Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 6, 20 February 2024


Open Access | Article

A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

Weicheng Wang * 1 , Jinye Ma 2
1 Moscow Aviation Institute
2 Moscow Aviation Institute

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 6, 54-72 Advances in Engineering Innovation,
Published 20 February 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Weicheng Wang, Jinye Ma. A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering. AEI (2024) Vol. 6: 54-72.

Abstract

The domain of aeronautical engineering and aero-engine engineering has witnessed considerable interest in the application of machine learning (ML) and deep learning (DL) techniques, revolutionizing various aspects of the field. This review provides a comprehensive review of the application of ML and DL in aerospace engineering and aero-engine engineering, focusing on aircraft aerodynamics, CFD, aircraft design and aeroacoustics and for aero-engine engineering focusing on health state evaluation, component optimization, blade defect detection and combustion. The review highlights the advantages and challenges of ML methods, presenting key concepts and strategies for ML. Furthermore, in terms of technical applications, DL has the potential to be on par with ML, despite being a branch of ML. The review further emphasizes the pressing requirement for a comprehensive examination of DL techniques concerning data-driven challenges within the realms of aerospace engineering and aero-engine engineering. It introduces representative DL methods and presents their mathematical definitions and illustrative applications.

Keywords

Machine learning, deep learning, aerospace engineering, aero-engine engineering, aerodynamic

References

1. European Commission. Directorate-General for Mobility and Transport, Directorate-General for Research and Innovation, Flightpath 2050: Europe’s vision for aviation: maintaining global leadership and serving society’s needs[J]. 2011.

2. Åkerman J. Sustainable air transport––on track in 2050[J]. Transportation Research Part D: Transport and Environment, 2005, 10(2): 111-126.

3. Goel A, Goel A K, Kumar A. The role of artificial neural network and machine learning in utilizing spatial information[J]. Spatial Information Research, 2023, 31(3): 275-285.

4. Grant C. Automated processes for composite aircraft structure[J]. Industrial Robot: An International Journal, 2006, 33(2): 117-121.

5. Corrochano, A.F. Neves, B. Khanal, S. Le Clainche, N.J. Lawson, Des of a slingsby firefly aircraft: unsteady flow feature extraction using pod and hodmd, J. Aerosp. Eng. 35(5) (2022) 04022063.

6. Brunton S L, Nathan Kutz J, Manohar K, et al. Data-driven aerospace engineering: reframing the industry with machine learning[J]. AIAA Journal, 2021, 59(8): 2820-2847.

7. Kani JN and Elsheikh AH. Dr-rnn: a deep residual recurrent neural network for model reduction. arXiv preprint arXiv:170900939, 2017.

8. Wu Y, Feng J. Development and application of artificial neural network[J]. Wireless Personal Communications, 2018, 102: 1645-1656.

9. Bu H, Huang X, Zhang X. An overview of testing methods for aeroengine fan noise[J]. Progress in Aerospace Sciences, 2021, 124: 100722.

10. Filippone A. Aircraft noise prediction[J]. Progress in Aerospace Sciences, 2014, 68: 27-63.

11. Liu Y, Dowling A P, Swaminathan N, et al. Prediction of combustion noise for an aeroengine combustor[J]. Journal of Propulsion and Power, 2014, 30(1): 114-122.

12. Moore A. A 3D prediction of the wing reflection of aero engine noise[C]//10th AIAA/CEAS Aeroacoustics Conf.. 2004: 2865.

13. Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255-260.

14. El Naqa I, Murphy M J. What is machine learning? [M]. Springer International Publishing, 2015.

15. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436-444.

16. Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016.

17. Mendez C, Le Clainche S, Moreno-Ramos R, et al. A new automatic, very efficient method for the analysis of flight flutter testing data[J]. Aerospace Science and Technology, 2021, 114: 106749.

18. Berke L, Patnaik S N, Murthy P L N. Optimum design of aerospace structural components using neural networks[J]. Computers & structures, 1993, 48(6): 1001-1010.

19. Hagan M T, Menhaj M B. Training feedforward networks with the Marquardt algorithm[J]. IEEE transactions on Neural Networks, 1994, 5(6): 989-993.

20. Rojas R. Neural networks: a systematic introduction[M]. Springer Science & Business Media, 2013.

21. Cunningham P, Cord M, Delany S J. Supervised learning[M]//Machine learning techniques for multimedia: case studies on organization and retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008: 21-49.

22. Le Clainche S, Ferrer E, Gibson S, et al. Improving aircraft performance using machine learning: a review[J]. Aerospace Science and Technology, 2023: 108354.

23. Alloghani M, Al-Jumeily D, Mustafina J, et al. A systematic review on supervised and unsupervised machine learning algorithms for data science[J]. Supervised and unsupervised learning for data science, 2020: 3-21.

24. Sorzano C O S, Vargas J, Montano A P. A survey of dimensionality reduction techniques[J]. arXiv preprint arXiv:1403.2877, 2014.

25. Van Der Maaten L, Postma E, Van den Herik J. Dimensionality reduction: a comparative[J]. J Mach Learn Res, 2009, 10(66-71).

26. Pezzotti N, Höllt T, Lelieveldt B, et al. Hierarchical stochastic neighbor embedding[C]//Computer Graphics Forum. 2016, 35(3): 21-30.

27. Kaelbling L P, Littman M L, Moore A W. Reinforcement learning: A survey[J]. Journal of artificial intelligence research, 1996, 4: 237-285.

28. Hester T, Vecerik M, Pietquin O, et al. Deep q-learning from demonstrations[C]//Proc. of the AAAI Conf. on artificial intelligence. 2018, 32(1).

29. Zhang W, Li H, Li Y, et al. Application of deep learning algorithms in geotechnical engineering: a short critical review[J]. Artificial Intelligence Review, 2021: 1-41.

30. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proc. of the thirteenth international Conf. on artificial intelligence and statistics. JMLR Workshop and Conf. Proc., 2010: 249-256.

31. Shrestha A, Mahmood A (2019) Review of deep learning algorithms and architectures. IEEE Access 7:53040–53065

32. Rumelhart D E, Durbin R, Golden R, et al. Backpropagation: The basic theory[M]//Backpropagation. Psychology Press, 2013: 1-34.

33. Medsker L R, Jain L C. Recurrent neural networks[J]. Design and Applications, 2001, 5(64-67): 2.

34. Pascanu R, Gulcehre C, Cho K, et al. How to construct deep recurrent neural networks[J]. arXiv preprint arXiv:1312.6026, 2013.

35. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.

36. Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks[J]. Pattern recognition, 2018, 77: 354-377.

37. O'Shea K, Nash R. An introduction to convolutional neural networks[J]. arXiv preprint arXiv:1511.08458, 2015.

38. Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: An overview[J]. IEEE signal processing magazine, 2018, 35(1): 53-65.

39. Abbas A, De Vicente J, Valero E. Aerodynamic technologies to improve aircraft performance[J]. Aerospace science and technology, 2013, 28(1): 100-132.

40. Swaddle J P, Lockwood R. Wingtip shape and flight performance in the European Starling Sturnus vulgaris[J]. Ibis, 2003, 145(3): 457-464.

41. K. Li, J. Kou, W. Zhang, Deep learning for multi-fidelity aerodynamic distribution modelling from experimental and simulation data, arXiv:2019 .12966v2, 2021.

42. Ling J, Kurzawski A, Templeton J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics, 2016, 807: 155-166.

43. E. Iuliano, E.A. Pérez, Application of Surrogate-Based Global Optimization to Aerodynamic Design, Springer, New York, 2016.

44. N. Van Nguyen, J. Lee, M. Tyan, et al., Repetitively enhanced neural networks method for complex engineering design optimisation problems, Aeronaut. J. 119 (2015) 1253–1270.

45. J.N. Kani, A.H. Elsheikh, Dr-rnn: a deep residual recurrent neural network for model reduction, arXiv:1709 .00939[cs .CE], 2017.

46. M.A.A. Oroumieh, S.M.B. Malaek, M. Ashrafizaadeh, et al., Dr-rnn: a deep residual recurrent neural network for model reduction, Aerosp. Sci. Technol. 26 (2013) 244–258.

47. S. Suresh, S.N. Omkar, V. Mani, T.N. Guru Prakash, Lift coefficient prediction at high angle of attack using recurrent neural network, Aerosp. Sci. Technol. 7(8) (2003) 595–602.

48. N.R. Secco, B.S.d. Mattos, Artificial neural networks to predict aerodynamic coefficients of transport airplanes, Aircr. Eng. Aerosp. Technol. 89 (2017) 211–230.

49. W. Hou, D. Darakananda, J.D. Eldredge, Machine-learning-based detection of aerodynamic disturbances using surface pressure measurements, AIAA J. 57(12) (2019) 5079–5093.

50. X. Wang, J. Kou, W. Zhang, Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks, Proc. Inst. Mech. Eng., G J. Aerosp. Eng. 233(14) (2019) 5107–5121.

51. Tao, J. and Sun, G., "Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization", Aerospace Science and Technology, vol. 92, pp. 722-737, Sep. 2019.

52. G. Sun, S. Wang, A review of the artificial neural network surrogate model-ing in aerodynamic design, Proc. Inst. Mech. Eng., G J. Aerosp. Eng. 233(16) (2019) 5863–5872.

53. Li J, Du X, Martins J R R A. Machine learning in aerodynamic shape optimization[J]. Progress in Aerospace Sciences, 2022, 134: 100849.

54. G. Sun, Y. Sun, S. Wang, Artificial neural network based inverse design airfoils and wings, Aerosp. Sci. Technol. 42 (2015) 415–428.

55. Xiang Z, Bao Y, Tang Z, et al. Deep reinforcement learning-based sampling method for structural reliability assessment[J]. Reliability Engineering & System Safety, 2020, 199: 106901.

56. Yasuda H, Yang J. Wingtip Deflection Monitoring and Prediction Based on Digital Image Correlation and Machine Learning Techniques[C]//European Workshop on Structural Health Monitoring. Cham: Springer International Publishing, 2022: 409-416.

57. Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, Charles-Alban Deledalle, Machine learning in acoustics: theory and applications, J. Acoust. Soc. Am. 146(5) (2019) 3590–3628.

58. Stéphane Moreau, The third golden age of aeroacoustics, Phys. Fluids 34(3) (2022) 031301.

59. Williams J E F. Aeroacoustics[J]. Annual Review of Fluid Mechanics, 1977, 9(1): 447-468.

60. Keating A, Beedy J, Shock R. Lattice Boltzmann simulations of the DLR-F4, DLR-F6 and variants[C]//46th AIAA Aerospace Sciences Meeting and Exhibit. 2008: 749.

61. Kusano K, Yamada K, Furukawa M. Aeroacoustic simulation of broadband sound generated from low-Mach-number flows using a lattice Boltzmann method[J]. Journal of Sound and Vibration, 2020, 467: 115044.

62. Zhenyu Tang, Hsien-Yu Meng, Dinesh Manocha, Learning acoustic scattering fields for dynamic interactive sound propagation, in: 2021 IEEE Virtual Reality and 3D User Interfaces (VR), 2021, pp.835–844.

63. Antonio Alguacil, Michaël Bauerheim, Marc C. Jacob, Stéphane Moreau, Pre-dicting the propagation of acoustic waves using deep convolutional neural networks, J. Sound Vib. 512 (2021) 116285.

64. Damjan Kužnar, Martin Možina, Marina Giordanino, Ivan Bratko, Improving vehicle aeroacoustics using machine learning, Eng. Appl. Artif. Intell. 25(5) (2012) 1053–1061.

65. Mario Rüttgers, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder, Andreas Lintermann, Prediction of acoustic fields using a lattice-Boltzmann method and deep learning, in: Heike Jagode, Hartwig Anzt, Guido Juckeland, Hatem Ltaief (Eds.), High Performance Computing, Springer International Publishing, Cham, 2020, pp.81–101.

66. Ghulam Moeen Uddin, Sajawal Gul Niazi, SyedMuhammad Arafat, Muham-madSajid Kamran, Muhammad Farooq, Nasir Hayat, Sher Afghan Malik, Abe Zeid, Sagar Kamarthi, Sania Saqib, IjazAhmad Chaudhry, Neural networks assisted computational aero-acoustic analysis of an isolated tire, Proc. Inst. Mech. Eng., Part D, J. Automob. Eng. 234(10–11) (2020) 2561–2577.

67. Jiaqing Kou, Laura Botero-Bolívar, Román Ballano, Oscar Marino, Leandro deSantana, Eusebio Valero, Esteban Ferrer, Aeroacoustic airfoil shape optimization enhanced by autoencoders, 2023.

68. Tensorflow documentation, https://www.tensorflow.org/ (accessed 9 May 2020).

69. Wu S, Akbarov A. Support Vector Regression for Warranty Claim Forecasting. European Journal of Operational Research 2011; 213(1): 196-204.

70. Shin J H, Jun H B. On condition based maintenance policy[J]. Journal of Computational Design and Engineering, 2015, 2(2): 119-127.

71. Matuszczak M, Żbikowski M, Teodorczyk A. Predictive modelling of turbofan engine components condition using machine and deep learning methods[J]. Eksploatacja i Niezawodność, 2021, 23(2): 359-370.

72. Tayarani-Bathaie S S, Vanini Z N S, Khorasani K. Dynamic neural network-based fault diagnosis of gas turbine engines[J]. Neurocomputing, 2014, 125: 153-165.

73. De Giorgi M G, Campilongo S, Ficarella A. A diagnostics tool for aero-engines health monitoring using machine learning technique[J]. Energy Procedia, 2018, 148: 860-867.

74. De Giorgi M G, Campilongo S, Ficarella A. Development of a real time intelligent health monitoring platform for aero-engine[C]//MATEC Web of Conf.s. EDP Sciences, 2018, 233: 00007.

75. Jayachandran A V T, Omar H H, Tkachenko A Y. Machine learning predictor for micro gas turbine performance evaluation[J]. Aeron Aero Open Access J., 2020, 4(4): 172-180.

76. Kuz'michev V S, Tkachenko A Y, Krupenich I N, et al. Composing a virtual model of gas turbine engine working process using the CAE system" ASTRA[J]. Research Journal of Applied Sciences, 2014, 9(10): 635-643.

77. Bartolini CM, Caresana F, Comodi G, et al. Application of artificial neural networks to micro gas turbines. Energy Convers Manag. 2011;52(1):781–788.

78. Chipperfield A, Fleming P. Multi objective gas turbine engine controller design using genetic algorithms. IEEE Transactions on Industrial Electronics. 1996;43(5):583–587.

79. Denton, J., 1997. “Lessons from rotor 37”. Journal of Thermal Science, 6(1), pp. 1–13.

80. Joly, M, Sarkar, S, & Mehta, D. "Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression." Proc of the ASME Turbo Expo 2018: Turbomachinery Technical Conf. and Exposition. Volume 2C: Turbomachinery. Oslo, Norway. June 11–15, 2018. V02CT42A050. ASME.

81. J. Aust, A. Mitrovic, and D. Pons, ‘‘Assessment of the effect of cleanliness on the visual inspection of aircraft engine blades: An eye tracking study,’’ Sensors, vol. 21, no. 18, p. 6135, Sep. 2021

82. Joly M, Sarkar S, Mehta D. Machine learning enabled adaptive optimization of a transonic compressor rotor with pre-compression[C]//Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2018, 51012: V02CT42A050.

83. B. Zhao, L. Xie, H. Li, S. Zhang, B. Wang, and C. Li, ‘‘Reliability analysis of aero-engine compressor rotor system considering cruise characteristics,’’ IEEE Trans. Rel., vol. 69, no. 1, pp. 245–259, Mar. 2020.

84. B. Sasi, B. P. C. Rao, and T. Jayakumar, ‘‘Dual-frequency eddy current non-destructive detection of fatigue cracks in compressor discs of aero engines,’’ Defence Sci. J., vol. 54, no. 4, pp. 563–570, Oct. 2004.

85. V. Ageeva, T. Stratoudaki, M. Clark, and M. G. Somekh, ‘‘Integrative solution for in-situ ultrasonic inspection of aero-engine blades using endoscopic cheap optical transducers (CHOTs),’’ in Proc. 5th Int. Symp. NDT Aerosp., 2013, pp. 1–9.

86. F. Zou, ‘‘Review of aero-engine defect detection technology,’’ in Proc. IEEE 4th Inf. Technol., Netw., Electron. Autom. Control Conf. (ITNEC), vol. 1, Jun. 2020, pp. 1524–1527.

87. W. K. Wong, S. H. Ng, and K. Xu, ‘‘A statistical investigation and optimization of an industrial radiography inspection process for aero-engine components,’’ Qual. Rel. Eng. Int., vol. 22, no. 3, pp. 321–334, 2006.

88. D. Li, Y. Li, Q. Xie, Y. Wu, Z. Yu, and J. Wang, ‘‘Tiny defect detection in high-resolution aero-engine blade images via a coarse-to-fine framework,’’ IEEE Trans. Instrum. Meas., vol. 70, pp. 1–12, 2021.

89. Biglari and W. Tang, ‘‘A review of embedded machine learning based on hardware, application, and sensing scheme,’’ Sensors, vol. 23, no. 4,p. 2131, Feb. 2023.

90. H. F. Le, L. J. Zhang, and Y. X. Liu, ‘‘Surface defect detection of industrial parts based on YOLOv5,’’ IEEE Access, vol. 10, pp. 130784–130794,2022.

91. Y. D. V. Yasuda, F. A. M. Cappabianco, L. E. G. Martins, and J. A. B. Gripp, ‘‘Aircraft visual inspection: A systematic literature review,’’ Comput. Ind., vol. 141, Oct. 2022, Art. no. 103695.

92. X. Tao, X. Gong, X. Zhang, S. Yan, and C. Adak, ‘‘Deep learning for unsupervised anomaly localization in industrial images: A survey,’’ IEEE Trans. Instrum. Meas., vol. 71, pp. 1–21, 2022

93. Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos, Machine learning for fluid mechanics, Annu. Rev. Fluid Mech. 52 (2020) 477–508.

94. R.W. Bilger, The role of combustion technology in the 21st century, 2011.

95. Stephen B. Pope, Small scales, many species and the manifold challenges of turbulent combustion, Proc. Combust. Inst. 34 (2013) 1–31.

96. E. Mastorakos, A. Giusti, Turbulent combustion modelling and experiments: recent trends and developments, Flow Turbul. Combust. 103() (11.2019) 847–869.

97. Kamila Zdybał, Mohammad Rafi Malik, Axel Coussement, James C. Sutherland, Alessandro Parente, Reduced-order modeling of reactive flows using data-driven approaches, in: N. Swaminathan, A. Parente (Eds.), Lecture Notes in Energy: Machine Learning and Its Application to Reacting Flows, Springer, 2022. Chapter 9.

98. Jacqueline H. Chen, Petascale direct numerical simulation of turbulent combustion -fundamental insights towards predictive models, Proc. Combust. Inst. 33 (2011) 99–123.

99. Matthias Ihme, Wai Tong Chung, Aashwin Ananda Mishra, Combustion ma-chine learning: principles, progress and prospects, Prog. Energy Combust. Sci. 91 (2022) 101010.

100. I.T. Jolliffe, Principal Component Analysis, Springer, New York, 1986.

101. Ulrich Maas, Dominique Thévenin, Correlation analysis of direct numerical simulation data of turbulent non-premixed flames, in: Symp (International) on Combustion, vol.27, 1998, pp.1183–1189.

102. C.E. Frouzakis, Y.G. Kevrekidis, J. Lee, K. Boulouchos, A.A. Alonso, Proper orthogonal decomposition of direct numerical simulation data: data reduction and observer construction, Proc. Combust. Inst. 28 (1.2000) 75–81.

103. R.S. Barlow, J.H. Frank, Effects of turbulence on species mass fractions in methane/air jet flames, 1998.

104. R.S. Barlow, G.J. Fiechtner, C.D. Carter, J.-Y. Chen, Experiments on the scalar structure of turbulent co/h 2 /n 2 jet flames, 2000.

105. Evatt R. Hawkes, Ramanan Sankaran, Jacqueline H. Chen, Sebastian A. Kaiser, Jonathan H. Frank, An analysis of lower-dimensional approximations to the scalar dissipation rate using direct numerical simulations of plane jet flames, Proc. Combust. Inst. 32(1) (2009) 1455–1463.

106. Naveen Punati, James C. Sutherland, Alan R. Kerstein, Evatt R. Hawkes, Jacque-line H. Chen, An evaluation of the one-dimensional turbulence model: com-parison with direct numerical simulations of co/h2 jets with extinction and reignition, Proc. Combust. Inst. 33 (2011) 1515–1522.

107. Naveen Punati, James C. Sutherland, Alan R. Kerstein, Evatt R. Hawkes, Jacque-line H. Chen, An evaluation of the one-dimensional turbulence model: com-parison with direct numerical simulations of co/h2 jets with extinction and reignition, Proc. Combust. Inst. 33 (2011) 1515–1522.

108. A.A. Mascarenhas, G. Bansal, J.H. Chen, Identification of intrinsic low dimensional manifolds in turbulent combustion using an isomap based technique, Technical report, Sandia National Lab. (SNL-CA), Livermore, California, United States, 2011.

109. Ehsan Fooladgar, Pál Tóth, Christophe Duwig, Characterization of flameless combustion in a model gas turbine combustor using a novel post-processing tool, Combust. Flame 204 (6.2019) 356–367.

110. Kamila Zdybał, Giuseppe D’Alessio, Gianmarco Aversano, Mohammad Rafi Malik, Axel Coussement, James C. Sutherland, Alessandro Parente, Advancing reactive flow simulations with data-driven models, in: Miguel A. Mendez, Andrea Ianiro, Bernd R. Noack, Steven L. Brunton (Eds.), Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning, Cambridge University Press, 2022. Chapter 15.

111. Kaidi Wan, Sandra Hartl, Luc Vervisch, Pascale Domingo, Robert S. Barlow, Christian Hasse, Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements, Combust. Flame 219 (9.2020) 268–274.

112. Kherlen Jigjid, Chitoshi Tamaoki, Yuki Minamoto, Ryota Nakazawa, Nakamasa Inoue, Mamoru Tanahashi, Data driven analysis and prediction of mild combustion mode, Combust. Flame 223 (1.2021) 474–485.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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20 February 2024
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Advances in Engineering Innovation
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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