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 interaction 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 also are 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, Project SAIL community, 2025).

Abstract
Underwater environments pose unique challenges to optical systems due to physical phenomena that induce severe data degradation. Current imaging sensors rarely address these effects comprehensively, resulting in the need to integrate complementary information sources. This article presents a multimodal data fusion approach to combine information from diverse sensing modalities into a single dense and accurate tridimensional representation. The proposed fusiNg tExture with apparent motion information for underwater Scene recOnstruction (NESO) encoder-decoder network leverages motion perception principles to extract relative depth cues, fusing them with textured information through an early fusion strategy. Evaluated on the FLSea-Stereo dataset, NESO outperforms state-of-the-art methods by 58.7%. Dense depth maps are achieved using multi-stage skip connections with attention mechanisms that ensure propagation of key features across network levels. This representation is further enhanced by incorporating sparse but millimeter-precise depth measurements from active imaging techniques. A regression-based algorithm maps depth displacements between these heterogeneous point clouds, using the estimated curves to refine the dense NESO prediction. This approach achieves relative errors as low as 0.41% when reconstructing submerged anode structures, accounting for metric improvements of up to 0.1124 m relative to the initial measurements. Validation at the ATLANTIS Coastal Testbed demonstrates the effectiveness of this multimodal fusion approach in obtaining robust tri-dimensional representations in real underwater conditions.

Abstract
The integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

Abstract
Solar energy is a compelling and growing avenue for transitioning towards reliance on sustainable and environmentally friendly energy sources. However, the performance of individual photovoltaic (PV) panels is vulnerable to defects inflicted by weather exposure. The industry currently addresses these challenges by conducting manual inspections at each PV installation, a process that is highly time-consuming, financially burdensome, difficult to scale, prone to human error, and sometimes unfeasible due to topographical constraints. This study aims to develop a solution that detects these defects in real time resorting to an Autonomous Aerial Vehicle (AAV) equipped with for a thermal and a visual sensor. This paper contributes three major advancements to PV defect detection: a robust, standardized dataset built following IEC TS 62446-3 specifications with annotations from a real PV power plant, a novel multi-spectral approach that leverages early fusion of visual and thermal data captured via an AAV, and benchmark performance metrics for defect detection using state-of-the-art AI models. Our implementation uses Real-Time DEtection TRansformer (RT-DETR) and You Only Look Once (YOLO) models, achieving a mean Average Precision@50 (mAP) of 94% for RT-DETR and a mAP@50 of nearly 95% for YOLOv11. These results demonstrate the effectiveness of our approach in real-world settings, addressing a significant operational challenge in PV plant maintenance while establishing new performance benchmarks for automated defect detection in industrial solar installations.

Abstract
Abandoned, Lost, or otherwise Discarded Fishing Gear (ALDFG) has a great impact on marine ecosystems. This is not only due to the direct contribution to marine litter production with particular emphasis on plastics but also to the effects of ghost fishing.The Nettag+ project aims to reduce these impacts by acting on three main lines of action: prevention, avoidance, and mitigation. In the first line, direct action and collaboration with fishers and nature protection organizations around Europe aim to establish the fishermen community as guardians of the ocean. These actions with active fishers' collaboration range from training and dissemination activities related to marine litter and ocean protection to direct measures in day-to-day work to minimize and recover litter from the sea.In the prevention line, an acoustic tag designed explicitly for the location of ALDFG was developed in collaboration with research institutions and fishing gear manufacturers. It can be integrated into the fishing equipment for future tracking and recovery. This tool can reduce lost fishing gear retrieval costs and is complemented with robotic solutions to support retrieving operations.To mitigate the effects of existing untagged ALDFG, multisensorial  detection algorithms are being developed to detect and map ALDFG on the sea and to take advantage of autonomous and robotic systems to perform this task.

Abstract
EMSO ERIC is a unique European distributed marine Research Infrastructure dedicated to the observation and study of the deep ocean in the long term in fixed regional areas. It provides different services of which access to its infrastructure by external users -engineers, scientists and researchers-, working both in the public and private sectors. The aim of this service, called physical access, is to facilitate access to instrumented platforms deployed at different sites across the European seas, from the seabed to the surface, in order to perform experiments in geosciences and engineering in real ocean conditions. Depending on the logistics and availability of each site, users may deploy their own platforms, instruments, systems or technologies to be tested by the existing equipment that, in this case, can provide reference measurements. Users may also deploy their own systems on the existing EMSO platforms, either in standalone mode or connected to them, receiving power and, in some cases, being able to transmit data by satellite or by cable, depending on the site. Projects requiring the use of several EMSO sites are also accepted. The host EMSO Regional Facility provides logistics and technical support in order to deploy and recover the systems, access the data and it may also offer training and co-development. EMSO ERIC launches the physical access call on a yearly basis and evaluates the received project proposals every two months. Access is free of charge and funding is available for travel, consumables, shipping, operations and hardware adaptations needed to run the project. Since 2022, when the first call was launched, ten projects with varied topics have been funded and are in different phases of execution.