The trend towards urbanization increases the need for highly available public transportation. Nevertheless, the majority of society demands for individual transport too. To address these demands in urban areas, intelligent transportation systems (ITS) aim at increasing capacity and safety while reducing costs, accidents, and environmental impact. To that end, both the railway industry and the automotive industry focus on automation, digitization, and wireless communications to cope with increasing numbers of vehicles and passengers. These two industries may rely on cooperative ITS (C-ITS) communicating in the same frequency band. Without appropriate measures, interference between the different radio technologies must be assumed and reliable communication for safety-critical applications cannot be guaranteed. To develop accurate and realistic interference models for current and future radio technologies, we conducted a four-day measurement campaign with the Deutsche Bahn (DB) advanced TrainLab on the Berlin "S üd-Ring" tracks. In this paper, we present an overview on C-ITS radio technologies, the measurement campaign, first results, and conclusions. An initial data analysis shows that adjacent channel interference can cause severe performance degradation on urban rail C-ITS if generated in line-of-sight (LOS) to the train with a significant number of interfering signals.

In this article, we describe in detail three seismic measurement campaigns based on refraction methods that we conducted at different sites in Bavaria, Germany. The measured data is published as an open data set. The particularity of this data set lies in its available ground truth information about each measurement site. Acquiring seismic data from sites with ground truth information is important for validation of seismic inversion algorithms. Since near-surface seismic field data with ground truth information is rather limited, we anticipate this data set to be a valuable contribution to the research community. For the measurements, three sites have been selected: (1) a gravel pit with a ground water layer, (2) a site above a highway tunnel and (3) a surface over underground tubes. The measurements have been conducted using line arrays of geophones, the Geode Seismograph from Geometrics Inc. and hammer strikes as seismic source. To obtain inversion results a travel time tomography based on first-arrivals within the software SeisImager is used. The inversion results show that we are able to image the ground water layer in the gravel pit, the highway tunnel and partly features of underground tubes. Furthermore, the results coincide with available ground truth information about the measurement sites. This paper summarizes the measurement campaigns and the respective data sets obtained through these campaigns. The data have been published by the authors as an open data set under the license CC BY 4.0 on figshare to make it available to the research community for validation of seismic data processing and inversion techniques.

We present a parallel implementation of a 2D seismic ray tracer on a graphics processing unit of the compact Jetson Nano by Nvidia. Ray tracing is commonly used in seismic imaging as an intermediate step in reconstructing subsurface structures. We employ a gradient-based ray tracer that requires a travel time map. Here, we make use of the fast iterative method that computes a travel time map in a parallel fashion. Since a ray path is independent from any other ray path, the tracing can be implemented in parallel as well. To this end, we use a graphics processing unit and implement ray tracing using the CUDA programming language. For performance evaluations we compare our implementation on the Jetson Nano to a state-of-the-art sequential seismic ray tracer on a desktop CPU and show that speedups with factors of up to three can be achieved. The results indicate that edge devices such as the Jetson Nano can play a relevant role for tomographic applications particularly in scenarios where mobility of processing devices and compactness are important.