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 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.

Abstract
The proliferation of wireless devices requires flexible network infrastructures to meet the increasing Quality of Service (QoS) requirements. Mobile Robotic Platforms (MRPs) acting as mobile communications cells are a promising solution to provide on-demand wireless connectivity in dynamic networking scenarios. However, the energy consumption of MRPs is a challenge that must be considered to maximize the availability of the wireless networks created. The main contribution of this paper is the experimental evaluation of the energy consumption of an MRP acting as a mobile communications cell. The evaluation considers different actions performed by a real MRP, demonstrating that energy consumption varies significantly with the type of action performed. The results obtained pave the way for optimizing MRP movement in dynamic networking scenarios, maximizing wireless network's availability while minimizing the MRP energy consumption.

Abstract
In the coming years, a wide range of underwater applications, including resource mining, marine research, and military operations will play an increasingly important role. The Internet of Underwater Things (IoUT) extends IoT principles to underwater environments, enabling connectivity between underwater devices and the Internet. However, high latency, intermittent connectivity, and security risks, such as privacy breaches, data tampering, and unauthorized access, pose major challenges to IoUT adoption. Existing security mechanisms fail in Delay-Tolerant Networks (DTNs) due to their reliance on centralized trust models. Blockchain provides a decentralized, immutable, and transparent solution for securing underwater communications. This paper introduces the Blockchain-Based Underwater Messaging System (BUMS), an innovative solution that ensures message integrity, confidentiality, and resilience in DTNs. Messages are immutably stored in blockchain blocks, while malicious nodes are autonomously detected and excluded without the need for a central authority. To evaluate its feasibility, we developed the Underwater Blockchain Simulator (UBS), a custom-tailored open-source simulator designed to test blockchain algorithms in underwater networks. Simulation results demonstrate that BUMS enhances security and network reliability while maintaining efficiency in high-latency underwater environments, making it a viable solution for secure IoUT-based communications.

Abstract