GNSS-RO Machine Learning Feature Sets used for Classification of Cubesat GNSS-RO Disturbances

General Description: This dataset includes feature sets extracted from GNSS-RO profiles used for multiclass classifcation model training and testing the classifier from Dittmann, Chang, & Morton (202?) Machine Learning Classification of Ionosphere and RFI Disturbances in Spaceborne GNSS Radio Occultation Measurements. In this work we apply a combination of physics-based feature engineering with data-driven supervised machine learning to improve classification of low earth orbit Spire Global GNSS radio occultation disturbances. Included in this dataset: data ├── converted_labels.pkl #(feature set catalogs) ├── **.pkl └── data ├── feature_set_all_single_file │ └── all_fdf_v2.pkl #(6 months of feature sets concatenated into single object) └── feature_sets ├── 2022.206.117.01.01.G23.SC001_0001.pkl #(individual profile feature sets) ├── 202***.pkl References: This conference proceedings and/or manuscript (TBA) for further description. This notebook for reproducing this experiment's result and using these datasets. The python library associated with this dataset.