Maintaining safety and cost-efficiency of air transport operations while increasing the capacity will push the next generation of ATM systems towards digitalization. In the mid-term, a digitalized system in the human operated ATM environment will be capable of delivering reliable predictive analytics based on automated information processing, providing decision support for human operators. SafeOPS supports these future services by investigating the use of big data analytics together with new risk assessment methodologies. ANSP and airlines are the relevant stakeholders of the aviation business, forming the SafeOPS consortium. Several research institutes complement the consortium. To ensure the high confidentiality levels of the associated datasets, SafeOPS utilizes DataBeacon, a platform that allows fusing and analyzing confidential aviation data. As an exemplary safety-critical scenario, SafeOPS considers go-arounds that are of high safety relevance for both, airlines and ANSPs. Based on successful unstable approach predictions, developed in the Horizon2020 project SafeClouds.eu, SafeOPS will carry out go-around predictions and analyze their impact onto the safety and resilience of ATM in detail. As recognized by the SESAR Single Programming Document, data-driven and machine learning technologies are a cost-efficient asset to reduce current fragmentation and upgrade inefficient old technologies. In turn, they introduce new challenges for ATM stakeholders, from controllers and their training to regulators and certification agencies. SafeOPS addresses some of these challenges by fostering the ATM modernization based on AI tools with an application on safety and resilience through an exemplary case study. It puts a special focus on the interaction among humans (controllers) and within the socio-technical system under the influence of this breakthrough technology. Therefore, it addresses both key performance areas from the Safety and Resilience ATM Master Plan.

* DIGITS-AU is the essential Airspace User complement to the DIGITS project (Demonstration of ATM Improvements Generated by Initial Trajectory Sharing). DIGITS was granted under H2020-SESAR-2015-2, SESAR.ID-VLD.Wave1-27-2015, and its continuation is dependent on the participation of Airspace Users (AU). * As required, DIGITS-AU brings together Airspace Users - who operate (even partially) in the airspace of ANSPs participating to DIGITS, namely DFS, ENAV, MUAC and NATS and - who will receive through this Call new onboard avionics capability which allows to downlink trajectory predictions, the so-called Extended Projected Profile (EPP), for sharing with Air Traffic Control. * This proposal addresses also the interface with other SESAR 2020 projects, by nature with PJ31 DIGITS, but also with PJs 01, 09, 10 and 18 in order to contribute to the identification of benefit potentials facilitated by this new airborne technology. The capture of real fuel burn reduction for DIGITS-AU flights is not possible due to the fact that ground ATC systems are used in an pre-operational or shadow mode, based on which air traffic controllers may not alter their instructions. Nevertheless "what-if" cases shall be identified as far as observations possibly allow and the theoretical fuel burn gain calculated.

Emissions from aircraft have adverse effects on the air quality in and around airports, contributing to public health concerns within neighboring communities. AVIATOR will adopt a multi-level measurement, modelling and assessment approach to develop an improved description and quantification of the relevant aircraft engine emissions, and their impact on air quality under different climatic conditions. Engine particulate and gaseous emissions in a test cell and on-wing from an in-service aircraft will be measured to determine pollutant plume evolution from the engine and APU exhaust. This will provide an enhanced understanding of primary emitted pollutants, specifically the nvPM and vPM (down to 10nm), and the scalability between the regulatory test cell and real environments. AVIATOR will develop and deploy across multiple airports, a proof-of-concept low cost sensor network for the monitoring of UFP, PM and gaseous species such as NOx and SOx, across airport and surrounding communities. Transport and impact of emissions from aircraft engines and APU will be monitored in this more complex environment through high fidelity and sensor measurements. Campaigns will be complemented by high-fidelity modelling of aircraft exhaust dynamics, microphysical and chemical processes within the plume. CFD, box, and airport air quality models will be applied, providing validated parameterizations of the relevant processes, applicable to standard dispersion modelling on the local scale. Working with the regulatory community, AVIATOR will develop improved guidance on measuring and modelling the impact of aircraft emissions with specific reference to UFP. Acknowledging the uncertainty surrounding health impacts of UFP, AVIATOR will work with the public health community to develop methodologies for the representative sampling of aircraft emissions. AVIATOR will provide airports and regulators with tools and guidance to improve the assessment of air quality in and around airports.

SafeClouds is a research project supported by EASA and powered by a full spectrum of aviation stakeholders (Airlines, Airports, ANSPs, Eurocontrol, Research Entities, Safety Agencies) that develops cutting-edge technologies for aviation safety assurance in a cost-effective manner. SafeClouds proposes a data-driven approach to achieve a deeper understanding of the dynamics of the system, where risks are pro-actively identified and mitigated in a continuous effort to enhance the already excellent European aviation safety records. SafeClouds develops an innovative aviation safety data analysis approach. Currently each stakeholder owns different isolated datasets and data-sharing paradigms are rare. However, the combination of those datasets is critical in discovering unknown safety hazards and in understanding and defining a performance-based system safety concept. The new data-driven paradigm, capable of extracting safety intelligence in a fast, connected and inexpensive way requires the collaboration of aviation and IT entities sharing their raw datasets, tools, techniques and information. SafeClouds high level objectives are: - To define a user-requirement driven approach for data mining in aviation safety, covering several current safety challenges within airlines and runway operations. - To develop novel data structures and safety intelligence representation. Given the complexity of the datasets, SafeClouds aims to solve their current challenges in data handling and knowledge discovery. - To develop the proof of concept and validate in a laboratory the safety data analysis paradigms, at different levels: historical analysis, predictive analytics, automatic safety data monitoring and unknown hazards identification. - To assemble a group of entities that encompasses the entire data-cycle for a unified, achievable vision for the future of safety analytics in Europe, including: users, data providers, data infrastructure researchers, operators and data scientists.