Large scale deployment of renewable energy (RE) is key to comply with the GHG emissions reduction set by the COP21 agreement. Despite cost competitive in many settings, RE diffusion remains limited largely due to its variability. This works as a major barrier to RE’s integration in electricity networks as knowledge of power output and demand forecasting beyond a few days remains poor. To help solve this problem, S2S4E will offer an innovative service to improve RE variability management by developing new research methods exploring the frontiers of weather conditions for future weeks and months. The main output of S2S4E will be a user co-designed Decision Support Tool (DST) that for the first time integrates sub-seasonal to seasonal (S2S) climate predictions with RE production and electricity demand. To support the dissemination of climate services, a pilot of the DST will be developed in two steps. The first will draw on historical case studies pointed as relevant by energy companies - e.g. periods with an unusual climate behaviour affecting the energy market. The second step will improve probabilistic S2S real-time forecasts built up into the DST and assess their performances in real life decision-making in these companies. This process will be co-designed with consortium’s partners which represent different needs and interests in terms of regions, RE sources (wind, solar and hydro) and electricity demand. Besides the partners, S2S4E will engage other users from the energy sector as well as other business areas and research communities to further explore DST application and impact. As a result, DST will enable RE producers and providers, electricity network managers and policy makers to design better informed S2S strategies able to improve RE integration, business profitability, electricity system management, and GHG emissions’ reduction. The long-term objective is to make the European energy sector more resilient to climate variability and extreme events.

The MONSOON vision is to provide Process Industries with dependable tools to help achieving improvements in the efficient use and re-use of raw resources and energy. MONSOON aims at establishing a data-driven methodology supporting the exploitation of optimization potentials by applying multi-scale model based predictive controls in production processes. MONSOON features harmonized site-wide dynamic models and builds upon the concept of the cross-sectorial data lab, a collaborative environment where high amounts of data from multiple sites are collected and processed in a scalable way. The data lab enables multidisciplinary collaboration of experts allowing teams to jointly model, develop and evaluate distributed controls in rapid and cost-effective way. Hybrid simulation and seamless integration techniques are adopted for rapid prototyping and deployment in real conditions. MONSOON will be developed and evaluated in two sites from the aluminium and plastics domains. The aluminium scenario will be focused on predictive monitoring of potlines, targeting early detection of anomalies and identification of potential optimization gains. Aluminium cases will be implemented in the plant with the highest primary aluminium production in the EU-28, namely the AP Dunkerque smelter, France. The plastics scenario will focus on fusing data from data-intensive in-mould sensors with information from higher SCADA levels, enabling early and precise identification of potential issues. This use case will be implemented in the GLN plant in Maceira-Leiria. MONSOON addresses the SPIRE vision, providing advantages for the European industry competitiveness and sustainability through the realization of an overarching monitoring and control infrastructure. MONSOON aims at creating synergies within and between the process industry sectors, boosting European industry in the worldwide race for competitiveness and sustainability.

The DOMINO-E project, proposed by a consortium of European organisations, including scientific institutes and SMEs and led by Airbus Defence and Space, aims at solving the key challenge of availability and reactivity of earth observations from space, by enabling multi-mission accessibility on a scalable and automated way. The implementation of a multi-mission/multi-sensor federation layer allows the end-user to address a variety of acquisition assets using scheduling and optimization algorithms. The orchestration between the users’ patrimonial missions and the third party missions is based on reactivity, persistence, precision and costs criteria, while user experience is improved thanks to cognitive assistants. The challenge is to overcome the current technological, architectural and economical roadblocks of existing mission ground segments: mono-mission architectures, un-harmonized interfaces between different ground segments, inexistent or crude multi-mission collaborative coverage and dispatch services. DOMINO-E consists of designing, analysing and modelling the multi-mission federation layer based on users’ requirements and is supported by demonstrations of added value services. A market analysis is performed to assess the commercial perspectives of multi-mission federation approach in terms of client acceptance in sharing assets, industry’s make or buy strategy and SME capability to build catalogues of multi-mission services. This innovative federation layer supports the change of space industry paradigm from instrumental push to end-client vertical needs pull and allows the EU space industry to embrace the emerging data driven space market. In such, DOMINO-E contributes to European non-dependence for the development of Earth-observation technologies and foster European competitiveness by supporting SMEs in developing multi-mission services agnostic to the end-to-end or ground segment systems integrators.

Internet of Things (IoT) is one of the next big concepts to support societal changes and economic growth, being one of the fastest growing ICT segments. A specific challenge is to leverage existing technology strengths to develop solutions that sustain the European industry and values. To address this, IoT-NGIN introduces novel research and innovation concepts, to establish itself as the “IoT Engine” that will fuel the Next Generation of IoT as a part of the European Next Generation Internet. First, IoT-NGIN uncovers a patterns based meta-architecture that encompasses evolving, legacy, and future IoT architectures. Second, it optimizes IoT/M2M and 5G/MCM communications, including using secure-by-design micro-services to extend the edge cloud paradigm. Thirdly, it enables user and self-aware, autonomous IoT systems through privacy-preserving federated ML and ambient intelligence, with AR support for humans. Finally, IoT-NGIN researches towards distributed IoT cybersecurity and privacy, for example, using Self-Sovereign Identities and interconnected DLTs to implement Meta-Level Digital Twins. IoT-NGIN will be validated via more than 30 types of heterogeneous IoT devices, ranging from tiny resource constrained IoT sensors to intelligent, autonomous buses, drones and robots, deployed in one of the most prestigious IoT/5G lab (OneLab) and five Living Labs: the “Twin Cities” Living Lab in Helsinki/Tallinn, a fully customizable Smart Agri Living Lab in Greece, the BOSCH and the ABB employee-friendly Industry 4.0 facilities in Barcelona and Helsinki, and a hybrid field-lab facility focused on Smart Energy, interconnecting Terni City and EDD Eurolab in Aachen. Beyond partners exploitation plans, to maximize impact and sustainability, IoT-NGIN will push all results via alliances, clusters, SDOs and DIHs, including FIWARE, BDVA, and AIOTI, offer developed software as open source, and organize open Open Calls to engage FGPA/ASIC/fabless and IoT application developers.

Discovering criminal networks and identifying their members is one of the primary aspects of LEAs' mission. ROXANNE will contribute towards this goal by bridging the strengths of speech and language technologies (SLTs), visual analysis (VA) and network analysis (NA). If funded, ROXANNE will achieve a significant increase in the speed of investigation processes and an improvement in identification of individuals by means of speech, in the scope of criminal cases where large amounts of lawfully intercepted communications (with multilingual attributes) are analysed. The technical development will be centred around the ROXANNE platform, which will enhance criminal network analysis capabilities by providing a framework for extracting evidence and actionable intelligence based on speech, language and video technologies. The intention is not to replace humans but automate time-consuming tasks, and support LEA decision-making. Its early version will offer preliminary SLT, VA and NA capabilities to collect end-user feedback. The final version will provide multilingual, probabilistic tools interfacing SLT and NA technologies, boosted by natural language processing (NLP) and relation analysis in the synoptic criminal activity graph. ROXANNE will achieve full compliance with relevant INTERPOL and EU legal and ethical frameworks, including innovative approaches to data protection management such as privacy by design. Special efforts will be expended to ensure ROXANNE outcomes achieve widespread adoption by law enforcement. The effort will be enhanced through a series of education and awareness campaigns and the direct involvement of LEAs from nine European countries, that will test our solutions on real case data. In addition, ROXANNE partner INTERPOL and EUROPOL (member of the External Advisory Board) will provide advice and guidance. The consortium has 24 partners with complementary skills, including leaders in key technology areas impacting criminal investigations.