The fifth generation (5G) wireless communications system offers faster data rates, lower latency, and higher number of interconnecting devices. Various 5G channel models were developed to study its stochastic characteristics prior to its implementation. These channel models generate multipath components that are grouped into clusters when they have similar properties in delay and angles. The multipaths and multipath clusters are used as datasets in multipath clustering which is used to examine the propagation properties of the 5G system. However, datasets are prone to outliers.


Electric power systems are comprised of cyber and physical components that are crucial to grid resiliency. Data from both components should be collected when modeling power systems: data from communication networks and intrusion detection systems; physical telemetry from sensors and field devices.


To test the effectiveness of different ambiguity models in representing real decision-making under ambiguity, we ran an incentivized experiment of choice under ambiguity. The study involved 310 participants recruited using the online international labor market, Amazon Mechanical Turk (MTurk), to participate in an experimental study implemented on the survey platform, Qualtrics. Each of the 310 subjects made 150 preference choices between two options involving variations of the four ambiguity problems with varying levels of ambiguity and risk.


A statistical subset of 385 PV modules was extracted from 10,413 silicon single-sided PV Modules from the California Solar Equipment database.

From the PV characteristic of the number of series PC cells Ns, open circuit voltage Voc, maximum power voltage Vmp, maximum power current Imp, and short circuit current Is, the solutions from the 5 Parameter Model were found for each.

5 parameter values: diode ideality factor η, series resistance Rs, parallel resistance Rp, photon light current IL, and diode reverse saturation current Io.


Replication data for the fsQCA model in: "Why do companies employ prohibited unethical artificial intelligence practices?"


Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they contact the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact–resistant dry electrode materials for wearable ECG monitoring.


Training people to think in opposites facilitates the falsification process in Wason’s rule discovery task.


This dataset contains information about published papers on how biological signals (ECG, EEG, EDA and MG + eye-tracking) are being used and collected in the field of video games. This dataset reflects the information published including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the main results reported from their use.


Oral health problems are closely associated with the analysis of dental tissue changes and the stomatologic treatment that follows. The associated paper explores the use of diffuse reflectance spectroscopy in the detection of dental tissue disorders. The data set includes 78 out of 343 measurements of teeth spectra in the wavelength range from 400 to 1700 nm. The proposed methodology focuses on computational and statistical methods and the use of these methods for the classification of dental tissue into two classes (healthy and unhealthy) by estimating the probability of class membership.