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.

Abstract
Non-pharmaceutical interventions (NPIs), such as lockdowns, travel restrictions, and social distancing mandates, play a critical role in controlling the spread of infectious diseases by shaping human mobility patterns. Using COVID-19 as a case study, this research investigates the relationships between NPIs, mobility, and the effective reproduction number (R-t) across 13 European countries. We employ XGBoost regression models to estimate missing mobility data from NPIs and missing R(t )values from mobility, achieving high accuracy. Additionally, using clustering techniques, we uncover national distinctions in social compliance. Northern European countries demonstrate higher adherence to NPIs than Southern Europe, which exhibits more variability in response to restrictions. These differences highlight the influence of cultural and social norms on public health outcomes. In general, our analysis reveals a strong correlation between NPIs and mobility reductions, highlighting the immediate impact of restrictions on population movement. However, the relationship between mobility and R(t )is weaker and more nuanced, reflecting the time delays involved, as changes in mobility take time to influence transmission rates. These results underscore the interdependence of restrictions, mobility, and disease spread while demonstrating the potential for data-driven approaches to guide policy decisions. Our approach offers valuable insights for optimizing public health strategies and tailoring interventions to diverse cultural contexts during future health crises.