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
Fishing for deep-sea species occurs on continental slopes, ridges, and seamounts. Fishing operations using fishing gears that contact the bottom (e.g., trawls and bottom longlines) may have significant impacts on Vulnerable Marine Ecosystems (VMEs). VMEs refer to marine ecosystems with a population or community of sensitive taxa or habitats that are likely to experience substantial alteration from short-term to chronic disturbance and that are unlikely to recover during the timeframe in which the disturbance occurs. The VME concept, introduced in the United Nations General Assembly Resolution 61/105, has been worldwide applied to the management of deep-sea fisheries. However, the effective identification and management of VMEs is highly constrained by the scarcity of data on VME indicator taxa. This data deficiency is usually surpassed by the use of VME predictive modelling. Video footage is a non-destructive method commonly used for exploring and investigating areas of seabed and for characterising and identifying habitat types. Remotely Operated Vehicles (ROVs) are one of the tools for seabed mapping. ROVs range in size from small observation-class to large work-class vehicles. Their sizes determine the payload, manoeuvrability, depth rating and ultimately uses of the vehicle. For epifaunal imaging, ROVs can be used in two modes: qualitative inspections and quantitative assessments. This paper presents the development of an innovative system composed of a compact support research vessel and a hybrid autonomous underwater vehicle capable of accurate georeferenced high-resolution imaging and profiling of the seabed for a detailed survey of the seabed for biodiversity studies. The experimental results obtained by the developed system in field work in real VME survey at 600m depth are presented.

Abstract
The increasing development of Unmanned Surface Vehicles (USVs) for various applications in open and shallow waters has increased demand for more advanced USVs with improved safety and navigation systems. This article introduces a collision avoidance system for USVs that complies with the International Regulations for Preventing Collisions at Sea (COLREG) rules, particularly rules 13 to 18 from Part B - Steering and Sailing. The system utilizes a three-block architecture for risk assessment, situation identification, and path replanning. Practical testing and validation were conducted using the Stonefish simulator, demonstrating the system's effectiveness in ensuring compliance with COLREG rules and facilitating safe navigation of USVs.

Abstract
This paper presents the Unmanned Aerial Vehicle (UAV) MANTIS, developed for indoor inventory management in large-scale warehouses. MANTIS integrates a visual odometry (VIO) system for precise localization, thus allowing indoor navigation in complex environments. The mechanical design was optimized for stability and maneuverability in confined spaces, incorporating a lightweight frame and efficient propulsion system. The UAV is equipped with an array of sensors, including a 2D LiDAR, six cameras, and two IMUs, which ensures accurate data collection. The VIO system integrates visual data with inertial measurements to maintain robust, drift-free localization. A behavior tree (BT) framework is responsible for the UAV mission planner assigned to the vehicle, which can be flexible and adaptive in response to dynamic warehouse conditions. To validate the accuracy and reliability of the VIO system, we conducted a series of tests using an OptiTrack motion capture system as a ground truth reference. Comparative analysis between the VIO and OptiTrack data demonstrates the efficacy of the VIO system in maintaining accurate localization. The results prove MANTIS, with the required payload sensors, is a viable solution for efficient and autonomous inventory management.

Abstract
Unmanned Aerial Vehicle (UAV) applications, particularly for indoor tasks such as inventory management, infrastructure inspection, and emergency response, are becoming increasingly complex with dynamic environments and their different elements. During operation, the vehicle's response depends on various decisions regarding its surroundings and the task goal. Reinforcement Learning techniques can solve this decision problem by helping build more reactive, adaptive, and efficient navigation operations. This paper presents a framework to simulate the navigation of a UAV in an operational environment, training and testing it with reinforcement learning models for further deployment in the real drone. With the support of the 3D simulator Gazebo and the framework Robot Operating System (ROS), we developed a training environment conceivably simple and fast or more complex and dynamic, explicit as the real-world scenario. The multi-environment simulation runs in parallel with the Deep Reinforcement Learning (DRL) algorithm to provide feedback for the training. TD3, DDPG, PPO, and PPO+LSTM were trained to validate the framework training, testing, and deployment in an indoor scenario.

Abstract
Abstract. A unique dataset of marine atmospheric electric field observations over the Atlantic Ocean is described. The data are relevant not only for atmospheric electricity studies, but more generally for studies of the Earth's atmosphere and climate variability, as well as space-earth interactions studies. In addition to the atmospheric electric field data, the dataset includes simultaneous measurements of other atmospheric  variables, including gamma radiation, visibility, and solar radiation. These ancillary observations not only support interpretation and understanding of the atmospheric electric field data, but are also of interest in themselves. The entire framework from data collection to final derived datasets has been duly documented to ensure traceability and reproducibility of the whole data curation chain. All the data, from raw measurements to final datasets, are preserved in data repositories with a corresponding assigned DOI. Final datasets are available from the Figshare repository (https://figshare.com/projects/SAIL_Data/178500) and computational notebooks containing the code used at every step of the data curation chain are available from the Zenodo repository (https://zenodo.org/communities/sail).