Abstract
The concept of the Digital Twin of the Ocean (DTO) has transitioned from a research vision to an operational paradigm in the ILIAD project. Several of the mature Digital Twin components are available as reusable, findable (through the Iliad Registry: https://iliad-registry.inesctec.pt) and interoperable application packages, enabling automated environmental monitoring and decision support. This contribution presents the Cretan Sea oil spill DTO, focusing on near-real-time oil spill detection and forecast.The presented system implements an end-to-end workflow based on Sentinel-1 SAR imagery, orchestrated through Common Workflow Language (CWL). Incoming satellite data are automatically ingested, processed, and analysed using containerized application packages, enabling scalable and reproducible execution across cloud and HPC infrastructures. Oil spill detection is performed using a deep learning approach based on a combination of FCOS and U-Net convolutional neural networks, trained to discriminate oil slicks from look-alike phenomena in SAR imagery. The results are systematically compared against a statistical detection methodology implemented via the SNAPpy library, providing robustness and methodological benchmarking.Detected oil spill events trigger downstream Digital Twin services, including high-resolution marine forecasting and oil spill transport modelling. The forecasting framework integrates dynamically downscaled atmospheric forcing from WRF, hydrodynamic fields from NEMO, and sea state information from WAVEWATCH III, providing coastal-scale predictions at kilometer resolution. Oil spill transport and fate are simulated using the already established and validated MEDSLIK-II software [1], with results visualized through operational web platforms to support rapid situational awareness. Additionally, a 4D immersive visualization tool is introduced to present the oil spill evolution and fate in an intuitive spatio-temporal environment, enhancing operational readiness and enabling first responders and non-expert stakeholders to rapidly interpret complex model outputs without reliance on conventional map-based products.By packaging satellite analytics, numerical modelling, and orchestration logic into reusable application packages, the system demonstrates how post-project DTO assets can be operationalized beyond the ILIAD lifecycle. The Cretan Sea DTO illustrates a transferable Digital Twin workflow for automated oil spill detection and response, supporting environmental monitoring authorities with timely, data-driven decision support.References[1] M. De Dominicis, N. Pinardi, G. Zodiatis, and R. Archetti, “MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations,” Geosci. Model Dev., vol. 6, pp. 1871–1888, 2013. doi: 10.5194/gmd-6-1871-2013

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.

Abstract
Reliable black start capability is a critical design requirement for offshore wind-hydrogen energy islands, directly influencing system availability, asset utilization, and the levelized cost of hydrogen production. This paper investi gates black start restoration strategies for autonomous offshore wind-to-hydrogen systems, focusing on the role of grid-forming converter technologies in enabling system recovery following total shutdown. A comparative analy sis of grid-forming battery storage and grid-forming wind turbine generation is conducted using electromagnetic transient simulations of a 300MW offshore wind farm coupled with a 240MW electrolyzer plant. Both technologies are evaluated within a combined soft and hard energization framework incorporating controlled voltage ramping, switchable reactive compensation, and sequential feeder energization. Battery-based grid-forming achieves faster voltage restoration and higher short-term overload capability, while wind turbine-based grid-forming provides superior frequency damping through higher virtual inertia. The combined energization strategy significantly re duces converter sizing requirements compared to pure soft energization, while switchable reactive compensation reduces reactive power burden by 94 percent during multi-feeder restoration. Strategic activation of electrolyzer auxiliary systems provides controllable load management that further attenuates frequency excursions during staged restoration. The findings provide practical design guidelines for black start technology selection in off shore wind-hydrogen systems, with direct implications for converter sizing, capital investment, and hydrogen production continuity.

Abstract
K-12 digital learning is increasingly shaped by emerging technologies layered onto existing digital infrastructures. In practice, the technologies that dominate attention, especially generative and assistive AI, arrive bundled with new assessment tensions, data flows, acquisition constraints, and inequities in access and support. This article proposes a practitioner-oriented framework for understanding emerging technologies as sociotechnical-immersive systems rather than standalone tools. The framework connects three lenses: (1) a macro sociotechnical circle that foregrounds policy, markets, equity, and governance; (2) a meso environment-design circle that analyzes how learning experiences are configured through system, narrative, and agency; and (3) a micro educational-approaches circle that focuses on the instructional activities educators enact within those environments, using the Immersive Learning Brain (ILB) as a map of practice and strategies. We developed this framework through practitioner sensemaking grounded in practitioner focus group data and aligned it with recent research syntheses on emerging technologies. We illustrate the framework through one worked example and two comparative mini-cases. We conclude with an agenda for researchers and practitioners focused on assessment, equitable infrastructure and support, data stewardship, and environment-design descriptions that move beyond technocentric labels.

Abstract
Innovation management encompasses a broad and complex organisational process that involves identifying and selecting new opportunities, implementing ideas, and capturing value from resulting innovations. The initial phase of this process, the Front End of Innovation (FEI), requires structured procedures to mitigate potential negative impacts across the innovation management chain. Research indicates that effective FEI activities correlate with improved innovation outcomes and a higher likelihood of successful innovation development. Despite its critical importance and the substantial technological demands of the military sector, the application of the FEI approach in defence contexts remains underexplored in academic literature, particularly within the unique circumstances of developing countries. This study employs the iterative design science research methodology to develop the InovaDefesa Ontology, a formal knowledge representation of the FEI phase, specifically tailored to address the challenges of the defence sector in developing economies. The artefact was evaluated through expert interviews, focus groups, and attribute agreement analysis. The proposed domain ontology offers a significant theoretical contribution by adapting and contextualising innovation management models within the military domain, thereby enhancing communication and coordination among stakeholders. On a practical level, it provides actionable insights and recommendations for public policies aimed at strengthening national innovation systems, building technological capacity, and fostering technological independence. These efforts are critical to achieving national sovereignty and advancing sustainable development in developing countries.

Abstract
Software architecture is reflected across multiple artifacts, making it difficult to communicate without proper documentation, which often becomes outdated or unreliable. We propose an approach to support Living Documentation by generating architectural diagrams from Docker Compose files. We implement our approach as a prototype tool that we name Infragenie and conduct an empirical study to show the viability of the approach. The study involved sending questionnaires to maintainers of 378 GitHub repositories. We received 36 responses. Infragenie-generated diagrams were rated as better or much better for most of the 12 projects with previous diagrams. Over 70% of the respondents agreed that our approach improved documentation completeness, consistency, and accessibility, and more than 90% recognized its effectiveness in capturing key architectural elements. We conclude that by using Docker Compose files we were able to provide useful architectural diagrams.