Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó
: figuras, tablas
- Autores:
-
Renteria Mena, Jackson Berney
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2025
- Institución:
- Universidad Tecnológica de Pereira
- Repositorio:
- Repositorio Institucional UTP
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utp.edu.co:11059/16492
- Acceso en línea:
- https://hdl.handle.net/11059/16492
https://repositorio.utp.edu.co/home
- Palabra clave:
- 620 - Ingeniería y operaciones afines
2. Ingeniería y Tecnología
Modelado hidrológico
Predicción multivariante
Nivel del agua
Redes neuronales no lineales
Modelos autorregresivos
ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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| dc.title.spa.fl_str_mv |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| title |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| spellingShingle |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó 620 - Ingeniería y operaciones afines 2. Ingeniería y Tecnología Modelado hidrológico Predicción multivariante Nivel del agua Redes neuronales no lineales Modelos autorregresivos ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos |
| title_short |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| title_full |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| title_fullStr |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| title_full_unstemmed |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| title_sort |
Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó |
| dc.creator.fl_str_mv |
Renteria Mena, Jackson Berney |
| dc.contributor.advisor.none.fl_str_mv |
Giraldo-Suárez, Eduardo Plaza Guinglia, Douglas |
| dc.contributor.author.none.fl_str_mv |
Renteria Mena, Jackson Berney |
| dc.contributor.researchgroup.none.fl_str_mv |
Ingeniería Eléctrica::Grupo de Investigación Control Automático |
| dc.subject.ddc.none.fl_str_mv |
620 - Ingeniería y operaciones afines |
| topic |
620 - Ingeniería y operaciones afines 2. Ingeniería y Tecnología Modelado hidrológico Predicción multivariante Nivel del agua Redes neuronales no lineales Modelos autorregresivos ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos |
| dc.subject.ocde.none.fl_str_mv |
2. Ingeniería y Tecnología |
| dc.subject.proposal.spa.fl_str_mv |
Modelado hidrológico Predicción multivariante Nivel del agua Redes neuronales no lineales Modelos autorregresivos |
| dc.subject.ods.none.fl_str_mv |
ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos |
| description |
: figuras, tablas |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-12-01T16:17:39Z |
| dc.date.available.none.fl_str_mv |
2025-12-01T16:17:39Z |
| dc.date.issued.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Doctorado |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_db06 |
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Text |
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info:eu-repo/semantics/doctoralThesis |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Renteria Mena, J. (2025). Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó. Universidad Tecnológica de Pereira. Disponible en: https://hdl.handle.net/11059/16492 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11059/16492 |
| dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Pereira |
| dc.identifier.reponame.none.fl_str_mv |
Repositorio Universidad Tecnológica de Pereira |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.utp.edu.co/home |
| identifier_str_mv |
Renteria Mena, J. (2025). Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó. Universidad Tecnológica de Pereira. Disponible en: https://hdl.handle.net/11059/16492 Universidad Tecnológica de Pereira Repositorio Universidad Tecnológica de Pereira |
| url |
https://hdl.handle.net/11059/16492 https://repositorio.utp.edu.co/home |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
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Lim, “Water level forecasting using deep learning time-series analysis: A case study of red river of the north,” Water, vol. 14, no. 12, p. 1971, 2022. [88] R. D. Pinzon Morales and Y. Hirata, “Bi-hemispherical neuronal network of the cerebellum with realistic climbing fiber reproduces asymmetrical motor learning during robot control,” Frontiers in Neural Circuits, vol. 8, 2014. [89] A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,” Neural networks, vol. 18, no. 5-6, pp. 602–610, 2005. [90] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. [91] M. Cho, C. Kim, K. Jung, and H. Jung, “Water level prediction model applying a long short-term memory (lstm)–gated recurrent unit (gru) method for flood prediction,” Water, vol. 14, no. 14, p. 2221, 2022. [92] S. Gillijns, O. B. Mendoza, J. Chandrasekar, B. L. R. D. Moor, D. S. Bernstein, and A. Ridley, “What is the ensemble kalman filter and how well does it work?,” in 2006 American Control Conference, pp. 6 pp.–, June 2006. [93] J. B. Renteria-Mena, D. Plaza, and E. Giraldo, “Multivariate hydrological modeling based on long short-term memory networks for water level forecasting,” Information, vol. 15, no. 6, p. 358, 2024. [94] J. B. Renteria-Mena, D. Plaza, and E. Giraldo, “Water-level forecasting based on an ensemble kalman filter with a narx neural network model,” in Engineering Proceedings, vol. 101, p. 2, 2025. [95] J. B. Renteria-Mena and E. Giraldo, “Predictive modeling of water level in the san juan river using hybrid neural networks integrated with kalman smoothing methods,” Information, vol. 15, no. 12, p. 754, 2024. Submission received: 22 Oct 2024; Revised: 20 Nov 2024; Accepted: 22 Nov 2024; Published: 26 Nov 2024. |
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Pereira, Risaralda, Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 de la Ley 23 de 1982. En concordancia suscribo (suscribimos) este documento en el momento mismo que hago (hacemos) entrega de mi (nuestra) OBRA a la Biblioteca “Jorge Roa Martínez” de la Universidad Tecnológica de Pereira. Manifiesto (manifestamos) que la OBRA objeto de la presente autorizaciónhttps://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessGiraldo-Suárez, EduardoPlaza Guinglia, DouglasRenteria Mena, Jackson BerneyIngeniería Eléctrica::Grupo de Investigación Control Automático2025-12-01T16:17:39Z2025-12-01T16:17:39Z2025Renteria Mena, J. (2025). Sistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del Chocó. Universidad Tecnológica de Pereira. Disponible en: https://hdl.handle.net/11059/16492https://hdl.handle.net/11059/16492Universidad Tecnológica de PereiraRepositorio Universidad Tecnológica de Pereirahttps://repositorio.utp.edu.co/home: figuras, tablasEsta investigación doctoral se centra en el desarrollo, la implementación y el análisis de modelos avanzados para la predicción multivariante a corto y largo plazo del nivel del agua en los sistemas hidrológicos del departamento del Chocó, Colombia, una región caracterizada por sus elevadas precipitaciones y su vulnerabilidad a las inundaciones. El estudio combina técnicas de redes neuronales no lineales y métodos lineales tradicionales para abordar la complejidad de la dinámica hidrológica de la región y mejorar la precisión de las predicciones en distintos horizontes temporales.This doctoral research focuses on the development, implementation, and analysis of advanced models for short- and long-term multivariate prediction of water levels in the hydrological systems of the department of Chocó, Colombia, a region characterized by high rainfall and vulnerability to flooding. The study combines nonlinear neural network techniques and traditional linear methods to address the complexity of the region's hydrological dynamics and improve the accuracy of predictions over different time horizons.Acknowledgments iii Abstract iv 1 Introduction 1 1.1 Problem statement -- 2 1.2 Justification -- 5 1.3 Objectives -- 8 1.3.1 General Objective -- 8 1.3.2 Specific Objectives -- 8 1.4 Study Area -- 9 1.4.1 Hydrological station measurements --12 1.5 Hydrologic Modeling -- 13 1.5.1 Hydrologic Cycle -- 15 1.5.2 Processes of the hydrological cycle -- 15 1.5.3 Hydrologic Model -- 16 1.6 Thesis Structure -- 17 1.7 Contributions -- 17 1.7.1 Chapter 2 Contributions -- 18 1.7.2 Chapter 3 Contributions -- 18 1.7.3 Chapter 4 Contributions -- 19 1.7.4 Chapter 5 Contributions -- 20 vi 2 Linear models for water level forecasting: implementation and analysis 21 2.1 Multivariable AR Data Assimilation for Water Level, Flow, and Precipitation Data -- 22 2.1.1 AR multivariable model -- 22 2.1.2 Multivarible AR Regularized Solution -- 23 2.1.3 AR Hydrological model -- 25 2.1.4 Experimental setup -- 26 2.1.5 Regularized AR multivariable estimation results -- 32 2.2 Multivariable ARX based forecasting for an early warning flood system 36 2.2.1 ARX multivariable model -- 37 2.2.2 Recursive Data Forecasting -- 38 2.2.3 Experimental setup ARX data forecasting -- 39 2.3 Academic discussion -- 43 2.4 Summary -- 43 2.5 Partial conclusions -- 43 3 Nonlinear models for water level forecasting: implementation and analysis 45 3.1 Multivariable NARX-based neural network models for short-term water level forecasting -- 46 3.1.1 Hydrological Variables -- 46 3.1.2 NARX Based Neural Network Structure -- 47 3.1.3 Experimental Setup -- 48 3.1.4 Estimation Results -- 48 3.2 Comparative Analysis of Nonlinear Methods for Multivariable Water Level Prediction: The Case Study of the Atrato River -- 52 3.2.1 Theoretical framework -- 52 3.2.2 Back-Propagation Nonlinear based Neural Network Structure -- 54 3.2.3 Regression metrics for the estimation of the quadratic error -- 56 3.2.4 Experimental setup -- 57 vii 3.2.5 Estimation results -- 58 3.3 Academic discussion -- 62 3.4 Summary -- 62 3.5 Partial conclusions -- 62 4 Water Level forecasting by Hybrid Models 64 4.1 Multivariate Hydrological Modeling Based on Long Short Term Memory Networks for Water Level Forecasting -- 65 4.1.1 Experimental setup -- 66 4.1.2 LSTM (Long Short-Term Memory) Network -- 66 4.1.3 Estimation Results -- 69 4.1.4 Forecasting Future Time Steps Based on Predictions and Forecasting Future Time Steps Based on Measurements -- 71 4.2 Water Level Forecasting based on an Ensemble Kalman Filter with NARX Neural Network Model -- 75 4.2.1 NARX neural network model for hydrologycal level forecasting -- 76 4.2.2 EnKF forecasting -- 78 4.2.3 Novelty of the proposed model NARX-EnKF -- 79 4.2.4 Experiment setup -- 80 4.2.5 NARX-EnKF Neural Network Validation Results -- 81 4.3 Academic discussion -- 92 4.4 Summary -- 92 4.5 Partial conclusions -- 92 5 Mechanism of Social Appropriation and Knowledge Transfer for the Riverside Communities of Choc ́o through a web platform and Virtual Environment 94 5.1 Choc ́o Early Warning Web Estimation System (SEAT-Web) -- 95 5.1.1 SEAT-Web Key Functionalities -- 95 5.1.2 Elements of SEAT-Web -- 95 5.2 Virtual Environment of an Early Warning System to Mitigate Flood Risks in the Department of Chocó -- 99 viii 5.2.1 Design and Construction of the 3D Model of the Hydrological Station -- 100 5.2.2 General aspect of the environment -- 107 5.3 Software registration -- 108 5.4 Summary -- 109 5.5 Partial conclusions -- 109 6 Conclusions and Final Remarks 110 6.1 Conclusions -- 110 6.2 Future works -- 111 6.3 Academic Discussion -- 113 References -- 114PregradoDoctor(a) en IngenieríaInverse dynamic problems137 páginasapplication/pdfengUniversidad Tecnológica de PereiraDoctorado en IngenieríaFacultad de IngenieríasPereira620 - Ingeniería y operaciones afines2. Ingeniería y TecnologíaModelado hidrológicoPredicción multivarianteNivel del aguaRedes neuronales no linealesModelos autorregresivosODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todosSistema de estimación del nivel del agua de alerta temprana para mitigar riesgos por inundaciones en comunidades ribereñas del departamento del ChocóTrabajo de grado - Doctoradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesisPereira, Risaralda, Colombia[1] K. G. M. Pacheco, “Con el agua al cuello”: Una historia de batallas perdidas contra el agua y desastres por inundaciones en colombia, 1950-2011,” Agua y territorio= Water and Landscape, no. 22, pp. 77–91, 2023.[2] H. Ayala Mosquera, “Extractivismo minero y sus efectos sobre el cambio clim ́atico en el choc ́o biogeogr ́afico colombiano.,” Bolet ́ın de Antropolog ́ıa, vol. 37, no. 64, 2022.[3] R. G ́omez, J. 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Submission received: 22 Oct 2024; Revised: 20 Nov 2024; Accepted: 22 Nov 2024; Published: 26 Nov 2024.Comunidad académica y científica, Estudiantes, Docentes, InvestigadoresPublicationORIGINALTrabajo_de_grado.pdfTrabajo_de_grado.pdfapplication/pdf14954431https://repositorio.utp.edu.co/bitstreams/76fd042e-d7d1-41b4-9078-3ba25fb3c3fe/downloadb9b175f19400ac7ee83f24f78ed77c99MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.utp.edu.co/bitstreams/af705685-a2f0-42b1-a2a1-21a1d108fd15/download73a5432e0b76442b22b026844140d683MD52THUMBNAILImagen2.pngimage/png58647https://repositorio.utp.edu.co/bitstreams/bc7dbaf1-7dfc-410a-8695-8c3d6746c566/downloadaedd7c4c0e87e2a479e00ff608e075a1MD53Trabajo_de_grado.pdf.jpgTrabajo_de_grado.pdf.jpgGenerated Thumbnailimage/jpeg6931https://repositorio.utp.edu.co/bitstreams/70df8eaa-6b06-4d76-aceb-851c8909ef1a/downloadc59edac6e6d03c31c9cbc7ce22f7a3f5MD55TEXTTrabajo_de_grado.pdf.txtTrabajo_de_grado.pdf.txtExtracted texttext/plain101637https://repositorio.utp.edu.co/bitstreams/87a28b44-e7e2-4f49-9c0e-1b4ccfdf87ad/download61b3ed4749cf8735b40d7b5976fd737cMD5411059/16492oai:repositorio.utp.edu.co:11059/164922025-12-02 05:00:12.404https://creativecommons.org/licenses/by-nc-nd/4.0/Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. 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Manifiesto (manifestamos) que la OBRA objeto de la presente autorizaciónopen.accesshttps://repositorio.utp.edu.coRepositorio de la Universidad Tecnológica de 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F1dG9yKSBwYXJhIGVqZXJjZXIgZXN0b3MgZGVyZWNob3Mgc29icmUgbGEgT2JyYSB0YWwgeSBjb21vIHNlIGluZGljYSBhIGNvbnRpbnVhY2nDs246PC9wPgogICAgPG9sIHR5cGU9ImEiPgogICAgICA8bGk+UmVwcm9kdWNpciBsYSBPYnJhLCBpbmNvcnBvcmFyIGxhIE9icmEgZW4gdW5hIG8gbcOhcyBPYnJhcyBDb2xlY3RpdmFzLCB5IHJlcHJvZHVjaXIgbGEgT2JyYSBpbmNvcnBvcmFkYSBlbiBsYXMgT2JyYXMgQ29sZWN0aXZhcy48L2xpPgogICAgICA8bGk+RGlzdHJpYnVpciBjb3BpYXMgbyBmb25vZ3JhbWFzIGRlIGxhcyBPYnJhcywgZXhoaWJpcmxhcyBww7pibGljYW1lbnRlLCBlamVjdXRhcmxhcyBww7pibGljYW1lbnRlIHkvbyBwb25lcmxhcyBhIGRpc3Bvc2ljacOzbiBww7pibGljYSwgaW5jbHV5w6luZG9sYXMgY29tbyBpbmNvcnBvcmFkYXMgZW4gT2JyYXMgQ29sZWN0aXZhcywgc2Vnw7puIGNvcnJlc3BvbmRhLjwvbGk+CiAgICAgIDxsaT5EaXN0cmlidWlyIGNvcGlhcyBkZSBsYXMgT2JyYXMgRGVyaXZhZGFzIHF1ZSBzZSBnZW5lcmVuLCBleGhpYmlybGFzIHDDumJsaWNhbWVudGUsIGVqZWN1dGFybGFzIHDDumJsaWNhbWVudGUgeS9vIHBvbmVybGFzIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhLjwvbGk+CiAgICA8L29sPgogICAgPHA+TG9zIGRlcmVjaG9zIG1lbmNpb25hZG9zIGFudGVyaW9ybWVudGUgcHVlZGVuIHNlciBlamVyY2lkb3MgZW4gdG9kb3MgbG9zIG1lZGlvcyB5IGZvcm1hdG9zLCBhY3R1YWxtZW50ZSBjb25vY2lkb3MgbyBxdWUgc2UgaW52ZW50ZW4gZW4gZWwgZnV0dXJvLiBMb3MgZGVyZWNob3MgYW50ZXMgbWVuY2lvbmFkb3MgaW5jbHV5ZW4gZWwgZGVyZWNobyBhIHJlYWxpemFyIGRpY2hhcyBtb2RpZmljYWNpb25lcyBlbiBsYSBtZWRpZGEgcXVlIHNlYW4gdMOpY25pY2FtZW50ZSBuZWNlc2FyaWFzIHBhcmEgZWplcmNlciBsb3MgZGVyZWNob3MgZW4gb3RybyBtZWRpbyBvIGZvcm1hdG9zLCBwZXJvIGRlIG90cmEgbWFuZXJhIHVzdGVkIG5vIGVzdMOhIGF1dG9yaXphZG8gcGFyYSByZWFsaXphciBvYnJhcyBkZXJpdmFkYXMuIFRvZG9zIGxvcyBkZXJlY2hvcyBubyBvdG9yZ2Fkb3MgZXhwcmVzYW1lbnRlIHBvciBlbCBMaWNlbmNpYW50ZSBxdWVkYW4gcG9yIGVzdGUgbWVkaW8gcmVzZXJ2YWRvcywgaW5jbHV5ZW5kbyBwZXJvIHNpbiBsaW1pdGFyc2UgYSBhcXVlbGxvcyBxdWUgc2UgbWVuY2lvbmFuIGVuIGxhcyBzZWNjaW9uZXMgNChkKSB5IDQoZSkuPC9wPgogIDwvbGk+CiAgPGJyLz4KICA8bGk+CiAgICBSZXN0cmljY2lvbmVzLgogICAgPHA+TGEgbGljZW5jaWEgb3RvcmdhZGEgZW4gbGEgYW50ZXJpb3IgU2VjY2nDs24gMyBlc3TDoSBleHByZXNhbWVudGUgc3VqZXRhIHkgbGltaXRhZGEgcG9yIGxhcyBzaWd1aWVudGVzIHJlc3RyaWNjaW9uZXM6PC9wPgogICAgPG9sIHR5cGU9ImEiPgogICAgICA8bGk+VXN0ZWQgcHVlZGUgZGlzdHJpYnVpciwgZXhoaWJpciBww7pibGljYW1lbnRlLCBlamVjdXRhciBww7pibGljYW1lbnRlLCBvIHBvbmVyIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhIGxhIE9icmEgc8OzbG8gYmFqbyBsYXMgY29uZGljaW9uZXMgZGUgZXN0YSBMaWNlbmNpYSwgeSBVc3RlZCBkZWJlIGluY2x1aXIgdW5hIGNvcGlhIGRlIGVzdGEgbGljZW5jaWEgbyBkZWwgSWRlbnRpZmljYWRvciBVbml2ZXJzYWwgZGUgUmVjdXJzb3MgZGUgbGEgbWlzbWEgY29uIGNhZGEgY29waWEgZGUgbGEgT2JyYSBxdWUgZGlzdHJpYnV5YSwgZXhoaWJhIHDDumJsaWNhbWVudGUsIGVqZWN1dGUgcMO6YmxpY2FtZW50ZSBvIHBvbmdhIGEgZGlzcG9zaWNpw7NuIHDDumJsaWNhLiBObyBlcyBwb3NpYmxlIG9mcmVjZXIgbyBpbXBvbmVyIG5pbmd1bmEgY29uZGljacOzbiBzb2JyZSBsYSBPYnJhIHF1ZSBhbHRlcmUgbyBsaW1pdGUgbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEgbyBlbCBlamVyY2ljaW8gZGUgbG9zIGRlcmVjaG9zIGRlIGxvcyBkZXN0aW5hdGFyaW9zIG90b3JnYWRvcyBlbiBlc3RlIGRvY3VtZW50by4gTm8gZXMgcG9zaWJsZSBzdWJsaWNlbmNpYXIgbGEgT2JyYS4gVXN0ZWQgZGViZSBtYW50ZW5lciBpbnRhY3RvcyB0b2RvcyBsb3MgYXZpc29zIHF1ZSBoYWdhbiByZWZlcmVuY2lhIGEgZXN0YSBMaWNlbmNpYSB5IGEgbGEgY2zDoXVzdWxhIGRlIGxpbWl0YWNpw7NuIGRlIGdhcmFudMOtYXMuIFVzdGVkIG5vIHB1ZWRlIGRpc3RyaWJ1aXIsIGV4aGliaXIgcMO6YmxpY2FtZW50ZSwgZWplY3V0YXIgcMO6YmxpY2FtZW50ZSwgbyBwb25lciBhIGRpc3Bvc2ljacOzbiBww7pibGljYSBsYSBPYnJhIGNvbiBhbGd1bmEgbWVkaWRhIHRlY25vbMOzZ2ljYSBxdWUgY29udHJvbGUgZWwgYWNjZXNvIG8gbGEgdXRpbGl6YWNpw7NuIGRlIGVsbGEgZGUgdW5hIGZvcm1hIHF1ZSBzZWEgaW5jb25zaXN0ZW50ZSBjb24gbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEuIExvIGFudGVyaW9yIHNlIGFwbGljYSBhIGxhIE9icmEgaW5jb3Jwb3JhZGEgYSB1bmEgT2JyYSBDb2xlY3RpdmEsIHBlcm8gZXN0byBubyBleGlnZSBxdWUgbGEgT2JyYSBDb2xlY3RpdmEgYXBhcnRlIGRlIGxhIG9icmEgbWlzbWEgcXVlZGUgc3VqZXRhIGEgbGFzIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEuIFNpIFVzdGVkIGNyZWEgdW5hIE9icmEgQ29sZWN0aXZhLCBwcmV2aW8gYXZpc28gZGUgY3VhbHF1aWVyIExpY2VuY2lhbnRlIGRlYmUsIGVuIGxhIG1lZGlkYSBkZSBsbyBwb3NpYmxlLCBlbGltaW5hciBkZSBsYSBPYnJhIENvbGVjdGl2YSBjdWFscXVpZXIgcmVmZXJlbmNpYSBhIGRpY2hvIExpY2VuY2lhbnRlIG8gYWwgQXV0b3IgT3JpZ2luYWwsIHNlZ8O6biBsbyBzb2xpY2l0YWRvIHBvciBlbCBMaWNlbmNpYW50ZSB5IGNvbmZvcm1lIGxvIGV4aWdlIGxhIGNsw6F1c3VsYSA0KGMpLjwvbGk+CiAgICAgIDxsaT5Vc3RlZCBubyBwdWVkZSBlamVyY2VyIG5pbmd1bm8gZGUgbG9zIGRlcmVjaG9zIHF1ZSBsZSBoYW4gc2lkbyBvdG9yZ2Fkb3MgZW4gbGEgU2VjY2nDs24gMyBwcmVjZWRlbnRlIGRlIG1vZG8gcXVlIGVzdMOpbiBwcmluY2lwYWxtZW50ZSBkZXN0aW5hZG9zIG8gZGlyZWN0YW1lbnRlIGRpcmlnaWRvcyBhIGNvbnNlZ3VpciB1biBwcm92ZWNobyBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYS4gRWwgaW50ZXJjYW1iaW8gZGUgbGEgT2JyYSBwb3Igb3RyYXMgb2JyYXMgcHJvdGVnaWRhcyBwb3IgZGVyZWNob3MgZGUgYXV0b3IsIHlhIHNlYSBhIHRyYXbDqXMgZGUgdW4gc2lzdGVtYSBwYXJhIGNvbXBhcnRpciBhcmNoaXZvcyBkaWdpdGFsZXMgKGRpZ2l0YWwgZmlsZS1zaGFyaW5nKSBvIGRlIGN1YWxxdWllciBvdHJhIG1hbmVyYSBubyBzZXLDoSBjb25zaWRlcmFkbyBjb21vIGVzdGFyIGRlc3RpbmFkbyBwcmluY2lwYWxtZW50ZSBvIGRpcmlnaWRvIGRpcmVjdGFtZW50ZSBhIGNvbnNlZ3VpciB1biBwcm92ZWNobyBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYSwgc2llbXByZSBxdWUgbm8gc2UgcmVhbGljZSB1biBwYWdvIG1lZGlhbnRlIHVuYSBjb21wZW5zYWNpw7NuIG1vbmV0YXJpYSBlbiByZWxhY2nDs24gY29uIGVsIGludGVyY2FtYmlvIGRlIG9icmFzIHByb3RlZ2lkYXMgcG9yIGVsIGRlcmVjaG8gZGUgYXV0b3IuPC9saT4KICAgICAgPGxpPlNpIHVzdGVkIGRpc3RyaWJ1eWUsIGV4aGliZSBww7pibGljYW1lbnRlLCBlamVjdXRhIHDDumJsaWNhbWVudGUgbyBlamVjdXRhIHDDumJsaWNhbWVudGUgZW4gZm9ybWEgZGlnaXRhbCBsYSBPYnJhIG8gY3VhbHF1aWVyIE9icmEgRGVyaXZhZGEgdSBPYnJhIENvbGVjdGl2YSwgVXN0ZWQgZGViZSBtYW50ZW5lciBpbnRhY3RhIHRvZGEgbGEgaW5mb3JtYWNpw7NuIGRlIGRlcmVjaG8gZGUgYXV0b3IgZGUgbGEgT2JyYSB5IHByb3BvcmNpb25hciwgZGUgZm9ybWEgcmF6b25hYmxlIHNlZ8O6biBlbCBtZWRpbyBvIG1hbmVyYSBxdWUgVXN0ZWQgZXN0w6kgdXRpbGl6YW5kbzogKGkpIGVsIG5vbWJyZSBkZWwgQXV0b3IgT3JpZ2luYWwgc2kgZXN0w6EgcHJvdmlzdG8gKG8gc2V1ZMOzbmltbywgc2kgZnVlcmUgYXBsaWNhYmxlKSwgeS9vIChpaSkgZWwgbm9tYnJlIGRlIGxhIHBhcnRlIG8gbGFzIHBhcnRlcyBxdWUgZWwgQXV0b3IgT3JpZ2luYWwgeS9vIGVsIExpY2VuY2lhbnRlIGh1YmllcmVuIGRlc2lnbmFkbyBwYXJhIGxhIGF0cmlidWNpw7NuICh2LmcuLCB1biBpbnN0aXR1dG8gcGF0cm9jaW5hZG9yLCBlZGl0b3JpYWwsIHB1YmxpY2FjacOzbikgZW4gbGEgaW5mb3JtYWNpw7NuIGRlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZWwgTGljZW5jaWFudGUsIHTDqXJtaW5vcyBkZSBzZXJ2aWNpb3MgbyBkZSBvdHJhcyBmb3JtYXMgcmF6b25hYmxlczsgZWwgdMOtdHVsbyBkZSBsYSBPYnJhIHNpIGVzdMOhIHByb3Zpc3RvOyBlbiBsYSBtZWRpZGEgZGUgbG8gcmF6b25hYmxlbWVudGUgZmFjdGlibGUgeSwgc2kgZXN0w6EgcHJvdmlzdG8sIGVsIElkZW50aWZpY2Fkb3IgVW5pZm9ybWUgZGUgUmVjdXJzb3MgKFVuaWZvcm0gUmVzb3VyY2UgSWRlbnRpZmllcikgcXVlIGVsIExpY2VuY2lhbnRlIGVzcGVjaWZpY2EgcGFyYSBzZXIgYXNvY2lhZG8gY29uIGxhIE9icmEsIHNhbHZvIHF1ZSB0YWwgVVJJIG5vIHNlIHJlZmllcmEgYSBsYSBub3RhIHNvYnJlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBvIGEgbGEgaW5mb3JtYWNpw7NuIHNvYnJlIGVsIGxpY2VuY2lhbWllbnRvIGRlIGxhIE9icmE7IHkgZW4gZWwgY2FzbyBkZSB1bmEgT2JyYSBEZXJpdmFkYSwgYXRyaWJ1aXIgZWwgY3LDqWRpdG8gaWRlbnRpZmljYW5kbyBlbCB1c28gZGUgbGEgT2JyYSBlbiBsYSBPYnJhIERlcml2YWRhICh2LmcuLCAiVHJhZHVjY2nDs24gRnJhbmNlc2EgZGUgbGEgT2JyYSBkZWwgQXV0b3IgT3JpZ2luYWwsIiBvICJHdWnDs24gQ2luZW1hdG9ncsOhZmljbyBiYXNhZG8gZW4gbGEgT2JyYSBvcmlnaW5hbCBkZWwgQXV0b3IgT3JpZ2luYWwiKS4gVGFsIGNyw6lkaXRvIHB1ZWRlIHNlciBpbXBsZW1lbnRhZG8gZGUgY3VhbHF1aWVyIGZvcm1hIHJhem9uYWJsZTsgZW4gZWwgY2Fzbywgc2luIGVtYmFyZ28sIGRlIE9icmFzIERlcml2YWRhcyB1IE9icmFzIENvbGVjdGl2YXMsIHRhbCBjcsOpZGl0byBhcGFyZWNlcsOhLCBjb21vIG3DrW5pbW8sIGRvbmRlIGFwYXJlY2UgZWwgY3LDqWRpdG8gZGUgY3VhbHF1aWVyIG90cm8gYXV0b3IgY29tcGFyYWJsZSB5IGRlIHVuYSBtYW5lcmEsIGFsIG1lbm9zLCB0YW4gZGVzdGFjYWRhIGNvbW8gZWwgY3LDqWRpdG8gZGUgb3RybyBhdXRvciBjb21wYXJhYmxlLjwvbGk+CiAgICAgIDxsaT4KICAgICAgICBQYXJhIGV2aXRhciB0b2RhIGNvbmZ1c2nDs24sIGVsIExpY2VuY2lhbnRlIGFjbGFyYSBxdWUsIGN1YW5kbyBsYSBvYnJhIGVzIHVuYSBjb21wb3NpY2nDs24gbXVzaWNhbDoKICAgICAgICA8b2wgdHlwZT0iaSI+CiAgICAgICAgICA8bGk+UmVnYWzDrWFzIHBvciBpbnRlcnByZXRhY2nDs24geSBlamVjdWNpw7NuIGJham8gbGljZW5jaWFzIGdlbmVyYWxlcy4gRWwgTGljZW5jaWFudGUgc2UgcmVzZXJ2YSBlbCBkZXJlY2hvIGV4Y2x1c2l2byBkZSBhdXRvcml6YXIgbGEgZWplY3VjacOzbiBww7pibGljYSBvIGxhIGVqZWN1Y2nDs24gcMO6YmxpY2EgZGlnaXRhbCBkZSBsYSBvYnJhIHkgZGUgcmVjb2xlY3Rhciwgc2VhIGluZGl2aWR1YWxtZW50ZSBvIGEgdHJhdsOpcyBkZSB1bmEgc29jaWVkYWQgZGUgZ2VzdGnDs24gY29sZWN0aXZhIGRlIGRlcmVjaG9zIGRlIGF1dG9yIHkgZGVyZWNob3MgY29uZXhvcyAocG9yIGVqZW1wbG8sIFNBWUNPKSwgbGFzIHJlZ2Fsw61hcyBwb3IgbGEgZWplY3VjacOzbiBww7pibGljYSBvIHBvciBsYSBlamVjdWNpw7NuIHDDumJsaWNhIGRpZ2l0YWwgZGUgbGEgb2JyYSAocG9yIGVqZW1wbG8gV2ViY2FzdCkgbGljZW5jaWFkYSBiYWpvIGxpY2VuY2lhcyBnZW5lcmFsZXMsIHNpIGxhIGludGVycHJldGFjacOzbiBvIGVqZWN1Y2nDs24gZGUgbGEgb2JyYSBlc3TDoSBwcmltb3JkaWFsbWVudGUgb3JpZW50YWRhIHBvciBvIGRpcmlnaWRhIGEgbGEgb2J0ZW5jacOzbiBkZSB1bmEgdmVudGFqYSBjb21lcmNpYWwgbyB1bmEgY29tcGVuc2FjacOzbiBtb25ldGFyaWEgcHJpdmFkYS48L2xpPgogICAgICAgICAgPGxpPlJlZ2Fsw61hcyBwb3IgRm9ub2dyYW1hcy4gRWwgTGljZW5jaWFudGUgc2UgcmVzZXJ2YSBlbCBkZXJlY2hvIGV4Y2x1c2l2byBkZSByZWNvbGVjdGFyLCBpbmRpdmlkdWFsbWVudGUgbyBhIHRyYXbDqXMgZGUgdW5hIHNvY2llZGFkIGRlIGdlc3Rpw7NuIGNvbGVjdGl2YSBkZSBkZXJlY2hvcyBkZSBhdXRvciB5IGRlcmVjaG9zIGNvbmV4b3MgKHBvciBlamVtcGxvLCBsb3MgY29uc2FncmFkb3MgcG9yIGxhIFNBWUNPKSwgdW5hIGFnZW5jaWEgZGUgZGVyZWNob3MgbXVzaWNhbGVzIG8gYWxnw7puIGFnZW50ZSBkZXNpZ25hZG8sIGxhcyByZWdhbMOtYXMgcG9yIGN1YWxxdWllciBmb25vZ3JhbWEgcXVlIFVzdGVkIGNyZWUgYSBwYXJ0aXIgZGUgbGEgb2JyYSAo4oCcdmVyc2nDs24gY292ZXLigJ0pIHkgZGlzdHJpYnV5YSwgZW4gbG9zIHTDqXJtaW5vcyBkZWwgcsOpZ2ltZW4gZGUgZGVyZWNob3MgZGUgYXV0b3IsIHNpIGxhIGNyZWFjacOzbiBvIGRpc3RyaWJ1Y2nDs24gZGUgZXNhIHZlcnNpw7NuIGNvdmVyIGVzdMOhIHByaW1vcmRpYWxtZW50ZSBkZXN0aW5hZGEgbyBkaXJpZ2lkYSBhIG9idGVuZXIgdW5hIHZlbnRhamEgY29tZXJjaWFsIG8gdW5hIGNvbXBlbnNhY2nDs24gbW9uZXRhcmlhIHByaXZhZGEuPC9saT4KICAgICAgICA8L29sPgogICAgICA8L2xpPgogICAgICA8bGk+R2VzdGnDs24gZGUgRGVyZWNob3MgZGUgQXV0b3Igc29icmUgSW50ZXJwcmV0YWNpb25lcyB5IEVqZWN1Y2lvbmVzIERpZ2l0YWxlcyAoV2ViQ2FzdGluZykuIFBhcmEgZXZpdGFyIHRvZGEgY29uZnVzacOzbiwgZWwgTGljZW5jaWFudGUgYWNsYXJhIHF1ZSwgY3VhbmRvIGxhIG9icmEgc2VhIHVuIGZvbm9ncmFtYSwgZWwgTGljZW5jaWFudGUgc2UgcmVzZXJ2YSBlbCBkZXJlY2hvIGV4Y2x1c2l2byBkZSBhdXRvcml6YXIgbGEgZWplY3VjacOzbiBww7pibGljYSBkaWdpdGFsIGRlIGxhIG9icmEgKHBvciBlamVtcGxvLCB3ZWJjYXN0KSB5IGRlIHJlY29sZWN0YXIsIGluZGl2aWR1YWxtZW50ZSBvIGEgdHJhdsOpcyBkZSB1bmEgc29jaWVkYWQgZGUgZ2VzdGnDs24gY29sZWN0aXZhIGRlIGRlcmVjaG9zIGRlIGF1dG9yIHkgZGVyZWNob3MgY29uZXhvcyAocG9yIGVqZW1wbG8sIEFDSU5QUk8pLCBsYXMgcmVnYWzDrWFzIHBvciBsYSBlamVjdWNpw7NuIHDDumJsaWNhIGRpZ2l0YWwgZGUgbGEgb2JyYSAocG9yIGVqZW1wbG8sIHdlYmNhc3QpLCBzdWpldGEgYSBsYXMgZGlzcG9zaWNpb25lcyBhcGxpY2FibGVzIGRlbCByw6lnaW1lbiBkZSBEZXJlY2hvIGRlIEF1dG9yLCBzaSBlc3RhIGVqZWN1Y2nDs24gcMO6YmxpY2EgZGlnaXRhbCBlc3TDoSBwcmltb3JkaWFsbWVudGUgZGlyaWdpZGEgYSBvYnRlbmVyIHVuYSB2ZW50YWphIGNvbWVyY2lhbCBvIHVuYSBjb21wZW5zYWNpw7NuIG1vbmV0YXJpYSBwcml2YWRhLjwvbGk+CiAgICA8L29sPgogIDwvbGk+CiAgPGJyLz4KICA8bGk+CiAgICBSZXByZXNlbnRhY2lvbmVzLCBHYXJhbnTDrWFzIHkgTGltaXRhY2lvbmVzIGRlIFJlc3BvbnNhYmlsaWRhZC4KICAgIDxwPkEgTUVOT1MgUVVFIExBUyBQQVJURVMgTE8gQUNPUkRBUkFOIERFIE9UUkEgRk9STUEgUE9SIEVTQ1JJVE8sIEVMIExJQ0VOQ0lBTlRFIE9GUkVDRSBMQSBPQlJBIChFTiBFTCBFU1RBRE8gRU4gRUwgUVVFIFNFIEVOQ1VFTlRSQSkg4oCcVEFMIENVQUzigJ0sIFNJTiBCUklOREFSIEdBUkFOVMONQVMgREUgQ0xBU0UgQUxHVU5BIFJFU1BFQ1RPIERFIExBIE9CUkEsIFlBIFNFQSBFWFBSRVNBLCBJTVBMw41DSVRBLCBMRUdBTCBPIENVQUxRVUlFUkEgT1RSQSwgSU5DTFVZRU5ETywgU0lOIExJTUlUQVJTRSBBIEVMTEFTLCBHQVJBTlTDjUFTIERFIFRJVFVMQVJJREFELCBDT01FUkNJQUJJTElEQUQsIEFEQVBUQUJJTElEQUQgTyBBREVDVUFDScOTTiBBIFBST1DDk1NJVE8gREVURVJNSU5BRE8sIEFVU0VOQ0lBIERFIElORlJBQ0NJw5NOLCBERSBBVVNFTkNJQSBERSBERUZFQ1RPUyBMQVRFTlRFUyBPIERFIE9UUk8gVElQTywgTyBMQSBQUkVTRU5DSUEgTyBBVVNFTkNJQSBERSBFUlJPUkVTLCBTRUFOIE8gTk8gREVTQ1VCUklCTEVTIChQVUVEQU4gTyBOTyBTRVIgRVNUT1MgREVTQ1VCSUVSVE9TKS4gQUxHVU5BUyBKVVJJU0RJQ0NJT05FUyBOTyBQRVJNSVRFTiBMQSBFWENMVVNJw5NOIERFIEdBUkFOVMONQVMgSU1QTMONQ0lUQVMsIEVOIENVWU8gQ0FTTyBFU1RBIEVYQ0xVU0nDk04gUFVFREUgTk8gQVBMSUNBUlNFIEEgVVNURUQuPC9wPgogIDwvbGk+CiAgPGJyLz4KICA8bGk+CiAgICBMaW1pdGFjacOzbiBkZSByZXNwb25zYWJpbGlkYWQuCiAgICA8cD5BIE1FTk9TIFFVRSBMTyBFWElKQSBFWFBSRVNBTUVOVEUgTEEgTEVZIEFQTElDQUJMRSwgRUwgTElDRU5DSUFOVEUgTk8gU0VSw4EgUkVTUE9OU0FCTEUgQU5URSBVU1RFRCBQT1IgREHDkU8gQUxHVU5PLCBTRUEgUE9SIFJFU1BPTlNBQklMSURBRCBFWFRSQUNPTlRSQUNUVUFMLCBQUkVDT05UUkFDVFVBTCBPIENPTlRSQUNUVUFMLCBPQkpFVElWQSBPIFNVQkpFVElWQSwgU0UgVFJBVEUgREUgREHDkU9TIE1PUkFMRVMgTyBQQVRSSU1PTklBTEVTLCBESVJFQ1RPUyBPIElORElSRUNUT1MsIFBSRVZJU1RPUyBPIElNUFJFVklTVE9TIFBST0RVQ0lET1MgUE9SIEVMIFVTTyBERSBFU1RBIExJQ0VOQ0lBIE8gREUgTEEgT0JSQSwgQVVOIENVQU5ETyBFTCBMSUNFTkNJQU5URSBIQVlBIFNJRE8gQURWRVJUSURPIERFIExBIFBPU0lCSUxJREFEIERFIERJQ0hPUyBEQcORT1MuIEFMR1VOQVMgTEVZRVMgTk8gUEVSTUlURU4gTEEgRVhDTFVTScOTTiBERSBDSUVSVEEgUkVTUE9OU0FCSUxJREFELCBFTiBDVVlPIENBU08gRVNUQSBFWENMVVNJw5NOIFBVRURFIE5PIEFQTElDQVJTRSBBIFVTVEVELjwvcD4KICA8L2xpPgogIDxici8+CiAgPGxpPgogICAgVMOpcm1pbm8uCiAgICA8b2wgdHlwZT0iYSI+CiAgICAgIDxsaT5Fc3RhIExpY2VuY2lhIHkgbG9zIGRlcmVjaG9zIG90b3JnYWRvcyBlbiB2aXJ0dWQgZGUgZWxsYSB0ZXJtaW5hcsOhbiBhdXRvbcOhdGljYW1lbnRlIHNpIFVzdGVkIGluZnJpbmdlIGFsZ3VuYSBjb25kaWNpw7NuIGVzdGFibGVjaWRhIGVuIGVsbGEuIFNpbiBlbWJhcmdvLCBsb3MgaW5kaXZpZHVvcyBvIGVudGlkYWRlcyBxdWUgaGFuIHJlY2liaWRvIE9icmFzIERlcml2YWRhcyBvIENvbGVjdGl2YXMgZGUgVXN0ZWQgZGUgY29uZm9ybWlkYWQgY29uIGVzdGEgTGljZW5jaWEsIG5vIHZlcsOhbiB0ZXJtaW5hZGFzIHN1cyBsaWNlbmNpYXMsIHNpZW1wcmUgcXVlIGVzdG9zIGluZGl2aWR1b3MgbyBlbnRpZGFkZXMgc2lnYW4gY3VtcGxpZW5kbyDDrW50ZWdyYW1lbnRlIGxhcyBjb25kaWNpb25lcyBkZSBlc3RhcyBsaWNlbmNpYXMuIExhcyBTZWNjaW9uZXMgMSwgMiwgNSwgNiwgNywgeSA4IHN1YnNpc3RpcsOhbiBhIGN1YWxxdWllciB0ZXJtaW5hY2nDs24gZGUgZXN0YSBMaWNlbmNpYS48L2xpPgogICAgICA8bGk+U3VqZXRhIGEgbGFzIGNvbmRpY2lvbmVzIHkgdMOpcm1pbm9zIGFudGVyaW9yZXMsIGxhIGxpY2VuY2lhIG90b3JnYWRhIGFxdcOtIGVzIHBlcnBldHVhIChkdXJhbnRlIGVsIHBlcsOtb2RvIGRlIHZpZ2VuY2lhIGRlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSBsYSBvYnJhKS4gTm8gb2JzdGFudGUgbG8gYW50ZXJpb3IsIGVsIExpY2VuY2lhbnRlIHNlIHJlc2VydmEgZWwgZGVyZWNobyBhIHB1YmxpY2FyIHkvbyBlc3RyZW5hciBsYSBPYnJhIGJham8gY29uZGljaW9uZXMgZGUgbGljZW5jaWEgZGlmZXJlbnRlcyBvIGEgZGVqYXIgZGUgZGlzdHJpYnVpcmxhIGVuIGxvcyB0w6lybWlub3MgZGUgZXN0YSBMaWNlbmNpYSBlbiBjdWFscXVpZXIgbW9tZW50bzsgZW4gZWwgZW50ZW5kaWRvLCBzaW4gZW1iYXJnbywgcXVlIGVzYSBlbGVjY2nDs24gbm8gc2Vydmlyw6EgcGFyYSByZXZvY2FyIGVzdGEgbGljZW5jaWEgbyBxdWUgZGViYSBzZXIgb3RvcmdhZGEgLCBiYWpvIGxvcyB0w6lybWlub3MgZGUgZXN0YSBsaWNlbmNpYSksIHkgZXN0YSBsaWNlbmNpYSBjb250aW51YXLDoSBlbiBwbGVubyB2aWdvciB5IGVmZWN0byBhIG1lbm9zIHF1ZSBzZWEgdGVybWluYWRhIGNvbW8gc2UgZXhwcmVzYSBhdHLDoXMuIExhIExpY2VuY2lhIHJldm9jYWRhIGNvbnRpbnVhcsOhIHNpZW5kbyBwbGVuYW1lbnRlIHZpZ2VudGUgeSBlZmVjdGl2YSBzaSBubyBzZSBsZSBkYSB0w6lybWlubyBlbiBsYXMgY29uZGljaW9uZXMgaW5kaWNhZGFzIGFudGVyaW9ybWVudGUuPC9saT4KICAgIDwvb2w+CiAgPC9saT4KICA8YnIvPgogIDxsaT4KICAgIFZhcmlvcy4KICAgIDxvbCB0eXBlPSJhIj4KICAgICAgPGxpPkNhZGEgdmV6IHF1ZSBVc3RlZCBkaXN0cmlidXlhIG8gcG9uZ2EgYSBkaXNwb3NpY2nDs24gcMO6YmxpY2EgbGEgT2JyYSBvIHVuYSBPYnJhIENvbGVjdGl2YSwgZWwgTGljZW5jaWFudGUgb2ZyZWNlcsOhIGFsIGRlc3RpbmF0YXJpbyB1bmEgbGljZW5jaWEgZW4gbG9zIG1pc21vcyB0w6lybWlub3MgeSBjb25kaWNpb25lcyBxdWUgbGEgbGljZW5jaWEgb3RvcmdhZGEgYSBVc3RlZCBiYWpvIGVzdGEgTGljZW5jaWEuPC9saT4KICAgICAgPGxpPlNpIGFsZ3VuYSBkaXNwb3NpY2nDs24gZGUgZXN0YSBMaWNlbmNpYSByZXN1bHRhIGludmFsaWRhZGEgbyBubyBleGlnaWJsZSwgc2Vnw7puIGxhIGxlZ2lzbGFjacOzbiB2aWdlbnRlLCBlc3RvIG5vIGFmZWN0YXLDoSBuaSBsYSB2YWxpZGV6IG5pIGxhIGFwbGljYWJpbGlkYWQgZGVsIHJlc3RvIGRlIGNvbmRpY2lvbmVzIGRlIGVzdGEgTGljZW5jaWEgeSwgc2luIGFjY2nDs24gYWRpY2lvbmFsIHBvciBwYXJ0ZSBkZSBsb3Mgc3VqZXRvcyBkZSBlc3RlIGFjdWVyZG8sIGFxdcOpbGxhIHNlIGVudGVuZGVyw6EgcmVmb3JtYWRhIGxvIG3DrW5pbW8gbmVjZXNhcmlvIHBhcmEgaGFjZXIgcXVlIGRpY2hhIGRpc3Bvc2ljacOzbiBzZWEgdsOhbGlkYSB5IGV4aWdpYmxlLjwvbGk+CiAgICAgIDxsaT5OaW5nw7puIHTDqXJtaW5vIG8gZGlzcG9zaWNpw7NuIGRlIGVzdGEgTGljZW5jaWEgc2UgZXN0aW1hcsOhIHJlbnVuY2lhZGEgeSBuaW5ndW5hIHZpb2xhY2nDs24gZGUgZWxsYSBzZXLDoSBjb25zZW50aWRhIGEgbWVub3MgcXVlIGVzYSByZW51bmNpYSBvIGNvbnNlbnRpbWllbnRvIHNlYSBvdG9yZ2FkbyBwb3IgZXNjcml0byB5IGZpcm1hZG8gcG9yIGxhIHBhcnRlIHF1ZSByZW51bmNpZSBvIGNvbnNpZW50YS48L2xpPgogICAgICA8bGk+RXN0YSBMaWNlbmNpYSByZWZsZWphIGVsIGFjdWVyZG8gcGxlbm8gZW50cmUgbGFzIHBhcnRlcyByZXNwZWN0byBhIGxhIE9icmEgYXF1w60gbGljZW5jaWFkYS4gTm8gaGF5IGFycmVnbG9zLCBhY3VlcmRvcyBvIGRlY2xhcmFjaW9uZXMgcmVzcGVjdG8gYSBsYSBPYnJhIHF1ZSBubyBlc3TDqW4gZXNwZWNpZmljYWRvcyBlbiBlc3RlIGRvY3VtZW50by4gRWwgTGljZW5jaWFudGUgbm8gc2UgdmVyw6EgbGltaXRhZG8gcG9yIG5pbmd1bmEgZGlzcG9zaWNpw7NuIGFkaWNpb25hbCBxdWUgcHVlZGEgc3VyZ2lyIGVuIGFsZ3VuYSBjb211bmljYWNpw7NuIGVtYW5hZGEgZGUgVXN0ZWQuIEVzdGEgTGljZW5jaWEgbm8gcHVlZGUgc2VyIG1vZGlmaWNhZGEgc2luIGVsIGNvbnNlbnRpbWllbnRvIG11dHVvIHBvciBlc2NyaXRvIGRlbCBMaWNlbmNpYW50ZSB5IFVzdGVkLjwvbGk+CiAgICA8L29sPgogIDwvbGk+CiAgPGJyLz4KPC9vbD4K 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