Abstract
Robust object detection in side-scan sonar (SSS) is challenging due to the impact of adversarial perturbations and environmental noise, which limit reliable deployment in autonomous underwater vehicles (AUVs). While existing approaches primarily focus on denoising techniques and data augmentation, they do not explicitly enhance the model's robustness against adversarial perturbations. To overcome these limitations, we introduce ROSAR, a framework that extends our prior work on knowledge distillation (KD) by integrating adversarial retraining, jointly tackling the dual challenges of model robustness and effi-ciency in SSS object detection. To support adversarial retraining and robustness evaluation, we introduce three novel, publicly available SSS datasets, each capturing different sonar setups and noise conditions. Furthermore, we define two SSS safety properties that guide the generation of adversarial datasets for retraining. Through a comparative analysis of projected gradient descent (PGD) and patch-based adversarial attacks, ROSAR demonstrates significant improvements in model robustness and detection accuracy under SSS-specific conditions, enhancing the model's robustness by up to 1.85%. ROSAR is publicly available at https://github.com/remaro-network/ROSAR-framework.

Abstract
The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes.

Abstract
Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.

Abstract
In our previous work, we explored a new invisible ECG biometrics approach that uses signals collected at the thighs using polymeric dry electrodes and sensors integrated into a toilet seat. However, the performance of the biometric templates remains unexplored. In this paper we evaluate how the ECG templates evolve, and the impact that potential changes may have on performance, using one case-study subject monitored over 31 days. This work is organized into two main parts. The first explores the morphological and physical traits of the subject throughout the 31 days based on data collected daily, three times per day at 6-hour intervals; in more than 80% of the sessions, all the signals were successfully acquired without showing noise nor movement artefacts. The second part is focused on evaluating the performance of Support Vector Machine (SVM) and Binary Convolutional Neural Network (BCNN) classifiers in the identification of the case study subject within a population of 10 individuals, covering an age range of (24 to 35 years); the top performer was the BCNN, achieving a perfect accuracy rate of 100% when tested on a group of two individuals.

Abstract
The sediment transport plays a major role in every aquatic ecosystem. However, the lack of instruments to monitor this process has been an obstacle to understanding its effects. We present the design of a single sensor built to measure water velocity, suspended sediment concentration and depth in situ, and how to associate the three variables to estimate and analyse sediment transport. During the laboratory calibrations, the developed instrument presented a resolution from 0.001 g/L to 0.1 g/L in the 0-12 g/L range for the measurement of suspended sediment concentration and 0.05 m/s resolution for 0-0.5 m/s range and 0.001 m/s resolution for 0.5-1 m/s range for the measurement of water velocity. The device was deployed for 6 days in an estuarine area with high sediment dynamics to evaluate its performance. During the field experiment, the sensor successfully measured the tidal cycles and consequent change of flow directions, and the suspended sediment concentration in the area. These measurements allowed to estimate water discharge and sediment transport rates during the different phases of tides, and the daily total volume of water and total amount of sediment passing through the estuary.

Abstract
Strategic term re-writing and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies to apply term re-write rules in defining largescale language transformations, while the latter is suitable to express context-dependent language processing algorithms. These two techniques can be expressed and combined via a powerful navigation abstraction: generic zippers. This results in a concise zipper-based embedding offering the expressiveness of both techniques. In addition, we increase the functionalities of strategic programming, enabling the definition of outwards traversals; i.e. outside the starting position. Such elegant embedding has a severe limitation since it recomputes attribute values. This paper presents a proper and efficient embedding of both techniques. First, attribute values are memoized in the zipper data structure, thus avoiding their re-computation. Moreover, strategic zipper based functions are adapted to access such memoized values. We have hosted our memoized zipper-based embedding of strategic attribute grammars both in the Haskell and Python programming languages. Moreover, we benchmarked the libraries supporting both embedding against the state-of-the-art Haskell-based Strafunski and Scala-based Kiama libraries. The first results show that our Haskell Ztrategic library is very competitive against those two well established libraries.