ORBIS aims to deliver a market-fit AI-powered mature and cost-effective solution that integrates constellations of satellites with autonomous unmanned aerial vehicles (UAVs) to achieve advanced secure real-time surveillance and fast search and rescue over vast maritime areas where illegal migrant activity could take place. To achieve that, ORBIS’ SMEs will collaborate closely to go beyond the current boundaries of knowledge and technology by: (i) integrating Synthetic Aperture Radar (SAR) satellites, optical Very High Resolution (VHR) satellites, and thermal infrared sources to achieve a continuous, high-accuracy monitoring system; (ii) leveraging advanced autonomous cost-effective UAVs and push their limits in terms of autonomy and sensing to support maritime border management applications, able to deal with extreme and diverse weather and sea conditions; (iii) exploiting cutting-edge Machine Learning (ML) models to anticipate potential threats in high-risk zones and optimise the surveillance UAV flight plans, making the most out of available flight time and sea area coverage; and (iv) fusing effectively the massive multi-modal data gathered during operation and train distributed ML models for real-time decision making that will ensure faster search and rescue of migration incidents. ORBIS will address early on key market, regulatory, ethical and security requirements towards accelerating access to the civil security market, while engaging with other EU SMEs and key market actors, following a clear innovation management strategy. Finally, ORBIS will document best practices and lessons learnt in clear replicable steps, acting as a successful case study that paves the way for others small innovators to in their go-to-market strategy. Regulatory and policy recommendations will also be elaborated as signposts for facilitating access to the civil security market, aligned with EU security policy priorities.

ΜΟΒΙ-TWIN argues that major global transition processes, such as green and digital transition, require to constantly redefine the changing nature of regional attractiveness for capturing shifts in the drivers and effects of spatial mobility. It builds on the idea that twin transition affects regional attractiveness -or otherwise, the drivers of human mobility- offering an opportunity to left-behind areas to attract new population based on characteristics related to living and environmental conditions and the changes it brings on job accessibility. It argues that this creates new spatial mobility patterns which tend to develop new equilibria between the different forms of mobility (permanent, circular, temporal), affecting both the ‘old’ and the ‘new’ sending and receiving regions. It uses biga data and agent-based modelling to analyse the impact of those changing patterns of spatial mobility on EU regions and envisage policies to counterbalance their effects in terms of demographics, society, welfare system and labour market. The project has a four-dimensional scope: (i) it will analyse the changing drivers of spatial mobility based on human mobility behaviour, encompassing the structural changes caused by twin transition in the definition of regional attractiveness; (ii) it will examine the new balance rising between the different forms of spatial mobility (permanent, circular, temporal) and their effects on EU regions following the changing nature of regional attractiveness; (iii) it will use agent-based modelling to capture and assess the impact of changing patterns of spatial mobility on EU regions in terms of demographics, society, welfare system and labour market; and (iv) it will use the insights to envisage place-based policies for harnessing the positive outcomes of twin transition. Specific attention will be placed on the ways in which the disruptions caused by the COVID-19 and Brexit have affected freedom of movement between EU regions.

In the context of modern technologies, the transition to 6G networks faces numerous challenges; therefore, novel solutions must be discovered, combined, and tested in order to achieve distributed smart connectivity, imperceptible latency, virtually infinite capacity, improved security/privacy, smart/adaptive networking, and energy efficiency. The 6G-ICARUS project will investigate, combine, and improve on current technologies in order to address numerous obstacles that 6G networks will face in order to define the future wireless networks (FWNs). 6G-ICARUS is focused on FWNs capable of meeting a variety of targets, resulting in better geographic coverage, particularly in urban areas, higher transmission rates, lower latency, provision of diverse services, processing of information generated from a massive volume of heterogeneous sources, inherent resilience to counter potential security threats, and making intelligent decisions. 6G-ICARUS is founded on three pillars: (i) Effective utilisation of reconfigurable intelligent surfaces suitably controlled by artificial intelligence software; (ii) Channel modelling for mmWave/subTHz communications; (iii) Multi-connectivity solutions for 6G mobile communications. The project will generate novel research results as well as novel original hardware supported by innovative software (techniques, algorithms) based mostly on machine learning and deep learning. Hardware and software will be combined and examined in real-world settings to fulfill certain situations, all of which are matched with real-world applications. There will be separate Proof of Concept studies to validate the technologies and methodologies under consideration.

TEXTaiLES goal is to redefine the digitization of Cultural Heritage (CH) textile objects and the development of AI-based processing tools, aiming to establish a standardized protocol that seamlessly integrates into the ECCCH. By studying past and present practices in textile digitization, we methodically explore their strengths and limitations. This comprehensive assessment, enriched by contributions from co-designer groups across various use cases, helps us identify the essentials of textile digitization. Our approach to standardization is all-encompassing. We develop advanced tools that address the entire digitization life cycle of textile objects, focusing on non-destructive methods. Using robotic, cost-effective multi-sensor solutions, we ensure the preservation of artefacts during digitization, complemented by reliable data analysis workflows. Innovation lies within the core of TEXTaiLES. We integrate state-of-the-art AI technologies to shed light on the intricate details of textiles. From revealing their weaving patterns and microstructures to identifying degradation factors, our AI-driven toolkit offers in-depth insights. It further excels in motion modeling, dynamics, and the reconstruction of fragmented or lost objects. All these capabilities merge into a groundbreaking 4D multi-scale annotation interface, coupled with a digital twin model and enhanced by human-in-loop interactions. Our commitment to the CH community remains strong. We aim to embed the tools and techniques developed by TEXTaiLES into archaeological research, facilitated by the ECCCH. By supporting interdisciplinary research communities and providing upskilling opportunities, we aim to raise the textile CH domain to new heights.

Future wireless networks (FWNs) will need to efficiently and flexibly provide diversified services such as enhanced mobile broadband access, ultra-reliable low-latency communications (URLLC), and massive machine-type communications . RECOMBINE joins the scientific excellence and expertise of key academic and industrial players into a joint collaborative effort to build the framework for the design oif FWNs beyond 5G that are able to support multiple operational standards for exploitation of intrinsic network heterogeneity; capable of processing information generated from a huge volume of heterogeneous sources and with sufficient intrinsic resilience to counter potential security threats. RECOMBINE will pursue innovations for advancing in the areas of mm-wave technology, licensed spectrum access, antenna design, channel propagation and modeling, and network prediction and quality of experience supported by artifical intelligence. In addition, RECOMBINE will integrate scientific and business model innovations for building a framework to fulfill the economic potential of FWNs.