{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:35:40Z","timestamp":1772724940670,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62101531"],"award-info":[{"award-number":["62101531"]}]},{"name":"National Natural Science Foundation of China","award":["61731022"],"award-info":[{"award-number":["61731022"]}]},{"name":"National Natural Science Foundation of China","award":["2019QZKK030701"],"award-info":[{"award-number":["2019QZKK030701"]}]},{"name":"National Natural Science Foundation of China","award":["XDA19090300"],"award-info":[{"award-number":["XDA19090300"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["62101531"],"award-info":[{"award-number":["62101531"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["61731022"],"award-info":[{"award-number":["61731022"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["2019QZKK030701"],"award-info":[{"award-number":["2019QZKK030701"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["XDA19090300"],"award-info":[{"award-number":["XDA19090300"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["62101531"],"award-info":[{"award-number":["62101531"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["61731022"],"award-info":[{"award-number":["61731022"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019QZKK030701"],"award-info":[{"award-number":["2019QZKK030701"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19090300"],"award-info":[{"award-number":["XDA19090300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications.<\/jats:p>","DOI":"10.3390\/rs16224251","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T12:04:25Z","timestamp":1731585865000},"page":"4251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution"],"prefix":"10.3390","volume":"16","author":[{"given":"Yujie","family":"Mao","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Guojin","family":"He","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Kashgar Aerospace Information Research Institute, Kashgar 844000, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"given":"Guizhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Kashgar Aerospace Information Research Institute, Kashgar 844000, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5594-0815","authenticated-orcid":false,"given":"Ranyu","family":"Yin","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Kashgar Aerospace Information Research Institute, Kashgar 844000, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"given":"Yan","family":"Peng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Kashgar Aerospace Information Research Institute, Kashgar 844000, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3469-2613","authenticated-orcid":false,"given":"Bin","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., and Zhang, L. (2020). Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models. Remote Sens. Environ., 237.","DOI":"10.1016\/j.rse.2019.111322"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Peng, X., He, G., Wang, G., Yin, R., and Wang, J. (2024). A Weakly Supervised Semantic Segmentation Framework for Medium-Resolution Forest Classification with Noisy Labels and GF-1 WFV Images. IEEE Trans. Geosci. Remote Sens., 62.","DOI":"10.1109\/TGRS.2024.3404953"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, R., He, G., Yin, R., Wang, G., Zhang, Z., Long, T., Peng, Y., and Wang, J. (2024). A Novel Weakly-Supervised Method Based on the Segment Anything Model for Seamless Transition from Classification to Segmentation: A Case Study in Segmenting Latent Photovoltaic Locations. Int. J. Appl. Earth Obs. Geoinf., 130.","DOI":"10.1016\/j.jag.2024.103929"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object Detection in Optical Remote Sensing Images: A Survey and a New Benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.inffus.2022.10.007","article-title":"Image Super-Resolution: A Comprehensive Review, Recent Trends, Challenges and Applications","volume":"91","author":"Lepcha","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-Based Super-Resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sun, J., Zhu, J., and Tappen, M.F. (2010, January 13\u201318). Context-Constrained Hallucination for Image Super-Resolution. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540206"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TPAMI.2010.25","article-title":"Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior","volume":"32","author":"Kim","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep Learning for Single Image Super-Resolution: A Brief Review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2016, January 8\u201316). Accelerating the Super-Resolution Convolutional Neural Network. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 10\u201317). SwinIR: Image Restoration Using Swin Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_17","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the Computer Vision\u2014ECCV 2018."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., and Shen, H. (2020, January 23\u201328). Single Image Super-Resolution via a Holistic Attention Network. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"ref_19","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., and Schmid, C. (2021, January 10\u201317). Segmenter: Transformer for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W. (2021, January 20\u201325). Pre-Trained Image Processing Transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., and Yu, F. (2023, January 1). Dual Aggregation Transformer for Image Super-Resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., and Dong, C. (2023, January 17\u201324). Activating More Pixels in Image Super-Resolution Transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Choi, H., Lee, J., and Yang, J. (2023, January 17\u201324). N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00206"},{"key":"ref_27","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., and Yang, X. (2024, January 7\u201311). Recursive Generalization Transformer for Image Super-Resolution. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TIP.2023.3349004","article-title":"TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution","volume":"33","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tan, Z., Lv, Q., Li, J., Zhu, B., and Liu, Y. (2024). An Efficient Hybrid CNN-Transformer Approach for Remote Sensing Super-Resolution. Remote Sens., 16.","DOI":"10.3390\/rs16050880"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shang, J., Gao, M., Li, Q., Pan, J., Zou, G., and Jeon, G. (2023). Hybrid-Scale Hierarchical Transformer for Remote Sensing Image Super-Resolution. Remote Sens., 15.","DOI":"10.3390\/rs15133442"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.inffus.2021.09.005","article-title":"Real-World Single Image Super-Resolution: A Brief Review","volume":"79","author":"Chen","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zabalza, M., and Bernardini, A. (2022). Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism. Remote Sens., 14.","DOI":"10.3390\/rs14122890"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1111\/j.1467-8306.2004.09402009.x","article-title":"On the First Law of Geography: A Reply","volume":"94","author":"Tobler","year":"2004","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Galar, M., Sesma, R., Ayala, C., Albizua, L., and Aranda, C. (2020). Super-Resolution of Sentinel-2 Images Using Convolutional Neural Networks and Real Ground Truth Data. Remote Sens., 12.","DOI":"10.3390\/rs12182941"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Salgueiro Romero, L., Marcello, J., and Vilaplana, V. (2020). Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks. Remote Sens., 12.","DOI":"10.3390\/rs12152424"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, J., Ma, Y., Chen, F., Shang, E., Yao, W., Zhang, S., and Yang, J. (2023). SA-GAN: A Second Order Attention Generator Adversarial Network with Region Aware Strategy for Real Satellite Images Super Resolution Reconstruction. Remote Sens., 15.","DOI":"10.3390\/rs15051391"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TPAMI.2018.2865304","article-title":"Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks","volume":"41","author":"Lai","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.neunet.2023.12.003","article-title":"Dual-Domain Strip Attention for Image Restoration","volume":"171","author":"Cui","year":"2024","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tsai, F.-J., Peng, Y.-T., Lin, Y.-Y., Tsai, C.-C., and Lin, C.-W. (2022, January 23\u201327). Stripformer: Strip Transformer for Fast Image Deblurring. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19800-7_9"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Fan, Y., Xiang, X., Demandolx, D., Ranjan, R., Timofte, R., and Van Gool, L. (2023, January 17\u201324). Efficient and Explicit Modelling of Image Hierarchies for Image Restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01753"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhao, H., Qiao, Y., and Dong, C. (2021, January 20\u201325). ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01184"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gu, J., and Dong, C. (2021, January 20\u201325). Interpreting Super-Resolution Networks with Local Attribution Maps. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00908"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4251\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:32:37Z","timestamp":1760113957000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":46,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224251"],"URL":"https:\/\/doi.org\/10.3390\/rs16224251","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}