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Datasets:
UARK-NED3
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BoilingBench-SeqReg

Modalities:
Image
Tags:
sequence-regression
pool-boiling
heat-flux-prediction
hydrophone
acoustic-emission
computer-vision
License:
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
              
              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 1694, 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 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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BoilingBench-SeqReg

BoilingBench-SeqReg is the self-contained model-and-test-data package distributed for SeqReg, an open sequence-regression package for experimental pool-boiling heat-flux prediction from hydrophone, AE-hit, and optical-image inputs.

This Hugging Face Dataset preserves the supplied release tree exactly: 79 files totaling approximately 3.12 GiB. It intentionally includes the same four model artifacts hosted at UARK-NED3/SeqReg, so users can obtain the documented model-and-test bundle from one release.

Contents

Models/ contains the pretrained artifacts:

  • HydReg.joblib — hydrophone sequence-regression model.
  • Hit2Flux_weights.h5 — AE-hit sequence-regression weights.
  • ImgReg.hdf5 and ImagePCA.pkl — optical-image sequence-regression artifacts.

TestData/ contains the supplied evaluation materials:

  • 1D-HydReg_TestData/testdataset.csv
  • 2D-Hit2Flux_TestData/ — 66 numbered text files
  • RawData/Sound.lvm and RawData/Temperature.lvm
  • ZippedData/ — packaged 1D HydReg, 2D Hit2Flux, 3D ImgReg, image, raw-AE, and raw-image test-data archives

The directory includes both directly accessible files and supplied ZIP archives. They are retained as released; do not treat similarly named files as independent experimental samples without inspecting the SeqReg loading and preprocessing workflow.

Intended use

Use this package with the canonical SeqReg repository, which provides dependencies, data-layout expectations, preprocessing, model loading, and tutorials. The appropriate pretrained artifact and configuration depend on modality:

  • HydReg: hydrophone data; documented with FFT preprocessing and SeqLen=4000.
  • Hit2Flux: AE-hit data; documented with FFT preprocessing, SeqLen=25, and sequence output.
  • ImgReg: optical images; documented with PCAnpy, pcskeep=40, and SeqLen=200.

Provenance, use limits, and safety

The canonical public release record cited by the SeqReg documentation is the SeqReg OSF project. This Hugging Face Dataset is a distribution mirror, not a replacement for its source record or the canonical software repository.

The artifacts were supplied as a release package and their model-file SHA-256 hashes are documented in the companion SeqReg model card. No new training, inference, or performance evaluation was performed during this hosting release. Evaluate model transfer before applying the artifacts to a different facility, fluid, surface, sensor chain, operating regime, or transient protocol.

HydReg.joblib and ImagePCA.pkl are serialized Python artifacts. Load only files obtained from this verified release and in a controlled environment.

License and attribution

SeqReg and the linked OSF project are released under Apache-2.0. Cite the applicable original publication and use the canonical repository for software issues and updates.

  • C. Dunlap, H. Pandey, E. Weems, and H. Hu, Nonintrusive Heat Flux Quantification Using Acoustic Emissions During Pool Boiling, Applied Thermal Engineering, 2023. https://doi.org/10.1016/j.applthermaleng.2023.120558
  • C. Dunlap, C. Li, H. Pandey, and H. Hu, Hit2Flux: A Machine Learning Framework for Boiling Heat Flux Prediction Using Hit-Based Acoustic Emission Sensing, AI in Thermal Fluids, 2025. https://doi.org/10.1016/j.aitf.2025.100002
  • C. Dunlap, C. Li, H. Pandey, Y. Sun, and H. Hu, A Temporal-Spatial Framework for Efficient Heat Flux Monitoring of Transient Boiling, IEEE Transactions on Instrumentation and Measurement, 2024. https://doi.org/10.1109/TIM.2024.3460944
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