The protection of European space infrastructures including the setting up of a Space Surveillance and Tracking (SST) capability at European level, represents a new priority of the EU space policy. The H2020 Work Programme for Space 2014-15 addresses challenges associated with this programmatic priority and ensures adequate support to the relative topics and actions. The main initiative towards the development of a SST capability at European level is represented by the SST Support Framework decision adopted by the Council and the European Parliament on the 16 of April 2014. The European Union Satellite Centre (EU SatCen) is an EU Agency linking space and security and with long experience in delivering geospatial imagery information services and products with various levels of classification to both civil and defence users. Its expertise in handling confidential information in a secure environment and its tight institutional links with the Member States has enabled the EU SatCen to build distinctive expertise on SST issues. The PASS (Preparation for the establishment of a European SST Service provision function) support action will aim at ensuring the transition of the EU SatCen SSA activities towards the Centre’s contribution to the setup of a SST service provision function within the SST Support Framework initiative by identifying necessary functional elements and interfaces with relevant SST actors, with particular emphasis on security aspects. PASS will strive to propose mature definitions of such interfaces, benefiting from the fruitful collaboration already established with the main actors involved. It will provide a technical contribution to this objective and to the future establishment of the SST capability by developing a representative SST service provision function pilot. Moreover, PASS will also contribute in exploring the different contexts and scenarios for the cooperation of the EU SatCen within the SST Support Framework

Currently, many processes, procedures, systems and equipment for information management and exchange in EU civilian external actions are in place, but these are often used in an incoherent way and lack interoperability, inhibiting CSDP entities and other stakeholders to build a common understanding of crisis management. CIVILEX aims to identify, characterise and model the communication and information systems in use within the EU Civilian missions, understand the stakeholders’ requirements and provide possible solutions to tackle by a future interoperable Situational Awareness, Information Exchange and Operational Control Platform. This would allow to constitute a more coherent and interoperable platform. As a result, common understanding and situational awareness about crisis management in EU civilian external actions will be enhanced since CIVILEX paves the way for such an infrastructure. The envisaged platform should facilitate the engagement of EU actors. This can include CSDP missions, EU Delegations, ECHO offices and Member State Embassies, but also UN offices and other non-EU actors. While the focus is on the civilian chain of command, it should also consider certain connections and overlaps with military actors. The need for coherence and interoperability is not new, nor is it a purely technical challenge. Clearly, there are certain legal, policy, institutional and human elements that function as facilitators and constrainers for better information exchange. CIVILEX, therefore, adds an innovative dimension by researching and helping to clarify the wider policy, institutional and operational issues of ICT systems in the context of CSDP and EU external action. The project would take a hybrid approach, aiming both for a technical solution for information exchange (a platform that stimulates low-level, secure ad-hoc communities of interest) and for institutional changes (leading to agreements on exchange formats for civilian parties).

Explainable Artificial Intelligence (AI) is key to achieving a human-centred and ethical development of digital and industrial solutions. ENEXA builds upon novel and promising results in knowledge representation and machine learning to develop scalable, transparent and explainable machine learning algorithms for knowledge graphs. The project focuses on knowledge graphs because of their critical role as enabler of new solutions across domains and industries in Europe. Some of the existing machine learning approaches for knowledge graphs are known to already provide guarantees with respect to their completeness and correctness. However, they are still impossible or impractical to deploy on real-world data due to the scale, incompleteness and inconsistency of knowledge graphs in the wild. We devise approaches that maintain formal guarantees pertaining to completeness and correctness while being able to exploit different representations of knowledge graphs in a concurrent fashion. With our new methods, we plan to achieve significant advances in the efficiency and scalability of machine learning, especially on knowledge graphs. A supplementary innovation of ENEXA lies in its approach to explainability. Here, we focus on devising human-centred explainability techniques based on the concept of co-construction, where human and machine enter a conversation to jointly produce human-understandable explanations. Three use cases on business software services, geospatial intelligence and data-driven brand communication have been chosen to apply and validate this new approach. Given their expected growth rates, these sectors will play a major role in future European data value chains.