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Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

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Data science Journal 2022年第1期21卷 8-8页

作者： Peng, Ge Lacagnina, Carlo Downs, Robert R. Ganske, Anette Ramapriyan, Hampapuram K. Ivánová, Ivana Wyborn, Lesley Jones, Dave Bastin, Lucy Shie, Chung-Lin Moroni, David F. Earth System Science Center/NASA MSFC IMPACT The University of Alabama in Huntsville HuntsvilleAL United States Barcelona Spain Columbia University PalisadesNY United States TIB – Leibniz Information Centre for Science and Technology Hannover Germany Science Systems and Applications Inc LanhamMD United States NASA Goddard Space Flight Center Greenbelt MD United States Curtin University Perth Australia National Computational Infrastructure Australian National University ACT Australia StormCenter Communications | GeoCollaborate HalethorpeMD United States Aston University Birmingham United Kingdom University of Maryland at Baltimore County BaltimoreMD United States NASA Goddard Space Flight Center GreenbeltMD United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re) use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles;ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets;and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity. © 2022 The Author(s).

关键词： Metadata

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Traffic Control Task Scheduling Optimization Approach of Urban Road Intersection Based on Edge Computing

Traffic Control Task Scheduling Optimization Approach of Urb...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Xiang Fu Fei Wang Nan Ji Yilong Ren School of Transportation Science and Engineering Beihang University BUAA Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Intelligent Transport System (ITS) R & D Center Shanghai Urban Construction Design and Research Institute (Group) Co. Ltd. Shanghai China

ISBN: (纸本)9781665412971

With the development of Connected Automated Vehicles (CAV), there will be a long period of mixed driving, which causes the traffic control task at intersections becomes much more complicated. The control tasks would include not only the traffic signal timing optimization but also the computing tasks for different levels of vehicles. Current control task management methods are proved to be inferior to solve this problem. In this paper, we propose an intersection control task scheduling optimization approach based on edge computing. Firstly, the traffic control edge computing system for intersections is constructed. Then, different types of control tasks are listed. Criticality and timeliness are introduced to calculate the control task's dynamic priority. The control task scheduling optimization model, which considering these two parameters is proposed. At last, the improved Hungarian algorithm is applied to solve the proposed task scheduling optimization model. simulation results show that our proposed methods can effectively reduce task processing costs and ensure the critical tasks execute firstly.

关键词： Processor scheduling Computational modeling Heuristic algorithms Traffic control Scheduling Timing Task analysis

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Radio dimming associated with filament eruptions in the meter and decimeter wavebands

arXiv

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arXiv 2025年

作者： Hou, Zhenyong Tian, Hui Yan, Jingye Madjarska, Maria S. Zhang, Jiale Xu, Yu Chen, Hechao Wu, Zhao Wu, Lin Lv, Xuning Yang, Yang Liu, Yujie Deng, Li Feng, Li Qiu, Ye School of Earth and Space Sciences Peking University Beijing100871 China Key Laboratory of Space Weather Chinese Academy of Sciences Beijing100871 China National Space Science Center Chinese Academy of Sciences Beijing100871 China Chengdu610041 China Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Space Research and Technology Institute Bulgarian Academy of Sciences Acad. Georgy Bonchev Str. Bl. 1 Sofia1113 Bulgaria School of Physics and Astronomy Yunnan University Kunming650050 China School of Space Science and technology Shandong University Weihai264209 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory CAS Nanjing210023 China School of Astronomy and Space Science Nanjing University Nanjing210023 China

Filament eruptions are considered to be a common phenomenon on the Sun and other stars, yet they are rarely directly imaged in the meter and decimeter wavebands. Using imaging data from the DAocheng solar Radio Telescope (DART) in the 150–450 MHz frequency range, we present two eruptive filaments that manifest as radio dimmings (i.e., emission depressions). Simultaneously, portions of these eruptive filaments are discernible as dark features in the chromospheric images. The sun-as-a-star flux curves of brightness temperature, derived from the DART images, exhibit obvious radio dimmings. The dimming depths range from 1.5% to 8% of the background level and show a negative correlation with radio frequencies and a positive correlation with filament areas. Our investigation suggests that radio dimming is caused by free-free absorption during filament eruptions obscuring the solar corona. This may provide a new method for detecting stellar filament eruptions. Copyright © 2025, The Authors. All rights reserved.

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Correction to: Venus Evolution Through Time: Key science Questions, Selected Mission Concepts and Future Investigations

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space science Reviews 2023年第8期219卷 1-5页

作者： Widemann, Thomas Smrekar, Suzanne E. Garvin, James B. Straume-Lindner, Anne Grete Ocampo, Adriana C. Schulte, Mitchell D. Voirin, Thomas Hensley, Scott Dyar, M. Darby Whitten, Jennifer L. Nunes, Daniel C. Getty, Stephanie A. Arney, Giada N. Johnson, Natasha M. Kohler, Erika Spohn, Tilman O’Rourke, Joseph G. Wilson, Colin F. Way, Michael J. Ostberg, Colby Westall, Frances Höning, Dennis Jacobson, Seth Salvador, Arnaud Avice, Guillaume Breuer, Doris Carter, Lynn Gilmore, Martha S. Ghail, Richard Helbert, Jörn Byrne, Paul Santos, Alison R. Herrick, Robert R. Izenberg, Noam Marcq, Emmanuel Rolf, Tobias Weller, Matt Gillmann, Cedric Korablev, Oleg Zelenyi, Lev Zasova, Ludmila Gorinov, Dmitry Seth, Gaurav Rao, C. V. Narasimha Desai, Nilesh LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Université Paris Cité Meudon France Université Paris-Saclay UVSQ DYPAC Versailles France Jet Propulsion Laboratory California Institute of Technology Pasadena USA NASA Goddard Space Flight Center Greenbelt USA Directorate of Science Solar System Section ESA’s European Space Research and Technology Centre Noordwijk Netherlands Mary W. Jackson NASA Headquarters NASA Science Mission Directorate Washington USA ESA’s European Space Research and Technology Centre Noordwijk Netherlands Planetary Science Institute Tucson USA Dept. Earth and Environmental Sciences Tulane University New Orleans USA International Space Science Institute Bern Switzerland Institute of Space Research Deutsches Zentrum für Luft- und Raumfahrt Berlin Germany School of Earth and Space Exploration Arizona State University Tempe USA Department of Atmospheric Oceanic and Planetary Physics Oxford University Oxford UK NASA Goddard Institute for Space Studies New York USA Theoretical Astrophysics Department of Physics and Astronomy Uppsala University Uppsala Sweden Department of Earth and Planetary Sciences University of California Riverside USA CNRS - Centre de Biophysique Moléculaire Orléans France Potsdam Institute for Climate Impact Research Potsdam Germany Department of Earth Sciences VU Amsterdam Amsterdam Netherlands Michigan State University East Lansing USA Department of Astronomy and Planetary Science Northern Arizona University Flagstaff USA Habitability Atmospheres and Biosignatures Laboratory University of Arizona Tucson USA Lunar and Planetary Laboratory University of Arizona Tucson USA Institut de physique du globe de Paris CNRS Université de Paris Paris France Institute of Planetary Research Deutsches Zentrum für Luft- und Raumfahrt Berlin Germany Dept. of Earth and Environmental Sciences Wesleyan University Middletown USA Department of Earth Sciences Royal Holloway University of London

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Wireless sensor network localization approach based on bayesian MDS 5th

Wireless sensor network localization approach based on bayes...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Pei, Zhongmin Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

ISBN: (纸本)9789811308956

The aim of wireless sensor networks (WSNs) are to perceive, collect and process the information of sensor nodes within the coverage of the network. As a bridge between the physical world and the digital one, WSNs have widely been applied in many fields. One of the key issues for most applications is to know the location of sensor nodes. Though localization algorithms based on multidimensional scaling (MDS), which only need several anchor nodes, have been proven to be robust with respect to range-based implementations, these methods are sensitive inaccuracy range measure. This article proposes a novel localization algorithm based on Bayesian MDS, named as MDS-MAP(P, B), where P and B denote the use of patching of local maps and Bayesian MDS, respectively. Experimental results in real-world systems show that our method is more robust and efficient. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Sensor nodes

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Denoising of hyperspectral images based on principal component analysis and adaptive sparse coding 10

Denoising of hyperspectral images based on principal compone...

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10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018

作者： Xiaorui, Song Lingda, Wu Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing101416 China

ISBN: (纸本)9781538684795

In view of the special properties of hyperspectral images(HSI) in the transform domain, in this paper, a new denoising method of HSI based on principal component analysis(PCA) and adaptive sparse coding is proposed. Firstly, the principal component image of each channel is obtained by performing PCA transform on the noisy HSI. Then, the first PCA output channels which contain a majority of the total energy of an HSI data cube are retained, and the rest PCA output channels which contain a small amount of energy, termed noise component images, are subjected to noise reduction through an adaptive sparse coding method. The encoding dictionaries are learned from each channel of noise component images by an approach of online dictionary learning. Finally, the denoised HSI is obtained by the inverse PCA transform. The proposed method takes the advantages of PCA and adaptive sparse representation that has better adaptability to the HSI. It not only performs better in denoising, but also preserves the details and alleviates the blocking artifacts well. The effectiveness of the proposed approach to hyperspectral denoising, termed PCASpC, is illustrated in a series of experiments with synthetic and realworld data where it outperforms the state-of-the-art. © 2018 IEEE.

关键词： Principal component analysis

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Visualization of pairwise data: An overview 3

Visualization of pairwise data: An overview

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3rd IEEE International Conference on Data science in Cyberspace, DSC 2018

作者： Pei, Zhong-Min Xiong, Ming-Hui Xiong, Wei Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

ISBN: (纸本)9781538642108

It is difficult for some applications to extract feature functions in the process of data visualization. Focus on the problem of large-scale pairwise data visualization, the paper first introduces and analysis the problem. Then the current mainstream methods for pairwise data visualization are analyzed. On the basis of active learning and configuration suture, the paper proposes a fast visualization method. The overall research framework, the key technologies are presented to provide some research ideas and recommendations for pairwise data visualization. © 2018 IEEE.

关键词： Data visualization

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Topological Connectivity Analysis on Freight Transportation Networks

Topological Connectivity Analysis on Freight Transportation ...

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作者： Xi-sheng SHEN Xiao-fang WANG Lian-yi ZHANG Xi-fu WANG School of Traffic and Transportation Beijing Jiaotong University School of Business Renmin University of China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center

The connections between nodes in Freight Transportation Networks(FTNs) are highly complex. With the topological modelling of real world FTNs, this paper introduces quantitative indicators, such as degree distribution and edge/node betweenness centrality, to analyze the topological connectivity. The exponential laws of degree distribution and edge/node betweenness centrality are shown by numerical results on the coal transportation network in Shanxi, China.

关键词： Topological connectivity Degree distribution Betweenness Freight transportation networks

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Robust Analysis of Freight Comprehensive Transportation Networks

Robust Analysis of Freight Comprehensive Transportation Netw...

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2019 International Conference on Applied Mathematics, Modeling, simulation and Optimization(AMMSO 2019)

作者： Xi-sheng SHEN Xiao-fang WANG Xi-fu WANG School of Traffic and Transportation Beijing Jiaotong University Science and Technology on Space System Simulation Laboratory Beijing Simulation Center School of Business Renmin University of China

The robustness of Freight Comprehensive Transportation Networks(FCTNs) refers to the ability that FCTNs can maintain their function or performance tolerating perturbations from uncertainties. To evaluate the robustness of FCTNs, this paper proposes several quantitative indicators including accessibility, delivery, carrying capacity and transferring capacity. Numerical results based on the case of coal comprehensive transportation networks in Shanxi, China show the effectiveness of the proposed method.

关键词： Robust analysis Freight Comprehensive Transportation Networks Evaluation indicators

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RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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