*.csv

AirIoT is a temporal dataset of air pollution concentration values measured for almost three years in Hyderabad, India. In AirIoT, a dense network of IoT-based PM monitoring devices equipped with low-cost sensors was deployed. The research focuses on two primary aspects: measurement and modelling. The team developed, calibrated, and deployed 50 IoT-based PM monitoring devices throughout Hyderabad, India, covering urban, semi-urban, and green areas.

This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected by User Equipment (UE) devices strategically positioned within a moving train, featuring the hexagonal frequency selective pattern on its windows. Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna.

All the data in this dataset corresponds to the research conducted in our work titled  "Enhancing Mobile Communication on Railways: Impact of Train Window Size and Coating". 

The dataset contains Moodle Log Reports of two batches of students. They used Moodle platform for their solo and team activities. The column includes Date, Time, User full name, Affected User, Event Context, Component, Event Name, Description, Origin and IP Address. The sensitive data like User name and IP address are removed in this Draft version dataset. Pivot table is used for filtering the data and visual charts and graphs are applied for understanding the data.

The prototype of the calibration is verified with a 12-bit SAR ADC manufactured in 28-nm standard CMOS process. It is based on non-binary weights differential SAR ADC with bottom-plate sampling. This data was captured using a logic analyzer. The data for fast Fourier transform (FFT) is an input 1 MHz sine wave at 50MS/s. The signal input amplitude is 15dbm. The sampling points are 131072. The MATLAB code includes both the original weight and the calibration weight.