Noise Content Research Articles

Expanding road noise cancellation to higher frequencies

Active vehicle Road Noise Cancellation (RNC) systems effectively reduce persistent vehicle rumble and booming noise which is very difficult to control with traditional NVH measures, particularly at low frequencies. Therefore, more and more vehicles are adapting RNC systems in their production applications. Most current RNC systems cancel low frequency structure-born noise, which is typically below 400 Hz. Although it provides appreciable noise attenuation, remaining high frequency noise contents still requires mitigation for a premium cabin experience. This study targets a next generation RNC system development which extends noise cancellation frequency range up to 1 kHz. High frequency RNC system (HF-RNC) development begins with analyzing limiting factors of the current RNC systems. Theoretical and practical limitations of the current system are discussed. Then, new technology and enablers to overcome those limitations are introduced. New enablers investigated herein in includes feedforward microphones, low latency system, headrest speakers and microphones, and head tracking system. HF-RNC system architecture is defined to comprise extended features and enablers. Low latency software is ported into a development platform which is based on a commercial DSP. The new HF-RNC system demonstrates noise cancellation up to 1 kHz when applied to an electric vehicle.

Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content. Denoising methods are needed to increase image quality. The purpose of this study was to investigate the performance, limitations, and behavior of a new 4D filtering method, called the 4D Similarity Filter (4DSF), to reduce image noise in temporal CT data. The 4DSF averages voxels with similar time-intensity curves (TICs). Each phase is filtered individually using the information of all phases except for the one being filtered. This approach minimizes the bias toward the noise initially present in this phase. Since the 4DSF does not base similarity on spatial proximity, loss of spatial resolution is avoided. The 4DSF was evaluated on a 12-phase liver dynamic CT angiography acquisition of 52 digital anthropomorphic phantoms, each containing one hypervascular 1cm lesion with a small necrotic core. The metrics used for evaluation were noise reduction, lesion contrast-to-noise ratio (CNR), CT number accuracy using peak-time and peak-intensity of the TICs, and resolution loss. The results were compared to those obtained by the time-intensity profile similarity (TIPS) filter, which uses the whole TIC for determining similarity, and the combination 4DSF followed by TIPS filter (4DSF + TIPS). The 4DSF alone resulted in a median noise reduction by a factor of 6.8, which is lower than that obtained by the TIPS filter at 8.1, and 4DSF + TIPS at 12.2. The 4DSF increased the median CNR from 0. 44 to 1.85, which is less than the TIPS filter at 2.59 and 4DSF + TIPS at 3.12. However, the peak-intensity accuracy in the TICs was superior for the 4DSF, with a median intensity decrease of -34 HU compared to -88 and -50 HU for the hepatic artery when using the TIPS filter and 4DSF + TIPS, respectively. The median peak-time accuracy was inferior for the 4DSF filter and 4DSF + TIPS, with a time shift of -1 phases for the portal vein TIC compared to no shift in time when using the TIPS. The analysis of the full-width-at-half-maximum (FWHM) of a small artery showed significantly less spatial resolution loss for the 4DSF at 3.2 pixels, compared to the TIPS filter at 4.3 pixels, and 3.4 pixels for the 4DSF + TIPS. The 4DSF can reduce noise with almost no resolution loss, making the filter very suitable for denoising 4D CT data for detection tasks, even in very low dose, i.e., very high noise level, situations. In combination with the TIPS filter, the noise reduction can be increased even further.

Advanced Data Processing of THz-Time Domain Spectroscopy Data with Sinusoidally Moving Delay Lines

AbstractWe provide a comprehensive technical analysis of the data acquisition process with oscillating delay lines for Terahertz-time domain spectroscopy. The utilization of these rapid stages, particularly in high-repetition-rate systems, is known to enable an effective reduction of noise content through averaging. However, caution must be exercised to optimize the data averaging process, with the goal of significantly optimizing the dynamic range (DR) and signal-to-noise ratio (SNR). Here we discuss some pitfalls to avoid and the effect of improper data handling on the dynamic range obtainable. A free and open-source program, called parrot (Processing All Rapidly & Reliably Obtained THz-traces), is provided alongside this publication to overcome the discussed pitfalls and facilitate the acceleration of experimental setups and data analysis, thereby enhancing signal fidelity and reproducibility.

Efficient recognition of fish feeding behavior: A novel two-stage framework pioneering intelligent aquaculture strategies

In the domain of aquaculture, the act of feeding fish is a pivotal factor influencing both the growth of the fish and the associated cultivation costs. The implementation of intelligent feeding strategies is a crucial prerequisite for maintaining fish health and minimizing costs, with the accurate discernment of fish feeding behavior serving as the fundamental basis for the realization of such strategies. Addressing the issues of high data redundancy and substantial noise content inherent in the datasets utilized by existing identification models, as well as the intricate design and suboptimal execution efficiency of the model structures, this study introduces a two-stage framework for discriminating fish feeding behavior. In the initial stage, a re-parameterizable multi-scale object detection model is established, facilitating the acquisition of the spatial distribution of fish schools. This process effectively eliminates redundant data and noise, decouples the training and inference processes of the model, equivalently transforms the complex network training weights, and simplifies the inference process of the model. An analysis of the dataset characteristics is conducted, leading to the optimization of the model detector’s design and a reduction in both the model parameters and computational requirements. In the subsequent stage, responding to the key data characteristics of the spatial distribution of fish schools, a lightweight behavior recognition model is designed. This model enables the rapid and accurate identification of fish feeding behavior. A plethora of experimental results demonstrate that the proposed method can achieve a high recognition accuracy (Acc 83.33%) while operating under the constraints of minimal model parameters and computations (6.45M Params, 8.135G FLOPs). This provides a robust model foundation for the industrial application of the algorithm, underscoring the significant potential of the proposed method in advancing intelligent feeding strategies in aquaculture.

Direct-Noise of an Ultrahigh-Bypass-Ratio Turbofan: Periodic-Sector vs Full-Annulus Large-Eddy Simulations

The tonal and broadband noise of an ultrahigh-bypass-ratio fan stage, which has been developed at École Centrale de Lyon, is studied using large-eddy simulations (LES). Wall-modeled LES of a periodic sector and a 360-deg full fan stage have been performed at approach conditions, which is a relevant operating point for aircraft noise certifications. The fan noise is directly obtained from the fully compressible LES, using a well-refined unstructured mesh, and compared with state-of-the-art analytical models. The impact of the periodic boundary conditions, which are often used for high-fidelity simulations of turbofan engines, is assessed. The results from the 360-deg and periodic-sector LES are compared from aerodynamic and acoustic perspectives, including an analysis of the mean and turbulent flow quantities and sound-pressure spectra. Aerodynamic parameters show similar results for both configurations. However, the fan blade loading is slightly reduced in the 360-deg- LES near the blade tip. Acoustically, lower sound power levels at the intake and exhaust sections of the fan stage are obtained in the 360-deg LES, when compared to the periodic sector LES, particularly at low and middle frequencies. This can be associated with lower coherence levels in the fan wakes and smaller spanwise correlation lengths at the trailing edge of the blades. The modal content of the acoustic field has also been analyzed in detail and shows that the periodic sector LES cannot correctly simulate the modal content of the fan noise.

Orientation-based solar noise impact on underwater and free-space optical wireless communication systems: experimental investigations

Solar noise, when it interferes with the received signal at the system receiver (Rx) of an optical wireless communication (OWC) system, degrades the system’s performance. The detrimental effect of solar noise on OWC systems has been well established in the literature. This work experimentally demonstrates solar noise interference in the OWC system by pointing the system Rx in various orientations in air and water mediums, e.g., 0° (Rx pointing horizontally leftward), 45°, 90° (Rx pointing vertically downward), 135°, 180° (Rx pointing horizontally rightward), 225°, 270° (Rx pointing vertically upward), and 315°. The experimental outcomes depict the signal’s noise content, spectral leakage, and roll-off rate variation at multiple Rx orientations. We also demonstrate the solar noise interference in transmitting an image through the outdoor underwater OWC link by pointing the system Rx in various orientations. Experimental demonstration confirms that the same OWC system never behaves identically in the presence of solar noise if the system Rx keeps changing its orientation during the maneuver.

Improving Digital Satellite Image for security purposes

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.

Background Seismic Noise Levels among the Caribbean Network and the Role of Station Proximity to Coastline

Abstract The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one-hour windows for over 15 yr of vertical-component data from the nine-station Caribbean network (CU) and look at background noise within the 0.05–300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75–3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4–8 s range more typical of globally observed secondary microseisms. A simple surface-wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast (<∼50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75–3 s. This may be because power from nearshore wave action at 0.75–3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of ∼25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.

Noise aware content-noise complementary GAN with local and global discrimination for low-dose CT denoising

In response to rising concerns over radiation exposure in computed tomography (CT) imaging, effective denoising methods for low-dose CT (LDCT) images are crucial. In recent years, the use of deep learning techniques especially generative adversarial networks (GANs) significantly enhanced the efficiency of LDCT denoising methods, surpassing traditional methods. However, GAN-based denoising methods often face challenges in preserving structural consistency and fine details. This study introduces a novel GAN framework with three accretions to enhance the effectiveness of LDCT denoising. Firstly, our generator is designed to leverage a complementary learning scheme between image noise and image content via two distinct paths. One path focuses on exploring the anatomical information of the image, while the second path is dedicated to learning the noise pattern. This complementary learning scheme provides stable noise cancellation while preserving the maximum structural information of the image. Subsequently, we propose a novel noise-conscious mean absolute error loss to address the challenge posed by the non-stationary characteristic of CT noise. In contrast to conventional MAE loss, this loss attentively prioritizes the different parts of the image based on the local distribution of noise in that region. We also incorporate a gradient-domain loss into the loss function, which inspires the generator to preserve precise image details through explicit guidance. Finally, this study adopted a U-Net-based design for the discriminator to better regularize the model by discriminating between the clean image and the denoised image at both global and local levels. The merit of this discriminator is that it can better adapt to the non-stationary environment of GAN training and guide the generator to produce denoised images that are locally and globally consistent. Our thorough experiments using abdominal CT and lung CT datasets demonstrate the superior performance of our approach compared to state-of-the-art methods.

Noise reduction analysis of deformation data based on CEEMD-PE-SVD modeling

In the field of high slope deformation monitoring, the deformation data obtained are often characterized by high volatility, strong nonlinearity, and noise content, due to the influence of factors such as the surrounding environment, human operation, and the complexity of the project. To overcome these problems, this paper proposes deformation data based on the coupling of complementary ensemble empirical modal decomposition (CEEMD), permutation entropy (PE), and singular value decomposition (SVD). Firstly, CEEMD decomposition is performed on the deformation data. Secondly, a randomness test is performed on the obtained modal components, and if the entropy value of the component arrangement entropy is greater than 0.5, SVD decomposition will be used for noise reduction. Finally, the components that meet the conditions are reconstructed to realize the noise reduction of the deformation data. The results show that the algorithm model has an excellent noise elimination effect, which can reflect the internal detail information of deformation data and provide a well-established method for future research on the processing of deformation monitoring data.

Fault Signal Perception of Nanofiber Sensor for 3D Human Motion Detection Using Multi-Task Deep Learning

Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception.

Accessibility and Exhibit Safety: The Importance of Sensory Maps

Museum accessibility, through audio description services, sighted guides, sensory maps/guides/social narratives, and more, is an essential part of exhibit safety and design. In the past decade, more museums recognize the importance of trigger warnings (a trigger being a sensory experience that may cause mental or physical discomfort, overload, or breakdown) for flashing lights, noise content, and movement to ensure visitors with epilepsy, tinnitus (ringing in the ears), PTSD, and mobility devices remain safe. But while more museums are developing resources and programming for neurodiverse individuals, including Autism Spectrum Disorder (ASD), many still do not understand how sensory maps, an inclusive and easy-to-create resource, are critical to visitor safety. A sensory map is an annotated layout of an exhibit, gallery, or entire museum that identifies areas with high and low sensory stimulation and the type of stimulation. Sensory overload/overstimulation can put visitors, staff, and collections at risk; sensory maps alleviate this risk by allowing visitors to self-select in or out of experiences, and in doing so, prioritize their own mental and bodily wellbeing. Highlighting how sensory maps are critical for visitor, staff, and object safety encourages museum boards of directors to allocate funding to create these resources and to seek out more funding for tailored programming and exhibit experiences. I met with accessibility professionals from the Metropolitan Museum of Art, the International Spy Museum, the Houston Museum of Art, and others to share their insights about how to create accessible sensory maps (physical and digital versions) and reach out to disabled communities.

Sentiment analysis of tweets using text and graph multi-views learning

With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches. However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts.

Data augmentation for 3D seismic fault interpretation using deep learning

Manual seismic interpretation of faults is a tedious and complicated process, which is prone to human error and bias. A semi-automatic approach for interpreting faults on seismic data is to use attributes that highlight discontinuities. These methods need to be optimized by the interpreter, thus constantly rely on the interpreter's knowledge. Recently, Machine Learning (ML) techniques in general and Convolutional Neural Networks (CNN) as part of Deep Neural Networks (DNN), in particular, have been used to detect and image faults on seismic data in order to make the process more automated and accurate. Supervised CNN learns and evolves from manually annotated or labeled fault interpretations.In this study, we have applied supervised 2D CNN to image faults on seismic data through binary segmentation. The study was performed on 3D marine seismic surveys in off-shore Norway collected from three separate locations along the Norwegian Continental Shelf, utilizing Efficient UNET CNN architecture. We applied data augmentation (geometric transformations) and hyperparameter tuning to improve the learning process and performance of the deep learning algorithm.We used noise content, acquisition type, imaging type, and fault scale as the main criteria to select seismic surveys. The application of data augmentation to the training and testing data generally led to improvement in the performance of 2D CNN on fault predictions, although the amount of improvement varied with respect to different surveys. The initial 2D CNN fault prediction improvement mainly relied on the quality, and size of faults present in the 3D seismic volumes. Further, improvement was achieved by the adjustment of certain hyperparameters affecting the training and testing process of the 2D CNN. However, we found little to no improvement on one seismic volume containing high levels of noise.

Sonic Bouquet: Decoding Cross-Modal Correspondences Between Timbre and Scent

The examination of cross-modal correspondences between auditory and olfactory senses opens up an intriguing perspective into the study of extra-musical meaning. In a behavioral experiment, musically trained participants were presented with 26 complex synthetic tones and 12 aromatic stimuli. Their task was to report potential associations between the two. The data analysis revealed that the majority of scents featured at least one association with a sound that was above chance. The salient acoustical correlates of basic aromatic categories could be summarized as follows: both fruity (e.g., cherry, melon, and pomegranate) and sour aromas exhibited a positive correlation with pitch. Fruity scents, in addition, were more likely to be associated with sounds featuring pronounced low harmonic partials (1st–4th), low noise content, low roughness, and a greater number of distinct pitches. Conversely, sour aromas were linked with stronger energy in the higher frequencies. Sweet scents correlated with sounds characterized by a lower spectral centroid, whereas aromas in the spicy/other category were associated with weak lower partials (fundamental frequency in particular) and stronger noisy components.

Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

SummaryDue to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively.

Interval Type-3 T-S fuzzy system for nonlinear aerodynamic modeling

This paper presents an aircraft related application of interval type-3 (IT3) T-S fuzzy system for nonlinear aerodynamic modeling from recorded flight data. The theoretical base developed for design of IT3 T-S fuzzy system is utilized and longitudinal aerodynamic coefficients are modeled. These longitudinal aerodynamic coefficients are related with lift force, drag force and pitching moment. The flight data used for simulation belongs to ATTAS aircraft which is operating in quasi steady stall maneuver flight condition. The applicability of IT3 fuzzy system for modeling data uncertainties are demonstrated by adding the external noise in recorded data and making uncertainties associated with unvarying and missing data. The IT3 T-S fuzzy modeling method can suitably characterize the complex and nonlinear behavior of the aircraft in the presence of uncertainty and unsteadiness created due to aerodynamic / propulsive / structural interactions, separated air flow and mass properties changes etc. The performance comparison of generated IT3 T-S fuzzy aerodynamic model is also carried out with other state of the art modeling methods, the proposed fuzzy model shows good prediction capability in presence of various form of data uncertainties with associated noise. It is also noticed that as noise content in data increases, the percentage improvement in accuracy (in comparison with other fuzzy models) also appears. However, this improvement is based on nature of application and data. For clean data, the performance of IT3 fuzzy system is almost equivalent to other modeling methods, but with the increase of noise content and uncertainty in data, the performance improvement of IT3 fuzzy system enhances. Thus, performance of the IT3 fuzzy systems becomes more appealing when, uncertainty arises in the data in form of noise or missing/unvarying data points.

Combined monthly GRACE-FO gravity fields for a Global Gravity-based Groundwater Product

SUMMARY The Combination Service for Time-variable Gravity fields (COST-G) operationally provides combinations of monthly Earth gravity field models derived from observations of the microwave ranging instrument of the GRACE Follow-on (GRACE-FO) satellite mission, applying the quality control and combination methodology originally developed by the Horizon 2020 project European Gravity Service for Improved Emergency Management for the data of the GRACE satellites. In the frame of the follow-up Horizon 2020 project Global Gravity-based Groundwater Product (G3P), the GRACE-FO combination is used to derive global grids of groundwater storage anomalies. To meet the user requirements and achieve optimal signal-to-noise ratio, the combination has been further developed and extended to incorporate: • new time-series based on the alternative accelerometer transplant product generated in the frame of the project by the Institute of Geodesy at the Graz University of Technology, which specifically improves the estimation of the C30 coefficient and also reduces the noise at medium to short wavelengths, and • the new time-series AIUB–GRACE-FO–RL02 of monthly GRACE-FO gravity fields, which is derived at the Astronomical Institute of the University of Bern by applying empirical noise modelling techniques. The COST-G quality control confirms the consistency of the contributing GRACE-FO time-series concerning the signal amplitude of seasonal hydrology in large river basins and the secular mass change in polar regions, but it also indicates rather diverse noise characteristics. The difference in the noise levels is taken into account in the combination process by relative weights derived by variance component estimation on the solution level. The weights are expected to be inverse proportional to the noise levels of the individual gravity field solutions. However, this expectation is violated when applying the weighting scheme as developed for the GRACE combination. The reason is found in the high-order coefficients of the gravity field, which are poorly determined from the low–low range-rate observations due to the observation geometry and suffer from aliasing due to the malfunctioning accelerometer onboard one of the GRACE-FO satellites. Hence, for the final G3P-combination a revised weighting scheme is applied where the gravity field coefficients beyond order 60 are excluded from the determination of the weights. The quality of the combined gravity fields is assessed by comparison of the noise content and the signal-to-noise ratio with the individual time-series. Independent validation is provided by the COST-G validation centre at the GFZ German Research Centre for Geosciences, where orbit fits of the low-flying Gravity and steady-state Ocean Circulation Explorer satellite are performed that confirm the high quality of the combined GRACE-FO gravity fields. By the end of the G3P project, the new combination scheme is implemented by COST-G as the new COST-G–GRACE-FO–RL02 and continued to be used for the operational GRACE-FO combination.

Influence of noise masking on leak pinpointing: Experimental analysis on a laboratory test rig for leak noise correlation.

The efficient management of water distribution networks requires the detection and mitigation of leaks that result in significant water losses, economic costs, and potential health hazards. This experimental study focuses on the optimal frequency range for accurately locating a water leak in a distribution network using the noise correlation approach while mitigating the influence of background noise associated with water consumption from users. Through laboratory experimental tests, a digital bandpass filter was defined in order to pre-process signals and to improve cross-correlation analysis within the pertinent frequency range. This was done by characterising the frequency content of leak-induced noise, noise masking resulting from water consumption, and background noise. The analysed signals were recorded using hydrophones installed within the pipes of a laboratory test rig under different conditions of water flow rate. The spectra analysis assessed the influence of varying water flow rates on the acoustic sound field within the pipeline system. Additionally, the results of the analysis made it possible to discriminate clearly between noise generated by leaks and noise attributed to water consumption. This distinction is useful for implementing suitable filters, and also to establish a threshold ratio of leak flow rate to water consumption flow rate for effective pre-filtering. This study aims to establish a methodological framework that can effectively differentiate between acoustic signals stemming from leaks and noise generated by other sources within the system, to improve leak location accuracy through the noise correlation approach.