The iSTORMY project will propose an innovative and interoperable hybrid stationary energy storage system based on: modular battery pack (stacks/modules) + modular power electronics (PE) interface + universal Self-healing energy management strategy (SH-EMS). In particular the project will investigate and demonstrate: 1. Modular battery pack with hybridization at stacks/modules level (incl. slave pBMS) with a new universal BMS (adaptive interfaces + accurate SoX) at the top of the battery system for easy and fast integration and control. The hybridization will consist of different battery types or same type with different capacities (first and second life) and a smart modular solution will be developed to integrate the cooling system among modules or stacks. 2. Modular PE interface based on SiC devices with high efficiency (topology + adaptive local controller) and Digital Twin modeling. 3. Universal SH-EMS (based on machine learning and online algorithms) including the aging and thermal constraints for failure mechanisms.

This project will focus on the development of technologies and methodologies which have the potential to save costs and time across the whole life cycle of the aircraft (design, production, maintenance, overhaul, repair and retrofit), including for certification aspects. Moreover it will also target the integration of additional functions or materials in structural components of the aircraft, the increased use of automation. The first proposed step is the introduction of the γ-TiAl alloy, a well known promising advanced material for aerospace applications and a revolutionary manufacturing technology. Its specific stiffness and strength, as compared to its low weight, potentially leads to large weight savings (50%), and therefore lower mechanical loads on thermomechanical stressed parts, compared to the common Ni based superalloys. The integration of new material and new manufacturing technology will positively impact several aspects of the manufacturing and maintenance chain, starting from the design, the production, the repair). The aim of this project is twofold: - On one side the work will be focused on the development and integration at industrial of a IPR protected gas atomization process for producing TiAl powders, whose properties must be highly stable from batch to batch. Thanks to the stability of the chemical and granulometric properties of the powders, the application of the Rapid Manufacturing technique to the production of TiAl components will be economically affordable. While this technique is by now well-known, its main drawback resides in the scarce quality of the starting powders. - The other main drawback for the wide industrial application of TiAl components is the integrated optimisation of all the machining steps, that means the setting up of machine tool characteristics and parameters, cutting tool geometry, substrate and coating materials, advanced lubrication technologies.

SHASAI targets the HW/SW security and AI-based high risk systems intersection, aiming to enhance the security, resilience, automated testing, and continuous assessment of AI systems. The rising interest in these systems makes them attractive targets for threat actors due to their complexity and valuable data. Ensuring the security of AI systems involves safeguarding AI models, datasets, dependencies, and securing the underlying HW/SW infrastructure. SHASAI takes a holistic approach of AI system security throughout their lifecycle stages. At requirement definition, SHASAI provides an enhanced risk assessment methodology for secure and safe AI. At design, SHASAI will propose secure and safe design patterns at SW and HW level to achieve trustworthy AI systems. During implementation, SHASAI provides tooling for a secure supply chain of the system by analyzing vulnerabilities in SW / HW dependencies, detecting poisoned data and backdoors in pretrained models, scanning for software vulnerabilities, hardening hardware platforms, and safeguarding intellectual property. At evaluation, SHASAI offers a virtual testing platform with automated attack and defense test suites to assess security against AI and infrastructure-specific threats. In operation, AI-enhanced security services continuously monitor the system, detect anomalies, and mitigate attacks using AI firewalls and attestation methods, ensuring availability and integrity. The feasibility of SHASAI methods and tools will be demonstrated in 3 real scenarios: 1. Agrifood industry: Cutting machines. 2. Health: Eye-tracking systems in augmentative and alternative communication. 3. Automotive: Tele-operated last mile delivery vehicle. Their heterogeneity and complementarity maximize the transferability of solutions. SHASAI will contribute to scientific, techno-economic, and societal impacts as it aligns with the CRA, EU AI Act, NIS2 and CSA, sharing and commercializing methods and tools to ensure trustworthy AI components.

PLIADES advances the SoA dataspaces reference architectures, towards a step change on the use of data as key enabler of technological advances in AI and Robotics. To this end, PLIADES researches into novel, AI-enabled tools to advance full data life cycles integration, both within and between data spaces. Sustainable data creation methods through data compression, filtering and normalization will be developed, to allow efficient and greener storage in a data-oriented future. Data privacy and sovereignty will be further ensured, through standards and decentralized protocols to protect data-producing organizations and citizens. Alongside, data sharing will be revolutionized through novel AI-based brokers and connectors using extended metadata, shaped through the project’s best practices and domain expert’s knowledge. On top of these, active data discovery services through cross domain AI connectors will allow creating linked data spaces, enabling interoperability between previously disconnected entities, while data quality assessment services will facilitate real time data evaluation. Extended synergies with EU initiatives will be established in order to contribute models, strategies and technologies for a Common European Data Space. Our outcomes will be evaluated in six use cases focusing on direct advancements in key AI and Robotics technologies for everyday use, oriented around multiple data spaces; mobility, healthcare, industrial, energy and green deal. Our use cases provide a challenging validation suite involving vast heterogeneous data creation, management and sharing while addressing full data lifecycles in multiple major domains. Through the developed ecosystem, CCAM and ADAS/AD car technologies will be enhanced, HRI for robot operators and healthcare patients will be reshaped, while further advanced, integrated data spaces will be deployed in the healthcare, manufacturing and green deal sectors aiming to reduce carbon footprints and shape a greener future.