Abstract
This study presents the development and testing of satellite antennas for the SONDA probe, an innovative deepsea monitoring system designed to be deployed by high-altitude balloons. The probe descends to the deep ocean, resurfaces, and transmits data while functioning as a drifter. The project faced unique design constraints, including the need for low-cost materials and lightweight construction for balloon deployment. These constraints ruled out traditional hermetic housings, necessitating alternative solutions for antenna protection. The work focused on custom ceramic patch antennas and their performance under various protective coatings, which affected the antennas' resonance and gain. Thinner layers effectively protected the antennas from high-pressure conditions and water ingress, maintaining functionality. Experiments on antenna height revealed optimal positioning above the water surface to minimize wave-induced signal interference. Hyperbaric chamber tests validated the mechanical integrity and functionality of the antennas under pressures equivalent to depths of 1500 m Antenna characterization techniques were employed in an anechoic chamber to validate antenna performance with the coating and to assess their correct operation after the hyperbaric tests. Field deployments demonstrated the antennas' capability to transmit data after diving. Challenges included communication delays, corrupted data, and mechanical vulnerabilities in materials. The findings emphasize the importance of rigorous mechanical design, material selection, and system optimization to ensure reliability in marine environments. This work advances the development of low-cost, lightweight, and modular probes for autonomous ocean monitoring, with potential applications in long-term drifter studies, real-time marine monitoring and oceanographic research.

Abstract
High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance by processing and interpreting visual data, enabling obstacle avoidance and ensuring LoS signal propagation. We propose a vision-aided Radio Access Network (RAN) based on the O-RAN architecture and capable of perceiving the surrounding environment. The vision-aided RAN consists of a gNodeB (gNB) equipped with a video camera that employs CV techniques to extract critical environmental information. An xApp is used to collect and process metrics from the RAN and receive data from a Vision Module (VM). This enhances the RAN's ability to perceive its surroundings, leading to better connectivity in challenging environments.

Abstract
This paper summarises the setup and results of the shared task on onomastic wordplay translation at the CLEF 2025 JOKER Lab, an earlier version of which was run at JOKER 2022 as a pilot task. The objective of the task is to translate wordplay concerned with proper names from English to French. Such wordplay, widespread in classic and modern creative writing, is particularly challenging to translate due to its idiosyncratic nature and cultural references. Four teams participated in this year’s task, submitting 20 runs. We describe our construction of the data set using for training and testing, the methods employed by the participating teams, and the results obtained for the runs and a naïve baseline in terms of various manually and automatically applied measures of translation quality. Despite notable advances, we find that translation of onomastic wordplay remains highly challenging, with fewer than 10% of manually evaluated translations judged as acceptable alternatives. Recurrent errors included untranslated source wordplay, overfitting to the training data, omission of surnames, and nonsensical generations.

Abstract

Abstract
A comprehensive understanding of the mechanisms underlying bone fatigue failure is crucial for advancing treatment strategies. In this regard, this study presents a novel approach to quantify crack propagation in cortical bone tissue through fatigue testing under mode I loading. To closely replicate real bone damage mechanisms, pre-cracked bone samples were subjected to cyclic loading. A compliance-based beam method and cubic B-spline interpolation method were employed to accurately extract fatigue coefficients and reduce experimental noise, yielding refined modified Paris law coefficients. A cohesive zone model for high-cycle fatigue was used to simulate crack propagation, capturing the nonlinear material response by means of the cohesive zone length, mimicking the non-negligible fracture process zone. The goal is to validate the followed experimental procedure. This study offers valuable insights into the fatigue and fracture mechanisms in cortical bone, providing a more accurate and realistic framework for characterizing fatigue life compared to previous methodologies. Coefficients produced from the cohesive model may be readily integrated into simulation tools commonly used in many areas of engineering, allowing biomechanical experts to create more robust designs that simulate actual world conditions for application in implants and orthopaedic structures.

Abstract
Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV positioning optimization. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3's performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.