Abstract
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures' operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

Abstract
This paper addresses the problem of high-bandwidth communication and data recovery from deep-sea semi-permanent robotic landers. These vehicles are suitable for long-term monitoring of underwater activities and to support the operation of other robotic assets in Operation & Maintenance (O&M) of offshore renewables. Limitations of current communication solutions underwater deny the immediate transmission of the collected data to the surface, which is alternatively stored locally inside each lander. Therefore, data recovery often implies the interruption of the designated tasks so that the vehicle can return to the surface and transmit the collected data. Resorting to a short-range and high-bandwidth optical link, an alternative underwater strategy for flexible data exchange is presented. It involves the usage of an AUV satellite approaching each underwater node until an optical communication channel is established. At this point, high-bandwidth communication with the remote lander becomes available, offering the possibility to perform a variety of operations, including the download of previously recorded information, the visualisation of video streams from the lander on-board cameras, or even performing remote motion control of the lander. All these three operations were tested and validated with the experimental setup reported here. The experiments were performed in the Atlantic Ocean, at Setubal underwater canyon, reaching the operation depth of 350m meters. Two autonomous robotic platforms were used in the experiments, namely the TURTLE3 lander and the EVA Hybrid Autonomous Underwater Vehicle. Since EVA kept a tether fibre optic connection to the Mar Profundo support vessel, it was possible to establish a full communication chain between a landbased control centre and the remote underwater nodes.

Abstract
This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

Abstract
This paper underscores the critical role of evolving tools for underwater search and rescue. Successful submarine crew rescue hinges on detecting, locating, and obtaining detailed information about the submerged vessel. Robotic systems, particularly ROVs and AUVs, emerge as invaluable tools, offering swift deployment times compared to manned submersibles. This study presents findings from Submarine Escape and Rescue (SMER) field trials conducted during the REPMUS 2023 naval military exercise off the west coast of Portugal, showcasing the effectiveness of these tools in real-world emergency situations. An initial multibeam sonar search from the surface with the Mar Porfundo ship was performed, followed by a close detailed inspection and visual survey with the EVA AUV of a target military submarine (NRP Arp (a) over tildeo) stationed on the sea bottom.

Abstract
This paper presents the results of field trials performed with the EVA autonomous underwater vehicle in the protection of critical infrastructures. The trials were conducted in the context of the REPMUS23 naval exercise organized by the Portuguese Navy. EVA was successfully deployed in a mission of detailed inspection of a submarine cable and in the detection and localization of a possible hostile attack with explosive charges. Multibeam sonar and structured laser light systems were also used to locate and obtain a detailed model of the TURTLE robotic lander deployed on the sea bottom.

Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.