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Tytuł artykułu

Possibility analysis of the LiDAR technique utilization to research the wear of rails and turnouts in tram tracks

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Warianty tytułu
PL
Analiza możliwości wykorzystania techniki LiDAR do badania zużycia szyn i rozjazdów w torach tramwajowych
Języki publikacji
EN
Abstrakty
EN
The introduction of the article highlights the importance of measuring the wear of rails and turnouts. The evolution of methods and devices used to measure the profiles of these elements is briefly presented. The principle of conducting research using the LiDAR technique is explained. The problem of geometric and structural differences of tram tracks in relation to classic railways is pointed out, and the resulting concerns about the possibility of adapting typical railway methods of measuring rail and turnouts profiles to tram tracks. The rest of the article describes the construction, basic parameters and method of operation of a precise stationary laser scanner, dedicated to measuring rail profiles and turnouts. Graphical analysis of the results for measurements carried out with the mentioned device on tram tracks are presented – for rails in curves with small radii, turnouts (half-switches and frogs), corrugated wear, and broken welds. The summary presents conclusions from the research conducted.
PL
We wstępie artykułu uwypuklono znaczenie pomiarów zużycia szyn i rozjazdów kolejowych. Przedstawiono w skrócie ewolucję metod i urządzeń stosowanych do pomiarów profili tych elementów. Wyjaśniono zasadę przeprowadzania badań z wykorzystaniem techniki LiDAR. Wskazano na problem odmienności geometrycznej i konstrukcyjnej torów tramwajowych w stosunku do klasycznej kolei i wynikające stąd obawy, o możliwość adaptowania typowo kolejowych metod pomiaru profili szyn i rozjazdów do torów tramwajowych. W dalszej części artykułu opisano budowę, podstawowe parametry oraz sposób obsługi precyzyjnego stacjonarnego skanera laserowego, dedykowanego do pomiaru profili szyn i rozjazdów kolejowych. Przedstawiono graficzne analizy wyników dla pomiarów przeprowadzonych wspomnianym urządzeniem w torach tramwajowych – dla szyn w łukach o małych promieniach, rozjazdów, zużycia falistego oraz złamań w spoinach. W podsumowaniu sformułowano wnioski z przeprowadzonych badań.
Rocznik
Strony
275--293
Opis fizyczny
Bibliogr. 70 poz., il., tab.
Twórcy
autor
  • Wrocław University of Science and Technology, Faculty of Civil Engineering, Wrocław, Poland
Bibliografia
  • [1] J. Šestáková and A. Pultznerová, “Diagnostics data in the framework of railway tracks maintenance”, IOP Conference Series: Materials Science and Engineering, vol. 1015, pp. 612-617, 2021, doi: 10.1088/1757-899X/1015/1/012062.
  • [2] J. Izvoltova, L. Izvolt, and J. Sestakova, “Analysis of Methods Used to Diagnostics of Railway Lines”, in Railway Transport Planning and Management, S. de Luca, R. Di Pace, and C.Fiori, Eds. IntechOpen, 2022, doi: 10.5772/intechopen.100835.
  • [3] H. Bryś, K. Ćmielewski, and I.Wilczyńska, “Profilograf do mechanicznego wyznaczania stopnia zużycia główki szyny podsuwnicowej”, Przegląd Geodezyjny, no. 8, pp. 10-12, 2021, doi: 10.15199/50.2021.8.1.
  • [4] Reichsbahnrat Mielich, Berlin-Grunewald: „Rad, schiene und wagenlauf”, Grossedeutscher Verkehr, Heft 5/6, pp. 150-161, 1941.
  • [5] Goldschmidt, Smart Rail Solutions, “Railprofile XY”. [Online]. Available:https://www.graw.com/en/track-measurement/railprofile-xy.html. [Accessed: 20. Sep. 2023].
  • [6] Goldschmidt, Smart Rail Solutions, “Railprofile 3D”. [Online]. Available:https://www.graw.com/en/track-measurement/railprofile-3d.html. [Accessed: 20. Sep. 2023].
  • [7] J. Ye, E. Stewart, and C. Roberts, “Use of a 3D model to improve the performance of laser-based railway track inspection”, Journal of Rail and Rapid Transit, vol. 233, no. 3, pp. 337-355, 2019, doi: 10.1177/0954409718795714.
  • [8] E. Abdelhameed, H. Sobhy, H. Zohny, M. Elhabiby, “Railway Inspection using Non-Contact Non-Destructive Techniques”, International Journal of Engineering and Applied Sciences (IJEAS), ISSN: 2394-3661, vol. 7, issue 8, pp. 51-56, August 2020, doi: 10.31873/IJEAS.7.08.13.
  • [9] E. Aldao, H. González-Jorge, L. M. González-deSantos, G. Fontenla-Carrera, and J. Martínez-Sánchez, “Validation of Solid-State LiDAR Measurement System for Ballast Geometry Monitoring in Rail Tracks”, MDPI, Infrastructures, vol. 8, no. 4, art. no. 63, 2023, doi: 10.3390/infrastructures8040063.
  • [10] P. Chen, Y. Hu, W-T. Li, and P-J. Wang, “Rail wear inspection based on computer-aided design model and point cloud data”, Advances in Mechanical Engineering, vol. 10, no. 12, pp. 1-9, 2018, doi: 10.1177/1687814018816782.
  • [11] A.M. Zarembski, G.T. Grissom, and T.L. Euston, “ On the Use of Ballast Inspection Technology For the Management of Track Substructure”, Transportation Infrastructure Geotechnology, vol. 1, pp. 83-109, 2014, doi: 10.1007/s40515-014-0004-5.
  • [12] M. Leslar, G. Perry, and K. McNease, “Using mobile lidar to survey a railway line for asset inventory”, in ASPRS 2010 Annual Conference, 26-30 April 2010, San Diego, USA, 2010. [Online]. Available:https://www.asprs.org/wp-content/uploads/2013/08/Leslar.pdf. [Accessed: 20. Sep. 2023].
  • [13] D. Morgan, “Using Mobile Lidar to Survey Railway Infrastructure. Lynx Mobile Mapper”, Lynx Sales Optech Incorporated, 2009. [Online]. Available:https://www.fig.net/resources/proceedings/2009/lakebaikal_2009_comm6/papers/06_daina{%}20morgan.pdf. [Accessed: 20. Sep. 2023].
  • [14] Y. Lou, T. Zhang, J. Tang, W. Song, Y. Zhang, and L. Chen, “A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data”, MDPI, Remote Sensing, vol. 10, no. 12, 2018, doi: 10.3390/rs10121998.
  • [15] M. Simmons and K. Wilczek, “Plasser EM120VT – Future Track Technology ->now”, presented at PWI: Railway Infrastructure – Delivering Digitally, 2 March 2023, Manchester, Great Britain, 2023. [Online]. Available:https://www.youtube.com/watch?v$=$\relax$\@@underline{\hbox{v5tuFoCX67o{&}t}}\mathsurround\z@$\relax$=$\relax$\@@underline{\hbox{32s}}\mathsurround\z@$\relax. [Accessed: 20. Sep. 2023].
  • [16] Y. Wang, W. Song, Y. Lou, Y. Zhang, F. Huang, Z. Tu, and Q. Liang, “Rail Vehicle Localization and Mapping With LiDAR-Vision-Inertial-GNSS Fusion”, IEEE Robotics and Automation Letters, vol. 7, no. 4, 2022, doi: 10.48550/arXiv.2112.08563.
  • [17] J. Kremer and A. Grimm, “The RailMapper – A Dedicated Mobile LiDAR Mapping System for Railway Networks”, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 39, pp. 477-482, 2012, doi: 10.5194/isprsarchives-XXXIX-B5-477-2012.
  • [18] X. Yu, W. He, X. Qian, Y. Yang, T. Zhang, and L. Ou, “Real-time rail recognition based on 3D point clouds”, IOP Publishing, Measurement Science and Technology, vol. 33, no. 10, 2022, doi: 10.1088/1361-6501/ac750c.
  • [19] L. Gézero and C. Antunes, “Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments”, MDPI, Infrastructures, vol. 4, no. 3, art. no. 46, 2019, doi: 10.3390/infrastructures4030046.
  • [20] D. Stein, M. Spindler, J. Kuper, and M. Lauer, “Rail Detection Using Lidar Sensors”, International Journal of Sustainable Development and Planning, vol. 11, no. 1, pp. 65-78, 2016, doi: 10.2495/SDP-V11-N1-65-78.
  • [21] H. Wang, J. Berkers, N. van den Hurk, and N.F. Layegh, “Study of loaded versus unloaded measurements in railway track inspection”, Measurement, vol. 169, 2021, doi: 10.1016/j.measurement.2020.108556.
  • [22] A.K. Singh, A. Swarup, A. Agarwal, and D. Singh, “Vision based rail track extraction and monitoring through drone imagery”, ICT Express, vol. 5, no. 4, pp. 250-255, 2019, doi: 10.1016/j.icte.2017.11.010.
  • [23] P. Lesiak, “Inspection and Maintenance of Railway Infrastructure with the Use of Unmanned Aerial Vehicles”, Problemy Kolejnictwa – Railway Report, no. 188, pp. 115-127, 2020, doi: 10.36137/1883E.
  • [24] W-G. Jeon and E-M. Kim, “Automated Reconstruction of Railroad Rail Using Helicopter-borne Light Detection and Ranging in a Train Station”, Sensors and Materials, vol. 31, no. 10, pp. 3289-3302, 2019, doi: 10.18494/SAM.2019.2433.
  • [25] M. Neubert, R. Hecht, C. Gedrange, M. Trommler, H. Herold, T. Krüger, and F. Brimmer, “Extraction of railroad objects from very high resolution helicopter-borne lidar and ortho-image data”, in GEOBIA 2008. International Society for Photogrammetry and Remote Sensing, 2008. [Online]. Available:https://www.isprs.org/proceedings/ XXXVIII/4-C1/Sessions/Session9/6718_Neubert_Proc_Pap.pdf. [Accessed: 20. Sep. 2023].
  • [26] J. Li, B. Ma, and H. Dong, “Detection of the Rail Profile Wear Based on Image Processing”, in 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), 28-30 July 2020, Shenyang, China. IEEE, 2020, pp. 582-585, doi: 10.1109/ICPICS50287.2020.9201956.
  • [27] Z. Ragala, A. Retbi, and S. Bennani, “Railway track faults detection based on image processing using MobileNet”, in The 7th International Conference on Smart City Applications, 19-21 October 2022, Castelo Branco, Portugal. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-4/W3-2022, doi: 10.5194/isprs-archives-XLVIII-4-W3-2022-135-2022.
  • [28] S. Hussain, I. Mubeen, N. Ullah, S.S. Ud Din Shah, B.A. Khan, M. Zahoor , R. Ullah , F.A. Khan, and M.A. Sultan, “Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review”, BioMed Research International, vol. 2022, art. no. 5164970, 2022, doi: 10.1155/2022/5164970.
  • [29] E. Samson and M. Noseworthy, “A review of diagnostic imaging approaches to assessing Parkinson’s disease”, Brain Disorders, vol. 6, art. no. 100037, 2022, doi: 10.1016/j.dscb.2022.100037.
  • [30] J. Makuch, “Pomiary zużycia przekroju szyn w torach pętli tramwajowej”, Przegląd Komunikacyjny, no. 6, pp. 2-7, 2019.
  • [31] J.Makuch, “Badania kształtu przekroju krzyżownic tramwajowych”, Przegląd Komunikacyjny, no. 4-5, pp. 32-38, 2022.
  • [32] J. Makuch, I. Gisterek, A. Hyliński, P. Lalewicz, and J. Gonera, “Analiza możliwości wykorzystania skanera laserowego do diagnostyki krzyżownic tramwajowych”, in Nowoczesne Technologie i Systemy Zarządzania w Transporcie Szynowym. Zeszyty Naukowo-Techniczne Stowarzyszenia Inżynierów i Techników Komunikacji Rzeczpospolitej Polskiej, Materiały Konferencyjne, vol. 2 (119). Kraków, 2019, pp. 133-148.
  • [33] J. Makuch, I. Gisterek, A. Hyliński, P. Lalewicz, and J. Gonera, “Analiza możliwości wykorzystania skanera laserowego do diagnostyki torów tramwajowych”, in Nowoczesne Technologie i Systemy Zarządzania w Transporcie Szynowym. Zeszyty Naukowo-Techniczne Stowarzyszenia Inżynierów i Techników Komunikacji Rzeczpospolitej Polskiej, Materiały Konferencyjne, vol. 2 (123). Kraków, 2021, pp. 255-270.
  • [34] Tramwaje we Wrocławiu, Wikipedia. [Online]. Available:https://pl.wikipedia.org/wiki/Tramwaje_we_Wroc\T1\lawiu. [Accessed: 20. Sep. 2023].
  • [35] C. Kraśkiewicz, A. Zbiciak, W. Oleksiewicz, and W. Karwowski, “Static and dynamic parameters of railway tracks retrofitted with under sleeper pads”, Archives of Civil Engineering, vol. 64, no. 4, pp. 187-201, 2018, doi: 10.2478/ace-2018-0070.
  • [36] C. Kraśkiewicz, A. Zbiciak, and A. Al-Sabouini-Zawadzka, “Laboratory tests of resistance environmental conditions of prototypical under sleeper pads applied in the ballasted track structures”, Archives of Civil Engineering, vol. 67, no. 3, pp. 319-331, 2021, doi: 10.24425/ace.2021.138058.
  • [37] C. Kraśkiewicz, A. Zbiciak, J. Medyński, and A. Al-Sabouini-Zawadzka, “Laboratory tests of selected prototype under sleeper pads (USPs) – pull-off strength determined after the weather resistance test”, Archives of Civil Engineering, vol. 69, no. 2, pp. 483-501, 2023, doi: 10.24425/ace.2023.145280.
  • [38] C. Kraśkiewicz, H. Anysz, A. Zbiciak, M. Płudowska-Zagrajek, and A. Al-Sabouini-Zawadzka, “Artificial neural networks as a tool for selecting the parameters of prototypical under sleeper pads produced from recycled rubber granulate”, Journal of Cleaner Production, vol. 405, 2023, doi: 10.1016/j.jclepro.2023.136975.
  • [39] A. Saenthon, S. Kiatwanidvilai, and S. Pumyoy, "Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection”, Sensors and Materials, vol. 32, no. 2, pp. 549-561, 2020, doi: 10.18494/SAM.2020.2529.
  • [40] R. Tang, L. De Donato, N. Besinovic, F. Flammini, R.M.P. Goverde, Z. Lin, R. Liu, T. Tang, V. Vittorini, and Z. Wang, “A literature review of Artificial Intelligence applications in railway systems”, Transportation Research Part C: Emerging Technologies, vol. 140, art. no. 103679, 2022, doi: 10.1016/j.trc.2022.103679.
  • [41] C. Yang, Y. Sun, C. Ladubec, and Y. Liu, “Developing Machine Learning-Based Models for Railway Inspection”, MDPI, Applied Sciences, vol. 11, no. 1, 2021, doi: 10.3390/app11010013.
  • [42] S. Kaewunruen and M. H. Osman, “Dealing with disruptions in railway track inspection using riskbased machine learning”, Scientific Reports, vol. 13, pp. 695-701, 2023, doi: 10.1038/s41598-023-28866-9.
  • [43] M.C. Nakhaee, D. Hiemstra, M. Stoelinga, and M. van Noort, “The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey”, in Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification, RSSRail 2019. Springer, 2019, pp. 91-05. [Online]. Available:https://ris.utwente.nl/ws/portalfiles/portal/167171939/ChenariyanNakhaee2019recent.pdf. [Accessed: 20. Sep. 2023].
  • [44] B. Firlik and M. Tabaszewski, “Monitoring of the technical condition of tracks based on machine learning”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 234, no. 7, pp. 702-708, 2019, doi: 10.1177/0954409719866368.
  • [45] A. Ji, W.L. Woo, E.W.L. Wong, and Y.T. Quek, “Rail track condition monitoring: a review on deep learning approaches”, Intelligence and Robotics, vol. 1, no. 2, pp. 151-175, 2021, doi: 10.20517/ir.2021.14.
  • [46] K. Oh, M. Yoo, N. Jin, J. Ko, J. Seo, H. Joo, and M. Ko, “A Review of Deep Learning Applications for Railway Safety”, MDPI, Applied Sciences, vol. 12, no. 20, art. no. 10572, 2022, doi: 10.3390/app122010572.
  • [47] Z. Chen, Q. Wang, K. Yang, T. Yu, J. Yao, Y. Liu, P Wang, and Q. He, “Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-Scan Images”, Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 11, pp. 888-901, 2021, doi: 10.1177/03611981211021547.
  • [48] J. Sresakoolchai and S. Kaewunruen, “Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning”, MDPI, Vibration, vol. 4, no. 2, pp. 341-356, 2021, doi: 10.3390/vibration4020022.
  • [49] S. Lu, J. Wang, G. Jing, W. Qiang, and M.M. Rad, “Rail Defect Classification with Deep Learning Method”, Acta Polytechnica Hungarica, vol. 19, no. 6, 2022. [Online]. Available:http://acta.uni-obuda.hu/Lu_Wang_Jing_ Qiang_MovahediRad_124.pdf. [Accessed: 20. Sep. 2023].
  • [50] Y. Santur, M. Karaköse, and E. Akın, “A New Rail Inspection Method Based on Deep Learning UsingLaser Cameras”, in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 16-17 September 2017, Malatya, Turkey. IEEE, 2017, doi: 10.1109/IDAP.2017.8090245.
  • [51] X. Gibert, V. M. Patel, and R. Chellappa, “Deep Multitask Learning for Railway Track Inspection”, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 153-164, 2017, doi: 10.1109/TITS.2016.2568758.
  • [52] D. Zheng, L. Li, S. Zheng, X. Chai, S. Zhao, Q. Tong, J. Wang, and L. Guo, “A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network”, Hindawi, Computational Intelligence and Neuroscience, vol. 2021, art. no. 2565500, 2021, doi: 10.1155/2021/2565500.
  • [53] H. Bałuch and I. Nowosińska, “Application of Artificial Neural Networks in Planning Track Superstructure Repairs”, Archives of Civil Engineering, vol. 66, no. 4, pp. 45-60, 2020, doi: 10.24425/ACE.2020.135208.
  • [54] H. Acikgoz and D. Korkmaz, “MSRConvNet: Classification of railway track defects using multi-scale residual convolutional neural network”, Engineering Applications of Artificial Intelligence, vol. 121, art. no. 105965, 2023, doi: 10.1016/j.engappai.2023.105965.
  • [55] M. Tabaszewski and B. Firlik, “Detection of the Presence of Rail Corrugation Using Convolutional Neural Network”, Engineering Transactions, vol. 70, no. 4, pp. 339-353, 2022, doi: 10.24423/EngTrans.2241.20221116.
  • [56] Z.M. Cınar, A.A. Nuhu, Q. Zeeshan, O. Korhan, M. Asmael, and B. Safaei, “Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0”, MDPI, Sustainability, vol. 12, no. 19, art. no. 8211, 2020, doi: 10.3390/su12198211.
  • [57] J.C.P. Cheng, W. Chen, K. Chen, and Q. Wang, “Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms”, Automation in Construction, vol. 112, art. no. 103087, 2020, doi: 10.1016/j.autcon.2020.103087.
  • [58] S. Goodarzi, H.F. Kashani, J. Oke, and C.L. Ho, “Data-driven methods to predict track degradation: A case study”, Construction and Building Materials, vol. 344, art. no. 128166, 2022, doi: 10.1016/j.conbuildmat.2022.128166.
  • [59] N. Davari, B. Veloso, G.A. Costa, P.M. Pereira, R.P. Ribeiro, and J. Gama, “A Survey on Data-Driven Predictive Maintenance for the Railway Industry”, MDPI, Sensors, vol. 21, no. 17, art. no. 5739, 2021, doi: 10.3390/s21175739.
  • [60] J. Neuhold, M. Landgraf, S. Marschnig, and P. Veit, “Measurement Data-Driven Life-Cycle Management of Railway Track”, Transportation Research Record, vol. 2674, no. 11, pp. 685-696, 2020, doi: 10.1177/0361198120946007.
  • [61] S. Sharma, Y. Cui, Q. He, R. Mohammadia, and Z. Lic, “Data-driven optimization of railway maintenance for track geometry”, Transportation Research Part C: Emerging Technologies, vol. 90, pp. 34-58, 2018, doi: 10.1016/j.trc.2018.02.019.
  • [62] C. Letot, P. Dersin, M. Pugnaloni, P. Dehombreux, G. Fleurquin, C. Douziech, and P. La-Cascia, “A data driven degradation-based model for the maintenance of turnouts: a case study”, IFAC-PapersOnLine, vol. 48, no. 21, pp. 958-963, 2015, doi: 10.1016/j.ifacol.2015.09.650.
  • [63] D. Ou, R. Xue, and K. Cui, “A Data-Driven Fault Diagnosis Method for Railway Turnouts”, Transportation Research Record, vol. 2673, no. 4, pp. 448-457, 2019, doi: 10.1177/0361198119837222.
  • [64] D. Kronmuller, “Accelerating Reliability – Centered Maintenance”, Global Railway Review. [Online Webinar, 27 May 2021]. Available:https://www.globalrailwayreview.com/webinar/121221/a-data-driven-approach-to-reliability-centred-maintenance/. [Accessed: 20. Sep. 2023].
  • [65] F. Balouchi, A. Bevan, and R. Formston, “Development of railway track condition monitoring from multi-train in-service vehicles”, Vehicle System Dynamics, International Journal of Vehicle Mechanics and Mobility, vol. 59, no. 9, pp. 1397-1417, 2021, doi: 10.1080/00423114.2020.1755045.
  • [66] H. Tsunashima, H. Ono, T. Takata, and S. Ogata, “Development and Operation of Track Condition Monitoring System Using In-Service Train”, MDPI, Applied Sciences, vol. 13, no. 6, art. no. 3835, 2023, doi: 10.3390/app13063835.
  • [67] Alstom, Press releases and news, “First infrastructure digital inspection for the Madrid Light train network”. [Online]. Available:https://www.alstom.com/press-releases-news/2020/9/first-infrastructure-digital-inspection-madrid-light-train-network. [Accessed: 20. Sep. 2023].
  • [68] R. Hobsch and L. Ammann, “Bring a new dimension to your public transportation network”, Global Railway Review. [Online Webinar, 2 Dec 2020]. Available:https://www.globalrailwayreview.com/webinar/112414/bringing-a-new-dimension-to-your-public-transportation-network/. [Accessed: 20. Sep. 2023].
  • [69] P. Pałyga and M. Migdal, “Projekt automatyzacji obchodów torów (AOT) w PKP Polskie Linie Kolejowe S.A.”, Nowoczesne Technologie i Systemy Zarządzania w Transporcie Szynowym. Kraków: Stowarzyszenie Inżynierów i Techników Komunikacji Rzeczpospolitej Polskiej, 2022, pp. 427-437.
  • [70] E. Kent, “AIVR – Delivering Digital Inspection”, presented at PWI: Railway Infrastructure – Delivering Digitally, 2 March 2023, Manchester, Great Britain, 2023. [Online]. Available:https://www.youtube.com/watch?v$=$\relax$\@@underline{\hbox{ibabAduBxR0}}\mathsurround\z@$\relax. [Accessed: 20. Sep. 2023].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e3950d04-c36e-4f48-b0d0-722f571c20a2
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