Tootfinder

FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research
Osama Abu Hamdan, Hao Che, Engin Arlsan, Md Arifuzzaman
https://arxiv.org/abs/2509.00621 https…

FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research
Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challe…

Profit Maximization for a Robotics-as-a-Service Model
Joo Seung Lee, Anil Aswani
https://arxiv.org/abs/2509.26595 https://arxiv.org/pdf/2509.26595

Profit Maximization for a Robotics-as-a-Service Model
The growth of Robotics-as-a-Service (RaaS) presents new operational challenges, particularly in optimizing business decisions like pricing and equipment management. While much research focuses on the technical aspects of RaaS, the strategic business problems of joint pricing and replacement have been less explored. This paper addresses the problem of profit maximization for an RaaS operator operating a single robot at a time. We formulate a model where jobs arrive sequentially, and for each, th…

Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework
Yiming Xu, Runzhi He, Hengzhi Ye, Minghui Zhou, Huaimin Wang
https://arxiv.org/abs/2507.21678 …

Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework
The maintenance risks of open source software (OSS) projects pose significant threats to the quality, security, and resilience of modern software supply chains. While prior research has proposed diverse approaches for predicting OSS maintenance risk -- leveraging signals ranging from surface features (e.g., stars, commits) to social network analyses and behavioral patterns -- existing methods often suffer from ambiguous operational definitions, limited interpretability, and datasets of insuffic…

Bridging Cloud Convenience and Protocol Transparency: A Hybrid Architecture for Ethereum Node Operations on Amazon Managed Blockchain
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
https://arxiv.org/abs/2507.18774

Bridging Cloud Convenience and Protocol Transparency: A Hybrid Architecture for Ethereum Node Operations on Amazon Managed Blockchain
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IA…

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction over Medium-range Forecast Periods
Takuya Inoue (Meteorological Research Institute, Tsukuba, Japan), Takuya Kawabata (Meteorological Research Institute, Tsukuba, Japan)
https://arxiv.org/abs/2507.18937

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction over Medium-range Forecast Periods
This study proposes a method that integrates convolutional neural networks (CNNs) with ensemble numerical weather prediction (NWP) models, enabling surface temperature forecasting at lead times beyond the short-range (five-day) forecast period. Owing to limited computational resources, operational medium-range temperature forecasts typically rely on low-resolution NWP models, which are prone to systematic and random errors. To resolve these limitations, the proposed method first reduces systema…

sci pol; NZ pol
ICYMI Another handbrake turn in research management, this time Aotearoa New Zealand under Christopher Luxon's National party coalition.
"New Zealand’s largest source of funding through competitive grants, the Endeavour Fund, remains stagnant at NZ$245 million but has *canceled all applications for the 2026 funding round to “reduce the operational burden of applying for funding”* during the mergers of Crown Research Institutes."
➡️ #NZ #researchfunding #SciPol

The CCOR Compact Coronagraphs for the Geostationary Operational Environmental Satellite-19 (GOES-19) and the Space Weather Follow On (SWFO) Missions
A. F. Thernisien, D. H. Chua, M. T. Carter, N. B. Rich, M. Noya, T. A. Babich, C. E. Crippa, B. Baugh, Y. Bordlemay, D. Socker, D. Biesecker, C. Korendyke, D. Wang, D. Vassiliadis, N-Y. Wang, S. Abbay, S. Bagnall, L. Balmaceda, S. Brown, J. Bonafede, D. Boyer, J. Declet, P. Cheng, K. Corsi, L. Cremerius, I. Chavis, J. Chiralo, G. Clifford,…

The CCOR Compact Coronagraphs for the Geostationary Operational Environmental Satellite-19 (GOES-19) and the Space Weather Follow On (SWFO) Missions
The CCOR Compact Coronagraph is a series of two operational solar coronagraphs sponsored by the National Oceanic and Atmospheric Administration (NOAA). They were designed, built, and tested by the U.S. Naval Research Laboratory (NRL). The CCORs will be used by NOAA's Space Weather Prediction Center to detect and track Coronal Mass Ejections (CMEs) and predict the Space Weather. CCOR-1 is on board the Geostationary Operational Environmental Satellite -U (GOES-U, now GOES-19/GOES-East). GOES-U wa…

The Effects of Communication Delay on Human Performance and Neurocognitive Responses in Mobile Robot Teleoperation
Zhaokun Chen, Wenshuo Wang, Wenzhuo Liu, Yichen Liu, Junqiang Xi
https://arxiv.org/abs/2508.18074

The Effects of Communication Delay on Human Performance and Neurocognitive Responses in Mobile Robot Teleoperation
Communication delays in mobile robot teleoperation adversely affect human-machine collaboration. Understanding delay effects on human operational performance and neurocognition is essential for resolving this issue. However, no previous research has explored this. To fill this gap, we conduct a human-in-the-loop experiment involving 10 participants, integrating electroencephalography (EEG) and robot behavior data under varying delays (0-500 ms in 100 ms increments) to systematically investigate…

A Fuzzy-Enhanced Explainable AI Framework for Flight Continuous Descent Operations Classification
Amin Noroozi, Sandaruwan K. Sethunge, Elham Norouzi, Phat T. Phan, Kavinda U. Waduge, Md. Arafatur Rahman
https://arxiv.org/abs/2508.14618

A Fuzzy-Enhanced Explainable AI Framework for Flight Continuous Descent Operations Classification
Continuous Descent Operations (CDO) involve smooth, idle-thrust descents that avoid level-offs, reducing fuel burn, emissions, and noise while improving efficiency and passenger comfort. Despite its operational and environmental benefits, limited research has systematically examined the factors influencing CDO performance. Moreover, many existing methods in related areas, such as trajectory optimization, lack the transparency required in aviation, where explainability is critical for safety and…

Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity
Qihang Chen
https://arxiv.org/abs/2509.14251 https://

Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity
Metro crew planning is a key component of smart city development as it directly impacts the operational efficiency and service reliability of public transportation. With the rapid expansion of metro networks, effective multi-line scheduling and emergency management have become essential for large-scale seamless operations. However, current research focuses primarily on individual metro lines,with insufficient attention on cross-line coordination and rapid replanning during disruptions. Here, a …

Flexible and High-Performance Radio Access Networks for upcoming Sixth-Generation (6G) Systems
Peter Schefczik, Umar Toseef, Paolo Baracca, Ralf Klotsche, Torsten Dudda, Mai-Anh Phan, Lorenzo Miretti, David Ginthoer, Bin Han
https://arxiv.org/abs/2509.24417

Flexible and High-Performance Radio Access Networks for upcoming Sixth-Generation (6G) Systems
The collaborative research project 6G-ANNA develops concepts for the 6G radio access network (RAN) architecture and technology components. Previous RAN generations have become inherently more complex and reach their limits in handling foreseen future traffic demands with their diverse characteristics in an efficient manner, e.g., for the use-case of mobile eXtended Reality (XR) on a massive scale. One main objective of 6G is to regain both operational and energy efficiency, i.e., by simplificat…

Ransomware Negotiation: Dynamics and Privacy-Preserving Mechanism Design
Haohui Zhang, Sirui Shen, Xinyu Hu, Chenglu Jin
https://arxiv.org/abs/2508.15844 https://

Ransomware Negotiation: Dynamics and Privacy-Preserving Mechanism Design
Ransomware attacks have become a pervasive and costly form of cybercrime, causing tens of millions of dollars in losses as organizations increasingly pay ransoms to mitigate operational disruptions and financial risks. While prior research has largely focused on proactive defenses, the post-infection negotiation dynamics between attackers and victims remains underexplored. This paper presents a formal analysis of attacker-victim interactions in modern ransomware incidents using a finite-horizon…

Technical Aspects of Plasma Operational Simulation (POPSIM): A Framework for Data-Driven Simulation and Control
Allen M. Wang, Zander Keith, Mark Dan Boyer, Andrew Oakleigh Nelson, Alex Saperstein, Alessandro Pau, Cristina Rea
https://arxiv.org/abs/2509.10244

Technical Aspects of Plasma Operational Simulation (POPSIM): A Framework for Data-Driven Simulation and Control
This paper reports on technical aspects of Plasma Operational Simulation (POPSIM), a research framework for data-driven simulation and control built in the machine learning framework JAX. The objective of the project is to address the extremely challenging simulation and modeling requirements of tokamak operations and control by combining simple principles-based models with data-driven models, spanning everything from power laws to new neural network architectures. This paper reports on key sof…

Architecting Human-AI Cocreation for Technical Services -- Interaction Modes and Contingency Factors
Jochen Wulf, Jurg Meierhofer, Frank Hannich
https://arxiv.org/abs/2507.14034

Architecting Human-AI Cocreation for Technical Services -- Interaction Modes and Contingency Factors
Agentic AI systems, powered by Large Language Models (LLMs), offer transformative potential for value co-creation in technical services. However, persistent challenges like hallucinations and operational brittleness limit their autonomous use, creating a critical need for robust frameworks to guide human-AI collaboration. Drawing on established Human-AI teaming research and analogies from fields like autonomous driving, this paper develops a structured taxonomy of human-agent interaction. Based…

Adaptive Network Security Policies via Belief Aggregation and Rollout
Kim Hammar, Yuchao Li, Tansu Alpcan, Emil C. Lupu, Dimitri Bertsekas
https://arxiv.org/abs/2507.15163

Adaptive Network Security Policies via Belief Aggregation and Rollout
Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response procedures and modifications to access controls, among others. Reinforcement learning methods have been proposed for automating such policy adaptations, but most of the methods in the research literature lack performance guarantees and adapt slowly to changes. In this paper, we address these limitations and present a …

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
Ray Wai Man Kong
https://arxiv.org/abs/2508.06518 https://arxiv.org/pd…

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanc…

Optimizing Microgrid Composition for Sustainable Data Centers
Julius Irion, Philipp Wiesner, Jonathan Bader, Odej Kao
https://arxiv.org/abs/2508.04284 https://

Optimizing Microgrid Composition for Sustainable Data Centers
As computing energy demand continues to grow and electrical grid infrastructure struggles to keep pace, an increasing number of data centers are being planned with colocated microgrids that integrate on-site renewable generation and energy storage. However, while existing research has examined the tradeoffs between operational and embodied carbon emissions in the context of renewable energy certificates, there is a lack of tools to assess how the sizing and composition of microgrid components a…

Broadband Simultaneous Beam Steering and Compressing Device Based on Subwavelength Protrusion Metallic Tunnels
Dongguo Zhang, Fei Sun, Qin Liao, Yichao Liu, Donguk Nam
https://arxiv.org/abs/2509.04856 …

Broadband Simultaneous Beam Steering and Compressing Device Based on Subwavelength Protrusion Metallic Tunnels
Beam steering and beamwidth compressing play a role in steering the beam and narrowing its half-power beamwidth, respectively, which are both widely applied in extending the effective operational range of 6G communications, IoT devices, and antenna systems. However, research on wave manipulation devices capable of simultaneously achieving both functionalities remains limited, despite their great potential for system miniaturization and functional integration. In this study, we design and realiz…

The economic, social & Environmental impact of Electric Vehicle (EV) adaptation on Bangladeshi Society
Abdullah Al Noman, Hasibul Hassan Siam
https://arxiv.org/abs/2508.08398

The economic, social & Environmental impact of Electric Vehicle (EV) adaptation on Bangladeshi Society
This study examines the economic social and environmental impacts of electric vehicle adoption in Bangladesh using survey data from 57 respondents and secondary research. Findings show strong public perception of electric vehicles as cost effective with ninety three percent agreement and environmentally beneficial with eighty two point five percent agreement. Electric vehicles have potential to reduce fuel imports lower operational costs and create over fifty thousand jobs by 2030. Socially ele…

Effects of Unplanned Incoming Flights on Airport Relief Processes after a Major Natural Disaster
Luka Van de Sype, Matthieu Vert, Alexei Sharpanskykh, Seyed Sahand Mohammadi Ziabari
https://arxiv.org/abs/2507.05150

Effects of Unplanned Incoming Flights on Airport Relief Processes after a Major Natural Disaster
The severity of natural disasters is increasing every year, impacting many people's lives. During the response phase of disasters, airports are important hubs where relief aid arrives and people need to be evacuated. However, the airport often forms a bottleneck in these relief operations due to the sudden need for increased capacity. Limited research has been done on the operational side of airport disaster management. Experts identify the main problems as, first, the asymmetry of information …

Understanding Demand for Shared Autonomous Micro-Mobility
Naroa Coretti Sanchez, Kent Larson
https://arxiv.org/abs/2508.03521 https://arxiv.org/pdf/2508.03…

Understanding Demand for Shared Autonomous Micro-Mobility
This study examines the behavioral and environmental implications of shared autonomous micro-mobility systems, focusing on autonomous bicycles and their integration with transit in the U.S. While prior research has addressed operational and lifecycle aspects, a critical gap remains in understanding which modes these services are likely to substitute, who is most inclined to adopt them, and how service attributes influence user decisions. We design a context-aware stated preference survey ground…

Evaluation of Large Language Model-Driven AutoML in Data and Model Management from Human-Centered Perspective
Jiapeng Yao, Lantian Zhang, Jiping Huang
https://arxiv.org/abs/2507.05962

Evaluation of Large Language Model-Driven AutoML in Data and Model Management from Human-Centered Perspective
As organizations increasingly seek to leverage machine learning (ML) capabilities, the technical complexity of implementing ML solutions creates significant barriers to adoption and impacts operational efficiency. This research examines how Large Language Models (LLMs) can transform the accessibility of ML technologies within organizations through a human-centered Automated Machine Learning (AutoML) approach. Through a comprehensive user study involving 15 professionals across various roles and…

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
Simon Pfreundschuh, Malarvizhi Arulraj, Ali Behrangi, Linda Bogerd, Alan James Peixoto Calheiros, Daniele Casella, Neda Dolatabadi, Clement Guilloteau, Jie Gong, Christian D. Kummerow, Pierre Kirstetter, Gyuwon Lee, Maximilian Maahn, Lisa Milani, Giulia Panegrossi, Rayana Palharini, Veljko Petkovi\'c, Soorok Ryu, Paolo San\`o, Jackson Tan

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, th…