RoBo6
홈페이지 · 논문 1편
Dataset contains light curves of 6 rocket body types from Mini Mega Tortora database (MMT)[^1]. The dataset was created to be used as a benchmark for rocket body light curve classification.
For more informations follow the original paper: _RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification_[^2]
Class labels:
- ARIANE 5 R/B
- ATLAS 5 CENTAUR R/B
- CZ-3B R/B
- DELTA 4 R/B
- FALCON 9 R/B
- H-2A R/B
## Dataset description
## Usage
``python
>>> from datasets import load_dataset
>>> dataset = load_dataset("kyselica/RoBo6", data_files={"train": "train.csv", "test": "test.csv"})
>>> dataset
DatasetDict({
train: Dataset({
features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],
num_rows: 5676
})
test: Dataset({
features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],
num_rows: 1404
})
})
`
- label - class name
- id - unique identifier of the light curve from MMT
- part - part number of the light curve
- period - rotational period of the object
- mag - relative path to the magnitude values file
- phase - relative path to the phase values file
- time - relative path to the time values file
Mean and standard deviation of magnitudes are stored in mean_std.csv file.
### File structure
- data directory contains 5 subdirectories, one for each class. Light curves are stored in file triplets in the following format:
- <track_id>_<#part>_mag.csv - magnitude values
- <track_id>_<#part>_time.csv - time values
- <track_id>_<#part>_phase.csv - phase angle values
where <track_id> is the unique identifier of the light curve from MMT, <\#part> is the part number of the light curve (some light curves are split into multiple parts).
- train.csv and test.csv - contains information about the train and test splits (label, id, part, period, mag, phase, time)
- mean_std.csv - contains mean and standard deviation for magnitudes, computed over the training set.
`
MMT Rocket Bodies
├── README.md
├── train.csv
├── test.csv
├── mean_std.csv
├── data
│ ├── ARIANE 5 R_B
│ │ ├── <track_id>_<\#part>_mag.csv
│ │ ├── <track_id>_<\#part>_time.csv
│ │ ├── <track_id>_<\#part>_phase.csv
│ ├── ATLAS 5 CENTAUR R_B
│ │ ├── ...
│ ├── CZ-3B R_B
│ │ ├── ...
│ ├── DELTA 4 R_B
│ │ ├── ...
│ ├── FALCON 9 R_B
│ │ ├── ...
│ ├── H-2A R_B
│ │ ├── ...
`
## Data preprocessing
To create data sutable for both CNN and RNN based models, the light curves were preprocessed in the following way:
1. Split the light curves if the gap between two consecutive measurements is larger than object's rotational period.
2. Split the light curves to have maximum span 1_000 seconds.
3. Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty.
5. Resample the light curves to 10_000 points with step 0.1 seconds.
4. Filter out light curves with less than 100 measurements.
## Citation
[^2]: RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification
`
@article{kyselica2024robo6,
title={RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification},
author={Kyselica, Daniel and {\v{S}}uppa, Marek and {\v{S}}ilha, Ji{\v{r}}{\'\i} and {\v{D}}urikovi{\v{c}}, Roman},
journal={arXiv preprint arXiv:2412.00544},
year={2024}
}
``
## References
[^1]: Karpov, S., et al. "Mini-Mega-TORTORA wide-field monitoring system with sub-second temporal resolution: first year of operation." Revista Mexicana de Astronomía y Astrofísica 48 (2016): 91-96.