{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:09:54Z","timestamp":1760058594988,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, CAAC","award":["FZ2022ZZ01","J2022-046","24CAFUC04015"],"award-info":[{"award-number":["FZ2022ZZ01","J2022-046","24CAFUC04015"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["FZ2022ZZ01","J2022-046","24CAFUC04015"],"award-info":[{"award-number":["FZ2022ZZ01","J2022-046","24CAFUC04015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address these limitations, this study proposes a novel detection framework, the Gaussian Detection (GaussianDet) Framework, that integrates probabilistic modeling with dynamic sample assignment to achieve more precise OD. The core design of this framework is inspired by the theory of geometric symmetry. Specifically, the radial symmetry of a two-dimensional Gaussian distribution is employed to capture the rotational and scale-invariant properties of Remote Sensing (RS) objects. By leveraging the axial symmetry of elliptical geometry, the proposed Gaussian Elliptical Intersection over Union (GEIoU) enables rotation-aligned matching, while Omni-dimensional Adaptive Assignment (ODAA) introduces dynamic symmetric constraints to optimize the spatial distribution of training samples. Specifically, a Flexible Bounding Box (FBBox) representation based on a 2D Gaussian distribution is introduced to more accurately characterize the shape, aspect ratio, and orientation of objects. In addition, the GEIoU is designed as a scale-invariant similarity metric to align regression loss with detection accuracy. To further enhance sample quality and feature learning, the ODAA strategy adaptively selects positive samples based on object scale and geometric constraints. Experimental results on the High-Resolution Ship Collection 2016 (HRSC2016) and University of Chinese Academy of Sciences\u2013Aerial Object Detection (UCAS-AOD) datasets demonstrate that GaussianDet achieves mean Average Precision (mAP) scores of 90.53% and 96.24%, respectively. These results significantly outperform existing Oriented Object Detection (OOD) methods, thereby validating the effectiveness of the proposed approach and providing a solid theoretical foundation for future research in Remote Sensing Object Detection (RSOD).<\/jats:p>","DOI":"10.3390\/sym17040594","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T09:06:51Z","timestamp":1744621611000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Symmetry-Driven Gaussian Representation and Adaptive Assignment for Oriented Object Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5128-5376","authenticated-orcid":false,"given":"Jiangang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianjin","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Control Technology Institute, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-5371","authenticated-orcid":false,"given":"Donglin","family":"Jing","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Control Technology Institute, Shanghai 201109, China"},{"name":"Research and Development Center of Infrared Detection Technology, China Aerospace Science and Technology Corporation, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3653-449X","authenticated-orcid":false,"given":"Ting","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5220\/0006120603240331","article-title":"A high resolution optical satellite image dataset for ship recognition and some new baselines","volume":"Volume 2","author":"Liu","year":"2017","journal-title":"Proceedings of the International Conference on Pattern Recognition Applications and Methods"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_4","first-page":"100","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_7","unstructured":"Ge, Z. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_8","unstructured":"Team, M.V. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_11","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_12","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2025, March 09). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_14","unstructured":"Lin, Y., Feng, P., Guan, J., Wang, W., and Chambers, J. (2019). IENet: Interacting embranchment one stage anchor free detector for orientation aerial object detection. arXiv."},{"key":"ref_15","first-page":"1","article-title":"MRDet: A multihead network for accurate rotated object detection in aerial images","volume":"60","author":"Qin","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_16","first-page":"1","article-title":"Object detection for aerial images with feature enhancement and soft label assignment","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_17","first-page":"1","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2021, January 2\u20139). R3det: Refined single-stage detector with feature refinement for rotating object. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 16\u201317). Feature selective anchor-free module for single-shot object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TIP.2022.3143690","article-title":"Refined one-stage oriented object detection method for remote sensing images","volume":"31","author":"Hou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","first-page":"1","article-title":"FRPNet: A feature-reflowing pyramid network for object detection of remote sensing images","volume":"19","author":"Wang","year":"2020","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_23","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2021, January 18\u201324). Rethinking rotated object detection with gaussian wasserstein distance loss. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_24","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-oriented scene text detection via rotation proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (2017). R2CNN: Rotational region CNN for orientation robust scene text detection. arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","first-page":"1922","article-title":"FCOS: A simple and strong anchor-free object detector","volume":"44","author":"Tian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","unstructured":"Zhu, B., Wang, J., Jiang, Z., Zong, F., Liu, S., Li, Z., and Sun, J. (2020). Autoassign: Differentiable label assignment for dense object detection. arXiv."},{"key":"ref_31","unstructured":"Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., and Chen, K. (2022). Rtmdet: An empirical study of designing real-time object detectors. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., and Yang, R. (2019, January 16\u201319). Iou loss for 2d\/3d object detection. Proceedings of the 2019 International Conference on 3D Vision (3DV), Qu\u00e9bec City, QC, Canada.","DOI":"10.1109\/3DV.2019.00019"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Raisi, Z., Naiel, M.A., Younes, G., Wardell, S., and Zelek, J.S. (2021, January 20\u201325). Transformer-based text detection in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00353"},{"key":"ref_36","unstructured":"Liu, L., Pan, Z., and Lei, B. (2017). Learning a rotation invariant detector with rotatable bounding box. arXiv."},{"key":"ref_37","unstructured":"Yang, X., and Yan, J. (2020, January 23\u201328). Arbitrary-oriented object detection with circular smooth label. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part VIII 16."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., and Yan, J. (2021, January 20\u201325). Dense label encoding for boundary discontinuity free rotation detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_39","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_40","first-page":"1","article-title":"Anchor-free oriented proposal generator for object detection","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, Z., Hou, B., Wu, Z., Ren, B., and Yang, C. (2023). FCOSR: A simple anchor-free rotated detector for aerial object detection. Remote Sens., 15.","DOI":"10.3390\/rs15235499"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). Orientation robust object detection in aerial images using deep convolutional neural network. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yang, X., Zhang, G., Wang, J., Liu, Y., Hou, L., Jiang, X., Liu, X., Yan, J., and Lyu, C. (2022, January 10\u201314). MMRotate: A Rotated Object Detection Benchmark Using PyTorch. Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), Lisbon, Portugal.","DOI":"10.1145\/3503161.3548541"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCAL Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The PASCAL Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning RoI transformer for oriented object detection in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Qian, W., Yang, X., Peng, S., Yan, J., and Guo, Y. (2021, January 2\u20139). Learning modulated loss for rotated object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i3.16347"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/JSTARS.2020.3036685","article-title":"Learning point-guided localization for detection in remote sensing images","volume":"14","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 5\u20139). Oriented object detection in aerial images with box boundary-aware vectors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2021, January 2\u20139). Dynamic anchor learning for arbitrary-oriented object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3115110","article-title":"Optimization for arbitrary-oriented object detection via representation invariance loss","volume":"19","author":"Ming","year":"2021","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ming, Q., Miao, L., Zhou, Z., Song, J., and Yang, X. (2021). Sparse label assignment for oriented object detection in aerial images. Remote Sens., 13.","DOI":"10.3390\/rs13142664"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1007\/s11263-022-01593-w","article-title":"On the arbitrary-oriented object detection: Classification based approaches revisited","volume":"130","author":"Yang","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3095186","article-title":"CFC-Net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images","volume":"60","author":"Ming","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isprsjprs.2023.01.001","article-title":"Task interleaving and orientation estimation for high-precision oriented object detection in aerial images","volume":"196","author":"Ming","year":"2023","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_56","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhu, J., Ruan, Y., Jing, D., Fu, Q., and Ma, T. (2025). PSMDet: Enhancing Detection Accuracy in Remote Sensing Images Through Self-Modulation and Gaussian-Based Regression. Sensors, 25.","DOI":"10.3390\/s25051285"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1109\/TCSVT.2022.3222906","article-title":"AO2-DETR: Arbitrary-oriented object detection transformer","volume":"33","author":"Dai","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_60","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, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:14:21Z","timestamp":1760030061000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040594"],"URL":"https:\/\/doi.org\/10.3390\/sym17040594","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,4,14]]}}}