OCEANIDS aims at building user-driven applications and tools, which act as an enabling technological layer for regional authorities & stakeholders in order to achieve a more resilient and inclusive systemic pathway to a Blue Economy in coastal regions. Brining spatial and non-spatial data & services under a single-access window platform for Climate-Informed Maritime Spatial Planning (CI-MSP), the project will allow a more integrated seascape management of coastal regions. The project delivers a Decision Support tool (the OCEANIDS Decision Support Platform - O-DSP), with an over-arching target to collect, harmonise and curate existing climate data services, making data accessible, reusable and interoperable for the development of local adaptation strategies. OCEANIDS facilitates access to knowledge, data & digital services critical for better understanding and managing climate risks, enhancing adaptive capacities and sup-porting transformative innovations. In addition, OCEANIDS sees inclusivity as an enabling, and required, factor towards a Blue Economy. The project has a strong focus on behavioural change, both on individual as well as on a systemic level, en-abling participating regions and communities to better understand and use potential social tipping points and systemic leverage points to accelerate transformative changes towards climate resilience. To achieve this, it promotes inclusive and deliberative governance through meaningful engagement and dialogue between citizens and stakeholders. This will be achieved using case-specific tools (i.e. ephemeral social networks) leveraging local citizens assemblies for bot-tom up deliberation, cultivating a culture of civic engagement, thus empowering individuals to take action in their own communities. Finally, the project will contribute to mobilising sustainable finance and resources towards adaptation at scale and closing climate protection gap.

Addressing critical challenges such as population displacement due to conflicts, exacerbated by factors like climate change, extreme weather events, food shortages, and poverty, remains paramount. The implementation of THEIA, integrating data fusion, processing, and analysis, particularly leveraging Geospatial Artificial Intelligence (GeoAI) and Machine Learning, is poised to enhance the efficacy of existing services significantly. Through the amalgamation of multi-temporal data and diverse datasets, THEIA empowers better decision-making and adapts to evolving policy and user needs. This technological advancement, bolstered by GeoAI, augments detection capabilities and ensures timely access to crucial information, bridging the gap between capabilities and stringent security demands. By integrating non-space data and end-user intelligence, THEIA’s supply chains add value not only at the operational level but also at regional and local levels, facilitating improved coordination. Furthermore, THEIA catalyzes fostering EU-independent capabilities and technologies, thereby bolstering the European space ecosystem's consolidation and ensuring the sustainable coexistence of legacy and New-Space solutions. Its services cater to a wide array of end-users, including EU entities such as SatCen and Frontex, Member State Ministries of Defence, Intelligence Agencies, Police Forces, NATO, and potentially Extra-EU National and Supranational Entities such as the United Nations.

MESEO aims to design, prototype and demonstrate an open, flexible and scalable multi-mission EO End-to-End system for massive processing. It will improve performance bottlenecks either on board or on ground while ensuring Space data sovereignty. The main goal of the proposed approach is to improve quality of service and timeline reactivity by improving the End-to-End EO chain at different levels and processing edges while reducing communication bandwidth and incorporating new technology optimised in power consumption and processing capabilities. MESEO will promote a collaborative digital ecosystem enabled by an EO Coordinator Centre together with distributed and heterogeneous Processing Functions. Those Functions will be exported through harmonized interfaces which will ensure the data sovereignty. EO ecosystem players will register their products/services as exported Functions into the ecosystem. The EO Coordinator Centre will control which registered Processing Function to activate in a transparent manner for optimal End-to-End performance, from either timing or quality point of view, for a given End-User request. MESEO specifically addressed both upstream and downstream parts of the EO data processing value chain in a transparent manner. Performance enhancements analysis will tackle major bottlenecks on each link of the EO data processing running either on board or on ground. MESEO proposed innovations will address such bottlenecks. Green Deal related pilot applications (methane monitoring and crops classification) will serve to demonstrate the performance enhancements due to the proposed innovations.

Space technology connected with Artificial Intelligence and machine learning techniques is one of the most rapidly developing field of science and also play a key role to control disaster by space like Covid-19 outbreak. While space technologies have been successfully applied to a small number of macroeconomy and heath care related matters over the last decade, there is neither a significant utilization of space elements nor a systematic analysis of needs for space assets in this sector yet. There are a significant number of indirect parameters observable from space that can be correlated to the impact on the economy of natural, health (including epidemic) and man-made disasters. Classical environment parameters (geographical, climatological and hydrogeological) and man induced impact on the environment (pollution, heat) can be combined with economic parameters of human activities impacted by the epidemic including transportation, industry, and commerce. Specific human activities can be directly correlated with the progression of the diseases i.e. increase of heat delivered by crematorium in the affected areas as well as in the dwelling areas due to lock-down restrictions. All these “observed parameters” need to be correlated to macro parameters related to the progress of the epidemic and its impact of the of the infection to the economy at different scales. At medium- and long-term time scale, this methodology enables the near real-time monitoring of macroeconomic parameters during the recovery phase following the end of the emergency outbreak. The project EYE intends to propose a prototype service based on Copernicus data, automatic image processing supported by artificial intelligence integrated with modelling and statistic and geospatial data into an IT platform able to provide econometric and epidemiologic nowcasting and forecasting data.

The MILLENION project focuses on modular scalability and accessibility aspects of trapped-ion quantum computers (QCs), tackling the transition from current laboratory-based experiments to industry-grade quantum computing technologies with technology readiness level above 8. The envisaged platform, which builds on top of the rack-mounted 50-qubit QC demonstrator realised in the flagship project AQTION, will offer a quantum advantage for various use-cases in a fully automated 100-qubit ion-trap QC. Our consortium will aggressively pursue disruptive development goals: (a) changing from one-dimensional strings of ions to two-dimensional arrays will allow us to support more than 1000 qubits; (b) consistently encoding quantum information in the electronic ground state of ion qubits enables error rates smaller than 10-4 per gate operation compatible with fault-tolerant error correction; and (c) implementing parallel gate operations will enable larger algorithmic depth. The new demonstrator device will be equipped with a hardware-optimised firmware suite and will be integrated in a high-performance computing (HPC) infrastructure to realise a QC/HPC solution, supporting standardised interfaces to various quantum software development kits with cloud accessibility. Finally, we will pave the way to scalable quantum computing by introducing long-range connectivity between quantum processors using photonic interconnects. We will combine these quantum information techniques with trap fabrication and packaging technologies which integrate optical and electronic components to achieve stable long-term operation in an industrial environment. These scientific and technological advances will provide a powerful hardware platform that can be exploited by partnering quantum software consortia to solve problems of major commercial and industrial importance such as computational problems in chemistry and machine learning.