esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.3 Extraction of Image Texture Features  
The spatial texture information of coal hyperspectral images can partially reflect differences in physical properties (e.g., moisture, cohesion) and chemical properties (e.g., ash content, volatile matter) of coal. To analyze the performance of different texture features for coal quality index determination, three texture extraction methods were implemented on coal hyperspectral data and the results were compared.  
Histogram statistics (HS) are commonly utilized in image analysis and processing [44]. In this study, the statistical features of the histogram including mean, variance, skewness, kurtosis, energy, and entropy denoted as HS_M, HS_V, HS_S, HS_K, HS_E1, and HS_E2 were employed as image texture features.  
Gray level co-occurrence matrix (GLCM) captures local gray variation characteristics by quantifying the combination and frequency of grayscale values in pixel pairs, thereby describing image texture [45]. In this study, six GLCM texture measures including contrast, dissimilarity, homogeneity, correlation, energy, and entropy were calculated and denoted as GLCM_C1, GLCM_D, GLCM_H, GLCM_C2, GLCM_E1, and GLCM_E2, respectively. The GLCM was extracted with the gray level N = 256 and the distance D = 1. To maintain rotational invariance, the texture information was computed at four orientation angles (\(\theta\) = 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\)) and averaged as the GLCM.  
Gabor filter (Gabor) is a linear, frequency, and orientation-selective filter based on a gaussian function modulating a sine wave, and is widely used in texture analysis and feature extraction in computer imaging [46]. Texture features can be extracted by the Gabor filter in various directions and scales. In this study, the Gabor filter calculated the average of the six feature values of contrast, dissimilarity, homogeneity, correlation, energy, and entropy in four directions (\(\theta\) = 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\)). The texture features were obtained using Gabor filters with frequencies \(F\) = 0.3, 0.6, 0.9 and denoted as \(F\_C1\), \(F\_D\), \(F\_H\), \(F\_C2\), \(F\_E1\), and \(F\_E2\), respectively.
Chem._**2021**, _885_, 115085. * Chromatography (2021)**6. Chromatography, Mass Spectrometry 6.1. _Challenges in Cannabis Testing_**J. P. Hopkins 1,*,* Aimone (2019)**J. P. Hopkins 1,*,* Alamone (2020)**J. P. Hopkins 4,*,* Alamone (2020)**J.  
syndrome and Dravet syndrome [2], which cause severe and frequent seizures which are often not manageable with traditional anti-epileptic medications, but also for chronic pain [3], terminal cancer [4], and multiple sclerosis [5]. National regulatory drug agencies have not yet agreed the testing required for cannabis, but generally, testing controls gross contamination, pesticides, mycotoxins, microbial contamination, and residual solvents, as well as cannabinoid contents and ratios. Often, recreational cannabis is also tested for its terpene profile which can reflect a user's subjective enjoyment, but these terpene fractions are of interest to medical researchers as well. The analysis of cannabis uses chromatographic techniques to separate isobaric components prior to analysis by mass spectrometry. Cannabis matrix is hugely complex--much more so than fruit and vegetables, for example, which also require similar testing for consumer safety. A particular challenge is the rapid contamination and resulting decline in sensitivity of mass spectrometers. We describe a mass spectrometer which does not suffer from sensitivity declines, as it does not use any voltages in the source and ion-transfer regions, instead using a novel laminar gas flow, combined with heated surfaces in the source and ion transfer regions to prevent contamination and resultant sensitivity loss. We present real-world data of the applicability of this technology in the analysis of very crude cannabis extracts. ## References  
* Bridgeman and Abazia (2017) Bridgeman, M.B. ; Abazia, D.T. Medicinal Cannabis: History, Pharmacology, and Implications for the Acute Care Setting. _Pharm. Ther._**2017**, 42, 1-9. * Epidyolex-Product Information. Available online: [[https://www.ema.europa.eu/en/medicines/human/EPAR/epidyolex#product-information-section](https://www.ema.europa.eu/en/medicines/human/EPAR/epidyolex#product-information-section)]([https://www.ema.europa.eu/en/medicines/human/EPAR/epidyolex#product-information-section](https://www.ema.europa.eu/en/medicines/human/EPAR/epidyolex#product-information-section)) (accessed on 8 November 2021). * Romero-Sandoval et al. (2017) Romero-Sandoval, E.A. ; Kolano, A.L. ; Alvarado-Vazquez, P.A. Cannabis and Cannabis for Chronic Pain. _Curr. Rheumatol. Rep._**2017**, 19, 67. * Daris et al. (2019) Daris, B.; Tancer Verboten, M.; Knez, Z.; Ferk, P. Cannabinoids in cancer treatment: Therapeutic potential and legislation. _Bosn. J. Basic Med. Sci._**2019**, 19, 14-23. * Gustavsen et al. (2021) Gustavsen, S.; Sondergaard, H.B. ; Linnet, K.; Thomsen, R.; Rasmussen, B.S. ; Sorensen, P.S. ; Sellebjerg, F.; Oturai, A.B. Safety and efficacy of low-dose medical cannabis oils in multiple sclerosis. _Mult. Scler. Relat. Disord._**2021**, 48, 102708.
## 3 Implementation and Experiments
### Heuristic vs. Exact Solution
One of the attributes, \"work class\" or \"occupation\", alone is useless for her, so either both should be suppressed or none of them. 4. Since she assumes discrimination because of age, sex, and race, at most one of these attributes should be suppressed. We generated the set of pattern vectors fulfilling her constraints (plus the all-\(\star\)-vector to remove outliers) and applied our greedy heuristic and the ILP implementation for \(k\in\{2,3\ldots,10,25,50,75,100\}\). The ILP implementation took up to six minutes to compute one single instance, whereas the greedy heuristic needs always less than one second. Moreover, the solution quality of the heuristic results is surprisingly good. The number of suppressed entries is at most \(1.31\) times the optimum. The ILP is slightly better concerning the measures \(\#h  
We introduced a promising approach to combinatorial data anonymization by enhancing the basic \(k\)-Anonymity problem with user-provided \"suppression patterns.\" It seems feasible to extend our model with weights on the attributes, thus making user influence on the anonymization process even more specific. A natural next step is to extend our model by replacing \(k\)-Anonymity by more refined data privacy concepts, such as domain generalization hierarchies [40], \(p\)-sensitivity [41], \(\ell\)-diversity [23] and \(t\)-closeness [38]. On the theoretical side, we did no extensive analysis of the polynomial-time approximability of Pattern-Guided \(k\)-Anonymity. Are there provably good approximation algorithms for Pattern-Guided \(k\)-Anonymity? Concerning exact solutions, are there further polynomial-time solvable special cases beyond Pattern-Guided \(2\)-Anonymity? On the experimental side, several issues remain to be attacked. For instance, we used integer linear programming in a fairly straightforward way almost without any tuning tricks (e.g., using the heuristic solution or \"standard heuristics\" for speeding up integer linear program solving). It also remains to perform tests comparing our heuristic algorithm against methods other than Mondrian (unfortunately, for the others, no source code seems to be freely available).
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
###### Abstract
Because the sparsity in the precision matrix implies the conditional independence of feature variables, the GLASSO can be understood as a de-noising operator to reflect the conditional independence structure in the underlying model. We focus on only the within-class covariance/precision, which is based on our assumption that empirical within-class covariance/precision has larger errors than empirical between-class covariance, because collecting sufficient utterances from the same class is relatively more difficult than collecting similar numbers of utterances from various classes. Therefore, it is expected that regularizing the within-class covariance/precision matrix may be more effective in terms of performance. The remainder of this paper is organized as follows. Section 2 outlines the preliminaries related to our research. Section 3 describes the GLASSO and related theory. Section 4 introduces the proposed method. Section 5 presents the experiments and their results. Section 6 discusses some issues about text-independent speaker verification and one more option for our solution. Finally, Section 7 concludes the paper.
## 5 International Standards and Current Regulations
### Comparison of Experimental and Theoretical Results  
The comparison of experimental and theoretical results reveals valuable insights into the structural performance of AW-based RC beams. Variations in predictive accuracy often stem from material properties, reinforcement configurations, and the complexity of theoretical models. For instance,  
* Coconut aggregate beams [26] showed up to a 10% deviation in failure load predictions, highlighting the need for further refinement in theoretical models for natural aggregate replacements.
* PO shell foamed concrete beams [69] exhibited variations in shear force prediction depending on reinforcement type and ratio, with deviations ranging between 5% and 15%.
* Rice straw Ash-RC beams [88] demonstrated deviations in flexural strength predictions, with experimental values being approximately 10% higher than theoretical predictions for certain reinforcement ratios.  
* Banana fiber-reinforced beams [80; 91] indicated consistent discrepancies in failure torque, with experimental results surpassing theoretical predictions by up to 20%, emphasizing the impact of fiber properties on load-bearing capacity.
* Palm kernel shell concrete without shear reinforcement [77] displayed deviations in shear force prediction, with experimental results nearly 25% higher than theoretical estimates, suggesting the need for incorporating material-specific calibration factors in theoretical equations.
* PO concrete beams with steel fibers [83] demonstrated deviations in predicted crack torque values of up to 15%, underscoring the importance of accurately modeling fiber-concrete interactions.  
These comparisons highlight the complexity of modeling AW-based concrete and underline the necessity for advancing theoretical frameworks to better reflect the unique properties and behaviors of these innovative materials.
###### Abstract  
Salt mining and shrimp farming have been practiced in the Non Thai district and the surrounding areas for more than 30 years, creating saline soil problems. To solve the soil salinity problem, soil salinity prediction and mapping utilizing the electromagnetic induction method (EMI) and spatial interpolation methods were examined in the Non Thai district, Nakhon Ratchasima province, Thailand. The research objectives were (1) to predict soil salinity using spatial interpolation methods and (2) to identify a suitable spatial interpolation method for soil salinity severity mapping. The research methodology consisted of five steps: apparent electrical conductivity (ECa) measurement using an electromagnetic induction (EMI) method; in situ soil sample collection and electrical conductivity of the saturated soil paste extract (ECe) measurement; soil electrical conductivity estimation using linear regression analysis (LRA); soil salinity prediction and accuracy assessment; and soil salinity severity classification and overlay analysis with relevant data. The result of LRA showed a strong positive relationship between ECe and ECa. The correlation coefficient (R) values of a horizontal measuring mode (HH) and a vertical measuring mode (VV) were 0.873 to 0.861, respectively. Four selected interpolation methods--Irverse Distance Weighting (IDW), Ordinary Kriging (OK), Ordinary CoKriging (OCK) with soil moisture content, and Regression Kriging (RK) without covariable factor--provided slightly different patterns of soil salinity prediction with HH and VV modes. The mean values of the ECe prediction from the four methods at the district level varied from 2156.02 to 2293.25 mS/m for HH mode and from 2377.38 to 2401.41 mS/m for VV mode. Based on the accuracy assessment with the rank-sum technique, the OCK is a suitable interpolation method for soil salinity prediction for HH mode. At the same time, the IDW is suitable for soil salinity prediction for the VV mode. The dominant soil salinity severity classes of the two measuring modes using suitable spatial interpolation methods were strongly and very strongly saline. Consequently, the developed research methodology can be applied to conduct soil salinity surveys to reduce costs and save time in other areas by government agencies in Thailand. Nevertheless, to apply the EMI method for soil salinity survey, the users should understand the principle of EMI and how to calibrate and operate the EM device properly for accurate ECa measurement.  
digital soil mapping; spatial interpolation method; soil salinity severity classification; electromagnetic induction method; EM38 sensor; Non Thai; Nakhon Ratchasima +
Footnote †: journal: Applied Sciences
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Temporal Variability  
Temporal variability delineates any variance of the statistical indices computed on the VIs partially or entirely explained by the temporal factor: vintage. The temporal variability represents an ordinary assessment to identify stable and unstable items affecting the crop's variability and performance [22; 44]. Hence, the temporal variability evaluation focuses on the ANOVA output listed in the \"vintage\" column in the tables listed among the results.  
Additionally, the actual contribution given by each year is depicted in the boxplots. The ANOVA confirmed the vintage effect on the VIs mean and CV values (Table 5) and the VIs development along the grapevine growing seasons (Table 6) [9]. Figures 9 and 14 refer the typicity of each vintage translated in VIs average values and VIs CV, indeed. Vintages are distinguished from each other for several reasons. In some cases, the temporal variability could overcome the spatial variability, and the climate assumes the main role, especially in non-irrigated crops [44, 45]. Additionally, human contributions should not be neglected. The vineyard management, particularly, the canopy arrangement and the pruning methods, are some main drivers boosting the temporal variability [22].  
Concerning the within-vineyard variability, vintage did not affect the main specific semivariogram's indices, except for the nugget. The spatial pattern of a vineyard variability is usually preserved across several vintages [4]. Except for the random effect, the nugget, which could be boosted from unpredictable causes, sustains the singularity of each vintage for grape and wine production. Hence, except for the structure of within-vineyard variability, the temporal effect was confirmed as one of the most critical drivers affecting the vineyard's remote monitoring through VIs. Based on the above-mentioned deductions, it is essential to analyse separately each vintage, focusing almost on the dispersion indices rather than the absolute values.
## 2 Coupled-Mode Equations  
The propagation of optical waves in asymmetric nonlinear couplers is governed by a pair of linearly-coupled nonlinear Schrodinger equations [22; 48];  
\[i\frac{\partial q_{1}}{\partial z}+i\beta_{11}\frac{\partial q_{ 1}}{\partial t}-\frac{\beta_{21}}{2}\frac{\partial^{2}q_{1}}{\partial t^{2}}+ \gamma_{1}|q_{1}|^{2}q_{1}+cq_{2}+\delta_{a}q_{1}=0, \tag{1}\] \[i\frac{\partial q_{2}}{\partial z}+i\beta_{12}\frac{\partial q_{ 2}}{\partial t}-\frac{\beta_{22}}{2}\frac{\partial^{2}q_{2}}{\partial t^{2}}+ \gamma_{2}|q_{2}|^{2}q_{2}+cq_{1}-\delta_{a}q_{2}=0, \tag{2}\]  
where \(q_{1}\), \(q_{2}\) and \(\gamma_{1}\), \(\gamma_{2}\) are amplitudes of slowly varying envelopes and nonlinearity coefficients in the two cores of the ANLDC, while \(\delta_{a}\) accounts for the phase-velocity difference between the cores. Further, \(\beta_{1j}\equiv 1/v_{jj}\) and \(\beta_{2j}\) (\(j=1,2\)) are the group-velocity and GVD parameters in the \(j\)-th core, and \(c\) is the coefficient of the linear coupling between the cores.  
To derive normalized coupled equations, we perform rescaling,  
\[q_{j}\equiv(\gamma_{1}L_{D})^{1/2}u_{j},\tau\equiv t-\beta_{11}z/T_{0},\xi \equiv z/L_{D}, \tag{3}\]  
where \(L_{D}=T_{0}^{2}/\left|\beta_{21}\right|\) is the dispersion length corresponding to a characteristic pulse width \(T_{0}\), the result being:  
\[i\frac{\partial u_{1}}{\partial\xi}+\frac{\sigma_{1}}{2}\frac{ \partial^{2}u_{1}}{\partial\tau^{2}}+\left|u_{1}\right|^{2}u_{1}+\kappa u_{2} +\chi u_{1}=0, \tag{4}\] \[i\frac{\partial u_{2}}{\partial\xi}+i\rho\frac{\partial u_{2}} {\partial\tau}+\kappa\frac{\sigma_{1}}{2}\frac{\partial^{2}u_{2}}{\partial \tau^{2}}+\Gamma\left|u_{2}\right|^{2}u_{2}+\kappa u_{1}-\chi u_{2}=0. \tag{5}\]  
Here, the normalized coupling coefficient is \(\kappa\equiv cL_{D}\), \(\sigma_{1}=+1\) and \(-1\) correspond to the anomalous and normal GVD in the first core, while the normalized phase- and group-velocity mismatches and differences in the GVD and effective mode areas are represented, respectively, by:  
\[\chi=\delta_{a}L_{D},\,\rho=(\beta_{12}-\beta_{11})L_{D}/T_{0},\,\alpha=\beta_ {22}/\beta_{21},\,\Gamma=\gamma_{2}/\gamma_{1}. \tag{6}\]  
To design such asymmetric fiber couplers and to calculate the asymmetry coefficients, we adopt physical parameters for the first core, corresponding to standard nonlinear directional couplers, as follows: \(\beta_{21}=0.02\) ps\({}^{2}\)/m, \(\gamma_{1}=10\) kW\({}^{-1}\)/m, \(T_{D}=50\) fs at wavelength \(\lambda=1.5\) um. Physical parameters for the second core are then determined by normalized coefficients, according to the design outlined above. Furthermore, in terms of this normalized system, we will call \"bar\" and \"cross\" the cores corresponding to Equations (4) and (5), respectively.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Methodology--Model Development
#### 2.3.4 Computation of Bio-Optical Parameters
Finally, the diffuse attenuation coefficient \(K_{d}(\lambda)\) is determined from Equation (14):  
\[K_{d}(\lambda)=m_{o}\times\alpha(\lambda)+m_{1}\times(1-m_{2} \times exp(-m_{3}\times\alpha(\lambda)))\times b_{b}(\lambda) \tag{14}\]  
where \(m_{o}\cong 1+0.005\times\theta_{s}\), in which \(\theta_{s}\) is the solar zenith angle; and \(m_{1}\), \(m_{2}\), and \(m_{3}\) are model constants with values of 4.18, 0.52, and 10.8, respectively, according to [PERSON] et al. (2002). Equation (14) is then transformed to Equation (15) as follows:  
\[K_{d}(\lambda)=(1+0.005\times\theta_{s})\times\alpha(\lambda)+4.18\times(1-0.5 2\times exp(-10.8\times\alpha(\lambda)))\times b_{b}(\lambda) \tag{15}\]
## 3 Results
### Experiment (3): Parameter-Based Absolute Accuracy for Single Strip Measurements  
The third experiment is an assessment based on the estimated table planes from Experiment (2). Compared to the point-based evaluation of the point clouds, this approach uses a parameter-based evaluation with the estimated plane parameters given in Equation (2). The normal vector defining the orientation of each plane in space is compared to the reference planes from the TLS point cloud. For better interpretation, the tilt is divided into a tilt along and one across the direction of flight. The computation is done using the known heading during the flight. With this tilt parameter, any misalignment of the UAV point cloud could be detected, especially due to errors in the bore-sight angles. The summarized results for the four flights and all included tables are shown in Figure 10 with the tilt across and in the direction of flight.  
The mean tilt across flight direction is \(-0.014^{\circ}\) with a RMSE of \(0.085^{\circ}\) and the mean tilt in flight direction is \(-0.052^{\circ}\) with a RMSE of \(0.052^{\circ}\). The tilt estimates for the precision are nearly the same and the standard deviation is within the same magnitude.  
With these results, various conclusions can be drawn about the precision and absolute accuracy. Due to the scan configuration, the roll angle and the corresponding calibration parameter can be analyzed perfectly. A systematic error in the roll angle would lead to an offset respectively tilt of the point clouds from the flights in the opposite direction. With the histogram of the tilt parameter across the flight direction, a systematic error can be excluded. The small systematic offset with the mean value of \(-0.052^{\circ}\) can be classified as non-significant. The standard deviation for both tilt directions is another measure of the high precision of the system for multiple flights. Based on the table size, a tilt of \(0.1^{\circ}\) would  
Figure 9: Height difference of estimated table heights for four flights.  
result in a height offset of less than 0.2 cm, which is within the noise level of the UAV-based laser scanning system and also the limitation imposed by the accuracy of the TLS reference.
## 4 Experiments
### Experiment I: Validation in CERN LHC Mockup  
The HRI cooperative behaviours module has been validated at the CERN experimental area [40] using two CERNBOT robots with the one-top-arm configuration in order to pick up and transport a pipe in a synchronized way (see Figure 14) by having a single operator in the loop (see Figure 1 and Section 4.5).  
For this experiment, the cooperative behaviours create leader-follower control commands that are activated by the operator via the mission plan module. The operator interacts with the system by moving the leading platform in velocity. The sequence of the mission plan and user interface interaction can be seen in Figure 15. Table 1 shows a comparison between the time-based efficiencies of the task with and without the cooperative behaviours. It should be noted that the operator was able to complete the task without programmed behaviours as well. Moreover, having synchronisation between the two platforms during the transport phase resulted in a much safer operation.