Abstract

Abstract
Accurate infrastructure-based UAV localization remains challenging in the presence of occlusions, clutter, and limited observability from single sensing modalities. We present a distributed multi-station tracking framework that fuses mmWave radar and monocular vision to achieve robust 3D position and velocity estimation. Building upon a prior single-station radar-vision tracker, we extend the approach to a network of three portable observation stations, each performing local multi-hypothesis tracking with uncertainty-aware Kalman filtering. Vision measurements provide angular constraints that improve radar data association and mitigate clutter-induced artifacts. Instead of transmitting raw detections, each station communicates a compact state estimate and covariance to a central fusion node, where an information-form filter produces a globally consistent estimate. The system is validated in indoor flight experiments using motion capture ground truth, while remaining fully independent of it during estimation. The fused solution achieves a 3D RMSE of 0.2342 m and improves robustness against degraded individual station estimates. These results highlight the potential of distributed radar-vision sensor networks for scalable and reliable infrastructure-based UAV localization.

Abstract
Maintaining volunteer engagement without sacrificing annotation quality is a central challenge for citizen-science and crowdsourcing systems. This paper presents a one-week quasi-experimental field trial of JellyfishGO! that tests how competitive and cooperative Games with a purpose (GWAP) mechanics affect motivation and data outcomes, involving 64 players (n = 64) organized in 32 two-person teams. We address two research questions (RQs): how motivation varies across Bartle player types (RQ1), and which mechanics best optimize engagement and data quality (RQ2). Results show that a Hybrid configuration (team matches + light inter-team leaderboard) substantially increased validated capture throughput versus solo-competitive play (Incidence Rate Ratio (IRR) ˜ 1.79, 95% Confidence Interval (CI) [1.37, 2.33]) and that this uplift was broadly distributed across Bartle player types. © 2026 IEEE.

Abstract
Abandoned mines are among the main sources of long-term soil contamination, often leaving behind persistent concentrations of potentially toxic elements (PTE) that pose environmental and health risks. Ribeiro da Serra Sb-Au mine, in Portugal, active from 1858 to 1890, has left a significant environmental legacy. This study mapped the spatial distribution of mine processing residues, elemental characterisation distinguishing between anthropogenic and natural enrichment by determining soil sample concentrations of PTE, Hg mobility, and Total Organic Carbon (TOC) quantification. Multivariate analysis, spatial interpolation and comparison with Enrichment Factor were employed with the aim of understanding the distributions and sources of PTEs in the soils. Mercury is still present at the site, revealing high mobile (18.72 mg kg(-1)) and semi-mobile (3.58 mg kg(-1)) concentrations accumulated in waste piles, a remnant of Au amalgamation processes. High Hg concentrations (20.75 mg kg(-1)) pose significant environmental risks, even after more than a century, such as bioaccumulation potential and soil toxicity. Also, there are high concentrations of Pb (449.03 mg kg(-1)) in the waste piles from the Sb processing. This study highlights the critical importance of interpreting natural enrichment values of elements of Hg (EF = 267) and Pb (EF = 18) to discern pollution pathways resulting from mining and processing activities. Interpretation of natural enrichment values leads to a more accurate evaluation of environmental contamination and its sources. The Enrichment Factor shows that Sb (EF = 133) and Hg (EF = 267) are the elements that present extremely high enrichment (EF > 40).

Abstract

Abstract
Deep learning has been extensively applied to medical imaging tasks over the past years, achieving outstanding results. However, the obscure reasoning of the models and the lack of supportive evidence causes both clinicians and patients to distrust the models' predictions, hindering their adoption in clinical practice. In recent years, the research community has focused on developing explanations capable of revealing a model's reasoning. Among various types of explanations, example-based explanations emerged as particularly intuitive for medical practitioners. Despite the intuitiveness and wide development of example-based explanations, no work provides a comprehensive review of existing example-based explainability works in the medical image domain. In this work, we review works that provide example-based explanations for medical imaging tasks, reflecting on their strengths and limitations. We identify the absence of objective evaluation metrics, the lack of clinical validation and privacy concerns as the main issues that hinder the deployment of example-based explanations in clinical practice. Finally, we reflect on future directions contributing towards the deployment of example-based explainability in clinical practice.