The FORESIGHT project aims to develop a federated cyber-range solution to enhance the preparedness of cyber-security professionals at all levels and advance their skills towards preventing, detecting, reacting and mitigating sophisticated cyber-attacks. This is achieved by delivering an ecosystem of networked realistic training and simulation platforms that collaboratively bring unique cyber-security aspects from the aviation, smart grid and naval domains. The proposed platform will extend the capabilities of existing cyber-ranges and will allow the creation of complex cross-domain/hybrid scenarios to be built jointly with the IoT domain. Emphasis is given on the design and implementation of realistic and dynamic scenarios that are based on identified and forecasted trends of cyber-attacks and vulnerabilities extracted from cyber-threat intelligence gathered from the dark web; this will enable cyber-security professionals to rapidly adapt to an evolving threat landscape. The development of advanced risk analysis and econometric models will prove to be valuable in estimating the impact of cyber-risks, selecting the most appropriate and affordable security measures, and minimising the cost and time to recover from cyber-attacks. Innovative training curricula, guiding cyber-security professionals to implement and combine security measures using new technologies and established learning methodologies, will be created and employed for training needs; they will be linked to professional certification programs and be supported by learning platforms. Aside from the development of skills, the project aims at a holistic approach to cyber-threat management with the ultimate goal of cultivating a strong security culture. As such, the project puts considerable emphasis on research and development (i.e. research on cyber-threats, development of novel ideas, etc) as the key to increasing training dynamics and awareness methods for exceeding the rate of evolution of cyber-attackers

Despite the huge public budget effort (6.000M€/year) for road pavement maintenance, the European road network (5.5M km) is not in an acceptable condition. Current pavement maintenance strategies are mainly based on corrective maintenance which is an inefficient and costly approach, with negative impact on pavement service life and road safety, and also on the environment. In order to be able to implement a maintenance strategy based on preventive operations of much lower cost carried out at the optimal moment (predictive maintenance), it is necessary to have continuous and accurate information of the pavement condition, something that is not possible at present due to the high cost of current inspection services. PAV-DT is a disruptive technology that can be installed in any customer vehicle (e.g. public road administrators and concessionaires or construction companies on performance-based maintenance contracts) in order to convert these vehicles into a very low-cost real-time pavement inspection equipment through its ordinary circulation. Additionally, thanks to our advanced algorithm and a cloud-based platform, customers will be able to access the latest available information on the pavement condition at any moment and receive information on which maintenance actions are really required, and exactly where they should be applied and when is the best moment to deploy a truly cost-effective maintenance strategy. PAV-DT consortium formed by APPLUS (Spain), BECSA (Spain), M&S (Austria), MICRO-SENSOR (Germany) and IMM-UPV (Spain) agreed on creating a Joint Venture for the commercial exploitation of the results as soon as the project is completed through a Product-as-a-service business model. Revenues of more than 38.8M€, with an associated profit of 25.5M€ and the creation of 52 new highly qualified jobs are expected within the first 3 years. Thanks to this investment in PAV-DT, these customers will experience savings of more than 746M€ over that period.

The MAGDA project aims at developing a toolchain for atmosphere monitoring, weather forecasting, and severe weather/irrigation/crop monitoring advisory, with GNSS (including Galileo) at its core, to provide useful information to agricultural operators. MAGDA will exploit the untapped potential of assimilating GNSS-derived, drone-derived, Copernicus EO-derived datasets, in situ weather sensors into very high-resolution, short-range (1-2 days ahead) and very short-range (less than 1 day ahead) numerical weather forecasts to provide improved prediction of severe weather events (rainfall, snow, hail, wind, heat and cold waves) as well as of weather-driven agriculture pests and diseases to the benefit of agriculture operations, also in light of ongoing effects of climate change. These targets will be achieved by setting up a database of variables of interest, and an assimilation system to feed a numerical weather prediction model, which in turn drives a hydrological model for irrigation performance and water accounting to assess water use and related productivity. In addition to already existing observational networks, new dedicated networks of sensors, including GNSS and drones, to monitor atmospheric variables at high spatial resolution will be deployed in the vicinity of large farms and cultivated areas, to provide data with high spatial and temporal resolutions for the assimilation into the weather model. The delivery of the augmented forecasts and irrigation advisories to farmers will be enabled by a dedicated dashboard and APIs to already existing Farm Management Systems. The tools developed within MAGDA will represent the technical and methodological components based on which services to support agricultural operations will be defined.

Research careers are often shaped early and can be difficult to redirect later, with many early-stage researchers (R1 and R2) in the EU facing challenges in navigating diverse career paths. Fragmented transitions between academia, industry, the public sector, and entrepreneurial ventures—along with the absence of cohesive frameworks—have hindered career mobility, skill development, and employability, particularly in fast-growing fields such as AI, digital transformation, and deep tech. NextTechTalents addresses these challenges by creating talent ecosystems that integrate these sectors, ensuring researchers gain the skills to transition between academia and non-academic environments, including entrepreneurial opportunities. By supporting researchers in building various skills, the project helps them commercialise innovations and pursue different ventures, contributing to a sustainable talent pool across the European Research Area. NextTechTalents, aligned with the goals of the European Research Area, aims to create a robust and sustainable talent ecosystem, fostering talent circulation across sectors and enhancing employability and mobility. The project develops tailored career services, mentoring programs, and training modules based on the ResearchComp framework to equip researchers with the tools for both academic and non-academic pathways. NextTechTalents, will create a comprehensive Handbook for HR managers and training providers within research and innovation organizations. This handbook will guide the integration of the NextTechTalents Training System, promote interdisciplinary skills acquisition, and strengthen collaboration across academia, industry, and entrepreneurial ventures. By addressing mobility, cross-sector employability, and inclusive career support, the project will help early-stage researchers thrive in evolving, competitive sectors.

Now that renewable energy generation is already competitive in cost with electricity obtained from fossil fuels, the development of efficient long term energy storage methods seems crucial for a faster transition to a net-zero greenhouse gas emissions EU economy. Power-to-X methods are promising due to their negligible discharge rate but up to now all the efforts have been based on the use of H2 obtained by electrolysis, and the TEAs have shown that the high cost of the electrolysers hinders greatly its possibilities of industrial use. EffiTorch aims at developing an alternative breakthrough technology for Power-to-X based on the direct splitting of CO2, using an ultra-high temperature thermal plasma, with the simultaneous valorisation of low value bio-waste, leading to the efficient production of syngas. EffiTorch aims to reach carbon efficiencies higher than 90% and energy efficiencies higher than 60%, outperforming the best solutions available presently. Some of the research groups in Effitorch have a vast experience in CO2 splitting using Microwave (MW) plasma torches. Nevertheless, recently a compound approach that combines CO2 splitting by thermal plasmas with a quenching using the very endothermic reverse Bouduard reaction (RBR) has been developed in China that vastly improves the promising results obtained in the splitting of CO2 , while solving one of the yet unresolved issues, that of the efficient separation of the gases obtained. EffiTorch aims to explore the possibilities offered by a much improved version of the experimental set-ups used by the Chinese groups, including additional sophistications like the ultrasonic atomization of a bio-oil obtained by Hydrothermal Liquefaction (HTL) from sewage sludge, the use of high temperature reactors with plasma confinement and the implementation of a secondary heating of the plasma by induction with HF frequency (100-400 KHz), that could improve the energy efficiency and reduce costs.