Separate Estimates Research Articles

Simple gunshot residue analyses for estimating firing distance: Investigation with four types of fabrics

This work presents two simple methods for estimating the firing distance from the gunshot residues (GSRs) on fabric targets. Four types of fabric targets, namely twill weave denim cotton-polyester (80/20), jersey knitting 100% cotton, plain weave cotton-polyester (80/20) and plain weave cotton-polyester (60/40), were employed. The firing tests were carried out using these white fabrics as targets at distances of 5–100 cm, respectively. In the first method, digital images of the black GSRs on fabric materials were recorded inside an illuminated box and the inverted gray intensity values were plotted against the firing distances. Since the plots of all fabrics are not significantly different, the estimation of firing distance employs the same exponential curve for all test fabrics. Although simple, the imaging method is not suitable for dark-colored materials. A chemical-based method was therefore developed as an alternative method. In the second method, a small disposable microfluidic paper-based analytical device (μPAD) was employed for detecting Pb(II) extracted from the GSRs. The μPAD method uses the measurement of the length of a narrow band of a pink color resulting from reaction between rhodizonate reagent and the Pb(II) extract. The plots indicated that the data of thick denim material are significantly different to other test fabrics which are much thinner. These three fabrics share the same estimation curve. However, it is recommended that the separate estimation curve for denim materials must be used. Both methods are suitable for short range firing distance, no further than 60 cm, since at greater distances the inverted gray intensity and the ‘band-length’ methods are unable to detect the GSRs.

Experimental evaluation of compressibility coefficients for fractures and intergranular pores of an oil and gas reservoir

The paper is devoted to studies of the volumetric response of rocks caused by changes in their stress state. Changes in the volume of fracture and intergranular components of the pore space based on measurements of the volume of pore fluid extruded from a rock sample with an increase in its all-round compression have been experimentally obtained and analyzed. Determination of the fracture and intergranular porosity components is based on the authors' earlier proposed method of their calculation using the values of longitudinal wave velocity and total porosity. The results of experimental and analytical studies of changes in porosity and its two components (intergranular and fractured) under the action of effective stresses are considered. This approach allowed the authors to estimate the magnitude of the range of changes in the volumetric compressibility of both intergranular pores and fractures in a representative collection of 37 samples of the Vendian-age sand reservoir of the Chayanda field. The method of separate estimation of the compressibility coefficients of fractures and intergranular pores is proposed, their values and dependence on the effective pressure are experimentally obtained. It is determined that the knowledge of the values of fracture and intergranular porosity volumetric compressibility will increase the reliability of estimates of changes in petrophysical parameters of oil and gas reservoirs caused by changes in the stress state during the development of hydrocarbon fields.

Olivine composition and reflectance spectroscopy relationship revisited from advanced MGM deconvolution based on synthetic and natural samples

The behavior of the three primary olivine absorptions near 1 μm (referred to as M1-1, M2, M1-2) is revisited by means of an advanced version of MGM analysis. The (M1-1, M2, M1-2) band center positions are used simultaneously to produce a constrained prediction across an extended range Fo#[0–90] of olivine solid solution composition. The established trend lines based on synthetic samples from the Suny and Bristol suites appear robust when implemented on natural samples, and represent an improvement over previous solutions. The methodology, based on a wavelength deconvolution along the entire visible and near-infrared range (540-2600 nm), and the trend lines derived from the current solution, perform well, even when considering the complex situation of chromite-bearing lunar olivines. Furthermore, through the use of separate and quadratic estimates to the trends, an assessment is offered to qualify the Fo# determination made on unknown samples and/or remote sensing observations.

Time and phase synchronization using clutter observations in airborne distributed coherent aperture radars

Airborne Distributed Coherent Aperture Radar (ADCAR) is one of the most promising next-generation radars to significantly improve target detection and discrimination abilities. However, time and phase synchronization among unit radars should be done before an ADCAR is intended to cohere on a potential target. To address this problem, a time and phase synchronization technique using clutter observations is proposed in this paper. Clutter returns from different azimuths and elevations on the surface of the earth are employed to calibrate system uncertainties. Two stages are mainly considered: a scene registration among range-Doppler units from different transmit/receive pairs is performed to enhance the clutter coherence in the first stage, followed by a joint estimation of those synchronization errors in the second stage. To relieve the computational burden, a novel Separable and Sequential Estimation (SSE) method is provided to separate the unknowns at the sacrifice of a range-Doppler unit. Moreover, performance analyses including the clutter coherence ability, estimation lower bound, and signal coherence loss are also performed. Finally, simulation results indicate that ADCAR time and phase synchronization is realized by using our methods.

Effect of wilting and lactic acid bacteria inoculant on in situ and in vitro determined protein value of grass silages

The objective of this study was to evaluate the effect of dry matter (DM) concentration and the use of lactic acid bacteria (LAB) inoculant on the protein value of grass silages. Grass samples containing 170 g DM/kg fresh material were wilted to different DM concentrations (310, 390, 420, 470, 530, 580, 600 g/kg fresh material) before ensiling. Half of the grass material at respective DM was treated with LAB, the other half remained untreated (control, CON). Ruminal degradation of organic matter (OM) and crude protein (CP) was determined using the in situ technique. For this, silage samples were incubated for 2, 4, 8, 16, 24, 48 and 72 h. Rumen undegraded CP (RUCP) was derived from in situ CP degradation. Microbial CP (MCP) synthesis was assumed to be 181 g per kg fermented OM. Post-ruminal CP (prCP) was defined as the sum of RUCP and MCP. Effective prCP in situ (prCPkin situ) was calculated for passage rates (k) of 0.02, 0.04 and 0.06/h. In addition, the modified Hohenheim gas test (modHGT) was used to determine effective prCP in vitro (prCPkin vitro) for the same passage rates. The DM linearly increased prCPk in situ in CON (P < 0.001) and LAB (P < 0.001). This was caused by both increased RUCP in the range of 580–600 g DM (P < 0.001) and increased MCP (P < 0.001) due to higher amount of water-soluble carbohydrates in the more intensively wilted silages. The use of LAB increased RUCP (P < 0.001) but decreased the calculated MCP content (P < 0.001). Therefore, these effects compensated each other leading to only slightly lower prCPk in situ for LAB (P < 0.05). The positive effect of DM on prCPkin situ was confirmed in vitro (P < 0.001) showing no effect of the inoculant. Based on both methods prCPk increased to a small extent with 2.3 – 4.9 g/kg OM (P < 0.001), when DM increases by 100 g/kg fresh material, depending on the assumed passage rate. Therefore, wilting and LAB addition to grass have the potential to improve protein value of grass silage. Both methods delivered similar estimates for effective prCP. But modHGT appeared to be superior to the in situ technique by being more sensitive, not requiring separate estimation of MCP and the assumption of a certain efficiency value, and by having lower operating expenses. The results of this study show the potential of the modHGT, however further studies are required to standardize and validate this technique and to evaluate the effect of wilting grass on animal performance.

A penalized likelihood for multispecies occupancy models improves predictions of species interactions.

Multispecies occupancy models estimate dependence among multiple species of interest from patterns of co-occurrence, but problems associated with separation and boundary estimates can lead to unreasonably large estimates of parameters and associated standard errors when species are rarely observed at the same site or when data are sparse. In this paper, we overcome these issues by implementing a penalized likelihood, which introduces a small bias in parameter estimates in exchange for a potentially large reduction in variance. We compare parameter estimates obtained from both penalized and unpenalized multispecies occupancy models fit to simulated data that exhibit various degrees of separation and to a real-word data set of bird surveys with little apparent overlap between potentially interacting species. Our simulation results demonstrate that penalized multispecies occupancy models did not exhibit boundary estimates and produced lower bias, lower mean squared error, and improved inference relative to unpenalized models. When applied to real-world data, our penalized multispecies occupancy model constrained boundary estimates and allowed for meaningful inference related to the interactions of two species of conservation concern. To facilitate the use of our penalized multispecies occupancy model, the techniques demonstrated in this paper have been integrated into the unmarked package in R programing language.

What drives the dividend decisions in BRICS countries?

Research background: We examine the dividend payout policies across companies listed on the main stock exchanges in Brazil, Russia, India, China, and South Africa (BRICS). Unlike the highly developed capital markets, the literature regarding dividend policy on BRICS? stock exchanges is scarce. &#x0D; Purpose of the article: The purpose of this paper is threefold: verification of the existence of dividend smoothing pattern; selection of the significant drivers that affect both dividend levels and dividend smoothing; examination of differences between dividend policy of cross- and single-listed companies.&#x0D; Methods: Based on a dataset of 564 companies that paid dividends for at least 11 consecutive years in the period of 1995?2015, we apply a GMM two-step estimator to assess the speed of dividend adjustment (SOA) coefficient. Further we employ the linear panel regression to indicate the individual and market determinants of the dividend levels and SOAs. In the latter case, we base on time series of the SOAs obtained from the rolling estimation technique. Finally, we conduct separate estimations for cross-listed companies.&#x0D; Findings &amp; value added: We confirm a moderate level of dividend smoothing within BRICS countries. Among the firm-level characteristics affecting the SOA the most important are: ownership dispersion, age and size of a firm, retained earnings, leverage, long term debt, asset tangibility, liquidity risk ratio, and issuing the depositary receipts (DR). Two relevant market factors are found: market capitalisation and turnover in relation to GDP. Similar characteristics have a significant impact on dividends? levels in the entire sample, whereas in the subsample of cross-listed companies fewer variables are significant. Our paper is the first comprehensive attempt to investigate the dividend policy and determinants of dividend smoothing among BRICS countries.

Joint estimation of monotone curves via functional principal component analysis

A functional data approach is developed to jointly estimate a collection of monotone curves that are irregularly and possibly sparsely observed with noise. In this approach, the unconstrained relative curvature curves instead of the monotone-constrained functions are directly modeled. Functional principal components are used to describe the major modes of variations of curves and allow borrowing strength across curves for improved estimation. A two-step approach and an integrated approach are considered for model fitting. The simulation study shows that the integrated approach is more efficient than separate curve estimation and the two-step approach. The integrated approach also provides more interpretable principle component functions in an application of estimating weekly wind power curves of a wind turbine.

Assessment of the greenness of micellar HPLC method for rapid separation and simultaneous estimation of chlorpheniramine maleate in presence of some co-administered drugs in three pharmaceutical dosage forms using single run

Abstract Sustainable chemistry established one of kind standards to maintain protection of environment through using safer mobile phase composition and/or lower solvent consumption. A fast green micellar HPLC method was developed and applied for the first time aiming at simultaneous determination of chlorpheniramine maleate, one of the most widely used antihistamine in combination with levochlopersatine fenodizoate or dextromethorphan hydrobromide or dexamethasone, in their pure forms, laboratory prepared mixtures and pharmaceutical dosage forms used in alleviating the symptoms of cough resulting from common colds and allergy. The separation was achieved on Kinetex C18 column (100 mm × 4.6 mm i.d., 2.6-μm particle size) using micellar aqueous mobile phase consisting of (30 mM sodium dodecyl sulfate and 50 mM sodium dihydrogen phosphate, pH 5) and ethanol (85:15) with UV detection at 230 nm. The four drugs were successfully separated using isocratic elution in a single run not exceeding 7 min. According to ICH guidelines, the method was confirmed to be linear, accurate and precise over the concentration ranges of 5–60 μg mL−1 for chlorpheniramine maleate, 10–100 μg mL−1 for levocloperastine fenodizoate and dextromethorphan hydrobromide and 5–30 μg mL−1 for dexamethasone. In addition, the greenness of the developed method was assessed using two different tools indicating their least hazardous effect on the environment.

Reducing automation risk through career mobility: Where and for whom?

AbstractAutomation risk prevails less in large cities compared to small cities but little is known about the drivers of this emerging urban phenomenon. A major challenge is that automation risk is quantified by work‐related tasks that allows for measurement through occupation, which is in turn implicitly related to local economic structure and to individual career paths. This paper examines the role of working in cities on changes in automation risk through individual career mobility. Using panel data on Swedish workers, we show that the metropolitan effect of reducing automation risk is mainly induced through inter‐firm job mobility. Separate estimates for different groups show that this effect accrues mostly to native, high‐skilled and male workers.

Estimation of canonical correlation directions: From Gaussian to sub-Gaussian population

Cai and Zhang (2018) established separate perturbation upper bound estimators for canonical correlation directions under centered Gaussian population and some conditions on the minimum singular value σr(S) of a correlation matrix S . They posed an open problem for the optimality of their estimators. In this paper, the optimality of Cai and Zhang’s estimation is firstly proved up to some multiplicated constants. Then motivated by Ma and Li’s work (Ma and Li, 2020), we give an upper bound estimation for centered sub-Gaussian population, and a better estimate for bounded sub-Gaussian population. Finally, all estimates are extended from centered population to non-centered one.

Depth to magnetic basement in the Anambra Basin, Benue Trough of Nigeria from aeromagnetic data: A prelude for hydrocarbon exploration

Data transformation, regional-residual separation, trend analysis, and analytic signal (AS) depth estimation were applied to aeromagnetic data covering the Anambra Basin, which is a major depocenter in the Benue Trough, southeast Nigeria, with the primary objectives of accentuating the attributes of magnetic sources and determining if sufficient sediment thickness exists for hydrocarbon generation, maturation, and expulsion. The application of data transformation techniques (such as map projection, merging, and reduction-to-pole) and regional-residual ensured the computation of a crustal magnetic field that would be suitable for magnetic analyses. Results indicate that the magnetic basement in the basin forms an undulating surface overlain by sediments with an average thickness ranging between 4 and 7.5 km, whereas the maximum thickness reaches 8 km in some areas. This depth range suggests a promising prospect for source-facies maturation and expulsion. We expect that areas in the study area with these appreciable sediment thicknesses, good preservation of graben fill, and suitable areal closures or fault structures would be favorable for hydrocarbon prospectivity.

Flexible parametric copula modeling approaches for clustered survival data.

Copula-based survival regression models, which consist of a copula function and marginal distribution (i.e., marginal survival function), have been widely used for analyzing clustered multivariate survival data. Archimedean copula functions are useful for modeling such dependence. For the likelihood inference, one-stage and two-stage estimation methods have been usually used. The two-stage procedure can give inefficient estimation results because of separate estimation of the marginal and copula's dependence parameters. The more efficient one-stage procedure has been mainly developed under a restrictive parametric assumption of marginal distribution due to complexity of the full likelihood with unknown marginal baseline hazard functions. In this paper, we propose a flexible parametric Archimedean copula modeling approach using a one-stage likelihood procedure. In order to reduce the complexity of the full likelihood, the unknown marginal baseline hazards are modeled based on a cubic M-spline basis function that does not require a specific parametric form. Simulation results demonstrate that the proposed one-stage estimation method gives a consistent estimator and also provides more efficient results over existing one- and two-stage methods. The new method is illustrated with three clinical data sets. The Appendix provides an R function so that the proposed method becomes directly accessible to interested readers.

Association of Wealth With Longevity in US Adults at Midlife

Wealthy adults tend to live longer than those with less wealth. However, a challenge in this area of research has been the reduction of potential confounding by factors associated with the early environment and heritable traits, which could simultaneously affect socioeconomic circumstances in adulthood and health across the life course. To identify the association between net worth at midlife and subsequent all-cause mortality in individuals as well as within siblings and twin pairs. This cohort study conducted a series of analyses using data from the Midlife in the United States (MIDUS) study, an ongoing national study of health and aging. The sample included adults (unrelated individuals, full siblings, and dizygotic and monozygotic twins) aged 20 to 75 years, who participated in wave 1 of the MIDUS study, which occurred from 1994 to 1996. The analyses were conducted between November 16, 2019, and May 18, 2021. Self-reported net worth (total financial assets minus liabilities) at midlife (the middle years of life). All-cause mortality was tracked over nearly 24 years of follow-up, with a censor date of October 31, 2018. Survival models tested the association between net worth and all-cause mortality. Discordant sibling and twin analyses compared longevity within siblings and twin pairs who, given their shared early experiences and genetic backgrounds, were matched on these factors. The full sample comprised 5414 participants, who had a mean (SD) age of 46.7 (12.7) years and included 2766 women (51.1%). Higher net worth was associated with lower mortality risk (hazard ratio [HR], 0.95; 95% CI, 0.94-0.97; P < .001). Among siblings and twin pairs specifically (n = 2490), a similar within-family association was observed between higher net worth and lower mortality (HR, 0.94; 95% CI, 0.91-0.97; P = .001), suggesting that the sibling or twin with more wealth tended to live longer than their co-sibling or co-twin with less wealth. When separate estimates were performed for the subsamples of siblings (HR, 0.94; 95% CI, 0.90-0.97; P = .002), dizygotic twins (HR, 0.94; 95% CI, 0.86-1.02; P = .19), and monozygotic twins (HR, 0.95; 95% CI, 0.87-1.04; P = .34), the within-family estimates of the net worth-mortality association were similar, although the precision of estimates was reduced among twins. This cohort study found that wealth accumulation at midlife was associated with longevity in US adults. Discordant sibling analyses suggested that this association is unlikely to be simply an artifact of early experiences or heritable characteristics shared by families.

Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

Data-driven algorithms are developed to fully automate sensor fault detection in systems governed by underlying physics, with a particular focus on the flight test setting. The proposed machine learning method uses a time series of typical behavior to approximate the evolution of measurements of interest by a linear time-invariant system. Given additional data from related sensors, a Kalman observer is used to maintain a separate real-time estimate of the measurement of interest. Sustained deviation between the measurements and the estimate is used to detect anomalous behavior. A decision tree, informed by integrating other sensor measurement values, is used to determine the amount of deviation required to identify a sensor fault. The method is validated by applying it to three test systems exhibiting various types of sensor faults: commercial flight test data, an unsteady aerodynamics model with dynamic stall, and a model for longitudinal flight dynamics forced by atmospheric turbulence. In the latter two cases, fault detection was tested for several prototypical failure modes. The combination of a learned dynamic model with the automated decision tree accurately detects sensor faults in each case.

Phenology and Fraser River sockeye salmon marine survival

Inspired by the pioneering work of Dr. Bill Peterson who demonstrated the utility of ocean indicators at predicting survival of coho and chinook salmon in the Columbia River, we investigated whether the phenology of primary productivity could explain variable marine survival of Fraser River sockeye salmon. Building on a study that had found a strong correlation between satellite-derived spring chlorophyll concentrations in Queen Charlotte Sound (British Columbia) and smolt survival, we hypothesized that smolt migration phenology could help to explain interannual survival differences among years. Applying a new migration model to 18 years of smolt migration data from Chilko Lake demonstrated that interannual differences in smolt migration timing were organized in up to 3 pulses of abundance with a general trend by the largest peak toward earlier peak migration dates over the time series (1998–2016). Analysis of satellite-derived fluorescence line height data within Queen Charlotte Sound identified 4 productivity domains through which most young sockeye salmon would migrate. Each domain had distinct seasonal productivity patterns. With these data, we were unable to demonstrate significant correlations between spring bloom dates in these domains and smolt marine survival, or between smolt migration timing and marine survival. Having separate survival estimates for each pulse and phenological indicators of the sockeye salmon prey base might improve our ability to test the hypothesis that phenology matters to sockeye salmon in the Queen Charlotte Sound region.

Multiple-echo steady-state (MESS): Extending DESS for joint T2 mapping and chemical-shift corrected water-fat separation.

PurposeTo extend the double echo steady‐state (DESS) sequence to enable chemical‐shift corrected water‐fat separation.MethodsThis study proposes multiple‐echo steady‐state (MESS), a sequence that modifies the readouts of the DESS sequence to acquire two echoes each with bipolar readout gradients with higher readout bandwidth. This enables water‐fat separation and eliminates the need for water‐selective excitation that is often used in combination with DESS, without increasing scan time. An iterative fitting approach was used to perform joint chemical‐shift corrected water‐fat separation and T2 estimation on all four MESS echoes simultaneously. MESS and water‐selective DESS images were acquired for five volunteers, and were compared qualitatively as well as quantitatively on cartilage T2 and thickness measurements. Signal‐to‐noise ratio (SNR) and T2 quantification were evaluated numerically using pseudo‐replications of the acquisition.ResultsThe water‐fat separation provided by MESS was robust and with quality comparable to water‐selective DESS. MESS T2 estimation was similar to DESS, albeit with slightly higher variability. Noise analysis showed that SNR in MESS was comparable to DESS on average, but did exhibit local variations caused by uncertainty in the water‐fat separation.ConclusionIn the same acquisition time as DESS, MESS provides water‐fat separation with comparable SNR in the reconstructed water and fat images. By providing additional image contrasts in addition to the water‐selective DESS images, MESS provides a promising alternative to DESS.

Regularized joint water-fat separation with B0 map estimation in image space for 2D-navigated interleaved EPI based diffusion MRI.

PurposeTo develop a new water–fat separation and B0 estimation algorithm to effectively suppress the multiple resonances of fat signal in EPI. This is especially relevant for DWI where fat is often a confounding factor.MethodsWater–fat separation based on chemical‐shift encoding enables robust fat suppression in routine MRI. However, for EPI the different chemical‐shift displacements of the multiple fat resonances along the phase‐encoding direction can be problematic for conventional separation algorithms. This work proposes a suitable model approximation for EPI under B0 and fat off‐resonance effects, providing a feasible multi‐peak water–fat separation algorithm. Simulations were performed to validate the algorithm. In vivo validation was performed in 6 volunteers, acquiring spin‐echo EPI images in the leg (B0 homogeneous) and head‐neck (B0 inhomogeneous) regions, using a TE‐shifted interleaved EPI sequence with/without diffusion sensitization. The results are numerically and statistically compared with voxel‐independent water–fat separation and fat saturation techniques to demonstrate the performance of the proposed algorithm.ResultsThe reference separation algorithm without the proposed spatial shift correction caused water–fat ambiguities in simulations and in vivo experiments. Some spectrally selective fat saturation approaches also failed to suppress fat in regions with severe B0 inhomogeneities. The proposed algorithm was able to achieve improved fat suppression for DWI data and ADC maps in the head–neck and leg regions.ConclusionThe proposed algorithm shows improved suppression of the multi‐peak fat components in multi‐shot interleaved EPI applications compared to the conventional fat saturation approaches and separation algorithms.

MODIFICATION OF SEPARATE RATIO TYPE EXPONENTIAL ESTIMATOR: A POST-STRATIFICATION APPROACH

In this research, modification of separate ratio type exponential estimator introduced in an earlier study is proposed. Expressions for the bias and mean square error (MSE) of the proposed estimator up to first degree of approximation are derived. The optimum value of the constant which minimize the MSE of the suggested estimator is also obtained. In the same vein, efficiency comparisons between the proposed estimator and some related existing ones under the case of post-stratification is conducted. Empirical studies have been conducted to demonstrate the efficiencies of the suggested estimators over other considered estimators. The proposed MSE and Percentage Relative Efficiency (PRE) were used to evaluate the achievement of the modified estimator.

Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach

Soil texture is an important compositional property where the separate estimates of sand, silt, and clay often does not add up to 100 % when predicted independently using digital soil mapping approach. An alternative is to use Remote sensing (RS) data, however, varying soil surface conditions, satellite pass day, and cloud cover on imagery often limit its estimation accuracies. Here, we examined an ensemble modeling approach to improve the estimation accuracy of soil textural fractions using Sentinel-2 (S2) data. Multispectral images (cloud free) from S2 mission for 63 different acquisition dates (between Jan. 2017 to Dec. 2020) were used as the RS data source. Spectral reflectance data over visible to near-infrared region (350−2500 nm at 1 nm interval) were also collected for each soil sample under laboratory conditions for comparison. Partial-least-square regression (PLSR), random forest (RF) and support vector regression (SVR) models were used to estimate sand, silt, and clay contents for both the laboratory-measured and S2 spectra. Resulting coefficient of determination (R2) and root-mean-squared error (RMSE) values for the laboratory-measured spectra were 0.44 and 14.9 % for sand contents, 0.32 and 16.23 % for silt contents, and 0.69 and 5.5 % for clay contents, respectively. The S2 datasets collected during Apr. 13, 2019 and Nov. 24, 2019 yielded best performance statistics for sand (R2 = 0.50, RMSE = 11.6 %) and silt (R2 = 0.46, RMSE = 5.8 %) contents, respectively. Similarly, the dataset collected on Jan. 3, 2018 yielded best performance for the clay contents (R2 = 0.44, RMSE = 6.9 %). Compared to S2 data of a single acquisition date, combination of S2 data from multiple acquisition dates through ensemble modeling could significantly improve the prediction accuracies for sand (R2 = 0.62, RMSE = 9.9 %), silt (R2 = 0.54, RMSE = 5.4 %), and clay contents (R2 = 0.54, RMSE = 6.5 %). These developed models allowed for the estimation of textural fractions at a resolution of 30 m.