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The findings show that UAV-based relays have the potential to play a vital role in emergency rescue.<\/jats:p>","DOI":"10.1515\/auto-2024-0032","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T14:22:34Z","timestamp":1736173354000},"page":"29-38","source":"Crossref","is-referenced-by-count":0,"title":["Concept study of an autonomous aerial mobile network relay for pre-hospital emergency care"],"prefix":"10.1515","volume":"73","author":[{"given":"Jonas","family":"Gruner","sequence":"first","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos Castelar","family":"Wembers","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tavia","family":"Plattenteich","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"},{"name":"Institute of Computer Engineering, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jasper","family":"Pflughaupt","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ievgen","family":"Zhavzharov","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georg","family":"Schildbach","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Rostalski","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering in Medicine, Universit\u00e4t zu L\u00fcbeck , L\u00fcbeck , Germany"},{"name":"Fraunhofer Research Institution for Individualized and Cell-based Medical Engineering , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"2025010614222790912_j_auto-2024-0032_ref_001","doi-asserted-by":"crossref","unstructured":"J. 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Sundqvist, \u201cCellular controlled drone experiment: evaluation of network requirements,\u201d Master\u2019s thesis, Aalto University. School of Electrical Engineering, 2015."},{"key":"2025010614222790912_j_auto-2024-0032_ref_007","doi-asserted-by":"crossref","unstructured":"S. C. Arum, D. Grace, and P. D. Mitchell, \u201cA review of wireless communication using high-altitude platforms for extended coverage and capacity,\u201d Comput. Commun., vol.\u00a0157, pp.\u00a0232\u2013256, 2020. https:\/\/doi.org\/10.1016\/j.comcom.2020.04.020.","DOI":"10.1016\/j.comcom.2020.04.020"},{"key":"2025010614222790912_j_auto-2024-0032_ref_008","doi-asserted-by":"crossref","unstructured":"I. Nagata, T. Abe, Y. Nakata, and N. Tamiya, \u201cFactors related to prolonged on-scene time during ambulance transportation for critical emergency patients in a big city in Japan: a population-based observational study,\u201d BMJ Open, vol. 6, no. 1, p. e009599, 2016. https:\/\/doi.org\/10.1136\/bmjopen-2015-009599.","DOI":"10.1136\/bmjopen-2015-009599"},{"key":"2025010614222790912_j_auto-2024-0032_ref_009","unstructured":"E. A. Ranquist, M. Steiner, and B. Argrow, \u201cExploring the range of weather impacts on UAS operations,\u201d in 18th Conference on Aviation, Range, and Aerospace Meteorology, American Meteorological Society, 2017."},{"key":"2025010614222790912_j_auto-2024-0032_ref_010","doi-asserted-by":"crossref","unstructured":"N. Schmid, J. Gruner, H. S. Abbas, and P. Rostalski, \u201cA real-time GP based MPC for quadcopters with unknown disturbances,\u201d in 2022 American Control Conference (ACC), IEEE, 2022, pp.\u00a02051\u20132056.","DOI":"10.23919\/ACC53348.2022.9867594"},{"key":"2025010614222790912_j_auto-2024-0032_ref_011","doi-asserted-by":"crossref","unstructured":"J. Gruner, N. Schmid, G. M\u00e4nnel, J. Grasshof, H. S. Abbas, and P. Rostalski, \u201cRecursively feasible model predictive control using latent force models applied to disturbed quadcopters,\u201d in 2022 IEEE 61st Conference on Decision and Control (CDC), IEEE, 2022, pp.\u00a01013\u20131020.","DOI":"10.1109\/CDC51059.2022.9992944"},{"key":"2025010614222790912_j_auto-2024-0032_ref_012","doi-asserted-by":"crossref","unstructured":"L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, \u201cPIXHAWK: a system for autonomous flight using onboard computer vision,\u201d in 2011 IEEE International Conference on Robotics and Automation, IEEE, 2011, pp.\u00a02992\u20132997.","DOI":"10.1109\/ICRA.2011.5980229"},{"key":"2025010614222790912_j_auto-2024-0032_ref_013","doi-asserted-by":"crossref","unstructured":"M. Colledanchise and P. Ogren, \u201cHow behavior trees modularize robustness and safety in hybrid systems,\u201d in 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2014.","DOI":"10.1109\/IROS.2014.6942752"},{"key":"2025010614222790912_j_auto-2024-0032_ref_014","doi-asserted-by":"crossref","unstructured":"E. Eyceyurt, Y. Egi, and J. Zec, \u201cMachine-learning-based uplink throughput prediction from physical layer measurements,\u201d Electronics, vol.\u00a011, no.\u00a08, p.\u00a01227, 2022. https:\/\/doi.org\/10.3390\/electronics11081227.","DOI":"10.3390\/electronics11081227"},{"key":"2025010614222790912_j_auto-2024-0032_ref_015","unstructured":"C. K. I. Williams and C. E. Rasmussen, Gaussian Processes for Machine Learning, vol.\u00a02 TS \u2013 RI, Cambridge, MA, MIT press, 2006."},{"key":"2025010614222790912_j_auto-2024-0032_ref_016","doi-asserted-by":"crossref","unstructured":"R. D. Taranto, S. Muppirisetty, R. Raulefs, D. Slock, T. Svensson, and H. Wymeersch, \u201cLocation-aware communications for 5G networks: how location information can improve scalability, latency, and robustness of 5g,\u201d IEEE Signal Process. Mag., vol. 31, no. 6, pp. 102\u2013112, 2014. https:\/\/doi.org\/10.1109\/msp.2014.2332611.","DOI":"10.1109\/MSP.2014.2332611"},{"key":"2025010614222790912_j_auto-2024-0032_ref_017","unstructured":"3GPP, Study on Channel Model for Frequencies from 0.5 to 100 GHz, Valbonne, ETSI, 2017, pp. 26\u201328."},{"key":"2025010614222790912_j_auto-2024-0032_ref_018","unstructured":"3GPP, Study on Enhanced LTE Support for Aerial Vehicles, Valbonne, 3GPP, 2017, pp. 26\u201327."},{"key":"2025010614222790912_j_auto-2024-0032_ref_019","unstructured":"E. Brochu, V. M. Cora, and N. de Freitas, \u201cA tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning,\u201d CoRR, 2010."},{"key":"2025010614222790912_j_auto-2024-0032_ref_020","unstructured":"Data for Good at Meta, \u201cGermany: high resolution population density maps + demographic estimates,\u201d [Online], 2020. Available at: https:\/\/data.humdata.org\/dataset\/germany-high-resolution-population-density-maps-demographic-estimates."},{"key":"2025010614222790912_j_auto-2024-0032_ref_021","doi-asserted-by":"crossref","unstructured":"S. Yang, N. Wei, S. Jeon, R. Bencatel, and A. Girard, \u201cReal-time optimal path planning and wind estimation using Gaussian process regression for precision airdrop,\u201d in 2017 American Control Conference (ACC), IEEE, 2017, pp.\u00a02582\u20132587.","DOI":"10.23919\/ACC.2017.7963341"},{"key":"2025010614222790912_j_auto-2024-0032_ref_022","unstructured":"A. G. D. G. Matthews, et al.., \u201cGPflow: a Gaussian process library using TensorFlow,\u201d J. Mach. Learn. Res., vol.\u00a018, no.\u00a040, pp.\u00a01\u20136, 2017."},{"key":"2025010614222790912_j_auto-2024-0032_ref_023","unstructured":"M. Abadi, et al.., \u201cTensorFlow: large-scale machine learning on heterogeneous systems,\u201d [Online], 2015. Available at: https:\/\/www.tensorflow.org\/."},{"key":"2025010614222790912_j_auto-2024-0032_ref_024","doi-asserted-by":"crossref","unstructured":"C. Castelar Wembers, J. Pflughaupt, L. Moshagen, M. Kurenkov, T. Lewejohann, and G. Schildbach, \u201cLiDAR-based automated UAV inspection of wind turbine rotor blades,\u201d J. Field Robot., vol. 41, no. 4, pp. 1116\u20131132, 2024. https:\/\/doi.org\/10.1002\/rob.22309.","DOI":"10.1002\/rob.22309"},{"key":"2025010614222790912_j_auto-2024-0032_ref_025","doi-asserted-by":"crossref","unstructured":"L. Afonso, N. Souto, P. Sebastiao, M. Ribeiro, T. Tavares, and R. Marinheiro, \u201cCellular for the skies: exploiting mobile network infrastructure for low altitude air-to-ground communications,\u201d IEEE Aerosp. Electron. Syst. 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