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Numerical simulations have been an increasingly important tool in space physics. Here, we introduce an open-source three-dimensional compressible Hall-Magnetohydrodynamic (MHD) simulation code LAPS (UCLA-Pseudo-Spectral, https://github.com/chenshihelio/LAPS). The code adopts a pseudo-spectral method based on Fourier Transform to evaluate spatial derivatives, and third-order explicit Runge-Kutta method for time advancement. It is parallelized using Message-Passing-Interface (MPI) with a "pencil" parallelization strategy and has very high scalability. The Expanding-Box-Model is implemented to incorporate spherical expansion effects of the solar wind. We carry out test simulations based on four classic (Hall)-MHD processes, namely, 1) incompressible Hall-MHD waves, 2) incompressible tearing mode instability, 3) Orszag-Tang vortex, and 4) parametric decay instability. The test results agree perfectly with theory predictions and results of previous studies. Given all its features, LAPS is a powerful tool for large-scale simulations of solar wind turbulence as well as other MHD and Hall-MHD processes happening in space. [ABSTRACT FROM AUTHOR]

For large eddy simulations (LES) of stratified turbulence in the strongly stratified regime, an artificial neural network (ANN) with five hidden layers is used to construct a sub-grid scale (SGS) model. The ANN is assessed by comparing it to the Smagorinsky model, the dynamic Smagorinsky model, the gradient model, and filtered direct numerical simulation data. In the a priori test, the SGS model using ANN performed better than the Smagorinsky model and the gradient model in terms of the correlation coefficient and relative error of the energy transfer rate. However, the ANN does not provide sufficient energy dissipation when it is applied to LES with a larger filter width because it overpredicts backscatter. To address this problem, we also trained a constrained ANN using a custom loss function that penalizes excessive backscatter. It is shown that the constrained ANN successfully predicts less backscatter, maintaining the high correlation coefficient without ad hoc clipping. These results show that ANN is a promising tool for realizing a highly accurate and stable SGS model for stratified turbulence. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2016–2017, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Three different techniques were used for obtaining measurements: Lucky Imaging, Speckle Interferometry and the direct vector autocorrelation (DVA) method. From our observations of 1,510 multiple stars, we obtained 2,918 new measurements with angular separations in the range 0″.1–20″ and an average accuracy of 0.019$$ 0.019 $$. The mean error on the position angles is 1°.1. Most of the position angles were determined without the usual 180° ambiguity with the application of the DVA technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV≈9−12$$ {m}_V\approx 9-12 $$) with large magnitude differences (up to ΔmV≈4.5$$ \Delta {m}_V\approx 4.5 $$). We have thus been able to measure six systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 593 double stars with a mean error of 0.1 mag. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 46 measurements with an angular separation smaller than the diffraction limit of our instrumentation. Except for a few objects that have been discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. We also report the measurements of 205 new double stars that we found in the files obtained during the observations. [ABSTRACT FROM AUTHOR]

This paper studies the dissipative filtering of uncertain singular time-varying delay Markovian jump systems (MJSs) with nonlinear perturbations and generally uncertain transition rates (GUTRs). We focus on the design of a mixed filter, which can include mode-dependent and mode-independent filters in a unified and simple structure. First, a new Lyapunov–Krasovskii functional is proposed through the delay-decomposition approach, and improved Wirtinger-based integral inequalities are used to obtain a sufficient condition to ensure the stochastic admissibility of the considered uncertain nonlinear singular time-varying delay MJSs with dissipative performance. Based on these ingredients, the explicit expression of the desired mixed filter is obtained. Finally, some numerical examples and the filter design of a two-loop circuit network reflect the less conservatism and effectiveness of the method in this article. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2013–2015, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Most of the observations were done in 2015, but some other observations of wide couples were done in 2013–2014 (0.3% of the total of the observations). Three different techniques were used for obtaining measurements: lucky imaging, speckle interferometry and the direct vector autocorrelation method. From our observations of 2837 multiple stars, we obtained 5182 new measurements with angular separations in the range of 0″$$ {}^{{\prime\prime} } $$.1–32″$$ {}^{{\prime\prime} } $$ and an average accuracy of 0″.018$$ {0}^{{\prime\prime} }.018 $$. The mean error on the position angles is 0°.8. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV≈9−11$$ {m}_V\approx 9-11 $$) with large magnitude difference (up to ΔmV≈4.5$$ \Delta {m}_V\approx 4.5 $$). We have thus been able to measure 21 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 1079 double stars with an estimated error of 0.2 mag. Except for a few objects that are discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 196 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. We also report measurements of the 164 new double stars that we found in the files obtained during the observations. Finally, from a study of the DR3 Gaia release, it is shown that all the objects of the Tycho Double Star Catalog that we did not resolve in 2015 are probably false detections by Tycho. [ABSTRACT FROM AUTHOR]

Gravity wave (GW) activities play a prominent role in the complex coupling process of wave–wave and wave–background circulation around mid-low-latitude and equatorial areas. The wavelengths are wide, from about 10 m to 100 km, with a period from minutes to hours. However, the oscillations of GW activities are apparently different between the period bands of 0.1 to 1 h (HF) and 1 to 5 h (LF). To further understand the characteristics of GW activities, the neutral winds during 2008–2009 with a resolution of 3 min obtained from a medium-frequency (MF) radar in Kunming (25.6° N, 103.8° E) were analyzed. Using two numerical filters, the HF and LF GWs were estimated. Interestingly, the power spectral density grows larger as the frequency increases. It linearly falls with decreasing frequency when the period is less than 2 h. The seasonal variations in both HF and LF GWs are strongly demonstrated in August–September, November, and February–March with maximum meridional variances of 1100 m2 s−2 and 500 m2 s−2 and maximum zonal variances of 800 m2 s−2 and 350 m2 s−2 in, respectively. The turbulent velocity was also calculated and shows similar oscillations with GW activities. Furthermore, the GW propagation direction exhibits strong seasonal variations, which may be dependent on the location of the motivating source and background wind. [ABSTRACT FROM AUTHOR]

Efforts to monitor the emissions and absorptions of atmospheric carbon dioxide (CO2) over the globe and to understand their varying regional patterns with greater accuracy have intensified in recent years. This study evaluates the performance of a new model coupling, ICO, built around the Laboratoire de Météorologie Dynamique atmospheric general circulation model (LMDZ) for simulating CO2 transport. ICO utilizes the new icosahedral hydrostatic dynamical core called Dynamico running on an unstructured grid, which enables potential improvements in spatial resolution at the Equator while removing artificial distortions and numerical filters at the poles. Comparisons with a reference configuration using a structured latitude-longitude grid reveal that ICO well captures seasonal variations in CO2 concentrations at surface stations. While not significantly enhancing the capture of complex seasonal patterns, ICO maintains comparable accuracy. Both configurations exhibit similar vertical CO2 concentration profiles and display a consistent bias in the lower stratosphere relative to observational data. ICO demonstrates advantages in computational efficiency and storage, thanks to its reduced cell count per level and a homogeneous grid structure. It holds promise for future developments, including with the LMDZ offline model and associated inversion system, which contribute to the Copernicus Atmosphere Monitoring Service. Overall, the ICO configuration showcases the efficacy of utilizing an unstructured grid for the physics, and the capability of Dynamico in accurately simulating CO2 transport. This study emphasizes the importance of advanced modeling approaches and high-resolution innovative grids in enhancing our understanding of the global carbon cycle and refining climate models. [ABSTRACT FROM AUTHOR]

Four tropical transition (TT) events in the North Atlantic basin are simulated with the Weather Research and Forecasting (WRF) and the HARMONIE‐AROME (HAR) models to study the main features of the horizontal kinetic energy (HKE) spectra of these kinds of high‐energetic atmospheric system. Though most of the times similar results are obtained with both models, HAR shows a more intense filtering and numerical dissipation, whereas WRF tends to represent overenergized spectra in the synoptic scale and especially at smaller wavelengths. Predictability is dissimilar for the four TTs studied due to the different spectral curve slope obtained for each case, ranging from unlimited to very poor predictability at synoptic scale. Additionally, an increased HKE is presented in the middle–upper troposphere spectra. A deep analysis of the different terms involved in the equation of the spectral energy budget is presented through a detailed study of one of these TTs. The role of all of them is studied, connecting the energy spectra and the meteorological processes involved. The energy budget terms related to the nonlinear spectral transfer, the three‐dimensional divergence, and diabatic process tendencies are identified as the key ones, whereas the potential and kinetic conversion terms and the vertical flux HKE and pressure divergence terms play a secondary role on modulating the spectrum behaviour. The major energetic contributions are found at the synoptic scale, but results show that a two‐dimensional energy cascade does not fully capture the whole spectrum of a TT. The role of convection, latent heat release, and moist convection outbursts is sketched and a link within different vertical levels is found. Results show that a high‐energetic system, such as a TT, can effectively alter the atmospheric energy behaviour. [ABSTRACT FROM AUTHOR]

A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky–Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control. [ABSTRACT FROM AUTHOR]

We examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors. [ABSTRACT FROM AUTHOR]

Knowledge of local mechanical behaviour of wood is especially important as silvicultural practices are modified to allow wood to compete as a relevant material in high technology applications. Challenges associated with identification of local mechanical behaviour have resulted in simplified test geometries designed to determine one or two constitutive parameters. The objective of this work was to design and simulate an entire experiment developed to simultaneously identify the earlywood and latewood orthotropic stiffnesses in loblolly pine in a single specimen and load geometry. The virtual experiment was capable of evaluating optimal orthotropy orientation for reduced identification errors and indicating most favourable choices for data smoothing filters and identification methodology. Additionally, certain ring spacing and latewood percentages were shown to produce large errors, but those combinations are unlikely to occur naturally. The simulation was able to identify Q11,Q22, and Q66 with approximately ±10% error; the Q12 error was larger with more scatter. The methodology presented here contributes to the best practices available for heterogeneous stiffness identification. [ABSTRACT FROM AUTHOR]

Atmospheric oscillations with daily periodicity are observed in in situ near-surface pressure, temperature, and winds observations and also in remotely sensed temperature and pressure observations of the Martian atmosphere. Such oscillations are interpreted as thermal tides driven by the diurnal cycle of solar radiation and occur at various frequencies, with the most prominent being the diurnal, semidiurnal, terdiurnal, and quadiurnal tides. Mars global circulation models reproduce these tides with varying levels of success. Until recently, both the MarsWRF and newly developed MarsMPAS models were able to produce realistic diurnal and semidiurnal tide amplitudes but predicted higher-order mode amplitudes that were significantly weaker than observed. We use linear wave analysis to show that the divergence damping applied within both MarsWRF and MarsMPAS is responsible for suppressing the amplitude of thermal tides with frequency greater than 2 per sol, despite being designed to suppress only acoustic wave modes. Decreasing the strength of the divergence damping in MarsWRF and MarsMPAS allows for excellent prediction of the higher-order tidal modes. This finding demonstrates that care must be taken when applying numerical dampers and filters that may eliminate some desired dynamical features in planetary atmospheres. [ABSTRACT FROM AUTHOR]

This paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario. [ABSTRACT FROM AUTHOR]

The peculiarities of applying the finite-difference method (FDM) for solving nonlinear dynamic problems of distributed systems (DS) in a flow are considered. The main limitations for using the FDM for numerical modeling of wave propagation and reflection in DS are shown to be the special features of the constitutive quasilinear equations. They necessitate the simultaneous calculation of the variables corresponding to fast and slow wave processes. The term "singularly perturbed system of equations" is used for such systems. These perturbations are the result of a significant difference in the propagation velocities of longitudinal, configurational, bending, and torsional waves in the DS at the physical level. Therefore, it is necessary to use special step-by-step methods of regularization and filtering of the numerical results. It imposes certain contstraints on the ability to model real processes and the accuracy of the results and forces the use of implicit difference schemes and high-frequency filtering. When solving the system of linear algebraic equations, taking into account the poor conditioning of the matrix of convective terms, the method of regularization was chosen by experimental calculation. Calculations according to the Crank–Nicolson difference scheme, even using coarse grids, can give the results with the required degree of accuracy and minimal time cost. Another picture is observed when comparing the results on coarse and fine grids for the Euler difference scheme. Inherent errors brought in by errors in approximating the missing boundary conditions lead to greater differences. [ABSTRACT FROM AUTHOR]

Boundary integral numerical methods are among the most accurate methods for interfacial Stokes flow, and are widely applied. They have the advantage that only the boundary of the domain must be discretized, which reduces the number of discretization points and allows the treatment of complicated interfaces. Despite their popularity, there is no analysis of the convergence of these methods for interfacial Stokes flow. In practice, the stability of discretizations of the boundary integral formulation can depend sensitively on details of the discretization and on the application of numerical filters. We present a convergence analysis of the boundary integral method for Stokes flow, focusing on a rather general method for computing the evolution of an elastic capsule or viscous drop in 2D strain and shear flows. The analysis clarifies the role of numerical filters in practical computations. [ABSTRACT FROM AUTHOR]

Regional seismic networks often observe artificially induced seismic events such as blasting and collapses. Misclassified seismic events in the earthquake catalog can therefore interfere with assessments of natural seismic activity. Traditional methods rely on the period, and phase features of seismic waves to determine the nature of seismic events. We designed three seismic event classifiers with reference to convolutional neural network structures such as VGGnet, ResNet, and Inception. The designed classifiers were tested and compared using three-channel seismic full-waveform time-series data and spectral data. Our classifiers are shown to only require 60 s of full-waveform seismic event data and first-arrival times for alignment; additional phase labeling or numerical filtering is unnecessary. Rapid classification of earthquakes, blasting, and mine collapses can be achieved within approximately 1 min of an event. As a test case, this study uses 6.4 k observations of actual local seismic events with magnitudes ranging from ML 0.6 to ML 4.5 obtained from 47 broadband seismic stations in the Henan Regional Network of the China Seismological Network Center; these observations include natural earthquakes, blasting, and collapse events. The results indicate that our classifiers can reach a lower classification magnitude limit of ML 0.6 and that their recall and accuracy exceed 90%, outperforming manually performed routine classifications and similar approaches. These findings provide an important reference for the rapid classification of small and medium earthquakes. [ABSTRACT FROM AUTHOR]

The full-cycle discrete Fourier transform (FCDFT) is commonly used to estimate phasors in numerical relays because of its immunity to harmonics and accurate identification of a decaying dc component (DDC) in a fault current. For fast protection of power systems, it is necessary to speed up the phasor estimation by using a data window below one cycle. The conventional least squares (LS) methods below one cycle approximate the DDC as the first several terms of the Taylor series expansion, which degrades the phasor-estimation accuracy. This paper proposes an LS method that uses Fourier analysis for approximation of the DDC to speed up phasor estimation without accuracy degradation. The phasor-estimation accuracy of the proposed method is investigated for the data-window length in simulations using synthetic data of fault currents containing DDC, harmonics, and measurement noise. The phasor-estimation speed is also investigated with respect to the delay of the antialiasing filter in the numerical relay. The proposed method is compared with the conventional LS and FCDFT methods in simulations using synthetic fault-current data. The feasibility of the proposed method is demonstrated through the phasor estimation of simulated transmission-line faults. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual double stars made in 2013–2014 with PISCO2 installed at the 76‐cm refractor of the Côte d'Azur Observatory in Nice (France). Our observation list contains orbital couples as well as double stars whose motion is still uncertain. Three different techniques were used for obtaining measurements: Lucky Imaging, Speckle Interferometry, and the Direct Vector Autocorrelation method. From our observations of 4,864 multiple stars, we obtained 4,952 new measurements with angular separations in the range 0″.1–14″ and an average accuracy of 0″.015. The mean error on the position angles is 1°.0. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV ≈ 9–11) with large magnitude differences (up to ΔmV ≈ 5). We have thus been able to measure 49 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 318 double stars with an estimated error of 0.2 mag. Except for a few objects that have been discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 455 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. Finally, we report 378 measurements of the 296 new double stars that we found in the files obtained during the observations. [ABSTRACT FROM AUTHOR]

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others. [ABSTRACT FROM AUTHOR]

We design moving horizon state estimators for a general model of bioprocesses. The underlying optimization is nonconvex due to the microbial growth kinetics, which are modeled as nonlinear functions. We relax the nonconvex growth constraints so that the optimization becomes a second-order cone program, which can be solved efficiently at large scales. Unfortunately, solutions to the relaxation can be inexact and thus lead to inaccurate state estimates. To recover feasible, albeit potentially locally optimal solutions, we use the concave–convex procedure, which here takes the form of a sequence of second-order cone programs. We find that the moving horizon state estimators outperform the unscented Kalman filter on numerical examples based on the gradostat and anaerobic digestion when there is high process noise or parameter error. • We design a moving horizon estimator for a general bioprocess model. • It is a convex relaxation, which can be solved using second-order cone programming. • When not exact, we use the convex-concave procedure to obtain feasible solutions. [ABSTRACT FROM AUTHOR]

This paper deals with join-semilattices whose sections, i.e. principal filters, are pseudocomplemented lattices. The pseudocomplement of a v b in the section [b,1] is denoted by a→b and can be considered as the connective implication in a certain kind of intuitionistic logic. Contrary to the case of Brouwerian semilattices, sections need not be distributive lattices. This essentially allows possible applications in non-classical logics. We present a connection of the semilattices mentioned in the beginning with the so-called non-classical implication semilattices which can be converted into I-algebras having everywhere defined operations. Moreover, we relate our structures to sectionally and relatively residuated semilattices which means that our logical structures are closely connected with substructural logics. We show that I-algebras form a congruence distributive, 3-permutable and weakly regular variety. [ABSTRACT FROM AUTHOR]

The Laplace operator is a differential operator which is used to detect edges of objects in digital images. This paper presents the properties of the most commonly used third-order 3>3 pixels Laplace contour filters including the difference schemes used to derive them. The authors focused on the mathematical properties of the Laplace filters. The basic reasons of the differences of the properties were studied and indicated using their transfer functions and modified differential equations. The relations between the transfer function for the differential Laplace operator and its difference operators were described and presented graphically. The impact of the corner elements of the masks on the results was discussed. This is a theoretical work. The basic research conducted here refers to a few practical examples which are illustrations of the derived conclusions. We are aware that unambiguous and even categorical final statements as well as indication of areas of the results application always require numerous experiments and frequent dissemination of the results. Therefore, we present only a concise procedure of determination of the mathematical properties of the Laplace contour filters matrices. In the next paper we shall present the spectral characteristic of the fifth order filters of the Laplace type. [ABSTRACT FROM AUTHOR]

Destructive aftershocks such as the Mw 7.2 Van earthquake on October 23, 2011, and the Hoy (Iran) earthquake with Mw 5.9 on February 23, 2020, occurred in the province of Van and its surroundings. In earthquake studies, the issue of examining the distribution and homogeneity of earthquake incidences with Geographic Information Systems (GIS) based via spatial autocorrelation techniques is frequently investigated. Van province and its surroundings are among the areas with high earthquake risk due to its location on the East Anatolian Compressive Tectonic Block. The aim of this study is to analyze the spatial patterns of earthquakes with magnitude Mw 4 and above that occurred in the province of Van and its surroundings during the instrumental period and to determine to cluster. Spatial cluster analyses play an important role in examining the distribution of seismicity. The data used in the study have been taken from the database system of the Earthquake Department of the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency. Moran's I and Getis-Ord Gi methods from spatial autocorrelation techniques were preferred on the earthquake data set to be used in this research. It has aimed to determine the dangerous areas by testing the earthquake distributions in clustered regions via spatial autocorrelation techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory prediction method based on graph attention network to accurately predict the trajectories of HDV (Human Drive Vehicles) around the ICV (Intelligent Connected Vehicles) under mixed traffic flow scenario on highways. Firstly, the vehicle trajectory data is filtered and smoothed to construct a trajectory prediction dataset containing map information. Secondly, the vehicle interaction relationship graph is constructed based on the position and behavior of vehicles. The high-dimensional spatial interaction relationship features between the target vehicle and surrounding vehicles are extracted using the graph attention network, which serves as input for the encoder-decoder model. Subsequently, an encoder-decoder model based on GRU (Gate Recurrent Unit) is employed to encode time-series features of vehicle trajectory data and generate future trajectories through decoding. Finally, experimental validation using NGSIM (Next Generation Simulation) datasets demonstrates that our proposed method achieves low displacement error in predicting vehicle trajectories compared to models such as GRU, and CNN-GRU (Convolutional Neural Network-Gate Recurrent Unit). • A trajectory prediction method is proposed to accurately predict the trajectories of HDV (Human Drive Vehicles) around the ICV (Intelligent Connected Vehicles) under mixed traffic flow scenario on highways. • The vehicle trajectory data is filtered and smoothed to construct a trajectory prediction dataset containing map information. • The T-GAT-GRU model is constructed, which can better extract the spatial interaction characteristics between vehicles. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel indoor PDR method, which uses wearable devices to collect pedestrian acceleration, gyroscope, geomagnetic and electromyographic signals, and other motion data in real-time. An adaptive Gaussian bilateral filtering (AGBF) algorithm is proposed for preprocessing the collected data. An improved step length estimation (SLE) model is also established based on surface electromyography and acceleration signals. The quaternion method is employed to calculate the heading angle, and the angle increment is acquired by integrating the inertial data, and then the heading information at the next moment. By improving the Unscented KF optimized by the particle filter, the heading angle deviation is corrected, reducing gyroscope drift errors. The current position of indoor pedestrians is estimated based on gait detection, SLE, and heading angle estimation. • The main contributions of this work are listed below:(I) This paper proposes an AGBF algorithm, which uses a numerical filter that adaptively adjusts the standard deviation and adopts a targeted denoising strategy, further enhancing the denoising performance. (II) The gait detection method based on the generalized likelihood ratio multi-threshold detection (GLR-MTD) algorithm can adaptively adjust the threshold and is suitable for pedometers, indoor positioning, and other fields. It has extensive universality and meets the needs of engineering applications. (III) We constructed a novel SLE method for the Linear multi-source information fusion (LM-SIF) model, which can provide accurate SLE for the PDR algorithm. (IV) An improved particle filter-unscented Kalman filter algorithm (IPF-UKF) is proposed to estimate the heading angle error. This algorithm can effectively improve the positioning accuracy of real-time pedestrian inertial navigation. (V) Due to infrastructure and historical data independence, PDR methods based on MEMS-IMU and surface electromyography (SEMG) sensors are very easy to integrate with any application that requires high-precision location services. To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results. [ABSTRACT FROM AUTHOR]

This study proposes a novel filtering method for unbiased minimum variance state estimation. A key challenge in the development of digital twinning and system identification of operational civil infrastructure is the difficulties associated with measurement of the often nonstationary, and stochastic unknown input to the system, e.g. traffic, earthquake, and wind loads. Herein a novel algorithm is presented for an unbiased output-only state estimation for linear state space systems with direct feedthrough. In this paper, the problem of state estimation for discrete time systems with unknown input, where no previous assumption is made regarding the probability distribution of the input, is addressed. In this regard, a two-stage output-only filter is proposed. The unbiasedness of the estimation is demonstrated, and the error covariance propagation over time is derived. It is shown that the unknown input of the system can be subsequently reconstructed via the system matrices, the measured responses, and the estimated state vector. A closed-form solution is provided for the parameters associated with pseudo-inverse in gain calculations to ensure the numerical stability of the filter. Three case studies including simulated and laboratory experiments for verification, validation, and cross comparison of the proposed method are presented. The first case study corresponds to a simulated shear building subjected to two different load time histories, a parametric sinusoidal load, and an earthquake ground acceleration. The second case study shows the results for a simulated experiment on a steel railway bridge subjected to a concentrated load. The third case study corresponds to a laboratory shear building subjected to ground motion and observed through accelerometers. The results of the experiments show a good agreement between the "true response" and the filter state estimation. Moreover, the proposed method is compared against a state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Edge computing furnishes communicational and computational mediators such as edge nodes (ENs) between vehicles and cloud platforms, accelerating interactive services such as route arrangement and congestion warning for the Internet of Vehicles (IoV). Implemented with the knowledge graph (KG), EN is further enabled to represent the structural relations between multi-source entities (e.g., vehicles and roads) for information reasoning tasks such as traffic flow prediction. However, due to the spatial precision dissimilarity, discontinuity, and relevance of the multi-source data, plus underestimating temporal consecutiveness in traditional rules, establishing KGs for ENs remains a challenge. Given this challenge, a temporal knowledge graph empowered reasoning model named TKGERM is developed. Firstly, confluent data for each EN are initiated by logical smoothing and filtering with multi-source data. Then, the temporal KGs are constructed by extending KG triples into temporal quadruples using timestamp information in the complex vector representation space. Furthermore, with the relative temporal information of past events, logical rules are explored by the temporal KG for traffic information reasoning. Finally, the time performance and effectiveness of TKGERM are evaluated by implementing a series of experiments on real-world data collected from the Bureau of Transportation in Guangdong, China. [ABSTRACT FROM AUTHOR]

Experimental and numerical approaches have their own advantages and limitations, in particular, when dealing with complex phenomena such as snow particles falling at moderate Reynolds numbers (Re). Time-resolved, three-dimensional particle tracking velocimetry (4D-PTV) experiments of free-falling, three-dimensional (3D)-printed snowflakes' analogs shed light on the elaborate falling dynamics of irregular snow particles but present a lower resolution (tracer seeding density) and a limited field of view (domain size) to fully capture the wake flow. Delayed-detached eddy simulations of fixed snow particles do not realistically represent all the physics of a falling ice particle, especially for cases with unsteady falling attitudes, but accurately predict the drag coefficient and capture the wake characteristics for steadily falling snowflakes. In this work, we compare both approaches on time- and space-averaged flow quantities in the snowflake wake. First, we cross validate the two approaches for low Re cases, where close agreement of the wake features is expected, and second, we assess how strongly the unsteady falling motion perturbs the average wake pattern as compared to a fixed particle at higher Re. For steadily falling snowflakes, the fixed-particle model can properly represent the wake flow with errors within the experimental uncertainty (±15%). At moderate/high Re (unsteady falling motion), larger differences are present. Applying a co-moving frame to the experimental data to account for the particle movement or filtering the numerical data on larger grids reduces these differences only to some extent, implying that an unsteady fall significantly alters the average wake structure as compared to a fixed particle model. [ABSTRACT FROM AUTHOR]

A neural network (NN) with one hidden layer is implemented to establish a relationship between the resolved-scale flow field and the subgrid-scale (SGS) stress for large eddy simulation (LES) of the Burgers equation. Five sets of input are considered for the neural network by combining the velocity gradient and the filter size. The training datasets are obtained by filtering the direct numerical simulation (DNS) results of the Burgers equation with random forcing function. The number of modes is sufficiently large (N = 65 536) to resolve extremely small scales of motion. In the a priori test, a correlation coefficient over 0.93 is achieved for the SGS stress between the NN models and the filtered DNS data. The results of the a posteriori test reveal that the obtained solutions are stable for all NN models without applying any stabilization techniques. However, not all NN models have a reasonable performance when embedded in the LES code. The applicability of the NN models to the Burgers equation with higher and lower viscosity is also investigated, and it is indicated that the most reliable NN models obtained in this paper can be applied to a set of parameters which are different from those used in training. The results of the SGS models constructed using the neural network are also compared with the existing models, and it is shown that the best obtained NN models outperform the Smagorinsky model and the gradient model, and are comparable to the dynamic Smagorinsky model. However, the NN models have an advantage over the dynamic Smagorinsky model in numerical cost. [ABSTRACT FROM AUTHOR]

For radar target tracking with non‐linear measurements, a sequential linear filtering method is proposed in this study, which includes a linear filter based on the position measurements and a linear sequential filter based on the range rate measurement. The linear radar measurement equation in the sequential filter is estimated based on the position filtering result. Furthermore, the feedback information during tracking is adaptively selected according to the quality of the angle measurements. When tracking the manoeuvring target, the proposed sequential linear filtering with adaptive information feedback is combined with the interacting multiple model (IMM) framework, and the IMM filter based on sequential linear filtering with adaptive model probability selection is proposed. Simulation results demonstrate the effectiveness of the proposed algorithms in non‐manoeuvring and manoeuvring target tracking. Compared with traditional algorithms, the tracking performance is improved by the proposed algorithms in both typical non‐manoeuvring and manoeuvring scenes. [ABSTRACT FROM AUTHOR]

Smoothing process can effectively improve the accuracy of the Global Navigation Satellite System (GNSS) position estimation algorithm. In this study, a Doppler‐aided GNSS position estimation algorithm with a B‐spline smoothing strategy is proposed. B‐spline signal processing is used to smoothen the code measurements with Doppler measurements, which were selected because they are more robust and immune to cycle slips than carrier phase measurements. Next, the mathematical expressions of the smoothing process and position estimation are derived. The smoothing process is based on the structure of a digital filter, which is especially suitable for real‐time applications. The Cramer–Rao lower bound of the proposed algorithm is derived. Moreover, a complete implementation structure of the proposed algorithm is presented. The validity of the proposed algorithm is verified through scenario simulation in terms of static, kinematic, and adverse environments. The advantages of the proposed algorithm are demonstrated by comparison with existing algorithms. [ABSTRACT FROM AUTHOR]

The observer design problem for a class of cell population balance models, describing the time evolution of the cell mass density distribution function and the substrate concentration in a continuous‐stirred tank bioreactor with irregular discrete‐time measurements of the cell mass distribution is considered. The model consists of a partial integro‐differential equation coupled with an ordinary differential equation. Using the theory of impulsive systems sufficient conditions for the input‐to‐state‐stability (ISS) of the observation error in the state‐space L1×ℝ+ with respect to the measurement uncertainty are derived in terms of the maximum time between successive measurements and the ISS gain. In absence of measurement uncertainty the convergence conditions imply exponential stability of the observation error dynamics. Besides these rigorous conditions, application‐oriented tuning guidelines are established. The theoretical results are illustrated with numerical simulations including a comparison with a continuous–discrete extended Kalman filter based on the numerical approximation, showing that a similar accuracy is achieved when using a finite‐dimensional approximation of the proposed impulsive observer scheme with a considerably lower computational effort. [ABSTRACT FROM AUTHOR]

High-intensity acoustic waves existing commonly in aeronautical and aerospace vehicles manifest nonlinear propagation behaviors. Large-amplitude vibration and irregular shape of the aerospace vehicles further complicate the acoustic responses. This paper is concerned with numerical analysis of finite-amplitude acoustic responses of complex-shaped vibration objects. The time-dependent effect of the solid boundary position due to the large-amplitude vibration of the objects is considered. A set of first-order differential equations is derived to govern the finite-amplitude acoustic wave. A fourth-order dispersion-relation-preserving finite difference formulation is employed to solve the nonlinear acoustic equations on a fixed Cartesian grid. Acoustic responses of the fluid and the vibration of the complex-shaped object are coupled by considering the compatibility conditions on the fluid-solid interface. A ghost-cell sharp-interface immersed boundary method is utilized to relax the conformity requirement between the computational grid and solid boundary. Numerical filters are employed in the computational procedure to suppress numerical oscillations. The present method is validated through several numerical tests. Numerical analysis of finite-amplitude acoustic responses of a complex-shaped object is performed. The nonlinear effect of a finite-amplitude acoustic wave, the time-dependent effect of solid boundary position, and the coupling effect between them on the propagation behaviors of nonlinear acoustic waves are discussed. [ABSTRACT FROM AUTHOR]

In deterministic estimation, applying a Kalman filter to a dynamical model based on partial differential equations is theoretically seducing but solving the associated Riccati equation leads to a so-called curse of dimensionality for its numerical implementation. In this work, we propose to entirely revisit the theory of Kalman filters for parabolic problems where additional regularity results proves that the Riccati equation solution belongs to the class of Hilbert-Schmidt operators. The regularity of the associated kernel then allows to proceed to the numerical analysis of the Kalman full space-time discretization in adapted norms, hence justifying the implementation of the related Kalman filter numerical algorithm with H-matrices typically developed for integral equations discretization. [ABSTRACT FROM AUTHOR]

Unwanted dispersive wiggles that arise from the leapfrog numerical scheme have been of interest in weather research and other fluid flow studies. A number of differing approaches have been developed over the last few decades to suppress such wiggles. For example, there are methods that use either smoothing filters or adaptive time-stepping. But, in addition to suppressing the wiggles, smoothing filters suppress the solution profile as well. In order to overcome this problem, in this paper, we construct a computationally efficient numerical scheme that simply eliminates the dispersive wiggles while keeping the solution profile intact. [ABSTRACT FROM AUTHOR]

A random distributed feedback fiber laser (DFB-FL), with an apodized fiber Bragg grating (FBG) or an apodized Distributed feedback fiber (DFB) optical filter, is proposed. This leads to a narrow band light generation from the random DFB-FL optical source. This is a theoretical work depending in numerical analysis. Our proposed system consists of a random DFB-FL, with input pump power and long fiber length, followed by an optical filter. The filter has high reflectivity, high side lobe suppression ratio, and narrow full-width half-maximum (FWHM). A suitable apodization function is selected in order to improve the performance using the FBG/DFB optical filter. The performance of proposed system is evaluated by determining its reflectivity, SLSR, and FWHM over a 100 km random DFB-FL. Our results reveal that a FWHM of 0.09 nm with a high SLSR of 12.336 dB is achievable when using a Hamming apodization profile. [ABSTRACT FROM AUTHOR]

• A macroscale model is developed to study electrostatic particle filtration. • Multi-layering is proposed as a means of protecting the bipolarly charged fibers. • Unipolar and bipolar charging are considered to lower the pressure drop. • Placing the unipolar layer upstream of the bipolar layer extends filter's life. The deposition of neutral and charged particles inside an electrostatically charged filter can affect its performance by decreasing its ability to capture additional particles and by increasing its pressure drop. The particle-loaded behavior of a composite filter comprised of three fibrous layers is studied in this paper using a macroscale simulation approach. Each of these fibrous layers were assumed to have fibers with the same amount of charge but different charge polarities of only unipolar positive, only unipolar negative, or only bipolar. The charged layers were stacked on top of one another in three different configurations with respect to the flow direction and their filtration efficiency and pressure drop were compared during particle loading with neutralized particles (having the Boltzmann equilibrium charge distribution), singly-charged particles, and neutral particles. Our simulations revealed that placing the bipolarly-charged layer downstream of the unipolarly charged layers helps to lower the negative impact of particle deposition on pressure drop and filtration efficiency of the resulting composite filter. This study introduces a design tool which can be used to optimize the configuration of a composite filter for enhanced particle capture efficiency and reduced pressure drop increase. [ABSTRACT FROM AUTHOR]

• Novel use of photon Doppler velocimeter for soil penetration testing. • PDV incorporated into vertical ballistic range to capture depth-dependent behavior. • High fidelity velocity and force time history in support of modeling. • Produces high fidelity velocity measurement without differentiation or integration. • Advantages, limitations, and details of implementation thoroughly described. This paper presents details for using a photon Doppler velocimeter (PDV) to produce high fidelity velocity penetration data from vertical penetration into granular soil targets. The PDV theory used for collecting data is presented, along with details relevant to soil penetration testing. Experimental aspects related to specific equipment used, measurement configurations implemented, power losses, and probe alignment are presented in order to facilitate replication. The PDV equipment were mounted on a vertical gas gun and used to measure conical and blunt nose rod projectile velocities in penetration into loosely packed and densely packed soil targets. An in-depth discussion of data smoothing and filtering, data sampling and repeatability is presented. Penetration tests in several soil targets are used to reveal important phenomenology of penetration in granular media. The roles of soil target bulk density and pore saturation are investigated in terms of Poncelet drag and bearing resistance parameters. It is shown that bulk density plays a major role in penetration resistance while saturation has a secondary effect. The Poncelet drag coefficient was found to be projectile nose shape dependent. The drag coefficient was as high as 2.0 in penetration of blunt nose projectiles in densely packed sand, while a conical nose reduced the drag coefficient to 1.4. Saturation reduced the drag coefficient by 32 % and 28 % in densely packed and loosely packed soil targets, respectively. [ABSTRACT FROM AUTHOR]

Due to the emergence of new network attack technologies, cloud manufacturing platforms may be subject to various network attacks at any time. Traditional network attack prediction aims to predict the upcoming types of network attacks with monitored data characteristics, and this prediction method cannot accurately learn the network attack traffic that the cloud manufacturing platform may suffer in the future. Network attack traffic prediction means the future network attack traffic prediction by using past data. Traditional machine learning approaches cannot investigate complex nonlinear features. Deep learning methods can investigate nonlinear characteristics, yet they suffer from the problem of overfitting. In addition to this, deep learning approaches suffer from gradient disappearance and explosion. To solve them, we design a network attack traffic method called S-Informer, which integrates the filter of Savitzky–Golay, the self-attention of ProbSparse in a generative decoder and an encoder for eliminating noise, reducing the network scale and improving the speed of prediction. Real-life dataset-based experimental results show that S-Informer achieves higher prediction accuracy than several commonly-used algorithms. [Display omitted] • A hybrid network attack detection method is given for a cloud manufacturing platform. • It adopts a Savitzky–Golay filter for data smoothing and noise elimination. • It adopts a self-attention mechanism of an encoder to reduce network scale. • It adopts a generative style decoder for prediction speed improvement. • A real-life dataset is used to show the accuracy of the prediction of the S-Informer. [ABSTRACT FROM AUTHOR]

Point-mass filter (PMF) is a numerical Bayesian filtering algorithm that estimates the probability density of state variables using a deterministically defined grid on state space. An important factor that determines the performance of the PMF is how accurately one guesses the probabilistically significant region (called a nonnegligible region) where a grid will be placed. In this article, we introduce the concept of boundary to express the nonnegligible region and propose a method to define a grid tailored to the nonnegligible region of posterior density by transporting boundary according to log-homotopy induced flow. The proposed method ensures accurate estimation performance and robustness especially for uncertain prior information. Terrain referenced navigation (TRN) is composed of a nonlinear observation model defined by terrains and an uncertain initial position. Therefore, it is a major application of the PMF, which can handle nonlinearity and has a more robust characteristic than other numerical filters. In order to compare the proposed PMF with the conventional PMFs, this article applies the proposed method to TRN. Monte Carlo simulation results show that the proposed algorithm is more accurate and robust than the conventional PMFs. [ABSTRACT FROM AUTHOR]

Using numerical filtering techniques allowing us to reduce noise from sferics, we are able to clearly study a new type of differently structured very low frequency (VLF) radio waves above f = 4 kHz at the ground station of Kannuslehto in northern Finland (KAN, MLAT = 64.4°N, L = 5.5). These emissions are intriguing, since they are detected at frequencies above half the electron gyrofrequency in the equatorial plane (fce) for the L-shell of Kannuslehto (fce ~ 5–6 kHz). They are commonly observed at Kannuslehto, but have also been infrequently reported at other stations, sometimes under different names. Their possible common origin and manner of propagation is still under investigation. This paper unifies the nomenclature by regrouping all these waves detected at frequencies higher than the local equatorial 0.5 fce at the L-shell of observation under the name of VLF bursty-patches. While these waves have different spectral features, they appeared mostly composed of hiss bursts with durations of a few seconds to several minutes. They also show periodic features with varying periodicity and shape. They are sometimes characterized by single bursts covering very large frequency ranges of several kHz. We also give a review of the different characteristics of VLF bursty-patches observed at Kannuslehto, which at the moment, is the station with the highest observation rate. We present recent observations between 2019 and 2021. [ABSTRACT FROM AUTHOR]

The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials—is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modeling that supports the use of image statistics heuristics in the judgement of metallicity—the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the exploration of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in perceiving differences in metal smoothness and coating bumpiness, and judging metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity. [ABSTRACT FROM AUTHOR]

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction. [ABSTRACT FROM AUTHOR]

The short-term rapid depressions in cosmic-ray (CR) flux intensity, historically referred to as Forbush decreases (FDs), are useful tools for testing some of the numerous hypotheses on space weather. Due to their long-term reliability and automated data acquisition, ground-based CR measurements by neutron monitors (NMs) provide the best direct means of comparing CR intensity variations during FDs with solar-wind disturbances in the vicinity of the Earth. Unfortunately, there is a mixture of high-magnitude FDs and CR diurnal anisotropies of enhanced amplitude during periods of high solar activity. The difficulties of isolating FDs from the superposed effects of diurnal anisotropies cast some doubt on the usefulness of FDs in space-weather studies. Using numerical filtering techniques over two isolated NMs' data (Apatity and Mount Washington) during high solar activity in 1972, we attempt to show that the slowly varying component of CR flux containing the CR anisotropies may be separated from the high-frequency part that contains the FDs. Subsequently, the rapidly varying component is passed to an FD location algorithm for accurate event timing as well as precise quantitative predictions, where the day-to-day amplitudes of the CR diurnal anisotropies are estimated from the slowly varying frequency component of the raw CR data. For effective comparison of event amplitudes and timing, the magnitudes of FDs from raw CR data are also estimated using an FD selection technique. [ABSTRACT FROM AUTHOR]

We present relative astrometric and photometric measurements of visual binaries made in 2011–2012, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as binaries whose motion is still uncertain. Three different techniques have been used for obtaining those measurements: Lucky imaging, speckle interferometry, and the direct vector autocorrelation method. From our observations of 3,136 multiple stars, we obtained 3,568 new measurements with angular separations in the range of 0″.1–10″ and an average accuracy of 0″.016. The mean error on the position angles is 1°.7. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems (mV ≈ 9–11) with large magnitude difference (up to ΔmV ≈ 5). We have thus been able to measure many systems containing red dwarf stars that had been poorly monitored since their discovery. We also measured the magnitude difference of the two components of 326 binaries with an estimated error of 0.2 mag. Except for a few objects that have been discussed, our measurements are in a good agreement with the ephemerides computed with published orbital elements, even for the binaries closer than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 196 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. We also obtained new measurements of the 15th magnitude multiple system CON37, and discovered a faint new double star in its vicinity. Finally, we report 47 measurements of 28 new binaries found during our observations. [ABSTRACT FROM AUTHOR]

Granular filters are used in hydraulic earthen structures as a natural barrier to protect base soils from erosion under seepage conditions. Their performance is greatly dependent on the pore space connectivity that provides transport pathways for migrating fine particles. Traditional filter design rules are based on characteristic particle diameters of the involved materials, which do not explicitly reflect the pore structure dimensions. Even though the literature offers some mathematical tools that can be used to characterize the opening size of granular filters, the underlying physical fundamentals are still not sufficiently understood. Therefore, this study aims to provide a plausible physical framework for determining the filter opening size by means of numerical simulations using the discrete element method. From the investigation of particle transport processes in polydisperse packings of spheres, the opening sizes associated to these numerical filters are defined on the basis of their constriction size distribution. A mathematical procedure is then introduced to determine the effective opening size of the filter and subsequently, a revised retention criterion for granular filters is proposed and validated with experimental data from the literature. The study findings can offer a valuable perspective for erosion assessment methods in both new and existing earthen structures. [ABSTRACT FROM AUTHOR]

The multiarea Thévenin equivalent (MATE) solution framework is used in this work to combine several subsystems, which are solved using different multiple and non-multiple time-step sizes. In the proposed asynchronous MATE-multirate (A-MATE-multirate) solution, each subsystem is solved with the integration step size that is most adequate for its own internal time constants. This paper relaxes the requirement of previous multirate techniques, in which the solution time-steps of different systems must be multiples or submultiples of each other. To this end, this paper introduces the concept of asynchronous updating, in which each subsystem is updated according to its own internal clock. This updating process is performed in a closed-form solution, with external subsystems represented by their interpolated Thévenin equivalents. Interpolation with future values without using extrapolation is possible in the electromagnetic transients program solution because the history source for future time-steps is calculated at previous time-steps. The exchange of information between subsystems is conditioned using filters to avoid numerical problems during the decimation of the fast subsystems and the interpolation of the slow subsystems. The effectiveness of the proposed methodology is verified with examples using multiple and non-multiple time-step sizes. [ABSTRACT FROM AUTHOR]

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure. [ABSTRACT FROM AUTHOR]