBZeWhQnmXLltmf//mf2/e///2YXn3yk5+0c845J6ZWrNuMdJREM4yAQOgY7JxQtexAYSAfnRBF5sl8Ih9uoBOLVOjA1BW/tZTIhSlpClNn/12DcmiEg0052V16y1veEms6P//5z+2YY46pyoxUlEQzTFDDH9PaFh+2Hh0283S9l9MQh7byHYkS+4K6UocFkLPXrvsZGaYwyPr3oeNyT2XkL4Jt+lxmpKIkmmGCOgcvWdIROIPBk5cwntCQC3aJA4PIWYSijqp6Hukd93BDSTTDhNQh0jsvnPdQmKZJ6jB6UpfYF9QRIxiRCHa+xgVE3uU61+GFstUPE+gkDO05rp6DDkHn4dMCADmRTonBgFCoG9UZgFhE1sTLlDi8UBLNMIL1l54glbQ9m8gnHZmHW/DTaZAryWZoUHdax9ICOiYnonKd6/BCSTTDABFK/I6y+yESrSMQx5vcbHHyPgxxbN0SXpLNYFAfHMhjLUs7T4LIR3Vd4vBC+ZmIYQBViOGHwVatetB270mfDPDAeGNbLwbSeThBumjRonjLt+w0g8Eht7e97W3xoiJ1BTmfcMIJ8Y1dTttqVMOvCLCbQ1yJwwMl0Qwj6CCcl9FIhiewiASbaRPhnByNX0EsMQgQzVvf+tb4jSROVDP645OchEE0mi5BNHw6k5O5JQ4PlFOnYYL4WraIBaOXAxn+g5Lb64M6g1AAoxleTmQUQ90B3BA5b3TzEmNZj4cPyhHNMIFq5AfDeAprFCPQUYiXYUTDFKpEDYxWMHfeeaetWbMmvvGCYQrFi4yAuoOsqbsFCxYE4ZQ4PFASzTCBamTqpIXgHDnR0JnoICXRDIbqB7JmpymvL2yNDlW31CHrXiUOD5RTp2EGo5ncCHQWUBztlEhQvYhgIBSIhbUaSIU1reIUtMThg/KOPcFQByoST4mhQT2JcDSlElGD3F3i8EBJNMMEEUk9MsnD6sWXSFDdFIlE9ZqbwwEiyxIl0Qwr9tcBGO4fbh3ld4F6JFPE71P9FcuLn50xfgGBD1/xPZoSJdEMK8qn1/BC9fn7TMyULScXvqaHgWDYGCC8bBcl0Qwr6nUIGplM7i/x2MjrE/fvG+GwO8Y3hiAWRi8QDeTCmhJgFFtcXxqpKIlmmDBUY/p96xy/z6AORSiPp95E3k9Gh+bQIOQCsWjkAuGovHn5MRANBw9zPBnl/H1DSTRPMNTg5C4xNOrVTx6mDlrsqHkdHyg0GsEWhiIAyIWTyrwisXPnziAXTYm09iYQhsnLxMu0ue7HW9YjASXRDCPyxiWo0ensh4bTNFSG2XlDH8lQnQDV4VAdkjqk3jBaB3mseuQ+IMv0BqKANDh5zKgEIgBD5Yd+0kA4gPyLBCMojvyQx5TTJ69br4CRXQPDBKqRRktjzhsg4frkAQ0OqMESx4G0SZMmhX8kg9EFHb8IvSuWg3qknvOmS+fmRUu9F5UDWfRrFIJOIHLj/nAPsOsBIiJ9TiDYGMKA/DLo5p5LBv3c65GKckQzTKBh6amaNzhBJCMofqin6EgDHb5YR/tDsY5JKwLPQTyjEY1aAPeJ/JCHmEirVx9AUTf3SPcJ0sDgx1a+pMXGT7pcHjCSejzXd6ShJJphAnN4GtL+iEMNV1AjHOmggzLqUH3srw5zIJ/XIToE7gX3hFEm+tGpjo5bRqSALTIq3ifJAnSgT+SCXzokI+R+5KW3qH8koGzpw4D9NdJ6UAOUPRIbHtB1Qwi4845Z7LRFFGUxGqUAdnoYpegBACRXhMrBlIr0WrdROOnRTRgGghnqnimPYj7Iq2z1ynCkoySag0Te0GiE+bC7CMJyA4pP45EK6g0yUOfDpo7yzih3MRwU0+k+MCXK3/bOUdRBOuRYo5GO/PAdRJOTSzE9IExmKLDOI9IbaShb+kEib1B0FKFeQ9PwGiM3DfuxGuaRDOqAa2fkIXcReRhukQbuYpwMpA8gjSLJEK/7kKcBfHBLxARJMfpAl+6tvvMM8rS5jseC7v1IREk0hwgaj3aahjJq3DkUN5KQ1wHXDskwFSnWg+oMYFPHMoLqtKhTIw8IA3LI0wh5WqXPpzV5OIBwtCNVLGs91JNBH3nUK89IQEk0h4jiGkAReRju3Iw05NdMvTE92R8kT0eXgdQhE+I0KhRERkxRIDC2kxmdQDjI0dnzdDL40ak80JHHk56PbDHiUdhQUDw6c0MY+WsEN9JQEs1BQA0FO9/pUEMuNsRiWN7wRgKK16lDcyCvl2I91asfkUk9IM+oAf0cyIM8+GgWZEOdDwXSYVQuyqA0uMlPIx6gcuZlLfqlUwYQT5kwORR/JKM8sHcIgGRo0CBvTKDoz6GnHDI06CP527dco+pAJMCoAeR1Q2fOm2Jef8U6zOWA4rFzEsKvepbZH4hHnntCWsqbpyNM7jwMyL8/SHbChAn7HEI80lGOaA4BeYfJTR5WBGE0ZiFvyEcqqCcIhpO/uFUHqqPcn9syRUimaIpQ3R5oHaNXJJOnIxxiyE8O53nW06343EhfvnU+UlASzSFADQeoMeXueiYH/uKT/EgA1wOhsFaid4pYkyFcJJFD9ahw1VU9fzEsR64D4M51F+NyyK8Rkfx5eD59AkUdgLB64YBwrp+6Keo60lFOnQ4BPKXpTHp35kBAdWsbVZ2AX0QY6j2b3zfkzSXvUHREOpAMHUmdbqiOJ6geHkvuQCBdBwOVQedpRAa4uV9yS24oEF8PSoMuPrbOYvVj6TpSUBLNIQCi4Un9eBuKdi+oejooOxr64bTDCXRESEULnPnojOvL60XXWw/D2QSlq6jzse5RLq8HQX49OfZX3jxO5AQUjk1ZuOeQzWOV60hBSTSHAEYzB0M0NEB1PAzrAoxqDodGJ1LRqIXOCOp1qgO5HtXBcF279OUo6i76dQ0C8dwTwvM4pZP+vNyPlSeQDHHU1+E0kj1UlGs0vwMUGycdtrjl+WQh7yC4ix0GP6TC+Q/WWtj+xa3y6umfg+ur19GGwuORfSzUy1vXpTiMypzLKg4grwcCyOWA6kl2LleUzaE4lWmkoBzRHAKGY0QDsOmw/FTu49V1qCDvYp48xTVywejJjtzjLd/+5Ml7qOZHumJcvbLWg9KqzKpvpZVe2cVRC+Gk4Z7o2pW34pUWKOzxguny4ThlPhiURHMI4MkO0TxeaI0GUP24acxsof6uGh75i1gYwVAulU1lBQfaXPI0Q0F5DAXlLZkDyVtp8vzlJj3unFiGKgNy3CdGm4rP9QyFPC4vQz1AZjxcyOdIR0k0hwAddX+sBpUDWRpYPXArWCR8sg5z0eHyxVzyx1BGjNyyHw/2J4++IophQ6Wvl7YI0qrzPlZecot88nylQ3HIYpAp2orP0xdBvIAchgfLSDi8VxLNIWColwIfC0MRDXpo1Gx7PlGffcx3iuo9rYcLQ+l7PM0NWeoDXTIHiscaJaBbJvfneXCfMFr0Vhkkp7RDYah45UH84brj+HhREs0hYLiJBnA7MCKbx6u7CHRpsRlDh1GnEQ41jwPBwTQz0lBWQJ09nnIeCNEU7WIZyQ89lIE6VP7I4cYU0+Sop09QHLtbEA32kYySaA4BEE3+UuWBQp1mf1VP40buYBshpJJvQSsvdZAiiK8XfjAoXtehNLG83AcK0lB3+yN0oGvGVh0pTNDhPepTkIzS1kMeJ33y5+GYI3/6ZPb/A712MKngkRiOAAAAAElFTkSuQmCC) | MCS CCD – Multi Channel Spectrometer High Sensitivity and Resolution MCS 시리즈는 높은 재현성과 Spectral Resolution을 제공합니다. Peltier cooling 방식 CCD로, UV sensitivity 및 안정적인 데이터를 수집합니다. |
특징 및 주요기능
Specification
|
Wavelength Range
|
Pixel count
|
Optical Resolution
(Raleigh criterion)
|
MCS FLEX CCD
|
190 - 980nm
|
1044(total)
|
3 - 4nm
|
![](data:image/png;base64,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|
PGS
– Plane Grating Spectrometer
The NIR Specialists
NIR spectroscopy를 위한 Spectral Sensor입니다. optics와 InGaAs detector array가 compact한 배열로 고정되어 있어 안정성 있는 데이터를 제공하여 in-line에도
적용 가능합니다
|
특징 및 주요기능
Compact, permanently aligned Robust and thermally stable Detector array with 256 or 512
pixels Excellent wavelength accuracy and
stability
Specification
|
Wavelength
Range
|
Pixel
count
|
Optical
Resolution
(Raleigh
criterion)
|
PGS-NIR
1.7 - 256
|
960
– 1690nm
|
256
|
8nm
|
PGS-NIR
1.7 – 512
|
960
– 1690nm
|
512
|
5nm
|
PGS-NIR
2.0 – 256
|
1340
– 2000nm
|
256
|
8nm
|
PGS-NIR
2.2 - 256
|
1000
– 2150nm
|
256
|
16nm
|
|