Abstract
Blue Growth, a term first coined by the European Commission as an initiative to harness the untapped potential of Europe's oceans, seas and coasts, identified rich marine resources as an unique asset for economic development in coastal regions and on islands. The European Commission has through the Blue Growth objectives for the first time highlighted marine sectors as unique market opportunities with high growth potential which carry socio-economic importance to the development of coastal regions. Particularly marine sectors such as aquaculture, marine robotics, and marine renewable energy which fulfil global needs in food safety and security, enable monitoring and exploration in harsh and remote conditions, and globally growing energy needs were recognized as catalysts to achieve sustainable development. Marine start-ups and small and medium-sized enterprises (SME) were identified as potential drivers in emerging marine sectors. However, they require support mechanisms tailored to their needs as they are competing for the same business and financial support as land-based SMEs, yet the research and development infrastructure is more difficult to access. ProtoAtlantic, an Interreg Atlantic Area funded project, provided marine-specific support mechanisms to marine start-ups and SMEs in emerging sectors, including business support through the accelerator and mentorship programs, enabling companies to fast track their product development through access to prototyping and testing facilities in all partner regions. The Interreg Atlantic Area encompasses partner regions in France, Ireland, Portugal, Scotland, and Spain. The consortium partners consist of Technopole Brest Iroise (Brest, France), University College Cork - UCC (Cork, Ireland), County Council Cork (Cork, Ireland), INESC TEC (Porto, Portugal), the European Marine Energy Centre - EMEC (Orkney, Scotland), EMERGE (Canary Islands, Spain), and the lead partner, Innovalia Association (Canary Islands, Spain). The strategic collaboration between the partners provided marine start-ups access to testing facilities in the Atlantic Ocean. The extreme living laboratories provided by EMEC, the LiR National Ocean Testing Facilities at UCC's Centre of Marine and Renewable Energy (MaREI centre), and INESC TEC promise harsh real-life conditions which test the suitability of marine technologies to the limit thereby providing start-ups and SMEs with an extra layer of confidence in developing their technologies. This cross-regional collaboration puts the ProtoAltantic program in a unique position, as it is the first of its kind to dedicate marine-specific support to marine start-ups and SMEs which have benefited from the opportunities that ProtoAtlantic has provided. ProtoAtlantic developed a holistic model for the prototyping and exploitation of innovative ideas in emerging maritime sectors. After the identification of ideas from the research community, start-ups, and SMEs with product innovation capacity in the maritime sector, an acceleration program with a normed and structured process was implemented, thus creating a unique ecosystem in the Atlantic that is addressing a co-creation paradigm with the local European start-ups communities and all the stakeholders.

Abstract
This paper addresses the development of a novel zero-shot learning method for remote marine litter hyperspectral imaging data classification. The work consisted of using an airborne acquired marine litter hyperspectral imaging dataset that contains data about different plastic targets and other materials and assessing the viability of detecting and classifying plastic materials without knowing their exact spectral response in an unsupervised manner. The classification of the marine litter samples was divided into known and unknown classes, i.e., classes that were hidden from the dataset during the training phase. The obtained results show a marine litter automated detection for all the classes, including (in the worst case of an unknown class) a precision rate over 56% and an overall accuracy of 98.71%.

Abstract
Scientific and environmental focused deep sea exploration is being expanded and as such a new class of Autonomous Underwater Vehicle (AUV) capable of accessing deep underwater sea bed environment for long periods of time is being deployed. This type of vehicle and the mission environment poses challenges to the mission development as these operations contain many systems that must work together to ensure that the mission requirements are met and that the vehicle is operated safely. As such, a solution based on the SMACC library for Robotic Operating System (ROS) was proposed and tested using a simulator. The results shown were based on the simulation of three missions representative of different scenarios for a deep sea exploration AUV and they were evaluated on the completion of the mission plan.

Abstract
Offshore wind turbine application has been widespread in the last years, with an estimation that in 2030 will reach a total capacity of 234GW. Offshore wind farms introduce advantages in terms of environmental impact (noise, impact on birds, disrupted landscapes) and energy production (34% onshore and 43% offshore). Still, they also introduce scientific challenges in developing methodologies that allow wind farm inspection (preventive maintenance) safety for humans. This paper presents a UAV approach for autonomous inspection of inland windturbine and describes the field tests in Penela, Portugal. From the state-of-the-art available wind turbine inspection, in 2015, we carried out the first autonomous inspection with a UAV. The inspection of wind blades offshore is an ongoing project; therefore, the paper also presents the preliminary results with a simulation environment to validate the 3D LiDAR and the inspection procedure with new challenges effects: floating platform, wind gusts, and unknown initial blade position.

Abstract
A major effort is put into the production of green energy as a countermeasure to climatic changes and sustainability. Thus, the energy industry is currently betting on offshore wind energy, using wind turbines with fixed and floating platforms. This technology can benefit greatly from interventive autonomous underwater vehicles (AUVs) to assist in the maintenance and control of underwater structures. A wireless charger system can extend the time the AUV remains underwater, by allowing it to charge its batteries through a docking station. The present work details the development process of a housing component for a wireless charging system to be implemented in an AUV, addressed as wireless charger housing (WCH), from the concept stage to the final physical verification and operation stage. The wireless charger system prepared in this research aims to improve the longevity of the vehicle mission, without having to return to the surface, by enabling battery charging at a docking station. This product was designed following a design for excellence (DfX) and modular design philosophy, implementing visual scorecards to measure the success of certain design aspects. For an adequate choice of materials, the Ashby method was implemented. The structural performance of the prototypes was validated via a linear static finite element analysis (FEA). These prototypes were further physically verified in a hyperbaric chamber. Results showed that the application of FEA, together with well-defined design goals, enable the WCH optimisation while ensuring up to 75% power efficiency. This methodology produced a system capable of transmitting energy for underwater robotic applications.

Abstract
The expansion of autonomous driving operations requires the research and development of accurate and reliable self-localization approaches. These include visual odometry methods, in which accuracy is potentially superior to GNSS-based techniques while also working in signal-denied areas. This paper presents an in-depth review of state-of-the-art visual and point cloud odometry methods, along with a direct performance comparison of some of these techniques in the autonomous driving context. The evaluated methods include camera, LiDAR, and multi-modal approaches, featuring knowledge and learning-based algorithms, which are compared from a common perspective. This set is subject to a series of tests on road driving public datasets, from which the performance of these techniques is benchmarked and quantitatively measured. Furthermore, we closely discuss their effectiveness against challenging conditions such as pronounced lighting variations, open spaces, and the presence of dynamic objects in the scene. The research demonstrates increased accuracy in point cloud-based methods by surpassing visual techniques by roughly 33.14% in trajectory error. This survey also identifies a performance stagnation in state-of-the-art methodologies, especially in complex conditions. We also examine how multi-modal architectures can circumvent individual sensor limitations. This aligns with the benchmarking results, where the multi-modal algorithms exhibit greater consistency across all scenarios, outperforming the best LiDAR method (CT-ICP) by 5.68% in translational drift. Additionally, we address how current AI advances constitute a way to overcome the current development plateau.