This dataset includes the rotor geometry (*.jpg) and motor parameters (*.csv) of interior permanent magnet synchronous motors. The rotor geometry covers three structures: 2D-, V-, and Nabla-structures. The motor parameters are generated by machine learning based on the finite element analysis results. The software JMAG Designer 19.1 was used for the finite element analysis.
Attention! This dataset is NOT the result of finite element analysis, but the data generated by machine learning. Check the paper (in preparation) for details.
We introduce a novel dataset of bee piping audio signals which was built by collecting 44 different recordings which were published by various beekeepers on the YouTube platform.Each recording has a duration varying from 2 to 13 seconds and is annotated according to the beekeeper comment respectively as Tooting or Quacking.We extracted the audio using ``YouTube soundtrack extraction'' from 14 distinct videos from which the signal is stored without a loss of quality into a WAVE file with a sampling frequency of F_s=22.05 kHz and a sample precision of 16 bits
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 .
Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,
humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the
past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the
agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather
Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.
Please download and unzip the following files corresponding to the three experiments described in our paper https://doi.org/10.3390/app11198860:
and follow the instructions in the ReadMe.pdfs.
Water consumption. Data recorded between 2017.1.1 and 2019.12.31.
This is a CSI dataset towards 5G NR high-precision positioning,
which is fine-grained, general-purpose and 3GPP R16 standards-complied.
5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).
Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.
The dataset_[SNR]_[date]_[time].mat contains:
1) a 4-D matrix, features, representing the feature data, and
2) a structure array, labels, labeling the ground truth of UE positions.
[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.
The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).
The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.
The value of Ng corresponds to the number of UEs in labels.
Colsed beta test is running.
In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.
In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.
Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.
To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.
1) Recruit participants for colsed beta test.
1)Expend our dataset with more CSI data with low SNR levels noise.
2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.
Closed beta test will start after suggestion collection.
1)Expend our dataset with more CSI data with different SNR levels noise.
2)Publish map files for Scenario 1 indoor office.
The University of Turin (UniTO) released the open-access dataset Stoke collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP).
Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.
The dermoscopic images considered in the paper "Dermoscopic Image Classification with Neural Style Transfer" are available for public download through the ISIC database (https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main). These are 24-bit JPEG images with a typical resolution of 768 × 512 pixels. However, not all the images in the database are in satisfactory condition.
This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a water distribution hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.
This dataset has related to the paper "A hardware-in-the-loop Water Distribution Testbed (WDT) dataset for cyber-physical security testing".
We provide four different acquisitions:
1) A normal acquisition without attacks ("normal.csv" for network traffic and "dataset_norm.csv" for physical measures)
2) Three acquisitions where different types of attacks and physical faults are reproduced ("attack_1.csv", "attack_2.csv" and "attack_3.csv" for network traffic and "dataset_att_1.csv", "dataset_att_2.csv" and "dataset_att_3.csv" for physical measures)
In addition to .csv files we provide four .pcap files ("attack_1.pcap", "attack_2.pcap", "attack_3.pcap" and "normal.pcap") which refer to network acquisitions for the four previous scenarios.
A README.xlsx file summarizes the key features of the entire dataset.