The growing integration of the Internet of Medical Things (IoMT) in critical infrastructure demands efficient and accurate Intrusion Detection Systems (IDS) tailored to their resource-constrained environments. In this work, we propose the Optimized Preprocessing Framework (OPF): A novel pre-processing methodology to develop a computationally optimized and robust against extreme class imbalance Artificial Intelligence-based IDS. Our methodology operates in two stages: First, a fully balanced dataset is created using Random Undersampling of the majority classes, and a Gini feature-based prioritization method is employed to efficiently reduce the feature space. Then, a Random Forest model is trained on a nearly balanced version of the dataset, generated using a hybrid resampling technique via logarithmic interpolation that employs SMOTE-based oversampling of minority classes and Random Undersampling of majority classes. This process preserves relative class order while compressing extreme disparities. Moreover, it significantly reduces the feature space and computational overhead. We validate the proposed approach on multiple real-world datasets, including CICIoMT2024, IoMT-TrafficData, and CICIoT2023. Results show that our method improves the F1-score metric, while reducing model complexity by 31.11%, 31.39%, and 78.16% in each dataset respectively, thereby making it suitable for deployment in practical IoMT environments.
The Media & Entertainment (M&E) sector is undergoing a profound technological and structural transformation. Audiences are shifting towards on-demand, immersive, and interactive content; AI and generative media are becoming integral to production and distribution; and live events increasingly rely on digital augmentation. Against this backdrop, 6G technologies—supported by innovations in AI, edge computing, non-terrestrial networks, sensing, and distributed intelligence—are poised to redefine how media is created, delivered, and experienced.Within this evolving landscape, the Smart Networks and Services Joint Undertaking (SNS JU) plays a central role in shaping Europe’s technological leadership. Through 22 dedicated projects and 46 use cases, SNS JU is developing and validating the network capabilities, architectural enablers, and experimental platforms that will make 6G-enabled M&E applications technically feasible, economically sustainable, and societally acceptable.SNS JU projects collectively address the stringent performance needs of next-generation M&E services—ultra-high bandwidth, sub-millisecond latency, massive device density, distributed compute, precise positioning, integrated sensing, and advanced security.This white paper provides an extensive analysis of the current status of the M&E sector, based on SNS JU experts and an extensive research of the industrial M&E domain, and the promising technologies and features of 6G that stand to bring M&E services to the 2030s.Despite strong progress from SNS JU projects, significant challenges remain for large-scale 6G adoption in the M&E sector. Achieving the advanced infrastructure required for sub-THz communications, distributed MIMO, and edge-centric architectures demands high investment, while uplink limitations still constrain volumetric capture and remote production. Immersive and metaverse applications also heighten privacy and security risks due to their dependence on sensitive biometric and behavioural data. Interoperability gaps between devices, platforms, and formats threaten ecosystem fragmentation, and today’s end-user devices—limited in battery, processing, display, and cost—continue to restrict the quality of immersive experiences.At the same time, 6G offers major opportunities to transform M&E through hybrid digital-physical events, immersive broadcasting, holographic telepresence, and cloud-native collaborative production. Ultra-high bandwidth, low latency, and distributed AI will support richer, more interactive content and personalized user experiences, while metaverse applications expand into culture, education, tourism, and industry. Sustainability will become a central value driver as carbon-aware streaming and energy-efficient delivery influence both operators and consumers. Early 6G adoption is expected in premium immersive services and professional production environments, broadening as devices mature and standards solidify. With continued investment, coordinated regulation, and leadership in R&I, Europe is well positioned to shape the global future of 6G-enabled media.
Beam shaping techniques enable tailored beam trajectories, offering unprecedented connectivity opportunities in wireless communications. Current approaches rely on flat apertures, which limit trajectory flexibility due to inherent geometric constraints. To overcome such restrictions, we propose adopting curved apertures as a more versatile alternative for beam shaping. We introduce a novel formulation for wave trajectory engineering that is compatible with arbitrarily shaped apertures. Theoretical and numerical analyses demonstrate that curved apertures offer improved control over wave propagation, are more resilient to phase control constraints, and achieve higher power density across a wider portion of the desired beam trajectory than flat apertures.
Living Labs (LLs) are key for collaborative and value-based innovation, though their relational and governance mechanisms are still being explored. This study focuses on examining how relational dynamics and community leadership influence the design, governance, and replicability of a Digital Living Labs (DLLs) methodology. The research examines the DLLs of Catalonia using a combination of 15 qualitative interviews and 104 survey responses, with a mixed-methods design adopted. This regional initiative is based on Quadruple Helix (4-H) collaboration and value-driven innovation. The findings show that inclusive participation is enabled through core relational infrastructures. These relationships are built on trust-building, collaboration, facilitation, and knowledge exchange. Community leaders complemented facilitators through harmonizing institutional objectives with local priorities, reinforcing distributed governance, and generating public value. Inclusion, equity, transparency, and solidarity were essential to engagement and collective ownership. The study’s results indicate that effective DLLs transferability depends more on reinforcing relational foundations and shared values than on replicating fixed structures.
