We report on high power microwave (HPM) susceptibility tests of vulnerable IT network components. The devices under test are five different commercial and industrial media converters, one military media converter and a shielded enclosure for commercial media converters. The report compares results of susceptibility tests in different configurations supplemented by transfer function measurements.

Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham’s line drawing algorithm) to account for the DSM’s underlying surface geometry. A proof-of-concept is developed utilizing Python’s TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations.

In this paper, we address the curse of dimensionality and scalability issues while managing vast volumes of multidimensional raster data in the renewable energy modeling process in an appropriate spatial and temporal context. Tensor representation provides a convenient way to capture inter-dependencies along multiple dimensions. In this direction, we propose a sophisticated way of handling large-scale multi-layered spatio-temporal data, adopted for raster-based geographic information systems (GIS). We chose Tensorflow, an open source software library developed by Google using data flow graphs, and the tensor data structure. We provide a comprehensive performance evaluation of the proposed model against r.sun in GRASS GIS. Benchmarking shows that the tensor-based approach outperforms by up to 60%, concerning overall execution time for high-resolution datasets and fine-grained time intervals for daily sums of solar irradiation [Wh.m-2.day-1].

This paper describes results on the development of a completely FOSS-based approach assessing the electricity production potential by building façade PV. To estimate solar irradiation the hemispherical view-shed approach described by (Fu, 1999) was used. Combining it with an approach to dissect walls into regular 3D hyper-points (1-meter spacing) the sun visibility and the sky viewshed throughout the year are calculated. This results in global irradiation per hyper-point. To estimate the economic potential of each façade element an economic model was developed. This is driven by technical parameters of the installation, such as module efficiency, installation and maintenance costs, figures about payback tariffs and envisaged module lifetime. The overall result is a city-wide PV suitability and economic potential map of every building façade. The processing is based on a city model in CityGML format using the 3DCityDB database and the spatial processing functionalities of PostGIS. A set of Python scripts has been developed as a central control instance and manage parallel processing of queries against the database to achieve scalability and improved performance. We run a case study with approximately 7000 single façade elements which are processed. Since we implemented a parallel computation of the façades running on an 80-core dedicated server machine, the completion for an entire city of about 3 million hyper-points points uses a decent amount of time for the given size of the data set. The chosen approach is highly scalable, robust and can be easily implemented through standard tools and libraries.