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
Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

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