Multi-Sensor Dataset of Ultrasonic and mmWave for Material Classification (MatSense2025)

Dataset Folder Structure:

datasets/│├── README-for-all.txt├──Materials' Thicknesses Details├── C4001 - Dataset/│   ││   ├── C4001 Reflected Signal Dataset – Multiple Materials & Thickness Levels/│   │   ├── C4001_AllMaterials_AllThickness.csv│   │   └── README.txt│   ││   └── C4001 Reflected Signal Dataset – Multiple Materials/│       ├── C4001_FiveMaterials_Only.csv│       └── README.txt|    |------ Raw Data|            |----- All Materials Raw Data│└── URM09 - Dataset/    │    ├── URM09 Reflected Signal Dataset – Multiple Materials & Thickness Levels/    │   ├── URM09_AllMaterials_AllThickness.csv    │   └── README.txt    │    └── URM09 Reflected Signal Dataset – Multiple Materials/        ├── URM09_SixMaterials_Only.csv        └── README.txt    |   |------ Raw Data    |            |----- All Materials Raw Data/////////////////////////////////////////////////////////////////////////////////////////////

Notes:1- Check the file "Materials' Thicknesses Details" to know the materials thicknesses used in this experiment.2- Read the methodology for data collection in paper.

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\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\How to use datasets:

1- Download the CSV files from this dataset.

2- Load the CSV files into your preferred programming environment(Python, MATLAB, R, Weka, etc.).

3- Select one or more datasets depending on your experiment needs:

A- AllMaterials_AllThickness → for general classification with multiple thickness levels.

B- Five/SixMaterials_Only → cleaner classification without thickness effects.

4- The label column shows the material and thickness for each sample(e.g., Plastic-2).

5- Use the feature columns (Mean, RMS, Energy, etc.) as inputs to machine learning algorithms.

6- Split the dataset into training and testing sets (e.g., 80% / 20%) or use N-Folds Cross Validation.

7- Train your ML model and evaluate performance (accuracy, precision, recall).

9- Cite this dataset in your research/publication when using it.///////////////////////////////////////////////////////////////////////////////////////////////////////