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Resource Optimization for LEO Constellation Networks: A Multi-satellite Cooperative Coverage Design

IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR Management Practices

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Yamini Kalva Anuradha Parasar Rajlaxmi Pramod Pujar K. Balasubramanian Zameer Pasha S. Ramya Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai India School of Liberal Education Galgotias University Dankaur India Department of Marketing Indira School of Business Studies PGDM Pune India Department of Computer Applications Kalasalingam Academy of Research and Education- Deemed to be University Krishnankoil India Department of Management St. Claret College (Autonomous) Bengaluru India Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. Data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps.

关键词： Training Accuracy Bidirectional long short term memory Machine learning Aging Feature extraction Convolutional neural networks Integrated circuit modeling Retirement Overfitting

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Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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ADHERENT-Net: An Adaptive Health Risk Estimation Network for Real-Time Environmental Tracking

ADHERENT-Net: An Adaptive Health Risk Estimation Network for...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： M. Priyadharshini V. Murugesh Subrata Chowdhury Bibhuti Bhusan Dash Utpal Chandra De Sudhansu Shekhar Patra Department of Computer Science and Engineering Faculty of Science and Technology(Icfai Tech) ICFAI Foundations for Higer Education Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE Sreenivasa Institute of Technology and Management Studies Chittoor Anthra Pradesh India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Air borne pollutants greatly affect people’s well-being, so more accurate predictions are essential for the assessment of urban risks. Adaptive Health Risk Estimation Network is proposed in this paper as ADHERENT-Net to predict immediate health risks from environmental data. The model uses LSTM networks for temporal patterns discovery and Dense Neural Network for general feature learning and an additional meta-learner that combines both branches for an enhanced prediction capability. This one is prevented by a particular choice of its loss function where underestimations of threats to health are punished severely enough to prompt timely actions. ADHERENT-Net has a good predictive accuracy: the coefficient of determination, R 2 , is equal to 0,90 and the mean absolute error (MAE) is equal to 0,15. The dual branch structure is capable of handling time-dependent and non-time-dependent data from each other and the meta-learner also increases flexibility in changing conditions such as air quality, and seasonality. Training employed miscellaneous environmental data set with the pre-processing of data completeness and normalized data. ADHERENT-Net creates a reliable foundation for real-time health risk prediction, which has substantial potential to enhance the efficiency of program interventions in urban areas. Future work will concern tuning of the elaborated model parameters, the validation in various scenarios, and the generalization of the framework in other healthcare settings.

关键词： Adaptation models Accuracy Adaptive systems Urban areas Neural networks Estimation Water quality Predictive models Real-time systems Long short term memory

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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ASDIA: An Adversarial Sample to Preserve Privacy Program in Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Tian, Shujuan Tan, Yi Wang, Han Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Department of Electrical Engineering and Computer Science University of Kansas 1520 W 15th St LawrenceKS United States Jinan University College of Information Science and Technology Guangzhou510632 China

Federated learning enables training across multiple entities while ensuring data security and the effectiveness of knowledge dissemination. Despite its benefits, it remains susceptible to privacy breaches by both external and internal adversaries, who may exploit data or model parameters to glean sensitive participant information or disrupt the training process, thus compromising participant privacy and security. This paper proposes a novel methodology, Adversarial Samples for Defense Inference Attack (ASDIA), aimed at dual protection of data privacy and model robustness within federated learning through adversarial samples and gradient reconstruction. ASDIA includes gradient processing approach before uploading: initially identifying privacy-sensitive gradient, followed by the injection of well-calibrated noise to these gradients. This method not only obfuscates the adversary's classification demarcations but also aids in model performance recovery, all the while maintaining computational efficiency. ASDIA reduces the efficacy of attacks to near-random guessing levels and shows better balance between the model utility and privacy protection compared to the most advanced defense strategies. Additionally, regarding model performance, ASDIA proves its merit across diverse datasets under overfitting and non-overfitting scenarios. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination

arXiv

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arXiv 2025年

作者： Chen, Weilin Cai, Ruichu Wan, Junjie Yang, Zeqin Hernández-Lobato, José Miguel School of Computer Science Guangdong University of Technology Guangzhou510006 China Peng Cheng Laboratory Shenzhen China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Long-term causal inference has drawn increasing attention in many scientific domains. Existing methods mainly focus on estimating average long-term causal effects by combining long-term observational data and short-term experimental data. However, it is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects, significantly limiting practical applications. In this paper, we propose several two-stage style nonparametric estimators for heterogeneous long-term causal effect estimation, including propensity-based, regression-based, and multiple robust estimators. We conduct a comprehensive theoretical analysis of their asymptotic properties under mild assumptions, with the ultimate goal of building a better understanding of the conditions under which some estimators can be expected to perform better. Extensive experiments across several semi-synthetic and real-world datasets validate the theoretical results and demonstrate the effectiveness of the proposed estimators. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

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FedEnv-Net: Federated Neural Network for Decentralized Health Risk Prediction in Dynamic Urban Environments

FedEnv-Net: Federated Neural Network for Decentralized Healt...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： M. Priyadharshini V. Murugesh Subrata Chowdhury Bibhuti Bhusan Dash Utpal Chandra De Sudhansu Shekhar Patra Department of Computer Science and Engineering Faculty of Science and Technology(Icfai Tech) ICFAI Foundations for Higher Education Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE Sreenivasa Institute of Technology and Management Studies Chittoor Anthra Pradesh India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The predictions of health risks in urban settings are constrained by factors such as pollution and temperature which change over time. Centralized models have problems with privacy and scalability that often do not allow implementing solutions in real-world large, and distributed systems. To this end, we introduce the FedEnv-Net which is a federated neural network to predict health risk in a decentralized manner. Using federated learning, FedEnv-Net train’s models directly on edge devices, where data is trained and only updates are shared. Dynamics of temporal patterns and features are predicted by LSTM branch and Dense branch respectively in the model. A Meta-Learner merges such branches for prediction and a tuned loss function for conservative health risk estimations. Although it ensures privacy of data, FedEnv-Net yields an R 2 score of 0.92 and an MAE of 0.14, which determines its predictive capability. Future work includes increasing the efficiency of the communication between the system components, broadening the number of input data sources and improving the level of privacy to make FedEnv-Net a versatile, privacy-preserving solution for real-time health risk assessment.

关键词： Privacy Federated learning Neural networks Urban areas Predictive models Real-time systems Data models Risk management Long short term memory Context modeling

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Challenges and Solutions for Securing Cloud-Based Virtual Private Networks (VPNs)

Challenges and Solutions for Securing Cloud-Based Virtual Pr...

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Automation and Computation (AUTOCOM), International Conference on

作者： Yuvraj Parmar Satish Kumar R V. Karthikeyan Gaurav Shukla Debahuti Mishra Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Adjunct Faculty School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud-based virtual private networks are becoming common as fewer connections to the company network make them cost-effective and convenient. Nonetheless, increased cyber-attacks and data breaches have made it incredibly difficult for organizations to secure these VPNs. In this technical abstract, we will highlight & describe issues with securing cloud-based VPNs, along with a few ideas for making them secure. In the first rung, there are those weaknesses of cloud-based VPN infrastructure. Since these VPNs are hosted on third-party cloud servers, they are susceptible to DoS and DDoS attacks. Moreover, due to the collaborative/inexpensive nature of cloud servers, they are also vulnerable when it comes to leaking data and getting unauthorized access. The second challenge is that managing the security of so many VPN connections becomes very difficult. Keeping track of all VPN users and their devices and which security protocols are being used becomes cumbersome for organizations, making detecting and responding quickly to a security breach difficult. Organizations should consider implementing multi-factor authentication and strong encryption protocols for their cloud-based VPNs to remedy these complications. Regular vulnerability assessments and penetration tests are also important to identify security risks. Also, a centralized network security management system can ensure more visibility and control of VPN connections.

关键词： Cloud computing Protocols Multi-factor authentication Data breach Denial-of-service attack Remote working Virtual private networks Encryption Security Servers

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Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound

Exploring Test Time Adaptation for Subcortical Segmentation ...

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IEEE International Symposium on Biomedical Imaging

作者： Joshua Omolegan Pak Hei Yeung Madeleine K. Wyburd Linde Hesse Monique Haak Intergrowth-21st Consortium Ana I. L. Namburete Nicola K. Dinsdale Oxford Machine Learning in NeuroImaging Lab University of Oxford Now at Google School of Computer Science and Engineering Nanyang Technological University Singapore Now at QuantCo Department of Obstetrics and Fetal Medicine Leiden University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen freehand US volumes often leads to a degradation of performance due to the vast differences in acquisition and alignment. In this work, we first demonstrate that test time adaptation (TTA) can be used to improve model performance in the presence of both real and simulated domain shifts. We further propose a novel TTA method by incorporating a normative atlas as a prior for anatomy. In the presence of various types of domain shifts, we benchmark the performance of different TTA methods and demonstrate the improvements brought by our proposed approach, which may further facilitate automated monitoring of fetal brain development. Our code is available at https://***/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation.

关键词： Degradation Deep learning Image segmentation Adaptation models Ultrasonic imaging Three-dimensional displays Codes Brain modeling Monitoring Biomedical imaging

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