Critical infrastructures for, e.g. the transport of goods and people or the supply of energy and water, are the lifelines of our globalized society. Impairments can result in enduring supply bottlenecks, significant disruptions to public safety or other dramatic consequences. Currently used monitoring processes for these infrastructures are costly, difficult to implement in isolated areas, nonuniform due to heterogenous technical solutions and poorly scalable or automatable. The project Instantaneous Infrastructure Monitoring by Earth Observation (IIMEO) contributes to an end-to-end solution for the operational real-time, high-resolution monitoring of critical infrastructure by means of an innovative observation payload for a future LEO constellation. Our system concept focusses on data availability within less than 1 hour from user request to information delivery by AI-based processing approaches implemented on space-qualified on-board hardware. To provide a persistent weather-independent monitoring service with an improved spatial resolution of up to 50 cm, we propose a novel sensor configuration consisting of a 35-GHz-SAR sensor in combination with optical cameras. Based on existing technology from scientific institutions and European space industry, hard- and software will be further developed up to TRL 6. Development will be carried out in close cooperation with a railway company as a pilot user to define use cases for commercial applications based on the requirements of industry and public services. The end-to-end prototype service including on-board processing will be demonstrated within a final flight campaign. A roadmap will describe the further exploitation of the project results and outline further applications of the technology to other infrastructure systems. A follow-up demonstrator mission in 2026/27 is envisaged to showcase the monitoring of railways from space on a global scale on TRL 7.

The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault-tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (predominately relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases. Moreover, as the volume and the value of natural language content constantly grows, built-in support for sophisticated text processing in data persistence architectures is increasingly becoming essential. The aim of TYPHON is to provide a methodology and an integrated technical offering for designing, developing, querying and evolving scalable architectures for persistence, analytics and monitoring of large volumes of hybrid (relational, graph-based, document-based, natural language etc.) data. TYPHON brings together research partners with a long track record of conducting internationally-leading research on software modelling, domain-specific languages, text mining and data migration, and of delivering research results in the form of robust and widely-used open-source software, industrial partners active in the automotive, earth observation, banking, and motorway operation domains, an industrial advisory board of world-class experts in the fields of databases, business intelligence and analytics, and large-scale data management, and a global consortium including more than 400 organisations from all sectors of IT.

Modern industrial companies aim to extend their products with services as fundamental value-added activities. The key potential of the concept of Product Service System (PSS), besides radical improvements in the use of products, is a reduction of environmental footprint of products and services. The overall footprint of PSS is still insufficiently investigated. The services within PSS are an important, insufficiently used source of (digitalized) data on the product and its use. It is likely that digital means facilitating provision of consistent “track and trace” information on the origin, composition and entire life cycle not only of a product but of all services offered and used around the product, will offer important contribution towards achievement of full circularity for manufacturing. The key idea of PSS-Pass is to investigate how extension of DPP to Digital Product Service System Passport (DPSSP) can be effectively achieved and how it will allow for improved circularity of the manufacturing industry. The overarching hypothesis is that LCA underpinned by Machine Learning (ML) methods and informed by dynamic data paves the way to more accurate LCA while supporting PSS life cycle decision making. The collected and sharable data from DPSSP will allow to effectively apply ML as well as Digital Twin (DT) for more reliable decision-making processes concerning circularity of PSS. The project will provide Methodological Framework for definition, development and update of DPSSP, Digital Environment for DPSSP built on existing interoperability architectures, set of ontologies for improved interoperability at DPSSP Environment, novel DT-based Simulation Framework, for modelling standardized and interoperable DTs for PSS lifecycle analysis, and AI based method/tool to forecasts the environmental impact of PSS. The PSS-Pass solutions will be tested and evaluated within 3 pilots in diverse sectors: home appliances, complex equipment, and textile industry.