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The dataset consists of undirected weighted multi-graphs stored in .pkl or .net formats. These undirected graphs form instances for the multi-trip multi-depot rural postman problem. The Multi-trip multi-depot Rural Postman Problem is a variant of the Capacitated Arc Routing Problem which is to find a set of routes for vehicles having limited capacity to traverse a set of arcs from a node called depot in an undirected graph in the least possible time. The dataset consists of instances generated by modifying instances from the literature and also real-world road networks.

Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.

Radar-based dynamic gesture recognition has a broad prospect in the field of touchless Human-Computer Interaction (HCI) due to its advantages in many aspects such as privacy protection and all-day working. Due to the lack of complete motion direction information, it is difficult to implement existing radar gesture datasets or methods for motion direction sensitive gesture recognition and cross-domain (different users, locations, environments, etc.) recognition tasks.

Good knowledge about a radio environment, especially about the radio channel, is a prerequisite to design and operate ultra-reliable communications systems. Radio Environment Maps (REMs) are therefore a helpful tool to gain channel awareness. Based on a user’s location, the channel conditions can be estimated in the surrounding of the user by extracting the information from the radio map. This data set contains two measured high-resolution REMs of an indoor environment.