esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper investigates the stability of SRM slopes and the large deformation process after failure using the material point strength reduction method. In this study, the comparison with pure soil slopes is emphasized, and the exact geometry of isotropic soil slopes as SRM slopes is established. The same numerical simulation is carried out for them, and the following conclusions are obtained.  
1. The safety coefficient of SRM slopes is higher than that of homogeneous soil slopes, i.e., stones are beneficial to maintain the stability of slopes. In this paper, the safety factor of pure soil slope is 1.4, and the maximum displacement of SRM slope at this discount factor has no sudden shift and is basically equal to 0. Until the reduction factor equals 1.7, the SRM slope has displacement, but the value is small, and the slope is still stable. Finally, the SRM slope is destabilized and destroyed when the reduction factor equals 1.8.
2. Before the reduction factor reaches the safety factor of the slope, a plastic zone has been formed in the slope from the bottom to the top of the slope. However, at this time, neither the pure soil slope nor the SRM slope has been damaged. When the reduction factor increases to the safety factor of the slopes or is even more significant than the safety factor, the slopes are immediately damaged, and the pure soil slopes can quickly restore stability after the failure. In contrast, the sliding of the SRM slopes lasts longer.
3. In extreme conditions, i.e., when the reduction factors are much more significant than the safety factors of slopes, the maximum displacement of pure soil slopes is more sensitive to this, while the maximum displacement of SRM slopes is less affected by the increase in reduction factors; in addition, the plastic zone of pure soil slopes basically does not change when the reduction factors are significant, either in terms of the extent of the plastic zone or the thickness of the sliding zone, while the plastic zone of SRM slopes develops towards the interior of the slope when the reduction factors are enormous.  
Conceptualization, Z.X. and C.L.; methodology, C.L.; software, F.F.; validation, F.F.; investigation, FW.; writing--original draft preparation, F.W.; writing--review and editing, F.W. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the Science and technology program in the science and technology department of Guizhou province ([2021] 486).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data that support the findings of this study are available from the corresponding author, [[PERSON]], upon reasonable request.  
The authors declare no conflict of interest.
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## Appendix A Formulation of the \(C_{ji}\) Matrix for Planar Transducers  
In Equation (8), it can be seen that \(\mathbf{g}_{ji}\) can be replaced with a matrix product between an \(N_{y}\times N_{z}N_{x}\) matrix \(\mathbf{C}_{ji}\) and the vector \(\boldsymbol{\gamma}\). As each column \(m\) of the matrix \(\mathbf{C}_{ji}\) is related to a point in \(\Omega\) through Equation (6), for each point \(\vec{r}=\) (x,z), the entries along only column \(m\) of \(\mathbf{C}_{ji}\) will be modified, as follows: \(m\) will be null everywhere except at line \(\tau\) that corresponds to time \(\tau/f_{s}=\lfloor(t-t_{0})f_{s}\rceil/f_{s}\) when the echo generated in \(\vec{r}\) arrives at \(\mathbf{v}_{i}\) (\(\lfloor\)\(\rceil\) represents the nearest integer function). Ideally (if \(\tau/f_{s}=t-t_{0}\)), the entry \(\mathbf{C}_{ji}[\tau,m]\) will take the value given by the geometric spreading term \(1/(4\pi^{2}d_{u}(\vec{r})d_{v}(\vec{r}))\) of the wave amplitude. However, as in most cases \(\tau/f_{s}\
eq t-t_{0}\), we propose to extrapolate the term \(1/(4\pi^{2}d_{uj}(\vec{r})d_{vi}(\vec{r}))\) into two entries of \(\mathbf{C}_{ji}[\tau,m]\), as follows:  
\[\begin{split}\mathbf{C}_{ji}[\tau,m]&=\sum\limits_ {p=1}^{N}\sum\limits_{u=1}^{N}\mathbf{C}_{\mathbf{u}_{y}i}[\tau,m]\\ \text{with:}&\mathbf{C}_{\mathbf{u}_{y}i}[\tau,m]= \begin{cases}\frac{a}{4\pi^{2}d_{uj}(\vec{r})d_{ui}(\vec{r})},&\text{for }\tau=\lfloor\frac{d_{uj}(\vec{r})+d_{uj}(\vec{r})}{c}f_{s} \rfloor\\ \frac{\beta}{4\pi^{2}d_{uj}(\vec{r})d_{uj}(\vec{r})},&\text{for }\tau=\lceil\frac{d_{uj}(\vec{r})+d_{uj}(\vec{r})}{c}f_{s} \rceil\\ 0,&\text{elsewhere}\end{cases}\end{split} \tag{1}\]  
where \(\lceil\) is the ceiling function. Linear interpolation, nearest-neighbor, and ceiling functions were compared. The lowest mean square error (compared to the reference using Field II) was achieved using the ceiling function. In Equation (1), the extrapolation coefficients \(\alpha\) and \(\beta\) are chosen in such a way as to minimize the echo discretization error, and they are computed using the following expression:  
\[\begin{split}&\text{argmin}_{(\alpha,\beta)}\bigg{\|}e(t)* \Big{[}\delta\big{(}t-\frac{d_{uj}(\vec{r})+d_{uj}(\vec{r})}{c}\big{)}-\\ &\big{(}\alpha\delta(t-\tau)+\beta\delta(t-\tau-\frac{1}{f_{s}}) \big{)}\Big{]}\bigg{\|}_{2}\end{split} \tag{2}\]
# Orifice versus Converging-Nozzle Grid Turbulence: A Wavelet Perspective
## 4 Results
### Wavelet Analysis  
Owing to its irregular nature, the flow turbulence characteristics fluctuate randomly in space and time domains, encompassing a wide range of eddying scales. Since the Fourier spectrum depicted in Figure 5 is restricted to the frequency domain, it is unable to identify or characterise time and space intermittencies of localised turbulent structures. Wavelet analysis decomposes a time series over a time-frequency space, thus enabling a better and more effective scrutiny of signal information than Fourier analysis. Wavelet transform can identify different frequency components of signals with sharper time resolution, disclosing the evolution of coherent structures in time. Furthermore, unlike the Fourier spectrum, the wavelet spectrum can handle local discontinuities existing in the flow field without having an effect on the entire spectrum [15].  
To extract the signal characteristics accurately, it is very important to identify the best convolution between the signal and the wavelet function, often referred to as the \"mother wavelet\". The mother wavelet is capable of deriving variations in the signal since it has a zero-mean value [20]. Numerous wavelet functions exist having a wave-like oscillatory construction which is suitable for multifarious applications. Although there is no standard technique for selecting the mother wavelet, one often used strategy is to employ a function with the same structure as the input signal. Various wavelet functions may be applied to the signal and the results compared to identify which one is optimal for a certain application [20; 34]. In the present study, the Daubechies basis function (db7) and the Morlet wavelet function are chosen as the mother wavelets for discrete and continuous wavelet transform, respectively. The selection of these mother wavelets is based on their lucidity in presenting the trend of the signal and previous turbulence studies [20; 34; 35].
# Green Synthesis Method and Application of NaP
## Results and Discussion
### The NaP Zeolite to Remove Ammonia Nitrogen
#### 3.7.3 Influence of Reaction Time  
According to the curve of adsorption time and removal rate in Figure 9, it can be seen that NaP zeolite is fast for adsorption of ammonia nitrogen. When the time exceeds 180 min, the adsorption reaches equilibrium and the removal rate reaches over 92.67%. At this point, zeolite adsorption NH4\({}^{+}\) reaches saturation, there will be ion exchange and adsorption equilibrium in the solution, even if the contact time is extended, the removal rate cannot be significantly increased [51]. Therefore, 180 min was selected as the optimal adsorption time.  
Figure 8: Effect of pH on ammonia nitrogen removal rate (adsorbent dosage: 20 g/L; contact time: 180 min).  
Figure 7: Effect of adsorbent dosage on Removal of ammonia nitrogen (pH = 7; contact time: 180 min).  
Through the analysis of the above results, we can conclude that the dosage of NaP zeolite is directly proportional to the removal rate, and has the characteristics of fast adsorption and slow equilibrium. The pH value of the solution has a great effect on the removal of ammonia nitrogen by zeolite, and the removal effect is best under neutral conditions. The best adsorption parameters were: zeolite dosage of 20 g/L, solution pH = 7, and adsorption time of 180 min.
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 5 Discussion  
This chapter introduces a dynamic target detection and tracking method that utilizes adaptive clustering and multiple classifiers. By implementing a target feature responder based on K-means clustering, adaptive classification training can be conducted according to the distinct main features of the refueling drogue in the training set. The detection and tracking outcomes from multiple classifiers are integrated and refined through a decision function, with the scorer being updated in real time based on depth estimation data and the motion status of the refueling drogue's outer ring. Additionally, the target state determination module identifies and selects the most suitable main classifier for optimal target detection.  
Subsequently, by employing a localization region completion mechanism, the method can estimate missing target sections using monocular image data and boundary constraints, facilitating emergency avoidance maneuvers in aerospace applications. The efficacy and reliability of the proposed method are substantiated on a digital simulation platform, which includes visual simulation software and physical flight experiments with unmanned aerial vehicles. Experimental data, along with comparisons to various tracking algorithms, confirm the high performance of this method, achieving an accuracy of 85.1% and a success rate of 95.2%. These data underscore both the effectiveness and the dependability of the approach.
Achieving an optimal balance between satellite geometry and computational efficiency is particularly difficult in dynamic environments, where satellite visibility is short-lived and time-constrained. To overcome the spatiotemporal constraints of dynamic LEO satellite-based opportunistic navigation, this paper proposes a multi-objective satellite selection model using a swarm intelligence optimization algorithm--the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). The model integrates opportunity signals from multiple LEO satellite constellations under dynamic conditions. By tracking and predicting satellite motion trajectories, it comprehensively considers factors such as Doppler geometric precision, signal strength, remaining visibility time, observation elevation angle, and receiver sensitivity. This multi-attribute optimization facilitates satellite selection, enabling the receiving terminal to dynamically and automatically fuse and switch signals based on satellite cognition. Consequently, the approach iteratively updates the solution by determining a guiding agent and archiving non-dominated solutions, ultimately identifying the satellite combination with the minimum DGDOP and the fewest satellites. This enables an optimal balance between satellite geometry and computational load under dynamic conditions. In our study, a dynamic application scenario is established to integrate observations from the Iridium, Iridium NEXT, Orbcomm, and Starlink constellations. A comparative analysis of satellite selection results is conducted using various optimization algorithms for multi-attribute decision-making. The results demonstrate that the NSWOA-based multi-attribute decision-making algorithm outperformed other methods in terms of optimization accuracy and computational efficiency. Building on this, a range-based positioning experiment using Doppler shifts from LEO satellite signals is conducted. The results show that the real-time performance and robustness of the navigation and positioning system based on the multi-attribute optimization satellite selection algorithm were significantly superior to conventional LEO satellite-based opportunistic navigation methods. Therefore, the proposed NSWOA-based multi-attribute satellite selection algorithm effectively enhances the real-time performance and robustness of multi-constellation LEO satellite-based dynamic opportunistic navigation.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 2 Materials and Methods
### Method
#### 2.1.2 Validation of Estimated GLASS GPP and NPP  
To assess the performance of the MuSyQ-NPP algorithm, FLUXNET GPP and BigFoot NPP were used to validate the GLASS GPP and NPP estimates. Specifically, eight-day GLASS GPP estimates were compared with ground level eight-day FLUXNET GPP, and the average GLASS NPP estimates in 2004, 2008 and 2012 at each Bigfoot site were compared with ground level yearly BigFoot NPP for 2000-2004.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
###### Abstract  
The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem.  
unit commitment (UC); competitive swarm optimizer; binary optimization Article
## 2 Background
### State Estimation
The \(P\) matrix is obtained based on the Jacobian matrix \(H=\frac{\partial f}{\partial x}\) and measurements weight \(W\) calculated as follows:  
\[P=H(H^{T}WH)^{-1}H^{T}W. \tag{5}\]  
Hence, the error in (3) is then composed by using the Innovation Index in (4) to obtain the Composed Measurement Error \(CME\) in its normalized form for each measurement \(i\) as follows:  
\[CME_{i}^{N}=\frac{r_{i}}{\sigma_{i}}\Bigg{(}\sqrt{1+\frac{1}{II_{i}^{2}}} \Bigg{)}. \tag{6}\]  
where \(r_{i}\) is the \(i\)th measurement mismatch which is the detectable part of the error, and \(\sigma_{i}\) is the standard deviation of the \(i\)th measurement. Therefore, the minimization problem in (2) should minimize the composed error in (6) instead of the residual [21].
## 4 System Design  
### Physical Layer: Data Collection  
There are three main functions of the present system: (i) gas monitoring and outlier detection for event identification; (ii) miner tracking and (iii) cloud computing. Acquiring data for air quality and miner tracking from an underground mine requires hardware such as SNs, RNs, gateways and MNs, which are carried by miners. Each hardware runs its own specific algorithm and communicates. Hardware design and power requirements vary greatly for each category. A detailed description of each type of hardware is given in the following subsections.
## 2 Description of Experimental Program
### Accelerated Carbonation Test  
The major object of this study is the effect of pozzolanic materials on carbonation resistance. For this, accelerated carbonation tests were performed. Carbonation is the chemical reaction wherein CO  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Composition** & **Fly Ash (FA)** & **Silica Fume (SF)** & **Nanosilica (NS)** \\  & _w/w (\%)_ & _w/w (\%)_ & _w/w (\%)_ \\ \hline Silicon dioxide (SiO\({}_{2}\)) & 61.18 & 97.38 & 98.82 \\ Aluminum oxide (Al2O\({}_{3}\)) & 19.47 & 0.37 & 0.08 \\ Iron oxide (Fe\({}_{2}\)O\({}_{3}\)) & 6.44 & 0.20 & 0.01 \\ Calcium oxide (CaO) & 4.51 & 0.41 & 0.02 \\ Magnesium oxide (MgO) & 0.07 & 0.29 & 0.0 \\ Sodium oxide (Na\({}_{2}\)O) & 2.30 & 0.17 & 0.84 \\ Potassium oxide (K\({}_{2}\)O) & 1.59 & 0.28 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Chemical compositions of pozzolanic materials.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & **W/B** & \multicolumn{8}{c}{**Unit Weight (kg/m\({}^{3}\))**} \\ \cline{2-10}  & **(\%)** & **Water** & **Cement** & **Fly Ash** & **Silica** & **Nano** & **Sall** & **Crushed** & **General** & **Chemical** \\  & **(\%)** & **Water** & **Cement** & **Fly Ash** & **Fume** & **Silica** & **Nano** & **Sand** & **Sand** & **Gravel** & **Admixture** \\ \hline OPC & 36.2 & 170 & 470 & - & - & - & 452 & 301 & 939 & 3.76 \\ FA15 & 36.2 & 170 & 400 & 71 & - & - & 445 & 297 & 925 & 3.76 \\ SF35 & 36.2 & 170 & 454 & - & 16 & - & 450 & 300 & 936 & 3.76 \\ NS20 & 36.2 & 170 & 461 & - & - & 9 & 448 & 299 & 931 & 3.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mixture design.  
gas diffuses into the concrete and reacts with Ca(OH)2 in the cement paste to form calcium carbonate as [18; 19]  
\[\ce{Ca(OH)2}+\ce{CO2}\ \rightarrow\ce{CaCO3}+\ce{H2O}. \tag{1}\]  
Carbonation of the concrete reduces its pH and removes the protective oxide film on the reinforcement steel, which can result in the corrosion of the reinforcement, and, therefore, must be avoided. The accelerated carbonation test was conducted based on the Korean Standard (KS F 2584) [20], as shown in Figure 1. The test specimen was sealed with epoxy coating except for the measurement section (both sides), and the carbonation depth was measured at one, two, four, and eight weeks after 28-day water curing by spraying with a 1% phenolphthalein solution. The test was performed using gas with a 5% CO2 concentration.
# Lateral Dynamic Response of Helical Pile in Viscoelastic Foundation Considering Shear Deformation
## 3 Governing Equations and Their Solutions
### Dynamic Equation of Helical Pile  
[PERSON] [47] modified the Euler beam theory and proposed a more accurate beam deformation theory which can consider the effects of shear deformation and rotational inertia on the lateral vibration characteristics of the pile. Furthermore, [PERSON] [48] found that the influence of rotational inertia of the beam cross-section in [PERSON]'s equation on the beam vibration characteristic was so small that it would not be considered in this paper. Figure 3 shows the schematic of the pile-soil interaction model, in which \(\varphi\) represents the rotation angle of the pile cross-section, \(\alpha\) denotes the inclination angle of the elastic axis, and \(\beta\) means the shear deformation angle, respectively.  
To solve the governing equation more conveniently and ensure the universality of the results, this article proposes the following assumptions:  
1. The pile's surrounding soil is a homogeneous, isotropic, and viscoelastic medium. Therefore, the stiffness and damping coefficients of the soil layers are both constants along the vertical direction.  
Figure 2: Cross-section diagram of helix.  
2. The pile body is a combination of circular and rectangular sections, and only the bending deformation of the pile is considered after simplification.
3. The pile-soil system is subjected to small deformations and strains during lateral vibration, and the longitudinal displacement of the pile's surrounding soil is ignored.
4. No relative sliding occurs at the pile-soil interface.
5. The influence of pile cap is not considered.
6. The harmonic excitation acts horizontally on the pile top.  
Utilizing the Timoshenko beam theory and combining it with the assumptions, the dynamic equilibrium equation of the helical pile can be derived as  
\[\left\{\begin{array}{l}k^{\prime}A_{\mathrm{P}}G_{\mathrm{P}}[\frac{\partial \varphi(z,t)}{\partial z}-\frac{\partial^{2}u(z,t)}{\partial z^{2}}]+k_{x} \cdot u(z,t)+\\ c_{x}\cdot\frac{\partial u(z,t)}{\partial t}+m_{\mathrm{P}}\cdot\frac{ \partial^{2}u(z,t)}{\partial t^{2}}=0\\ E_{\mathrm{P}}^{\prime}l_{\mathrm{P}}^{\prime}\cdot\frac{\partial^{2}\varphi( z,t)}{\partial z^{2}}+k^{\prime}A_{\mathrm{P}}G_{\mathrm{P}}[\frac{\partial u(z,t)}{ \partial z}\\ -\varphi(z,t)]=0\end{array}\right., \tag{7}\]  
where \(u(z,t)\) and \(\varphi(z,t)\) denote the LDD and rotation angle of the cross-section of the pile element segment, respectively; \(G_{\mathrm{P}}\), \(m_{\mathrm{P}}\), and \(A_{\mathrm{P}}\) represent the shear modulus, pile element mass, and cross-section area of the helical pile, respectively; \(k^{\prime}\) denotes the shear coefficient of the cross-section, which is related to the shape of the cross-section. The meanings of the other symbols are consistent with the previous text.
## 4 Data Analysis and Findings
### Convergent Validity
#### 4.1.2 Composite Reliability and Average Variance Extracted  
The Composite Reliability (\(\rho\)c) and Average Variance Extracted (AVE) tests require all variables to receive a rating of AVE > 0.5 and \(\rho\)c > 0.6 so that respectively, the degree of variance between the indicators is justified and the overall reliability of the collection of indicators used in the research is validated against the variable redundancy measures. Accordingly, all variables surpassed the AVE > 0.5 and \(\rho\)c > 0.6 threshold (Table 3).
## 4 Results and Discussion
### Metals Determined in Analyzed Products by Atomic Absorption Spectrometry
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Product** & **Fe** & **Zn** & **Cu** & **Mn** & **Mg** \\ \hline P-1 & 66.1 \(\pm\) 2.5 \({}^{\rm bc}\) & 5.7 \(\pm\) 0.6 \({}^{\rm f}\) & - & 58.7 \(\pm\) 3.3 \({}^{\rm e}\) & 4147.8 \(\pm\) 558.4 \({}^{\rm fg}\) \\ \hline P-2 & 9.8 \(\pm\) 0.3 \({}^{\rm d}\) & 22.3 \(\pm\) 0.2 \({}^{\rm e}\) & - & 195.2 \(\pm\) 8.2 \({}^{\rm e}\) & 2019.9 \(\pm\) 116.1 \({}^{\rm h}\) \\ \hline P-3 & - & 20.7 \(\pm\) 0.6 \({}^{\rm e}\) & 0.8 \(\pm\) 0.1 \({}^{\rm d}\) & 114.3 \(\pm\) 6.9 \({}^{\rm e}\) & 3387.9 \(\pm\) 148.3 \({}^{\rm g}\) \\ \hline P-4 & 26.2 \(\pm\) 3.8 \({}^{\rm d}\) & 139.4 \(\pm\) 7.1 \({}^{\rm a}\) & 2.5 \(\pm\) 0.2 \({}^{\rm d}\) & 993.4 \(\pm\) 9.2 \({}^{\rm c}\) & 7746.8 \(\pm\) 143. 8 \({}^{\rm b}\) \\ \hline P-5 & 14.8 \(\pm\) 0.7 \({}^{\rm d}\) & 43.1 \(\pm\) 0.7 \({}^{\rm d}\) & - & 1086.2 \(\pm\) 21.9 \({}^{\rm c}\) & 9946.8 \(\pm\) 383.4 \({}^{\rm a}\) \\ \hline P-6 & 207.5 \(\pm\) 23.1 \({}^{\rm a}\) & 42.0 \(\pm\) 1.5 \({}^{\rm d}\) & 5.2 \(\pm\) 0.1 \({}^{\rm c}\) & 1498.9 \(\pm\) 81.6 \({}^{\rm b}\) & 6315.0 \(\pm\) 241.1 \({}^{\rm c}\) \\ \hline P-7 & 107.1 \(\pm\) 10.1 \({}^{\rm b}\) & 40.5 \(\pm\) 2.7 \({}^{\rm d}\) & 9.9 \(\pm\) 0.7 \({}^{\rm a}\) & 2897.8 \(\pm\) 103.9 \({}^{\rm a}\) & 4978.1 \(\pm\) 229.5 \({}^{\rm cf}\) \\ \hline P-8 & 91.1 \(\pm\) 4.4 \({}^{\rm c}\) & 48.9 \(\pm\) 2.1 \({}^{\rm c}\) & 7.9 \(\pm\) 0.9 \({}^{\rm b}\) & 1131.4 \(\pm\) 75.6 \({}^{\rm c}\) & 6773.3 \(\pm\) 507.0 \({}^{\rm c}\) \\ \hline P-9 & 90.9 \(\pm\) 4.3 \({}^{\rm bc}\) & 53.6 \(\pm\) 3.8 \({}^{\rm c}\) & 7.9 \(\pm\) 0.7 \({}^{\rm b}\) & 1511.7 \(\pm\) 25.8 \({}^{\rm b}\) & 5796.0 \(\pm\) 176.5 \({}^{\rm de}\) \\ \hline P-10 & 180.2 \(\pm\) 13.1 \({}^{\rm a}\) & 93.0 \(\pm\) 5.6 \({}^{\rm b}\) & 7.7 \(\pm\) 0.2 \({}^{\rm b}\) & 843.5 \(\pm\) 29.5 \({}^{\rm d}\) & 5465.2 \(\pm\) 145.6 \({}^{\rm de}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Concentrations of analyzed metals in P-1–P-10 products [mg/kg]. Each analysis was conducted three times. The letters next to values represent [PERSON]’s HSD post hoc results, different letters (a–g) indicate significant differences between homogeneous groups of yerba mate products for each bioelement (\(p<0.05\)). method using the usual 30-50 g of the product could be an important source of these microelements in the daily diet [43].