Abstract
This paper presents the perception system designed for the underwater mine exploration UNEXMIN robot. This autonomous underwater vehicle was designed in the context of the European 112020 ENEXIVIIN project to explore flooded underground mines The presented work addresses the sensor choice and placement options, the characterization of the system with results obtained in test tank and on field missions in mines. The perception software and computational architecture is also discussed with details on its distributed features. This perception system is comprised of one multibeam imaging/profiling sonar, one mechanically scanning sonar, digital cameras and a set of custom developed laser based structured light systems. The presented results from the Kaatiala mine (Finland) field trials and the Idrija mine tests (Slovenia) are discussed and allow for the performance analysis of the system.

Abstract
Emerging opportunities in the exploration of inland water bodies, such as underwater mining of flooded open pit mines, require accurate real-time positioning of multiple underwater assets. In the mining operation scenarios, operational requirements deny the application of standard acoustic positioning techniques, posing additional challenges to the localization problem. This paper presents a novel underwater localization solution, implemented for the VAMOS! project, based on the combination of raw measurements from a short baseline (SBL) array and an inverted ultrashort baseline (iUSBL). An extended Kalman filter (EKF), fusing IMU raw measurements, pressure observations, SBL ranges, and USBL directional angles, estimates the localization of an underwater mining vehicle in 6DOF. Sensor bias and the speed of sound in the water are estimated indirectly by the filter. Moreover, in order to discard acoustic outliers, due to multipath reflections in such a confined and cluttered space, a data association layer and a dynamic SBL master selection heuristic were implemented. To demonstrate the advantage of this new technique, results obtained in the field, during the VAMOS! underwater mining field trials, are presented and discussed.

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.

Abstract
The atmospheric electric field is a key characteristic of the Earth system. Despite its relevance, oceanic measurements of the atmospheric electric field are scarce, as typically oceanic measurements tend to be focused on ocean properties rather than on the atmosphere above. This motivated the set-up of an innovative campaign on board the sail ship NRP Sagres focused on the measurement of the atmospheric electric field in the marine boundary layer. This paper describes the monitoring system that was developed to measure the atmospheric electric field during the planned circumnavigation expedition of the sail ship NRP Sagres.

Abstract
This paper presents the development of a hyperbaric system able to collect, transport and maintain deep-sea species in controlled condition from the sea floor up to the surface (HiperSea System). The system is composed by two chambers coupled with a transference set-up. The first chamber is able to reach a maximum of 1km depth collecting both benthic and pelagic deep-sea species. The second chamber is a life support compartment to maintain the specimens alive at the surface, in hyperbaric conditions.

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.