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 oceans have the capability to support the current transitions occurring within our societies, including the implementation of clean energy production and storage technologies and new paths for sustainable food production. These transitions are, nonetheless, many times dependent on innovative technologies which require long paths of technology maturation before they can fit the existing ecosystems and markets. One critical step for technology validation is the demonstration stage in real offshore conditions, which is necessary to validate the performance of the proposed technologies, as well as their reliability and economic viability. In this respect, Portugal has been the testbed of several ocean-based technologies, including the Windfloat device, and possesses the necessary infrastructures to implement and test further innovative concepts and designs. Still, these infrastructures are currently underutilized, which means more technology developers could be testing and implementing their technologies in the country. This paper presents the OceanACT initiative, which is being led by five partners, + ATLANTIC, CEIIA, Fórum Oceano, INESC TEC and WavEC, aiming to promote and manage the existing offshore testing infrastructures in the country. The vision and the strategic path for the initiative, as well as the available infrastructures, and its respective metocean conditions, are presented here. This initiative intends to attract new technology developers to the country, and consequently generate relevant socioeconomic benefits, such as the attraction of investment, the inclusion of the national industry into the supply chain of these innovative projects, and the creation of highly qualified jobs.

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
In recent years, autonomous underwater vehicles (AUVs) have gained prominence in the most varied fields of application of underwater missions. The most common solution for recharging their batteries still implies removing them from the water, which is exceptionally costly. The use of Inductive Power Transfer (IPT) technologies allows to mitigate the associated costs and to extend the vehicles' operation time. In consequence, a prototype has been developed, whose objective is to integrate commercially available IPT technologies, while allowing the employment by most of the AUVs. The prototype is a funnel structure and its counterpart aimed to be fixed to a docking station and the AUV respectively. When coupled, it enables the batteries to recharge by electromagnetic induction. Energy transmission has been tested, resulting in encouraging results. This particular solution achieved over 90% efficiency during underwater experiments. The next objective will be to develop a commercial version of the prototype, that allows a direct, practical and effective use of wireless charging technologies within this particular scenario.

Abstract
The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

Abstract
The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.