Abstract
Integrating citizen science with digital twin technology represents a significant development in oceanographic research and marine management. This paper examines how the Iliad project has successfully developed a comprehensive suite of digital twins of the ocean (DTOs) that leverage citizen science contributions to enhance data coverage, improve modelling accuracy, and foster public engagement with marine ecosystems. Through innovative technological solutions, including semantic interoperability frameworks, mobile applications, knowledge graphs, and gamification approaches, the project demonstrates the reciprocal benefits between citizen scientists, scientific research and digital twin ecosystems. The developments presented in this work illustrate how engaging the public in scientific research not only broadens the data foundation for digital twins but also creates pathways for citizens to gain valuable insights from these sophisticated digital representations of ocean environments.

Abstract
Developed within the Iliad Digital Twin of the Ocean (DTO) project, Coastal Crete provides advanced marine forecasting for oil spill detection and response. The system integrates satellite data, in-situ observations, and machine learning to predict oil spill trajectories and minimize environmental impacts. Using a multi-model approach, it combines WRF-DA, NEMO, and WAVEWATCH III models for high-resolution forecasts. Making use of Sentinel-1 SAR imagery, a deep learning approach was developed for near-real-time oil spill detection. The methodology is based on a U-net Neural Network, which is compared with the statistical methodology based on pythons' SNAPpy library. The operational forecasting system employs MEDSLIK-II for oil spill transport modeling and visualization via the GeoMachine platform, ensuring rapid decision-making for marine safety and environmental protection.

Abstract
The application of digital twin technology to the ocean is often referred to as Digital Twins of the Ocean (DTO). One notable initiative funded under Horizon Europe programs - Green Deal is the ILIAD - Digital Twin of the Ocean project. One of the objectives of ILIAD is to establish interoperable, data-intensive, and cost-effective DTO pilots. This paper focuses on one such pilot dedicated to environmental monitoring and water quality assessment associated with the OceanLab infrastructure in the Trondheim Fjord, Norway. This paper outlines the architecture and concept of the pilot while providing detailed insights into its application for various what-if scenarios. The scenario presented in this paper is a case study that analyzes the impact of a hypothetical oil spill at the Trondheim terminal. It focuses on the spread of surface oil over a 30-hour period using various pilot modules. The paper also discusses the potential replication of this study in another geographical location.

Abstract
ILIAD focuses on developing an ecosystem of interoperable Digital Twins for the Ocean by connecting to existing ocean data infrastructures, enhancing ocean data infrastructures with additional observation technologies and citizen science, employing numerical models and executing AI models, and aiding operational decision-making of marine and maritime activities. This work focuses on the diverse ILIAD Pilots and emphasizes sensors, data collection, and data management. Emphasis is given on new, low-cost sensors, their objectives, the novel technical aspects, the generated data, and how they can be used in the ILIAD project framework and the operation of ILIAD DTs.

Abstract
Integrating Internet of Things (IoT) technologies into physical education (PE) presents opportunities for improving the methodologies for collecting, analysing, and managing student performance data. However, it also introduces technical challenges, particularly related to the real-time handling and protection of sensitive data in dynamic training environments. This paper presents a comprehensive solution outline based on a private local network architecture that supports scalable sensor data processing, real-time database integration, and mobile application interfaces. The proposed distributed system ensures data integrity, low-latency communication, and secure access while enabling educators to monitor student performance in real-time and review historical data. The system supports more personalised, data-driven training strategies by providing actionable insights for sports education. © © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
Throughout the Virtual Reality (VR) literature, we find different terms to define the same concepts as well as the same terms addressing different concepts. This issue can easily cause misinterpretations and difficulty in the analysis of papers from different authors. This work addresses this terminology confusion through a detailed analysis of current key concepts, how they have been employed, comparing them to other concepts, and proposing adaptations to their definitions to reduce conceptual overlap while preserving the original terms. In this work, we reviewed widely used terms in VR: Fidelity, Realism, Immersion, Presence, and Coherence. We also identified and discussed derivative terms, such as Place Illusion, Plausibility Illusion, Sensorimotor Contingencies, Multisensory, Virtual Content, Objective and Subjective Realism, and Objective and Subjective Internal Coherence. We proposed how these distinct concepts can be separated, merged, and linked, providing a clearer terminology for future use and discussing the implications of this terminology.