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2024年05月26日

United Nations Global Geodetic Centre of Excellence UN cam...

Photo Attribution: "https://www.unbonn.org/news/new-un-organisation-bonn-2021/In 2020 the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) welcomed and supported the offer from the Federal Republic of Germany to establish and host a Global Geodetic Centre of Excellence (UN-GGCE) at the United Nations Campus in Bonn, Germany. With the conclusion and signing of the Agreement on the Operationalization of the UN-GGCE by the United Nations and the Federal Ministry of the Interior and Community, UN-GGCE is ready to start of work. The State Secretary at the Federal Ministry of the Interior and Community (BMI) Juliane Seifert emphasizes:"With the successful conclusion of the Agreement for the United Nations Global Geodetic Centre of Excellence Germany once more proved itself to be a reliable international partner. The Centre of Excellence is going to provide important contributions to a UN Member State agreed worldwide geodetic infrastructure. This infrastructure is the reliable long-term foundation for applications like satellite navigation, space-borne Earth observation as well as monitoring of the UN Sustainable Development Goals 2030."Recognizing the growing need for a high quality and sustainable Global Geodetic Reference Frame (GGRF) to support good policy development and decision-making for inclusive social progress, increasing environmental sustainability and vibrant economic development, the General Assembly on 26 February 2015 adopted resolution 69/266, entitled 'A Global Geodetic Reference Frame for Sustainable Development'. The resolution recognizes the importance of international cooperation, as no one country can do this alone, to realize the GGRF and services to underpin global navigation satellite systems technology and provide the framework for all geospatial activity, as a key enabler of geospatial data interoperability and data integration, and sustainable development. The resolution also recognizes the economic and scientific importance of and the growing demand for an accurate and stable global geodetic reference frame for the Earth that allows the interrelationship of measurements taken anywhere on the Earth and in space, combining geometric positioning and gravity field-related observations, as the basis and reference in location and height for geospatial information, which is used in many Earth science and societal applications, including sea-level and climate change monitoring, natural hazard and disaster management and a whole series of industrial applications including mining, agriculture, transport, navigation and construction) in which precise positioning introduces efficiencies.UN-GGCE's overarching goal is to assist Member States and geodetic organizations to coordinate and collaborate to sustain, enhance, access and utilise an accurate, accessible and sustainable GGRF to support science, society and global development. The objective is to support, within available resources, the implementation of General Assembly resolution 69/266 through strengthening and advancing: global geodetic cooperation and coordination; worldwide geodetic infrastructure; standards and policies; education, training and capacity development; and communication and awareness.

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2024年05月12日

中国宇航学会编制并发布《中国宇航学会重要学术会议指南（2024）》...

在第八个“全国科技工作者日”来临之际，中国宇航学会围绕“弘扬科学家精神，勇当高水平科技自立自强排头兵”主题，开展“航天科技工作者服务周”系列活动，广泛传播学会服务理念，精准对接航天科技工作者需求，打通“宣传、关心、服务”科技工作者的最后一公里。为促进宇航领域学术会议规范管理，提升学术会议质量，推动学术会议品牌建设，提高科技工作者对高质量学术会议信息获取的便捷性，中国宇航学会编制并发布《中国宇航学会重要学术会议指南（2024）》（以下简称《指南2024》）。中国宇航学会向所属47个分支机构征集高水平年度会议，根据会议的学术影响力、举办的规范性和机制性，遴选出20项宇航领域重要学术会议，其中国际会议7项，国内会议13项。以下分支机构推荐会议入选《指南2024》：深空探测技术专业委员会空间太阳能电站专业委员会空间控制专业委员会空间遥感专业委员会空气动力与飞行力学专业委员会空天动力燃烧与传热专业委员会航天医学与空间生物学专业委员会发射工程与地面设备专业委员会电推进专业委员会质量与可靠性专业委员会计量与测试专业委员会无人飞行器分会临近空间产业工作委员会自2021年起，中国宇航学会开展发布《中国宇航学会重要学术会议指南》工作，旨在通过持续发布指南，与相关单位共同打造权威学术会议，推动宇航领域学术会议质量提升。中国宇航学会将选择优秀会议进一步推荐至中国科协等上级有关部门。

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2024年05月12日

《外空条约》与外空利用新发展研讨会举办

4月24日，2024年中国航天大会《外空条约》与外空利用新发展研讨会在武汉大学举办。研讨会由中国宇航学会主办，中国宇航学会航天政策与法律专业委员会、武汉大学法学院、武汉大学国际法治研究院、武汉大学国际法研究所联合承办。三位致辞嘉宾及论坛主席（由左至右）中国科学院院士、国际宇航科学院院士于登云，武汉大学党委常务副书记沈壮海，外交部参赞董志华，外交部条法司一秘徐宇，中国空间技术研究院总法律顾问王冀莲，武汉大学国际法治研究院院长肖永平，武汉大学法学院副院长祝捷出席开幕式。沈壮海副书记、于登云院士、董志华参赞先后致辞。开幕式由研讨会主席、武汉大学周鲠生讲席黄解放教授主持。主题报告和讨论由武汉大学国际法研究所苏金远教授主持。安徽大学法学院讲师许丰娜博士、外交学院国际法系讲师陈南睿博士、中南财经政法大学法学院雷益丹副教授、海南大学法学院李杜副教授、中国政法大学国际法学院讲师唐雅博士、深圳大学法学院助理教授龙杰博士、华东政法大学国际法学院蒋圣力副教授、南京航空航天大学人文与社会科学学院聂明岩副教授、北京交通大学法学院讲师颜永亮博士先后逐条解释《外空条约》主要条款，讨论其在当前面临的挑战，梳理相关进程进行，并对相关国际法规则的完善提出建议。来自政府部门、航天单位等专家进行了研讨。专题报告和讨论后，研讨会副主席王冀莲总法律顾问进行了总结。此次研讨会作为中国宇航学会航天政策与法律专业委员会《外空条约》研究的首轮工作，实现了既定目标，对进一步推进研究工作具有重大意义。

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2024空天信息大会（第五届）筹备视频

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太空计算系统

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INTERNATIONAL CONFERENCE

第14届联合国全球地理空间信息管理专家委员会（UN-GGIM）...

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Meeting Time ： 7 -9 August 2024

Meeting Place：美国纽约联合国总部

Nature of the meeting：政府性质会议

会议时间：2024年8月7日至9日会议地点：美国纽约联合国总部会议性质：政府性质会议会议内容：联合国全球地理空间信息管理The Committee of Experts on Global Geospatial Information Management (UN-GGIM) was established to provide the leadership to ensure that geospatial information and resources are coordinated, maintained, accessible and able to be leveraged by Member States and society to find sustainable solutions for social, economic, and environmental development. The Committee provides a forum for coordination and dialogue with and among Member States and relevant international organizations on enhanced cooperation in the field of global geospatial information management for the achievement of its operations focused on the Sustainable Development Goals (SDGs) and the United Nations Integrated Geospatial Information Framework (UN-IGIF), to strengthen and ensure its continued effectiveness and benefits to all Member States.

中国航天大会

Meeting Time ： 2024年4月23-26日

Meeting Place：湖北省武汉市

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6th Summit for Space Sustainability

Meeting Time ： July 11-12 2024

Meeting Place：Tokyo Japan

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国际宇航大会 2024

Meeting Time ： 14 - 18 OCTOBER 2024

Meeting Place：MILAN ITALY

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PUBLICATIONS

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A Joint Communication and Computation Framework for Digita...

In this article, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) need to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. For the special case with one physical device, the optimal number of transmission times is revealed. Based on the theoretical findings, the original problem is transformed into a simplified problem and the optimal device scheduling can be found. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.Published in: IEEE Journal of Selected Topics in Signal Processing ( Volume: 18, Issue: 1, January 2024)

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Deep Reinforcement Learning for Energy Efficiency Maximiza...

Federated learning (FL) is a promising solution for preserving user privacy in Internet of Things (IoT) networks thanks to its distributed computing feature. Furthermore, over-the-air FL (AirFL) can leverage the superposition property of wireless channels to achieve fast model aggregation through concurrent analog transmissions. To make AirFL sustainable for energy-constrained IoT devices, we apply simultaneous wireless information and power transfer (SWIPT) at the base station to broadcast the global model and charge local devices during the model training process. To characterize the optimality gap between the aggregated FL model and the ideal FL model brought by signal misalignment, channel fading, and random noise in the model distribution and aggregation processes, we prove the convergence of SWIPT-based AirFL to show the precise impact of up- and down-link communications on the learning performance. We formulate a long-term energy efficiency (EE) maximization problem and propose a deep reinforcement learning algorithm with a collaborative double-agent approach to optimize resource allocation strategies while guaranteeing learning performance. Numerical results demonstrate that the proposed algorithm can achieve a maximum of 41% improvement in EE under various network settings compared with benchmark schemes, and the learning performance of SWIPT-based AirFL can be improved significantly by alleviating transmission errors.Published in: IEEE Transactions on Green Communications and Networking ( Volume: 8, Issue: 1, March 2024)

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Distributed Machine Learning for UAV Swarms: Computing, Se...

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 5, 01 March 2024)

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Delay-Optimized Edge Caching in Integrated Satellite-Terre...

X. Zhu, C. Jiang, Z. Yang and H. Wang, "Delay-Optimized Edge Caching in Integrated Satellite-Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3355139.In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.Published in: IEEE Internet of Things Journal ( Early Access )链接地址：https://ieeexplore.ieee.org/document/10402004

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Wisdom makes the sky and counts the future

We will make space services online and space data more valuable

Wisdom makes the sky and counts the future

We will make space services online and space data more valuable