SALSA is an exploratory research project relating to multi-source ADS-B system. A multi-source ADS-B system that combines the benefit of all possible type of relays (space, maritime, air or ground based) of ADS-B messages could provide a global surveillance system to overcome the prevailing continuous surveillance constraints in the non-radar airspace (NRA). By bringing Space based ADS-B with other sources of surveillance based on ground, air and oceanic relays, a system of system architecture is conceived; upon its benefits, new separation standards are validated through analysis using theoretical modelling for separation standard and airspace capacity, in the context of NRA. Reduction in separation minimum and in the number of standards will bring significant benefits to ATC/ATM operations with improved aircraft surveillance and airspace management. These two aspects, namely, a system-of-system concept for multi-source ADS-B architecture and analytical modelling for enhanced separation minima and airspace capacity in the context of NRA define the scope of SALSA. The analysis will also consider different scenarios of separation minima Vs. ADS-B message update rate. The study will assess the impact of performance of such a system of systems approach in the context of separation standards; it will provide an assessment of the procedural impact and impact to flight safety due to the revised minima and the system configuration. A set of recommendations to SESAR JU and other stake-holders and industry partners will be provided in order to purse the outcome of the study towards higher technology readiness level (TRL) and eventual implementation.

The Air Traffic Control - Trajectory Based Operations (ATC-TBO) project proposes to validate TBO SESAR Solutions for flights in the tactical execution phase for en-route and TMA operations, thus contributing to the finalisation of the SESAR Phase C developed in SESAR 2020 and the realisation of the Digital European Sky vision in SESAR Phase D. The results are expected to contribute to capacity, operational efficiency, safety, environment and cost efficiency. The SESAR Solutions, with the TRL/EOCVM levels at the start and end of the project are: • Solution #4: Improved automation in sector planning and separation management via enhanced trajectory predictions and controller tools, including the completion of Solution 53A from SESAR 2020 to V3 (Initial = TRL4/V2; Target = TRL6/V3) • Solution #5: Improved air/ground trajectory synchronisation via lateral and vertical complex CPDLC clearances to support TBO, including completion of Solution 56 from SESAR 2020 to V3 (Initial = TRL4/V2; Target = TRL6 /V3) • Solution #6: Advanced separation management function concepts and tools, making use of more reliable trajectory information and advanced use of datalink/CPDLC (Initial = TRL1/2 (V0/V1); Target = TRL4/V2) The ATC TBO project consortium - with EUROCONTROL as the project coordinator, and steering from the PMB and the SJU - brings together 14 ANSPs, 1 airspace user, 10 airborne and ground industry partners, 5 research establishments, and 1 university. 14 EU member states, Switzerland and the UK are represented. The proposal describes 21 validation exercises including real-time simulations, fast-time simulations, and expert groups. The 'design', 'development & validation', and 'data pack & maturity gate' phases are planned for the second half of 2023, 2024, 2025 and the first half of 2026.

The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making. The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules. The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.

The Project encompasses the industrial research aimed to timely and efficiently create and use airspace capacity, in combination with targeted, effective demand and/or capacity measures. As such, it will focus on advanced levels of dynamic airspace configuration, Leveraging different virtualization models, digital INAP applications as well as Network-wide monitoring, all with high levels of automation. The project addresses the R&I need for on-demand air traffic services reflective of traffic demand, and the continuity of ATM service despite disruption. The project exploits the latest advancements in artificial intelligence and machine learning, to supply a variety of supporting toolsets to ATM stakeholders that enable rapid exploration of options for the deployment of capacity-on-demand solutions, whenever and wherever required. The benefits include increased en-route capacity and improved cost-efficiency of ATS provision, without compromising the current safety levels.

The Master Planning and Monitoring project (so called AMPLE3 ) is to support the SESAR3 Joint Undertaking (S3JU) in planning and reporting on deployment activities in deployment (covering both industrialisation and implementation), and when need be, to contribute to the update the European ATM MP. The project provides Content Integration (CI) service to S3JU in support of the development activities and the delivery of a coherent set of SESAR Solutions aligned at content level with the S3JU MAWP direction and ambitions declined from the European ATM MP. While earlier SESAR Programmes addressed phases A, B and C of the SESAR Vision, the SESAR 3 JU Multi Annual Work Programme (MAWP) presents the strategic Research and Innovation (R&I) roadmaps of the Digital European Sky (DES) Programme to achieve also Phase D of the SESAR Vision. The MAWP identified several Transversal Activities (TA) to make the DES Programme not just a list of disconnected projects but a coherent programme, supporting delivery of SESAR Solutions.