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…

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…

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…

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 …

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…

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella, Sebastiano Panichella
https://arxiv.org/abs/2510.09396

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering fa…

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…

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…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

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…

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…

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…

Smart Contract-Enabled Procurement under Bounded Demand Variability: A Truncated Normal Approach
Jinho Cha, Youngchul Kim, Junyeol Ryu, Sangjun Park, Jeongho Kang, Hyeyoung Hwang
https://arxiv.org/abs/2510.07801

Smart Contract-Enabled Procurement under Bounded Demand Variability: A Truncated Normal Approach
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution. While existing research emphasizes the technical feasibility of smart contracts, the operational and economic implications of adoption under moderate uncertainty remain underexplored. We propose a multi-supplier model in which a centralized retailer jointly determines the optimal smart contract adoption intensity and …

The dawn of alchemical free-energy methods in biomolecular simulations
Daniele Macuglia, Giovanni Ciccotti, Beno\^it Roux
https://arxiv.org/abs/2510.04917 https://

The dawn of alchemical free-energy methods in biomolecular simulations
From the onset of fundamental statistical mechanical constructs formulated in the late 19th century, alchemical free-energy methods slowly emerged and transitioned to become operational tools of biomolecular simulation applicable to a wide range of problems including protein-ligand binding for drug discovery research. This article reconstructs how statistical mechanical approaches such as thermodynamic integration and free-energy perturbation were reconfigured in the early 1980's to address the…

SPARTA: Python-Based Automated Spectral Parameter Analysis and Assessment System for Resonance Tracking
Kerem Semiz
https://arxiv.org/abs/2510.05832 https://

SPARTA: Python-Based Automated Spectral Parameter Analysis and Assessment System for Resonance Tracking
Accurately determining resonance frequencies and quality factors (Q) is crucial in accelerator physics and radiofrequency engineering, as these factors have direct impacts on system design, operational stability, and research results. The methods currently employed to facilitate resonance analysis are mostly manual, requiring operators and physicists to estimate resonant parameters and examine scattering parameter (S-parameter) data from vector network analyzers (VNAs). The current techniques t…