Short-term Noise Research Articles

Short-term noise annoyance towards drones and other transportation noise sources: A laboratory study.

Noise from unmanned aerial vehicles, commonly referred to as "drones," will likely shape our acoustic environment in the near future. Yet, reactions of the population to this new noise source are still little explored. The objective of this study was to investigate short-term noise annoyance reactions to drones in a controlled laboratory experiment. Annoyance to (i) two quadcopters of different sizes in relation to common contemporary transportation noise sources (jet aircraft, propeller aircraft, helicopters, single car passbys), and (ii) different drone maneuvers (takeoff; landing; high, medium, and low flybys) flown at different speeds and elevations was systematically assessed. The results revealed that, at the same sound exposure level, drones are perceived as substantially more annoying than other airborne vehicles and passenger cars. Furthermore, for drone maneuvers, landings, and takeoffs are more annoying than flybys, as are maneuvers flown at low speed. Different loudness metrics (LAE, LDE, effective perceived noise level, psychoacoustic loudness level) accounted for drone noise annoyance ratings to an equal degree. An analysis of psychoacoustic parameters highlighted the significant link between drone noise annoyance and tonality, sharpness, and loudness level. The results suggest a different perception and an increased annoyance potential of drones, which will likely require specifically tailored legislation.

Non-linear associations between noise level and people's short-term noise annoyance in different activity contexts

Recent research has become increasingly interested in the on-linear associations between noise levels and people's short-term noise annoyance. However, there has been limited investigation into measuring short-term noise annoyance and how different activity contexts may affect these non-linear associations. To address this research gap, this study measured people's short-term noise annoyance using real-time Ecological Momentary Assessment (EMA) data and the Day Reconstruction Method's (DRM) recalled data. Corresponding noise levels were captured using Global Positioning Systems and portable noise sensors. Employing the Shapley additive explanations method, we examined the non-linear associations between noise level and people's real-time and recalled noise annoyance across different activity contexts. The results indicated that 1) People had greater sensitivity to noise levels in real-time annoyance (non-linear association threshold: 60 dB) compared to recalled annoyance, which had a higher non-linear association threshold of 70 dB. 2) The non-linear associations between noise level and people's real-time/recalled noise annoyance varied between different activity contexts. People tended to be more sensitive to noise in real-time annoyance than recalled annoyance on travel routes and at workplaces. 3) Among the factors examined, the contribution of noise level varied across activity contexts. Noise level contributed more significantly to people's real-time noise annoyance in outdoor recreational sites and on travel routes. These findings enhance our understanding of the non-linear association between noise level and people's short-term noise annoyance, moving beyond the linear paradigm. Policymakers should consider the non-linear relationships and different activity contexts when implementing noise control measures.

Effects of noise exposure on stress hormone changes during task performance in young Korean men: quasi-experimental study

ABSTRACT Numerous studies have suggested that noise exposure might be associated with changes in stress hormone levels. However, quantitative evidence for these effects in humans is rare and remains controversial. This study aimed to investigate the acute effects of exposure to noise and its different levels on stress hormone changes in task performance. Quasi-experimental noise exposure environment was established for 90 male university student volunteers in their twenties, and each was exposed to different noise levels during task performance. The stress hormones tested included cortisol, adrenocorticotropic hormone (ACTH), adrenaline, and noradrenaline. A one-way ANOVA was performed to investigate differences in hormone levels measured in the three groups according to the noise exposure levels (35, 45, or 75 dB). Analysis of covariance (ANCOVA) was used to adjust for confounding factors that might affect hormone levels. After adjusting for confounders, significant exposure-dependent differences were found in hormone levels in salivary cortisol, serum cortisol, serum ACTH, and serum adrenaline. The amount of hormonal increase in 75 dB exposure group compared to 35 or 45 dB groups was detected. Similar results were also seen in the rate of change analysis. Our findings indicate that short-term noise exposure during task performance elevates stress hormone levels. Further, the extent of stress hormone alterations varies with noise exposure levels. Changes in hormone levels are an objective measure that may be used to identify health effects and stress responses in various noise environments.

Comprehensive Analysis on GPS Carrier Phase under Various Cutoff Elevation Angles and Its Impact on Station Coordinates’ Repeatability

When processing the carrier phase, the global navigation satellite system (GNSS) grants the highest precision for geodetic measurements. The analysis centers (ACs) from the International GNSS Service (IGS) provide different data such as precise clock data, precise orbits, reference frame, ionosphere and troposphere data, as well as other geodetic products. Each individual AC has its own strategy for delivering the abovementioned products, with one of the key elements being the cutoff elevation angle. Typically, this angle is arbitrarily chosen using generic values without studying the impact of this choice on the obtained results, in particular when very precise positions are considered. This article addresses this issue. To this end, the article has two key sections, and the first is to evaluate the impact of using the two different cutoff elevation angles that are most widely used: (a) 3 degrees cutoff and (b) 10 degrees cutoff elevation angle. This analysis is completed in two major parts: (i) the analysis of the root mean square (RMS) for the carrier phase and (ii) the analysis of the station position in terms of repeatability. The second key section of the paper is a comprehensive carrier phase analysis conducted by adopting a new approach using a mean of the 25-point average RMS (A-RMS) and the single-point RMS and using an ionosphere-free linear combination. By using the ratio between the 25-point average RMS and the single-point RMS we can define the type of scatter that dominates the phase solution. The analyzed data span a one-year period. The tested GNSS stations belong to the EUREF Permanent Network (EPN) and the International GNSS Service (IGS). These comprise 55 GNSS stations, of which only 23 GNSS stations had more than 95% data availability for the entire year. The RMS and A-RMS are analyzed in conjunction with the precipitable water vapor (PWV), which shows clear signs of temporal correlation. Of the 23 GNSS stations, three stations show an increase of around 50% of the phase RMS when using a 3° cutoff elevation angle, and only four stations have a difference of 5% between the phase RMS when using both cutoff elevation angles. When using the A-RMS, there is an average improvement of 37% of the phase scatter for the 10° cutoff elevation angle, whereas for the 3° cutoff elevation angle, the improvement is around 33%. Based on studying this ratio, four stations indicate that the scatter is dominated by the stronger-than-usual dominance of long-period variations, whereas the others show short-term noise. In terms of station position repeatability, the weighted root mean square (WRMS) is used as an indicator, and the results between the differences of using a 3° and 10° cutoff elevation angle strategy show a difference of −0.16 mm for the North component, −0.21 mm for the East component and a value of −0.75 mm for the Up component, indicating the importance of using optimal cutoff angles.

Frequency drift characterization of a laser stabilized to an optical fiber delay line

Lasers stabilized to optical fiber delay lines have been shown to deliver a comparable short-term (<1 s) frequency noise performance to that achieved by lasers stabilized to ultra-low expansion (ULE) cavities, once the linear frequency drift has been removed. However, for continuous stable laser operations, the drift can be removed only when it can be predicted, e.g., when it is linear over very long timescales. To date, such long-term behaviour of the frequency drift in fiber delay lines has not been, to the best of our knowledge, characterised. In this work we experimentally characterise the frequency drift of a laser stabilised to a 500 m-long optical fiber delay line over the course of several days. We show that the drift still follows the temperature variations even when the spool temperature is maintained constant with fluctuations below tens of mK. Consequently, the drift is not linear over long timescales, preventing a simple feed-forward compensation. However, here we show that the drift can be reduced by exploiting the high level of correlation between laser frequency and the fiber temperature. In our demonstration, by applying a frequency correction proportional to temperature readings, a calculated frequency drift of less than 16 Hz/s over the several days of our test was obtained, corresponding to a 23-fold improvement from uncorrected values.

Analysis of road traffic noise in an urban area in Croatia using different noise prediction models

Abstract Road traffic noise is the second largest environmental stressor in urban areas in Europe after air pollution. The harmful effects of noise arise mainly from the stress response it triggers in the human body, which can have significant consequences for physical and mental health. Therefore, the use of a reliable noise prediction model is an important prerequisite for the quality assessment of a number of residents exposed to excessive noise levels and for the selection of appropriate noise mitigation measures. In this study, the analysis of road traffic noise in an urban street in the narrower centre of the Croatian capital Zagreb was performed using four noise prediction models: "RLS-90", "RLS-19", “NMPB-Routes-96 (SET-RA-CERTU-LCPC-CSTB)”, and “CNOSSOS-EU”. LimA V2021 noise prediction software was used for the analysis, and the noise modelling results were validated with short-term noise measurements. The main objective of the research presented in the article was to test the “CNOSSOS-EU” method, recently introduced in Croatian noise control practice, and to gain initial insights into which of the aforementioned noise prediction models is the most reliable for the assessment of road traffic noise in urban environments in Croatia. A comparison of the noise modelling results with the results of short-term noise measurements has shown that the German national calculation methods “RLS-90” and “RLS-19” as well as the “CNOSSOS-EU” method provide significantly more accurate noise predictions than the “NMPB-Routes-96 (SETRA-CERTU-LCPC-CSTB)” method.

Noise and thermal performance of a sub-attofarad capacitance sensor for precision measurements, with applications in gravitational wave detectors.

We describe the design principles, fabrication, and characterization of a precision AC resonant capacitance bridge (RCB) sensor, based on a resonant differential planar printed circuit board transformer with a solid (ungapped) MnZn ferrite core, demonstrating a short-term sensitivity at 293K of 0.225 ± 0.005 aF/√Hz at around 120kHz resonance frequency and 1Hz Fourier measurement frequency. At 120K, the RCB short term noise sensitivity is 0.118 ± 0.005 aF/√Hz. We compare the ungapped configuration to five different RCBs: three with a core gap of 65 μm and two with a core gap of 130 μm. Their average room temperature short term noise sensitivities are 0.30 ± 0.01 and 0.45 ± 0.01 aF/√Hz, while the cryogenic operation of these transformers at 120K resulted in averaged sensitivities of 0.23 ± 0.01 and 0.36 ± 0.01 aF/√Hz, respectively. Multi-hour room temperature runs, with one core of each of the three gap types, proved the stability of their long-term sensitivities of 0.234 ± 0.005, 0.338 ± 0.009, and 0.435 ± 0.010 aF/√Hz for the ungapped (40-h duration) and the 65 and 130 μm (28-h duration) cores, respectively. At 0.1mHz, a critical frequency for space gravitational wave detectors, the respective sensitivities are 0.25 ± 0.02, 0.35 ± 0.02, and 0.53 ± 0.07 aF/√Hz. Measurements with the ungapped transformer configuration for temperatures from 325 to 349K further validate the dependence of the noise model on temperature and permeability. The performance of our RCB with an ungapped core matches the calculated performance value and shows an improvement in signal-to-noise ratio of two or more compared with capacitance bridges developed for similar applications. A further factor of about two noise reductions is achieved by cooling to 120K.

Onboard sound emission measurements of unmanned aerial vehicles for psychoacoustic experiments

With the prospect of small UAVs being more commonly used for tasks like surveillance or parcel transport within inhabited areas, demand for noise regulations arises. The noise assessment of UAV operations can be investigated through laboratory listening experiments using auralized sound stimuli. Sound recordings of source signals for dynamic flight operations have to be performed outdoors where measurements with stationary microphones impose uncertainties in backpropagation and suffer from bad signal-to-noise ratio due to the weak source and the presence of ambient sounds. To overcome these difficulties, this contribution presents an approach for acquiring UAV source signals using onboard microphones. A lightweight setup for time synchronous recordings of sound using MEMS microphones, the UAV position and the rotational speed of each rotor was developed and applied to two quadcopters of 0.9 and 6.3 kg mass. Source directivity and propagation effects were added to the source signals to obtain clean sound pressure signals at virtual listener positions. The resulting stimuli were successfully used in a listening experiment on short-term noise annoyance.

The Impact of the Federal Reserve Raises the Interest Rates on the US Stock Market Index: The Dynamic Perspective

In order to battle growing inflation, the Federal Reserve raised interest rates in March 2022 for the first time since 2018. This action signals the conclusion of the Federal Reserve's accommodative monetary policy adopted during the pandemic, coinciding with significant inflationary trends in the United States. One of the main focuses that this may bring is the effect on stock price. This article will use past data on NASDAQ stock prices to forecast future trends and will discuss the analysis. At the end, the research finds that Feds policy has let NASDAQ index decrease than expected trends in a long-term perspective. This suggests that the Fed's policy may have a more pronounced impact on long-term trends rather than short-term fluctuations. It also implies that investors should adopt a strategic, long-term approach to their investments, rather than reacting to short-term market noise. Investors may also consider implementing risk management strategies, including stop-loss orders and options contracts, to protect against sudden market downturns that may result from interest rate hikes.

Analysis of the short-term noise situation around Bangkok International Airport prior to the second phase of airport expansion begins

Short-term noise measurements carried out around the Bangkok International Airport during 2014-2015 provided baseline datasets of noise levels to illustrate the overview of actual aircraft noise exposure to people living underneath the flight path. The datasets will be useful for social surveys to draw the dose-response relationship curve, validation of noise prediction studies, and noise management plans around the airport before the second phase expansion. This study proposes a post-processing tool for extracting aircraft noise events due to aircraft flyovers from the measurements. The actual noise exposure levels of aircraft fly-over noise events were reported to reflect the existing noise situation around the airport and contribute to the analysis of other studies conducted during the same period.

The Stochastic Climate Model helps reveal the role of memory in internal variability in the Bohai and Yellow Sea

Hasselmann’s theory elucidates how short-term random noise leads to longer-term unprovoked variations, i.e., red spectra. Here, we study ensembles of numerical model simulations of the hydrodynamics of the Bohai and Yellow Sea concerning internal variability formation. Short(/long) term variations are associated with small(/large) spatial scales, and the internal variability of long-term temporal and large-scale variations is markedly enhanced, even without external forcing on these scales, when the tides are turned off. This pattern is well explained by Hasselmann’s theory. A critical element in this theory is the concept of memory, which in our ensembles exhibits a scale dependence that aligns with the scale-dependent nature of redness. Additionally, this framework clarifies why there is a significant reduction of long-term fluctuations during winter and when tides are active: the system’s memory is notably diminished under these conditions.

An approach to on/off testing for wind farm noise assessment

In Victoria, assessment of operational wind farm noise is usually undertaken using a regression approach based on NZS6808. This adopts a comparison of long-term operational sound level and wind speed measurements with background sound levels measured prior to the construction of the wind energy facility (WEF), taken at sensitive receivers near to the site. Where no background noise levels have been measured or there are changes in the environment that may make background noise levels unrepresentative, it can be difficult to positively demonstrate compliance with required noise limits, and the results can be inconclusive. On/Off testing is described in NZS6808 as an alternative to conducting sound measurements correlated with wind data, and relies on the wind farm being turned off for a short period to allow a short-term background noise level to be determined, against which the “adjacent” operational noise levels can be compared. However, the approach is not documented in detail in NZS6808 and on/off testing is rarely undertaken in Australia. This paper describes a detailed approach that has been adopted for recent on/off testing undertaken at a large WEF in Australia and outlines the key aspects of the testing which are necessary to conduct a meaningful assessment.

PCB Coil Enables In Situ Calibration of Magnetoelectric Sensor Systems

Abstract Accurate calibration is key for any reliable sensor system. Magnetoelectric (ME) sensors, in particular, are influenced in their operating point by external parameters such as the Earth’s magnetic field or the ambient temperature. In this paper, we introduce a new planar coil design for the generation of a magnetic test field within the plane of the ME sensor. Furthermore, we implemented a method for measuring the sensor behaviour using a short-term magnetic noise signal. The combination of the printed circuit board (PCB) coil and the accelerated sensor characterization method allows the sensor system to be calibrated at the measurement site (in situ) without the need for laboratory equipment. We can show that the presented method for calibration achieves high-quality results in 10 seconds for a sensor affected by external interference fields.

Monitoring performance of slopes via ambient seismic noise recordings: Case study in a colluvium deposit

Long-term monitoring of slopes is of significance for engineering geology research and geo-disaster prevention. There is a growing need to develop fast, nondestructive and affordable techniques that can detect gradual and cyclic changes inside slopes. For this purpose, we propose a fast and low-cost computational framework based on processing the single-point ambient seismic noise recordings by the horizontal-to-vertical spectral ratio (HVSR) method. To test the efficiency of the proposed framework, we conduct a demonstration study in a road-cut slope in colluvium deposits in Southwest China. First, we carry out short-term ambient seismic noise surveys on the slope, delineate the shear wave velocity (Vs) structure of the slope, verify and establish this structure as the reference Vs model for the slope. Then we conduct the long-term monitoring of the ambient seismic noise on two profiles of the slope, calculate the HVSR curves and observe the time-dependent variations of the predominant peaks that reflect temporal changes of subsurface interfaces. Finally, we perform the Vs inversion to investigate changes in the Vs structure with rainfall. Through the monitoring, we identify the rainfall-induced slope failure and discover that both predominant frequency and shallow subsurface Vs of slope are negatively correlated with rainfall. The HVSR calculation, the predominant peak identification and the Vs inversion can all be implemented in minutes, which is much faster than the array-based surface wave method. The theoretical analysis and the demonstration application show that the framework we proposed in this study has great potential for monitoring changes in performance of slopes.

QUANTITATIVE INVESTMENT DECISIONS BASED ON MACHINE LEARNING AND INVESTOR ATTENTION ANALYSIS

According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning. We first adopt the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition technology to separate the short-term noise, cycle transformation and long-term trend from the original series, and use the CSI 500 Baidu index series to reflect the investors’ attention, which provides data support for establishing a more effective forecasting model. Then, the CEEMDANBP neural network model is designed based on the obtained effective information of low-frequency trend series, investor attention index and CSI 500 stock index futures market transaction data. Finally, an Attention-based Dual Thrust quantitative trading strategy is proposed and optimized. The optimized Attention-based Dual Thrust strategy solves the core problem of breakout interval determination, effectively avoids the risk of subjective selection, and can meet investors’ different risk preferences. The quantitative investment decision-making model based on CEEMDAN-BP neural network utilizes the advantages of different algorithms, avoids some defects of a single algorithm, and can make corresponding adjustments according to changes in investors’ attention and the occurrence of emergencies. The results show that considering investor attention can not only improve the predictive ability of the model, but also reduce the cognitive bias of the market, effectively control risks and obtain higher returns.

Obesity-related cardiometabolic indicators modify the associations of personal noise exposure with heart rate variability: A further investigation on the Study among Obese and Normal-weight Adults (SONA)

Elucidating the associations between environmental noise and heart rate variability (HRV) would be beneficial for the prevention and control of detrimental cardiovascular changes. Obese people have been found to manifest heightened susceptibility to the adverse effects of noise on HRV. However, the underlying mechanisms remain unclear. Based on 53 normal-weight and 44 obese young adults aged 18–26 years in Beijing, China, this study aimed to investigate the role of obesity-related cardiometabolic indicators for associations between short-term environmental noise exposure and HRV in the real-world context. The participants underwent personal noise exposure and ambulatory electrocardiogram monitoring using portable devices at 5-min intervals for 24 continuous hours. Obesity-related blood pressure, glucose and lipid metabolism, and inflammatory indicators were subsequently examined. Generalized mixed-effect models were used to estimate the associations between noise exposure and HRV parameters. The C-peptide, homeostasis model assessment of insulin resistance (HOMA-IR), and leptin levels were higher in obese participants compared to normal-weight participants. We observed amplified associations between short-term noise exposure and decreases in HRV among participants with higher C-peptide, HOMA-IR, and leptin levels. For instance, a 1 dB(A) increment in 3 h-average noise exposure level preceding each measurement was associated with changes of −0.20% (95%CI: −0.45%, 0.04%) and −1.35% (95%CI: −1.85%, −0.86%) in standard deviation of all normal to normal intervals (SDNN) among participants with lower and higher C-peptide levels, respectively (P for interaction <0.05). Meanwhile, co-existing fine particulate matter (PM2.5) could amplify the associations between noise and HRV among obese participants and participants with higher C-peptide, HOMA-IR, and leptin levels. The more apparent associations of short-term exposure to environmental noise with HRV and the effect modification by PM2.5 may be partially explained by the higher C-peptide, HOMA-IR, and leptin levels of obese people.

LEO Satellite Clock Modeling and Its Benefits for LEO Kinematic POD

High-accuracy Low Earth Orbit (LEO) satellite clock and orbital products are preconditions to realize LEO augmentation for high-accuracy GNSS-based positioning on the ground. There is a high correlation between the orbit and clock parameters in the kinematic Precise Orbit Determination (POD) process. While future LEO satellites are planned to be equipped with better clocks, the benefits of modeling high-stability LEO satellite clocks are not yet thoroughly investigated, particularly when mid- to long-term systematic effects induced by the complex LEO relativistic effects and the external environment remain in the clocks. Through clock modeling, this study attempts to reduce not only the short-term noise of radial kinematic orbits, but also mis-modeled effects caused by, e.g., real-time GNSS orbital and clock errors. To explore the benefits of clock modeling, the clocks need to be first detrended by the mid- to long-term systematic effects. While over-detrending limits the orbital improvements, weak detrending would also hamper strong clock modeling and easily lead to performance degradations. A balance between the strengths of the detrending and the model thus needs to be investigated for different clock types. In this study, the Piece-Wise Linear (PWL) model of different time lengths and a 2.5-state filter with different strengths (h values) are tested using real data from GRACE FO-1 with an Ultra-Stable Oscillator (USO) on board. Using the CNES real-time GPS products, it was found that when detrending the clocks with a smoothing window of 300 to 500 s, one could generally expect an improvement larger than 10% in the estimation of radial orbits when applying a PWL model with a length from 300 to 1200 s. Improvements of this size can also be expected when using the 2.5-state model with h−1 (for Flicker Frequency Noise) from 10−28 to 10−30.

Adaptive Power Line Communication for Low-Data-Rate Control and Sensing

Unreliable and range-limited communication restricts the effectiveness of many demand-side management schemes. For electrical loads with infrequent switching needs, untapped opportunities still exist to facilitate the reliable deployment of power line communication (PLC) for demand-side management applications. Inexpensive low-data-rate PLC, typically robust against power-line attenuation, can enable reliable load control in a building or facility. Yet, effective low-data-rate PLC schemes are susceptible to harmful and time-varying power-line noise levels. This paper presents approaches for adapting low-data-rate PLC to short-term and long-term noise variations often present in PLC channels. The proposed approaches enhance low-data-rate PLC performance, and further improve the reliability of load-control schemes that only require low data rates. Field demonstrations in a 24-floor apartment building with challenging conditions validate the proposed schemes.

Research of Dynamic Characteristics of Bearing Reducers of the TwinSpin Class in the Start-Up Phase and in the Initial Operating Hours

This paper describes the results of research in the field of monitoring dynamic characteristics of the TwinSpin cycloid bearing reducer made by SPINEA. The research in this area resulted from the requirement to monitor the condition of high-precision bearing reducers and to assess the non-linear behavior of the kinematic structures of the monitored bearing reducers, specifically to determine the optimal start-up time of a particular bearing reducer class and to plan its maintenance. The condition of contact surfaces in the operational process was identified, and the lubricant applied to the monitored class of reducers was assessed. Analyses of the applied lubricant were carried out in order to identify and monitor changes in the technical condition of the bearing reducer. The aim of the measurements was to verify the start-up time of the bearing reducer, to assess the correlation between the total content of iron particles in the lubricant and an increase in kinematic backlash in the engagement of cycloidal wheels and reducer bearings after start-up and after about 1000 h of operation, and to propose recommendations to the operator for the implementation of short-term dynamic vibration, noise, and temperature tests before the reducer itself is put into operation.

Allan Variance Characterization of Compact Fourier Transform Infrared Spectrometers.

Handheld Fourier transform infrared (FT-IR) spectrometers are very promising candidates for several applications where accurate real-time material detection and quantification are needed. Due to their compact size, their mode of operation which does not allow for long warm-up time, and changing environmental conditions, these spectrometers suffer from short-term noise and long-term instabilities which affect their performance. In this work, the effect of long-term multiplicative instabilities on the signal-to-noise ratio (S/N), measured using the 100% line-method, is studied. An expression for the variance, in this case, is deduced. The Allan variance technique is used to identify and quantify the presence of the different types of noises. The methodology is applied to a commercial NeoSpectra scanner module from Si-Ware Systems, Inc.