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Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string |
|---|
0 0.5000 0.8006 1.0000 0.0892 |
0 0.5000 0.1233 1.0000 0.0795 |
0 0.5000 0.3055 1.0000 0.0560 |
0 0.5000 0.7834 1.0000 0.0458 |
0 0.5000 0.0680 1.0000 0.0845 |
0 0.3942 0.1384 0.7883 0.0645 |
0 0.5000 0.6208 1.0000 0.0873 |
0 0.5000 0.1256 1.0000 0.0332 |
0 0.5000 0.5254 1.0000 0.0509 |
0 0.5000 0.1265 1.0000 0.0955 |
0 0.5000 0.2406 1.0000 0.0150 |
0 0.5000 0.1116 1.0000 0.0659 |
0 0.5000 0.1548 1.0000 0.0344 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1765 1.0000 0.0885 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.4196 1.0000 0.0444 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.4793 0.1548 0.9586 0.0344 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1765 1.0000 0.0885 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.2633 0.4196 0.5266 0.0444 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1765 1.0000 0.0885 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1765 1.0000 0.0885 |
0 0.8377 0.1121 0.3246 0.0334 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1765 1.0000 0.0885 |
0 0.5000 0.1121 1.0000 0.0334 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.4340 0.1765 0.8679 0.0885 |
0 0.5000 0.1121 1.0000 0.0334 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.1121 1.0000 0.0334 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.4623 0.1121 0.9246 0.0334 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.7243 1.0000 0.0995 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.6208 1.0000 0.0873 |
0 0.5000 0.1256 1.0000 0.0332 |
0 0.5000 0.5254 1.0000 0.0509 |
0 0.5000 0.1265 1.0000 0.0955 |
0 0.5000 0.2406 1.0000 0.0150 |
0 0.5000 0.1116 1.0000 0.0659 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.6656 1.0000 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.7763 1.0000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.4864 0.6656 0.9728 0.0108 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.3000 0.7763 0.6000 0.0984 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.8971 0.8320 0.2058 0.0323 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.0624 1.0000 0.0450 |
0 0.5000 0.8320 1.0000 0.0323 |
0 0.5000 0.3649 1.0000 0.0406 |
0 0.5000 0.0624 1.0000 0.0450 |
Power Spectrum Signal Detection Dataset
Dataset Overview
This dataset is the simulation dataset used in the paper:
"One Dimensional Detection for Radio Signals Based on SpecUNeSt-MLP"
It is designed for one-dimensional radio signal detection based on power spectral density (PSD) data. The dataset focuses on detecting the frequency regions occupied by valid radio signals in power spectrum measurements, which can be formulated as a single-class one-dimensional semantic segmentation task.
The complete dataset generation code is provided in the Code folder.
- Running
Gen_raw_IQdata.mgenerates the original simulated IQ signals. - Running
Gen_dataset_spectrum_detect.mprocesses the generated IQ data and produces the corresponding power spectrum signal detection dataset.
The dataset released here contains only a subset of the complete simulated data for demonstration and research purposes.
Task Description
The objective of this dataset is to detect signal-occupied frequency bands from one-dimensional power spectrum signals.
Each sample represents a power spectral density sequence, and the corresponding label indicates the frequency locations where valid signals exist.
The task can be regarded as a binary semantic segmentation problem:
- Input: One-dimensional power spectrum sequence
- Output: Signal occupancy mask in the frequency domain
Data Format
Each data sample is stored as a binary file (.bin) with:
Data shape: (8192,)
Data type: float32
The physical meaning of each sample is the power spectral density (PSD) of a received signal.
The PSD values are non-negative real numbers representing the signal energy distribution along the frequency axis.
Example: sample.bin | └── [P1, P2, ..., P8192]
where each element corresponds to one frequency bin.
Label Format
The labels are stored in YOLO-style annotation files (.txt).
Each line in a label file represents one narrowband signal component contained in the corresponding spectrum sample.
The format is: class_id x_center y_center width height
The meaning of each column is:
| Column | Description |
|---|---|
| 1 | Class index |
| 2 | Time center position |
| 3 | Frequency center position |
| 4 | Time width |
| 5 | Frequency bandwidth |
Since this dataset focuses on one-dimensional frequency-domain detection, only the frequency-related information is required:
- Frequency center position (
column 3) - Frequency bandwidth (
column 5)
These two values can be used to generate the corresponding binary segmentation mask.
For samples without valid signals, the corresponding label file is empty.
Dataset Generation Process
The complete generation pipeline is:
Simulated IQ Signal Generation | | v Gen_raw_IQdata.m | v Raw IQ Signal Dataset | | v Gen_dataset_spectrum_detect.m | v Power Spectrum Detection Dataset
Citation
If you use this dataset in your research, please cite:
Citation
If you use this dataset in your research, please cite: @article{specunestmlp, title={One Dimensional Detection for Radio Signals Based on SpecUNeSt-MLP}, author={}, journal={}, year={} }
License
This dataset is released for academic research purposes only.
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