Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data.

This study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important

characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five

The images containing honey bees were extracted from the video recorded  in the Botanic Garden of the University of Ljubljana, where a beehive with a colony of the Carnolian Grey, the native Slovene species, is placed. We set the camera above the beehive entrance and recorded the honey bees on the shelf in front of the entrance and the honey bees entering and exiting the hive. With such a setup, we ensured a non-invasive recording of the honey bees in their natural environment. The dataset contains 65 images of size 2688 x 1504 pixels.

This dataset consists of temporal and temperature drift characteristics of Si3N4-gate iSFET andsupplementary files