The Horizon Europe AGEMERA Project: Innovative Non-Invasive Geophysical Methodologies for Mineral Exploration

The AGEMERA project [1], which is an acronym for Agile Exploration and Geo-Modelling for European Critical Raw Materials, employs three non-invasive survey methods for mineral exploration: a passive seismic method to assess bedrock hardness and rock type boundaries; an integrated, multi-sensing fixed-wing drone system for measuring conductivity, magnetism, and radioactivity; and a multidetector system based on muon detection for detailed 2D, 3D, and 4D density profiles of large-volume rock bodies (with the 4th dimension being time). The technologies are designed to map geological structures in scenarios where traditional methods are either environmentally unsound or socially challenging. By the project's conclusion, these methods are anticipated to achieve a Technological Readiness Level (TRL) of 5 within a three-year timeline. The technologies vary in their operational capacities, including acquisition time, depth penetration, area coverage, and volume assessment. The multi-sensing drone effectively probes to 300-500 meter depth and can survey vast areas, up to hundreds of square kilometres, in a single campaign. Muography, on the other hand, can reach depths of up to 1000 metres and cover large volumes, up to a cubic kilometre. Passive seismic analysis, meanwhile, can survey any area and depth while a larger depth usually implies a lower resolution. While these techniques, especially when combined with deep 3D muography, may require extended periods for data collection, the valuable insights they offer make them a worthwhile investment. After conducting these innovative, non-invasive geophysical surveys, the findings will be consolidated in a web-based data repository. This repository will be accessible for in-depth analysis to enhance our understanding of critical raw material distribution.The project receives funding from the Horizon Europe program (Grant agreement ID: 101058178)

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Technical Progress after Year 2

As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "The article is a co-development of many authors from many organizations. Only the first author affiliation was provided on the article PDF. The following additional author affiliations are noted: Kaspars Ozols (Institute of Electronics and Computer Science, Latvia); Rihards Novickis (Institute of Electronics and Computer Science, Latvia); Aleksandrs Levinskis (Institute of Electronics and Computer Science, Latvia)." The original article PDF remains unchanged.Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.Peer reviewe

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3