In this dataset, we provided the raw analog-to-digital-converter (ADC) data of a 77GHz mmwave radar for the automotive object detection scenario. The overall dataset contains approximately 19800 frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has 4 dimension: samples (fast time), chirps (slow time), transmitters, receivers. The experiment radar was assembled from the TI AWR 1843 board, with 2 horizontal transmit antennas and 4 receive antennas.

The greenhouse remote sensing image dataset we produced contains 2101 tiles and 23914 greenhouses. And in the data set, 37.9% of dense scenes were added, so that the model trained through this data set could better adapt to the dense scene detection task.

The concentration of sea ice is essential for determining crucial climate factors. Together with sea ice thickness, it is possible to determine significant air-sea fluxes and atmospheric heat transfer. In this study, the SARAL/AltiKa Sea Ice Algorithm is used to determine the monthly sea ice concentration (SIC) in the Arctic (SSIA). For the period from April 2013 to December 2020, data from the dual-frequency microwave radiometer (23.8 GHz and 37 GHz) on the SARAL/AltiKa satellite are used to compute SIC. 

This is the dataset we collected for the article "Scalable Undersized Dataset RF Classification: Using Convolutional Multistage Training". 17 objects were collected in the laboratory and scanned using a 'cw radar' setup featuring 2x UWB antennas (1 transmit antenna, 1 receive antenna), inside anechoic chamber. There was no clutter added in the experiment.