Considering the socio-ecological transition of Europe 2030, and towards a more resilient and informed community, focusing on the forests that are near wildfire risk, TREEADS aims to build upon state-of-the-art high TRL products and unite them in a holistic Fire Management platform that optimize and reuse per phase the available Socio-technological Resources in all three main phases of Wildfires. For the prevention and preparedness TREEADS propose the use of a real-time risk evaluation tool that can receive multiple classification inputs and work with a new proposed neural network-powered Risk factor indicator. To create a model of Fire adapted communities (FAC) in parallel to insurance incentives, TREEADS will use alkali activated construction materials (AAM) integrating post-wildfires wood ashes (PWA) for fire-resilient buildings and infrastructure. TREEADS also uses a variety of technological solutions such as the Copernicus infrastructure, and a swarm of small drones customized for accurate forest supervision. In the area of Detection TREEADS propose a variety of toolsets that will accommodate most needs. Stemming from Virtual reality for the training, wearables for the protective equipment of the emergency responders. to UAV (drones), UAG and airships for improving capacity in temporal and spatial analysis as well as to increase the inspected area coverage. Last, TREEADS will build a new land and field-based restoration initiative that will use all modern techniques such as agroforestry, drones for seed spread, Internet of things sensors that will be able to adapt the seeding process based on the ground needs and on the same time with the help of AI to determine post-fire risks factors. TREEADS solution will be demonstrated and validated under real operating conditions. Demonstration will involve Eight complex pilot implementations executed in seven EU countries and in Taiwan.

Facing to the challenge of future highly automated vehicles, where occupants can freely orient themselves to engage in non-driving activities. This new environment prompts questions about how car occupants will actually sit, what activities they will engage in, and how they will informed through the HMI to keep them in the loop if necessary. AWARE2ALL aims to pave the way towards Highly Automated Vehicles (HAVs) deployment in traffic, by effectively addressing the changes in road safety and changes in the interaction of different road users caused by the emergence of HAV through the development of innovative technologies along with the corresponding assessment tools and methodologies. AWARE2ALL will develop safety and HMI systems that will be interrelated through achieving a holistic understanding of the scene to ensure safe operation of the HAV. AWARE2ALL proposes a common conceptual universal safety framework for considering Human Machine Interaction (HMI). The project will be built on the results of projects funded under H2020 and other R&D programmes addressing the identification of new safety-critical situations and the most likely positions and postures considering the expected HAV applications. The main objective of AWARE2ALL is to address the new safety challenges posed by the introduction of HAVs in mixed road traffic, through the development of inclusive and innovative safety (passive and active) and HMI (interior and exterior) systems that will consider the variety of population and will objectively demonstrate relevant improvements in mixed traffic safety.

Software is everywhere and the productivity of Software Engineers has increased radically with the advent of new specification, design and programming paradigms and languages. The main objective of the project DECODER is to introduce radical solutions to increase productivity and by means of new languages that improve the situation by abstractions of the formalisms used today for requirements analysis and specification. We will develop a methodology and tools to improve the productivity of the software development process for medium-criticality applications in the domains of IoT, Cloud Computing, and Operating Systems by combining Natural Language Processing techniques, Modelling techniques and Formal Methods. The combination is a novel approach that permits a smooth transition from informal requirements engineering to deployment and maintenance phases. A radical improvement is expected from the management and transformation of informal data into material (herein called ‘knowledge’) that can be assimilated by any party involved in a development process. Thus, the DECODER project will 1) introduce new languages to represent knowledge in a more abstract manner, 2) develop transformations leading from informal material into specifications and code and vice-versa, 3) define and prototype a Persistent Knowledge Monitor for managing all relevant knowledge, and 4) develop a prototype IDE. The project will automate the transformation steps using existing techniques from the Big Data (knowledge extraction), Model-Driven Engineering (knowledge representation and refinement), and Formal Methods (specifications and proofs). The project will produce a novel Framework combining these techniques and demonstrate its efficiency on several uses cases belonging to the beforehand mentioned domains. The project expects an average benefit of 20% in terms of efforts on these use-cases and will provide recommendations on how to generalise the approach to other medium-criticality domains.

Air travel is an important supplement to other means of regional transportation and in some regions the only viable solution. However, the regional transport market is highly competitive, and air travel must demonstrate that it can compete with hi-speed trains, cars or coaches. One major issue is the perception of the high CO2 and greenhouse gas (GHG) emissions by planes for these short journeys. Hybridization of the propulsion system enables combining the technical components benefits, thereby achieving an overall efficiency improvement. It is a concept well understood by the general public, due to the hybridization of automobiles, and can bring a real impact on the reduction of GHG emissions. While enabling rather than limiting new sources of energy in aviation, an ambitious path to reduction of greenhouse gases and the pathway to a cleaner and greener aviation for regional flights is built upon three pillars: • Electric hybridization • An ultra-efficient turboprop thermal engine • 100% SAF compatibility HE-ART will be a first step on the path to hybridization of regional aircraft and a reduction of up to XX% of GHG. This will be accomplished by achieving a strategic global objective of demonstrating, within 36 months, the viability of a hybrid electric thermal turboprop (e-TP) within the scope of a dedicated integrated ground test demonstrator. HE-ART will build on existing state of the art integrating the following technologies into the demonstrator: • Core thermal engine • Electric motor and power electronics • Gearbox • Propeller • Nacelle and heat exchanger This will be executed in conjunction with the Top-Level Aircraft Requirements that will be defined in parallel Clean Aviation research projects. To ensure that these requirements are met in full, HE-ART brings together the leading engine manufacturers, Rolls Royce and Safran with leading airframers, ATR, Airbus and Leonardo along with key manufacturers and research organisations.

During the past few years many projects and initiatives were undertaken deploying and testing Automated Vehicles (AVs) for public transportation and logistics. However in spite of their ambition, all of these projects stayed on the level of elaborated experimentation and never reached the level of a large-scale commercial deployment of transport services. The reasons for this are many, the most important being the lack of economically viable and commercially realistic models, the lack of scalability of the business and operating models, and the lack of user oriented services required for large end-user adoption of the solutions. The ULTIMO project will create the very first economically feasible and sustainable integration of AVs for MaaS public transportation and LaaS urban goods transportation. ULTIMO aims to deploy in three sites in Europe 15 or more multi-vendor SAE L4 AVs per site. A user centric holistic approach, applied throughout the project, will ensure that all elements in a cross-sector business environment are incorporated to deliver large-scale on-demand, door-to-door, well-accepted, shared, seamless-integrated and economically viable CCAM services. We target the operation without safety driver on-board, in a fully automated and mission management mode with the support of innovative user centric passenger services. ULTIMO’s innovative transportation models are designed for a long-term sustainable impact on automated transportation in Europe, around the globe and on society. The composition of the consortium ensures the interoperability between multiple stakeholders by making adoption of new technology at minimum costs and maximum safety. The integration of the ongoing experiments of previous AV-demonstrator projects ensures highest possible technical and societal impacts from the very beginning of the project, as well as during the project lifetime and even long after its completion.