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.

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.

PEARL project aims at carrying out the SESAR performance management process, under the leadership of SESAR 3 JU. This implies to reconcile and map the performance assessments and results delivered by the R&I projects with the SESAR performance ambitions in the ATM Master Plan. To achieve this goal, PEARL Project will conduct the following activities: -A priori estimation of performance contributions from SESAR Solutions -Consolidation of performance assessments coming from SESAR Solutions to deliver PAGAR report -Provision support and guidance to Solutions through Communities of Practice (in performance domains as Operational Performance, Safety, (cyber) Security, Human Performance, Environment, CBA, Digitalisation and U-Space), as part of a Quality Assurance process -Conduct multi-SESAR Solution network impact simulations, looking for indications of cross-effects of joint deployment Complementarily, on specific request from SESAR 3 JU, PEARL project will maintain SESAR Performance Framework and related material, as well as providing support to Maturity Gates. It will also be flexible for the provision of additional ad-hoc activities, to be agreed with SESAR 3 JU.

The European airspace above FL550 is expected to become an area where operations will significantly expand as a result of increased new entrants presence representing both aviation and space domains, with estimated business values in the order of billions of euros per year. The European Air Traffic Management (ATM) system, including ATC’s operational procedures and systems, needs modernization and adaptation to allow seamless integration of Higher Altitude Operations (HAO) because of vastly different characteristics of vehicles and associated operational, safety and performance requirements. ECHO-2 will build on the deliverables of the ECHO (European Concept of Higher airspace Operations) project, starting from the Concept of Operations, to propose validated solutions paving the way towards the operational integration of HAO in ATM, on three directions: - Space Launch Real Time Monitoring Module and associated working station for the European ATM Network Manager for increased situational awareness at Network level, supporting Air Traffic Flow Management as well as crisis and contingency management. - Procedural Package covering specific ground and air-ground operational integration issues (including for communications, navigation and surveillance means) associated with the integration of High-Altitude Platform Systems (HAPS) operations into ATM - Procedural Package covering specific ground and air-ground operational integration issues (including for communications, navigation and surveillance means) associated with the integration of supersonic, hypersonic and suborbital vehicles operations into ATM. These solutions will propose all the elements needed by the European ATM Network operational stakeholders for a rapid, harmonized and cost-effective implementation of a European approach towards the integration between ATM and HAO, at TRL6 maturity. They will also include assessments of potential specific related standardization and regulatory activities.

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.