Abstract
Quality control represents a critical function in industrial environments, ensuring that manufactured products meet strict standards and remain free from defects. In highly regulated sectors such as the pharmaceutical industry, traditional manual inspection methods remain widely used. However, these are time-consuming and prone to human error, and they lack the reliability required for large-scale operations, highlighting the urgent need for automated solutions. This is crucial for industrial applications, where environments evolve and new defect types can arise unpredictably. This work proposes an automated visual defect detection system specifically designed for pharmaceutical bottles, with potential applicability in other manufacturing domains. Various methods were integrated to create robust tools capable of real-world deployment. A key strategy is the use of incremental learning, which enables machine learning models to incorporate new, unseen data without full retraining, thus enabling adaptation to new defects as they appear, allowing models to handle rare cases while maintaining stability and performance. The proposed solution incorporates a multi-view inspection setup to capture images from multiple angles, enhancing accuracy and robustness. Evaluations in real-world industrial conditions demonstrated high defect detection rates, confirming the effectiveness of the proposed approach.

Abstract
In football, where minor differences can significantly affect outcomes and performance, automatic video analysis has become a critical tool for analyzing and optimizing team strategies. However, many existing solutions require expensive and complex hardware comprising multiple cameras, sensors, or GPS devices, limiting accessibility for many clubs, particularly those with limited resources. Using images and video from a moving camera can help a wider audience benefit from video analysis, but it introduces new challenges related to motion. To address this, we explore an alternative homography estimation in moving camera scenarios. Homography plays a crucial role in video analysis, but presents challenges when keypoints are sparse, especially in dynamic environments. Existing techniques predominantly rely on visible keypoints and apply homography transformations on a frame-by-frame basis, often lacking temporal consistency and facing challenges in areas with sparse keypoints. This paper explores the use of estimated motion information for homography computation. Our experimental results reveal that integrating motion data directly into homography estimations leads to reduced errors in keypoint-sparse frames, surpassing state-of-the-art methods, filling a current gap in moving camera scenarios.

Abstract
This paper presents an optimized framework for Post-Disaster Search and Rescue (PDSR) that leverages multiple Unmanned Aerial Vehicles (UAVs) equipped with integrated radar and communication capabilities to simultaneously address sensing and connectivity requirements. The proposed solution includes a scalable system architecture and an optimization strategy that enable the rapid deployment of UAV swarms with diverse sensing, communication, and edge-enabled coordination features, ensuring enhanced performance in real-world disaster environments. The proposed approach formulates and solves a 3D UAV positioning and power allocation problem to maximize sensing performance and communication efficiency over multiple targets in designated zones. Due to the NP-hard and combinatorial nature of the problem, we propose a Distributed Joint Radar-Communication (DJRC) solution. This solution employs an efficient reward for potential actions and consistently selects the best action that maximizes the reward while ensuring both communications and sensing performance. Simulation results demonstrate significant performance improvements of the proposed solution over state-of-the-art radar- or communication-centric methods, with polynomial complexity dependent on the number of UAVs and linear dependence on the iteration count.

Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as key enablers in Non-Terrestrial Networks (NTNs) to provide flexible wireless coverage, particularly in infrastructure-limited scenarios. In our previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, a pioneering solution for the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). SUPPLY ensures continuous Ground User (GU) coverage while minimizing propulsion energy consumption. However, its quadratic time complexity in the GU grouping phase imposes scalability constraints, especially in large-scale and time-sensitive scenarios. In this paper, we propose eSUPPLY, a computationally efficient enhancement to SUPPLY. By increasing the step size between candidate Flying Access Point (FAP) positions during the GU grouping phase, eSUPPLY significantly reduces the size of the optimization problem. Simulation results demonstrate up to a 97% reduction in execution time, with only a marginal increase in the number of FAPs and energy consumption, enabling realtime operation in large-scale, dynamic Flying Networks (FNs). © 2025 IEEE.

Abstract
This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial Vehicle (UAV) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC translates high-level natural language commands into executable UAV mission code, bridging the gap between operator intent and UAV behaviour. FLUC is evaluated using three open-source LLMs - Qwen 2.5, Gemma 2, and LLaMA 3.2 - across scenarios involving code generation and mission planning. Results show that Qwen 2.5 excels in multi-step reasoning, Gemma 2 balances accuracy and latency, and LLaMA 3.2 offers faster responses with lower logical coherence. A case study on energy-aware UAV positioning confirms FLUC's ability to interpret structured prompts and autonomously execute domain-specific logic, showing its effectiveness in real-time, mission-driven control.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly employed to enable wireless communications, serving as communications nodes. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). We also developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate UAV energy consumption. However, MUAVE was designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that enables the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. We then use eMUAVE to evaluate the energy consumption of rotary-wing and fixed-wing UAVs in reference and random networking scenarios. The results show that rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following SUPPLY-defined trajectories.