Abstract
The knowledge of the precise 3D position of a target in tracking applications is a fundamental requirement. The lack of a low-cost single sensor capable of providing the three-dimensional position (of a target) makes it necessary to use complementary sensors together. This research presents a Local Positioning System (LPS) for outdoor scenarios, based on a data fusion approach for unmodified UAV tracking, combining a vision sensor and mmWave radar. The proposed solution takes advantage of the radar's depth observation ability and the potential of a neural network for image processing. We have evaluated five data association approaches for radar data cluttered to get a reliable set of radar observations. The results demonstrated that the estimated target position is close to an exogenous ground truth obtained from a Visual Inertial Odometry (VIO) algorithm executed onboard the target UAV. Moreover, the developed system's architecture is prepared to be scalable, allowing the addition of other observation stations. It will increase the accuracy of the estimation and extend the actuation area. To the best of our knowledge, this is the first work that uses a mmWave radar combined with a camera and a machine learning algorithm to track a UAV in an outdoor scenario. © 2025 IEEE.

Abstract

Abstract
Radon (Rn-222) is a unique atmospheric tracer, since it is an inert gaseous radionuclide with a predominantly terrestrial source and a short half-life (3.8232 (8) d), enabling quantification of the relative degree of recent (< 21 d) terrestrial influences on marine air masses. High quality measurements of atmospheric radon activity concentration in remote oceanic locations enable the most accurate identification of baseline conditions. Observations of GHGs under baseline conditions, representative of hemispheric background values, are essential to characterise long-term changes in hemispheric-mean GHG concentrations, differentiate between natural and anthropogenic GHG sources, and improve understanding of the global carbon budget.The EU-funded project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) will establish world-leading high-quality atmospheric measurements of radon activity concentration and of selected GHG concentrations (CO2, and CH4) at a remote oceanic location, the Eastern North Atlantic (ENA) facility, managed by the Atmospheric Radiation Measurement (ARM) programme (Office of Science from the U.S. Department of Energy), located on Graciosa Island (Azores archipelago), near the middle of the north Atlantic Ocean. These observations will provide an accurate, time-varying atmospheric baseline reference for European greenhouse gas (GHG) levels, enabling a clearer distinction between anthropogenic emissions and slowly changing background levels. NuClim will also enhance measurement of atmospheric radon activity concentration at the Mace Head Station, allowing the identification of latitudinal gradients in baseline atmospheric composition, and supporting the evaluation of the performance of GHG mitigation measures for countries in the northern hemisphere.The high-quality nuclear and GHG observations from NuClim, and the resulting classification of terrestrial influences on marine air masses, will assist diverse climate and environmental studies, including the study of pollution events, characterisation of marine boundary layer clouds and aerosols, and exploration of the impact of natural planktonic communities on GHG emissions. This poster presents an overview of NuClim, outlines the project objectives and methodologies, and summarises the relevant data products that will be made available to the climate community.Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515.

Abstract
Project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration and ambient radioactivity to advance climate science and improve radiation protection and nuclear surveillance capabilities. It is supported by new metrological capabilities developed in the EMPIR project 19ENV01 traceRadon. This work reviews the scientific objectives of project NuClim in terms of both climate science and radiological protection, and provides an overview of the NuClim field campaign and the various nuclear measurements being implemented within the scope of the project.

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
There is a strong need to motivate students to learn science, technology, engineering, and mathematics (STEM) subjects. This is a problem not only at lower educational levels, but also at college institutions. With this idea in mind, the School of Engineering of the Porto Polytechnic (ISEP) Electrical Engineering Department decided, in 2021, to launch a robotics competition in order to foster students' interest in the areas of robotics and automation. This event, named Robotics@ISEP Open, aims to raise awareness of the area of electronics, computing, and robotics among students, involving them in the use of techniques and tools in this area, and encompasses three distinct robotics competitions covering both manipulator arms and mobile robots. It is based on two main points of interest: (i) robotic competitions and (ii) outside class training in robotics, aimed at students who want support to participate in competitions. Since its first edition, the event has grown and internationalized and has already become a milestone in the academic life of ISEP. This paper presents the motivations that led to the creation of this event, its main organizational aspects, and the competitions that are part of it, as well as some results gathered from the experience accumulated in organizing it. © 2025 IEEE.