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Calibration of the Solar X-ray Detector on-board the Macao science Satellite-1B for Soft X-ray Detection

science China Earth sciences 2025年第1期68卷 11-26页

作者： Man-Hei NG Xiaoping ZHANG Jianwu CHEN Yongqiang SHI Fuchang ZUO Wenxi PENG Weichun FU Liansheng LI Pengwei LUO Chi-Long TANG Guozheng ZHANG Zhiwu MEI Li WANG Cunhui LI Jiaming OU Yanyan Xu State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao999078China Space Optoelectronic Measurement and Perception Laboratory Beijing Institute of Control EngineeringBeijing100190China Institute of High Energy Physics Chinese Academy of SciencesBeijing100049China Science and Technology on Vacuum Technology and Physics Laboratory Lanzhou Institute of PhysicsLanzhou730000China China National Space Administration(CNSA) Macao Center for Space Exploration and ScienceMacao999078China

The Solar X-ray Detector(SXD)on-board the Macao science Satellite-1B(MSS-1B)was successfully launched via the Chinese Long March-2C rocket on 21 May 2023,and commenced operations in early June of the same *** MSS-1B/Soft X-ray Detection Units(SXDUs)employ two silicon drift detectors(SDDs),providing a wide range of energy spectra spanning from 0.7 to 24 ***,the SXDUs deliver a high-resolution capability of 0.14 keV@5.9 keV and operate with a time cadence of 1 ***,we perform thorough calibrations of the MSS-1B/SXDUs,employing a combination of ground experiments and *** addition,quantitative analysis comparing the flux measurements obtained by the MSS-1B/SXDUs to the data collected by the Geostationary Operational Environmental Satellite(GOES),provides compelling evidence of their ***,the preliminary spectral analysis results showcase the robustness and expected performance of the MSS-1B/SXDUs,unlocking their potential for facilitating the study of dynamic evolution of solar ***,the innovative MSS-1B/Solar X-ray Detector facilitates concurrent observations of solar soft and hard X-rays,thereby making valuable contributions to the advancements in solar research.

关键词： Macao science Satellite-1B(MSS-1B) Calibration Solar X-Ray Detector Spectrometer Soft X-rays Solar flares Solar abundances

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Adaptive Spatial Modeling and Multi-Scale Attention Aggregation for Semantic Segmentation of Remote Sensing Images 2

Adaptive Spatial Modeling and Multi-Scale Attention Aggregat...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Wenying Zhang, Yuchuan Jin, Yuxuan Wang, Yupei Chen, Liang Beijing Institute of Technology Chongqing Innovation Center National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing China Jianghan University School of Artificial Intelligence Wuhan China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

As remote sensing technology advances and high-resolution sensors are deployed, the analysis of high-resolution remote sensing images encounters challenging issues, such as intra-class variability, and inter-class similarity, due to the unobvious appearance and complicated background. The high-resolution remote sensing images pose critical issues for precise semantic segmentation due to these fundamental issues. To this end, we propose an adaptive hierarchical aggregation network. Different from the previous approach of modeling semantic interdependencies solely within a single feature map in the channel dimension, we introduce multi-scale channel-wise feature aggregation to capture global contextual dependencies. Meanwhile, we design an adaptive detail-aware module to model long-range spatial dependencies, adaptively extracting effective details from different levels as compensation. Additionally, this module utilizes depth-wise separable convolution to selectively filter out irrelevant spatial details from lower-level feature maps. The outputs of the two modules are fused layer by layer to achieve refined feature representation, thereby generating precise pixel-level segmentation results. Our experiments are conducted on the Vaihingen dataset and the Potsdam dataset. The experimental results demonstrate that the proposed algorithm surpasses several existing state-of-the-art approaches. © 2024 IEEE.

关键词： Semantic Segmentation

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Multi-View Joint Target Selection for Relativistic-Perturbed Starlight Navigation

Multi-View Joint Target Selection for Relativistic-Perturbed...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhao, Yaru Ge, Dantong Xiong, Kai School of Aerospace Engineering Beijing Institute of Technology Beijing China Key Laboratory of Autonomous Navigation Control for Deep Space Exploration Beijing Institute of Technology Beijing China National Key Laboratory of Space Intelligent Control Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350384185

Autonomous navigation is one of the key technologies for spacecraft to realize long-term space operation and carry out science missions. In order to meet the requirement of high precision autonomous navigation, the effects of the gravitational deflection of light, stellar aberration and measurement bias of sensor are considered to establish a relativistic-perturbed starlight navigation scheme. To obtain high quality measurements, a multi-probe star sensor composed of multiple field of view probes is adopted for observation. A polygonal star pair selection method is proposed to conduct multi-view joint target selection. By constructing irregular polygons to select high quality star pair observations, the accuracy of navigation estimation is expected to be improved. The performance of relativistic-perturbed starlight navigation using the multi-view joint target selection method is then verified through a numerical simulation. © 2024 IEEE.

关键词： Probes

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A Navigation and control Framework of Quadrupedal Robot for Autonomous Exploration in Cave Environments 16th

A Navigation and Control Framework of Quadrupedal Robot for ...

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16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Hu, Yong Jiang, Lisong Kang, Ke Cao, Dandan Zhou, Zhongda Liu, Junfeng Wang, Yong Li, Maodeng Hu, Haidong National Key Laboratory of Science and Technology On Space Intelligent Control Beijing Institute of Control Engineering Beijing100190 China China Academy of Space Technology Hangzhou Institute Hangzhou310001 China

ISBN: (纸本)9789819964796

This article introduces XiaoTian, a quadrupedal robot with high mobility, dynamic motion skills, and autonomous ability. The robot uses backdriveable joint actuators with high force density, and robustness to impacts during trotting. Considering the specific application environment, we design the overall navigation and control framework of the robot, including the motion controlling, mapping, and planning methods. The experiment proved that XiaoTian can perform walking gaits, dynamically trot on the uneven ground and achieve autonomous exploration in the dark cave environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Navigation

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Data-Driven control of Distributed Event-Triggered Network Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Deep Reinforcement Learning-Based intelligent Decision-Making for Orbital Game of Satellite Swarm 29th

Deep Reinforcement Learning-Based Intelligent Decision-Makin...

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29th International Conference on Computational and Experimental Engineering and sciences, ICCES 2023

作者： Yu, Weizhuo Yue, Xiaokui Huang, Panxing Liu, Chuang National Key Laboratory of Aerospace Flight Dynamics School of Astronautics Northwestern Polytechnical University Xi’an710072 China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China

ISBN: (纸本)9783031429866

Recent years have witnessed the rapid development of aerospace science and technology, and the orbital game technology has shown great potential value in the field of failed satellite maintenance, debris removal, etc. In this case, orbital game is often characterized by nonlinear dynamic model, unknown state information, high randomness, but the existing approaches to deal with game problem are difficult to be applied. The analytical method based on game theory is only applicable to simple scenarios, and it is challenging to find the optimal strategy for such complex scenarios as satellite swarm game. It should be noted that deep reinforcement learning has some research basis in the cooperative decision-making and control of multi-agents. In view of its powerful perception and decision ability, this paper applies deep reinforcement learning to solve the orbital game problem of satellite swarm. Firstly, the game scenario is modeled, where typical constraints, e.g., minimum time, optimal fuel, and collision avoidance, are taken into consideration in the game process, and then the multi-agent reinforcement learning algorithm is developed to solve the optimal maneuver strategy. The algorithm is based on the Actor-Critic architecture and uses a centralized training and decentralized execution approach to solve the optimal joint maneuver strategy. For different task scenarios, the action space, state observation space, and reward space are designed to introduce more rewards that match the specific game tasks to make the algorithm converge quickly, so that the satellite swarm emerges and executes better intelligent strategies to complete the corresponding game task. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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Solar evaporation for simultaneous oil-water separation and electricity generation with Janus wood-based absorbers

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Frontiers of Environmental science & Engineering 2024年第2期18卷 13-23页

作者： Yue Yang Ze Fu Qi Zhang Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological EngineeringNanjing University of Science&TechnologyNanjing 210094China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesKey Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)Jiangsu Key Laboratory of Artificial Functional MaterialsNanjing UniversityNanjing 210093China

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m2 is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.

关键词： Oily wastewater Carbonized wood Salinity gradient Electricity generation Solar irradiation

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A Novel Principal Component Analysis Algorithm for Fault Detection 43

A Novel Principal Component Analysis Algorithm for Fault Det...

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43rd Chinese control Conference, CCC 2024

作者： Shenhang, Wang Kun, Tian Wei, Zheng Mengyu, Liu Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China

ISBN: (纸本)9789887581581

In modern industry, fault detection is very important in high-cost and safety-critical processes. Principal component analysis (PCA) is an effective data-driven approach which is widely used in this field. Conventional PCA is time-invariant, which often lead to false alarms. In order to overcome the false alarms, recursive PCA algorithms have been proposed for adaptive process. However, the amount of calculation of the algorithm is very great because of frequently updated information. In this paper, a novel recursive PCA method is proposed for solving the question by introducing the standard residual method. The standard residual method serves as an indicator of the level of consistency among data, which is used to decide whether or not to update the PCA model. The proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Errors

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