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Wendelstein 7-X (W7-X) is currently the largest optimized stellarator in operation in the world. Its main objective is to demonstrate long pulse operation and to investigate the suitability of this type of fusion device for a power plant. Maintaining the safety of the first wall is critical to achieving the desired discharge times of approximately 30 min while keeping a steady-state condition. We present a deep learning-based solution to detect the unexpected plasma-wall and plasma-object interactions, so-called hot-spots, in the images of the Event Detection Intelligent Camera (EDICAM) system. These events can pose a serious threat to the safety of the first wall, therefore, to the operation of the device. We show that sufficiently training a neural network with relatively small amounts of data is possible using our approach of mixing the experimental dataset with new images containing so-called synthetic hot-spots generated by us. Diversifying the dataset with synthetic hot-spots increases performance and can make up for the lack of data. The best performing YOLOv5 Small model processes images in 168 ms on average during inference, making it a good candidate for real-time operation. To our knowledge, we are the first ones to be able to detect events in the visible spectrum in stellarators with high accuracy, using neural networks trained on small amounts of data while achieving near-real-time inference times. [ABSTRACT FROM AUTHOR]

The detachment regime has a high potential to play an important role in fusion devices on the road to a fusion power plant. Complete power detachment has been observed several times during the experimental campaigns of the Wendelstein 7-X (W7-X) stellarator. Automatic observation and signaling of such events could help scientists to better understand these phenomena. With the growing discharge times in fusion devices, machine learning models and algorithms are a powerful tool to process the increasing amount of data. We investigate several classical supervised machine learning models to detect complete power detachment in the images captured by the Event Detection Intelligent Camera System (EDICAM) at the W7-X at each given image frame. In the dedicated detached state the plasma is stable despite its reduced contact with the machine walls and the radiation belt stays close to the separatrix, without exhibiting significant heat load onto the divertor. To decrease computational time and resources needed we propose certain pixel intensity profiles (or intensity values along lines) as the input to these models. After finding the profile that describes the images best in terms of detachment, we choose the best performing machine learning algorithm. It achieves an F1 score of 0.9836 on the training dataset and 0.9335 on the test set. Furthermore, we investigate its predictions in other scenarios, such as plasmas with substantially decreased minor radius and several magnetic configurations. [ABSTRACT FROM AUTHOR]

Measuring high-level corruption is subject to extensive scholarly and policy interest, which has achieved moderate progress in the last decade. This article develops two objective proxy measures of high-level corruption in public procurement: single bidding in competitive markets and a composite score of tendering 'red flags'. Using official government data on 2.8 million contracts in twenty-eight European countries in 2009–14, we directly operationalize a common definition of corruption: unjustified restriction of access to public contracts to favour a selected bidder. Corruption indicators are calculated at the contract level, but produce aggregate indices consistent with well-established country-level indicators, and are also validated by micro-level tests. Data are published at http://digiwhist.eu/resources/data/. [ABSTRACT FROM AUTHOR]

Gas chimneys, fluid-escape pipes, and diffused gas clouds are common geohazards above or below most petroleum reservoirs and in some CO2 storage sites. However, the processes driving the formation of such structures are poorly understood, as are the time scales associated with their growth or their role as long-term preferential fluid-migration pathways in sedimentary basins. We present results from a multidisciplinary study integrating advanced seismic processing techniques with high-resolution simulations of geological processes. Our analyses indicate that time-dependent rock (de)compaction yields ascending solitary porosity waves forming high-porosity and high-permeability vertical chimneys that will reach the surface. The size and location of chimneys depend on the reservoir topology and compaction length. Our simulation results suggest that chimneys in the studied area could have been formed and then lost their connection to the reservoir on a time scale of a few months. [ABSTRACT FROM AUTHOR]