Tootfinder

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida
https://arxiv.org/abs/2510.05698

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforcem…

"Environmental damage is putting European way of life at risk, says report"
#Europe #Environment

Placing the Near-Earth Object Impact Probability in Context
C. R. Nugent, K. P. Andersen, James M. Bauer, C. T. Jensen, L. K. Kristiansen, C. P. Hansen, M. M. Nielsen, C. F. Vesterg{\aa}rd
https://arxiv.org/abs/2508.02418

Placing the Near-Earth Object Impact Probability in Context
Near-Earth objects (NEOs) have the potential to cause extensive damage and loss of life on Earth. Advancements in NEO discovery, trajectory prediction, and deflection technology indicate that an impact could be prevented, with sufficient warning time. We derive an impact frequency of NEOs 140m and larger, using the NEOMOD2 NEO population model and JPL Horizons. We then place that frequency in context with other preventable causes of death; allowing for comparison between a planet-wide event and…

AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Roshan Kenia, Anfei Li, Rishabh Srivastava, Kaveri A. Thakoor
https://arxiv.org/abs/2510.00882

AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Glaucoma is a progressive eye disease that leads to optic nerve damage, causing irreversible vision loss if left untreated. Optical coherence tomography (OCT) has become a crucial tool for glaucoma diagnosis, offering high-resolution 3D scans of the retina and optic nerve. However, the conventional practice of condensing information from 3D OCT volumes into 2D reports often results in the loss of key structural details. To address this, we propose a novel hybrid deep learning model that integra…

Safety Assurance for Quadrotor Kinodynamic Motion Planning
Theodoros Tavoulareas, Marzia Cescon
https://arxiv.org/abs/2507.17679 https://arxiv.org/pdf/2507…

Safety Assurance for Quadrotor Kinodynamic Motion Planning
Autonomous drones have gained considerable attention for applications in real-world scenarios, such as search and rescue, inspection, and delivery. As their use becomes ever more pervasive in civilian applications, failure to ensure safe operation can lead to physical damage to the system, environmental pollution, and even loss of human life. Recent work has demonstrated that motion planning techniques effectively generate a collision-free trajectory during navigation. However, these methods, w…

Nighttime Cloud Detection, Tracking and Prediction with All-Sky Cameras
Sebastian Buntin, Chris M. Copperwheat, Helen E. Jermak
https://arxiv.org/abs/2507.21711 https://

Nighttime Cloud Detection, Tracking and Prediction with All-Sky Cameras
This paper presents a novel method for real-time nighttime cloud detection, tracking, and prediction using all-sky cameras, aimed at enhancing the efficiency of ground-based robotic telescopes. Ground-based telescopes are vulnerable to adverse weather conditions, particularly cloud cover, which can lead to the loss of valuable observation time and potential damage to the telescope. Existing methods for cloud detection have limitations in accuracy, particularly under varying illumination conditi…

A Deep Dive into the Impact of Solar Storms on LEO Satellite Networks
Eunju Kang, Alagappan Ramanathan, Sangeetha Abdu Jyothi
https://arxiv.org/abs/2509.19647 https://

A Deep Dive into the Impact of Solar Storms on LEO Satellite Networks
Low Earth Orbit (LEO) satellite networks are an important part of the global communication infrastructure today. Despite ongoing efforts to improve their resilience, they remain vulnerable to component damage and deorbiting under harsh space weather conditions. Prior work identified a modest but noticeable impact on LEO satellite network performance during solar storms, typically manifesting as an immediate rise in packet loss and a sustained increase in round-trip time (RTT). However, these st…