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. 

RSSI measurements collected by four anchors while receiving messages from a single mobile node transmitting advertisement and extended advertisement messages in all BLE channels (both primary and secondary advertisement channels).  Tests conducted in 10x10 m office area (no large obstacles), with 4 anchors located in the corners of the area.

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.

Columns are genes, miRNAs, drugs, or cnv. Rows are patient identifiers or cell lines.

This dataset includes the measured Downlink (DL) signal-to-noise ratios (SNRs) at the User Equipments (UEs), adopting one of the beams of the beamforming codebook employed at the Base Stations (BSs). First, we configured a system-level simulator that implements the most recent Third Generation Partnership Project (3GPP) 3D Indoor channel models and the geometric blockage Model-B to simulate an indoor network deployment of BSs and UEs adopting Uniform Planar Arrays (UPAs) and a codebook based transmission.

This is a subset of the ASHRAE Global Comfort Database that we used in our study to prove that Deep learning methods performs better than shallow methods predicting the thermal sensation.

Any work using this dataset should cite this paper as follows:

Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.

Abstract

This file contains one-year measurements of demand (average 11 kWh/day), Electric vehicle charging (3 kW rating), and PV generation (3.3 kWp) for a household in London, UK. 

This dataset is associated with the following paper: 

A. A. R. Mohamed, R. J. Best, X. A. Liu and D. J. Morrow, "A Comprehensive Robust Techno-Economic Analysis and Sizing Tool for the Small-Scale PV and BESS," in IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2021.3107103.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established.