Abstract
The use of autonomous vehicles in maritime operations is a technological challenge. In the particular case of autonomous aerial vehicles (UAVs), their application ranges from inspection and surveillance of offshore power plants, and marine life observation, to search and rescue missions. Manually landing UAVs onboard water vessels can be very challenging due to limited space onboard and wave agitation. This paper proposes an autonomous solution for the task of landing commercial multicopter UAVs with onboard cameras on water vessels, based on the detection of a custom landing platform with computer vision techniques. The autonomous landing behavior was tested in real conditions, using a research vessel at sea, where the UAV was able to detect, locate, and safely land on top of the developed landing platform.

Abstract

Abstract
This work addresses reconstructing an ancient mining site in three-dimensional (3D) modelling with robotic systems, processing the information from two visible spectrum cameras. The developed solution, GeoTec System, was validated in an underground environment in the Monastery of Tibães (Braga, NW Portugal). This study was developed under the MineHeritage project's scope, aiming to attain society on the importance of raw materials across a historical approach. The outputs acquired from the datasets developed in a successful 3D reconstruction of the main gallery and secondary tunnels of the Aveleiras mine in Tibães. However, the investigation is still ongoing to contribute to applying 3D reconstruction technologies, GIS-based mapping and geovisualization techniques in the underground heritage environment.

Abstract
Gamma radiation over the Atlantic Ocean was measured continuously from January to May 2020 by a NaI(Tl) detector installed on board the Portuguese navy's ship NRP Sagres. Enhancements in the gamma radiation values are identified automatically by an algorithm for detection of anomalies in mean and variance as well as by visual inspection. The anomalies are typically +50% above the background level and relatively rare events (similar to<10% of the days). All the detected anomalies are associated with simultaneous precipitation events, consistent with the wet deposition of scavenged radionuclides. The enhancements are detected in the open ocean even at large distances (+500 km) from the nearest coastline. Back trajectories reveal that half of these events are associated with air masses experiencing continental land influences, but the other half do not display evidence of recent land contact. The enhancements in gamma radiation very far from land and with no evidence of continental fetch from back trajectories are difficult to explain as resulting only from radionuclides with a terrestrial source such as radon and its progeny. Further investigation and additional measurements are needed to improve understanding on the sources of ambient radioactivity in the open ocean and assess whether gamma radiation in the marine environment is influenced not only by radionuclides of terrestrial origin, but also cosmogenic radionuclides, like Beryllium-7, formed in the upper atmosphere but with the ability to be transported downward and serve as a tracer of the aerosols to which it attaches. Plain Language Summary Radioactive elements such as the noble gas radon and those produced by its radioactive decay are naturally present in the environment and used as tracers of atmospheric transport and composition. In particular, the noble gas radon, being inert and of predominantly terrestrial origin, is used to identify pristine marine air masses with no land contamination. Precipitation over land typically brings radon from the atmosphere to the surface, enhancing gamma radiation on the ground, but such enhancements have not been identified before nor expected over the ocean due to the low amount of radon typical of marine air masses. Here we report, for the first time, gamma radiation enhancements associated with precipitation in the oceanic environment, using measurements performed over the Atlantic Ocean in a campaign onboard the Portuguese navy ship NRP Sagres.

Abstract
In the present day, unmanned aerial vehicle (UAV) technology is being used for a multitude of inspection operations, including those in offshore structures such as wind-farms. Due to the distance of these structures to the coast, drones need to be carried to these structures via ship. To achieve a completely autonomous operation, the UAV can greatly benefit from an autonomous surface vehicle (ASV) to transport the UAV to the operation location and coordinate a successful landing between the two. This work presents the concept of a four-link parallel platform to perform wave-motion synchronization to facilitate UAV landings. The parallel platform consists of two base floaters connected with rigid rods, linked by linear actuators to a top mobile platform for the landing of a UAV. Using an inverse kinematics approach, a study of the position of the cylinders for greater range of motion and a workspace analysis is achieved. The platform makes use of a feedback controller to reduce the total motion of the landing platform. Using the robotic operating system (ROS) and Gazebo to emulate wave motions and represent the physical model and actuator system, the platform control system was successfully validated.

Abstract
Structures associated with offshore wind energy production require an arduous and cyclical inspection and maintenance (O&M) procedure. Moreover, the harsh challenges introduced by sub-sea phenomena hamper visibility, considerably affecting underwater missions. The lack of quality 3D information within these environments hinders the applicability of autonomous solutions in close-range navigation, fault inspection and intervention tasks since these have a very poor perception of the surrounding space. Deep learning techniques are widely used to solve these challenges in aerial scenarios. The developments in this subject are limited regarding underwater environments due to the lack of publicly disseminated underwater information. This article presents a new underwater dataset: NEREON, containing both 2D and 3D data gathered within real underwater environments at the ATLANTIS Coastal Test Centre. This dataset is adequate for monocular depth estimation tasks, which can provide useful information during O&M missions. With this in mind, a benchmark comparing different deep learning approaches in the literature was conducted and presented along with the NEREON dataset.