Abstract
This paper focuses on an innovative application of deep learning (DL) techniques, particularly 3D convolutional neural networks (CNNs), for land cover classification using multispectral Sentinel-2 (S-2) data. In this study, we evaluated the performance of window-pixel-wise 2D, 3D, and 3D Multiscale CNN architectures for land cover classification. 3D and 3D multiscale CNNs were using the spectral dimension as the third dimension for convolutions. Methodology was applied to classify large area (23,217 km2) in Czechia according to the Land use, land-use change and forestry (LULUCF) categories, a key sector in greenhouse gas inventories. The input dataset included S-2 data, along with NDVI, NDVI variance, and SRTM elevation data, all resampled to the 10 m S-2 grid and forming multi-dimensional input 5 x 5 pixel patches. The results show that a 3D CNN with 3 x 3 x 3 spatial-spectral filters and classical training achieved the best F1 score of 0.84, outperforming other proposed CNN architectures and a baseline Random Forest classifier. The study highlights the ability of 3D CNNs to integrate spatial-spectral information, making them highly effective for multispectral data analysis, even with limited (small) training ground truth datasets. This approach provides valuable information for researchers seeking to optimize DL methods for land cover classification, particularly for applications aligned with the LULUCF frameworks.

Abstract
The Haka'a'Museum workshop in Madeira explores how augmented reality (AR) enhances marine conservation education. This one-day, hands-on experience engages participants in co-creating AR experiences that make complex environmental issues more accessible. Following a structured approach, participants explore museum exhibits, collaborate on AR concepts, implement content using no-code tools, and evaluate their experiences. Leveraging Madeira's unique marine ecosystem, the workshop addresses ocean pollution, climate change, and sustainability. Data from AR interactions will inform the best practices for museum education. Ultimately, the workshop fosters awareness and action for ocean sustainability, redefining how museums educate through immersive technology.

Abstract
Background Intrusion Tolerant Systems (ITS) aim to maintain system security despite adversarial presence by limiting the impact of successful attacks. Current ITS risk managers rely heavily on public databases like NVD and Exploit DB, which suffer from long delays in vulnerability evaluation, reducing system responsiveness.Objective This work extends the HAL 9000 Risk Manager to integrate additional real-time threat intelligence sources and employ machine learning techniques to automatically predict and reassess vulnerability risk scores, addressing limitations of existing solutions.Methods A custom-built scraper collects diverse cybersecurity data from multiple Open Source Intelligence (OSINT) platforms, such as NVD, CVE, AlienVault OTX, and OSV. HAL 9000 uses machine learning models for CVE score prediction, vulnerability clustering through scalable algorithms, and reassessment incorporating exploit likelihood and patch availability to dynamically evaluate system configurations.Results Integration of newly scraped data significantly enhances the risk management capabilities, enabling faster detection and mitigation of emerging vulnerabilities with improved resilience and security. Experiments show HAL 9000 provides lower risk and more resilient configurations compared to prior methods while maintaining scalability and automation.Conclusions The proposed enhancements position HAL 9000 as a next-generation autonomous Risk Manager capable of effectively incorporating diverse intelligence sources and machine learning to improve ITS security posture in dynamic threat environments. Future work includes expanding data sources, addressing misinformation risks, and real-world deployments.

Abstract
Virtual presence demands ultra-low latency, a factor that centralized architectures, by their nature, cannot minimize. Local peer-to-peer architectures offer a compelling alternative, but also pose unique challenges in terms of network infrastructure. This paper introduces a prototype leveraging Conflict-Free Replicated Data Types (CRDTs) to enable real-time collaboration in a shared virtual environment. Using this prototype, we investigate latency, synchronization, and the challenges of decentralized coordination in dynamic non-Byzantine contexts. We aim to question prevailing assumptions about decentralized architectures and explore the practical potential of P2P in advancing virtual presence. This work challenges the constraints of mediated networks and highlights the potential of decentralized architectures to redefine collaboration and interaction in digital spaces.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
Computer simulations, which are performed at a single wavelength at a time, have been traditionally used to estimate the optical properties of tissues. The results of these simulations need to be interpolated. For a broadband estimation of tissue optical properties, the use of computer simulations becomes time consuming and computer demanding. When spectral measurements are available for a tissue, the use of the photon diffusion approximation can be done to perform simple and direct calculations to obtain the broadband spectra of some optical properties. The additional estimation of the reduced scattering coefficient at a small number of discrete wavelengths allows to perform further calculations to obtain the spectra of other optical properties. This study used spectral measurements from the heart muscle to explain the calculation pipeline to obtain a complete set of the spectral optical properties and to show its versatility for use with other tissues for various biophotonics applications.