Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read SynthSoM is an open synthetic dataset that pairs ray-traced wireless channel data with mmWave radar, RGB, depth, and LiDAR in frame-aligned air-ground scenarios, and validates the data via sim-to-real path-loss prediction at 89.28%…

desk verdict Ambitious open SoM dataset with real engineering value; validation is too narrow and one weather claim is physically reversed. read the letter →

arxiv 2501.07459 v2 pith:25M27OS3 submitted 2025-01-13 eess.SP

Xiang Cheng , Ziwei Huang , Yong Yu , Lu Bai , Mingran Sun , Zengrui Han , Ruide Zhang , Sijiang Li This is my paper · ORCID
classification eess.SP
keywords SynthSoMdatasetmulti-modalsensing-communicationSynesthesiaofMachinesmmWaveradarray-tracingsimulationpathlossLiDARpointcloudsair-groundcooperativescenario
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces SynthSoM, an open synthetic dataset in which wireless communication data and multi-modal sensory data are generated in the same simulated world and aligned frame by frame. The dataset spans five air-ground cooperative scenarios and combinations of weather, time of day, agent density, frequency band, and antenna type, and contains channel matrices and path loss, mmWave radar waveforms and point clouds, RGB images, depth maps, and LiDAR point clouds. The authors' central claim is that this synthetic data is realistic enough to support Synesthesia of Machines research: they show qualitative consistency with measured phenomena and report a train-on-synthetic/test-on-real path-loss prediction accuracy of 89.28% against 90.35% for train/test on real data. If the transferability claim holds, SynthSoM offers a common, customizable benchmark for studying how communications and sensing can reinforce each other.

What carries the argument

The load-bearing mechanism is the four-step simulation platform: high-fidelity scenario construction by importing identical STL models into AirSim, WaveFarer, and Wireless InSite; comprehensive condition simulation covering weather, time of day, agent density, frequency band, and antenna type; dynamic scenario generation in which SUMO-generated trajectories are imported into all three software; and automatic data collection and export. The alignment guarantee rests on exporting the same STL models with no rotation, scaling, or origin offset, and setting identical coordinates, a 1:1 scale, and the same world origin in each software. This is what makes the communication data, RF sensory data, and non-RF sensory data correspond snapshot by snapshot.

What would settle it

Run the same train-on-synthetic/test-on-real protocol on a different modality from the dataset, such as mmWave radar object detection or channel-matrix prediction, across the other four scenarios, and measure the accuracy gap relative to train-on-real. If the gap exceeds about ten percentage points, or if frame-level cross-modal matching between RGB, LiDAR, and radar point clouds fails on a noticeable fraction of snapshots, the paper's transferability and precise-alignment claims would be refuted.

Watch

Extended reading notes

Core claim

The central discovery is that a single simulation platform can produce precisely aligned communication and multi-modal sensing data at scale, and that the resulting synthetic data transfers to the real world for a representative learning task. The platform combines AirSim for RGB, depth, and LiDAR, WaveFarer for mmWave radar, and Wireless InSite for ray-traced channel data, using identical STL models, coordinates, weather conditions, and trajectories across the three software. The validation compares train-on-synthetic/test-on-real and train-on-real/test-on-real path-loss prediction, finding accuracies of 89.28% and 90.35%, a gap under 10% that the authors treat as acceptable for a synthetic dataset. The paper therefore frames SynthSoM as a consistent open benchmark for cross-comparing, calibrating, and baselining SoM-related algorithms.

Load-bearing premise

The whole-dataset claim rests on the assumption that transferability demonstrated for path-loss prediction from RGB and depth in one campus scenario extends to every modality and every scenario-condition combination in the dataset, and that the three simulators are aligned closely enough that no quantitative registration error undermines the multi-modal correspondences.

Editorial extensions

If this is right

  • SynthSoM supplies frame-aligned channel matrices, mmWave radar, RGB, depth, and LiDAR for the same agents, enabling multi-modal models that jointly exploit communications and sensing.
  • The reported train-on-synthetic/test-on-real accuracy of 89.28% suggests that models trained on SynthSoM can be applied to real measurements with a small performance drop, at least for path-loss prediction.
  • The platform's four-step pipeline can be reused to generate new scenarios beyond the five included areas, because scenario files, conditions, and trajectories are modular inputs.
  • Consistent data across multiple conditions supports cross-comparison of SoM algorithms, model calibration, and transfer-learning baselines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The transferability evidence is based on one task, path-loss prediction from RGB and depth in a single campus scene; extending the same train-on-synthetic/test-on-real protocol to mmWave radar point clouds or channel matrices across the other four scenarios is a direct test the authors did not report.
  • If alignment is as precise as claimed, a cross-modal retrieval benchmark that matches an RGB frame to its corresponding LiDAR scan and radar point cloud should achieve near-perfect accuracy, offering an inexpensive independent check of the alignment claim.
  • The dataset's scale of 743.79 GB may make full downloads impractical for many groups; providing scenario-specific subsets or a smaller core sample would lower the barrier to adoption and broaden the benchmark's use.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper introduces SynthSoM, a large synthetic multi-modal sensing-communication dataset intended to support research on Synesthesia of Machines (SoM). The dataset is generated by a custom platform that integrates AirSim, WaveFarer, and Wireless InSite, with scenarios, dynamic trajectories, weather, time-of-day, agent density, frequency band, and antenna type aligned across the three simulators. It contains RF channel data (path loss and channel matrices), mmWave radar waveforms and point clouds, and non-RF sensory data (RGB images, depth maps, LiDAR point clouds), organized into five air-ground scenarios. The paper validates the dataset in two ways: a statistics-based qualitative comparison with real-world measurements and an ML-based transferability experiment (train on synthetic, test on real; train on real, test on real) for path-loss prediction from RGB and depth data. The central claim is that SynthSoM is realistic and precisely aligned enough to serve as a valid open benchmark for SoM-related algorithms.

Significance. If the validation were commensurate with the claims, SynthSoM would be a valuable community resource: it is openly released, large in scale, and broader in modality coverage than existing datasets such as ViWi, DeepSense 6G, and M3SC. The construction pipeline is described in enough detail to be reproducible, and the accompanying code and measurement campaign are concrete strengths. The paper also makes a useful comparison with prior datasets. However, the load-bearing validation evidence is much narrower than the dataset-level claims: only one task (path-loss prediction from RGB/depth), one matched campus scenario, and none of the other modalities or condition axes are quantitatively validated. In addition, the weather-related path-loss statement in Technical Validation is physically inconsistent as written. These gaps do not invalidate the dataset itself, but they do require either additional validation or a substantially more cautious statement of what has been demonstrated.

major comments (5)
  1. [Technical Validation, ML-Based Evaluation Metrics] The TSTR/TRTR experiment validates only path-loss prediction from RGB images and depth maps in a single campus scenario whose simulation parameters are matched to the measurement setup. The dataset claim in the abstract and Usage Notes is that SynthSoM provides consistent data for cross-comparing SoM algorithms across five scenarios, multiple conditions, and several modalities (channel matrices, radar waveforms/point clouds, LiDAR). The current evidence does not support that generalization. The authors should either add quantitative validation for at least one additional modality and condition axis, or explicitly restrict the transferability claim to the validated path-loss task and scenario.
  2. [Technical Validation, Figure 6(g)-(i) and accompanying text] The text states: 'In comparison with sunny days, path loss is much smaller on rainy and snowy days attributed to the obvious rain and snow attenuation at the mmWave frequency band.' This is physically inverted: rain and snow attenuation are additional loss mechanisms and should increase path loss, not decrease it. If the underlying data actually show smaller path loss in rain/snow, the weather-condition subset is systematically invalid; if the data show larger path loss, the sentence and the caption must be corrected. The same paragraph also refers to 'sunny, rainy, and sunny days' where the last should be 'snowy days.' Because the weather axis is one of the paper's advertised condition dimensions, this must be resolved before the dataset-level validity claim can be accepted.
  3. [Technical Validation, ML-Based Evaluation Metrics, accuracy formula] The accuracy metric is defined as A = |PLpr - PLgt| / PLgt, which is the absolute relative error, not an accuracy. The reported values of 89.28% and 90.35% are then presented as prediction accuracies. If the formula is a typo and the intended metric is accuracy = 1 - |PLpr - PLgt| / PLgt, the formula and surrounding explanation must be corrected. As written, the metric contradicts the reported numbers and makes the central transferability result impossible to interpret.
  4. [Technical Validation, ML-Based Evaluation Metrics, Figures 11(a)-(b)] The 89.28% versus 90.35% TSTR/TRTR comparison is reported without error bars, repeated runs, or statistical significance testing. Since MLP training involves stochastic initialization and minibatch order, a single run provides no measure of the variability of the 1.07 percentage point gap. The authors should report results over multiple seeds or otherwise quantify the uncertainty, or the claim that the gap is 'acceptable' is not supported.
  5. [Methods, High-Fidelity Scenario Construction and Dynamic Scenario Generation] The 'precise alignment' of AirSim, WaveFarer, and Wireless InSite is a central design claim, but the paper provides no quantitative alignment error or consistency check. Stating that STL import preserves scale and origin, and that trajectories are imported from SUMO, is not the same as demonstrating that the multi-modal data streams are aligned at each snapshot to a specified tolerance. The authors should report at least one quantitative check, such as re-projection of 3D points into the RGB/depth images or comparison of a known reference path across simulators, or they should soften the 'precise alignment' claim accordingly.
minor comments (5)
  1. [Table 3] The loss function is listed as 'MESLoss'; this should be 'MSELoss' (mean squared error loss).
  2. [Background & Summary] The phrase 'a certain intelligent agent destiny' should be 'a certain intelligent agent density.'
  3. [Technical Validation, Figure 6 text] The sentence 'Figures 6(g)-(i) demonstrate path loss heatmaps in the urban crossroad scenario on sunny, rainy, and sunny days' should say 'sunny, rainy, and snowy days' to match the figure caption.
  4. [Technical Validation, Figure 6(a)-(b) text] Path loss values are reported in dBm, but path loss is a ratio and should be expressed in dB. The figure labels correctly use 'dB'; the text should be consistent.
  5. [Technical Validation, ML-Based Evaluation Metrics] The references to 'Table 6' and 'Tables 4, 5' should be more explicit, e.g., 'Table 6 in [47] and Tables 4-5 in [48]', since the current phrasing is ambiguous.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the transferability claim is tested against independent real-world measurements, and the self-citations are motivational rather than load-bearing.

full rationale

The paper's core claim—that SynthSoM data can transfer to the real world—is checked by a TSTR experiment in which a model is trained on synthetic RGB and depth images and tested on held-out real path-loss measurements collected at Peking University. The target path loss is produced by Wireless InSite ray-tracing with simulation parameters matched to the measurement campaign, but the accuracy values (89.28% TSTR vs. 90.35% TRTR) are empirical outcomes rather than quantities fitted from the real data. The synthetic pipeline does not use the real path-loss labels as inputs, so the prediction is not forced by construction. The self-citations to the authors' SoM concept paper (ref. 1) and M3SC dataset (ref. 21) provide background and motivation; they do not determine the contents, statistics, or validation results of SynthSoM, so they are not load-bearing. The alignment across AirSim, WaveFarer, and Wireless InSite is achieved by shared STL models and coordinates, which is a construction choice rather than a circular derivation. The main limitations—that TSTR covers only path loss/RGB/depth in one matched campus scenario and that the rainy/snowy path-loss sentence appears physically inverted ('path loss is much smaller on rainy and snowy days attributed to the obvious rain and snow attenuation')—are correctness and generalization concerns, not circularity. The dataset is open-sourced and benchmarked against external measurements, making the validation self-contained.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim does not hinge on fitted parameters. The listed free parameters are hand-chosen scenario settings (rain rate, temperature, sun angle) that influence data appearance, but they are not tuned to match the validation target. The key axioms are the simulators' physical fidelity and the generalization of the single-scenario transferability result. No new physical entities are introduced.

free parameters (4)
  • rain_rate = 50 mm/hr
    Set from the LISA method (ref. 30); hand-chosen weather condition, not fitted to validation target.
  • snow_rate = 10 mm/hr
    Set from the LISA method (ref. 30).
  • weather_temperature_and_humidity = 22.2 C, 100% humidity (rain); -10 C, 20% humidity (snow)
    Hand-chosen conditions for the weather simulation in WaveFarer and Wireless InSite.
  • sun_elevation_and_skylight_parameters = Dawn: Sunheight=0, Elevation=0, Skylight=1.0; Morning: Sunheight=1.0, Elevation=90, Skylight=2.0; Night: Sunheight=-1…
    Hand-chosen values used to represent times of day in AirSim.
assumptions (4)
  • domain assumption AirSim, WaveFarer, and Wireless InSite produce physically representative multi-modal data.
    Invoked throughout Methods; the paper's validity claim depends on these simulators being reliable proxies for real sensors and channels.
  • domain assumption Precise alignment holds from identical STL models, 1:1 scale, world origin alignment, and manually aligned coordinates.
    Invoked in the 'Precise Alignment' paragraph; no quantitative registration error is reported.
  • domain assumption The TSTR transferability on path loss generalizes to all modalities and scenarios in the dataset.
    Entered in the 'ML-Based Evaluation Metrics' section; asserted, not demonstrated, for radar and LiDAR.
  • domain assumption LISA augmentation faithfully represents LiDAR behavior in rain and snow.
    Invoked in 'Comprehensive Scenario Condition Simulation'; accepted from ref. 30 without independent verification in this paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)." pith.science (2026). https://pith.science/paper/25M27OS3

@misc{pith2026250107459,
  author       = {Pith},
  title        = {Pith review of: SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/25M27OS3}},
  note         = {Machine review of arXiv:2501.07459}
}
read the original abstract

Given the importance of datasets for sensing-communication integration research, a novel simulation platform for constructing communication and multi-modal sensory dataset is developed. The developed platform integrates three high-precision software, i.e., AirSim, WaveFarer, and Wireless InSite, and further achieves in-depth integration and precise alignment of them. Based on the developed platform, a new synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM), named SynthSoM, is proposed. The SynthSoM dataset contains various air-ground multi-link cooperative scenarios with comprehensive conditions, including multiple weather conditions, times of the day, intelligent agent densities, frequency bands, and antenna types. The SynthSoM dataset encompasses multiple data modalities, including radio-frequency (RF) channel large-scale and small-scale fading data, RF millimeter wave (mmWave) radar sensory data, and non-RF sensory data, e.g., RGB images, depth maps, and light detection and ranging (LiDAR) point clouds. The quality of SynthSoM dataset is validated via statistics-based qualitative inspection and evaluation metrics through machine learning (ML) via real-world measurements. The SynthSoM dataset is open-sourced and provides consistent data for cross-comparing SoM-related algorithms.

Figures

Figures reproduced from arXiv: 2501.07459 by the authors.

Figure 1
Figure 1. Framework of constructing the developed simulation platform for the generation of the SynthSoM dataset. agents in AirSim, WaveFarer, and Wireless InSite at other snapshots. The fourth step, i.e., data collection and export, aims to collect and export communication and multi-modal sensory data automatically. High-Fidelity Scenario Construction Procedures of high-fidelity scenario construction in the developed simulat… view at source ↗
Figure 2
Figure 2. Air-ground multi-link cooperative scenarios in the SynthSoM dataset. (a) Urban crossroad scenario. (b) Urban wide lane scenario. (c) Urban overpass scenario. (d) Suburban fork scenario. (e) Mountain road scenario. ban Crossroad, key parameters, including sensor position, sensor parameter, and RSF position, can be found in https: //github.com/ZiweiHuang96/SynthSoM/blob/main/urban_crossroad/README.md. To achieve the p… view at source ↗
Figure 3
Figure 3. Comprehensive conditions in the SynthSoM dataset taking the urban wide lane scenario as an example. (a) Sunny day. (b) Rainy day. (c) Snowy day. (d) Morning. (e) Night. (f) High intelligent agent density. (g) Medium intelligent agent density. (h) Low intelligent agent density. (i) Sub-6 GHz band. (j) mmWave band. (k) SISO condition. (l) MIMO condition. (m) Massive MIMO condition. Then, the temperature and humidity a… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Dynamic scenarios at Snapshot 700, Snapshot 900, and Snapshot 1100 taking the urban wide lane scenario as an example. (a) AirSim, Snapshot 700. (b) AirSim, Snapshot 900. (c) AirSim, Snapshot 1100. (d) Wireless InSite, Snapshot 700. (e) Wireless InSite, Snapshot 900. (f…
Figure 5
Figure 5. Figure 5: Data directory hierarchy and content of the SynthSoM dataset. Overall, the SynthSoM dataset contains 140K sets of channel matrices, 18K sets of path loss, 136K sets of mmWave radar waveforms with 38K radar point clouds, 145K RGB images, 290K depth maps, and 79K sets of…
Figure 6
Figure 6. Figure 6: Visualization and statistical properties of RF channels under different conditions. (a) Path loss heatmap in urban crossroad scenario with sub-6 GHz frequency band. (b) Path loss heatmap in urban crossroad scenario with mmWave frequency band. (c) Propagation paths in m…
Figure 7
Figure 7. Figure 7: Visualization of RF sensory data under different intelligent agent densities. (a) mmWave radar point cloud under high intelligent agent density. (b) mmWave radar point cloud under medium intelligent agent density. (c) mmWave radar point cloud under low intelligent agen…
Figure 8
Figure 8. Figure 8: Visualization of non-RF sensory data under different conditions. (a) RGB image on sunny days. (b) RGB image on rainy days. (c) RGB image on snowy days. (d) Depth map on sunny days. (e) Depth map on rainy days. (f) Depth map on snowy days. (g) LiDAR point cloud on sunny…
Figure 9
Figure 9. Figure 9: Measurement campaign at the campus of Peking University. (a) Location of the measurement campaign. (b) Propagation environment of the measurement. in the vicinity, as shown in [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: ML experiments of TSTR and TRTR. (a) (b) 0 20 40 60 80 100 120 140 160 0.7 0.75 0.8 0.85 0.9 0.95 1 Train on synthetic, test on real (TSTR) Train on real, test on real (TRTR) 0.82 0.09 0.05 0.04 0 0.85 0.07 0.06 0.02 0 0~11% 11%~14% 14%~17% 17%~20% 20%~23% 0 0.1 0.2 0…
Figure 11
Figure 11. Figure 11: Path loss prediction results of TSTR and TRTR. (a) Prediction accuracy at all snapshots of data in the testing set. (b) Probability of prediction relative error at all snapshots of data in the testing set. world via ML-based evaluation metrics. As a result, the SynthS…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems

    eess.SP 2025-09 conditional novelty 4.0 of 10

    MMICM, a proposed AI framework for 6G, claims to map camera/LiDAR/radar observations to radio channel parameters, but is presented with architecture detail and qualitative examples only, no quantitative validation.

Reference graph

Works this paper leans on

48 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [1]

    Cheng, X. et al. Intelligent multi-modal sensing-communication integration: Synesthesia of Machines. IEEE Commun. Surv. & Tutorials 26, 258–301, https://doi.org/10.1109/COMST.2023.3336917 (2024)

    arXiv 2024

  2. [2]

    Chen, J. et al. Computer vision aided codebook design for MIMO communications systems. IEEE Transactions on Wirel. Commun. 22, 3341–3354, https://doi.org/10.1109/TWC.2022.3217664 (2023)

    arXiv 2023

  3. [3]

    & Saad, W

    Mundlamuri, R., Gangula, R., Thomas, C.K., Kaltenberger, F. & Saad, W. Sensing aided channel estimation in wideband millimeter-wave MIMO systems. In 2023 IEEE International Conference on Communications Workshops(ICC Workshops), 1404–1409, https://doi.org/10.1109/ICCWorkshops57953.2023.10283636 (IEEE,2023)

    arXiv 2023

  4. [4]

    & Wollherr, D

    Steyer, S., Lenk, C., Kellner, D., Tanzmeister, G. & Wollherr, D. Grid-based object tracking with nonlinear dynamic state and shape estimation. IEEE Transactions on Intell. Transp. Syst. 21, 2874–2893, https://doi.org/10.1109/TITS.2019.2921248 (2020)

    arXiv 2020

  5. [5]

    & Alkhateeb, A

    Charan, G., Alrabeiah, M. & Alkhateeb, A. Vision-aided 6G wireless communications: Blockage prediction and proactive handoff. IEEE Transactions on Veh. Technol.70, 10193–10208, https://doi.org/10.1109/TVT.2021.3104219 (2021)

    arXiv 2021

  6. [6]

    He, H. et al. Integrated sensing and communication in an optical fibre. Light. Sci. & Appl. 12, 25–38, https://doi.org/10. 1038/s41377-022-1067-1 (2023)

    work page 2023

  7. [7]

    Liu, F. et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond. IEEE J. on Sel. Areas Commun. 40, 1728–1767, https://doi.org/10.1109/JSAC.2022.3156632 (2022)

    work page arXiv 2022

  8. [8]

    M., Knightly, E

    Jornet, J. M., Knightly, E. W. & Mittleman, D. M. Wireless communications sensing and security above 100 GHz.Nat. Commun. 14, 841–850, https://doi.org/10.1038/s41467-023-36621-x (2023)

    work page doi:10.1038/s41467-023-36621-x 2023

Show all 48 references
  1. [9]

    Chen, X. et al. Follownet: A comprehensive benchmark for car-following behavior modeling. Sci. Data 10, 828–842, https://doi.org/10.1038/s41597-023-02718-7 (2023)

    2023 doi

  2. [10]

    Jeon, K. et al. Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators. Nat. Commun. 15, 129–141, https://doi.org/10.1038/s41467-023-44620-1 (2024)

    2024 doi

  3. [11]

    Aguirre, F. et al. Hardware implementation of memristor-based artificial neural networks. Nat. Commun. 15, 1974–2013, https://doi.org/10.1038/s41467-024-45670-9 (2024)

    2024 doi

  4. [12]

    Li, X. et al. A high-resolution climate simulation dataset for the past 540 million years. Sci. Data 9, 371–380, https: //doi.org/10.1038/s41597-022-01490-4 (2022)

    2022 doi

  5. [13]

    Shift: A synthetic driving dataset for continuous multi-task domain adaptation

    Sun, T.et al. Shift: A synthetic driving dataset for continuous multi-task domain adaptation. In Proceedings of theIEEE/CVF Conference on Computer Vision and Pattern Recognition, 21371–21382, https://doi.org/10.1109/CVPR52688.2022.02068 (IEEE, 2022)

  6. [14]

    Xu, R. et al. OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), 2583–2589, https://doi.org/10.1109/ICRA46639. 2022.9812038 (IEEE, 2022)

    2022

  7. [15]

    Qi, B. et al. An open time-series simulated dataset covering various accidents for nuclear power plants. Sci. Data 9, 766–779, https://doi.org/10.1038/s41597-022-01879-1 (2022)

    2022 doi

  8. [16]

    Manivasagam, S. et al. LiDARsim: Realistic lidar simulation by leveraging the real world. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11167–11176, https://doi.org/10.1109/CVPR42600.2020.01118 (IEEE, 2020)

  9. [17]

    & Urtasun, R

    Geiger, A., Lenz, P., Stiller, C. & Urtasun, R. Vision meets robotics: The KITTI dataset. The Int. J. Robotics Res. 32, 1231–1237, https://doi.org/10.1177/0278364913491297 (2013)

    2013 doi

  10. [18]

    Alkhateeb, A. et al. Deepsense 6G: A large-scale real-world multi-modal sensing and communication dataset. IEEE Commun. Mag. 61, 122–128, https://doi.org/10.1109/MCOM.006.2200730 (2023)

    2023 doi

  11. [19]

    Li, J. et al. A synthetic digital city dataset for robustness and generalisation of depth estimation models. Sci. Data 11, 301–313, https://doi.org/10.1038/s41597-024-03025-5 (2024)

    2024 doi

  12. [20]

    & Alkhateeb, A

    Alrabeiah, M., Hredzak, A., Liu, Z. & Alkhateeb, A. ViWi: A deep learning dataset framework for vision-aided wireless communications. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 1–5, https://doi.org/10.1109/ VTC2020-Spring48590.2020.9128579 (IEEE, 2020)

    2020

  13. [21]

    Cheng, X. et al. M3SC: A generic dataset for mixed multi-modal (MMM) sensing and communication integration. China Commun. 20, 13–29, https://doi.org/10.23919/JCC.fa.2023-0268.202311 (2023). 15/17

    2023

  14. [22]

    & Kapoor, A

    Shah, S., Dey, D., Lovett, C. & Kapoor, A. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and Service Robotics: Results of the 11th International Conference, 621–635, https://doi.org/10.1007/ 978-3-319-67361-5_40 (Springer, 2018)

  15. [23]

    Cityengine

    Esri. Cityengine. https://www.esri.com/en-us/arcgis/products/arcgis-cityengine/resources

  16. [24]

    Unreal engine 4

    UE4. Unreal engine 4. https://www.unrealengine.com

  17. [25]

    Roadrunner

    MathWorks. Roadrunner. https://ww2.mathworks.cn/products/roadrunner.html

  18. [26]

    Wavefarer

    Remcom. Wavefarer. https://www.remcom.com/wavefarerautomotive-radar-software

  19. [27]

    Wireless insite

    Remcom. Wireless insite. https://www.remcom.com/wireless-insite-em-propagation-software

  20. [28]

    & Koltun, V

    Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A. & Koltun, V . CARLA: An open urban driving simulator. Preprint at https://arxiv.org/abs/1711.03938 (2017)

    2017 arXiv

  21. [29]

    Rong, G. et al. LGSVL simulator: A high fidelity simulator for autonomous driving. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1–6, https://doi.org/10.1109/ITSC45102.2020.9294422 (IEEE, 2020)

    2020

  22. [30]

    Lidar light scattering augmentation (LISA): Physics-based simulation of adverse weather conditions for 3D object detection

    Kilic, V .et al. Lidar light scattering augmentation (LISA): Physics-based simulation of adverse weather conditions for 3D object detection. Preprint at https://arxiv.org/abs/2107.07004 (2021)

    2021 arXiv

  23. [31]

    Studio, B. Blender. https://www.blender.org/

  24. [32]

    Huang, Z. et al. A mixed-bouncing based non-stationarity and consistency 6G V2V channel model with continuously arbitrary trajectory. IEEE Transactions on Wirel. Commun.https://doi.org/10.1109/TWC.2023.3293024 (2023)

    2023

  25. [33]

    Yang, M. et al. Non-stationary vehicular channel characterization in complicated scenarios. IEEE Transactions on Veh. Technol. 70, 8387–8400, https://doi.org/10.1109/TVT.2021.3096973 (2021)

    2021

  26. [34]

    Lopez, P. A. et al. Microscopic traffic simulation using SUMO. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2575–2582, https://doi.org/10.1109/ITSC.2018.8569938 (IEEE, 2018)

    2018

  27. [35]

    Cheng, X. et al. SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM). figshare https://doi.org/10.6084/m9.figshare.28123646 (2025)

    2025 doi

  28. [36]

    & Joseph, W

    De Beelde, B., Tanghe, E., Yusuf, M., Plets, D. & Joseph, W. Radio channel modeling in a ship hull: Path loss at 868 MHz and 2.4, 5.25, and 60 GHz. IEEE Antennas Wirel. Propag. Lett. 20, 597–601, https://doi.org/10.1109/LAWP.2021.3058439 (2021)

    2021

  29. [37]

    Measurement-based analysis of channel capacity in aircraft cabin distributed MIMO system

    Yan, Y .et al. Measurement-based analysis of channel capacity in aircraft cabin distributed MIMO system. In 2015 IEEE 4th Asia-Pacific Conference on Antennas and Propagation (APCAP), 18–22, https://doi.org/10.1109/APCAP.2015.7374255 (IEEE, 2015)

    2015

  30. [38]

    Miao, H. et al. Sub-6 GHz to mmWave for 5G-advanced and beyond: Channel measurements, characteristics and impact on system performance. IEEE J. on Sel. Areas Commun. 41, 1945–1960, https://doi.org/10.1109/JSAC.2023.3274175 (2023)

    2023

  31. [39]

    Rain rate and rain attenuation over millimeter waves in Tropical regions based on real measurements

    Tashan, W.et al. Rain rate and rain attenuation over millimeter waves in Tropical regions based on real measurements. In 2021 IEEE 15th Malaysia International Conference on Communication (MICC) , 120–125, https://doi.org/10.1109/ MICC53484.2021.9642095 (IEEE, 2021)

    2021

  32. [40]

    & Salous, S

    Abdulali, M. & Salous, S. Snow attenuation measurement for short-range mmWave fixed link. In2024 4th URSI Atlantic Radio Science Meeting (AT-RASC), 1–4, https://doi.org/10.46620/URSIATRASC24/IOAQ7456 (URSI, 2024)

    2024 doi

  33. [41]

    & Liu, H

    Gao, X., Xing, G., Roy, S. & Liu, H. Experiments with mmWave automotive radar test-bed. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1–6, https://doi.org/10.1109/IEEECONF44664.2019.9048939 (IEEE, 2019)

    2019

  34. [42]

    Norouzian, F. et al. Attenuation at millimeter wave and low-THz frequencies. IEEE Trans. Antennas Propag. 68, 421–431, https://doi.org/10.1109/TAP.2019.2938735 (2020)

    2020

  35. [43]

    Norouzian, F. et al. Experimental study on low-THz automotive radar signal attenuation during snowfall. IET Radar Sonar Navig. 13, 1421–1427, https://doi.org/10.1049/iet-rsn.2018.5644 (2019)

    2019

  36. [44]

    & Takeda, K

    Zhang, Y ., Carballo, A., Yang, H. & Takeda, K. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey. ISPRS J. Photogramm. Remote. Sens. 196, 146–177, https://doi.org/10.1016/j.isprsjprs.2022.12.021 (2023). 16/17

    2023 doi

  37. [45]

    G., Schirrmeister, R

    Hartmann, K. G., Schirrmeister, R. T. & Ball, T. Eeg-gan: Generative adversarial networks for electroencephalograhic (EEG) brain signals. Preprint at https://doi.org/10.48550/arXiv.1806.01875 (2018)

    2018 doi arXiv

  38. [46]

    Jensen, R. I. T. et al. A synthetic data set to benchmark anti-money laundering methods. Sci. Data 10, 661–670, https: //doi.org/10.1038/s41597-023-02569-2 (2023)

    2023 doi

  39. [47]

    & Lee, O

    Lee, J. & Lee, O. CTGAN and TGAN? Which one is more suitable for generating synthetic eeg data. J. Theor. Appl. Inf. Technol. 99, 2359–2372, https://www.jatit.org/volumes/V ol99No10/15V ol99No10.pdf (2021)

    2021

  40. [48]

    Yuan, R. et al. A synthetic dataset of danish residential electricity prosumers. Sci. Data 10, 371–385, https://doi.org/10. 1038/s41597-023-02271-3 (2023). Acknowledgements This work was supported in part by the National Natural Science Foundation of China under Grant 62125101...

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.