Datasets:

BdezuSZo
/

turbosens1

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
                  import h5py
              ModuleNotFoundError: No module named 'h5py'
              
              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/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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TurboSens1

The first scenario of the TurboSens turbofan degradation benchmark — an interactive simulator dataset that pairs sensor observations with the full ten-dimensional ground-truth health state of the engine at every flight cycle, enabling a direct inverse probing protocol for any self supervised world model.

TurboSens2 (the second, harder scenario with fouling, phantom observation and region archetypes) is released as a separate dataset.

Splits

Split	Episodes	Total flights	Episode length	Notes
`train`	400	4,000,000	10,000 (capped)	4 archetypes, 10 events
`test`	40	400,000	10,000 (capped)	held out, same generator and prior
`test_hard`	40	400,000	10,000 (capped)	OOD: stronger degradation, modified maintenance

Schema (HDF5 columns)

Path	Shape	Description
`sensors`	(N, 7, 12)	Sensor stream (7 channels x 12 flight phase contexts)
`observation.state`	(N, 10)	Ground truth mechanical wear $\mathbf{s}_t$ (probing target)
`action`	(N, 1)	Maintenance action index
`event_mask`	(N,)	Boolean: any event fired at $t$
`event_types`	(N, 10)	Per event type rising edge flag
`weather`	(N, 1)	Ambient temperature deviation
`ep_offset`, `ep_len`	(N_ep,)	Per episode flat offsets
`ep_meta/{archetype, archetype_onset, eol_triggered, success_coeff}`	(N_ep,)	Episode metadata

File-level attributes

scenario: "turbosens1"
n_episodes, n_timesteps
action_names: [do_nothing, fan_overhaul, hpc_overhaul, turbine_overhaul, full_overhaul, targeted_patch]
archetype_names: [A_compressor, B_fan_booster, C_turbine, D_balanced]
event_names: abstract effect-typed labels — [perm_step_1, trans_drift_1, trans_anomaly_1, perm_step_2, trans_fouling_1, trans_anomaly_2, perm_drift_1, perm_drift_2, sensor_pulse_1, trans_drift_2]
sensor_names: [HPC_Tout, HP_Nmech, HPC_Tin, LPT_Tin, Fuel_flow, HPC_Pout_st, LP_Nmech]

Loading

import h5py

with h5py.File("turbosens1_train.h5", "r") as f:
    sensors = f["sensors"][:]                  # (N, 7, 12)
    state   = f["observation.state"][:]       # (N, 10)
    action  = f["action"][:]                  # (N, 1)
    ep_off  = f["ep_offset"][:]               # (N_ep,)
    ep_len  = f["ep_len"][:]                  # (N_ep,)

Or via the HuggingFace datasets library (downloads only; HDF5 must be parsed with h5py):

from huggingface_hub import hf_hub_download
import h5py

p = hf_hub_download(repo_id="<namespace>/turbosens1",
                    filename="turbosens1_train.h5", repo_type="dataset")
with h5py.File(p, "r") as f:
    ...

Inverse probing protocol

Pretrain any world model self supervised on the train split's sensor stream alone.
Freeze the encoder; train a small probe head on top of the frozen embeddings to predict $\mathbf{s}_t$.
Evaluate on test and test_hard. Report per-component Pearson, RMSE, $R^2$.

Reference baselines (JEPA, AR-LSTM, RSSM) and probe code live in the companion GitHub repository.

Versioning

Generated by turbosens1@v1.0.0 (deterministic simulator, stamped in SIM_VERSION).

Caveats and intended use

Synthetic data, not a calibration of any real fleet. Stochastic event rates and magnitudes are mathematical abstractions and do not reflect operational fleet failure statistics.
Single domain. Cross-domain generalisation claims should not be made from TurboSens alone.
Linear probe sufficiency. The protocol assumes a linear probe is expressive enough; encoders that encode the state in a non-linearly decodable form will appear to fail at probing — informative, not definitive about representation quality.

Full RAI metadata is in croissant.json.

License

CC-BY-4.0.

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Total file size:

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