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This paper investigates the practicality of an autonomous and resilient positional navigation using corner cube reflectors on the lunar surface. The proposed approach stands as an efficient navigation solution when the current system, e.g. GPS, is unavailable due to being attacked, jammed or spoofed by a third party. The paper provides a complete setup of the mathematical models using a nonlinear least squares algorithm and covariance analysis. A study is done that determines how the blockage of the Earth affects the proposed solution’s ability to perform positional navigation. Numerical simulations are presented to show the accuracy, performance and practicality of the proposed solution. The results of the proposed solution are compared with the results of an Earth-based ground station system that uses radio frequency to track the given satellite. The results show that the lunar reflector-based system generates more accurate results than the Earth-based ground station system. [ABSTRACT FROM AUTHOR]

Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimation, which occur even with state-of-the-art data collection and processing methods. Within this paper, we provide an Bayesian approach based on a spike-and-slab prior for the regression coefficients, which allows for simultaneous handling of variable selection and estimation in combination with handling of missing values in covariate data. The paper also discusses the implementation of the approach using Markov chain Monte Carlo techniques and provides results for simulated data sets and an empirical illustration based on data from the German National Educational Panel Study. The suggested Bayesian approach is compared to other statistical and machine learning frameworks such as Lasso, ridge regression, and Elastic net, and is shown to perform well in terms of estimation performance and variable selection accuracy. The simulation results demonstrate that ignoring the handling of missing values in data sets can lead to the generation of biased selection results. Overall, the proposed Bayesian method offers a holistic, flexible, and powerful framework for variable selection in the presence of missing covariate data. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a significant factor in the climate system. The accurate determination of SM has high importance in food production to satisfy the increasing demand for food and the chemical processes of soil. This paper applies decision tree regression to estimate SM considering different parameters including air temperature, time, relative humidity, and soil temperature. The presented method holds a mighty advantage to determine SM since the stimulant of the decision tree regression is an algorithm that generates a decision tree from given instances. Besides, usage of decision tree regression provides an opportunity to save time. Numerical results show that the presented method offers a high coefficient of determination value (R2), low mean squared error (MSE), and mean absolute error (MAE). The depth of the decision tree equals to five by providing higher fitness values than other depth levels. The best fitness values in the training stage are 0.00019, 0.007, and 0.842 for MSE, MAE, and R2, respectively. In conclusion of the paper, applied decision tree regression can handle the data of SM estimation in satisfying fitness criterion. [ABSTRACT FROM AUTHOR]

Several experimental evidences and field data documented that zooplankton may alter its behavioral response in the presence of toxic phytoplankton, reducing its consumption to the point of starvation. This paper is devoted to the mathematical study of such interactions of toxic phytoplankton with grazer zooplankton. The non-toxic phytoplankton is assumed to adopt a density-dependent refuge strategy to avoid over-predation by zooplankton. Both groups of phytoplankton are assumed to suffer direct harm from anthropogenic toxicants, while zooplankton is affected indirectly by ingesting contaminated phytoplankton. We calibrate the proposed model with the field data from Talsari and Digha Mohana, India, and estimate some crucial model parameters consistent with the behavior of the observed data. Our results demonstrate that zooplankton grazing on toxic phytoplankton plays a key role in the emergence or mitigation of plankton blooms. We also highlight the system's potential to exhibit multiple stable configurations under the same ecological conditions. The plankton system experiences significant regime shifts, which are explored through various bifurcation scenarios, such as transcritical and saddle-node bifurcations. These shifts are influenced by changes in refuge capacity, species growth rates, and environmental carrying capacity. Furthermore, we incorporate environmental variations due to seasonal periodic or almost periodic changes, allowing the refuge parameter to be time-dependent. We observe that the forced system exhibits double periodic solutions. Moreover, stronger seasonal variations in the refuge pattern lead to irregular chaotic blooms. In conclusion, the results offer valuable insights into the sustainability of biodiversity, potentially shedding light on the origin of diverse plankton bloom phenomena. [ABSTRACT FROM AUTHOR]

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed. [ABSTRACT FROM AUTHOR]

In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, ... , and k inflated points with probabilities w 0 , w 1 , ... , and w k , respectively. By choosing the appropriate values for the weights w 0 , ... , w k , various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero–k-inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results. [ABSTRACT FROM AUTHOR]

The paper presents a methodology of preparing and conducting airborne gravimetry survey using an inertial measuring unit (IMU) or strapdown inertial navigation system as airborne gravimeter. We also discuss the key aspects of developing appropriate postprocessing algorithms and software and their application to gravimeter raw data processing. Solving the strapdown inertial airborne gravimetry problem, we use readings of the IMU sensors (accelerometers and gyroscopes) as primary information. The second equally important source of information is raw (pseudorange, Doppler pseudorange rate, and carrier phase) measurements from the GNSS receivers on board the aircraft and on the ground. The developed methodology, algorithms, and practical recommendations presented in the paper are based on the authors' long-term experience in airborne gravimetry. [ABSTRACT FROM AUTHOR]

Many issues in real life are riddled with confusion, vagueness, and ambiguity. The Topp-Leone distribution is a significant one-parameter probability distribution in the context of classical probability theory. There is a gap in the literature when it comes to dealing with circumstances involving interval-valued data with a classical Topp-Leone distribution. So, in this connection, neutrosophic Topp-Leone distribution is presented in this paper as an extension of the traditional Topp-Leone distribution. The new neutrosophic distribution takes into account the indeterminacy and crisp form of interval-valued distributions. The suggested distribution's mathematical features were derived, including moments and related measures, quantile function, survival, hazard, inverted hazard functions, and mills ratio. Maximum likelihood is used to estimate model parameters. Finally, a complex dataset is utilized to show the usefulness of the proposed distributions. [ABSTRACT FROM AUTHOR]

While the evolution of state uncertainties from Gaussian to non-Gaussian is well understood in geocentric orbits, the only data product made publicly available by the United States Space Force, the Two-Line Element, does not include state uncertainties at all. With a new international focus on cislunar space situational awareness, it is useful to determine how this complex dynamical environment influences trajectory uncertainties and the resultant implications for data association, orbit determination, and force model algorithms. This paper utilizes a new tensor eigenpair measure of nonlinearity (TEMoN) to quantify the nonlinearity of gravitational forces in the cislunar regime. This measure is then compared with various characterizations of Gaussian distributions to determine the value of TEMoN at which trajectory uncertainties become non-Gaussian. This novel advancement combines the cumulative effect of time and physical location of the trajectory into one value. The result is a predictive method that obviates highly parameterized Monte Carlo runs, and allows an objective assessment of how sensor tasking cadence, measurement uncertainty, and force model selection can be balanced to enable nascent cislunar space situational awareness capabilities with legacy space surveillance network assets. [ABSTRACT FROM AUTHOR]

In this paper, interval observers are designed for linear dynamic systems described by continuous-time models with exogenous disturbances, measurement noises, and parametric uncertainties. Jordan canonical form-based relations are presented for an interval observer that estimates the set of admissible values of a given linear function of the system state vector. The theoretical results are illustrated by a practical example. [ABSTRACT FROM AUTHOR]

The estimation of agricultural output is an important issue in the digital development of modern agriculture. Using embedded technology, this paper designs a multimedia platform for digital estimation of agricultural output. The terminal design of the multimedia platform selects the Intel Xscale series PXA255 processor as the core processor, uses multiple sensors to collect agricultural output-related environmental parameters and selects the digital media processing chip TMS320DM642 for multimedia processing. The software used selects the grey relational Support Vector Machine (SVM) algorithm to estimate agricultural output. First, the collected agricultural sown area, labor force, mechanical power, irrigated area, agricultural production environment and other parameters are collected as input to grey relational SVM. Second, the estimated results are outputted. Finally, the results of agricultural output estimation are digitally displayed by a display interface on the embedded multimedia terminal. The experimental results show that the prediction error is less than 1%, and the prediction accuracy is higher than 99%, which validates that the method can accurately predict agricultural output with an abundance of applications. [ABSTRACT FROM AUTHOR]

The multimedia big data has tendency of fast growth over time span due to basic characteristics like volume, variety and velocity. Sample based estimates are used to compute the unknown population parameter. The multimedia big data is characterized various features who are prominent in terms of identification and analysis. Social media platforms are major sources of generating big data by virtue of communication among registered users in the form of text, video and images data. Registered users are also growing with drastic speed. When a user registers on a portal, default digital storage space is allotted by the system, who increases over time domain. A monitoring system is required to anticipate the increment and to alert managers of data centers for further enhancement of infrastructure. In the case of medical diagnostics, the CT-scan and MRI equipment produce the huge amount of scan files data while done over the large number of patients. These files are used to store in memory of the system for at least a prefixed duration. The digital file size of such reports, pixel densities and intensity of contents are the prime parameters of interest while comparing the quality of similar types of machines. Doctors and patients on social media platform used to exchange digital medical reports like X-ray, CT-scan, MRI, cancer diagnostics occupying the default storage. A guess value of digital file size can be helpful for the determination of expected amount of digital storage for users to be allocated to the medical processionals or other such. This paper presents sample based estimation methods for estimating the average file size over several time points. Confidence intervals are used as a tool of comparisons. A new simulation procedure is also suggested for comparative results of confidence intervals. At multiple optimum values of constant, the proposed sample based estimation methods perform better and the proposed simulation method is also result oriented. Strategy of using support information of other multimedia variable in estimation procedure is found useful and effective. Findings of the paper are numerically supported and significant percentage gain observed for proposed in the setup of multimedia big data floating over social media platforms. [ABSTRACT FROM AUTHOR]

Soil plays a vital role in the climate system. This paper performs decision tree regression to estimate soil moisture (SM) by considering different parameters that include air temperature, time, relative humidity, and soil temperature. Besides, this paper investigates the effects of the parameters of decision tree regression by utilizing the response surface. The obtained estimation results of two distinct soil areas, Field and Forest, indicate that two different soil areas have distinct estimation quality. Furthermore, numerical results of the training stage show that the estimation of SM for Field and Forest soil performing decision tree regression offers 0.0019 and 0.0025 mean absolute error (MAE), respectively. Moreover, numerical results show that the interaction of the parameters of the performed algorithm plays a vital role in the estimation stage of Field and Forest soils. [ABSTRACT FROM AUTHOR]

The class of α -stable distributions is widely used in various applications, especially for modeling heavy-tailed data. Although the α -stable distributions have been used in practice for many years, new methods for identification, testing, and estimation are still being refined and new approaches are being proposed. The constant development of new statistical methods is related to the low efficiency of existing algorithms, especially when the underlying sample is small or the distribution is close to Gaussian. In this paper, we propose a new estimation algorithm for the stability index, for samples from the symmetric α -stable distribution. The proposed approach is based on a quantile conditional variance ratio. We study the statistical properties of the proposed estimation procedure and show empirically that our methodology often outperforms other commonly used estimation algorithms. Moreover, we show that our statistic extracts unique sample characteristics that can be combined with other methods to refine existing methodologies via ensemble methods. Although our focus is set on the symmetric α -stable case, we demonstrate that the considered statistic is insensitive to the skewness parameter change, so our method could be also used in a more generic framework. For completeness, we also show how to apply our method to real data linked to financial market and plasma physics. [ABSTRACT FROM AUTHOR]

This paper proposes a networked control architecture that provides robustness not only to parametric uncertainties in the model but also to packet dropouts and time-varying network-induced delays. Contrary to the common approach of nonlinear optimization solvers, the estimation and control approaches stand out by providing a direct solution derived from an Extended Kalman Filter and a Recursive Robust Linear-Quadratic Regulator. Moreover, we prove that the optimal robust solution for a single agent suffices for the control of a homogeneous multi-agent scenario. Numerous simulations analyze the impact of different network conditions on the proposed architecture for a multi-agent scenario. Finally, the architecture was implemented and made available as a Robot Operating System (ROS) package, and trajectory-tracking missions were experimentally carried out to show the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

In parametric models, minimising different estimators of the Kullback–Leibler divergence between the empirical distribution function and the true distribution function yield the maximum likelihood estimator (MLE) and the maximum spacings product estimator. This approach has been extended in the literature to minimise some estimators of Csisźar divergence between the empirical distribution function and the true distribution function. Such estimators based on disjoint spacings have recently been studied in the literature. This paper considers analogues of these estimators based on overlapping sample spacings. The estimators have been found to be consistent and asymptotically normally distributed under a broad set of regularity conditions. Asymptotically and for any fixed order of spacings, such estimators are at least as good as the corresponding estimators based on non-overlapping spacings. Simulation studies show that some of these estimators perform better than the MLE for contaminated models. An application to real data reveals that the considered estimators can perform better than the MLE for parsimonious models. [ABSTRACT FROM AUTHOR]

From the point of view of agriculture, ecology, or environmental engineering, the capability of forecasting meteorological variables in the long and short term is crucial. Short-term forecasts enabling the planning of field work in agriculture, management of mass events, or tourism are important, while long-term forecasts related to advancing climate change are also very interesting. In the literature, there are known many approaches that can be used to forecast climate time series. The most common is based on the statistical modelling of the corresponding data, and the prediction is made on the fitted model. There are known one-dimensional approaches, where single variables are modeled separately; however, in the last decade, there appears a new trend which assumes the importance of the relationship between different time series. This is the approach considered in this paper. We propose to examine the climate data (temperature and precipitation) using the multidimensional vector autoregressive model (VAR). However, because in the time series we observe non-Gaussian behaviour, the classical VAR model can not be applied and the multidimensional Gaussian noise is replaced by the α - stable one. This model was previously analyzed by the authors in the context of financial data description where also non-Gaussian characteristics are observed. The main goal of this paper is to answer the question whether there are reasons to go from the Gaussian model to the generalized models, like α - stable based. The second purpose is to link total precipitation data with temperature time series. In the classical approach, precipitation was treated as a variable not correlated with temperature, which, as we will show in the paper, is inconsistent with reality. We hope the presented in this paper results open new areas of interest related to climate data modelling and prediction. [ABSTRACT FROM AUTHOR]

Recently, state-of-the-art crowd counting methods have focused more on predicting a density map and then obtaining the final aggregated count. In 2018, a typical density map-based network for congested scene recognition called CSRNet was proposed, and it achieved better crowd counting performance than previous methods with a simple architecture. It utilizes the first 10 layers from VGG-16 as the front end and deploys dilated convolutional layers as the back-end to generate high-quality density maps. CSRNet has been demonstrated on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the World Expo'10 dataset, and the UCSD dataset) and delivered great performance. To obtain better performance, in this paper, we propose a small network as a new component that generates a counting residual estimation, and we combine our component with CSRNet. We demonstrate this combined network on three datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, and the World Expo'10 dataset) and compare the results with those of CSRNet. The results show that our method has significantly improved the results of CSRNet. Through a series of experiments, such as ablation experiments and control experiments, we demonstrate the effectiveness of our method. In the future, we will apply our method to other networks to achieve better results. [ABSTRACT FROM AUTHOR]

This research aims to see how well back-propagation neural network (BPNN) approaches perform in estimating peak ground acceleration in the Himalayan region using various forms of input data (i.e., magnitude, focal depth, hypocentre and S-wave avg. velocity). These criteria were derived from the Himalayan Earthquake Catalogue, which includes all minor and significant earthquakes and their aftershock sequences in prior years. The analysis of high-quality seismological recorded data is important for earthquake prediction. The BPNN is employed for predicting the strong ground motion parameters such as peak ground acceleration, peak ground velocity and peak ground displacement using different activation functions at different layers. This research paper focuses on the model's training to analyze the dataset, including a few parameters to estimate the acceleration of the ground. Translated data into seismicity indicators, which are mathematically derived parameters. These seismicity indicators were utilized for the training of the BPNN to improve decision-making and for peak ground acceleration estimation. Artificial neural network models were used to predict the ground motion intensity measures for future events. The predictive parameters' contributions to the prediction of the considered intensity measurements are finally explored via sensitivity analysis. This study uses the multi-layer BPNN model to discover components in the Himalayan region, using the actual incidence of earthquake seismicity indicators as input and goal vectors. Finally, the BPNN model produces a good estimate for the peak ground acceleration of earthquakes. [ABSTRACT FROM AUTHOR]

This paper deals with estimating the underlying parameter of a length-biased inverse Gaussian distribution, when the observations are prone to length-biased sampling. Length-biased sampling occurs when the observations of smaller lengths or dimensions are neglected from the sample. We focus on a particular type of sequential fixed-accuracy confidence interval for estimation purposes. This method proves to be both time and cost efficient as one is able to perform the estimation using an optimal number of observations according to some set criteria. We discuss the applicability of our proposed method with regards to estimating the cluster size of the "Eastern Tropical Pacific Dolphins", which are often vulnerable to length biasedness. [ABSTRACT FROM AUTHOR]

This paper proposes an online identification and compensation scheme for a distorted waveform's harmonic and interharmonic content in electrical circuits. The proposed novel identification scheme allows the simultaneous estimation of the harmonic components' frequency, amplitude, and phase. One of the main characteristics of the proposed online identification scheme is how the harmonics and interharmonics are estimated using a fast-convergent state estimator based on the state-space representation of the harmonic content. The proposed estimator has a decentralized approach that relies on efficient implementation, a relevant result for real-time feedback control applications, such as power conditioners control. The decentralized structure allows, if needed, selective harmonic suppression. The paper exposes a case study of identifying and suppressing the harmonic and interharmonic content produced by a three-phase load connected to the electrical power grid to demonstrate the proposed estimator's efficiency. Hardware-in-the-loop and simulation results validate the identifier effectiveness to determine in real time the harmonic content and how it can be used to reduce the total harmonic distortion through a nonlinear optimal tracking control scheme. [ABSTRACT FROM AUTHOR]

Orbits of objects in cislunar space are, in general, non-Keplerian due to the influence of the Moon's gravity and cannot be generically parameterized by a simple set of characteristics. Objects are also fainter and move relatively more slowly when viewed from Earth. Detection and tracking are expected to be significantly more difficult and, as a consequence, orbit determination becomes more challenging. In this paper we review a subset of possible orbits and their expected astrometric and photometric signatures from the perspective of hypothetical ground-based electro-optical sensors on Earth. Although a multitude of orbits are possible, we focus on special types of orbits that are closed in the synodic frame (i.e., periodic) and emanate from the libration points of the Earth-Moon system. We investigate three separate elemental periodic orbit families that have been differentially corrected in a high-fidelity dynamical system: H1, L1, and W4W5. For each family, we set objects at different locations at different epochs and simulate the expected observational features (e.g., right ascension, declination, visual magnitude) based on faceted satellite models. In this study, we show how Gaussian mixture model estimation filters behave when processing different observation sets, specifically varying data cadence, data density, data quality, and data span. Convergence and uncertainty bounds are shown to have a strong dependence on the observational data composition (affecting the accuracy of fitting orbits) and a notable correlation to orbital stability (affecting the ability to predict/correct orbits). [ABSTRACT FROM AUTHOR]

On the basis of power and exponential transformations, an original family of distributions is created and studied. Thanks to two tuning parameters, it has the capacity to generalize a few of the present families, which have been widely used in recent data. As a first contribution of this paper, a theoretical result is demonstrated on the admissible values of these parameters. Then, some of the properties of the family are established, including functional series expansions, quantile function, and moments. After that, we exemplify the theory; a special case based on the Weibull distribution is emphasized. It can be described as a new two-parameter lifetime distribution. It is of interest because of its original and flexible functionalities in terms of probability density and hazard rate shapes, and manageable properties, including a closed form expression of the quantile function. Five different and efficient estimation methods are examined for the point estimation of the parameters of this special distribution. A simulation work is performed to compare these estimation methods. Finally, we apply the special distribution to two real-world datasets to illustrate its usefulness, and compare it to some competitors, including some based on the Weibull distribution. According to the data analysis, the proposed distribution works well. [ABSTRACT FROM AUTHOR]

The reliability of autonomous landing of the UAVs in an unknown or unprepared environment can be improved by the application of image-based sensors. This paper investigates the landing control of a multirotor UAV by controlling the optical flow and estimation of vertical distance supported by a low-cost monocular camera. Landing control and vertical distance estimation using just a camera as the sensor, makes the proposed approach well-suited for emergency scenarios in GPS denied environment. To develop the optical flow-based controller strategy, an appropriate nonlinear model is proposed by combining the optical flow equations and the kinematics of vertical landing. In the control loop of the multirotor landing, an estimation of the vertical distance is required, which is provided by the application of the developed model and the optical flow derived from camera images. RLS algorithm and the EKF have been applied to estimate the vertical distance. The controller output is determined by using the NDI algorithm, the augmented state-space form of the equations, and the estimated states based on optical flow. An experimental setup is developed for the tasks of optical flow extraction, vertical distance estimation, and control. Additionally, the developed estimation and controller strategy are applied to the nonlinear dynamics of a quadrotor to demonstrate the applicability of the proposed models and algorithms for landing control. According to the results, the proposed optical flow-based control strategy can support a smooth landing for the multirotor UAV while its performance is dependent on the quality of estimation. There is a steady-state error in tracking the optical flow due to the estimation error of the vertical distance. The EKF-based algorithm has better performance in terms of estimation accuracy respecting the RLS, and therefore supports a better landing performance. [ABSTRACT FROM AUTHOR]

Resources of mineral deposits are commonly estimated using data extracted from rock cores obtained by drilling. Information of good quality and quantity is essential to achieve good estimates (unbiased and precise). Frequently, the geomodeller is required to provide an estimate of the grade of each block to be mined. Some deposits have more erratic spatial grade distributions than others, and therefore a better understanding of closely spaced samples is required. Given the importance of samples, due to their influence on the precision and accuracy of the model built from these collected data, it is deemed important to optimize their spacing (or sampling grid). The idea is to obtain information which will lead to the desired model precision while minimizing the overall sampling cost. Commonly, mineral deposit regions with high grade variability require closely spaced samples. In this paper, the variability is measured by the grades' coefficient of variation. A chart relating the sample spacing to the estimation error for various data coefficients of variation and block geometries of three different deposits is proposed. A case study illustrates the results obtained for a copper grade, an iron grade, and an aluminum oxide grade. The aim of this paper is to establish a fast and reliable way to obtain the maximum error for a given sample spacing that may be used for short-term geological modeling and mine planning at different ore deposits. [ABSTRACT FROM AUTHOR]

This paper develops a Kalman filter-based method to estimate the magnitude and phases of currents and voltages of a single-phase transmission line. Unlike common-place practices, in which phasors are estimated by using Fourier-based or least squares methods, the standard Kalman filter algorithm is used. We represent the transmission line by using a steady-state model in which the phasors of voltages and currents are considered to be in the complex d - q domain. We then show the estimation performance of the filter-based estimator by means of a numerical simulation of a medium-length transmission line and compare the proposed method to standard methods for phasor estimation. The results presented show that the filter is highly efficient and encourages future research on the estimation of the electric parameters of transmission lines by using the proposed method. We conclude the paper by reasoning that the proposed estimation method could be used in phasor measurement devices for improved monitoring and control capabilities of electrical systems. [ABSTRACT FROM AUTHOR]

Recent research syntheses have identified several potentially high-leverage teaching strategies for improving low-attaining secondary school students' learning of mathematics. These strategies include the structured use of representations and manipulatives and an emphasis on derived facts and estimation. This paper reports on 70 semi-structured interviews conducted with low-attaining students in Years 9–10 (ages 13–15) in England. The interviews addressed the students' perceptions of learning mathematics and the teaching strategies that they experienced and believed were most helpful. Many students reported rarely using number lines, not spontaneously estimating answers and being unfamiliar with derived facts. During the interviews, with minimal direction, students often showed that they were well able to make use of these strategies; however, they did not report making spontaneous use of them independently. We conclude that many of the most well-evidenced and recommended strategies to support low-attaining students in mathematics appear to be unfamiliar and unvalued, and we discuss how this might be addressed. [ABSTRACT FROM AUTHOR]

The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns. [ABSTRACT FROM AUTHOR]

The time series with periodic behavior, such as the periodic autoregressive (PAR) models belonging to the class of the periodically correlated processes, are present in various real applications. In the literature, such processes were considered in different directions, especially with the Gaussian-distributed noise. However, in most of the applications, the assumption of the finite-variance distribution seems to be too simplified. Thus, one can consider the extensions of the classical PAR model where the non-Gaussian distribution is applied. In particular, the Gaussian distribution can be replaced by the infinite-variance distribution, e.g. by the α -stable distribution. In this paper, we focus on the multidimensional α -stable PAR time series models. For such models, we propose a new estimation method based on the Yule-Walker equations. However, since for the infinite-variance case the covariance does not exist, thus it is replaced by another measure, namely the covariation. In this paper we propose to apply two estimators of the covariation measure. The first one is based on moment representation (moment-based) while the second one—on the spectral measure representation (spectral-based). The validity of the new approaches are verified using the Monte Carlo simulations in different contexts, including the sample size and the index of stability of the noise. Moreover, we compare the moment-based covariation-based method with spectral-based covariation-based technique. Finally, the real data analysis is presented. [ABSTRACT FROM AUTHOR]

This paper introduces a position sensorless operation of a brushless DC (BLDC) motor based on an efficient state estimation algorithm. In the proposed method, the speed and rotor position estimation is performed only by measuring the phase currents and line voltages. Estimated values are used to determine the commutation logic and speed controller. The line voltage difference is employed, which eliminates the need for neutral. The states estimation is based on the particle filter that doesn't have the limitations and drawbacks of the Extended Kalman Filter. First, there is no assumption for the probability density functions of the measurement and process noise, it works for Gaussian and non-Gaussian noises. Second, it is not based on any linearization and can efficiently estimate the states of nonlinear systems. Moreover, a suitable method is adopted for resampling step, which improves the estimation performance and does not increase the complexity. The simulation and experimental results demonstrate the effectiveness of the proposed sensorless method. The proposed method is more robust than other common techniques like EKF at different sampling times. The experimental results show the possibility of the BLDC motor states estimation with appropriate accuracy in steady-state and dynamic operation. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the estimation problem for the extreme value parameters in the case of stationary -mixing serials with heavy-tailed distributions. We first introduce two families of estimators generalizing the Hill's estimator. And from those families, three asymptotically unbiased estimators of the extreme value index are established. Our reflection is based on the generalized Jackknife methodology which consists of taking any pair of three special cases of our family of estimators to cancel the bias term. The resulting estimators are also used to deduce three asymptotically unbiased estimators of the extreme quantiles. In a simulation survey, the performance of our proposed methods are compared to alternative estimators recently introduced in the literature. Finally, our methods are applied to high financial losses data in order to estimate the Value-at-Risk of the daily stock returns on the S&P500 index. [ABSTRACT FROM AUTHOR]

In this paper, we consider the vector autoregressive time series with multidimensional α -stable noise where 1 < α < 2 . This model takes into account the short-term dependence in the multidimensional data and possible large observations (outliers). However, for the processes based on the α -stable distribution, the classical dependence measures known as the covariance function or the correlation function are not defined due to the diverging second moment. And therefore, the classical estimation method related to the covariance-based Yule–Walker (Y-W) equations should not be used in this case. Here, we propose to generalize the Y-W equations by replacing the covariance function with the covariation function which is a properly defined dependence measure for the symmetric α -stable distribution. Based on the covariation-based Yule–Walker equations, we propose a new estimation method and we demonstrate its efficiency by conducting a simulation study on the trajectories of two- and three-dimensional α -stable VAR(1) time series. Moreover, we compare the new covariation-based technique with the classical Yule–Walker method based on the covariance function. We emphasize that the estimation technique introduced in this paper extends the generalized Yule–Walker method introduced by Gallagher (Stat Prob Lett 53:381–390, 2001) for one-dimensional α -stable autoregressive models. The theoretical results are illustrated by the real data analysis of a bivariate data set describing the daily prices of KGHM and copper. [ABSTRACT FROM AUTHOR]

Let be a partition of the interval constructed by the rule where . We introduce the sequence of numbers by setting where are the Fibonacci numbers (). The main result of this paper is the following: Theorem. : where . [ABSTRACT FROM AUTHOR]

The periodic behavior of real data can be manifested in the time series or in its characteristics. One of the characteristics that often manifests the periodic behavior is the sample autocovariance function. In this case, the periodically correlated (PC) behavior is considered. One of the main models that exhibits PC property is the periodic autoregressive (PARMA) model that is considered as the generalization of the classical autoregressive moving average (ARMA) process. However, when one considers the real data, practically the observed trajectory corresponds to the "pure" model with the additional noise which is a result of the noise of the measurement device or other external forces. Thus, in this paper we consider the model that is a sum of the periodic autoregressive (PAR) time series and the additive noise with finite-variance distribution. We present the main properties of the considered model indicating its PC property. One of the main goals of this paper is to introduce the new estimation method for the considered model's parameters. The novel algorithm takes under consideration the additive noise in the model and can be considered as the modification of the classical Yule–Walker algorithm that utilizes the autocovariance function. Here, we propose two versions of the new method, namely the classical and the robust ones. The effectiveness of the proposed methodology is verified by Monte Carlo simulations. The comparison with the classical Yule–Walker method is presented. The approach proposed in this paper is universal and can be applied to any finite-variance models with the additive noise. [ABSTRACT FROM AUTHOR]

Heart disease has become one of the world's most dangerous and serious diseases due to the difficulty in identifying it. Machine learning may assist the medical field by providing accurate and timely illness diagnoses. The main objective is to develop an expert system that can identify heart illness early and assist cardiologists in making more accurate diagnoses. This paper examines many aspects of heart illness and develops a model using supervised learning techniques like Logistic Regression, Naïve Bayes, decision trees, K-nearest neighbor (KNN), random forest, Support Vector Machine (SVM), and the XG Boost methods. It utilizes a dataset of the Cleveland catalog of heart patients at UCI. There are around 303 occurrences plus 76 characteristics within the collection. Only 14 out 76 characteristics are tested, despite their importance in proving the effectiveness of alternative algorithms. The accuracy of different classifiers are,in Naive Bayes 83.51%, SVM 84.79%, Decision Tree 77.5%, Random Forest 87.94%, and KNN 80.21%, Logical Regression 85.0% is obtained. Gradient Boost has the highest accuracy rate of 95.62%, Sensitivity of 92.3%, Precision 83.7%, and specificity of 93.8 Percent in the case of same data set we have applied for other classifier. [ABSTRACT FROM AUTHOR]

The choice of an estimation method has received considerable attention in the Operations Research literature. In this paper we depart from the standard use of linex and double-linex loss functions which are widely used in parameter estimation and forecasting problems and we propose a non-standard use for them. Specifically, we propose to use the corresponding linex and double-linex error densities as models for the errors of a regression problem when more emphasis should be placed on over-estimation or under-estimation of errors. The new techniques are applied to synthetic as well real data concerning the role of management in production as well as to an application of forecasting volatility in intradaily data. [ABSTRACT FROM AUTHOR]

For both clients and developers in the development of software, the effort estimate is vital. Lower efforts may lead to poorly created processes, low-quality and delayed budgets being improperly approved by the management. This could lead to a failure to complete the task in the defined schedule. The assessed parameter helps to share the information needed to accomplish project results successfully. The main parameters for the software projects include time, conditions, people, infrastructure, materials and funds and hazards. For this reason, large evaluations of various elements, including effort and cost, are vital to a project. If the estimate is less than necessary, the advancement of the project is also lacking in cash and time. The paper's main aim is to evaluate the software development estimate to improve current RE, MRE, MMRE and PRED methodologies. [ABSTRACT FROM AUTHOR]

Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were conducted at seven sampling sites along the Yangtze River Estuary (YRE) during summer, autumn, and winter 2021 to analyze the concentrations of seven heavy metals (As, Cd, Cr, Pb, Cu, Ni, Zn) in water and surface sediments. The order of heavy metal concentrations in water samples from highest to lowest was Zn > As > Cu > Ni > Cr > Pb > Cd, while that in surface sediments samples was Zn > Cr > As > Ni > Pb > Cu > Cd. Human health risk assessment of the heavy metals in water samples indicated a chronic and carcinogenic risk associated with As. The risks of heavy metals in surface sediments were evaluated using the geo-accumulation index (Igeo) and potential ecological risk index (RI). Among the seven heavy metals, As and Cd were highly polluted, with Cd being the main contributor to potential ecological risks. Principal component analysis (PCA) was employed to identify the sources of the different heavy metals, revealing that As originated primarily from anthropogenic emissions, while Cd was primarily from atmospheric deposition. To further analyze the influence of water quality indicators on heavy metal pollution, an artificial neural network (ANN) model was utilized. A modified model was proposed, incorporating biochemical parameters to predict the level of heavy metal pollution, achieving an accuracy of 95.1%. This accuracy was 22.5% higher than that of the traditional model and particularly effective in predicting the maximum 20% of values. Results in this paper highlight the pollution of As and Cd along the YRE, and the proposed model provides valuable information for estimating heavy metal pollution in estuarine water environments, facilitating pollution prevention efforts. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

In this paper, we are basically interested in the issue of estimation parameters for continuous q-distributions. The parameters estimation and simulation studies of three classical continuous Lindley, gamma and exponential q-distributions are elaborated. For the parameters estimation problem the moment method is used. The effectiveness of the proposed models are highlighted through simulation studies for different q parameters values and samples sizes of the Lindley, gamma and exponential q-distributions. [ABSTRACT FROM AUTHOR]

We begin with a generic expression of an optimal fixed-sample-size n∗ which has an expression λg(휃) with λ > 0 and g(휃) > 0 where 휃 is an unknown parameter. A consistent estimator of 휃 is a sample mean of independent and identically distributed (i.i.d.) random variables. Under fairly relaxed set of conditions on g(.), we have developed a general theory of three-stage sampling strategy detailing requisite mathematical techniques for proving both asymptotic (as λ → ∞ ) first-order and second-order analyses. We believe that this theory is broad and rich especially since the technicalities developed are not tailored to fit a specific inference problem of choice. We have validated this sentiment with the help of illustrations which cannot be handled satisfactorily by improvising upon some of the existing methodologies. For example, (i) Illustration 1 proposes a three-stage strategy under Linex loss in a recently developed inference problem; (ii) Illustration 2 handles estimation of 휃 in a Uniform(0,휃) distribution which obviously stays outside an exponential family; and (iii) Illustration 3 incorporates expressions of g(.) functions which no existing paper's analyses could treat. [ABSTRACT FROM AUTHOR]

Production of diploid males is disadvantageous for populations of haplodiploid species, because it increases the genetic load and decreases fitness. In social species, the damage becomes more serious because of increased colony mortality and decreased colony growth rates. As a parameter to quantify diploid male production, the proportion of diploids that are males, Φ , has been estimated using multiple-loci marker genotypes. In these studies, Φ is separately estimated for individual marker locus, and then the estimates are averaged over marker loci. In this paper, we propose a simple method for combining genotypes of multiple-marker loci to obtain a single estimate of Φ , which is expected to enhance the quality of estimate. As an application, Φ in a threatened bumble bee population in Japan is estimated from genotype data of multiple microsatellite loci. Under the separate analysis of individual marker locus the detectability of diploid males, measured by the probability that a diploid is heterozygous, is within the range of 0.196–0.554, whereas the detectability from the proposed method increases to 0.834, giving an estimate of Φ with a higher degree of precision. [ABSTRACT FROM AUTHOR]

The dynamic general nesting spatial econometric model for spatial panels with common factors is the most advanced model currently available. It accounts for local spatial dependence by means of an endogenous spatial lag, exogenous spatial lags, and a spatial lag in the error term. It accounts for dynamic effects by means of the dependent variable lagged in time, and the dependent variable lagged in both space and time. Finally, it accounts for global cross-sectional dependence by means of cross-sectional averages or principal components with heterogeneous coefficients, which generalizes the traditional controls for time-invariant and spatial-invariant variables by unit-specific and time-specific effects. This paper provides an overview of the main arguments in favor of each of these model components, as well as some potential pitfalls. [ABSTRACT FROM AUTHOR]

This special issue of the Journal of the German Statistical Society presents 14 papers with surveys on the development and new topics in econometrics. The articles aim to demonstrate how German econometricians see the discipline from their specific view. They briefly describe the main strands and emphasize some recent methods. [ABSTRACT FROM AUTHOR]

Compound Cox processes (CCP) are flexible marked point processes due to the stochastic nature of their intensity. This paper states closed-form expressions of their counting and time statistics in terms of the intensity and of the mean processes. They are forecast by means of principal components prediction models applied to the mean process in order to reach attainable results. A proposition proves that only weak restrictions are needed to estimate the probability of a new occurrence. Additionally, the phase type process is introduced, which important feature is that its marginal distributions are phase type with random parameters. Since any non-negative variable can be approximated by a phase-type distribution, the new stochastic process is proposed to model the intensity process of any point process. The CCP with this type of intensity provides an especially general model. Several simulations and the corresponding study of the estimation errors illustrate the results and their accuracy. Finally, an application to real data is performed; extreme temperatures in the South of Spain are modeled by a CPP and forecast. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of functional observer design for systems with non-commensurate time varying delays. The main procedure of three types of functional observers are presented in this article, more precisely, observers with delays dependent, observers without internal delays and delay-free functional observers. A necessary condition for the existence of these observers is given as rank equality. The design conditions for functional observers are presented in terms of linear matrix inequalities (LMIs). The solution of these LMIs is used to determine the observers parameters. Finally, some numerical examples are given at the end of the paper to show the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

During heavy rainfalls, the surface soil on a slope may be eroded and the erosion is much dependent on the tensile strength of soil. In addition, the tensile strength of soil is also one of the governing factors which affect the cracking of soil. It is noted that the water content of soil has a significant effect on the tensile strength of soil. The experimental works for measurement of the tensile strength of soil in laboratory (especially for the unsaturated soil) are challenging, and the indirect estimation techniques draw a lot of attentions among practical engineers. In this paper, the tensile strength of soil is estimated from the soil–water characteristic curve (SWCC) by adopting the concept of pore-size distribution function. The proposed equations were verified with the experimental data from published literature. The comparison results show that the estimated tensile strengths agree well with the experimental results of the selected soils from published literatures. In addition, the variations of SWCC due to different initial densities and fine contents are also considered and the effects of the variation of SWCC on the estimated tensile strength are also investigated and discussed in this paper. It is observed that the modified SWCCs incorporating the effects of different initial densities and fine contents are able to provide a more accurate estimation of the tensile strength of unsaturated soil. [ABSTRACT FROM AUTHOR]

This paper explores an alternative interpolation approach for the line-of-sight measurements in Laplace's method for angles-only initial orbit determination (IOD). The classical implementation of the method uses Lagrange polynomials to interpolate three or more unit line-of-sight (LOS) vectors from a ground-based or orbiting site to an orbiting target. However, such an approach does not guarantee unit magnitude of the interpolated line-of-sight path except at the measurement points. The violation of this constraint leads to unphysical behavior in the derivatives of the interpolated line-of-sight history, which can lead to poor initial orbit determination (IOD) performance. By adapting a spherical interpolation method used in the field of computer graphics, we can obtain an interpolated line-of-sight history that is always unit norm. This new spherical interpolation method often leads to significant performance improvements in Laplace's IOD method, with comparable robustness to measurement noise as the traditional interpolation methods. This paper also demonstrates the benefit of interpolating through more than three data points, which is easily enabled up to an arbitrary number of measurements by recursive definitions of the interpolations. [ABSTRACT FROM AUTHOR]

Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. The estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers. [ABSTRACT FROM AUTHOR]

In 1992, in a famous paper, Deville and Särndal proposed the calibration method in order to adjust samples on known population totals. This paper had a very important impact in the theory and practice of survey statistics. In this paper, we propose a rigorous formalization of the calibration problem viewed as an optimization problem. We examine the main calibration functions and we discuss the question of the existence of solutions. We also propose an alternate way of solving the optimization problem given by the calibration principle. We finally present a set of simulations in order to compare the different methods. [ABSTRACT FROM AUTHOR]