This dataset provides Channel Impulse Response (CIR) measurements from standard-compliant IEEE 802.11ay packets to validate Integrated Sensing and Communication (ISAC) methods. The CIR sequences contain reflections of the transmitted packets on people moving in an indoor environment. They are collected with a 60 GHz software-defined radio experimentation platform based on the IEEE 802.11ay Wi-Fi standard, which is not affected by frequency offsets by operating in full-duplex mode.
The dataset is divided into two parts:


 Millimeter-wave (mmWave) spectrum with wide bandwidth provides a promising solution to enable high throughput in next-generation wireless agricultural networks, characterized by swarms of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels due to the dynamic crop growth behavior.


This data provides realized gain values for a handset operating at 28 GHz, with 3 4x1 linear antenna arrays placed around the handset along the right edge, bottom edge and back face of the handset. Beam steering was carried out at each of these antenna arrays and results for the handset with and without the hand phantom are included to show the effect that the introduction of the hand phantom has on the realized gain of the handset.


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.


A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.


The dataset contains measurement results of Radar Cross Section of different Unmanned Aerial Vehicles at 26-40 GHz. The measurements have been performed fro quasi-monostatic case (when the transmitter and receiver are spatially co-located) in the anechoic chamber. The data shows how radio waves are scattered by different UAVs at the specified frequency range.