The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard machine learning operations (MLOps) tools and platforms and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper presents AI4EOSC, a federated, open-source platform designed to operationalize the full AI/ML life-cycle within the European Open Science Cloud (EOSC) ecosystem. Our methodology tackles the fragmentation of distributed research infrastructures by integrating a modular and distributed architecture comprising an AI development platform, a serverless AI-as-a-Service layer, and a federated orchestration model that is able to integrate heterogeneous computing and storage resources from distributed e-infrastructures. AI4EOSC also introduces a “FAIR-by-design” approach that enforces metadata standardization (via MLDCAT-AP) and W3C PROV-compliant provenance tracking through a platform-integrated CI/CD pipeline. The added value of AI4EOSC is demonstrated through the delivery of a diverse set of community installations, which show consistent and seamless deployment across heterogeneous cloud providers. These installations are validated by a set of scientific cases, showing how our work reduces the manual burden on researchers while ensuring high levels of reproducibility and interoperability and providing a unified environment for the development, training, and production of AI/ML models in the EOSC.

The Synthetic Aperture Radar operating in P-band and performing a helical flight pattern promises to be a potential tool for underground tomography, featuring high resolution and penetration. Based on the success of detecting ant nests beneath the industrial forest, a new experiment was carried out to detect buried bodies. Two sets of flights were performed and validated. Six bodies buried 0.5 m to 1.5 m deep, containing the remains of cow and pork, were located with a detection rate of 100% and a false alarm rate of 0%. Also, the ability to detect change across two sets of flights was verified by locating a bucket filled up with a large tarpaulin piece in the first set of flights.

The use of radio frequency identification technology (RFID) has grown significantly in recent years. Since some chips have now memory capability, it is possible to read and write them, easing the tracing of different parts/components. In addition, and in opposition to standard technology for identification, e.g. barcode, a RFID reader can detect multiple chips simultaneously.Our team is recently exploring the potential of passive RFID tags to be used for package tracing. However, since these chips use energy from electromagnetic waves for communication and to power themselves, the communication range is very low, which could be an obstacle for some applications.In this article, we present a proof-of-concept of a motorized system to increase the read volume of the traditional RFID readers. The system resembles a robot with a tool specifically designed to support the reader. It will have four degrees of freedom controlled by different servomotors that will allow the reader to move to different zones. The new concept was compared with the traditional static RFID reader. While the static reader showed a reduced detection volume, 8400mm 3 , our approach proved, in our preliminary study, to guarantee an higher detection volume, 2.89×10 7 mm 3 , corroborating its potential to read RFID tags inserted into standard packages.Overall, the preliminary results show that our system has the potential to increase the detection range of RFID tags and to facilitate future package tracking systems.

The Radio Frequency Identification (RFID) technology has been used mainly to manage products and have stock control in real time. This technology is commonly used in the form of tags that are positioned outside of the object. However, the RFID insertion strategies are still sub-optimal, thus, there has been attempted to create methods to insert labels during the plastic injection process. However, must of the available insertion strategies do not satisfy the needs of large companies, since they are not standard. So, the objective of this study is to present a proof of concept of a new RFID tags insertion strategy adapted to plastic parts. The system uses a robot to pick up an RFID chip and insert it into a cavity of a mold. Then it will take some of the same material and with an ultrasound welder the plastic material will be melted to close the structure.For this project, we started by carrying out some experiments to understand the limitations of the available RFID chips, such as: maximum distance that can be detected, maximum temperature without damage when subjected to a welding process. With these experiments we will validate our proof of concept. Through these experiments we conclude that chips are detected at greater distances if they are centered with the reader’s antenna. Moreover, it was possible to confirm that they supported high temperatures.Overall, the current results corroborate the potential of this technique for the insertion of RFID in standard processes of the plastic industry.

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge.