This is the dataset used for pre-training in "ReasonBERT: Pre-trained to Reason with Distant Supervision", EMNLP'21.

There are two files:

sentence_pairs_for_pretrain_no_tokenization.tar.gz -> Contain only sentences as evidence, Text-only

table_pairs_for_pretrain_no_tokenization.tar.gz -> At least one piece of evidence is a table, Hybrid

The data is chunked into multiple tar files for easy loading. We use WebDataset, a PyTorch Dataset (IterableDataset) implementation providing efficient sequential/streaming data access.

For pre-training code, or if you have any questions, please check our GitHub repo https://github.com/sunlab-osu/ReasonBERT

Below is a sample code snippet to load the data

import webdataset as wds

# path to the uncompressed files, should be a directory with a set of tar files
url = './sentence_multi_pairs_for_pretrain_no_tokenization/{000000...000763}.tar'
dataset = (
  wds.Dataset(url)
  .shuffle(1000) # cache 1000 samples and shuffle
  .decode()
  .to_tuple("json")
  .batched(20) # group every 20 examples into a batch
)

# Please see the documentation for WebDataset for more details about how to use it as dataloader for Pytorch
# You can also iterate through all examples and dump them with your preferred data format

Below we show how the data is organized with two examples.

Text-only

{'s1_text': 'Sils is a municipality in the comarca of Selva, in Catalonia, Spain.', # query sentence
 's1_all_links': {
  'Sils,_Girona': [[0, 4]],
  'municipality': [[10, 22]],
  'Comarques_of_Catalonia': [[30, 37]],
  'Selva': [[41, 46]],
  'Catalonia': [[51, 60]]
 }, # list of entities and their mentions in the sentence (start, end location)
 'pairs': [ # other sentences that share common entity pair with the query, group by shared entity pairs
  {
    'pair': ['Comarques_of_Catalonia', 'Selva'], # the common entity pair
    's1_pair_locs': [[[30, 37]], [[41, 46]]], # mention of the entity pair in the query
    's2s': [ # list of other sentences that contain the common entity pair, or evidence
     {
       'md5': '2777e32bddd6ec414f0bc7a0b7fea331',
       'text': 'Selva is a coastal comarque (county) in Catalonia, Spain, located between the mountain range known as the Serralada Transversal or Puigsacalm and the Costa Brava (part of the Mediterranean coast). Unusually, it is divided between the provinces of Girona and Barcelona, with Fogars de la Selva being part of Barcelona province and all other municipalities falling inside Girona province. Also unusually, its capital, Santa Coloma de Farners, is no longer among its larger municipalities, with the coastal towns of Blanes and Lloret de Mar having far surpassed it in size.',
       's_loc': [0, 27], # in addition to the sentence containing the common entity pair, we also keep its surrounding context. 's_loc' is the start/end location of the actual evidence sentence
       'pair_locs': [ # mentions of the entity pair in the evidence
        [[19, 27]], # mentions of entity 1
        [[0, 5], [288, 293]] # mentions of entity 2
       ],
       'all_links': {
        'Selva': [[0, 5], [288, 293]],
        'Comarques_of_Catalonia': [[19, 27]],
        'Catalonia': [[40, 49]]
       }
      }
    ,...] # there are multiple evidence sentences
   },
 ,...] # there are multiple entity pairs in the query
}

Hybrid

{'s1_text': 'The 2006 Major League Baseball All-Star Game was the 77th playing of the midseason exhibition baseball game between the all-stars of the American League (AL) and National League (NL), the two leagues comprising Major League Baseball.',
 's1_all_links': {...}, # same as text-only
 'sentence_pairs': [{'pair': ..., 's1_pair_locs': ..., 's2s': [...]}], # same as text-only
 'table_pairs': [
  'tid': 'Major_League_Baseball-1',
  'text':[
    ['World Series Records', 'World Series Records', ...],
    ['Team', 'Number of Series won', ...],
    ['St. Louis Cardinals (NL)', '11', ...],
  ...] # table content, list of rows
  'index':[
    [[0, 0], [0, 1], ...],
    [[1, 0], [1, 1], ...],
  ...] # index of each cell [row_id, col_id]. we keep only a table snippet, but the index here is from the original table.
  'value_ranks':[
    [0, 0, ...],
    [0, 0, ...],
    [0, 10, ...],
  ...] # if the cell contain numeric value/date, this is its rank ordered from small to large, follow TAPAS
  'value_inv_ranks': [], # inverse rank
  'all_links':{
    'St._Louis_Cardinals': {
     '2': [
      [[2, 0], [0, 19]], # [[row_id, col_id], [start, end]]
     ] # list of mentions in the second row, the key is row_id
    },
    'CARDINAL:11': {'2': [[[2, 1], [0, 2]]], '8': [[[8, 3], [0, 2]]]},
  }
  'name': '', # table name, if exists
  'pairs': {
    'pair': ['American_League', 'National_League'],
    's1_pair_locs': [[[137, 152]], [[162, 177]]], # mention in the query
    'table_pair_locs': {
     '17': [ # mention of entity pair in row 17
       [
        [[17, 0], [3, 18]],
        [[17, 1], [3, 18]],
        [[17, 2], [3, 18]],
        [[17, 3], [3, 18]]
       ], # mention of the first entity
       [
        [[17, 0], [21, 36]],
        [[17, 1], [21, 36]],
       ] # mention of the second entity
     ]
    }
   }
 ]
}

SpecTf: Transformers Enable Data-Driven Imaging Spectroscopy Cloud Detection

Summary

Manuscript in review. Preprint: https://arxiv.org/abs/2501.04916

This repository contains the dataset used to train and evaluate the Spectroscopic Transformer model for EMIT cloud screening.

• spectf_cloud_labelbox.hdf5
  • 1,841,641 Labeled spectra from 221 EMIT Scenes.
• spectf_cloud_mmgis.hdf5
  • 1,733,801 Labeled spectra from 313 EMIT Scenes.
  • These scenes were speciffically labeled to correct false detections by an earlier version of the model.
• train_fids.csv
  • 465 EMIT scenes comprising the training set.
• test_fids.csv
  • 69 EMIT scenes comprising the held-out validation set.

v2 adds validation_scenes.pdf, a PDF displaying the 69 validation scenes in RGB and Falsecolor, their existing baseline cloud masks, as well as their cloud masks produced by the ANN and GBT reference models and the SpecTf model.

Data Description

221 EMIT Scenes were initially selected for labeling with diversity in mind. After sparse segmentation labeling of confident regions in Labelbox, up to 10,000 spectra were selected per-class per-scene to form the spectf_cloud_labelbox dataset. We deployed a preliminary model trained on these spectra on all EMIT scenes observed in March 2024, then labeled another 313 EMIT Scenes using MMGIS's polygonal labeling tool to correct false positive and false negative detections. After similarly sampling spectra from these scenes, A total of 3,575,442 spectra were labeled and sampled.

The train/test split was randomly determined by scene FID to prevent the same EMIT scene from contributing spectra to both the training and validation datasets.

Please refer to Section 4.2 in the paper for a complete description, and to our code repository for example usage and a Pytorch dataloader.

Each hdf5 file contains the following arrays:

• 'spectra'
  • Top-of-Atmosphere reflectance calculated from the EMIT L1B Radiance product
  • Float64 of shape (n, 268)
• 'fids'
  • The FID from which each spectrum was sampled
  • Binary string of shape (n,)
• 'indices'
  • The (col, row) index from which each spectrum was sampled
  • Int64 of shape (n, 2)
• 'labels'
  • Annotation label of each spectrum
    • 0 - "Clear"
    • 1 - "Cloud"
    • 2 - "Cloud Shadow" (Only for the Labelbox dataset, and this class was combined with the clear class for this work. See paper for details.)
      • label[label==2] = 0
  • Int64 of shape (n,2)

Each hdf5 file contains the following attribute:

• 'bands'
  • The band center wavelengths (nm) of the spectrum
  • Float64 of shape (268,)

Acknowledgements

The EMIT online mapping tool was developed by the JPL MMGIS team. The High Performance Computing resources used in this investigation were provided by funding from the JPL Information and Technology Solutions Directorate.

This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

© 2024 California Institute of Technology. Government sponsorship acknowledged.

On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios

This repository contains the 3DO dataset proposed in [1].

PyTroch Dataloader

A minimal PyTorch dataloader for the 3DO dataset is provided at: https://github.com/StrohmayerJ/3DO

Dataset Description

The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (Note: The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)

The directories 3DO/d1/, 3DO/d2/, and 3DO/d3/ contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., 3DO/d1/w1/) contains a csiposreg.csv file storing the raw WiFi packet time series and a csiposreg_complex.npy cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, csiposreg_complex.npy is automatically generated by the provided dataloader.)

Dataset Structure:

/3DO

├── d1 <-- day 1 subdirectory

  └── w1 <-- sequence subdirectory

     └── csiposreg.csv <-- raw WiFi packet time series

     └── csiposreg_complex.npy <-- CSI time series cache

├── d2 <-- day 2 subdirectory

├── d3 <-- day 3 subdirectory

In [1], we use the following training, validation, and test split:

Subset Day Sequences

Train 1 w1, w2, w3, s1, s2, s3, l1, l2, l3

Val 1 w4, s4, l4

Test 1 w5 , s5, l5

Test 2 w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5

Test 3 w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4

w = walking, s = sitting and l= lying

Note: On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. https://doi.org/10.1007/978-3-031-78354-8_13

BibTeX citation:

@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin", title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios", booktitle="Pattern Recognition", year="2025", publisher="Springer Nature Switzerland", address="Cham", pages="194--211", isbn="978-3-031-78354-8" }

Spectroscopic Transformer for Improved EMIT Cloud Masks

Summary

Manuscript in preparation/submitted.

This repository contains the dataset used to train and evaluate the Spectroscopic Transformer model for EMIT cloud screening.

• spectf_cloud_labelbox.hdf5
  • 1,841,641 Labeled spectra from 221 EMIT Scenes.
• spectf_cloud_mmgis.hdf5
  • 1,733,801 Labeled spectra from 313 EMIT Scenes.
  • These scenes were speciffically labeled to correct false detections by an earlier version of the model.
• train_fids.csv
  • 465 EMIT scenes comprising the training set.
• test_fids.csv
  • 69 EMIT scenes comprising the held-out validation set.

Data Description

221 EMIT Scenes were initially selected for labeling with diversity in mind. After sparse segmentation labeling of confident regions in Labelbox, up to 10,000 spectra were selected per-class per-scene to form the spectf_cloud_labelbox dataset. We deployed a preliminary model trained on these spectra on all EMIT scenes observed in March 2024, then labeled another 313 EMIT Scenes using MMGIS's polygonal labeling tool to correct false positive and false negative detections. After similarly sampling spectra from these scenes, A total of 3,575,442 spectra were labeled and sampled.

The train/test split was randomly determined by scene FID to prevent the same EMIT scene from contributing spectra to both the training and validation datasets.

Please refer to Section 4.2 in the paper for a complete description, and to our code repository for example usage and a Pytorch dataloader.

Each hdf5 file contains the following arrays:

• 'spectra'
  • Top-of-Atmosphere reflectance calculated from the EMIT L1B Radiance product
  • Float64 of shape (n, 268)
• 'fids'
  • The FID from which each spectrum was sampled
  • Binary string of shape (n,)
• 'indices'
  • The (col, row) index from which each spectrum was sampled
  • Int64 of shape (n, 2)
• 'labels'
  • Annotation label of each spectrum
    • 0 - "Clear"
    • 1 - "Cloud"
    • 2 - "Cloud Shadow" (Only for the Labelbox dataset, and this class was combined with the clear class for this work. See paper for details.)
      • label[label==2] = 0
  • Int64 of shape (n,2)

Each hdf5 file contains the following attribute:

• 'bands'
  • The band center wavelengths (nm) of the spectrum
  • Float64 of shape (268,)

Acknowledgements

The EMIT online mapping tool was developed by the JPL MMGIS team. The High Performance Computing resources used in this investigation were provided by funding from the JPL Information and Technology Solutions Directorate.

This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

© 2024 California Institute of Technology. Government sponsorship acknowledged.

This repository contains the Wallhack1.8k dataset for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the CAD models (of 3D-printable parts) of the WiFi systems proposed in [2].

PyTroch Dataloader

A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: https://github.com/StrohmayerJ/wallhack1.8k

Dataset Description

The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).

To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:

LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system

LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system

NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system

NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system

These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.

To extract the 52 L-LTF subcarriers used in [1], the following indices of A are to be selected:

52 L-LTF subcarriers

csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]

Additional 56 HT-LTF subcarriers can be selected via:

56 HT-LTF subcarriers

csi_valid_subcarrier_index += [i for i in range(66, 94)]
csi_valid_subcarrier_index += [i for i in range(95, 123)]

For more details on subcarrier selection, see ESP-IDF (Section Wi-Fi Channel State Information) and esp-csi.

Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the spectrograms/ directory.

The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]

Spectrogram index: [0, ..., n]

Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."

Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.

Dataset Overview:

Table 1: Raw WiFi packet sequences.

Scenario System "no presence" / label 0 "walking" / label 1 "walking + arm-waving" / label 2 Total

LoS BQ b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

LoS PIFA b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

NLoS BQ b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

NLoS PIFA b1.csv w1.csv, w2.csv, w3.csv, w4.csv and w5.csv ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv

4 20 20 44

Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.

Scenario System

"no presence" / label 0

"walking" / label 1

"walking + arm-waving" / label 2 Total

LoS BQ 149 154 155

LoS PIFA 149 160 152

NLoS BQ 148 150 152

NLoS PIFA 143 147 147

589 611 606 1,806

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].

[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 42-56). Cham: Springer Nature Switzerland, doi: https://doi.org/10.1007/978-3-031-63211-2_4.

[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: https://doi.org/10.1109/ICIP51287.2024.10647666.

BibTeX citations:

@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}@INPROCEEDINGS{10647666, author={Strohmayer, Julian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, year={2024}, volume={}, number={}, pages={3594-3599}, keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32}, doi={10.1109/ICIP51287.2024.10647666}}