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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Poster: Reporting Unique-Cause MC/DC Score Using Formal Verification

Poster: Reporting Unique-Cause MC/DC Score Using Formal Veri...

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Monika Rani Golla Sangharatna Godboley Avijit Das P. Radha Krishna Dept. of Computer Science and Engineering National Institute of Technology Warangal Warangal India Dept. of Computer Science and Engineering NITMiner Technologies National Institute of Technology Warangal Warangal India Electronics & Radar Development Establishment Defence Research & Development Organisation Bengaluru India

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Unique-Cause MC/DC (UCM) is the most desired form of MC/DC in many safety-critical applications. For a given predicate, the UCM considers the independent pair of each condition by flipping the corresponding condition and fixing the other conditions. For the given N conditions in a predicate, the existing static symbolic execution tool, CBMC, generates MC/DC (Modified Condition/Decision) goal constraints of size, N + 1 that constitutes its minimal independent pairs. However, we propose a novel UCM Sequence Generator (UCM-Gen) that generates all possible inequality comparisons of the sequences/combinations of the N conditions which helps in computing the independent pairs further. The UCM-Gen outputs UCM Annotated Program which when given to the program verifiers, produces the UCM Score (%). In our work, we have considered CBMC to get the SAT/UNSAT results for each sequence of the UCM Annotated Program. Upon analysing these results, we calculate the total number of independently affected conditions (i.e., I value) for all the predicates in the given program. Furthermore, this work is compared with the CBMC's mode of MC/DC implementation. Interestingly, our proposed approach based UCM score (%) is always greater than the CBMC's MC/DC score (%) and hence claiming that their corresponding test cases contribute in effective bug finding.

关键词： Software testing computer bugs Generators Formal verification

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Energy-Efficient Satellite IoT Optical Downlinks Using Weather-Adaptive Reinforcement Learning

arXiv

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arXiv 2025年

作者： Fettes, Ethan Madoery, Pablo G. Yanikomeroglu, Halim Kurt, Gunes Karabulut Naik, Abhishek Bellinger, Colin Martel, Stéphane Ahmed, Khaled Siddiqui, Sameera Lab Department of Systems and Computer Engineering Carleton University Canada Poly-Grames Research Center Department of Electrical Engineering Polytechnique Montréal Montréal Canada National Research Council Canada Canada Defence Research and Development Canada Canada Satellite Systems MDA Canada

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don’t factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values. © 2025, CC BY.

关键词： Delay tolerant networks

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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QS-CPABE: Quantum-Safe CP-ABE with policy hiding and verifiable policy update for cloud storage

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Journal of Information Security and Applications 2025年 92卷

作者： Gudipati, Sravya Pasupuleti, Syamkumar Padmavathy, R. Quantum safe cryptography Lab Institute for Development and Research in banking technology Telangana Hyderabad500057 India Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Hanamkonda506004 India

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) ensures data privacy and fine-grained access control in a cloud environment through its one-to-many encryption feature. However, most of the existing CP-ABE schemes cannot resist quantum attacks as they are constructed from bilinear pairing assumptions. Recently, several lattice-based CP-ABE schemes have been proposed to defend against quantum attacks, but these schemes do not provide mechanisms to hide the policy attributes along with a verifiable policy updating mechanism which is crucial for dynamic access control management in cloud environments. To address these issues, this paper proposes a Quantum-Safe CP-ABE scheme with policy hiding and verifiable policy updates for cloud storage. The security of QS-CPABE is proven against the Chosen Plaintext Attack (CPA) under the Module Learning with Errors (M-LWE) assumptions. The theoretical and experimental analyses demonstrate that QS-CPABE is practical and efficient. © 2025

关键词： Authorization

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One-Step Laser-Induced Dissolvable PVA Mask for 3D Soft Carbon Electrode Array 38

One-Step Laser-Induced Dissolvable PVA Mask for 3D Soft Carb...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Wang, Xuanqi Xue, Kai Bai, Ruiyu Huang, Ye Zhang, Zimo You, Xiaoli Wang, Minghao Chang, Honglong Ji, Bowen Northwestern Polytechnical University Unmanned System Research Institute China Northwestern Polytechnical University National Key Laboratory of Unmanned Aerial Vehicle Technology Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology China Northwestern Polytechnical University Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical Engineering China Northwestern Polytechnical University Sanhang Institute for Brain Science and Technology China Northwestern Polytechnical University Institute of Medical Research China Hangzhou Dianzi University Moe Engineering Research Center of Smart Microsensors and Microsystems College of Electronics and Information China

ISBN: (纸本)9798331508890

This paper introduces a 3D soft carbon electrode array, fabricated through a one-step laser-induced process integrating a water-dissolvable polyvinyl alcohol (PVA) mask. This approach achieves precise conical microstructures and robust conductive channels using hybrid MWCNT@PDMS conductive paste. The soft carbon electrode sites with a 3D conical microstructure are designed to enhance proximity to neural tissue, thereby enhancing electrocorticogram (ECoG) signal quality and enabling effective neural stimulation. The electrode array demonstrates remarkable stretchability (25% over 1,000 cycles without failure) and stability (equivalent to 66 days in vivo). Compared to conventional materials, our electrodes exhibit superior electrochemical properties, including lower impedance (9.7 kΩ at 1 kHz) and higher signal-to-noise ratio (SNR, 61.7 dB). Thus, this one-step process is a cost-effective, scalable solution for recording and modulating central or peripheral neural tissues. © 2025 IEEE.

关键词： Carbon electrodes

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Small-Scale Dataset for Machine Learning Based Network Intrusion Detection

Small-Scale Dataset for Machine Learning Based Network Intru...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Mohammad Azmi Ridwan Nurul Asyikin Mohamed Radzi Faris Syahmi Samidi Suhaila Sulaiman Kaiyisah Hanis Mohd Azmi Wan Siti Halimatul Munirah Wan Ahmad Institute of Power Engineering Universiti Tenaga Nasional Selangor Malaysia Department of Electronic Systems Engineering MJIIT-UTM Kuala Lumpur Malaysia Institute For Research Development & Innovation IMU Kuala Lumpur Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

An intrusion detection system (IDS) was introduced to detect various intrusive attempts on the network. Several machine learning (ML) techniques have been incorporated to build robust and intelligent IDS. researchers are maximizing public intrusion datasets, such as the KDD CUP 99, NSL-KDD and the CICIDS-2018 datasets, to train the ML algorithms. However, public intrusion datasets have large data sizes, which cause a high training time for the ML algorithms. Moreover, public intrusion datasets have high-dimensional input features, thus requiring tedious feature extraction methods. Furthermore, these datasets have an imbalance ratio between normal and attack traffic, which leads to less efficient training supplied to the ML algorithms, resulting in low accuracy. An intrusion dataset that can reduce the training and processing time whilst having a balanced dataset between normal and attack traffic must be proposed to overcome these issues. Hence, a small-scale and robust intrusion dataset is developed in this paper to train the ML classifier. Simulation results show that the proposed dataset improves the accuracy of the ML classifier by 19.15%, 99.26% and 40.48% considering accuracy, recall and F-measure, respectively, compared with the CICIDS-2018 public dataset.

关键词： Training Measurement Accuracy Machine learning algorithms Runtime Simulation Machine learning Feature extraction Denial-of-service attack Unsupervised learning

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MOAT: towards safe BPF kernel extension 24

MOAT: towards safe BPF kernel extension

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Hongyi Lu Shuai Wang Yechang Wu Wanning He Fengwei Zhang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology and Department of Computer Science and Engineering Southern University of Science and Technology and Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology and Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

ISBN: (纸本)9781939133441

The Linux kernel extensively uses the Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. The BPF employs a verifier to check the security of user-supplied BPF code statically. Recent attacks show that BPF programs can evade security checks and gain unauthorized access to kernel memory, indicating that the verification process is not flawless. In this paper, we present MOAT, a system that isolates potentially malicious BPF programs using Intel Memory Protection Keys (MPK). Enforcing BPF program isolation with MPK is not straightforward; MOAT is designed to alleviate technical obstacles, such as limited hardware keys and the need to protect a wide variety of BPF helper functions. We implement MOAT on Linux (ver. 6.1.38), and our evaluation shows that MOAT delivers low-cost isolation of BPF programs under mainstream use cases, such as isolating a BPF packet filter with only 3% throughput loss.

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Editorial: Urban multi-energy system networks with high proportion of renewable energy

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Frontiers in Energy research 2025年 13卷

作者： Ding, Xiaoyi Xiang, Yue Morais, Hugo School of Water Resources and Hydroelectric Engineering Xi’an University of Technology Xi’an China Department of Electrical Engineering Sichuan University Chengdu China Institute of Systems and Computer Engineering: Research and Development Higher Technical Institute University of Lisbon Lisbon Lisbon Portugal

Faced with environment pollution and climate change, it is essential to develop advanced low-carbon energy system based on renewable sources. Micro-energy networks with a high proportion of renewable energy are one of the most important methods to achieve the complementary use of renewable energy and fossil energy with high efficiency of energy utilization. research on urban-level energy networks would be an important step for applying and integrating renewable sources. Faced with high penetration rate of renewable energy, the robust ability and safety operation are challenging from both sides of energy generation and the electrical grid. Meanwhile, the increasing number of electrical and fuel cell powered vehicles put additional difficulties for optimization and scheduling. Therefore, research on urban energy systems with a high proportion of renewable energy is extremely *** topic (“Urban Multi-energy System Networks with High Proportion of Renewable Energy”) aims to look into the key problems existing in the integrated multi-energy network system for the urban level and provide a detailed theoretical basis and experimental data for the design and operation of integrated micro-energy networks at the international academic level. The topic received several submissions within the field of renewable energy systems, including modeling and optimization, grid supply, the interaction between grid and EV/fuel cell cars, and advanced controlling strategy of off-grid/grid-connected microgrid systems, ***-carbon development of energyFeng et al. [1] modeled the natural gas system, power system, and their coupling components, and proposed a low-carbon economic control framework based on MASAC. The objective was to minimize both operational costs and carbon emissions. Simulation results using a nodal system demonstrated that this method significantly reduces costs and emissions compared to traditional approaches. Hu et al. [2] developed a low-carbon scheduling mod

关键词： renewable energy electric vehicle (EV) dynamic control multi-energy system synthetic fuel

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