Abstract
The deep ocean is vast and challenging to observe; however, it is key to knowledge of the sea and its impact on global climate. Fixed sea observing points (such as the EMSO observing nodes) provide a limited view and are complemented by expensive oceanographic campaigns with systems demanding high logistical requirements such as deep-sea ROVs.  These costs not only limit our capability for key ocean data collection in the deep but also introduce their own environmental costs.Emerging challenges in knowledge and pressure on the exploration of the deep ocean demand new technological solutions for monitoring and safeguarding the marine ecosystem.Innovative robotic technologies such as the TURTLE robotic deep-sea landers can combine long-term permanence at the seabed with mobility and dynamic reconfigurability in spatial and temporal deep-sea observation.Robotic systems of a heterogeneous nature (from conventional gliders, AUVs, or robotic landers) can be combined with standard and new sensing systems, such as bottom-deployed sensor nodes, moored systems, and cabled points when feasible.These systems can provide underwater localization services for the different assets, energy supply and high bandwidth data transfer with robotic docking stations for other mobile elements. An example of the synergy obtained with these new systems is the possibility of using robotic landers as carriers of EGIM (EMSO Generic Instrument Module) sensor payloads, providing power and data storage and flexibility in the deployment and recovery process.This approach, partly taken in the EU-funded Trident project to develop technical solutions for cost-effective and efficient observation of environmental impacts on deep seabed environments, allows for a substantial reduction in the operational and logistic requirements for deep-sea observation, greatly reducing the need for costly oceanographic campaigns or the use of expensive (economic and logistical) deep sea ROV systems.In this work, we present some of the new developments and discuss the transition from existing technological solutions to new ones integrating these recent developments.

Abstract
The UNEXMIN (Horizon 2020) and UNEXUP (EIT RawMaterials) projects developed a novel technology to send robots and even autonomously deliver optical images, 3D maps and other georeferenced scientific data from flooded underground environments, like abandoned mines, caves or wells. The concept turned into a market ready solution in seven years, where the last few years of field trials of the development beautifully demonstrating the technology's premier capabilities. Here in this paper, we focus on the wide variety of environments, circumstances and measurements where the UNEXMIN technology can be the best solution or the only solution to deliver certain research or engineering data. These are obtained from both simple and complex environments like different mines and caves, small and large cavities, long and tight tunnels and shafts, different visibility conditions, even different densities of the liquid medium where UX robots operated.

Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.

Abstract
Water bodies are complex and interconnected systems that play a crucial role in both our environment and our economy. Studying these water bodies is therefore essential but collecting and analyzing water samples can be challenging, particularly when dealing with large volumes of water. This article presents a system capable of autonomously filtering large volumes of water through standard marine biology filters and preserving them to later be analyzed. The system's preliminary results are presented in this paper.

Abstract
This paper underscores the critical role of evolving tools for underwater search and rescue. Successful submarine crew rescue hinges on detecting, locating, and obtaining detailed information about the submerged vessel. Robotic systems, particularly ROVs and AUVs, emerge as invaluable tools, offering swift deployment times compared to manned submersibles. This study presents findings from Submarine Escape and Rescue (SMER) field trials conducted during the REPMUS 2023 naval military exercise off the west coast of Portugal, showcasing the effectiveness of these tools in real-world emergency situations. An initial multibeam sonar search from the surface with the Mar Porfundo ship was performed, followed by a close detailed inspection and visual survey with the EVA AUV of a target military submarine (NRP Arp (a) over tildeo) stationed on the sea bottom.

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.