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Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 28 October 2024 / Approved: 28 October 2024 / Online: 28 October 2024 (10:47:14 CET) Utebayeva, D.; Ilipbayeva, L.; Seidaliyeva, U.; Yembergenova, A.; Matson, E. T. Deep Learning Models for Predicting Drone Sound Distances: Lightweight, Fusion and Hybridization Approaches. Preprints 2024, 2024102156.
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 16 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (08:31:53 CEST) Seidaliyeva, U.; Ilipbayeva, L.; Utebayeva, D.; Smailov, N.; T.Matson, E. T. LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques. Preprints 2024, 2024101344.
4.1. Data Preparation Data preparation is an integral part of any deep-learning-based system. The Data Preparation section first started with collecting drone sounds from different scenarios, as shown in Figure 6. The main content of the database was created by testing the flights of DJI Phantom 1 and DJI Phantom 2 without and with a load at a distance of 2–100 m from the microphone. The microphone is placed on a table height of 75 cm.
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