{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T13:37:19Z","timestamp":1770730639086,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Project of Sichuan Science and Technology Department","award":["23ZDYF0290"],"award-info":[{"award-number":["23ZDYF0290"]}]},{"name":"Key Research and Development Project of Sichuan Science and Technology Department","award":["23ZDXM23"],"award-info":[{"award-number":["23ZDXM23"]}]},{"name":"Key Research and Development Project of Sichuan Science and Technology Department","award":["2024NSFSC0841"],"award-info":[{"award-number":["2024NSFSC0841"]}]},{"name":"Soft Science Project of China Meteorological Administration","award":["23ZDYF0290"],"award-info":[{"award-number":["23ZDYF0290"]}]},{"name":"Soft Science Project of China Meteorological Administration","award":["23ZDXM23"],"award-info":[{"award-number":["23ZDXM23"]}]},{"name":"Soft Science Project of China Meteorological Administration","award":["2024NSFSC0841"],"award-info":[{"award-number":["2024NSFSC0841"]}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["23ZDYF0290"],"award-info":[{"award-number":["23ZDYF0290"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["23ZDXM23"],"award-info":[{"award-number":["23ZDXM23"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2024NSFSC0841"],"award-info":[{"award-number":["2024NSFSC0841"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Demodulation and decoding are pivotal for the eLoran system\u2019s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.<\/jats:p>","DOI":"10.3390\/rs16173349","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T09:21:00Z","timestamp":1725873660000},"page":"3349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Research on ELoran Demodulation Algorithm Based on Multiclass Support Vector Machine"],"prefix":"10.3390","volume":"16","author":[{"given":"Shiyao","family":"Liu","sequence":"first","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Baorong","family":"Yan","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Yu","family":"Hua","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Shougang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Key Laboratory of Time and Frequency Standards, Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Jun","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9852-9233","authenticated-orcid":false,"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Dong","family":"Yang","sequence":"additional","affiliation":[{"name":"Sichuan Meteorological Service Centre, Chengdu 610072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Son, P.W., Park, S.G., Han, Y., Seo, K., and Fang, T.H. (2023). Demonstration of the Feasibility of the Korean eLoran System as a Resilient PNT in a Testbed. Remote Sens., 15.","DOI":"10.3390\/rs15143586"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dardanelli, G., and Maltese, A. (2022). On the accuracy of cadastral marks: Statistical analyses to assess the congruence among GNSS-based positioning and official maps. Remote Sens., 14.","DOI":"10.3390\/rs14164086"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, K.Q., Yuan, J.B., Yan, W.H., Yang, C.Z., Guo, W., Li, S.F., and Hua, Y. (2022). A Shrink-Branch-Bound Algorithm for eLoran Pseudorange Positioning Initialization. Remote Sens., 14.","DOI":"10.3390\/rs14081781"},{"key":"ref_4","first-page":"11","article-title":"GNSS Vulnerability Analysis and Assessment","volume":"46","author":"Zhao","year":"2014","journal-title":"J. Aeronaut. Astronaut. Aviat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1017\/S0373463308005213","article-title":"GPS Jamming and the Impact on Maritime Navigation","volume":"62","author":"Grant","year":"2009","journal-title":"J. Navig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6864","DOI":"10.48084\/etasr.3908","article-title":"Complexity and Limitations of GNSS Signal Reception in Highly Obstructed Environments","volume":"11","author":"Hussain","year":"2021","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_7","first-page":"42","article-title":"Globle Navigation Satellite System (GNSS) Spoofing: A Review of Growing Risks and Mitigation Steps","volume":"6","author":"Dinesh","year":"2013","journal-title":"Def. ST Tech. Bull."},{"key":"ref_8","unstructured":"Van Willigen, D., Offermans, G.W.A., and Helwig, A.W.S. (1998, January 20\u201323). EUROFIX: Definition and current status. Proceedings of the IEEE 1998 Position Location and Navigation Symposium, Palm Springs, CA, USA."},{"key":"ref_9","unstructured":"Wu, H.T. (2002). Study on the Independent Time Service and Date Communication over Loran-C. [Ph.D. Thesis, University of Chinese Academy of Sciences (National Time Service Center)]. Available online: https:\/\/d.wanfangdata.com.cn\/thesis\/W010252."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Johnson, G.W., Swaszek, P.F., Hartnett, R.J., Shalaev, R., and Wiggins, M. (2007, January 16\u201317). An Evaluation of eLoran as a Backup to GPS. Proceedings of the 2007 IEEE Conference on Technologies for Homeland Security, Woburn, MA, USA.","DOI":"10.1109\/THS.2007.370027"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yan, W.H., Zhao, K.J., Li, S.F., Wang, X.H., and Hua, Y. (2020). Precise Loran-C Signal Acquisition Based on Envelope Delay Correlation Method. Sensors, 20.","DOI":"10.3390\/s20082329"},{"key":"ref_12","first-page":"3592","article-title":"A Cycle Identification Algorithm for enhanced LOng RAnge Navigation Signal Based on Skywave Reconstruction Technology","volume":"44","author":"Liu","year":"2022","journal-title":"J. Electron. Inf."},{"key":"ref_13","first-page":"356","article-title":"Design and Implementation of Loran-C Datalink","volume":"4","author":"Li","year":"2006","journal-title":"Inf. Elect. Eng."},{"key":"ref_14","first-page":"1407","article-title":"Research of Loran-C data demodulation and decoding technology","volume":"33","author":"Li","year":"2012","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MAES.2007.4350283","article-title":"Loran Data Modulation: A Primer [AESS Tutorial IV]","volume":"22","author":"Lo","year":"2007","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yuan, J.B., Yan, W.H., Li, S.F., and Hua, Y. (2020). Demodulation Method for Loran-C at Low SNR Based on Envelope Correlation\u2013Phase Detection. Sensors, 20.","DOI":"10.3390\/s20164535"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lyu, B.Y., Hua, Y., Yan, W.H., Yuan, J.B., and Li, S.F. (2022, January 18\u201320). Data demodulation algorithm of enhanced Loran system. Proceedings of the International Conference on Electronic Information Technology (EIT 2022), Chengdu, China.","DOI":"10.1117\/12.2638821"},{"key":"ref_18","unstructured":"Williams, P., and Last, D. (2023, January 3\u20136). Modelling Loran-C envelope-to-cycle differences in mountainous terrain. Proceedings of the 32nd Annual Meeting, International Loran Association, Boulder, CO, USA. Available online: https:\/\/www.loran.org\/proceedings\/Meeting2003\/Session6\/WmsLastILA03ECD.pdf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yan, W.H., Dong, M., Li, S.F., Yang, C.Z., Yuan, J.B., Hu, Z.P., and Hua, Y. (2022). An eLoran Signal Cycle Identification Method Based on Joint Time\u2013Frequency Domain. Remote Sens., 14.","DOI":"10.3390\/rs14020250"},{"key":"ref_20","first-page":"767","article-title":"Research on GRI Combination Design of eLORAN System","volume":"44","author":"Liu","year":"2022","journal-title":"J. Electron. Inf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1002\/navi.142","article-title":"Analysis, modelling and mitigation of cross-rate interference in eLoran","volume":"63","author":"Safar","year":"2016","journal-title":"J. Navig."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1017\/S0373463399008231","article-title":"Eurofix system and its developments","volume":"52","author":"Offermans","year":"1999","journal-title":"J. Navig."},{"key":"ref_23","unstructured":"(2018). Transmitted Enhanced Loran (eLoran) Signal Standard for Tri-State Pulse Position Modulation (Standard No. SAE 9990\/1-2018). Available online: https:\/\/www.antpedia.com\/standard\/1532228581.html."},{"key":"ref_24","unstructured":"Wu, H.T., Li, X.H., Zhang, H.J., Gao, H.J., and Bian, Y.J. (2002, January 31). UTC message broadcasting over Loran-C data channel. Proceedings of the 2002 IEEE International Frequency Control Symposium and PDA Exhibition (Cat. No.02CH37234), New Orleans, LA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TAES.2007.4285358","article-title":"Loran data modulation: Extensions and examples","volume":"43","author":"Lo","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_26","unstructured":"Helwig, A., Offermans, G., Stout, C., and Schue, C. (2011). eLoran System Definition and Signal Specification Tutorial, International Loran Association. Available online: https:\/\/www.sigidwiki.com\/images\/c\/ca\/UrsaNav_ILA-40_eLoran_System_Definition_%26_Signal_Specification_Tutorial.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sooch, S.K., Gupta, M., and Kumar, R. (2020, January 5\u20136). Implementing Cyclic Redundancy Check as Error Correction Technique in HDLC. Proceedings of the International Conference on Research in Management & Technovation, Nagpur, India.","DOI":"10.15439\/2020KM13"},{"key":"ref_28","first-page":"8","article-title":"Design of HDLC Controller with CRC Generation Using VHD","volume":"4","author":"Ahmad","year":"2014","journal-title":"Int. J. Mod. Eng. Res."},{"key":"ref_29","first-page":"12","article-title":"Based on reed solomon code design of a flash memory controller","volume":"34","author":"Wu","year":"2011","journal-title":"Electr. Measur. Technol."},{"key":"ref_30","first-page":"1632","article-title":"Application of low complexity Reed-Solomon decoder in seismic exploration","volume":"31","author":"Wang","year":"2010","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_31","unstructured":"Li, S.F. (2013). Study on the Methods and Techniques of eLoran Signal Received. [Ph.D. Thesis, University of Chinese Academy of Sciences (National Time Service Center)]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Truong, T.X., Nhu, V.-H., Phuong, D.T.N., Nghi, L.T., Hung, N.N., Hoa, P.V., and Bui, D.T. (2023). A New Approach Based on Tensor Flow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas. Remote Sens., 15.","DOI":"10.3390\/rs15143458"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, W., Song, C., Liu, C., Fu, Q., An, T., Wang, Y., Sun, X., Wen, N., Tang, H., and Wang, Q. (2023). A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China. Remote Sens., 15.","DOI":"10.3390\/rs15143483"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification, Springer.","DOI":"10.1007\/978-1-4899-7641-3"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1162\/089976698300017575","article-title":"Properties of support vector machines","volume":"10","author":"Pontil","year":"1998","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"ref_39","unstructured":"Zhou, Z.H. (2016). Machine Learning, Tsinghua University Press. [1st ed.]."},{"key":"ref_40","first-page":"31","article-title":"Support Vector Machines and Kernel Methods: The New Generation of Learning Machines","volume":"23","author":"Cristianini","year":"2002","journal-title":"AI Mag."},{"key":"ref_41","unstructured":"Yang, C.C., Lee, W.J., and Lee, S.J. (2006, January 16\u201321). Learning of kernel functions in support vector machines. Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s10115-019-01335-4","article-title":"Parameter investigation of support vector machine classifier with kernel functions","volume":"61","author":"Tharwat","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1260\/1748-3018.8.2.163","article-title":"Kernel parameter selection for support vector machine classification","volume":"8","author":"Liu","year":"2014","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Thomasberger, A., Nielsen, M.M., Flindt, M.R., Pawar, S., and Svane, N. (2023). Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15143600"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2721","DOI":"10.1007\/s10916-011-9748-4","article-title":"Diagnosis of diabetes diseases using an artificial immune recognition system2 (AIRS2) with fuzzy k-nearest neighbor","volume":"36","author":"Chikh","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Akbulut, Y., Sengur, A., Guo, Y., and Smarandache, F. (2017). NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier. Symmetry, 9.","DOI":"10.3390\/sym9090179"},{"key":"ref_54","first-page":"1","article-title":"Learning k for kNN Classification","volume":"8","author":"Zhang","year":"2017","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TSM.2007.907607","article-title":"Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes","volume":"20","author":"He","year":"2007","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1186\/s40064-016-2941-7","article-title":"The distance function effect on k-nearest neighbor classification for medical datasets","volume":"5","author":"Hu","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A., and Bahrami, S. (2020). Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12020266"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7174","DOI":"10.1016\/j.eswa.2010.04.014","article-title":"A fall detection system using k-nearest neighbor classifier","volume":"37","author":"Liu","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_59","first-page":"24","article-title":"KNN-CF Approach: Incorporating Certainty Factor to kNN Classification","volume":"11","author":"Zhang","year":"2010","journal-title":"IEEE Intell. Inform. Bull."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","article-title":"A fuzzy k-nearest neighbor algorithm","volume":"15","author":"Keller","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3349\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:52:15Z","timestamp":1760111535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":60,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173349"],"URL":"https:\/\/doi.org\/10.3390\/rs16173349","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]}}}