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| 1 | A survey of federated learning for edge computing:Research problems and solutions显示文摘Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data.It can provide better data privacy be-cause training data are not transmitted to a central server.Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dis-persed edge devices.To build such an edge federated learning system,we need to tackle a number of technical challenges.In this survey,we provide a new perspective on the applications,development tools,communication efficiency,security&privacy,migration and scheduling in edge federated learning. | Qi Xia Winson Ye Zeyi Tao Jindi Wu Qun Li | 2021 | High-Confidence Computing2021，1，1： | 1 |
| 2 | The Solar Upper Transition Region Imager(SUTRI)Onboard the SATech-01 Satellite显示文摘The Solar Upper Transition Region Imager(SUTRI)onboard the Space Advanced Technology demonstration satellite(SATech-01),which was launched to a Sun-synchronous orbit at a height of~500 km in 2022 July,aims to test the on-orbit performance of our newly developed Sc/Si multi-layer reflecting mirror and the 2k×2k EUV CMOS imaging camera and to take full-disk solar images at the Ne VII 46.5 nm spectral line with a filter width of~3 nm.SUTRI employs a Ritchey-Chrétien optical system with an aperture of 18 cm.The on-orbit observations show that SUTRI images have a field of view of~416×416 and a moderate spatial resolution of~8″without an image stabilization system.The normal cadence of SUTRI images is 30 s and the solar observation time is about16 hr each day because the earth eclipse time accounts for about 1/3 of SATech-01's orbit period.Approximately15 GB data is acquired each day and made available online after processing.SUTRI images are valuable as the Ne VII 46.5 nm line is formed at a temperature regime of~0.5 MK in the solar atmosphere,which has rarely been sampled by existing solar imagers.SUTRI observations will establish connections between structures in the lower solar atmosphere and corona,and advance our understanding of various types of solar activity such as flares,filament eruptions,coronal jets and coronal mass ejections. | Xianyong Bai Hui Tian Yuanyong Deng Zhanshan Wang Jianfeng Yang Xiaofeng Zhang Yonghe Zhang Runze Qi Nange Wang Yang Gao Jun Yu Chunling He Zhengxiang Shen Lun Shen Song Guo Zhenyong Hou Kaifan Ji Xingzi Bi Wei Duan Xiao Yang Jiaben Lin Ziyao Hu Qian Song Zihao Yang Yajie Chen Weidong Qiao Wei Ge Fu Li Lei Jin Jiawei He Xiaobo Chen Xiaocheng Zhu Junwang He Qi Shi Liu Liu Jinsong Li Dongxiao Xu Rui Liu Taijie Li Zhenggong Feng Yamin Wang Chengcheng Fan Shuo Liu Sifan Guo Zheng Sun Yuchuan Wu Haiyu Li Qi Yang Yuyang Ye Weichen Gu Jiali Wu Zhe Zhang Yue Yu Zeyi Ye Pengfeng Sheng Yifan Wang Wenbin Li Qiushi Huang Zhong Zhang | 2023 | Research in Astronomy and Astrophysics2023，23，6： | 1 |
| 3 | Design,Fabrication and Assembly of the Solar Upper Transition Region Imager(SUTRI)显示文摘The Solar Upper Transition Region Imager(SUTRI)focuses on the solar transition region to achieve dynamic imaging observation of the upper transition region.In this paper,we report the optical system design,mechanical design,ultrasmooth mirror manufacture and measurement,EUV multilayer film coating,prelaunch installation and calibration for the SUTRI payload at IPOE,Tongji University.Finally,the SUTRI carried by the SATech-01 satellite was successfully set to launch.All functions of this telescope were normal,and the observation results obtained in orbit were consistent with the design. | Zhanshan Wang Jun Yu Runze Qi Chunling He Zhengxiang Shen Li Jiang Weichen Gu Jiali Wu Zhe Zhang Pengfeng Sheng Yifan Wang Yue Yu Zeyi Ye JingJing Xia Yujie Xing Hongfei Jiao Bin Ma Qiushi Huang Zhong Zhang Xianyong Bai Yuanyong Deng Hui Tian Nange Wang Lun Shen Song Guo Lei Jing Xiaobo Chen Yang Gao Xiaocheng Zhu Xiaofeng Zhang Junwang He Qi Shi Shuo Liu Xingzi Bi | 2023 | Research in Astronomy and Astrophysics2023，23，9： | 0 |
| 4 | A review of seismo-electromagnetic research in China显示文摘The seismo-electromagnetic(EM)method is an important geophysical method that plays a major role in the observation of seismic anomalies related to earthquake precursors.It is the most promising method for a breakthrough in short-term earthquake prediction.The digital transformation and network upgrading implemented in the“Ninth five-year plan”and“Tenth five-year plan”have optimized the original observation system,improved the quality of observed data,enriched the seismicelectromagnetic information,and enhanced the analysis capability and timeliness of seismic-electromagnetic anomalies.These improvements are of major importance for the research on seismo-electromagnetics as well as for the development of new technologies.Since the beginning of the 21 st century,China has launched a satellite named CSES that was designed for the generation and study of seismo-electromagnetic data and built a high-power transmitting source and a new CSELF observation network that is used specifically for earthquake monitoring.This platform has promoted the full-time three-dimensional EM monitoring and the identification of earthquake anomalies.Based on the study of anomalies related to earthquake precursors,the physical and numerical simulations,and the study on generation mechanism of anomalies in China,we summarize the characteristics of earthquake EM anomalies and discuss the advantages and disadvantages of different EM observation methods.Finally,considering the related questions of the seismo-electromagnetic prediction and implementing the recent developments both in China and abroad,we review the current status of seismo-electromagnetic research and propose strategies for future research. | Guoze ZHAO Xuemin ZHANG Juntao CAI Yan ZHAN Qinzhong MA Ji TANG Xuebin DU Bing HAN Lifeng WANG Xiaobin CHEN Qibin XIAO Xiangyu SUN Zeyi DONG Jijun WANG Jihong ZHANG Ye FAN Tao YE | 2022 | Science China Earth Sciences2022，65，7： | 0 |
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