[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120173-en":3,"doc-seo-120173-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120173,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Environmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks","Environmental Surveillance through Machine Learning proposes using interconnected optical mesh networks for early earthquake detection and localization by leveraging existing terrestrial fiber infrastructure. The work employs a waveplate model to simulate fiber-cable strains from real ground displacement data of seven earthquakes, generating polarization evolution datasets to train and validate a machine learning detector for primary wave arrivals. Validation exceeds 95% accuracy, and testing with a M4.3 earthquake uses three interconnected mesh networks as a smart sensing grid to triangulate epicenter coordinates and estimate fiber-to-epicenter distance, enabling earlier municipal warnings.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nEnvironmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks  \nOriginal  \nEnvironmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks / Awad, Hasan; Usmani, Fehmida; Virgillito, Emanuele; Bratovich, Rudi; Proietti, Roberto; Straullu, Stefano; Aquilino, Francesco; Pastorelli, Rosanna; Curri, Vittorio. -In: SENSORS. -ISSN 1424-8220. -ELETTRONICO. -24:10(2024) .[10.3390/s24103041]  \nAvailability:  \nThis version is available at: 11583/2987077 since: 2024-05-18T07:55:00Z  \nPublisher: MDPI  \nPublished  \nDOI:10.3390/s24103041  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n07 November 2024  \n sensors   \nArticle  \nEnvironmental Surveillance through Machine  \nLearning-Empowered Utilization of Optical Networks  \nHasan Awad 1, *, Fehmida Usmani 1,2, Emanuele Virgillito 1 , Rudi Bratovich 3, Roberto Proietti 1, Stefano Straullu 4 , Francesco Aquilino 4 , Rosanna Pastorelli 3 and Vittorio Curri 1  \nCitation: Awad, H.; Usmani, F.; Virgillito, E.; Bratovich, R.; Proietti, R.; Straullu, S.; Aquilino, F.; Pastorelli, R.; Curri, V. Environmental Surveillance through Machine LearningEmpowered Utilization of Optical Networks. Sensors 2024, 24, 3041 . [https://doi.org/10.3390/s24103041](https://doi.org/10.3390/s24103041)  \nAcademic Editor: Gabriele Bolognini and Yuliya Semenova  \nReceived: 12 March 2024  \nRevised: 5 May 2024  \nAccepted: 9 May 2024  \nPublished: 10 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Electronics and Telecommunications, Polytechnic University of Turin, 10129 Turin, Italy; fehmida.usmani@polito.it (F.U.); emanuele.virgillito@polito.it (E.V.); roberto.proietti@polito.it (R.P.); vittorio.curri@polito.it (V.C.)  \n2 School of Electrical Engineering and Computer Science (SEECS), National University of Sciences & Technology (NUST), Islamabad 45400, Pakistan  \n3 SM-Optics, 20093 Cologno Monzese, Italy; [rudi.bratovich@sm-optics.com](rudi.bratovich@sm-optics.com) (R.B.); [rosanna.pastorelli@sm-optics.com](rosanna.pastorelli@sm-optics.com) (R.P.)  \n4 LINKS Foundation, 10129 Turin, Italy; [stefano.straullu@linksfoundation.com](stefano.straullu@linksfoundation.com) (S.S.); [francesco.aquilino@linksfoundation.com](francesco.aquilino@linksfoundation.com) (F.A.)  \n* Correspondence: hasan.awad@polito.it  \nAbstract: We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation f","cbCailUpbsdN05Dg","https://ap.wps.com/l/cbCailUpbsdN05Dg","pdf",1524555,1,14,"English","en",105,"# Abstract\n# Introduction\n## Earthquake risk and monitoring challenges\n# Method Overview\n## Optical mesh sensing grid and waveplate-based simulation\n## Machine learning detection of primary waves\n# Validation and Testing\n## Accuracy results and detection latency\n## Epicenter localization and early-warning timeline","[{\"question\":\"How does the system detect earthquakes early in this approach?\",\"answer\":\"It uses interconnected optical mesh networks and a machine learning model trained to detect primary wave arrivals that precede destructive surface waves.\"},{\"question\":\"What data and modeling are used to train the machine learning model?\",\"answer\":\"The method integrates real ground displacement records from seven earthquakes and uses a waveplate model to simulate strains in fiber cables, producing polarization evolution data for training and validation.\"},{\"question\":\"How are epicenter coordinates and warning times determined during testing?\",\"answer\":\"Testing with an M4.3 earthquake uses three interconnected mesh networks as a smart sensing grid, applies triangulation to localize the epicenter and estimates fiber-to-epicenter distance, yielding detection latency to support early warning from nearby to more distant areas.\"}]","Environmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks | 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does the system detect earthquakes early in this approach?","Question",{"text":75,"@type":76},"It uses interconnected optical mesh networks and a machine learning model trained to detect primary wave arrivals that precede destructive surface waves.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and modeling are used to train the machine learning model?",{"text":80,"@type":76},"The method integrates real ground displacement records from seven earthquakes and uses a waveplate model to simulate strains in fiber cables, producing polarization evolution data for training and validation.",{"name":82,"@type":73,"acceptedAnswer":83},"How are epicenter coordinates and warning times determined during testing?",{"text":84,"@type":76},"Testing with an M4.3 earthquake uses three interconnected mesh networks as a smart sensing grid, applies triangulation to localize the epicenter and estimates fiber-to-epicenter distance, yielding detection latency to support early warning from 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