Pith. sign in

REVIEW 3 major objections 2 minor 28 references

Deformation Driven Suction Cups: A Mechanics-Based Approach to Wearable Electronics

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read The paper claims that suction-cup geometry must be matched to substrate stiffness, with narrow, tall domes outperforming wide, flat cups on soft skin.

desk verdict The abstract promises a mechanics paper on suction cups, but the supplied full text is an unrelated bioacoustics paper, so nothing in the abstract can be checked. read the letter →

arxiv 2508.11838 v2 pith:AISBC3DE submitted 2025-08-15 physics.med-ph cond-mat.mtrl-sciphysics.app-ph

Seola Lee , Andrew Akerson , Roham Pardakhtim , Ehsan Hajiesmaili , Kevin Rhodes , Zidong Li , Andrew Stanley , Amirhossein Amini
show 3 more authors
Daniele Piazza Chiara Daraio Tianshu Liu
This is my paper
classification physics.med-phcond-mat.mtrl-sciphysics.app-ph
keywords suctionadhesionwearableelectronicscompliantsubstratesrecoverablevolumecontactmechanicscupgeometryskinnegativepressure
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 aims to establish that suction adhesion to soft, deformable surfaces such as skin is governed by the cup's geometry and the substrate's stiffness, so cup shape should be tailored to the surface it grips. It argues that wide, flat suction cups work well on rigid surfaces but fail on soft tissue because the substrate bulges into the chamber and destroys the negative pressure, while narrow, tall domes preserve more recoverable chamber volume and produce stronger suction. The authors combine analytical modeling, numerical simulation, and experiments to support this rule, and add a soft tacky layer designed from contact mechanics to seal rough, dry skin. If the framework is right, designers of wearable electronics can choose suction-cup shape from tissue stiffness instead of relying on straps, chemical adhesives, or trial and error.

What carries the argument

The load-bearing mechanism is the recoverable chamber volume of the suction cup: the negative pressure that creates adhesion comes from elastic deformation on pressing and recovery on releasing, and this volume is the quantity the analytical and numerical models track. Cup aspect ratio and substrate stiffness enter through how much the substrate intrudes into the chamber; the contact-mechanics model governs the tacky interfacial layer that seals rough, dry skin. The 'recoverable volume' is the central object that carries the argument.

What would settle it

Take a soft, flat silicone substrate with known modulus and run a geometric sweep of suction cups from wide-and-flat to narrow-and-tall, measuring the negative pressure at pull-off. The central claim predicts a crossover: wide flat cups detach at lower pressures on the soft substrate than narrow tall cups, while the reverse holds on a rigid plate. Separately, measure whether the tacky interfacial layer restores a seal on rough, dry skin (e.g., increased pull-off force or leak-free pressure hold) relative to a bare cup.

Watch

Extended reading notes

Core claim

On its own terms, the core discovery is a transferable design rule: suction performance on a compliant substrate is set by the interaction between cup geometry and substrate compliance through the recoverable volume of the cup chamber. Wide and flat geometries, which retain large chambers on rigid surfaces, lose suction on skin because the soft substrate intrudes into the chamber; narrow and tall domes resist that intrusion, keep more recoverable volume, and generate higher negative pressure. The paper further claims that a tacky interfacial layer, selected with a contact-mechanics model, can restore sealing on rough, dry skin. The result is presented as a mechanics-based foundation for skin

Load-bearing premise

The framework assumes that real skin can be represented by a small set of continuum parameters—substrate compliance and interfacial adhesion—and that the 'rough, dry skin' case is captured by a tacky layer whose contact model is itself idealised.

Editorial extensions

If this is right

  • Wearable-electronics designers can pick suction-cup geometry directly from the stiffness of the target tissue, replacing trial-and-error with a quantitative rule.
  • Suction adhesion becomes a model-driven alternative to straps and chemical adhesives, with repeatable attachment and removal that does not degrade skin.
  • The tacky interfacial layer extends the design space to rough, dry surfaces like untreated skin, where bare cups fail to seal.
  • The same analysis can be applied to other soft, deformable substrates, provided their mechanical behaviour is within the continuum assumptions.

Reading between the lines

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

  • My editorial note: the full-text content supplied with this review is a different manuscript (on animal vocalization encoding), so this reading is based on the title, abstract, and stated claims; the experiments and derivations themselves were not available to check.
  • If the recoverable-volume rule is correct, a stiffness-versus-optimal-aspect-ratio chart could be generated for any soft substrate, turning the qualitative 'soft vs. hard' guidance into a design graph.
  • A natural extension is dynamic loading: cyclic press-release on skin will excite viscoelastic and time-dependent substrate behaviour, which the current continuum parameters likely do not capture; testing hold time and cyclic detachment would show whether the rule survives real use.
  • The same mechanics could transfer to other compliant surfaces, such as internal tissue or wet soft materials, but the tacky-layer contact model would need re-validated for those interfaces.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The submission, arXiv:2508.11838, presents an abstract claiming a mechanics-based framework for suction-cup adhesion on compliant substrates, with analytical modeling, numerical simulations, and experiments that yield geometry-specific design rules for wearable electronics. However, the full text supplied is not the suction-cup paper. It is the ICLR 2026 bioacoustics paper 'AVEX: What Matters for Animal Vocalization Encoding' (arXiv:2508.11845v3), which concerns self-supervised and supervised training of animal vocalization encoders. None of the claimed content—equations, simulations, or experiments related to suction cups, substrate intrusion, recoverable volume, or interfacial adhesion—appears anywhere in the manuscript. The abstract's central claims are therefore entirely unsupported by the provided text.

Significance. If the abstract's claims were backed by the described modeling, simulation, and experiments, the paper could be significant for wearable electronics: a quantitative relation between suction-cup geometry, substrate compliance, and adhesion would provide falsifiable design rules and could guide device design on skin. However, no such evidence is present in this submission. The manuscript as supplied contains no derivations, no numerical results, no experimental data, and no figures or tables relevant to suction adhesion. The significance assessment cannot go beyond the abstract, and the abstract alone is not a sufficient basis for a scientific publication.

major comments (3)
  1. [Full text (entire manuscript)] The full text is the bioacoustics paper 'AVEX: What Matters for Animal Vocalization Encoding' (arXiv:2508.11845v3), not a suction-cup mechanics paper. The abstract of arXiv:2508.11838 claims analytical modeling, numerical simulations, and experiments establishing that wide, flat cups fail on soft skin while narrow, tall domes preserve recoverable volume and generate stronger suction. None of this content appears: there are no equations, no finite-element or analytical model, no substrate-compliance parameter, no experimental protocol, and no data on suction performance. The central claim of the paper is therefore unverifiable from the submitted manuscript.
  2. [§3 Methods; Table 3] The Methods section describes training data and architectures for audio encoders; Table 3 reports benchmark results for bioacoustic models. These have no connection to suction cups, substrate intrusion, or interfacial adhesion. The abstract's assertion that cup geometry should be tailored to substrate stiffness is not derived or tested anywhere. Even the most basic definitions—e.g., 'recoverable volume,' 'substrate intrusion,' and the proposed contact mechanics for the tacky layer—are absent.
  3. [§5 Conclusion] The conclusion summarizes findings about self-supervised and supervised bioacoustic representation learning. It does not mention suction cups, skin adhesion, or wearable electronics. Thus the manuscript's stated conclusions are inconsistent with its own full text. Regardless of the physical plausibility of the abstract's design rules, the submission provides no evidence that could be checked, replicated, or compared against.
minor comments (2)
  1. [Header] The manuscript header shows arXiv:2508.11845v3, which does not match the submission identifier arXiv:2508.11838. This is consistent with the full text being a different paper.
  2. [Title and abstract] The title and abstract describe a suction-cup mechanics paper, but the body is an unrelated bioacoustics paper. The mismatch should have been caught before submission.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the claimed derivation is absent from the supplied text, making the central claim unverifiable but not circular.

full rationale

Walking the claimed derivation chain is impossible because the supplied full text (AVEX: What Matters for Animal Vocalization Encoding, arXiv:2508.11845v3) does not contain the suction-cup paper described in the abstract. The abstract's load-bearing claim—that analytical modeling, numerical simulation, and experiments show wide flat cups fail on soft substrates while narrow tall domes preserve recoverable volume and generate stronger suction—is asserted, but none of the equations, simulation setups, or experimental measurements are present. The circularity pass examines whether predictions reduce by construction to fitted inputs, definitions, or self-citations. Here there is no derivation text at all: no analytic model is stated, no parameter is fitted, no result is predicted from an equation, and no cited uniqueness theorem or prior ansatz is invoked to forbid alternatives. Consequently none of the enumerated circularity patterns can be exhibited, and hard rule 1 forbids flagging circularity without a quotable reduction. The abstract/full-text mismatch is a serious evidentiary failure and an omitted proof, and I flag it here: the central claim is unverifiable from this submission. But unverifiability is not circularity. Under the rubric, the honest finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

Only the abstract was reviewable because the supplied full text is a different paper (AVEX, arXiv 2508.11845v3). No free parameters could be audited; the axioms above are the assumptions visible in the abstract's framing. A complete ledger requires the actual manuscript.

assumptions (3)
  • domain assumption Skin and other compliant substrates can be characterized by a tractable substrate compliance/stiffness that enters the analytical and numerical models.
    Abstract: suction performance depends on cup geometry, substrate compliance, and interfacial adhesion. The framework's predictions require real skin behavior to live in this parameterization.
  • ad hoc to paper Elastic deformation and recovery of the cup is the dominant attachment mechanism, so the model's mechanics captures the failure modes such as substrate intrusion.
    Abstract: the system attaches through elastic deformation and recovery, and wide flat cups fail on soft skin due to substrate intrusion into the chamber. Both statements are only meaningful inside the proposed model.
  • domain assumption A contact-mechanics model of a soft, tacky interfacial layer describes sealing on rough, dry skin.
    Abstract: we introduce a soft, tacky interfacial layer informed by a contact mechanics model. The layer's effectiveness on rough skin is asserted, not independently evidenced in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deformation Driven Suction Cups: A Mechanics-Based Approach to Wearable Electronics." pith.science (2026). https://pith.science/paper/AISBC3DE

@misc{pith2026250811838,
  author       = {Pith},
  title        = {Pith review of: Deformation Driven Suction Cups: A Mechanics-Based Approach to Wearable Electronics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AISBC3DE}},
  note         = {Machine review of arXiv:2508.11838}
}
read the original abstract

Wearable electronics are emerging as essential tools for health monitoring, haptic feedback, and human-computer interactions. While stable contact at the device-body interface is critical for these applications, it remains challenging due to the skin's softness, roughness, and mechanical variability. Existing methods, such as grounding structures or adhesive tapes, often suffer from contact loss, limited repeatability, and restrictions on the types of electronics they can support. Suction-based adhesives offer a promising alternative by generating negative pressure without requiring tight bands or chemical adhesives. However, most existing cup designs rely on rigid-surface assumptions and overlook mechanical interactions between suction cups and skin. Inspired by traditional cupping therapies, we present a suction-based adhesive system that attaches through elastic deformation and recovery. Using analytical modeling, numerical simulations, and experiments, we present a mechanics-based framework showing how suction performance depends on cup geometry, substrate compliance, and interfacial adhesion. We show that cup geometry should be tailored to substrate stiffness. Wide, flat suction cups perform well on rigid surfaces but fail on soft ones like skin due to substrate intrusion into the chamber. Narrow and tall domes better preserve recoverable volume and generate stronger suction. To improve sealing on rough, dry skin, we introduce a soft, tacky interfacial layer informed by a contact mechanics model. Using our design principles for skin suction adhesives, we demonstrate secure attachment of rigid and flexible components including motion sensors, haptic actuators, and electrophysiological electrodes across diverse anatomical regions. These findings provide a fundamental basis for designing the next generation of skin-friendly adhesives for wearable electronics.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

28 extracted references · 14 canonical work pages

  1. [3]

    We include the original tables side-by-side for comparison

    To disentangle the effects of the data mix and answering the question: ”does including general sound in training give better representation?” we report the averaged results without ESC-50. We include the original tables side-by-side for comparison. 23 Published as a conference paper at ICLR 2026 Figure 6: Supervised encoders outperform self-supervised on ...

    work page 2026

  2. [5]

    bio” dataset. “- Bio + General

    From a baseline of (focal) bioacoustic data only, we show the performance of adding general audio, adding soundscape recordings, and ablating different taxonomic groups (whales, and then all taxa but birds.) Adding general audio to the training mix improved results overall, but in particular transferred consistently across our vocal repertoire datasets. T...

    work page arXiv 2026

  3. [6]

    Julie E Elie and Frederic E Theunissen

    URLhttps://arxiv.org/abs/2505.03071. Julie E Elie and Frederic E Theunissen. The vocal repertoire of the domesticated zebra finch: a data- driven approach to decipher the information-bearing acoustic features of communication signals. Animal cognition, 19(2):285–315,

    arXiv

  4. [8]

    Masato Hagiwara

    URLhttps: //arxiv.org/abs/2407.21783. Masato Hagiwara. Aves: Animal vocalization encoder based on self-supervision. InIEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE,

    arXiv

  5. [9]

    Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed

    URLhttps://arxiv.org/abs/2312.07439. Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. Hubert: Self-supervised speech representation learning by masked prediction of hidden units.IEEE/ACM transactions on audio, speech, and language processing, 29:3451–3460,

    arXiv

  6. [11]

    A collection of fully-annotated soundscape record- ings from the Northeastern United States, September 2022a

    Stefan Kahl, Russell Charif, and Holger Klinck. A collection of fully-annotated soundscape record- ings from the Northeastern United States, September 2022a. URLhttps://doi.org/10. 5281/zenodo.7079380. Stefan Kahl, Connor M Wood, Philip Chaon, M Zachariah Peery, and Holger Klinck. A collection of fully-annotated soundscape recordings from the western unit...

    doi:10.5281/zenodo

  7. [12]

    Robust detection of overlapping bioacoustic sound events,

    12 Published as a conference paper at ICLR 2026 Louis Mahon, Benjamin Hoffman, Logan S James, Maddie Cusimano, Masato Hagiwara, Sarah C Woolley, and Olivier Pietquin. Robust detection of overlapping bioacoustic sound events,

    work page 2026

  8. [13]

    Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen

    URLhttps://arxiv.org/abs/2503.02389. Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen. Metrics for polyphonic sound event detection.Applied Sciences, 6(6):162, 2016a. Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen. TUT database for acoustic scene classi- fication and sound event detection. In2016 24th European Signal Processing Conference (EU-...

    arXiv

Show all 28 references
  1. [14]

    Ines Nolasco, Ilyass Moummad, Dan Stowell, and Emmanouil Benetos

    Accessed via gbif.org 2023-05-09. Ines Nolasco, Ilyass Moummad, Dan Stowell, and Emmanouil Benetos. Acoustic identification of individual animals with hierarchical contrastive learning. InIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE,

    2023

  2. [15]

    URLhttps://doi.org/10.1038/ s41597-025-05281-5

    doi: 10.1038/s41597-025-05281-5. URLhttps://doi.org/10.1038/ s41597-025-05281-5. Data descriptor for the DCLDE 2026 killer-whale annotation dataset. Michael A Pardo, Kurt Fristrup, David S Lolchuragi, Joyce H Poole, Petter Granli, Cynthia Moss, Iain Douglas-Hamilton, and Georg...

    2026 doi

  3. [17]

    Can masked autoencoders also listen to birds?, 2025a

    Lukas Rauch, Ren´e Heinrich, Ilyass Moummad, Alexis Joly, Bernhard Sick, and Christoph Scholz. Can masked autoencoders also listen to birds?, 2025a. URLhttps://arxiv.org/abs/ 2504.12880. Lukas Rauch, Raphael Schwinger, Moritz Wirth, Ren ´e Heinrich, Denis Huseljic, Marek Herde...

    arXiv

  4. [18]

    NatureLM-audio: an audio-language foundation model for bioacoustics

    13 Published as a conference paper at ICLR 2026 David Robinson, Marius Miron, Masato Hagiwara, and Olivier Pietquin. NatureLM-audio: an audio-language foundation model for bioacoustics. InThe Thirteenth International Conference on Learning Representations,

    2026

  5. [20]

    Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M Dahdul, Charles Stewart, Tanya Berger-Wolf, et al

    URL https://arxiv.org/abs/2406.01253. Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M Dahdul, Charles Stewart, Tanya Berger-Wolf, et al. Bioclip: A vision foundation model for the tree of life. InProc...

    arXiv

  6. [22]

    Attention is all you need.Advances in neural informa- tion processing systems, 30,

    14 Published as a conference paper at ICLR 2026 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural informa- tion processing systems, 30,

    2026

  7. [23]

    Tweet- ybert: Automated parsing of birdsong through self-supervised machine learning.bioRxiv, pp

    George Vengrovski, Miranda R Hulsey-Vincent, Melissa A Bemrose, and Timothy J Gardner. Tweet- ybert: Automated parsing of birdsong through self-supervised machine learning.bioRxiv, pp. 2025–04,

    2025

  8. [24]

    Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov

    URLhttps://arxiv.org/abs/2404.16436. Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation. InIEEE International Conference on Acousti...

    arXiv

  9. [25]

    Superb: Speech processing universal performance benchmark.arXiv preprint arXiv:2105.01051,

    Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y Lin, Andy T Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, et al. Superb: Speech processing universal performance benchmark.arXiv preprint arXiv:2105.01051,

    arXiv

  10. [26]

    15 Published as a conference paper at ICLR 2026 Table 5: Training hyperparameters for all model variants Model Training Data Stage-1 Stage-1 Epochs LR Batch Optimizer Weight Scheduler Warmup Epochs LR Size Decay Steps EfficientNet Variants EffNetB0-AS AudioSet NA NA 50 5e-4 25...

    2026

  11. [1998]

    Investigating self-supervised speech models’ ability to classify animal vocalizations: The case of gibbon’s vocal signatures

    Jules Cauzinille, Benoˆıt Favre, Ricard Marxer, Dena Clink, Abdul Hamid Ahmad, and Arnaud Rey. Investigating self-supervised speech models’ ability to classify animal vocalizations: The case of gibbon’s vocal signatures. InInterspeech 2024, pp. 132–136. ISCA; ISCA,

    2024

  12. [2013]

    doi: 10.1121/ 1.4799597

    ISSN 1939-800X. doi: 10.1121/ 1.4799597. URLhttps://doi.org/10.1121/1.4799597. Joseph Turian, Jordie Shier, Humair Raj Khan, Bhiksha Raj, Bj ¨orn W Schuller, Christian J Stein- metz, Colin Malloy, George Tzanetakis, Gissel Velarde, Kirk McNally, et al. Hear: Holistic evaluatio...

    1939 doi

  13. [2015]

    Audio set: An ontology and human-labeled dataset for audio events

    11 Published as a conference paper at ICLR 2026 Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In2017 IEEE international conferen...

    2026

  14. [2016]

    URLhttps://asa.scitation.org/doi/abs/10.1121/2.0000358

    doi: 10.1121/2.0000358. URLhttps://asa.scitation.org/doi/abs/10.1121/2.0000358. Julian C. Sch ¨afer-Zimmermann, Vlad Demartsev, Baptiste Averly, Kiran Dhanjal-Adams, Mathieu Duteil, Gabriella Gall, Marius Faiß, Lily Johnson-Ulrich, Dan Stowell, Marta B. Manser, Marie A. Roch, ...

    doi

  15. [2020]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human langua...

    2019

  16. [2021]

    doi: https://doi.org/10.1016/j.eswa.2021.115270

    ISSN 0957-4174. doi: https://doi.org/10.1016/j.eswa.2021.115270. URLhttps://www.sciencedirect. com/science/article/pii/S0957417421007016. Stefan Kahl, Connor M Wood, Maximilian Eibl, and Holger Klinck. Birdnet: A deep learning solution for avian diversity monitoring.Ecological...

    2021

  17. [2023]

    Curriculum learning

    2https://github.com/earthspecies/avex 10 Published as a conference paper at ICLR 2026 Yoshua Bengio, J´erˆome Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pp. 41–48,

    2026

  18. [2024]

    Tweetynet: a neural network that enables high-throughput, automated anno- tation of birdsong.BioRxiv, pp

    Yarden Cohen, David Nicholson, Alexa Sanchioni, Emily K Mallaber, Viktoriya Skidanova, and Timothy J Gardner. Tweetynet: a neural network that enables high-throughput, automated anno- tation of birdsong.BioRxiv, pp. 2020–08,

    2020

  19. [2025]

    Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, and Furu Wei

    URLhttps://arxiv.org/abs/2506.00343. Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, and Furu Wei. Beats: Audio pre-training with acoustic tokenizers. InInternational Conference on Machine Learning, pp. 5178–5193. PMLR,

    arXiv

  20. [2026]

    From massive detections and localisations of orca at orcalab over three years to real-time survey joint to environmental conditions

    M Poupard, P Best, M Ferrari, P Spong, H Symonds, J-M Pr ´evot, T Soriano, and H Glotin. From massive detections and localisations of orca at orcalab over three years to real-time survey joint to environmental conditions. Ine-Forum Acusticum 2020, pp. 3235–3237,

    2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.