Add .mat archive, relative-path split matching the registry, dataset card

OracleDatasetProcessed-arranged.tar.gz

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README.md

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+---
+pretty_name: RepresentationLearning ORACLE triplets
+tags:
+- wireless
+- rf-fingerprinting
+- channel-estimation
+- cfo-estimation
+- iq-samples
+size_categories:
+- 100K<n<1M
+---
+
+# RepresentationLearning dataset
+
+Preprocessed WiFi packets for three wireless tasks, used to train and attack the
+multi-task representations in
+[RepresentationLearning](https://github.com/nasimsoltani/RepresentationLearning).
+Pretrained models and the run registry are in
+[`Aadharsh/RepresentationLearning-models`](https://huggingface.co/Aadharsh/RepresentationLearning-models).
+
+The packets are derived from the [ORACLE RF fingerprinting dataset](https://genesys-lab.org/oracle)
+(16 USRP X310 transmitters recorded at 11 distances, 2–62 ft).
+
+## Files
+
+| File | Size | Contents |
+|---|---|---|
+| `OracleDatasetProcessed-arranged.tar.gz` | 3.3 GB | 416,244 `.mat` files (see below) |
+| `rf_partition_dict_0.5.pkl` | 5.8 MB | train/val/test split and CFO normalisation constants |
+
+## Layout
+
+```bash
+tar xzf OracleDatasetProcessed-arranged.tar.gz
+```
+
+The archive has a doubled top-level directory. The `.mat` files are in
+`OracleDatasetProcessed-arranged/OracleDatasetProcessed-arranged/`; point
+`DATA_BASE_PATH` there.
+
+One sample is a **triplet** sharing the suffix `_run1_Radio<r>_<d>ft_<i>.mat`:
+
+| Prefix | Variables | Task |
+|---|---|---|
+| `RFfingerprinting` | `Packet` (4000×1 complex), `Radio` (label, `"Radio0"`–`"Radio15"`) | RF fingerprinting (16 classes) |
+| `CFOEstimation` | `LSTF` (160×1 complex), `CFO` (scalar, Hz) | CFO estimation |
+| `ChannelEstimation` | `LLTF` (160×1 complex), `EstChannnel` (52×1 complex) | Channel estimation |
+
+`EstChannnel` really is spelled with three n's. The archive also contains 24,525
+`CFOEstimation` files without RF/channel partners; no split references them.
+
+## Split
+
+`rf_partition_dict_0.5.pkl` is a dict:
+
+| Key | Value |
+|---|---|
+| `train` / `val` / `test` | lists of `RFfingerprinting_*.mat` filenames: 91,323 / 13,070 / 26,180 |
+| `mean_cfo`, `std_cfo`, `max_cfo` | CFO statistics of the training split, used to normalise CFO targets |
+
+Filenames are relative to `DATA_BASE_PATH`. The registry's test metrics for runs
+from July 2025 onward were computed on this split, so use it unchanged when
+comparing with the registry.
+
+## Loading
+
+```bash
+git clone https://github.com/nasimsoltani/RepresentationLearning.git
+cd RepresentationLearning && cp .env.example .env   # set DATA_BASE_PATH and PKL_FILE_PATH
+python code/rep_lr/inference.py                     # evaluate the best released model
+```
+
+Each input is normalised to unit RMS and split into I/Q channels by
+`code/rep_lr/py_datasets.py`.
+
+## Citation
+
+The ORACLE authors ask that any publication using their data cite:
+
+```bibtex
+@inproceedings{sankhe2019oracle,
+  title={ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks},
+  author={Sankhe, Kunal and Belgiovine, Mauro and Zhou, Fan and Riyaz, Shamnaz and Ioannidis, Stratis and Chowdhury, Kaushik},
+  booktitle={IEEE INFOCOM 2019 - IEEE Conference on Computer Communications},
+  pages={370--378},
+  year={2019}
+}
+```

rf_partition_dict_0.5.pkl

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