Abstract
Current space debris observations and tracking aren't able to detect smaller debris, which poses a significant risk to space activities. This paper analyses the performance of a star tracker for detecting small space debris. This novel approach aims at improving our understanding of these objects. The ESA MASTER (Meteoroid and Space Debris Terrestrial Environment Reference) model is used to study the probability of space debris detection for a specific population of interest. Moreover, the maximum distance a space debris can be detected was analysed based on PROOF (Program for Radar and Optical Observation Forecasting) and using the camera characteristics, specifically by computing the signal-to-noise ratio as a function of debris size and material. This star tracker's maximum distance performance results are then applied together with detectability constraints to simulate, using ESA/ESOC GODOT libraries, when a debris is observed by the camera in space. The results demonstrate that the optical device could detect smaller debris in some of the orbits indicated by MASTER. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Abstract
Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness.

Abstract

Abstract
Lock-free data structures have become increasingly significant due to their algorithmic advantages in multi-core cache-based architectures. Safe Memory Reclamation (SMR) is a technique used in concurrent programming to ensure that memory can be safely reclaimed without causing data corruption, dangling pointers, or access to freed memory. The ERA theorem states that any SMR method for concurrent data structures can only provide at most two of the three main desirable properties: Ease of use, Robustness, and Applicability. This fundamental trade-off influences the design of efficient lock-free data structures at an early stage. This work redesigns a previous lock-free hash map to fully exploit the properties of the ERA theorem and to leverage the characteristics of multi-core cache-based architectures by minimizing the number of cache misses, which are a significant bottleneck in multi-core environments. Experimental results show that our design outperforms the previous design, which was already quite competitive when compared against the Concurrent Hash Map design of the Intel's TBB library.

Abstract
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. These compressed models are especially essential when implementing multi-model fusion solutions where multiple models are required to operate simultaneously. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

Abstract
We propose an approach to cluster and classify compositional data. We transform the compositional data into directional data using the square root transformation. To cluster the compositional data, we apply the identification of a mixture of Watson distributions on the hypersphere and to classify the compositional data into predefined groups, we apply Bayes rules based on the Watson distribution to the directional data. We then compare our clustering results with those obtained in hierarchical clustering and in the K-means clustering using the log-ratio transformations of the data and compare our classification results with those obtained in linear discriminant analysis using log-ratio transformations of the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.