Comparison of machine learning path loss model for wireless sensor networks in cassava crops
Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio...
- Autores:
-
Barrios-Ulloa, Alexis
De-La-Hoz-Franco, Emiro
Cama-Pinto, Alejandro
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13112
- Acceso en línea:
- https://hdl.handle.net/11323/13112
https://repositorio.cuc.edu.co/
- Palabra clave:
- Decision tree
K-Nearest-Neighbors
Machine learning
Path loss model
Radio wave propagation
Random Forest (RF)
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| spellingShingle |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| title_short |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_full |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_fullStr |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_full_unstemmed |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_sort |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| dc.creator.fl_str_mv |
Barrios-Ulloa, Alexis De-La-Hoz-Franco, Emiro Cama-Pinto, Alejandro |
| dc.contributor.author.none.fl_str_mv |
Barrios-Ulloa, Alexis De-La-Hoz-Franco, Emiro Cama-Pinto, Alejandro |
| dc.subject.proposal.eng.fl_str_mv |
Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| topic |
Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| description |
Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio wave propagation. In this study, we evaluate three path loss models obtained from machine learning: K-Nearest-Neighbors, Random Forest, and Decision Tree. The measurements were carried out on a cassava crop, one of Colombia's most important agricultural products. Compared to vegetation models, the use of ML allows for predictions with reduced error. |
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2023-11-22 |
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2024-07-04T13:14:06Z |
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2024-07-04T13:14:06Z 2026-11-22 |
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A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224. |
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https://hdl.handle.net/11323/13112 |
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10.1109/C358072.2023.10436224 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224. 979-8-3503-4180-5 10.1109/C358072.2023.10436224 Corporación Universidad de la Costa REDICUC - Repositorio CUC 979-8-3503-4179-9 |
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https://hdl.handle.net/11323/13112 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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2023 IEEE Colombian Caribbean Conference (C3) |
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[1] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908. [2] A. Barrios-Ulloa, P. Ariza-Colpas, H. Sánchez-Moreno, A. P. QuinteroLinero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285. [3] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, Support Vector Machine for Path Loss Predictions in Urban Environment, vol. 12255 LNCS. Springer International Publishing, 2020. [4] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365. [5] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256. [6] O. O. Shoewu, L. A. Akinyemi, and L. Oborkhale, “Towards Developing Path loss Models for Dryland and Wetland Environments,” IEEE AFRICON Conf., vol. 2019-Septe, 2019, doi: 10.1109/AFRICON46755.2019.9134041. [7] E. Greenberg and E. Klodzh, “Comparison of deterministic, empirical and physical propagation models in urban environments,” in 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), 2015, pp. 1–5, doi: 10.1109/COMCAS.2015.7360394. [8] M. Ayadi, A. Ben-Zineb, and S. Tabbane, “A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks,” IEEE Trans. Antennas Propag., vol. 65, no. 7, pp. 3675–3683, 2017, doi: 10.1109/TAP.2017.2705112. [9] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990– 1010, 2022, doi: 10.4236/ojapps.2022.126068. [10] A. Zappone, M. Di Renzo, M. Debbah, T. T. Lam, and X. Qian, “ModelAided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization,” IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 60–69, 2019, doi: 10.1109/MVT.2019.2921627. [11] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318. [12] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAVEnabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709. [13] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A MachineLearning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468. [14] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169–4188, 2021, doi: 10.1007/s11276-021-02682-3. [15] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020. [16] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778. [17] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744. [18] İ. Yazici, I. Shayea, and J. Din, “A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems,” Eng. Sci. Technol. an Int. J., vol. 44, 2023, doi: 10.1016/j.jestch.2023.101455. [19] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi:10.1109/MOCAST52088.2021.9493374. [20] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967. [21] [J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© Copyright 2024 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfBarrios-Ulloa, AlexisDe-La-Hoz-Franco, EmiroCama-Pinto, Alejandro2024-07-04T13:14:06Z2026-11-222024-07-04T13:14:06Z2023-11-22A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224.979-8-3503-4180-5https://hdl.handle.net/11323/1311210.1109/C358072.2023.10436224Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/979-8-3503-4179-9Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio wave propagation. In this study, we evaluate three path loss models obtained from machine learning: K-Nearest-Neighbors, Random Forest, and Decision Tree. The measurements were carried out on a cassava crop, one of Colombia's most important agricultural products. Compared to vegetation models, the use of ML allows for predictions with reduced error.6 páginasapplication/pdfengIEEEBarranquilla, Colombiahttps://ieeexplore-ieee-org.ezproxy.cuc.edu.co/document/10436224Comparison of machine learning path loss model for wireless sensor networks in cassava cropsCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852023 IEEE Colombian Caribbean Conference (C3)[1] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908.[2] A. Barrios-Ulloa, P. Ariza-Colpas, H. Sánchez-Moreno, A. P. QuinteroLinero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285.[3] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, Support Vector Machine for Path Loss Predictions in Urban Environment, vol. 12255 LNCS. Springer International Publishing, 2020.[4] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365.[5] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256.[6] O. O. Shoewu, L. A. Akinyemi, and L. Oborkhale, “Towards Developing Path loss Models for Dryland and Wetland Environments,” IEEE AFRICON Conf., vol. 2019-Septe, 2019, doi: 10.1109/AFRICON46755.2019.9134041.[7] E. Greenberg and E. Klodzh, “Comparison of deterministic, empirical and physical propagation models in urban environments,” in 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), 2015, pp. 1–5, doi: 10.1109/COMCAS.2015.7360394.[8] M. Ayadi, A. Ben-Zineb, and S. Tabbane, “A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks,” IEEE Trans. Antennas Propag., vol. 65, no. 7, pp. 3675–3683, 2017, doi: 10.1109/TAP.2017.2705112.[9] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990– 1010, 2022, doi: 10.4236/ojapps.2022.126068.[10] A. Zappone, M. Di Renzo, M. Debbah, T. T. Lam, and X. Qian, “ModelAided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization,” IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 60–69, 2019, doi: 10.1109/MVT.2019.2921627.[11] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318.[12] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAVEnabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709.[13] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A MachineLearning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468.[14] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169–4188, 2021, doi: 10.1007/s11276-021-02682-3.[15] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020.[16] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778.[17] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744.[18] İ. Yazici, I. Shayea, and J. Din, “A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems,” Eng. Sci. Technol. an Int. J., vol. 44, 2023, doi: 10.1016/j.jestch.2023.101455.[19] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi:10.1109/MOCAST52088.2021.9493374.[20] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967.[21] [J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220.61Decision treeK-Nearest-NeighborsMachine learningPath loss modelRadio wave propagationRandom Forest (RF)PublicationORIGINALComparison of machine learning path loss model.pdfComparison of machine learning path loss model.pdfArtículoapplication/pdf1351662https://repositorio.cuc.edu.co/bitstreams/c28862cc-00b3-4df6-8ca0-efd2764adcac/download4e4d0b98b583f5bba23cac2dc8dd5e69MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/c7185e3c-b142-4277-a184-5328b646a6dd/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTComparison of machine learning path loss model.pdf.txtComparison of machine learning path loss model.pdf.txtExtracted 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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