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# How to Run
1. Execute `process1.py` to convert the `csv` file into a `pkl` file.
2. Run one of the `process2` scripts based on your requirements:
**Note:** We use `-1000` to represent the position of package loss. You can apply various interpolation methods to fill these gaps. We highly encourage you to try our CSI-BERT model to recover the lost packages. ([CSI-BERT](https://github.com/RS2002/CSI-BERT), [CSI-BERT2](https://github.com/RS2002/CSI-BERT2))
(1) If you want to process each record into a long sequence, run `process2.py`. You can refer to `dataset.py` in [CSI-BERT2](https://github.com/RS2002/CSI-BERT2) for guidance.
(2) If you prefer to split each record into multiple fixed-length samples, run `process2-split.py` and modify the `length` parameter in the code to your desired length. You can refer to `dataset.py` in [CSI-BERT](https://github.com/RS2002/CSI-BERT), [CrossFi](https://github.com/RS2002/CrossFi), [KNN-MMD](https://github.com/RS2002/CrossFi), and [LoFi](https://github.com/RS2002/LoFi/tree/main/network_examples) for usage instructions.
(3) `process2-squeeze-split.py` functions similarly to `process2-split.py`, but it excludes all lost packages. |