Abstract
The electrification of the transport sector is a critical element in the transition to a low-emissions economy, driven by the widespread adoption of electric vehicles (EV) and the integration of renewable energy sources (RES). However, managing the increasing demand for EV charging infrastructure while meeting carbon emission reduction targets is a significant challenge for charging station operators. This work introduces a novel carbon-aware dynamic pricing framework for EV charging, formulated as a chance-constrained optimization problem to consider forecast uncertainties in RES generation, load, and grid carbon intensity. The model generates day-ahead dynamic tariffs for EV drivers with a certain elastic behavior while optimizing costs and complying with a carbon emissions budget. Different types of budgets for Scope 2 emissions (indirect emissions of purchased electricity consumed by a company) are conceptualized and demonstrate the advantages of a stochastic approach over deterministic models in managing emissions under forecast uncertainty, improving the reduction rate of emissions per feasible day of optimization by 24 %. Additionally, a surrogate machine learning model is proposed to approximate the outcomes of stochastic optimization, enabling the application of state-of-the-art explainability techniques to enhance understanding and communication of dynamic pricing decisions under forecast uncertainty. It was found that lower tariffs are explained, for instance, by periods of higher renewable energy availability and lower market prices and that the most important feature was the hour of the day.

Abstract
With the increasing adoption of mobile robots for transporting components across several locations in industries, congestion problems appear if the movement of these robots is not correctly planned. This paper introduces a fleet management system where a central agent coordinates, plans, and supervises the fleet, mitigating the risk of deadlocks and addressing issues related to delays, deviations between the planned paths and reality, and delays in communication. The system uses the TEA* graph-based path planning algorithm to plan the paths of each agent. In conjunction with the TEA* algorithm, the concepts of supervision and graph-based environment representation are introduced. The system is based on ROS framework and allows each robot to maintain its autonomy, particularly in control and localization, while aligning its path with the plan from the central agent. The effectiveness of the proposed fleet manager is demonstrated in a real scenario where robots operate on a shop floor, showing its successful implementation.

Abstract
Cloud computing technologies offer significant advantages in scalability and performance, enabling rapid deployment of applications. The adoption of microservices-oriented architectures has introduced an ecosystem characterized by an increased number of applications, frameworks, abstraction layers, orchestrators, and hypervisors, all operating within distributed systems. This complexity results in the generation of vast quantities of logs from diverse sources, making the analysis of these events an inherently challenging task, particularly in the absence of automation. To address this issue, Machine Learning techniques leveraging Large Language Models (LLMs) offer a promising approach for dynamically identifying patterns within these events. In this study, we propose a novel anomaly detection framework utilizing a microservices architecture deployed on Kubernetes and Istio, enhanced by an LLM model. The model was trained on various error scenarios, with Chaos Mesh employed as an error injection tool to simulate faults of different natures, and Locust used as a load generator to create workload stress conditions. After an anomaly is detected by the LLM model, we employ a dynamic Bayesian network to provide probabilistic inferences about the incident, proving the relationships between components and assessing the degree of impact among them. Additionally, a ChatBot powered by the same LLM model allows users to interact with the AI, ask questions about the detected incident, and gain deeper insights. The experimental results demonstrated the model's effectiveness, reliably identifying all error events across various test scenarios. While it successfully avoided missing any anomalies, it did produce some false positives, which remain within acceptable limits.

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. © 2025 Elsevier B.V., All rights reserved.