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This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images.

One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards.

Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.

The post-synthesis procedure for cyclic amine (morpholine and 1-methylpiperazine) modified mesoporous MCM-48 and SBA-15 silicas was developed. The procedure for preparation of the modified mesoporous materials does not affect the structural characteristics of the initial mesoporous silicas strongly. The initial and modified materials were characterized by XRD, N2 physisorption, thermal analysis, and solid-state NMR. The CO2 adsorption of the obtained materials was tested under dynamic and equilibrium conditions. The NMR data revealed the formation of different CO2 adsorbed forms. The materials exhibited high CO2 absorption capacity lying above the benchmark value of 2 mmol/g and stretching out to the outstanding 4.4 mmol/g in the case of 1-methylpiperazin modified MCM-48. The materials are reusable, and their CO2 adsorption capacities are slightly lower in three adsorption/desorption cycles.

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.

This is the abstract volume of the Citizen Science SDG Conference titled "Knowledge for Change: A decade of Citizen Science (2020-2030) in support of the SDGs" that took place on 14.-15. October 2020 as a hybrid conference (in Berlin and online). This conference was an official event of Germany’s 2020 EU Council presidency. The conference took place as part of the CS-SDG project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000014.

This is the conference programme of the Citizen Science SDG Conference titled "Knowledge for Change: A decade of Citizen Science (2020-2030) in support of the SDGs" that took place on 14.-15. October 2020 as a hybrid conference (in Berlin and online). This conference was an official event of Germany’s 2020 EU Council presidency. The conference took place as part of the CS-SDG project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000014.

An article about the Powerskin+ project in the Open Access Government publication.

In early 2020, the EOSC Community took another crucial step on the road to the development and implementation of the European Open Science Cloud, as seven key EOSC-related Horizon 2020 projects signed a Collaboration Agreement in support of the EOSC Governance. The Agreement involves all the projects supported within the INFRAEOSC-05-2018-2019 call. The Agreement provides a useful framework for all parties to collaborate on a wide range of topics, in order to enhance synergies in all mutual activities related to the EOSC. The projects also agreed on a Joint Activity Plan, which will guide them towards the first iteration of EOSC. Overlaps and complementarities among projects were identified, as well as specific areas for potential cooperation, ultimately aimed at the development of a common strategy to synchronise activities with the EOSC Working Groups. Between April and May 2020, EOSCsecretariat.eu collected the position papers on EOSC compiled by the INFRAEOSC 5b projects, the subgroup that specifically includes the four regional projects covering all corners of Europe, as well as the thematic project ExPaNDS. We would like to thank the five Horizon 2020 projects which have contributed to the making of this compilation of EOSC position papers: EOSC-Nordic (GA No. 857652), EOSC-Pillar (GA No. 857650), EOSC-synergy (GA No. 857647), ExPaNDS (GA No. 857641), and NI4OS-Europe (GA No. 857645).

Short presentation of the RAILS project published in ERCIM NEWS 121.