In line with the European Green Deal target of reaching carbon neutrality in the aviation industry by 2050, breakthrough technologies related to direct (100% hydrogen) combustion systems will be researched, prototyped and integrated onto a modern donor aeroengine for ground testing (starting in late 2024) in Project CAVENDISH. This aeroengine test on liquid hydrogen will be a first of a kind in Europe and the cornerstone to further in-flight demonstration, eventually leading to product development aimed at meeting Europe’s and the industry’s ambition for the entry in service (EIS) of commercial, mass-transport, hydrogen-fuelled aircraft in 2035. CAVENDISH’s second objective will be to work on system and powerplant aircraft integration with several established airframers and a supplemental type certificate organisation to define certification pathways and formulate a route to permit to fly. This activity will directly benefit the flight test of the donor engine scheduled for the next phase of the Clean Aviation programme. CAVENDISH will also explore alternative enabling technologies in the form of a dual fuel combustor system (capable of operating on 100% hydrogen and 100% SAF) and in the form of a cryo-compressed tank system. Both these technologies will offer flexibility and could ease the introduction of hydrogen in aviation. CAVENDISH brings together expertise-leading European organizations in aeronautics, power and propulsion, combustion, fuel and controls systems and aircraft. It builds on multiple national technology programmes heralding from the UK, Germany, France and the Netherlands, and is in effect the marriage and acceleration of these technology pathways into an early demonstration and a first minimum viable product (MVP) of a liquid hydrogen combusting aeroengine. The project is also connected to activities in other Clean Aviation calls, on SMR and Certification activities specifically, notably project proposals HEAVEN and CONCERTO.

This proposal follows the PIRANIA-MS project submitted in 2021 to ANR-ASTRID. As a result of the committee’s evaluation report, comments were taken into account for this new submission. As a consequence, our proposal has been reviewed by tightening our ambitions. We have worked closely together to limit the ambitions to achievable objectives in the field of expertise of TéSA and INP Toulouse laboratories. As a result, PIRANIA-MMV proposes to adapt state-of-the-art anomaly detection methods to provide decision support mechanisms to operational people by determining the probability of anomalies in a tactical maritime scenario. The project aims at developing methods based on signal processing technics combined with Artificial Intelligence (Machine Learning, Neural Networks) and modeling for detecting association anomalies of various sensor measures and ship platform trajectory anomalies. The scientific issue is thus linked to the theme of data processing and exploitation and the one related to the processing of massive data from heterogeneous sensors. The strategy proposed in this project is to jointly use Radar and AIS time series to detect anomalies in vessel paths. This detection of anomalies can be done at the level of the associations of the Radar and AIS time series or at the level of parameters estimated from the ship’s trajectories. In both cases, we propose to develop new anomaly detection methods adapted to the joint processing of Radar and AIS data: Association anomalies: We propose to modify existing anomaly detection methods based on the LoOP (Local Outlier Probabilities) method or the One-class SVM method to take heterogeneous Radar and AIS data into account. We also propose to analyse the potential of dictionary learning methods for detecting anomalies in Radar and AIS times series. These methods have been used successfully for the analysis of satellite telemetry. Trajectory anomalies: The idea is to use an anomaly detection algorithm such as the One-class SVM algorithm to detect abnormal trajectories even if the boat has not completed its full journey. The innovative part will be here to determine the parameters adapted to the monitoring of ship trajectories. For both kinds of anomalies, the use of active learning methods will be also considered to take advantage of a possible user feedback that would confirm the normal or abnormal character of some trajectories.

The OPTIMA project falls within the scope of the topic S2R-OC-IP2-02-2019 call – Support to development of demonstrator platform for Traffic Management – which is connected with the complementary topics S2R-CFM-IP2-01-2019, S2R-CFM-CCA-01-2019. The OPTIMA project will address the design and development of a Communication Platform to manage the link with different services (multimodal operational systems), supporting TMS applications. In this sense, the Communication Platform will link TMS applications with Traffic Management, Traffic Control, Maintenance/Energy Management and signalling field infrastructure systems. In particular, the following activities are linked with the main objectives of OPTIMA: • use of Integration Layer to integrate real-time data from the rail business service, external sources, services running in the Application Framework and operator workstations; • development, validation and verification of: o middleware of Integration Layer (or if you prefer: Integration Layer constituents and its interfaces); o software clients for connecting several rail business services and external services; o Application Framework constituents and its interfaces; o enhanced integration of the standardized operator workstations; o definition of detailed data structure according the Conceptual Data Model; o first level support for testing prototypes of complementary projects; • provision of a fully available and documented communication platform for installing and testing complementary projects prototypes. In addition, OPTIMA will disseminate project findings to relevant stakeholders and communities and will ensure the sustainability and impact of the results coming from the own activities. The Consortium is well balanced with research organizations, industrial rail stakeholders and Infrastructure Managers actively performing Traffic Management and Traffic Control in dedicated Control centers, some of them with previous participation in S2R projects.