{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:08:21Z","timestamp":1792688901236,"version":"4.5.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-21467-8","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T15:12:39Z","timestamp":1761750759000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optimizing deep learning models for on-orbit deployment through neural architecture search"],"prefix":"10.1038","volume":"15","author":[{"given":"Roberto","family":"Del Prete","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parampuneet Kaur","family":"Thind","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Mazzeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Whitley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Papa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Long\u00e9p\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Meoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"21467_CR1","doi-asserted-by":"crossref","unstructured":"Emery, W. & Camps, A. Introduction to Satellite Remote Sensing: Atmosphere, Ocean, Land and Cryosphere Applications (Elsevier, 2017).","DOI":"10.1016\/B978-0-12-809254-5.00011-7"},{"key":"21467_CR2","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1038\/nclimate1908","volume":"3","author":"J Yang","year":"2013","unstructured":"Yang, J. et al. The role of satellite remote sensing in climate change studies. Nat. Clim. Change 3, 875\u2013883 (2013).","journal-title":"Nat. Clim. Change"},{"key":"21467_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2012.06.003","volume":"37","author":"JE Patino","year":"2013","unstructured":"Patino, J. E. & Duque, J. C. A review of regional science applications of satellite remote sensing in urban settings. Comput. Environ. Urban Syst. 37, 1\u201317 (2013).","journal-title":"Comput. Environ. Urban Syst."},{"key":"21467_CR4","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/S0065-308X(00)47012-0","volume":"47","author":"S Goetz","year":"2000","unstructured":"Goetz, S., Prince, S. & Small, J. Advances in satellite remote sensing of environmental variables for epidemiological applications. Adv. Parasitol. 47, 289\u2013307 (2000).","journal-title":"Adv. Parasitol."},{"key":"21467_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2024.3425961","author":"D Tuia","year":"2024","unstructured":"Tuia, D. et al. Artificial intelligence to advance earth observation: A review of models, recent trends, and pathways forward. IEEE Geosci. Remote Sensing Magazine. https:\/\/doi.org\/10.1109\/MGRS.2024.3425961 (2024).","journal-title":"IEEE Geosci. Remote Sensing Magazine."},{"key":"21467_CR6","doi-asserted-by":"crossref","unstructured":"Meoni, G. et al. Unlocking the use of raw multispectral earth observation imagery for onboard artificial intelligence. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing. (2024).","DOI":"10.1109\/JSTARS.2024.3418891"},{"key":"21467_CR7","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1109\/JSTARS.2024.3509734","volume":"18","author":"D Rijlaarsdam","year":"2025","unstructured":"Rijlaarsdam, D. et al. The next era for earth observation spacecraft: An overview of cognisat-6. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing 18, 2450\u20132463. https:\/\/doi.org\/10.1109\/JSTARS.2024.3509734 (2025).","journal-title":"IEEE J. Selected Topics Appl. Earth Observations Remote Sensing"},{"key":"21467_CR8","doi-asserted-by":"publisher","first-page":"16939","DOI":"10.1038\/s41598-022-19437-5","volume":"12","author":"V R\u016f\u017ei\u010dka","year":"2022","unstructured":"R\u016f\u017ei\u010dka, V. et al. Rav\u00e6n: Unsupervised change detection of extreme events using ml on-board satellites. Sci. Rep. 12, 16939 (2022).","journal-title":"Sci. Rep."},{"key":"21467_CR9","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.actaastro.2023.05.001","volume":"210","author":"A Paravano","year":"2023","unstructured":"Paravano, A., Locatelli, G. & Trucco, P. What is value in the new space economy? The end-users\u2019 perspective on satellite data and solutions. Acta Astronautica 210, 554\u2013563 (2023).","journal-title":"Acta Astronautica"},{"key":"21467_CR10","doi-asserted-by":"crossref","unstructured":"Del Prete, R. et al. Enhanced maritime monitoring via onboard processing of raw multi-spectral imagery by deep learning. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, 1713\u20131717 (IEEE, 2024).","DOI":"10.1109\/IGARSS53475.2024.10641068"},{"key":"21467_CR11","doi-asserted-by":"crossref","unstructured":"Sch\u00f6ttl, F., Spichtinger, A., Franquinet, J. & Langer, M. Real-time on-orbit fire detection on forest-2. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, 2360\u20132364 (IEEE, 2024).","DOI":"10.1109\/IGARSS53475.2024.10641618"},{"key":"21467_CR12","doi-asserted-by":"publisher","first-page":"17651","DOI":"10.1109\/JSTARS.2024.3434437","volume":"17","author":"N Long\u00e9p\u00e9","year":"2024","unstructured":"Long\u00e9p\u00e9, N. et al. Simulation of multispectral and hyperspectral eo products for onboard machine learning application. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing 17, 17651\u201317665. https:\/\/doi.org\/10.1109\/JSTARS.2024.3434437 (2024).","journal-title":"IEEE J. Selected Topics Appl. Earth Observations Remote Sensing"},{"key":"21467_CR13","doi-asserted-by":"crossref","unstructured":"Rijlaarsdam, D. et al. The next era for earth observation spacecraft: An overview of cognisat-6. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing. (2024).","DOI":"10.36227\/techrxiv.170862166.69578112\/v1"},{"key":"21467_CR14","unstructured":"Marin, A. et al. Phi-sat-2: Onboard ai apps for earth observation. Proc. Space Artif. Intell 6 (2021)."},{"key":"21467_CR15","unstructured":"Mu\u00f1oz, M. M.\u00a0C. et al. Mantis, a 12u smallsat mission taking advantage of super-resolution and artificial intelligence for high-resolution imager. In Small Satellite Conference (2024)."},{"key":"21467_CR16","doi-asserted-by":"publisher","first-page":"5113","DOI":"10.1007\/s10462-020-09816-7","volume":"53","author":"T Choudhary","year":"2020","unstructured":"Choudhary, T., Mishra, V., Goswami, A. & Sarangapani, J. A comprehensive survey on model compression and acceleration. Artif. Intell. Rev. 53, 5113\u20135155 (2020).","journal-title":"Artif. Intell. Rev."},{"key":"21467_CR17","doi-asserted-by":"crossref","unstructured":"Elsken, T., Metzen, J.\u00a0H. & Hutter, F. Neural architecture search: A survey (2019). arXiv:1808.05377.","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"21467_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110052","volume":"147","author":"M Poyser","year":"2024","unstructured":"Poyser, M. & Breckon, T. P. Neural architecture search: A contemporary literature review for computer vision applications. Pattern Recognit. 147, 110052. https:\/\/doi.org\/10.1016\/j.patcog.2023.110052 (2024).","journal-title":"Pattern Recognit."},{"key":"21467_CR19","unstructured":"Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, 6105\u20136114 (PMLR, 2019)."},{"key":"21467_CR20","doi-asserted-by":"crossref","unstructured":"Tan, M. et al. Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2820\u20132828 (2019).","DOI":"10.1109\/CVPR.2019.00293"},{"key":"21467_CR21","doi-asserted-by":"crossref","unstructured":"Su, X. et al. Vitas: Vision transformer architecture search. In European Conference on Computer Vision, 139\u2013157 (Springer, 2022).","DOI":"10.1007\/978-3-031-19803-8_9"},{"key":"21467_CR22","doi-asserted-by":"crossref","unstructured":"Wang, S., Xie, T., Cheng, J., Zhang, X. & Liu, H. Mdl-nas: A joint multi-domain learning framework for vision transformer. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20094\u201320104 (2023).","DOI":"10.1109\/CVPR52729.2023.01924"},{"key":"21467_CR23","unstructured":"Cai, H., Zhu, L. & Han, S. ProxylessNAS: Direct neural architecture search on target task and hardware. In International Conference on Learning Representations (2019)."},{"key":"21467_CR24","doi-asserted-by":"publisher","first-page":"6092","DOI":"10.1109\/TGRS.2020.3020424","volume":"59","author":"C Peng","year":"2020","unstructured":"Peng, C., Li, Y., Jiao, L. & Shang, R. Efficient convolutional neural architecture search for remote sensing image scene classification. IEEE Trans. Geosci. Remote Sensing 59, 6092\u20136105 (2020).","journal-title":"IEEE Trans. Geosci. Remote Sensing"},{"key":"21467_CR25","doi-asserted-by":"publisher","first-page":"10978","DOI":"10.3390\/app122110978","volume":"12","author":"C Li","year":"2022","unstructured":"Li, C. et al. Efficient object detection in sar images based on computation-aware neural architecture search. Appl. Sci. 12, 10978 (2022).","journal-title":"Appl. Sci."},{"key":"21467_CR26","unstructured":"Kadway, C. et al. Zero-shot embedded neural architecture search for on-board satellite tasks & hardware accelerators. In Proceedings of SPAICE2024: The First Joint European Space Agency\/IAA Conference on AI in and for Space, 175\u2013179 (2024)."},{"key":"21467_CR27","doi-asserted-by":"crossref","unstructured":"Gholami, A. et al. A survey of quantization methods for efficient neural network inference. In Low-power computer vision, 291\u2013326 (Chapman and Hall\/CRC, 2022).","DOI":"10.1201\/9781003162810-13"},{"key":"21467_CR28","doi-asserted-by":"publisher","first-page":"2900","DOI":"10.1109\/TPAMI.2023.3334614","volume":"46","author":"Y He","year":"2023","unstructured":"He, Y. & Xiao, L. Structured pruning for deep convolutional neural networks: A survey. IEEE Trans. Pattern Anal. Machine Intell. 46, 2900\u20132919 (2023).","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"21467_CR29","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge distillation: A survey. Int. J. Computer Vision 129, 1789\u20131819 (2021).","journal-title":"Int. J. Computer Vision"},{"key":"21467_CR30","unstructured":"Zhang, B., Cheng, D., Zhang, Y., Liu, F. & Tian, J. Lossless model compression via joint low-rank factorization optimization. arXiv preprint arXiv:2412.06867 (2024)."},{"key":"21467_CR31","unstructured":"He, Y., Jiang, L. & Wu, D. Convolutional neural network compression based on low-rank decomposition. arXiv preprint arXiv:2408.16289 (2024)."},{"key":"21467_CR32","doi-asserted-by":"crossref","unstructured":"Rigamonti, R., Sironi, A., Lepetit, V. & Fua, P. Learning separable filters. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).","DOI":"10.1109\/CVPR.2013.355"},{"key":"21467_CR33","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M. & de Freitas, N. Predicting parameters in deep learning (2014). arXiv:1306.0543."},{"key":"21467_CR34","doi-asserted-by":"publisher","first-page":"7682","DOI":"10.1109\/TPAMI.2024.3392941","volume":"46","author":"L Papa","year":"2024","unstructured":"Papa, L., Russo, P., Amerini, I. & Zhou, L. A survey on efficient vision transformers: Algorithms, techniques, and performance benchmarking. IEEE Trans. Pattern Anal. Machine Intell. 46, 7682\u20137700. https:\/\/doi.org\/10.1109\/TPAMI.2024.3392941 (2024).","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"21467_CR35","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3390\/informatics8040077","volume":"8","author":"A Alqahtani","year":"2021","unstructured":"Alqahtani, A., Xie, X. & Jones, M. W. Literature review of deep network compression. Informatics 8, 77. https:\/\/doi.org\/10.3390\/informatics8040077 (2021).","journal-title":"Informatics"},{"key":"21467_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3623402","volume":"14","author":"B Rokh","year":"2023","unstructured":"Rokh, B., Azarpeyvand, A. & Khanteymoori, A. A comprehensive survey on model quantization for deep neural networks in image classification. ACM Trans. Intell. Syst. Technol. 14, 1\u201350. https:\/\/doi.org\/10.1145\/3623402 (2023).","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"21467_CR37","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/tc.2020.2995593","volume":"70","author":"C Gong","year":"2021","unstructured":"Gong, C. et al. Vecq: Minimal loss dnn model compression with vectorized weight quantization. IEEE Trans. Comput. 70, 696\u2013710. https:\/\/doi.org\/10.1109\/tc.2020.2995593 (2021).","journal-title":"IEEE Trans. Comput."},{"key":"21467_CR38","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MSP.2017.2765695","volume":"35","author":"Y Cheng","year":"2018","unstructured":"Cheng, Y., Wang, D., Zhou, P. & Zhang, T. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Process. Magazine 35, 126\u2013136 (2018).","journal-title":"IEEE Signal Process. Magazine"},{"key":"21467_CR39","doi-asserted-by":"crossref","unstructured":"Kokhazadeh, M., Keramidas, G., Kelefouras, V. & Stamoulis, I. Denseflex: A low rank factorization methodology for adaptable dense layers in dnns. In Proceedings of the 21st ACM International Conference on Computing Frontiers, 21\u201331 (2024).","DOI":"10.1145\/3649153.3649183"},{"key":"21467_CR40","doi-asserted-by":"publisher","first-page":"3048","DOI":"10.1109\/TPAMI.2021.3055564","volume":"44","author":"L Wang","year":"2021","unstructured":"Wang, L. & Yoon, K.-J. Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks. IEEE Trans. Pattern Anal. Machine Intell. 44, 3048\u20133068 (2021).","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"21467_CR41","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland, J. H. Genetic algorithms. Sci. Am. 267, 66\u201373 (1992).","journal-title":"Sci. Am."},{"key":"21467_CR42","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"LP Kaelbling","year":"1996","unstructured":"Kaelbling, L. P., Littman, M. L. & Moore, A. W. Reinforcement learning: A survey. J. Artif. Intell. Res. 4, 237\u2013285 (1996).","journal-title":"J. Artif. Intell. Res."},{"key":"21467_CR43","unstructured":"Zela, A., Siems, J. & Hutter, F. Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search. arXiv preprint arXiv:2001.10422 (2020)."},{"key":"21467_CR44","unstructured":"Ru, B., Wan, X., Dong, X. & Osborne, M. Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels. arXiv preprint arXiv:2006.07556 (2020)."},{"key":"21467_CR45","unstructured":"Lopes, V.\u00a0F. Improving Neural Architecture Search With Bayesian Optimization and Generalization Mechanisms. Ph.D. thesis, Universidade da Beira Interior (Portugal) (2024)."},{"key":"21467_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110624","volume":"146","author":"Z Cai","year":"2023","unstructured":"Cai, Z., Chen, L., Zeng, S., Lai, Y. & Liu, H.-L. Est-nas: An evolutionary strategy with gradient descent for neural architecture search. Appl. Soft Comput. 146, 110624 (2023).","journal-title":"Appl. Soft Comput."},{"key":"21467_CR47","doi-asserted-by":"publisher","first-page":"89602","DOI":"10.1109\/ACCESS.2021.3090918","volume":"9","author":"S Santra","year":"2021","unstructured":"Santra, S., Hsieh, J.-W. & Lin, C.-F. Gradient descent effects on differential neural architecture search: A survey. IEEE Access 9, 89602\u201389618 (2021).","journal-title":"IEEE Access"},{"key":"21467_CR48","unstructured":"Liu, H., Simonyan, K. & Yang, Y. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)."},{"key":"21467_CR49","doi-asserted-by":"publisher","first-page":"9339","DOI":"10.1109\/JSTARS.2022.3217047","volume":"15","author":"G Liu","year":"2022","unstructured":"Liu, G., Li, Y., Chen, Y., Shang, R. & Jiao, L. Pol-nas: A neural architecture search method with feature selection for polsar image classification. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing 15, 9339\u20139354 (2022).","journal-title":"IEEE J. Selected Topics Appl. Earth Observations Remote Sensing"},{"key":"21467_CR50","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/rs15010091","volume":"15","author":"P Gudzius","year":"2022","unstructured":"Gudzius, P., Kurasova, O., Darulis, V. & Filatovas, E. Automl-based neural architecture search for object recognition in satellite imagery. Remote Sensing 15, 91 (2022).","journal-title":"Remote Sensing"},{"key":"21467_CR51","doi-asserted-by":"crossref","unstructured":"Cassimon, A., Reiter, P., Mercelis, S. & Mets, K. Designing a classifier for active fire detection from multispectral satellite imagery using neural architecture search. arXiv preprint arXiv:2410.05425 (2024).","DOI":"10.1109\/JSTARS.2025.3556550"},{"key":"21467_CR52","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Doll\u00e1r, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2980\u20132988, https:\/\/doi.org\/10.1109\/ICCV.2017.324 (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"21467_CR53","unstructured":"NVIDIA Corporation. Nvidia jetson agx orin series (2022). Accessed: 2025-05-02."},{"key":"21467_CR54","unstructured":"Intel Corporation. Intel\u00ae movidius$$^\\text{TM}$$ myriad$$^\\text{ TM }$$ x vision processing unit (2017). Accessed: 2025-05-02."},{"key":"21467_CR55","unstructured":"Pultarova, T. Spacex to launch 1st space-hardened nvidia ai gpu on upcoming rideshare mission (2024). Accessed: 2025-05-02."},{"key":"21467_CR56","unstructured":"Ubotica Technologies. D-orbit\u2019s wild ride ion mission (2021). Accessed: 2025-05-02."},{"key":"21467_CR57","doi-asserted-by":"crossref","unstructured":"Vasu, P. K.\u00a0A., Gabriel, J., Zhu, J., Tuzel, O. & Ranjan, A. Mobileone: An improved one millisecond mobile backbone. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 7907\u20137917 (2023).","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"21467_CR58","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"21467_CR59","unstructured":"European Space Agency (ESA). The discovery campaign on cognitive cloud computing (2025). Accessed on 2025-09-03."},{"key":"21467_CR60","doi-asserted-by":"crossref","unstructured":"Meoni, G. et al. E2e: Onboard satellite real-time classification of thermal hotspots events on optical raw data. Astrodynamics 1\u201317 (2025).","DOI":"10.1007\/s42064-024-0249-x"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21467-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21467-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21467-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T15:13:05Z","timestamp":1761750785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21467-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21467"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-21467-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6753230\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]},"assertion":[{"value":"26 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37783"}}