These data were taken from three oscilloscopes (Tektronix TPS 2014B, 2024B and MDO4104C) connected to an inductive power transfer system utilizing the three-phase to single-phase midpoint matrix converter with a free-wheeling switch. They were taken under various transient and steady-state conditions. The 4 attached ZIP files contain 21 CSV files in total, with its own README.txt describing the data and oscilloscope channel configurations. Additionally, each ZIP file is supplied with a MATLAB m-file script to plot the data.

To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .

This dataset presents a collection of coordinates that belongs to paths generated with a 3D disjstkra algorithm,in diferents enviroments,with a grid size equal to one. The output is a six dimension vector that represents the action taken by the agent (z+,z-,y+,y-,x+,x-) based on his pose, sensors readings and the target. 

We have prepared a synthetic dataset to detect and add new devices in DynO-IoT ontology. This dataset consists of 1250 samples and has 35 features, such as feature-of-interest, device, sensor, sensor output, deployment, accuracy, unit, observation, actuator, actuation, actuating range, tag, reader, writer, etc.