Maritime Domain Awareness is the combination of activities, events and threats in the maritime environment that could have impact on marine activities and EU territory. During the past decades, advances in Information and Communication Technologies have provided better means to monitor and analyse vessel activity. Today private and public source of data such the Automatic Identification System or space related data can be combined with Vessel Traffic Services, Vessel Traffic Management Systems and Vessel Traffic Monitoring & Information Systems data enabling the development of value added information resulted by the combination of such data. European waters are navigated daily by some 12,000 vessels, which share their positions to avoid collisions, generating a huge number of positional messages every month. It is important that this overabundance of information will not overwhelm the marine operator in charge for decision-making. The challenge is twofold: on one side encourage the exchange of heterogeneous data among administration valorising the CISE network currently in place, on the other exploit at the best these datasets by means of automated processing in a way to minimise false alert that might results by an incorrect processing or interpretation of the results. PROMENADE will improve solutions for the vessel tracking, behaviour analysis and automatic anomaly detection by means of the application Artificial Intelligence (AI) and Big Data (BD) technologies, and to promote collaborative exchange of information between maritime surveillance authorities, shortening the time to market and assuring the compliance with legal and ethical regulations. An open, service-based toolkit implementing “state of art” AI / BD techniques also benefiting of HPC (High Performance Computing) platform is the core activity of the project. The project’s developments will be demonstrated and evaluated in 3 operational scenarios and 1 simulated defined by Border Guards Authorities.

The future of wireless communications will go beyond connecting people or things to connecting smart robots or unmanned vehicles without human intervention. Only sub-THz frequencies can offer the spectrum to achieve unprecedented communication throughput. Yet current technologies suffer from barriers that prevent mass-market operations, such as high cost, limited bandwidth or power-hungry hardware not compatible with autonomous systems. HERMES proposes the fusion of Artificial Intelligence (AI) and deep sub-micron CMOS technology to open a new generation of wireless transceivers. The project will focus on sub-THz frequencies between 140 and 160GHz, and will break new ground in the conversion of information from digital to THz using European CMOS technology to develop a highly integrated transceiver. For the first time, HERMES will deliver to the telecommunications industry a disruptive way of designing transceivers, with impact on production of billions of units that can be implemented in any autonomous system to communicate. This new wireless link will be a springboard for an innovation leap in the robotics and the security industry. Our ambitious and risky approach goes significantly beyond the SoA: we will demonstrate that the power of AI developed by computer sciences research and associated with an original electronics signal processing technique can push CMOS technology to release outstanding performances. The project will produce a chipset of a low-cost radio that exchanges tens of Gbps and will test it in use cases of unmanned vehicles applied to maritime border surveillance operated by civil authorities. The HERMES consortium brings together 6 partners from 5 European countries bridging the full value chain of academic research, technology transfer and industry.

Current evidence suggests that the global fight against corruption faces serious challenges: policy decisions are not well informed, the corruption landscape is complex and enormous, while measuring corruption is so far mostly based on subjective approaches, and there is lack of appropriate technological tools to support anti-corruption. To address these challenges, FALCON is designed and dedicated to support the composition, update and management of comprehensive corruption intelligence pictures, within domains and jurisdictions of interest. This will be accomplished following a multi-actor, evidence-based, data-driven approach, building upon existing assets and prior work of consortium partners. FALCON will, first, develop and validate objective and actionable indicators (individual and composite) of corruption that can be used to inform policy decisions. Second, it will design, implement and integrate powerful data analytics tools, data pipelines and applications that support the management of the entire lifecycle of corruption intelligence pictures. This will enable comprehensive corruption risk assessment, informed policy making, and improved anticorruption law enforcement. FALCON will be piloted in four corruption domains – corruption schemes at border crossings, sanction circumvention by kleptocrats/oligarchs, public procurement fraud, conflicts of interest of politically exposed persons (PEPs) – involving law enforcement experts (police authorities and border guards) from six (6) European countries and other key actors (i.e., GovTech providers, academia, financial intermediaries, policy makers, NGOs, and civil society). FALCON’s implementation will be incremental and iterative, forming synergies between SSH and technological expertise, and will adhere to the principles of Trustworthy AI and responsible research and innovation. Lastly, FALCON has defined specific key exploitable results and performance indicators for measuring its progress and success.

Maritime surveillance is crucial for achieving maritime situational awareness (MSA) and to effectively prevent, protect, and respond to critical security challenges (e.g., illicit commercial activities, irregular migration, pollution, terrorist attacks). CROSSMARE aims to overcome existing challenges in border and maritime surveillance by introducing a layered maritime surveillance network and an interoperability framework that integrates the space, air, surface, and underwater domains within a comprehensive monitoring picture, enhancing MSA and surveillance capabilities. To achieve this, a consortium of 17 big industries, SMEs, RTOs, and end-users a) utilize cutting-edge technologies in the context of maritime surveillance: High Altitude Platform Stations; Remotely Piloted Aircraft Systems; High-Res SAR Sat imagery; Unmanned Aerial Vehicles; Autonomous Underwater & Unmanned Surface Vehicles; novel Flash LiDAR sensors; as well as legacy surveillance systems, b) apply innovative AI algorithms; Large Language and Visual Models; Advanced multi-modal data fusion, c) extend the capabilities of existing C2 and HMIs, also supporting intelligent mission management of manned and unmanned systems, d) and support the interoperability of novel and legacy surveillance systems, improving cross-border and cross-agency collaboration. To this end, CROSSMARE aims to demonstrate interoperable data exchange with CISE and proposes an extension to the current CISE data and service model, enhancing existing and enabling new functionalities. The project will validate the proposed technologies through a series of validation activities: a) continuous integration testing, b) 2 Small Scale Tests (Italy, Lithuania), c) a Table Top Exercise (Romania), and d) a Full Scale Demonstration (Spain) involving real assets, end-users and relevant stakeholders. A Security and Privacy-by-design framework will ensure compliance with fundamental rights, privacy and personal data protection regulations.

The mission of PREVISION is to empower the analysts and investigators of LEAs with tools and solutions not commercially available today, to handle and capitalize on the massive heterogeneous data streams that must be processed during complex crime investigations and threat risk assessments. With criminals being ever more determined to use new and advanced technology for their cause, the aim is to establish PREVISION as an open and future-proof platform for providing cutting-edge practical support to LEAs in their fight against terrorism, organised crime and cybercrime, which represent three major cross-border security challenges that are often interlinked. PREVISION provides advanced near-real-time analytical support for multiple big data streams (coming from online social networks, the open web, the Darknet, CCTV and video surveillance systems, traffic and financial data sources, and many more), subsequently allowing their semantic integration into dynamic and self-learning knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals, cybercriminal organisations and organised crime groups, giving rise to enhanced situational awareness in these fields. PREVISION has a pan-European engagement and support agenda for LEAs: ten (10) different LEAs and practitioners take part in its consortium, while additional ones (including Europol) have joined its external advisory board. A strong inter-disciplinary dimension, combining technological expertise with sociological, psychological, linguistic and data science models, will lead to a common strategic approach for predicting abnormal and deviant behaviour, radicalisation potential, threat risks for soft targets, and cybercrime trends at different timescales. PREVISION will conduct demonstrations on five representative and complementary use cases, under real-life operational conditions, in full compliance with fundamental rights and applicable legislation.