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This paper presents the results of a listening experiment designed to assess annoyance and perceived loudness (PL) for several unmanned aircraft system (UAS) operations, with the listener simulated in indoor and outdoor positions. This research investigated (i) how participant responses change depending on UAS operation, (ii) which broadband metrics are most suitable for representing annoyance and PL, (iii) differences in noise level required to result in equal participant responses to different operations, and (iv) which sound quality metrics (SQMs) are significant for UAS noise perception. Results indicate annoyance and PL responses were greatest for landing operations with flyovers being the least annoying or loud. LAeq, LASmax, and loudness (N5) were the strongest predictors in representing annoyance. Offset analysis predicted small differences in annoyance responses between flyovers and other operations, but also indicated that flyovers would require an increase to LASmax of 3.3 to 6.3 dB compared to other operations to achieve equal PL. Loudness was the most significant SQM, with minor contributions from impulsivity for annoyance and PL when outside, and tonality for PL when indoors. These findings contribute to the understanding of UAS noise perception for the development of metrics and assessment methods accounting for the characteristics of UAS operations.
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http://dx.doi.org/10.1121/10.0024522 | DOI Listing |
Environ Monit Assess
September 2025
Department of Water Resources Study and Research, Water Research Institute, Tehran, Iran.
Small glaciers situated in high mountainous areas are experiencing notable declines, characterized by unprecedented rates of ice loss in recent years. This study investigates the recent changes in surface elevation and mass loss occurring between 2010 and 2023 within the Alamkouh Glacier over three subperiods, one of the biggest glaciers in Iran and the Middle East. These assessments are derived from a combination of high-resolution LiDAR data in 2010 (with a spatial resolution of 20 cm) and multi-temporal surveys conducted using unmanned aerial vehicles (UAVs) in 2018, 2020, and 2023 (with spatial resolutions varied from 10 to 20 cm).
View Article and Find Full Text PDFPLoS One
September 2025
Hydraulic Engineering and Water Management, School of Architecture and Civil Engineering, University of Applied Sciences, Saarbrücken, Germany.
Soil erosion is an ongoing environmental problem. To address this issue, calibrated erosion models are used to forecast areas vulnerable to erosion and to determine appropriate preventive measures. Model calibrations are based on erosion data recorded using different techniques such as photogrammetry from an unmanned aerial vehicle (UAV).
View Article and Find Full Text PDFScience
September 2025
Department of Agricultural, Food and Resource Economics, Michigan State University, East Lansing, MI, USA.
Rapid growth in drone use is upending expectations but also inducing trade-offs.
View Article and Find Full Text PDFSci Adv
September 2025
Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
Turbulence-induced vibrations pose substantial risks to aircraft structural integrity and flight stability, particularly in unmanned aerial vehicles (UAVs), where real-time impact monitoring and lightweight protection are critical. Here, we present a bioinspired twist-hyperbolic metamaterial (THM) integrated with a triboelectric nanogenerator (TENG) for simultaneously impact buffering and self-powered sensing. The THM-TENG protector exhibits tunable stiffness (40 to 4300 newtons per millimeter), ~70% impact energy absorption, and achieves a specific energy absorption of ~0.
View Article and Find Full Text PDFPLoS One
August 2025
Information Network Security Administration (INSA), Aerospace Division, Addis Ababa, Ethiopia.
This paper presents a novel hybrid combined neural network and fuzzy logic adaptive proportional, integral, and derivative(NNPID+FPID) control strategy that integrates neural networks and fuzzy logic for optimizing Unmanned Aerial Vehicle(UAV) dynamics by tuning the gains of a PID controller. The proposed approach leverages the strengths of each technique by applying neural networks to fine-tune the y and ψ states, while fuzzy logic enhances the performance of x, z, ϕ, and θ dynamics. A single-layer neural network with 10 hidden neurons is utilized to adjust PID gains for the y and ψ states using proportional, integral, and derivative errors ([Formula: see text]) as inputs.
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