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In structural health monitoring (SHM), an important issue is the limited availability of measurement data due to the spatial sparsity of sensors installed on the structure. These measurements are insufficient to accurately depict the actual dynamic behavior and response of the structure. Therefore, full‐field (i.e., every degree of freedom) structural response reconstruction based on sparse measured data has drawn a lot of attention in recent years. Kalman filter (KF) is an effective technology for response reconstruction (also known as state estimation), providing an optimal solution for systems that can be well‐represented by a fully known Gaussian linear state‐space model. This implies that both the process noise and measurement noise follow known zero‐mean Gaussian distribution, which is impractical in many civil engineering applications considering the unavoidable modeling errors and variations of environmental conditions. To address this challenge, a data‐physics hybrid‐driven method, i.e., KalmanNet, is proposed in this study for response reconstruction of partially known systems. By integrating a recurrent neural network (RNN) module into the KF framework, KalmanNet can efficiently learn and compute the Kalman gain using available monitoring data, without any Gaussian assumptions or explicit noise covariance specifications (e.g., covariance matrices of process and measurement noise). Both numerical and experimental investigations are conducted to validate this method. The results demonstrate that under the influence of non‐Gaussian noise and modeling errors, KalmanNet can effectively and accurately reconstruct the structural response from sparse measurements in real‐time and has higher accuracy and robustness compared to traditional KF even with optimal parameter settings. [ABSTRACT FROM AUTHOR]

The observation of the gate-controlled supercurrent (GCS) effect in superconducting nanostructures increased the hopes for realizing a superconducting equivalent of semiconductor field-effect transistors. However, recent works attribute this effect to various leakage-based scenarios, giving rise to a debate on its origin. A proper understanding of the microscopic process underlying the GCS effect and the relevant time scales would be beneficial to evaluate the possible applications. In this work, we observed gate-induced two-level fluctuations between the superconducting state and normal state in Al/InAs nanowires (NWs). Noise correlation measurements show a strong correlation with leakage current fluctuations. The time-domain measurements show that these fluctuations have Poissonian statistics. Our detailed analysis of the leakage current measurements reveals that it is consistent with the stress-induced leakage current (SILC), in which inelastic tunneling with phonon generation is the predominant transport mechanism. Our findings shed light on the microscopic origin of the GCS effect and give deeper insight into the switching dynamics of the superconducting NW under the influence of the strong gate voltage. It was recently shown that a voltage could be used to control a supercurrent in superconducting nanostructures, much like a transistor, however, the exact origin of this effect has been debated. Here Elalaily et al. show via noise measurements that the suppression of the supercurrent in the superconducting device arises due to excitation emission by inelastic tunneling of the electrons through the trap states created by stressing the oxide layer between the gate and the NW under a high electric field. [ABSTRACT FROM AUTHOR]

Accurate local temperature measurement at micro and nanoscales requires thermometry with high resolution because of ultra-low thermal transport. Among the various methods for measuring temperature, optical techniques have shown the most precise temperature detection, with resolutions reaching (~10−9 K). In this work, we present a nanomechanical device with nano-Kelvin resolution (~10−9 K) at room temperature and 1 atm. The device uses a 20 nm thick silicon nitride (SiN) membrane, forming an air chamber as the sensing area. The presented device has a temperature sensing area >1 mm2 for micro/nanoscale objects with reduced target placement constraints as the target can be placed anywhere on the >1 mm2 sensing area. The temperature resolution of the SiN membrane device is determined by deflection at the center of the membrane. The temperature resolution is inversely proportional to the membrane's stiffness, as detailed through analysis and measurements of stiffness and noise equivalent temperature (NET) in the pre-stressed SiN membrane. The achievable heat flow resolution of the membrane device is 100 pW, making it suitable for examining thermal transport on micro and nanoscales. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin. [ABSTRACT FROM AUTHOR]

• We analyze the impact of plasma noise on Taiji using Wind's electron number density data. • Lomb–Scargle method is used to estimate the spectrum of plasma noise in 10−6 to 10−2 Hz. • The spectrum of plasma noise is fitted by power law and its effect on TDI is estimated. • It provides a basis for estimating plasma noises in similar gravitational wave observatories. In space-based laser interferometric gravitational wave antennas, plasma from the solar wind is an important noise floor source for laser interferometric measurements between two distant spacecraft. In this study, we aim to analyze the impact of solar wind on the inter-satellite laser interferometric measurements of the Taiji mission, a space-based laser interferometric gravitational wave antenna proposed by the University of Chinese Academy of Sciences. Based on the 6.5-year electron number density data from the National Aeronautics and Space Administration Wind spacecraft and considering the uneven sampling characteristics of the data, we employed the Lomb–Scargle spectral analysis method to study the impact of solar wind plasma on intersatellite laser interferometric measurements within the frequency range of 1 × 10 - 6 Hz to 0.01 Hz. The study shows that the effect of the plasma on the intersatellite laser interferometric measurement is approximately 1 pm/ Hz at 3.3 mHz, which is one order of magnitude smaller than the requirements on the displacement noise of the interferometric measurement of the Taiji mission, indicating that the noise due to plasma will not affect the aim of the Taiji mission to detect gravitational waves. The research can be applied to Taiji, Laser Interferometer Space Antenna, and future gravitational wave detection missions. [ABSTRACT FROM AUTHOR]

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to deal with the measurement delays and noise while improving convergence performance, the platoon control problem of vehicular systems is studied under the nonsingular terminal sliding mode control (NTSMC) framework. A sliding mode observer (SMO) is proposed to estimate the states affected by measurement delays and noise. A distributed NTSMC scheme is developed for the platooning of the vehicular systems and ensures the convergence of the sliding mode surface affected by measurement delays and noise. One salient feature of the proposed SMO is that it can handle time-varying measurement delays rather than constant ones. Moreover, the control law is free of initial spacing error conditions under the employed coupled spacing policy. Numerical simulations are finally provided to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Based on the performance and acoustic data files of reduced-size open-rotor engines in low-speed wind tunnels, the static sound pressure level was derived by converting the 1-foot lossless spectral density into sound-pressure-level data, the background noise was removed, and the results were corrected according to the environmental parameters of the low-speed wind tunnels. In accordance with the requirements of Annex 16 of the Convention on International Civil Aviation Organization and Part 36 of the Civil Aviation Regulations of China on noise measurement procedures, the takeoff trajectory was physically modeled; the static noise source was mapped onto the takeoff trajectory to simulate the propagation process of the noise during takeoff; and the 24 one-third-octave center frequencies that corresponded to the SPL data were corrected for geometrical dispersion, atmospheric absorption, and Doppler effects, so that the takeoff noise could be corrected to represent a real environment. In addition, the audible processing of noise data with a 110° source pointing angle was achieved, which can be useful for enabling practical observers to analyze the noise characteristics. [ABSTRACT FROM AUTHOR]

Today, one of the main challenges in the quantum infrared detection field is to increase the operating temperature in order to reduce size, weight and power-cost (SWAP). However, this leads to two major issues: an increase in the number of random telegraph signal (RTS) pixels and a residual fixed pattern noise (RFPN) instability. To identify these noise sources and assess the performance of a component, numerous measurements at the operating temperature are necessary, which can be laborious. Nevertheless, there exists a noise quality indicator for detectors at high temperatures known as the Tobin factor. In this paper, our objective is to investigate and understand the relationship between random telegraph signal (RTS) pixels and the Tobin factor. The aim of this study is to evaluate the relevance of the Tobin factor extracted at high temperatures to forecast noise performance at operating temperatures. [ABSTRACT FROM AUTHOR]

To cope with the well-known peaking phenomenon and noise sensitivity in the application of the High-Gain observer, a parameter tuning method based on the LPV/LMI approach for a 2nd-order cascade observer structure is proposed in this paper. Compared to other high-gain observer methods, this method can significantly reduce the infimum of gain in the observer, thereby reducing the peak phenomenon of state estimation and the influence of measurement output noise. By transforming the observer structure into a Luenberger-like structure, the parameters of the observer can be solved by one linear matrix inequality (LMI) with a high-gain effect or a 2n of LMI sets (LMIs) without a high-gain effect. Then by decomposing the nonlinear part of the system dynamics into high-dimensional and low-dimensional parts, we could solve the adjustable number 2js of LMIs can be solved to obtain the result with limited high-gain effect. Stability analysis based on the Lyapunov method proves the convergence of this method, and the effectiveness of this method is verified through applications to one single-link mechanical arm model and a vehicle trajectory estimation application. [ABSTRACT FROM AUTHOR]

This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) simulations, we analyze the spatial distribution of magnetic forces and their harmonics. Eight ferrite shape models were examined, focusing on both outer and inner air gaps. Model #8 (reduced outer air gap) and Model #9 (reduced inner air gap) were experimentally validated. Noise measurements indicated that Model #8 reduced 120 Hz harmonic noise components, while Model #9 increased them due to enhanced excitation forces. Current measurements confirmed that Model #9 achieved higher efficiency, with RMS current reduced to 94.54% of the base model. The study reveals a trade-off between performance and noise: inner air gap reduction significantly boosts efficiency but raises noise levels, whereas outer air gap reduction offers balanced improvements. These findings provide insights for optimizing induction cooktop designs, aiming for quieter operation without compromising efficiency. [ABSTRACT FROM AUTHOR]

Among non-destructive testing methods, a group dedicated to the assessment of the state of residual stresses can be distinguished. The method of measuring residual stresses using the Barkhausen noise method has many advantages, as evidenced by the number of publications. The residual stresses in metal products are important for the further processing of such metal, such as laser cutting or bending. The results presented in this work are of an experimental nature, and the presented method of calibration of measuring heads shows how various research techniques can be used to correlate results. The research was carried out for structural steel due to the market share of this type of steel. The method can be used to measure the residual stresses in ferromagnetic metal products in order to assess their directions and quantify them. A prerequisite for the use of this measurement method is that the amplitude and geometry of the Barkhausen noise are adequately correlated to the specific values of the state of stress depending on the tested steel grade or other metals. In this study, a method for calibrating measuring sensors for the residual stress measurements is presented, as developed by the authors. The method involved conducting bending tests in both numerical modeling and experimental tests. During the bending tests, changes in the magnetic field (Barkhausen noise waveform) were recorded, taking into account the state of elastic stresses. Correlating the results of the numerical calculations and Barkhausen noise measurements made it possible to determine the quantitative values of the residual stresses in the steel sheets. Thanks to the method used, very accurate measurement is possible, and the obtained results are repeatable. [ABSTRACT FROM AUTHOR]

Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup. [ABSTRACT FROM AUTHOR]

Here, we report on a simplified laser frequency noise measurement technique employing an acousto-optic modulator, a delay line, and a real-time oscilloscope. The technique is a slight modification of the typical delayed heterodyne method. Instead of using a swept frequency spectrum to analyze the laser emission spectrum, the waveform captured on a real-time oscilloscope is used to directly calculate the laser frequency noise. The oscilloscope bandwidth and sampling requirements can be kept modest by choosing a modulator driven at a few hundred megahertz, making this technique attractive for a large number of laboratories. We show the frequency noise measurements of two different lasers with linewidths at 2.7 kHz and 2 MHz. We took the opportunity to investigate the noise floor of the frequency noise measurement system, and we found that the noise floor of the frequency noise measurement depends on the power level of the laser that is being characterized, with the kilohertz linewidths laser requiring more power to reduce the noise floor to acceptable levels. [ABSTRACT FROM AUTHOR]

This paper aims to conduct an acoustic analysis through noise measurements of a hybrid propulsion system intended for implementation on a drone, from which the main noise sources can be identified for further research on noise reduction techniques. Additionally, the noise was characterized by performing spectral analysis and identifying the tonal components that contribute to the overall noise. The propelling force system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The propulsion system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The electric current produced by the generator powers an electric ducted fan (EDF). The engineturbo-engine was tested in free-field conditions for noise generation at different speeds, and for this, an array of microphones was installed, positioned polarly around the system and near the intake and exhaust. Consequently, based on the test results, the acoustic directivity was plotted, revealing that the highest noise levels are at the front and rear of the engine. The noise level at a distance of 1.5 m from the turboengine exceeds 90 dBA at all tested speeds. Spectral analyses of both the far-field acoustic signals (measured with a polar microphone array) and the near-field signals (microphones positioned near the intake and exhaust) revealed that the primary contributors to the overall noise are the micromotor's compressor, specifically the gas dynamic phenomena in the fan (BPF and 2× BPF). Thus, it was determined that at the intake level, the main noise contribution comes from the high-frequency components of the compressor, while at the exhaust level, the noise mainly originates from the combustion chamber, characterized by low-frequency components (up to 2 kHz). The findings from this study have practical applications in the design and development of quieter drone propulsion systems. By identifying and targeting the primary noise sources, engineers can implement effective noise reduction strategies, leading to drones that are less disruptive in urban environments and other noise-sensitive areas. This can enhance the acceptance and deployment of drone technology in various sectors, including logistics, surveillance, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment's quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1 dB(A) near a religious place to 82.4 dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning. [ABSTRACT FROM AUTHOR]

The advancement of urban rail transit is increasingly confronted with environmental challenges related to vibration and noise. To investigate the critical issues surrounding vibration propagation and the generation of structure-borne noise, a two-story frame building was selected for on-site measurements of both vibration and its induced structure-borne noise. The collected data were analyzed in both the time and frequency domains to explore the correlation between these phenomena, leading to the proposal of a hybrid prediction method for structural noise that was subsequently compared with measured results. The findings indicate that the excitation of structure-borne noise produces significant waveforms within sound signals. The characteristic frequency of the structure-borne noise is 25–80 Hz, as well as that of the train-induced vibration. Furthermore, there exists a positive correlation between structural vibration and structure-borne noise, whereby increased levels of vibration correspond to more pronounced structure-borne noise; additionally, indoor distribution patterns of structure-borne noise are non-uniform, with corner wall areas exhibiting greater intensity than central room locations. Finally, a hybrid prediction methodology that is both semi-analytical and semi-empirical is introduced. The approach derives dynamic response predictions of the structure through analytical solutions, subsequently estimating the secondary noise within the building's interior using a newly formulated empirical equation to facilitate rapid predictions regarding indoor building vibrations and structure-borne noises induced by subway train operations. [ABSTRACT FROM AUTHOR]

Aiming to reduce noise pollution and optimize the acoustic quality in highway tunnels, the noise characteristics on different types of pavements were analyzed and compared in this research, based on the on-site noise measurement in two tunnels with the free fields as a control group. Specifically, the noise characteristics include two aspects: various noise and noise time attenuation performance. Various noise includes on-board sound intensity (OBSI) noise and cabin noise. The noise time attenuation performance uses the indicator of reverberation time. Three types of pavements were measured, including dense-graded asphalt concrete (DAC) and single-layered and double-layered porous asphalt (PA) pavement. The results showed that, for the same type of pavement, compared with the free fields, the difference in OBSI noise in tunnels was within a range of less than 1 dBA; the cabin noise increased by 3.4 dBA~6.6 dBA. The noise level in tunnels was greater than that outside tunnels, and the longer tunnel exhibited higher traffic noise and worse noise time attenuation performances. For the same tunnel, PA pavement could reduce the cabin noise by 3.8 dBA~6.7 dBA. PA pavement also exhibited shorter reverberation time. The application of PA pavement could effectively improve the acoustic quality in the tunnel. This research contributes to noise pollution abatement and the improvement of the comfort and safety of drivers in tunnels. [ABSTRACT FROM AUTHOR]

The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters. • Noise is modeled as a GSTM distribution. • Sliding window uses multiple measurements to resist dynamic outliers. • VB method and MHE framework combine for joint inference of unknown parameters. • The superiority of the proposed filter has been confirmed through simulations. [ABSTRACT FROM AUTHOR]

The characteristics of a mechanical structure under operating conditions may differ from those in a static state. It is often more desirable to obtain the frequency response function (FRF) of the operating structure in engineering applications. While operational modal analysis (OMA) can estimate modal parameters during operation, it fails to provide mass-normalized mode shapes for FRF synthesis. This paper presents a new method using experimental modal analysis (EMA) to compensate for the absent information in OMA. It categorizes operational mode shapes into changed ones and those that remain the same compared to the static state, applying different scaling techniques accordingly. This method adapts to changes in dynamic characteristics without altering the operating conditions. Stability is emphasized throughout the process. Two examples are provided to verify the method, considering noise and incompleteness in measurement, and disturbances in dynamic properties. The proposed method is proven to be feasible and reliable to capture the changes in operational FRFs. [ABSTRACT FROM AUTHOR]

Distributed state estimation is one of the critical technologies in the field of target tracking, where the process noise and measurement noise may have a heavy-tailed distribution. Traditionally, heavy-tailed distributions like the student-t distribution are employed, but our observation reveals that Gaussian noise predominates in many instances, with occasional outliers. This sporadic reliance on heavy-tailed distributions can degrade performances or necessitate frequent parameter adjustments. To overcome this, we introduce a novel distributed consensus multi-distribution state estimation method that combines Gaussian and student-t filters. Our approach establishes a system model using both Gaussian and student-t distributions. We derive a multi-distribution filter for a single sensor, assigning probabilities to Gaussian and student-t noise models. Parallel estimation under both distributions, utilizing Gaussian and student-t filters, allows us to calculate the likelihood of each distribution. The fusion of these results yields a mixed-state estimation and corresponding error matrix. Recognizing the increasing degrees of freedom in the student-t distribution over time, we provide an effective approximation. An information consensus strategy for multi-distribution filters is introduced, achieving global estimation through consensus on fused local filter results via interaction with neighboring nodes. This methodology is extended to the distributed case, and the recursive process of the distributed multi-distribution consensus state estimation method is presented. Simulation results demonstrate that the estimation accuracy of the proposed algorithm improved by at least 20% compared to that of the traditional algorithm in scenarios involving both Gaussian and heavy-tailed distributions. [ABSTRACT FROM AUTHOR]

The impact of urban traffic on human health is significant. This research conducts field measurements in Guangzhou, China, focusing on a building situated near subgrade roads and viaducts to investigate the characteristics of airborne and structure-borne noise generated by these infrastructures. The analysis involves the use of both sound pressure level and overall sound pressure level, as well as an examination of the transfer function between outdoor and indoor noise levels. The findings indicate that traffic-related airborne noise demonstrates a characteristic frequency at 1000 Hz in this scenario, while viaduct- and building-generated structure-borne noise is predominantly distributed at lower frequencies. Additionally, it is worth noting that structural vibrations generate significantly less energy compared to airborne traffic noise sources. The variation in outdoor road noise across different floors over the entire frequency range demonstrates an initial increase followed by a decrease with rising floor height due to air damping effects as well as sound barriers' attenuation properties. These results enhance engineers' understanding of urban traffic-induced airborne or structure-borne noise while establishing foundational data for designing layouts integrating urban buildings with roads. [ABSTRACT FROM AUTHOR]

The measurement of the abundance of galaxy clusters in the Universe is a sensitive probe of cosmology, which depends on both the expansion history of the Universe and the growth of structure. Density fluctuations across the finite survey volume add noise to this measurement, this is often referred to as super-sample covariance (SSC). For an unbinned cluster analysis, such noise has not been included in the cluster likelihood, since the effect of SSC was small compared to the Poisson shot-noise for samples of a few hundred clusters. For upcoming large cluster surveys such as the Rubin LSST, which will deliver catalogues of tens of thousands of clusters, this effect will no longer be negligible. In this paper, we propose a new hybrid likelihood based on the Gauss-Poisson Compound model (GPC), by using infinitesimal mass bins and standard redshift bins. This likelihood has the advantages of an unbinned Poisson likelihood while successfully incorporating the effects of SSC. Using a simulated dark matter halo catalogue, we find that the hybrid likelihood, accounting for both Poisson noise and SSC, increases the dispersion of the parameter posteriors by 20 per cent when using 100 000 clusters compared to the standard unbinned likelihood, based on Poisson statistics only. [ABSTRACT FROM AUTHOR]

Silicon spin qubits are promising candidates for scalable quantum computers, due to their coherence and compatibility with CMOS technology. Advanced industrial processes ensure wafer-scale uniformity and high device yield, but traditional transistor processes cannot be directly transferred to qubit structures. To leverage the micro-electronics industry expertise, we customize a 300 mm wafer fabrication line for silicon MOS qubit integration. With careful optimization of the gate stack, we report uniform quantum dot operation at the Si/SiO2 interface at mK temperature. We measure a record-low average noise with a value of 0.61 μ eVH z − 0.5 at 1 Hz and even below 0.1 μ eVH z − 0.5 for some operating conditions. Statistical analysis of the charge noise measurements show that the noise source can be described by a two-level fluctuator model. This reproducible low noise level, in combination with uniform operation of our quantum dots, marks CMOS manufactured spin qubits as a mature platform towards scalable high-fidelity qubits. [ABSTRACT FROM AUTHOR]

This study investigates the impact of different passenger rolling stock structures and train configurations on vibrations and noise generated during operation. Vibroacoustic measurements were performed during acceleration, constant speed, and braking phases to analyze the effects using statistical analysis according to the relevant standards, revealing a statistically significant relationship between the equivalent noise level and vibration dose. In the context of the expanding Warsaw metro network, which is adding new lines and modern rolling stock, trends in the development of metro rolling stock were analyzed using five different types of metro vehicles, from the oldest to the newest designs. Vehicle performance was ranked in the context of ride comfort using standards and combined vibration and noise measures. The research results allowed for a collective comparative assessment of the construction of individual types of passenger rolling stock in terms of vibroacoustic phenomena, thanks to which it is possible to assess the impact of modern solutions and the justification for investing in modern rolling stock. Newer trains generally record significantly lower vibration and noise levels. The difference between the oldest and the most modern vehicle types is 57% for the vibration acceleration level (0.08 ms−2 RMS) and 66% for the noise level (2.2 dB LAeq). [ABSTRACT FROM AUTHOR]

Exposure and damage caused by noise have been reported in many countries around the world. However, few nationwide studies explored the association of residential environmental noise with depressive symptoms, this study aims to examine this association. The Korean Community Health Survey at the individual-level and the Korean Environmental Noise Measurement Database at the regional-level were used. A total of 30,630 individuals were eligible for the analysis. Multilevel model framework was applied to account for the clustered structure of the regional-level data in which individual-level data containing demographic characteristics and health information were nested. As a result of the analysis, Individuals living in the highest environmental noise area had a 1.55 times higher likelihood of experiencing depressive symptoms than those living in the lowest environmental noise area (95% CI, 1.04–2.31). After stratified analysis according to depressive symptom severity, individuals residing in areas with the highest environmental noise exposure had significantly higher odds of mild (aOR, 1.46; 95% CI, 1.02–2.07) and moderate symptoms (aOR, 1.70; 95% CI, 1.00–2.91). In conclusion, the higher the residential environmental noise, the higher the possibility of mild-to-moderate depressive symptoms. Our findings suggest the need for continued attention to and management of noise pollution, which has the potential to adversely affect individual's mental health. [ABSTRACT FROM AUTHOR]

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) faces a significant challenge in accurately detecting damage due to noise in acquired signals in composite plates, which can adversely affect reliability. Specific noise reduction techniques tailored to SHM signals are developed to tackle this issue. Gaussian smoothing proves effective in reducing noise and enhancing signal features, thereby facilitating the identification of damage‐related information. Optimization algorithms play a crucial role in damage detection, especially when integrated with smoothing and fusion techniques, as they provide optimal solutions to SHM challenges. A model‐updating‐based optimization algorithm is proposed for detecting damage in structures using condensed frequency response functions (CFRFs), even in the presence of various types of noise and measurement errors. The CFRF signals are first smoothed using an optimized Gaussian smoothing technique as part of the proposed method. Then, the proposed methodology integrates diverse smoothed signals using a raw data fusion approach, including those from different excitations, frequency ranges, and sensor placements. Fused smoothed signals are then fed into a new objective function, incorporating mutual information (MI) and Gaussian smoothing to mitigate correlated coloured noise. The proposed objective function also introduces a hyperparameter tuning of Gaussian smoothing to enhance its performance. Optimization via the improved reptile search algorithm (IRSA) updates the objective function, optimizing damage and smoothing parameters. The hybrid method detects damage in numerical composite laminated plates with different layers and boundary conditions, demonstrating its effectiveness as an SHM technique. Comparative evaluations of other state‐of‐the‐art methods show that the proposed method outperforms its counterparts, making it a promising damage detection approach to address the noise challenge in the SHM field. [ABSTRACT FROM AUTHOR]

The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further. [ABSTRACT FROM AUTHOR]

Palatine and pharyngeal tonsil hypertrophy may lead to dysfunction of the auditory tube due to a propensity for infection, potentially giving rise to otitis media. This is a quantitative and longitudinal study, developed from 2019 to 2021, at the State University of Campinas (UNICAMP). The studied sample comprised 15 participants aged 5 to 12 years (mean 7.9 years), 12 male and 3 female, arranged into two groups: children diagnosed with pharyngeal and/or palatine tonsil hypertrophy who were candidates for surgery (G1), and children who were later evaluated after surgery (G2). As part of the test, an otoscopy and measurements of logoaudiometry, pure-tone threshold audiometry, wideband tympanometry (ambient and peak pressure), and otoacoustic emissions (TEOAEs and DPOAEs, both at ambient and peak pressure) were all performed. There were statistically significant differences between phases in pure-tone audiometry, in terms of 226 Hz tympanometry, wideband tympanometry in peak pressure conditions, in the amplitude measurement TEOAEs in both pressure conditions, in DPOAEs in ambient pressure conditions, and in the signal/noise measurement in both pressures in DPOAEs. Overall, it was found that hearing tests were different for subjects with palatine and pharyngeal tonsil hypertrophy compared to the post-surgical group. [ABSTRACT FROM AUTHOR]

We propose a broadband noise test method (10 Hz–1 MHz) using a direct power method to measure the excess noise of InGaAs/InP avalanche photodiodes (APDs) with a separate absorption, grading, charge, and multiplication structure. By this means, the impact ionization coefficient ratio (the k value) can be accurately determined. Compared with previous studies, this method can distinguish between low-frequency noise and excess noise, so for an appropriate high frequency spectral range, the excess noise can be analyzed without mixing in other noise information. Three different APDs fabricated in-house with different diameters (12, 25, and 60 µm) were investigated using the above-mentioned method under temperatures varying from −10 to −40 °C. Our findings showed that the k value of the APDs decreased as the temperature decreased. When the temperature decreased to −40 °C, the k value of the 12, 25, and 60 µm diameter diodes was as low as 0.1, 0.3, and 0.2, respectively. The measurement results are consistent with our device design and not only provide useful information for the future selection of device materials and structures but also demonstrate the feasibility of the broadband noise measurement method in the accurate determination of the k value. It indicates that low excess noise can be achieved by utilizing the temperature dependence of k. [ABSTRACT FROM AUTHOR]

In India, railway is the major transportation mode for carrying goods and people. The tracks for the movement of the rail were initially constructed in the city for the pre-eminence and expediency of the vantage of the people. Rapid modernization and increasing population in the city crammed the area around the railway tracks. Moving rail on the tracks passing through the city is not compatible, which is creating problems for the nearby residents. In the urban and suburban regions, the railway noise has become a major problem. This study was conducted to examine the perception of the physiological and psychological effects of railway noise in the nearby areas of railway stations in Delhi, India. For this purpose, 10 sites near the railway station were selected for the study. To assess the impact of railway noise pollution on the health of humans, a questionnaire survey was conducted. The data of 344 individuals were collected through the questionnaire survey and analyzed to get the perception towards railway noise. Noise level was monitored by a Sound Level Meter (SLM) and the equivalent noise level (Leq) in dB(A) was used to compute the noise pollution in three shifts, i.e., morning, noon, and evening time. Results showed that 57.65% of female and 86.11% of male respondents in the survey reported the disturbance due to railway noise. The level of noise pollution was found higher in the evening time as compared to the noon and morning period, which exceeds the limit set by the Central Pollution Control Board (CPCB) at all the monitored locations. Findings of the study show that the primary cause of the health problems is railroad noise, which is negatively impacting the health of the residents, who are living in the proximity of the rail track region. The perception survey reported that headache, sleep disturbance, irritation, and stress are common health issues among the locals residing around the railway track proximity in Delhi. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy. [ABSTRACT FROM AUTHOR]

In this article, we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions. We focus on the fluid–structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources. These are a discretized baffled piston using the discrete calculation method (DCM), a closed cylindrical volume using the boundary element method (BEM) and radiating elastic disks in a cubic enclosure solved with the finite element method (FEM). We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models. Selected space-resolved waveforms are compared with experimental results as well as with a fluid–structure interaction finite element model. The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted, and the baffled pistons limitations caused by a lack of edge radiation effects are investigated. [ABSTRACT FROM AUTHOR]

Image processing systems can be used to measure the accuracy of 3D-printed objects. These systems must compare images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. Consequently, the integrity of the accuracy measurement process is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel by pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pore size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing. [ABSTRACT FROM AUTHOR]

A new afterburner installation is proposed, fueled with pure hydrogen (100%H2) or hydrogen–methane mixtures (60% H2 + 40% CH4, 80% H2 + 20% CH4) for use in cogeneration applications. Two prototypes (P1 and P2) with the same expansion angle (45 degrees) were developed and tested. P1 was manufactured by the classic method and P2 by additive manufacturing. Both prototypes were manufactured from Inconel 625. During the tests, analysis of flue gas (CO2, CO, and NO concentration), PIV measurements, and noise measurements were conducted. The flue gas analysis emphasizes that the behavior of the two tested prototypes was very similar. For all three fuels used, the CO2 concentration levels were slightly lower in the case of the additive-manufactured prototype P2. The CO concentration levels were significantly higher in the case of the additive-manufactured prototype P2 when 60% H2/40% CH4 and 80% H2/20% CH4 mixtures were used as fuel. When pure H2 was used as fuel, the measured data suggest that no additional CO was produced during the combustion process, and the level of CO was similar to that from the Garrett micro gas turbine in all five measuring points. The NO emissions gradually decreased as the percentage of H2 in the fuel mixture increased. The NO concentration was significantly lower in the case of the additive-manufactured prototype (P2) in comparison with the classic manufactured prototype (P1). Examining the data obtained from the PIV measurements of the flow within the mixing region shows that the highest axial velocity component value on the centerline was measured for the P1 prototype. The acoustic measurements showed that a higher H2 concentration led to a reduction in noise of approximately 1.5 dB for both afterburner prototypes. The outcomes reveal that the examined V-gutter flame holder prototype flow was smooth, without any perpendicular oscillations, without chaotic motions or turbulent oscillations to the flow direction, across all tested conditions, keeping constant thermal power. [ABSTRACT FROM AUTHOR]

Indoor air quality has become a more prominent concern since the arrival of the COVID-19 pandemic. Manufacturing industries have always been prone to occupational health risks, which depend on the dynamics of the production shop floors. The furniture industry is one of these sectors with a unique work environment. Although a typical furniture manufacturing facility involves physical, chemical, and noise pollution-producing elements, this industry has been studied relatively less for indoor air quality and noise-related risks. This study investigated nine furniture manufacturing organizations’ indoor air quality and noise pollution levels through comprehensive quantitative techniques. The results of the measurements were compared against reference values set by specific guidelines to explore the degree of occupational health risk associated with the World Health Organization's (WHO) suggested levels. Repetitive measurements from five pre-designated workstations were taken at each facility. The study’s results indicated that organization size and department were significant factors for PM 2.5 and HCHO parameters, while only department type was substantial for noise exposure levels. However, across all departments and organization sizes, LAeq noise levels were below the safety threshold of 85 dB(A). Most organizations presented a lack of proper use of personal protective equipment and poor ventilation across shop floors.

This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to re6ne the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, e7ectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only o7ers a robust framework for analyzing dam deformation trends but also sets a new standard in the 6eld, providing a new solution for assessing structural integrity in hydrological engineering. [ABSTRACT FROM AUTHOR]

In practical radar systems, changes in the target aspect toward the radar will result in glint noise disturbances in the measurement data. The glint noise has heavy-tailed characteristics and cannot be perfectly modeled by the Gaussian distribution widely used in conventional tracking algorithms. In this article, we investigate the challenging problem of tracking a time-varying number of maneuvering targets in the context of glint noise with unknown statistics. By assuming a set of models for the possible motion modes of each single-target hypothesis and leveraging the multivariate Laplace distribution to model measurement noise, we propose a robust interacting multi-model multi-Bernoulli mixture filter based on the variational Bayesian method. Within this filter, the unknown noise statistics are adaptively learned while filtering and the predictive likelihood is approximately calculated by means of the variational lower bound. The effectiveness and superiority of our proposed filter is verified via computer simulations. [ABSTRACT FROM AUTHOR]

The work tests and analyzes the engine noise distribution in a car with and without active control, where the car's built-in loudspeakers are used as secondary sources to attenuate the engine noise inside the whole cabin. To analyze the sound field in detail, the noise level at 52 microphones on the passenger ears' plane is measured and presented. The distributions of the primary overall noise, first four order noise of the engine, and the sound field generated by the subwoofer at engine orders' frequencies are reported, and the noise reductions corresponding to overall engine noise and each order's noise are analyzed. Besides, jury test is presented and compared with the calculated loudness of residual noise to evaluate the acoustic comfort improvement of the system. The experiment results show that the overall noise reduction is achieved inside the whole cabin by controlling the first four orders' engine noise. The average noise reduction of the measurement area inside the cabin is 6.2 dB and 2.9 dBA. Besides, 93% of the participants feel the acoustic comfort is improved with the active noise control system on. [ABSTRACT FROM AUTHOR]

Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process. [ABSTRACT FROM AUTHOR]

Acoustic measurements were performed using microphone downstream of a 2-D wind turbine blade section in wind tunnel. The experiments have been carried out in both static and oscillatory pitching cases. The latter is usually experienced by the blades in actual circumstances. The microphone was 1.5 chords downstream of the airfoil and the measurements were conducted at three transverse positions, i.e. behind the trailing edge, midway between the trailing edge and the ground and very close to the ground. A CFD simulation of the flowfield has also been conducted using Fluent to correlate the acoustic behavior to the phenomena observed in the flowfield around the blade. The results show that the acoustic noise heard by a listener located on the ground is higher and stronger than that positioned downstream of the trailing edge, showing the ground effect on acoustic noise reverberation. The aerodynamic noise heard by the listener, changes from a treble to bass sound as the angle of attack increases. Beyond stall, the flow is dominated by the vortices shed into wake and the acoustic noises would be at very low frequencies which would result in a bass sound accompanied by structural vibration. In high angle of attack range, such noises can hardly be heard by a normal person but have a very destructive role on blade structure. [ABSTRACT FROM AUTHOR]

Both constrained state estimation and constrained reference tracking could be formulated as optimization problems, and the symmetry between these two problems could be shown. As variables in the stochastic model, both the mean of process noise and the mean of measurement noise do not have to be zero. The main contributions of this paper are that we take the mean of process noise and the mean of measurement noise, which do not have to be zero, and show the symmetry between constrained state estimation and constrained reference tracking for this case. In other words, we study the symmetry between state estimation and reference tracking with an additional constraint on the measurement noise and the process noise. By this symmetric relationship, the constrained state estimation problem can be solved as a constrained reference tracking problem, and vice versa, despite the presence of more general process and measurement noise. [ABSTRACT FROM AUTHOR]

Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise. For n-qubit systems, our algorithm, which employs the easily prepared initial state 0 n 0 n assumed to be noiseless, efficiently estimates k-RDMs with O ̃ (k n k ) state copies and O ̃ ( n ) calibration measurements for GTM noise with constant fidelities. We show that our algorithm is robust against noise types like depolarizing, damping, and X-rotation noise with constant strengths, showing scalings akin to prior CS algorithms for fermions but with better noise resilience. Numerical simulations confirm our algorithm's efficacy in noisy settings, suggesting its viability for near-term quantum devices. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems. [ABSTRACT FROM AUTHOR]

Featured Application: The used method was introduced by the Railway Administration's methodological regulations for the determination of points of primary and secondary system points and is currently used in connection with the determination of the spatial position of the center of the track. This article deals with the analysis of the accuracy of the geodetic real-time GNSS measurement procedure used in railway operating conditions in the Czech Republic. The purpose was to determine to what extent the operating conditions affect the accuracy of the measurement result and whether an accuracy of standard deviation σx,y = 5 mm in the horizontal plane could be achieved. The use of geodetic GNSS equipment with an IMU unit was also tested. The accuracy obtained in operational conditions is compared with the accuracy obtained on a calibration base using the same measurement procedure. The consistency between the accuracy of the primary system (satellite-based) and the secondary system (terrestrially measured by the traverse method) is also discussed. The analysis includes the issue of residual inhomogeneities of the uniform transformation key when converted to the Czech national coordinate system S-JTSK. It is shown that a homogeneous accuracy in coordinate standard deviation better than σx,y = 5 mm can be achieved. The results indicate that the accuracy under operational conditions is two–three times worse than the accuracy achieved by the same procedure under ideal conditions on a calibration base. This is due to the non-ideal observing conditions, i.e., horizon occlusion by overlays, surrounding vegetation and multipath effects. It has been shown that the effect of multipath can be reduced by repeating short observations 3–4 h apart. Older GNSS instruments using an IMU unit in combination with an electronic compass (eCompass) are at risk of a systematic bias of up to several tens of millimeters, which can be detected by rotating the antenna by 180°. The current uniform transformation key used in the Czech Republic for the conversion of GNSS coordinates into the national system has residual geometric inhomogeneities (p = 0.90 to 10 mm/km, sporadically up to 20 mm/km), which metrologically deteriorate the results of the calculation of the terrestrially measured secondary system inserted into the GNSS measured primary system. Achieving homogeneous accuracy in coordinate standard deviation in a horizontal plane better than σx,y = 5 mm has been demonstrated in non-ideal railway operating conditions with increased risk of multipath. The innovative aspect of the approach used is that it simplifies and thus increases the efficiency of the measurement with respect to the availability of GPS, GLONASS, Galileo and BeiDou satellites, as well as reducing the effect of multipath on the noise by repeating the measurement procedure. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

This study aimed to determine the risk level of noise, which is an important physical risk, in small and medium-sized furniture industry enterprises. The noise levels of the circular sawing machines, edge banding machines, and mitre cutting machines, which are among the main processing machines of the sector, were measured. The study was carried out in 32 furniture businesses. The possible risks of noise on the operators of the machines in question and other employees were evaluated. Noise level measurements were made with the help of TESTO 815 measuring device. Dunnett's T3 test was used to detect differences in noise levels for machine operators and other employees. It was determined that the edge banding machine does not pose an occupational health and safety risk in terms of noise risk factors. However, the mitre cutting machine and the circular sawing machine pose a risk for the machine operator in active production by creating noise above the established exposure limit value. The mitre cutting machine carries the same risk for the machine operator when it is in operation but in passive production. The results revealed the need for personal protective equipment for machine operators for mitre cutting and circular sawing machine. [ABSTRACT FROM AUTHOR]

Traffic-related noise pollution is a recurring problem in large and medium-sized cities. The objective of this study is to quantify traffic-noise levels along selected urban arterials and model the generated traffic noise based on traffic and pavement characteristics. Urban arterial and collector streets in Irbid city, Jordan, were taken as a case study. The city is considered an example of a medium-sized city. 65 urban arterial and collector sections were selected to achieve the stated objective. For each section, ten noise measurements were taken using a time interval of 2.5 minutes for each observation. The statistical pass-by method was used to measure 650 externalnoise observations. In addition, data on traffic, pavement and section geometric characteristics was obtained through field measurements. The collected traffic characteristics, including traffic flow, percentage of trucks and speed in each direction of travel, were obtained. In addition to the measurement of pavement macrotexture depth, an international roughness index was measured using a smartphone application. Investigation of the collected noise data indicated that urban streets experienced high noise levels, reaching maximum and average values of 82 and 77.2 dB(A), respectively. The results of the analyses showed that an increase in each of the included traffic characteristics resulted in significantly higher noise levels. For example, an increase in speed from 35 to 55 km/h would increase noise by 2.7 dB(A). In addition, the interaction term of roughness index and pavement macrotexture depth was found to increase the generated noise. Finally, the results of the analysis indicated that both multivariate linear -and exponential-regression models are suitable to model the generated traffic noise. Each model explained approximately 54% of the noise variability. Probably, traffic composition and vehicle-power type heterogeneity might reduce the level of explained variability. [ABSTRACT FROM AUTHOR]

Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized for measuring corona discharge currents at sub-ampere levels. However, these approaches were not suitable for continuously recording the vast range of lightning currents. For this sake, we have developed a lightning current measurement system equipped with a shock-tolerant low-noise amplifier module. With the system installed on a tall tower, sub-ampere level currents just before the lightning initiation were observed for the first time. To confirm the authenticity of the recorded currents, the background noise of the measurement system and surrounding environment were identified, and a digital multi-frequency notch filter was proposed for de-noising. Results show that the system can achieve a current identification level of 50 mA even in complex electromagnetic environments, while having a measurement capability of 220 kA. [ABSTRACT FROM AUTHOR]

Quantitative characterization of freestream fluctuations in a hypersonic flow facility is important for proper interpretation of results from any boundary-layer flow transition experiments conducted in the facility. Direct measurement of freestream flow quantities (like pressure, temperature, velocity) using a physical probe is not possible due to the modulation introduced by the leading shock wave and the downstream flow region. A transfer function that models the complex modulation process, which is specific to a given probe geometry, is therefore needed to estimate the freestream flow condition from the physical probe output. The general practice across different experimental hypersonic flow facilities globally is to employ custom designed probes for estimation of freestream fluctuations. This hinders a direct comparison of freestream fluctuations between two different flow facilities since such a comparison is limited by the accuracy of the two different transfer functions that are used. The present work is motivated by the need to have a standard probe geometry and an associated transfer function for enabling an objective comparison of freestream fluctuations across experimental facilities. A wedge probe along with a linear transfer function used in the recent work by Wagner et al. (J Fluid Mech 842:495–531, 2018. https://doi.org/10.1017/jfm.2018.132) is proposed for consideration as a standard hypersonic freestream flow characterization method. The utility of the measurement method is demonstrated by making spectral measurements of freestream pressure fluctuations at Mach 6 in the Roddam Narasimha Hypersonic Wind Tunnel at IISc and performing a direct quantitative comparison with the results obtained by Wagner et al. in the RWG Ludwieg tube (Göttingen, Germany). [ABSTRACT FROM AUTHOR]