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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

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The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

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International Soil and Water Conservation Research 2020年第2期8卷 195-204页

作者： Ayoub Lazaar Abdul Mounem Mouazen Kamal EL Hammouti Michael Fullen Biswajeet Pradhan Muhammad Sohail Memon Karim Andich Abdelilah Monir Laboratory of Applied Geosciences Department of GeologyFaculty of ScienceMohammed First UniversityOujdaMorocco Division of Mechatronics Biostatistics and Sensors(MeBioS)Faculty of Bioscience EngineeringKasteelpark Arenberg 30B-3001 HeverleeBelgium Faculty of Science and Engineering University of WolverhamptonWolverhamptonWV11LYUnited Kingdom Center for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney2007NSWAustralia Faculty of Agricultural Engineering Sindh Agriculture UniversityTandojam70060Pakistan Laboratory of Applied Geomatics and Soil Science National Institute of Agronomic Research(INRA)OujdaMorocco Department of Mathematics EDP and Scientific Computing TeamMoulay Ismail UniversityMéknesMorocco

Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical re-agents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0-20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley-Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350-2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2=0.93 and RMSE=0.13 compared to the contact probe protocol with an R2=0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for pre-dicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the pre-diction of SOM for (PG) setup were at:424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

关键词： Soil organic matter VIS-NIR spectroscopy Reflectance spectra SOM estimation SOM analysis

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ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification

arXiv

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arXiv 2021年

作者： Wang, Jinping Tan, Xiaojun Lai, Jianhuang Li, Jun Xiang, Canqun School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing School of Computer Science and Engineering Sun Yatsen University Ministry of Education Guangzhou510006 China Zhuhai519082 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from hyperspectral images (HSIs). However, the sampling locations of the convolutional kernels of capsules are fixed and cannot be adaptively changed according to the inconsistent semantic information of HSIs. Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field. Note that this unit can learn more discriminative representations of HSIs with fewer parameters. Specifically, two cascaded ASP-based convolution operations (ASPConvs) are applied to input images to learn relatively high-level semantic features, transmitting hierarchical structures among capsules more accurately than the use of the most fundamental features. Furthermore, the semantic features are fed into ASP-based conv-capsule operations (ASPCaps) to explore the shapes of objects among the capsules in an adaptive manner, further exploring the potential of capsule networks. Finally, the class labels of image patches centered on test samples can be determined according to the fully connected capsule layer. Experiments on three public datasets demonstrate that ASPCNet can yield competitive performance with higher accuracies than state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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HUNTER: AI based holistic resource management for sustainable cloud computing

arXiv

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

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Recent progress of integrated circuits and optoelectronic chips

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Science China(information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Discovery of terahertz-frequency orbitally-coupled magnons in a kagome ferromagnet

arXiv

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arXiv 2024年

作者： Che, Mengqian Chen, Weizhao Wang, Maoyuan Bartram, F. Michael Liu, Liangyang Dong, Xuebin Liu, Jinjin Li, Yidian Lin, Hao Wang, Zhiwei Liu, Enke Yao, Yugui Yuan, Zhe Zhang, Guang-Ming Yang, Luyi State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Center for Advanced Quantum Studies Department of Physics Beijing Normal University Beijing100875 China Department of Physics Xiamen University Xiamen361005 China Beijing National Center for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Fudan University Shanghai200433 China Frontier Science Center for Quantum Information Beijing100084 China

In ferromagnetic materials, magnons - quanta of spin waves - typically resonate in the gigahertz range. Beyond conventional magnons, while theoretical studies have predicted magnons associated with orbital magnetic moments, their direct observation has remained challenging. Here, we present the discovery of two distinct terahertz orbitally-coupled magnon resonances in the topological kagome ferromagnet Co3Sn2S2. Using time-resolved Kerr rotation spectroscopy, we pinpoint two magnon resonances at 0.61 and 0.49 THz at 6 K, surpassing all previously reported magnon resonances in ferromagnets due to strong magnetocrystalline anisotropy. These dual modes originate from the strong coupling of localized spin and orbital magnetic moments. These findings unveil a novel category of magnons stemming from orbital magnetic moments, and position Co3Sn2S2 as a promising candidate for high-speed terahertz spintronic applications. © 2024, CC BY.

关键词： Magnetocrystalline anisotropy

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Adaptive Stepwise Feature Selection Approach for EEG-Based Epileptic Seizure Classification

Adaptive Stepwise Feature Selection Approach for EEG-Based E...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sunday Timothy Aboyeji Wenfang Zhou Yuan Tao Mingxing Zhu Oluwarotimi Williams Samuel Ijaz Ahmad Guoru Zhao Ji Yi Michael Chi Fai Tong Xin Wang Yi Guo Shixiong Chen CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Otolaryngology Luohu District People's Hospital Shenzhen China Department of Otorhinolaryngology Peking University Shenzhen Hospital Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen Shenzhen China School of Computing and Engineering University of Derby Derby UK Department of Ophthalmology School of Medicine Johns Hopkins University Baltimore USA Department of Otorhinolaryngology Head and Neck Surgery The Chinese University of Hong Kong Hong Kong China Department of Neurology Shenzhen People’s Hospital Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advancements in feature selection (FS) optimization algorithms have influenced the field of epileptic seizure classification. However, integrating these optimization algorithms into machine learning (ML) models often creates time complexity, limiting their clinical deployment. To address this issue, we propose an innovative adaptive stepwise FS method tailored for epileptic seizure detection (ESD). First, a discrete wavelet transform (DWT) was applied to the preprocessed signal to get three levels of the db4 wavelet family within the frequency range pertinent to epileptic seizure classification. Linear and nonlinear features are then extracted from each level of the DWT. The selected features are initially ranked using the minimum relevance, maximum redundancy (mRMR) FS technique. After that, a stepwise FS approach was applied to the ranked features to optimize the performance of Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) classifiers. The experiment was performed on a publicly accessible CHB-MIT dataset in a patient-independent approach. The model's performance was assessed using accuracy, sensitivity, and specificity. The results show an improved performance of the ML models with the integration of stepwise algorithm into the mRMR technique. Among the classifiers, RF exhibited superior performance with accuracy, sensitivity, and specificity of 87.69%, 91.53%, and 83.86%, respectively, when 12 features were selected. Our proposed stepwise feature selection method (PSFS) performs similarly to generalize forward feature selection (GFFS), with an average accuracy of 88.37% and 88.57%, respectively across selected features with less computation. This makes PSFS a very efficient and effective FS in epileptic seizure classification.

关键词： Support vector machines Radio frequency Accuracy Redundancy Sensitivity and specificity Feature extraction Brain modeling Classification algorithms Discrete wavelet transforms Optimization

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SECP-AKE: Secure and efficient certificateless-password-based authenticated key exchange protocol for smart healthcare systems

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Journal of systems Architecture 2025年 167卷

作者： Zhiqiang Zhao Xuexian Hu Yining Liu Jianghong Wei Yuanjun Xia Yangfan Liang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou 450000 Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325027 Zhejiang China School of Computer and Information Security Guilin University of Electronic Technology Guilin 541004 Guangxi China College of Information Science and Engineering the Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems the Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province and the Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 Zhejiang China

Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes still suffer from incomplete security properties, imperfect authentication mechanisms, and inadequate privacy preservation. Therefore, this paper presents SECP-AKE, a secure and efficient certificateless-password-based authenticated key exchange protocol for IoT-based smart healthcare, which enables batch authentication, resists physical attacks, and provides strong anonymity. Specifically, using certificateless cryptography, the SECP-AKE protocol enables batch authentication of authorized users and devices while also resolving the key escrow problem. In particular, the SECP-AKE protocol incorporates Physical Unclonable Functions (PUFs) to resist physical attacks, thus enhancing device security and ensuring reliable medical service delivery. Additionally, the design of a pseudonym update mechanism can achieve user unlinkability, thereby providing enhanced privacy preservation. The results from both formal verification using SVO logic and informal security analyses demonstrate that the SECP-AKE protocol is secure and offers more comprehensive security properties. Meanwhile, the use of a well-known automated security verification tool Scyther further evaluates the protocol’s security reliability. Ultimately, comparative experiments on communication overhead and computational overhead demonstrate that the SECP-AKE protocol is efficient and feasible compared to state-of-the-art existing works.

关键词：

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

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

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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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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