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
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines.

Abstract
The International Symposium on Applied Reconfigurable Computing (ARC) is an annual forum for the discussion and dissemination of research, notably applying the Reconfigurable Computing (RC) concept to real-world problems. The first edition of ARC took place in 2005, and in 2024, ARC celebrated its 20th edition. During those 20 years, the field of reconfigurable computing saw a tremendous growth in its underlying technology. ARC contributed very significantly to the presentation and dissemination of new ideas, innovative applications, and fruitful discussions, all of which have resulted in the shaping of novel lines of research. Here, we present selected papers from the first 20 years of ARC, that we believe represent the corpus of work and reflect the ARC spirit by covering a broad spectrum of RC applications, benchmarks, tools, and architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

Abstract
A evolução das tecnologias digitais e das redes de comunicação favorecem o surgimento de uma sociedade conectada que desafia a educação a ampliar os espaços do ensinar e do aprender para além da fisic

Abstract
Federated Learning (FL), a distributed learning mechanism where data is decentralized across multiple devices and periodic gradient updates are shared, is an alternative to centralized training that aims to address privacy issues arising from raw data sharing. Despite the expected privacy benefits, prior research showcases the potential privacy leakage derived from overfitting, exploited by passive attacks. However, limited attention has been given to understanding and defending against active threats that increase model leakage by interfering with the training process, instead of relying on overfitting. This work addresses this gap by introducing Active Attribute Inference (AAI*), a novel active attack that encodes sensitive attribute information by making any targeted training sample leave a distinguishable footprint on the gradient of maliciously modified neurons [8]. Results, using two real-world datasets, show that it is possible to successfully encode sensitive information incurring a small error in terms of neuron activation. More importantly, on a practical scenario, AAI. can improve upon a state-of-theart approach by achieving over 90% of restricted ROC AUC, therefore increasing model leakage. To defend against such active attacks, this work introduces several attack detection strategies tailored for different levels of the defender's knowledge. Including the novel White-box Attack Detection Mechanism (WADM*) that detects abnormal changes in weights distribution, and two black-box strategies based on the monitorization of model performance. Results show that the detection rate can be 100% on both datasets. Remarkably, WADM. reduces any attack to random guessing while preserving model utility, offering significant improvements over existing defenses, particularly when clients are non-IID. By proposing active attacks against well-generalized models and effective countermeasures, this research contributes to a better understanding of privacy in FL systems.