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Advancements in IoT system security: a reconfigurable intelligent surfaces and backscatter communication approach

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Abstract

In 6 G wireless networks, secure communication is crucial due to the inherent susceptibility of electromagnetic waves to eavesdropping. Reconfigurable intelligent surfaces (RIS) and backscatter communication technologies offer promising solutions by securely directing signals to authorized users, even in the presence of multiple passive eavesdroppers equipped with multi-antenna setups. This paper proposes a novel RIS-enhanced backscatter communication system that utilizes radio frequency (RF) signals from a power beacon (PB) to transmit confidential information to multiple authorized users, each equipped with a single antenna. To optimize system performance, the deep deterministic policy gradient (DDPG) algorithm is employed to dynamically control RIS beamforming and mitigate eavesdropping attempts by adversaries using linear decoding techniques. Simulation results demonstrate that the proposed DDPG-based strategy significantly improves multicast secrecy rates while satisfying transmit power and unit modulus constraints. Compared to conventional optimization methods, the DDPG algorithm enhances the alignment of RIS reflections toward intended users and minimizes signal leakage to eavesdroppers. This research highlights how RIS and backscatter communication technologies can enhance security and energy efficiency in 6 G networks, providing a scalable solution to reduce eavesdropping threats in future wireless systems.

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References

  1. Shiu Y-S, Chang SY, Wu H-C, Huang SC-H, Chen H-H (2011) Physical layer security in wireless networks: a tutorial. IEEE Wirel Commun 18(2):66–74. https://doi.org/10.1109/MWC.2011.5751298

    Article  MATH  Google Scholar 

  2. Mukherjee A, Fakoorian SAA, Huang J, Swindlehurst AL (2014) Principles of physical layer security in multiuser wireless networks: a survey. IEEE Commun Surv Tutor 16(3):1550–1573. https://doi.org/10.1109/SURV.2014.012314.00178

    Article  MATH  Google Scholar 

  3. Feng Y, Yan S, Yang Z, Yang N, Yuan J (2018) User and relay selection with artificial noise to enhance physical layer security. IEEE Trans Veh Technol 67(11):10906–10920. https://doi.org/10.1109/TVT.2018.2870280

    Article  MATH  Google Scholar 

  4. Li Q, Yang L (2019) Beamforming for cooperative secure transmission in cognitive two-way relay networks. IEEE Trans Inf Forensics Secur 15:130–143. https://doi.org/10.1109/TIFS.2019.2918431

    Article  MATH  Google Scholar 

  5. Wang W, Teh KC, Li KH (2017) Artificial noise aided physical layer security in multi-antenna small-cell networks. IEEE Trans Inf Forensics Secur 12(6):1470–1482. https://doi.org/10.1109/TIFS.2017.2663336

    Article  MATH  Google Scholar 

  6. Trappe W (2015) The challenges facing physical layer security. IEEE Commun Mag 53(6):16–20. https://doi.org/10.1109/MCOM.2015.7120011

    Article  MATH  Google Scholar 

  7. Jin C, Hu F, Ling Z, Mao Z, Chang Z, Li C (2022) Transmission optimization and resource allocation for wireless powered dense vehicle area network with energy recycling. IEEE Trans Veh Technol 71(11):12291–12303. https://doi.org/10.1109/TVT.2022.3195216

    Article  Google Scholar 

  8. Lipps C, Herbst J, Reddy R, Franke L, Becker S, Rahm M, Schotten HD (2022) Reconfigurable intelligent surfaces: a physical layer security perspective. In: 2022 4th International Conference on Data Intelligence and Security (ICDIS), pp. 174–181. IEEE

  9. Zhang S, Huang W, Liu Y (2024) A systematic survey on physical layer security oriented to reconfigurable intelligent surface empowered 6g. Comput Secur. https://doi.org/10.1016/j.cose.2024.104100

    Article  MATH  Google Scholar 

  10. Ahmed M, Wahid A, Laique SS, Khan WU, Ihsan A, Xu F, Chatzinotas S, Han Z (2023) A survey on STAR-RIS: use cases, recent advances, and future research challenges. IEEE Internet Things J. 10(16):14689–14711. https://doi.org/10.1109/JIOT.2023.3279357

    Article  Google Scholar 

  11. Zhu Y, Mao B, Kato N (2022) A dynamic task scheduling strategy for multi-access edge computing in IRS-aided vehicular networks. IEEE Trans Emerg Top Comput 10(4):1761–1771. https://doi.org/10.1109/TETC.2022.3153494

    Article  MATH  Google Scholar 

  12. Hashida H, Kawamoto Y, Kato N, Iwabuchi M, Murakami T (2022) Mobility-aware user association strategy for IRS-aided mm-wave multibeam transmission towards 6G. IEEE J Sel Areas Commun 40(5):1667–1678. https://doi.org/10.1109/JSAC.2022.3143216

    Article  Google Scholar 

  13. Hashida H, Kawamoto Y, Kato N (2020) Intelligent reflecting surface placement optimization in air-ground communication networks toward 6G. IEEE Wirel Commun 27(6):146–151. https://doi.org/10.1109/MWC.001.2000142

    Article  MATH  Google Scholar 

  14. Xu S, Liu J, Rodrigues TK, Kato N (2022) Robust multiuser beamforming for IRS-enhanced near-space downlink communications coexisting with satellite system. IEEE Internet Things J 9(16):14900–14912. https://doi.org/10.1109/JIOT.2021.3112595

    Article  Google Scholar 

  15. Abideen SZU, Wahid A, Kamal MM (2024) Adaptive security solutions for noma networks: the role of ddpg and RIS-equipped UAVS. Int J Electr, Energy Power Syst Eng 7(3):158–174

    Google Scholar 

  16. Liu R, Zheng S, Wu Q, Jiang Y, Zhang N, Liu Y, Di Renzo M, et al (2024) Sustainable wireless networks via reconfigurable intelligent surfaces (RISS): Overview of the etsi isg ris. arXiv preprint arXiv:2406.05647

  17. Waleed S, Ullah I, Khan WU, Rehman AU, Rahman T, Li S (2021) Resource allocation of 5g network by exploiting particle swarm optimization. Iran J Comput Sci 4(3):211–219

    Article  MATH  Google Scholar 

  18. Han K, Huang K (2017) Wirelessly powered backscatter communication networks: modeling, coverage, and capacity. IEEE Trans Wirel Commun 16(4):2548–2561. https://doi.org/10.1109/TWC.2017.2665629

    Article  MATH  Google Scholar 

  19. Abideen SZU, Wahid A, Kamal MM, Hussain T, Jan N (2024) Enhancing noma network security with RIS-UAV integration: exploring PPO technique. Asian Bull. Big Data Manag 4(02):4

    Article  Google Scholar 

  20. Wahid A, Abideen SZU, Ahmed M, Khan WU, Sheraz M, Chee T, Lee YL (2024) Advanced security measures in coupled phase-shift STAR-RIS networks: a DRL approach. J King Saud Univ-Comput Inf Sci 36(9):102215

    Google Scholar 

  21. Zhou H, Hu C, Liu X (2024) An overview of machine learning-enabled optimization for reconfigurable intelligent surfaces-aided 6g networks: From reinforcement learning to large language models. arXiv preprint arXiv:2405.17439

  22. Puspitasari AA, Lee BM (2023) A survey on reinforcement learning for reconfigurable intelligent surfaces in wireless communications. Sensors 23(5):2554

    Article  MATH  Google Scholar 

  23. Li B, Liu W, Xie W (2024) Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: a DRL approach. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2024.03.002

    Article  MATH  Google Scholar 

  24. Tang W, Dai JY, Chen M, Li X, Cheng Q, Jin S, Wong K-K, Cui TJ (2019) Programmable metasurface-based RF chain-free 8PSK wireless transmitter. Electron Lett 55(7):417–420

    Article  Google Scholar 

  25. Tang W, Dai JY, Chen MZ, Wong K-K, Li X, Zhao X, Jin S, Cheng Q, Cui TJ (2020) MIMO transmission through reconfigurable intelligent surface: system design, analysis, and implementation. IEEE J Sel Areas Commun 38(11):2683–2699. https://doi.org/10.1109/JSAC.2020.3007055

    Article  MATH  Google Scholar 

  26. Jung M, Saad W, Debbah M, Hong CS (2021) On the optimality of reconfigurable intelligent surfaces (RISS): passive beamforming, modulation, and resource allocation. IEEE Trans Wireless Commun 20(7):4347–4363. https://doi.org/10.1109/TWC.2021.3058366

    Article  MATH  Google Scholar 

  27. Khan I, Zhang K, Wu Q, Ullah I, Ali L, Ullah H, Rahman SU (2022) A wideband high-isolation microstrip MIMO circularly-polarized antenna based on parasitic elements. Materials 16(1):103

    Article  MATH  Google Scholar 

  28. Khan HU, Sohail M, Ali F, Nazir S, Ghadi YY, Ullah I (2023) Prioritizing the multi-criterial features based on comparative approaches for enhancing security of IoT devices. Phys Commun 59:102084

    Article  MATH  Google Scholar 

  29. Zhao W, Wang G, Atapattu S, Tsiftsis TA, Tellambura C (2020) Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system? IEEE Commun Lett 24(6):1342–1346. https://doi.org/10.1109/LCOMM.2020.2980510

    Article  MATH  Google Scholar 

  30. Xu S, Liu J, Cao Y (2022) Intelligent reflecting surface empowered physical-layer security: signal cancellation or jamming? IEEE Internet Things J 9(2):1265–1275. https://doi.org/10.1109/JIOT.2021.3079325

    Article  Google Scholar 

  31. Wang C, Li Z, Zheng T-X, Ng DWK, Al-Dhahir N (2021) Intelligent reflecting surface-aided secure broadcasting in millimeter wave symbiotic radio networks. IEEE Trans Veh Technol 70(10):11050–11055. https://doi.org/10.1109/TVT.2021.3108452

    Article  Google Scholar 

  32. Yao J, Wu T, Zhang Q, Qin J (2020) Proactive monitoring via passive reflection using intelligent reflecting surface. IEEE Commun Lett 24(9):1909–1913. https://doi.org/10.1109/LCOMM.2020.3001255

    Article  Google Scholar 

  33. Zhao H, Shuang Y, Wei M, Cui TJ, Hougne Pd, Li L (2020) Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat Commun 11(1):3926

    Article  MATH  Google Scholar 

  34. Xu S, Liu J, Zhang J (2021) Resisting undesired signal through IRS-based backscatter communication system. IEEE Commun Lett 25(8):2743–2747. https://doi.org/10.1109/LCOMM.2021.3077093

    Article  MATH  Google Scholar 

  35. Ali BS, Ullah I, Al Shloul T, Khan IA, Khan I, Ghadi YY, Abdusalomov A, Nasimov R, Ouahada K, Hamam H (2024) ICS-IDS: application of big data analysis in Ai-based intrusion detection systems to identify cyberattacks in ICS networks. J Supercomput 80(6):7876–7905

    Article  Google Scholar 

  36. Renzo MD, Debbah M, Phan-Huy D-T, Zappone A, Alouini M-S, Yuen C, Sciancalepore V, Alexandropoulos GC, Hoydis J, Gacanin H (2019) Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come. EURASIP J Wirel Commun Netw 2019(1):1–20

    Article  Google Scholar 

  37. Wu Q, Zhang R (2019) Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag 58(1):106–112

    Article  MATH  Google Scholar 

  38. Zhao M-M, Wu Q, Zhao M-J, Zhang R (2020) Intelligent reflecting surface enhanced wireless networks: two-timescale beamforming optimization. IEEE Trans Wirel Commun 20(1):2–17

    Article  MATH  Google Scholar 

  39. Goel S, Negi R (2008) Guaranteeing secrecy using artificial noise. IEEE Trans Wirel Commun 7(6):2180–2189

    Article  MATH  Google Scholar 

  40. Jian M, Alexandropoulos GC, Basar E, Huang C, Liu R, Liu Y, Yuen C (2022) Reconfigurable intelligent surfaces for wireless communications: overview of hardware designs, channel models, and estimation techniques. Intell Converg Netw 3(1):1–32

    Article  MATH  Google Scholar 

  41. Zhu H, Zhan J, Lam C-T, Chen B, Ng BK (2024) Machine learning based blind signal detection for ambient backscatter communication systems. IEEE Trans Cognitive Commun Netw. https://doi.org/10.1109/TCCN.2024.3457532

    Article  MATH  Google Scholar 

  42. Yang G, Xu X, Liang Y-C, Di Renzo M (2021) Reconfigurable intelligent surface-assisted non-orthogonal multiple access. IEEE Trans Wirel Commun 20(5):3137–3151

    Article  Google Scholar 

  43. Cheng X, Lin Y, Shi W, Li J, Pan C, Shu F, Wu Y, Wang J (2021) Joint optimization for RIS-assisted wireless communications: from physical and electromagnetic perspectives. IEEE Trans Commun 70(1):606–620

    Article  MATH  Google Scholar 

  44. Yang H, Liu S, Xiao L, Zhang Y, Xiong Z, Zhuang W (2023) Learning-based reliable and secure transmission for UAV-RIS-assisted communication systems. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2023.3336535

    Article  MATH  Google Scholar 

  45. Guo K, Wu M, Li X, Lin Z, Tsiftsis TA (2024) Joint trajectory and beamforming optimization for federated DRL-aided space-aerial-terrestrial relay networks with RIS and RSMA. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2024.3468298

    Article  Google Scholar 

  46. Zhao Y, Clerckx B (2024) Riscatter: Unifying backscatter communication and reconfigurable intelligent surface. IEEE Journal on Selected Areas in Communications

  47. ElMossallamy MA, Zhang H, Song L, Seddik KG, Han Z, Li GY (2020) Reconfigurable intelligent surfaces for wireless communications: principles, challenges, and opportunities. IEEE Trans Cognitive Commun Netw 6(3):990–1002. https://doi.org/10.1109/TCCN.2020.2992604

    Article  MATH  Google Scholar 

  48. Özdogan Ö, Björnson E, Larsson EG (2020) Intelligent reflecting surfaces: physics, propagation, and pathloss modeling. IEEE Wirel Commun Lett 9(5):581–585. https://doi.org/10.1109/LWC.2019.2960779

    Article  MATH  Google Scholar 

  49. Zhao W, Wang G, Atapattu S, Tsiftsis TA, Ma X (2020) Performance analysis of large intelligent surface aided backscatter communication systems. IEEE Wirel Commun Lett 9(7):962–966. https://doi.org/10.1109/LWC.2020.2976934

    Article  MATH  Google Scholar 

  50. Abeywickrama S, You C, Zhang R, Yuen C (2021) Channel estimation for intelligent reflecting surface assisted backscatter communication. IEEE Wirel Commun Lett 10(11):2519–2523. https://doi.org/10.1109/LWC.2021.3106165

    Article  MATH  Google Scholar 

  51. Jia X, Zhou X, Niyato D, Zhao J (2022) Intelligent reflecting surface-assisted bistatic backscatter networks: joint beamforming and reflection design. IEEE Trans Green Commun Netw 6(2):799–814. https://doi.org/10.1109/TGCN.2021.3127190

    Article  MATH  Google Scholar 

  52. Nemati M, Ding J, Choi J (2020) Short-range ambient backscatter communication using reconfigurable intelligent surfaces. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), pp. 1–6. https://doi.org/10.1109/WCNC45663.2020.9120813

  53. Idrees S, Jia X, Durrani S, Zhou X (2022) Design of intelligent reflecting surface (IRS)-boosted ambient backscatter systems. IEEE Access 10:65000–65010. https://doi.org/10.1109/ACCESS.2022.3184017

    Article  Google Scholar 

  54. Liang Y-C, Zhang Q, Larsson EG, Li GY (2020) Symbiotic radio: cognitive backscattering communications for future wireless networks. IEEE Trans Cognitive Commun Netw 6(4):1242–1255. https://doi.org/10.1109/TCCN.2020.3023139

    Article  MATH  Google Scholar 

  55. Ma H, Zhang H, Zhang N, Wang J, Wang N, Leung VCM (2022) Reconfigurable intelligent surface with energy harvesting assisted cooperative ambient backscatter communications. IEEE Wirel Commun Lett 11(6):1283–1287. https://doi.org/10.1109/LWC.2022.3164257

    Article  MATH  Google Scholar 

  56. Zhao Y, Clerckx B (2022) RIScatter: unifying backscatter communication and reconfigurable intelligent surface. arXiv preprint arXiv:2212.09121

  57. Fara R, Phan-Huy D-T, Ratajczak P, Ourir A, Di Renzo M, De Rosny J (2021) Reconfigurable intelligent surface-assisted ambient backscatter communications - experimental assessment. In: 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, pp. 1–7. https://doi.org/10.1109/ICCWorkshops50388.2021.9473842

  58. Zuo J, Liu Y, Yang L, Song L, Liang Y-C (2021) Reconfigurable intelligent surface enhanced noma assisted backscatter communication system. IEEE Trans Veh Technol 70(7):7261–7266

    Article  Google Scholar 

  59. Zhuang Y, Li X, Ji H, Zhang H (2022) Exploiting intelligent reflecting surface for energy efficiency in ambient backscatter communication-enabled noma networks. IEEE Trans Green Commun Netw 6(1):163–174

    Article  MATH  Google Scholar 

  60. Xu S, Liu J, Zhang J (2021) Resisting undesired signal through IRS-based backscatter communication system. IEEE Commun Lett 25(8):2743–2747

    Article  MATH  Google Scholar 

  61. Xu S, Liu J, Cao Y (2021) Intelligent reflecting surface empowered physical-layer security: signal cancellation or jamming? IEEE Internet Things J 9(2):1265–1275

    Article  Google Scholar 

  62. Loku Galappaththige D, Rezaei F, Tellambura C, Herath S (2023) RIS-empowered ambient backscatter communication systems. IEEE Wirel Commun Lett 12(1):173–177. https://doi.org/10.1109/LWC.2022.3220158

    Article  Google Scholar 

  63. Ullah I, Noor A, Nazir S, Ali F, Ghadi YY, Aslam N (2024) Protecting iot devices from security attacks using effective decision-making strategy of appropriate features. J Supercomput 80(5):5870–5899

    Article  Google Scholar 

  64. Wu N, Wang X, Fei Z, Xia F, Huang J, Nallanathan A (2024) Ris-assisted integrated sensing and backscatter communications for future iot networks. IEEE Internet Things Magazine 7(4):44–50. https://doi.org/10.1109/IOTM.001.2300184

    Article  Google Scholar 

  65. Tu Z, Long R, Pei Y, Liang Y-C (2024) RIS-enabled full-duplex backscatter communication in multi-user symbiotic radio. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2024.3439622

    Article  MATH  Google Scholar 

  66. Zou Y, Liu Y, Mu X, Zhang X, Liu Y, Yuen C (2023) Machine learning in RIS-assisted noma iot networks. IEEE Internet Things J 10(22):19427–19440. https://doi.org/10.1109/JIOT.2023.3245288

    Article  Google Scholar 

  67. Jia X, Zhou X (2021) IRS-assisted ambient backscatter communications utilizing deep reinforcement learning. IEEE Wirel Commun Lett 10(11):2374–2378. https://doi.org/10.1109/LWC.2021.3100901

    Article  Google Scholar 

  68. Idrees S, Jia X, Khan S, Durrani S, Zhou X (May, 2022) Deep learning based passive beamforming for IRS-assisted monostatic backscatter systems. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, pp. 8652–8656. https://doi.org/10.1109/ICASSP43922.2022.9746747

  69. Gao J, Khandaker MRA, Tariq F, Wong K-K, Khan RT (Sept, 2019) Deep neural network based resource allocation for V2X communications. In: 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA, pp. 1–5. https://doi.org/10.1109/VTCFall.2019.8891446

  70. Dai L, Jiao R, Adachi F, Poor HV, Hanzo L (2020) Deep learning for wireless communications: an emerging interdisciplinary paradigm. IEEE Wirel Commun 27(4):133–139. https://doi.org/10.1109/MWC.001.1900491

    Article  Google Scholar 

  71. François-Lavet V, Henderson P, Islam R, Bellemare MG, Pineau J (2018) An introduction to deep reinforcement learning. Found Trends ® in Mach Learn 11:219–354

    Article  MATH  Google Scholar 

  72. Ullah I, Adhikari D, Su X, Palmieri F, Wu C, Choi C (2024) Integration of data science with the intelligent iot (iiot): current challenges and future perspectives. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2024.02.007

    Article  Google Scholar 

  73. Zhang Z, Zhang D, Qiu RC (2019) Deep reinforcement learning for power system applications: an overview. CSEE J Power Energy Syst 6(1):213–225

    MATH  Google Scholar 

  74. Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285

    Article  MATH  Google Scholar 

  75. Ullah I, Khan IU, Ouaissa M, Ouaissa M, El Hajjami S (2024) Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  76. Xiang J, Li Q, Dong X, Ren Z (2019) Continuous control with deep reinforcement learning for mobile robot navigation. In: 2019 Chinese Automation Congress (CAC), pp. 1501–1506. IEEE

  77. Fujimoto S, Hoof H, Meger D (2018) Addressing function approximation error in actor-critic methods. In: International Conference on Machine Learning, pp. 1587–1596. PMLR

  78. Yang H, Xiong Z, Zhao J, Niyato D, Xiao L, Wu Q (2020) Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications. IEEE Trans Wirel Commun 20(1):375–388

    Article  Google Scholar 

  79. Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30

  80. Haarnoja T, Zhou A, Hartikainen K, Tucker G, Ha S, Tan J, Kumar V, Zhu H, Gupta A, Abbeel P, et al (2018) Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905

  81. Tang W, Chen MZ, Chen X, Dai JY, Han Y, Di Renzo M, Zeng Y, Jin S, Cheng Q, Cui TJ (2020) Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans Wirel Commun 20(1):421–439

    Article  MATH  Google Scholar 

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SZA contributed to conceptualization, methodology, software, writing—original draft, and software. AW contributed to conceptualization, methodology, and software; NS and MMK contributed to conceptualization and methodology; NI contributed to software conceptualization and methodology; NS and YID contributed to validation, resources, writing—review, and editing; MAK contributed to validation, resources; AA contributed to resources, validation, and software; IU contributed to validation, writing—review, and editing, supervision, project administration, and funding acquisition

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Correspondence to Inam Ullah.

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Abideen, S.Z.U., Wahid, A., Kamal, M.M. et al. Advancements in IoT system security: a reconfigurable intelligent surfaces and backscatter communication approach. J Supercomput 81, 362 (2025). https://doi.org/10.1007/s11227-024-06819-x

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