Measurement Continuity under Sensor Deployment Strategies in Polar Field Stations: Field Study
Keywords:
Polar Instrumentation, Sensor Deployment, Data Continuity, Environmental Monitoring, Measurement ContinuityAbstract
Polar field stations are critical hubs for monitoring global climate dynamics, yet the harsh environments of these regions present severe challenges to sensor deployment and measurement continuity. This paper presents a comprehensive field study evaluating various sensor deployment strategies across Antarctic and Arctic research stations. We examine the impact of extreme temperatures, high winds, and icing on sensor survivability and data transmission. Our findings highlight the importance of localized shielding, robust power management, and adaptive data buffering techniques in maintaining uninterrupted observational series. Through empirical data collected over a three-year observation period, we compare standard open-air deployments with specialized protective configurations, assessing their relative performance in preserving meteorological and geophysical measurements. Ultimately, this work provides a set of validated design guidelines and deployment topologies intended to optimize the resilience and longevity of polar scientific instrumentation networks.References
1. European Union (EU).
2025. Available online: https://health.ec.europa.eu/one-health/overview_en (accessed on 21 November 2025).
2. World Health Organization (WHO).
2017. Available online: https://www.who.int/news-room/questions-and-answers/item/one-health (accessed on 22 November 2025).
3. Su, W.; Liu, H.; Li, T.; Lv, X.; Rui, H.; Huang, W.; Wang, Z.; Li, Y. Jointly Optimizing Deployment and Antenna of Base Stations Using Hierarchical Reinforcement Learning. ACM Trans. Knowl. Discov. Data 2026, 20, 1–25.
4. World Health Organization (WHO).
2025. Available online: https://www.who.int/health-topics/one-health#tab=tab_1 (accessed on 22 November 2025).
5. Korean Council of Science Editors. (2022). Charting variety, scope, and impact of open access diamond journals in various disciplines and regions: A survey-based observational study. Science Editing, 9(2), 120–135.
6. Moynihan, D. P. (2008). The dynamics of performance management. Georgetown University Press.
7. Mohammed, S.A.; Murad, S.S.; Albeyboni, H.J.; Soltani, M.D.; Ahmed, R.A.; Badeel, R.; Chen, P. Supporting Global Communications of 6G Networks Using AI, Digital Twin, Hybrid and Integrated Networks, and Cloud: Features, Challenges, and Recommendations. Telecom 2025, 6, 35.
8. Muraoka, T.; Avellaneda, C.N. Do the networks of inter-municipal cooperation enhance local government performance? Local Gov. Stud. 2021, 47, 616–636.
9. Hegele, Y. The impact of department structure on policy-making: How portfolio combinations affect interdepartmental coordination. Public Policy Adm. 2020, 36, 429–451.
10. Jung, K.; Song, M.; Park, H.J. The dynamics of an interorganizational emergency management networks: Interdependent and independent risk hypotheses. Public Adm. Rev. 2019, 79, 225–235.
11. Barth, M.; Boriboonsomsin, K. Real-world Carbon Dioxideimpacts of traffic congestion. Transp. Res. Rec. 2008, 2058, 163–171.
12. Zhen, Y.; Tao, L.; Wu, D.; Tang, T.; Wang, R. Energy-Saving Control Strategy for Ultra-Dense Network Base Stations Based on Multi-Agent Reinforcement Learning. Digit. Commun. Netw. 2025, 11, 1007–1017.
13. Wang, T.; Cheng, K.; Du, X. Multi-Agent Independent PPO-Based Automatic ECN Tuning for High-Speed Data Center Networks. In Proceedings of the 2025 IEEE International Conference on Cluster Computing (CLUSTER), Edinburgh, UK, 2–5 September 2025; pp. 1–11.
14. Li, Y.; Shan, C. Impacts of cooperative adaptive cruise control links on driving comfort under vehicle-to-vehicle communication. J. Adv. Transp. 2022, 2022, 7248854.
15. Lowe, R.; Wu, Y.; Tamar, A.; Harb, J.; Abbeel, P.; Mordatch, I. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017), Long Beach, CA, USA, 4–9 December 2017; Curran Associates, Inc.: Red Hook, NY, USA, 2017; pp. 6379–6390.
16. Luo, B.; Lu, X.; Lu, W.; Han, H.; Huang, G.; Zhang, Y. Neural Adaptive Video Streaming via Imitation Learning and Reinforcement Learning. In Proceedings of the 2024 IEEE 10th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE), Guangzhou, China, 27–30 November 2024; pp. 1–4.
17. Guan, W.; Zhang, H. Introduction. In Network Slicing for Future Wireless Communication; Wireless Networks; Springer Nature: Cham, Switzerland, 2024; pp. 1–12.
18. Hodge, N. How to Address Low-Probability, High-Impact Risks. Risk Manag. 2021, 68, 26–29.
19. Bourdin, S. Does the Cohesion Policy Have the Same Influence on Growth Everywhere? A Geographically Weighted Regression Approach in Central and Eastern Europe. Econ. Geogr. 2018, 95, 256–287.
20. Nature-Based Solutions (NBS).
2025. Available online: https://research-and-innovation.ec.europa.eu/research-area/environment/nature-based-solutions_en (accessed on 21 November 2025).
21. Lin, T.; Du, J.; Zhang, H.; Nallanathan, A.; Wang, J. PPO-Based Energy-Efficient Power Control and Spectrum Allocation in In-Vehicle HetNets. In Proceedings of the GLOBECOM 2023—2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 4–8 December 2023; pp. 6334–6339.
22. Lin, X.; Liu, A.; Han, C.; Liang, X.; Sun, Y.; Ding, G.; Zhou, H. Intelligent Adaptive MIMO Transmission for Nonstationary Communication Environment: A Deep Reinforcement Learning Approach. IEEE Trans. Commun. 2025, 73, 5965–5979.
23. Hu, H.; Tang, H.; Zhang, R.; Jiang, F.; Ding, Z.; Niyato, D. Joint Scheduling and Power Control in AoI-Oriented WP-IoT Networks: An HVF-Based PPO Approach. IEEE Trans. Veh. Technol. 2025, 75, 6876–6881.
24. Suzuki, T.; Matsuda, K.; Kumagae, K.; Tobisawa, M.; Hoshino, J.; Itoh, Y.; Harada, T.; Matsuoka, J.; Kagawa, T.; Hattori, K. Acquisition of cooperative control of multiple vehicles through reinforcement learning utilizing vehicle-to-vehicle communication and map information. J. Robot. Mechatron. 2024, 36, 642–657.
25. Available online: https://www.who.int/publications/m/item/one-health-definitions-and-principles (accessed on 22 November 2025).
26. Life Cycle Assessment (LCA).
2022. Available online: https://pre-sustainability.com/articles/life-cycle-assessment-lca-basics/ (accessed on 22 November 2025).
27. Patel, B.K.; Chapman, C.G.; Luo, N.; Woodruff, J.N.; Arora, V.M. Impact of mobile tablet computers on internal medicine resident efficiency. Arch. Intern. Med. 2012, 172, 436–438.
28. Wu, Z.; Fang, H.; Tang, J.; Yang, X. Lightweight Adaptive PPO-AHP Enhanced Algorithm for Task Offloading in Vehicular Edge Computing. In Proceedings of the 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 30 June–5 July 2025; pp. 1–9.
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