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Land Surface Displacement Geohazards Monitoring Using Multi-temporal InSAR Techniques

Journal of Geodesy and Geoinformation science 2021年第1期4卷 77-87页

作者： Guang LIU Perski ZBIGNIEW Salvi STEFANO Thiebes BENNI Lixin WU Jinghui FAN Shibiao BAI Lianhuan WEI Shiyong YAN Rui SONG Bignami CHRISTIAN Tolomei CRISTIANO Stefan SCHNEIDERBAUER Joao Sousa JOAQUIM 无 Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Carpathian Branch Polish Geological Institute-National Research InstituteCracow 31-560Poland National Institute of Geophysics and Volcanology Roma 605-00143Italy German Committee for Disaster Reduction Bonn 1353113Germany School of Resources and Civil Engineering Northeastern UniversityShenyang 110819China Division of Remote Sensing for Environment China Aero Geophysical Survey&Remote Sensing Center for Natural ResourcesBeijing 100083China School of Marine Science and Ocean Engineering Nanjing Normal UniversityNanjing 210046China School of Environment Science and Spatial Informatics China University of Mining and TechnologyXuzhou 221116China Mullard Space Science Laboratory Department of Space and Climate Physics University College London Holmbury RH56NTUK Eurac Research Bolzano 39100Italy School of Science and Technology University of Trás-os-Montes e Alto DouroVila Real 5000-801Portugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and Science(INESC-TEC)Porto 4200-465Portugal

China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every *** occur not only in remote areas but also in highly populated *** the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various ***,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination *** movements detected in the different study areas were cross validated with different SAR sensors and truth data.

关键词： Dragon-4 project Sentinel-1 GF-3 landslide geohazards InSAR

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Dynamical Models of the Milky Way in Action space with LAMOST DR8 and GAIA EDR3

arXiv

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arXiv 2025年

作者： Sun, Guang-Chen Wang, Qiao Mao, Shude Li, Yichao Long, Richard J. Ding, Ping-Jie Wang, Yougang Zhang, Xin Chen, Xuelei College of Sciences Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China Department of Astronomy Tsinghua University Beijing100084 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China National Frontiers Science Center for Industrial Inteligence and Systems Optimization Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China

This work explores dynamical models of the Milky Way (MW) by analyzing a sample of 86,109 K giant stars selected through cross-matching the LAMOST DR8 and Gaia EDR3 surveys. Our earlier torus models in Wang et al. (2017) did not include Gaia data, making them incompatible with the new sample’s proper motion distributions. Here, we refine the construction of action-based, self-consistent models to constrain the three-dimensional velocity distribution of K giants over a larger parameter space, drawing on a series of existing MW models. This approach produces several new MW models. Our best-fit model for the local kinematics near the Sun indicates a MW virial mass of 1.35 ×1012M☉, a local stellar density of 0.0696 M☉pc−3, and a local dark matter density of 0.0115 M☉pc−3. Our main conclusion supports a thicker and more extended thick disk, alongside a cooler thin disk, compared to the best-fitting model in Wang et al. (2017). Near the Sun, our model aligns well with observations, but is less satisfactory at distances far from the Galactic center, perhaps implying unidentified structures. Further high-precision observations will be critical for understanding the dynamics in these outer Galactic regions, and will require a more realistic model. Copyright © 2025, The Authors. All rights reserved.

关键词： Kinematics

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A KDC-enhanced closest point method for thermal analysis on curved geometries using LS-GFDM

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Applied Mathematics Letters 2025年 171卷

作者： Zhuochao Tang Jiming Gu Bin Chen Zhuojia Fu School of Management Science and Engineering Anhui University of Technology Maanshan 243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Maanshan 243002 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Engineering Science Hohai University Nanjing 211100 China

This paper presents a Krylov-deferred correction (KDC)-enhanced closest point method (CPM) integrated with the least-squares generalized finite difference method (LS-GFDM) for thermal analysis on curved geometries. To overcome the inherent limitations of conventional single-step/multi-step temporal schemes in long-time simulations, we systematically unify three advanced components: (i) CPM’s embedding strategy that transforms surface PDEs into extended problems over Cartesian grids, (ii) LS-GFDM’s spatial discretization employing dual point sets to enhance applicability beyond conventional GFDM, and (iii) KDC’s predictor–corrector framework that iteratively refines temporal solutions to achieve high-order accuracy. Building upon our previous work (Tang et al. 2025) where LS-GFDM was integrated with CPM using explicit Euler temporal schemes, this study constitutes a critical advancement by replacing low-order temporal discretization with the KDC methodology. Numerical experiments demonstrate that our KDC-CPM-LSGFDM framework achieves superior numerical performance.

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An Approach on Visual Detecting Multi-Targets in the Unstructured and Complex Scenes Based on RGB-D Images 5

An Approach on Visual Detecting Multi-Targets in the Unstruc...

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5th International Conference on Automation, Control and Robotics Engineering, CACRE 2020

作者： Tan, Qimeng Sun, Yikun Xin, Pengfei Lin, Yimin Ma, Chao Wang, Xinyu Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing China Beijing Special Engineering Design and Research Institute Propellant Injection Laboratory Beijing China Beijing University of Posts Telecommunications Institute of Optical Communication Optoelectronics Beijing China Beijing Information Science and Technology University School of Instrument Science and Optoelectronic Engineering Beijing China

ISBN: (纸本)9781728198880

The technology of detecting and identifying multitarget is of significance for robotic visual navigation within the unknown, unstructured, complex scenes, whose result could be considered as important references of 3D map building and path planning for mobile robot. Traditional algorithms of target detection can be applied to 2D images without depth information generally, which is disturbed by few factors such as illumination, view and scale easily. Therefore, an approach on visual detecting multi-target of unstructured and complex scenes based on RGBD images is proposed in this article to solve the above problem, which is composed of extracting descriptor of rotation and scale invariance feature, local encoding of targets, random ferns classifier training, Hough map generation, Hough voting theoretical model and local maximum search. Experimental results have shown that the proposed approach reduce the calculation of extracting and matching local feature, improve the accuracy of object recognition and detection in unknown complex environments, be capable of well robust against few disturbing factors i.e. rotation, scale, illumination, occlusion and non-rigid body deformation. © 2020 IEEE.

关键词： Motion planning

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Not all Neoproterozoic continental crust exposed in the Sulu belt was deeply subducted

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Lithos 2025年 510-511卷

作者： Wang, Zhuocheng Wang, Lu Brown, Michael Johnson, Tim E. Kirkland, Christopher L. Clark, Chris Blereau, Eleanore State Key Laboratory of Geological Processes and Mineral Resources Center for Global Tectonics School of Earth Sciences China University of Geosciences Wuhan 430074 China Laboratory for Crustal Petrology Department of Geology University of Maryland College Park MD United States School of Earth and Planetary Sciences The Institute for Geoscience Research (TIGeR) (†Timescales of Mineral Systems Group) Space Science and Technology Centre Curtin University GPO Box U1987 Perth 6845 WA Australia Institute of Geophysics and Tectonics School of Earth and Environment University of Leeds United Kingdom

A newly identified tectonic sliver of low-grade Neoproterozoic continental crust comprising hybrid granitoids is exposed between Triassic ultrahigh-pressure (UHP) metamorphic rocks of the Sulu belt and the Cretaceous Laoshan granite. Outcrop evidence of magma mingling combined with the granitic mineral assemblages and primary igneous microstructures suggest >100 km difference in likely depth of subduction compared to the proximal UHP metamorphic rocks. The granitoids are cut by a brittle-to-ductile shear zone and extensional fractures that focussed fluid flow and allowed low-temperature fluid–rock alteration. Integrated accessory mineral geochronology from zircon, allanite and apatite records crystallization ages of c. 835–700 Ma and alteration ages of c.120–90 Ma. There is no evidence of the Triassic UHP metamorphic event recorded in the Neoproterozoic granitoids. The hybrid granitoids were likely generated during Neoproterozoic rifting of Rodinia, forming part of the northern margin of the Yangtze craton, but they did not experience deep subduction like other Neoproterozoic continental crust within the Sulu belt. The adjacent Cretaceous Laoshan granite was emplaced when all units were at shallow crustal depths, after >100 km of exhumation of the UHP eclogites. The Cretaceous ages retrieved from the hybrid granitoids date the brittle deformation and fluid alteration of these rocks in the Laoshan granite aureole. Cretaceous subduction retreat (slab roll back) of the paleo-Pacific plate caused extension and lithosphere thinning of the upper plate (eastern China), forming extensional shear zones and core complexes, associated with numerous granitoid intrusions, including the Laoshan granite. The extensional geological setting also facilitated preservation of this thin sliver of hybrid granitoids at the margin of the Laoshan granite adjacent to the UHP metamorphic terrane, and likely contributed to the final exhumation of the UHP metamorphic rocks of the Sulu belt. Our

关键词： Accessory mineral geochronology Allanite Exhumation Hybrid granitoid Laoshan granite Sulu belt

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Mid-wave infrared super-resolution imaging based on compressive calibration and sampling

arXiv

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arXiv 2021年

作者： Jin, Xiao-Peng Zhao, Qing Liu, Xue-Feng Xiong, An-Dong School of Physics Beijing Institute of Technology Beijing100081 China Key Laboratory of Electronics and Information Technology for Space Systems National Space Science Center Chinese Academy of Sciences Beijing100049 China Beijing Academy of Quantum Information Sciences Beijing100193 China University of Chinese Academy of Sciences Beijing100049 China

Mid-wave infrared (MWIR) cameras for large number pixels are extremely expensive compared with their counterparts in visible light, thus, super-resolution imaging (SRI) for MWIR by increasing imaging pixels has always been a research hotspot in recent years. Over the last decade, with the extensively investigation of the compressed sensing (CS) method, focal plane array (FPA) based compressive imaging in MWIR developed rapidly for SRI. This paper presents a long-distance super-resolution FPA compressive imaging in MWIR with improved calibration method and imaging effect. By the use of CS, we measure and calculate the calibration matrix of optical system efficiently and precisely, which improves the imaging contrast and signal-to-noise ratio (SNR) compared with previous work. We also achieved the 4 × 4 times super-resolution reconstruction of the long-distance objects which reaches the limit of the system design in our experiment. Copyright © 2021, The Authors. All rights reserved.

关键词： Infrared radiation

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993-2017

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国家科学评论（英文版） 2022年第5期9卷 149-161页

作者： Zhen Zhang Benjamin Poulter Sara Knox Ann Stavert Gavin McNicol Etienne Fluet-Chouinard Aryeh Feinberg Yuanhong Zhao Philippe Bousquet Josep G.Canadell Anita Ganesan Gustaf Hugelius George Hurtt Robert B.Jackson Prabir K.Patra Marielle Saunois Lena H?glund-lsaksson Chunlin Huang Abhishek Chatterjee Xin Li Department of Geographical Sciences University of MarylandCollege ParkMD 20742USA Biospheric Sciences Laboratory NASA Goddard Space Flight CenterGreenbeltMD 20771USA Department of Geography University of British ColumbiaVancouver V6T 1Z2Canada Global Carbon Project CSIRO Oceans and AtmosphereCanberraACT 2601Australia Department of Earth and Environmental Sciences University of Illinois ChicagoChicagoIL 60607USA Department of Earth System Science Stanford UniversityStanfordCA 94305USA Institute for Data Systems and SocietyMassachusetts Institute of TechnologyCambridgeMA 02139USA College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266000China Laboratoire des Sciences du Climat et de I'Environment LSCE-IPSL(CEA-CNRS-UVSQ)Université Paris-SaclayGif-sur-Yvette 91191France School of Geographical Sciences University of BristolBristol BS8 1RLUK Department of Physical Geography and Bolin Centre for Climate Research Stockholm UniversityStockholm SE-10691Sweden Department of Earth System Science Stanford UniversityStanfordCA 94305USA Woods Institute for the Environment and Precourt Institute for Energy Stanford UniversityStanfordCA 94305USA Research Institute for Global Change JAMSTECYokohama 236-0001Japan International Institute for Applied Systems Analysis(IIASA) Laxenburg A-2361Austria Northwest Institute of Eco-Environment and Resources Chinese Academy of SciencesLanzhou 730000China Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMD 20771USA Universities Space Research Association ColumbiaMD 21046USA Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijing 100101China

Atmospheric methane(CH4)concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to *** hypotheses have been proposed to explain the temporal variations in CH4 growth,and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities,agriculture and natural wetlands,or to a decrease in the atmospheric chemical ***,we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic *** emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture,landfill and waste sectors were responsible for 53±13％of the renewed growth over the period 2007-2017 compared to 2000-2006;industrial fossil fuel sources explained an additional 34±24％,and wetland sources contributed the least at 13±9％.The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 *** finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate *** findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

关键词： greenhouse gas carbon cycle climate mitigation wetland methane isotope

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Near-space Communications: the Last Piece of 6G space-Air-Ground-Sea Integrated Network Puzzle

arXiv

引用

arXiv 2023年

作者： Liu, Hongshan Qin, Tong Gao, Zhen Mao, Tianqi Ying, Keke Wan, Ziwei Qiao, Li Na, Rui Li, Zhongxiang Hu, Chun Mei, Yikun Li, Tuan Wen, Guanghui Chen, Lei Wu, Zhonghuai Liu, Ruiqi Chen, Gaojie Wang, Shuo Zheng, Dezhi School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Advanced Research Institute of Multidisciplinary Sciences 100081 Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Jiaxing314000 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Jinan250307 China Department of Mathematics Southeast University Nanjing210096 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China 5GIC & 6GIC Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom China Academy of Space Technology Beijing100094 China

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics and unique problems of NS-COM network. Afterwards, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite (StratoSat) -to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic MIMO, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, StratoSat-based radar detection and imaging, NS-COM assisted enhanced global navigation system, NS-COM assisted intelligent unmanned system and free space optical (FSO) communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture. Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation systems

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