esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0-80 min), drying temperature (40, 50, and 60 \({}^{\circ}\)C), and two levels of IR power (0.32 and 0.49 W/cm\({}^{2}\)) for the prediction of moisture ratio (_MR_). After applying different models, several performance prediction indices, i.e., correlation coefficient (\(R^{2}\)), mean square error index (_MSE_), and mean absolute error (_MAE_) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the _MR_ in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher \(R^{2}\) and lower _MSE_.  
drying; parboiled hulls; artificial neural networks; neuro-fuzzy inference; support vector regression +
Footnote †: journal: Applied Sciences
## 1 Introduction
On the basis of comparable pore size distributions in powder and laser powder-bed parts, [PERSON] et al. [16] suggested that most of the small (\(<\)15 \(\upmu\)m) spherical pores originate from the powder, except for parts produced in keyhole mode. The fraction of small spherical pores was lower in the SLM parts than in the powder and was further reduced by remelting, as it provides the possibility for entrapped gas to escape. The so-called trapped gas pores were also suggested to have their origin in the gaseous atmosphere in laser powder-bed AM. Another challenging feature of SLM is the occurrence of thermal stresses during the part building. Thermal stresses are a result of thermal gradients in the built stage and are therefore strongly related to the processing conditions. Depending on the geometry, such stresses can cause shape changes during the SLM process and residual stresses in the as-built component. The latter can lead to deformation upon removing the component from its constraining support or the base plate [17]. [PERSON] et al. [5] investigated the curvature of rectangular specimens after different island based scan strategies and found more deformation after a least heat influence (LHI) scan order. They proposed that the observed effects are caused by steeper thermal stress gradients as a consequence of less preheating for the LHI scan strategy. [PERSON] et al. [18] modeled the influence of different scan strategies on specimen deformation and residual stress, and found that the in-out and out-in spiral strategies result in a positive and negative deformation, respectively. [PERSON] and [PERSON] [19] proposed a model for a single layer plate, showing how the distortion occurring during laser scanning is more significant than those caused by (the release of) residual stresses. Recently, [PERSON] and [PERSON] [20] utilized a low-fidelity modeling technique for optimization of SLM scanning strategies. They presented a method for generating optimized cellular scanning strategies. The scanning strategies were optimized with regard to introducing least residual stress in the as-built component. In the present work we investigate the influence of their cellular scan strategy in SLM parts on surface topography, porosity, and microstructure.
## 3 Control Method and Control Characteristics of the Switching Process of the DFVSPS Unit
### Generating Mode
#### 3.1.2 Voltage Regulation and Grid Connection  
After the DFVSPS unit started in generating mode and the speed reached the rated speed, the voltage on the stator side of the DFIM needs to be adjusted to meet the grid-connected conditions. When grid connection is performed, the magnitude, frequency, phase and phase sequence of the voltage on the stator side of DFIM are required to be the same as the grid voltage [27].  
Figure 8: Waveforms of start-up in generating mode. (**a**) guide vane opening; (**b**) mechanical power; (**c**) speed.  
Before the grid connection, the stator side of DFIM is open, and the current on the stator side is 0. Thus, \(i_{sd}=0\), \(i_{sq}=0\), \(T_{e}=0\). Substituting this constraint relationship into the stator and rotor flux equation and the voltage equation, Formula (13) can be obtained:  
\[\left\{\begin{array}{l}U_{sd}=\frac{d\psi_{sd}}{dt}-\omega_{1}\psi_{sq}=L_{m} \frac{d\dot{m}_{id}}{dt}-\omega_{1}L_{m}i_{rq}\\ U_{sq}=\frac{d\psi_{sq}}{dt}+\omega_{1}\psi_{sd}=L_{m}\frac{d\dot{m}_{sq}}{dt}+ \omega_{1}L_{m}i_{rd}\end{array}\right.. \tag{13}\]  
When grid voltage orientation is used and the stator resistance is ignored, \(U_{sd}=U_{s}\), \(U_{sq}=0\). Since the dynamic performance of the stator side is not highly required during the grid-connecting process, the dynamic process of the flux linkage is neglected, and the stator-side voltage equation can be further simplified and the following relationship can be obtained:  
\[\left\{\begin{array}{c}i_{rd}=0\\ i_{rq}=-\frac{U_{s}}{\omega_{1}L_{m}}\end{array}\right.. \tag{14}\]  
The above formula indicates that the voltage on the stator side of DFIM can be adjusted by regulating the \(q\)-axis current to reach the grid-connected conditions. The simulation is performed in MATLAB/Simulink, and the waveform is shown in Figure 9. The unit is connected to the grid at t = 90.4 s. In addition, it can be found that the voltage difference before the grid connection is very small.
## 2 Materials and Methods
### Case Study of the Selected Area: The Skikda Region  
The study area is the isolated rural village of \"Salah Bouchaour\", located in Skikda Province, Algeria (Latitude: 36.7\({}^{\circ}\) N, Longitude: 6.86\({}^{\circ}\) E). Situated at an elevation of approximately 120 m above sea level, the village is home to some 600 inhabitants who rely primarily on small-scale agriculture (olives, cereals, and vegetables). The local climate is Mediterranean semi-arid, with hot, dry summers (average daily solar irradiation of roughly 5 kWh/m\({}^{2}\)) and mild, wetter winters. Annual mean wind speeds of 4-6 m/s make the site suitable for small wind turbines, while irrigation water is drawn from a shallow aquifer fed by seasonal rainfall. Access to the nearest grid connection is over 15 km away, so the community currently depends on diesel generators for electricity--highlighting the potential impact of a hybrid solar-wind-hydraulic storage system.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 7 Conclusions  
Creating maximum simultaneous D2D link schedules for dealing with increasing traffic loads is a challenging problem in modern cellular scenarios due to the need for reducing the load on base stations and because of their high computational complexity. To date, many link scheduling optimization algorithms have been developed, but most of them are not well suited for working online and hence cannot meet the real-time requirements of modern network infrastructures. In this paper, we used a machine learning-based strategy to maximize the number of simultaneously active D2D links without interfering with cellular communications. Based on the physical interference model, we designed a link scheduling algorithm based on a DNN to predict device activity in forming D2D links in order to reduce computation times-costs. Since the application of deep learning is still an emerging research topic in network scheduling, we empirically determined the proper operating parameters via specifically crafted experiments aiming at optimizing the model performance. The parameters chosen in this way were the number of hidden layers, the size of the training set, the batch size, and the learning rate. Simulation experiments showed that the proposed approach can reach more than 90% of the optimum results quality. At the same time, we learned that the influence of base station power is greater than the threshold of an SINR on the number of D2D links. Through the experiments, we also learned that the influence of the number of D2D links changes in presence of different base stations' coverage areas. Furthermore, we also analyzed the reasons for these factors. For future work, we are going to consider more solutions, like transfer learning and reinforcement learning for the more general case that D2D links may be scheduled in a multi-cell.  
Conceptualization: B.Y., X.Z., E.C., and I.Y. Formal analysis: B.Y. and E.C. Funding acquisition: X.Z. and I.Y. Investigation: B.Y., X.Z., and E.C. Methodology: X.Z. and E.C. Performance evaluation: B.Y. and E.C. Writing, original draft: B.Y. and X.Z. Writing, review and editing: F.P. and I.Y. FundingThis work was supported by the National Key Research and Development Program of China (2016 YFB0200902) as well as the Soonchunhyang University Research Fund. Conflicts of InterestThe authors declare no conflict of interest.
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 3 Satellite Network Capacity Calculation Method
### Average Hop Count
\(\left|C_{A}-\tilde{C}_{A}\right|\) portrays the effect of non-cooperative jamming on the network capacity of \(\Psi_{A}\). Based on the network capacity obtained from the calculation method, we analyze the trend of the jamming effects of various jamming parameters of \(\Psi_{B}\).
# Feasible Trajectories Generation for Autonomous Driving Vehicles
## 2 Vehicle Kinematic Model  
A typical passenger vehicle can be modelled as a simple draw of four wheels, where two front wheels are connected to the steering system, \(\phi\), and rotate in a maximum angle range of \(+/-45^{\circ}\) (degrees) to the vehicle body. The distance between the center of the front wheels and the center of the rear wheels is called the vehicle wheelbase (\(l\)). In our model, we select the wheelbase length of \(l=2\) m. The wheel radius of four wheels is assumed to be \(r=0.25\) m. The vehicle position (\(x\) and \(y\)) is identified at the center of the rear wheels. The vehicle body angle, \(\theta\), is also identified along the axe \(x\). It is assumed that the vehicle position is always identical to the GPS and LIDAR systems with exact \(x\) and \(y\) coordinates. The vehicle body angle \(\theta\) and the steering angle \(\psi\) are assumed to also always be identical to the 3D sensors. The kinematic model of a vehicle is shown in Figure 1.  
The vehicle moves forward and reverses and is controlled by the engine angular and steering angular velocity. A vehicle driven by the rear wheels and moving forward is presented in Equation (1):  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\\ \dot{\phi}\end{array}\right]=\left[\begin{array}{c}cos\theta\\ sin\theta\\ \frac{tan\phi}{l}\\ 0\end{array}\right]rv_{1}+\left[\begin{array}{c}0\\ 0\\ 0\\ 1\end{array}\right]v_{2} \tag{1}\]  
Figure 1: Vehicle kinematic diagram.  
The vehicle driven by the rear wheels and moving in reverse is obtained in Equation (2), where the vehicle speed is assigned in a negative sign:  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\\ \dot{\phi}\end{array}\right]=-\left[\begin{array}{c}cos\theta\\ sin\theta\\ \frac{tan\theta}{l}\\ 0\end{array}\right]rv_{1}+\left[\begin{array}{c}0\\ 0\\ 0\\ 1\end{array}\right]v_{2} \tag{2}\]  
The vehicle driven by the front wheels is formulated in Equation (3):  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\\ \dot{\phi}\end{array}\right]=\left[\begin{array}{c}cos\theta cos\phi\\ sin\theta cos\phi\\ \frac{tan\phi}{l}\\ 0\end{array}\right]rv_{1}+\left[\begin{array}{c}0\\ 0\\ 0\\ 1\end{array}\right]v_{2} \tag{3}\]  
where \(\left[x,y,\theta,\phi\right]^{\prime}\) are the state and output variables. The two control inputs are \(v_{1}\) and \(v_{2}\), where \(v_{1}\) is the angular speed of the driven wheels, therefore \(rv_{1}\) is the vehicle speed (km/h); and \(v_{2}\) is the steering angular speed in revolutions per minute (rpm). The two models in Equations (1) and (2) are mainly used to calculate and simulate the vehicle performances. The next part presents a basic method for trajectory generation.
# Microbial-Based Biotechnology: Production and Evaluation of Selenium-Tellurium Nanoalloys
## 3 Results
### Morphological Characterization  
The Se-NPs appeared spherical (Figure 3a), and the Te-NPs were rod-shaped (Figure 3b). SeTe-NAs were acicular with a poly-amorphous structure (Figure 3c) and were obviously heterogeneous in size. Prolonging the incubation period increased the number and size of these particles. The particle size distribution of Se-NPs shows that the diameter is 206 \(\pm\) 33 nm. The Te-NP's diameter is 62 \(\pm\) 13 nm and the length is 176 \(\pm\) 32 nm. The diameter and length of SeTe-NPs are 74 \(\pm\) 18 nm and 247 \(\pm\) 33 nm, respectively. Figure 4a shows the peak at 1.4 keV confirming the presence of Se, while the peaks at 3.8 and 4.1 keV (Figure 4b) indicate a predominantly Te composition. Figure 4c includes peaks at 1.4, 3.8, and 4.1 keV, indicating the presence of Se and Te as alloys. The carbon (C) and gold (Au) peaks are correlated with the underlying coverslip.  
The prepared samples were measured with an AFS instrument to determine the concentration present in both liquids and NPs. According to the experimental design, Se and Te were added such that the Se/(Se + Te) molar ratio tends to decrease throughout the sample. The Se/(Se + Te) molar ratio correlation remains unchanged regardless of the factors. A linear regression model (Figure 5) was constructed to establish the correlation between samples. Regression line y = 0.9910X + 1.393 with R\({}^{2}\) = 0.9960. The molar concentration ratio in the NPs is equal to the molar concentration ratio of the starting liquid medium. Bacteria converted inorganic Te and Se into elemental Te and Se [34; 35; 35]. Even SeTe-NPs are found in the same concentration molar ratio as those with inorganic Te and Se present in it. This indicates that the bacterial biotransformation efficiency of the initial selenite and tellurite concentrations remains constant.  
Figure 2: Samples containing only Se-NPs (tube no. 1), only Te-NPs (tube no. 15), and different combinations of the nanoalloy SeTe-NAs (tubes from 2 to 14) (from left to right). B refers to the control of Se or Te (without Se and Te) as the tube contains only MRS medium.  
Figure 3: Particle size distribution and SEM images of (**a**) Se-NPs at 30,000\(\times\), (**b**) Te-NPs at 18,000\(\times\), and (**c**) SeTe-NAs at 25,000\(\times\) magnification.  
Figure 4: _Cont._  
Figure 4: Energy dispersive spectra measured the elemental composition of the sample surface of (a) Se-NPs, (b) Te-NPs, and (c) SeTe-NAs.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML** & \multirow{2}{*}{**Strength**} & \multirow{2}{*}{**Weakness**} & \multirow{2}{*}{**FL Study**} \\
**Algorithms** & & & \\ \hline \multirow{4}{*}{AE} & AE is mainly designed for dimensional feature reduction and denoising medical datasets via an unsupervised learning method. AE aims to recreate effective compact and effective feature representation. & An autoencoder may exclude essential & \\  & & & \\ \hline \multirow{4}{*}{CNN} & Performs well on medical image classification tasks such as prediction of COVID-19 using X-ray images & The training process of CNN that contains multiple layers will be time-consuming if the client in the FL environment does not have powerful computation resources. & [18; 20; 38; 39; 40; 41; 42] \\ \hline \multirow{4}{*}{GAN} & Generate a synthetic sample of medical data for limited quantity in experiments datasets. & Training GAN is challenging due to the unstable training process, no standard metric evaluation, and numerous trial-and-error experiments required for effective outcomes. & [43; 44] \\ \hline \multirow{4}{*}{LSTM} & Performs well on time series or sequential medical datasets, for instance, detection of human activity recognition. & Due to the vanishing and exploding gradient challenges, training LSTM is difficult. & [45] \\ \hline \multirow{4}{*}{MLP} & Good generalization performance on tabular medical datasets such as mortality prediction based on drug data & MLP is limited to learning elementary problems. Additionally, it is feature-scaling sensitive and involves setting numerous hyperparameters such as the number of hidden neurons and layers. & [46; 47; 48; 49; 50; 51] \\ \hline \multirow{4}{*}{SVM} & SVM is capable of modeling nonlinear decision boundaries and a variety of kernels are available. Additionally, it is highly resistant to overfitting, particularly in high-dimensional space. & SVM is memory-consuming, more difficult to modify because of critically selecting the appropriate kernel, and does not scale well to more extensive datasets. & [52] \\ \hline \multirow{4}{*}{U-Net} & Achieve accurate results when performing segmentation tasks on medical image datasets, for example, when segmenting brain tumors disease using brain magnetic resonance medical images. & U-Net model development is time-consuming because the network must be operated independently for each patch, and redundancy due to overlapping patches. Additionally, a tradeoff exists between the precision of localization and the utilization of context. & [38; 53] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of machine learning algorithms performing on federated learning, along with strengths and weaknesses.
## 2 Materials and Methods
### Electrophysiology  
Borosilicate glass electrodes were pulled using a PC-10 puller (Narishge, Amityville, NY, USA). The microelectrodes were filled with 3 M KCl and had tip resistances between 0.5-1.5 M\(\Omega\). Voltage-clamping of oocytes was achieved through a Turbo-tec 03X amplifier connected to a 1440 A Axon Instruments digitizer (Molecular Devices, San Jose, CA, USA). Voltage protocols and current recording were controlled with Clampex software. ChR2 was activated via a 1 mm light guide connected to an Omicron LEDMOD V2 300 mW LED module with an emission wavelength of 470 nm. The membrane voltage was varied from \(-\)100 mV to +40 mV in 20 mV steps. Sodium solutions consisted of 100 mM NaCl, 2 mM MgCl\({}_{2}\), 0.1 CaCl\({}_{2}\), 5 mM MOPS/5 mM MES at pH 7.5 and 6.0, respectively. Reversal potentials were determined from current-voltage curves at I\({}_{\text{p}}\) and I\({}_{\text{ss}}\). Kinetic parameters were determined by fitting photocurrent traces with monoexponetial (T\({}_{\text{decay}}\)) or biexponential equations (T\({}_{\text{off}}\)).
## 3 Numerical Analysis Results
### Nut Height (Number of Engaged Threads) Effect on SCF  
Three round type nuts with three different nut heights (49 mm, 44.5 mm, and 40 mm) and number of engaged threads (9, 8, and 7) were examined through 2D FE analysis to determine if the design parameter is a meaningful factor in reducing SCFs as shown in Figure 11.  
Table 7 indicates that nut height or number of engaged threads is not a critical factor in reducing SCF.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nut Type (2D)** & **Norm Stress at** & **Bolt Maximum Stress** & **SCF** & **Reduction** & **No. of Engaged** \\  & **Shank (MPa)** & **at Thread (MPa)** & **SCF** & **Reduction** & **Threads** \\ \hline Standard & 649 & 1975 & 3.04 & 0\% (base) & 8 \\ Round & 649 & 1564 & 2.41 & 21\% & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis results from 2D FE model.  
Figure 11: Nut height and number of engaged threads of M42 round nut.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nut Height** & **Norm Stress (MPa)** & **Max. Stress (MPa)** & **Number of Engaged Threads** & **SCF** \\ \hline
49 mm & 650 & 1664 & 9 & 2.56 \\
44.5 mm & 650 & 1675 & 8 & 2.58 \\
40 mm & 650 & 1691 & 7 & 2.60 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SCFs based on change of nut height.  
Figure 10: Finite element model (two-dimensional (2D) and 3D model).
## 2 Results and Discussion  
### Characterization of PPy Fiber  
The morphological surface structure of the PPy-coated fiber was studied using a field-emission scanning electron microscope (FE-SEM). The SEM images of the PPy-coated platinized fiber are shown in Figure 1. It is clear that the surface of the coating is saliently porous. As depicted in Figure 1a, the nanofibers of PPy are clearly obvious. Additionally, as revealed in Figure 1b, a dense nanofibrous coating is deposited on the whole surface of the steel surface. The tubular opening of the top section can be obviously observed. Figure 1a also shows aligned PPy nanofibers vertically erecting on the steel surface.
## 3 Results
### Partial Least Squares Discriminant Analysis  
In order to investigate the element profile of samples from different soil types, PLS-DA as an effective feature selection and classifier was applied. The sampling points were divided into two groups: sandy (CEC \(<\) 15 meq) and clay (CEC \(>\) 15 meq). The obtained classification model had very good performance (Figure 2a) and outlines the elements (VIP \(>\) 1) with significant discriminative power (Figure 2b). The regression vectors of both groups (Figure 2c,d) present the element profiles of each group.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Soil Samples**} & \multicolumn{3}{c}{**Plant Samples**} \\ \hline
**Element** & **Mean** & **Median** & **(Min-Max)** & **Mean** & **Median** & **(Min–Max)** \\ \hline Ag & 0.62 & 0.21 & 0.002–8.89 & 0.19 & 0.14 & 0.044–0.71 \\ \hline Al & 32,125 & 33,675 & 12,491–57,054 & 196 & 84 & 33.8–943 \\ \hline As & 5.24 & 4.93 & 1.36–9.87 & 0.083 & 0.067 & 0.001–0.21 \\ \hline Ba & 159 & 145 & 74–303 & 29.3 & 20.5 & 4.03–68.9 \\ \hline Be & 1.23 & 1.33 & 0.36–2.68 & 0.022 & 0.001 & 0.001–0.15 \\ \hline Bi & 0.53 & 0.52 & 0.24–1.10 & 0.50 & 0.42 & 0.18–2.10 \\ \hline Ca & 8134 & 7882 & 1996–26,874 & 21,261 & 19,641 & 8848–35,366 \\ \hline Cd & 0.97 & 0.70 & 0.14–6.25 & 0.62 & 0.33 & 0.061–5.31 \\ \hline Co & 22.7 & 21.3 & 6.70–58.9 & 0.34 & 0.25 & 0.07–1.15 \\ \hline Cr & 39.1 & 37.4 & 15.3–74.0 & 2.17 & 2.47 & 0.60–4.01 \\ \hline Cu & 386 & 300 & 51.9–1288 & 18.2 & 17.8 & 6.14–40.8 \\ \hline Fe & 41,069 & 41,387 & 13,989–97,297 & 334 & 267 & 138–854 \\ \hline K & 3344 & 2957 & 1081–6076 & 33,444 & 39,417 & 1769–64,474 \\ \hline La & 20.4 & 19.1 & 11.5–61.8 & 0.31 & 0.18 & 0.062–0.93 \\ \hline Mg & 11,516 & 11,066 & 2670–25,999 & 6147 & 5689 & 2438–11,364 \\ \hline Mn & 1321 & 1114 & 561–5461 & 109 & 80.1 & 35.5–298 \\ \hline Mo & 3.92 & 2.57 & 0.99–14.6 & 1.58 & 0.73 & 0.15–19.8 \\ \hline Na & 880 & 713 & 169–2493 & 447 & 164 & 35.2–3382 \\ \hline Ni & 27.9 & 25.5 & 11.6–59.6 & 1.07 & 0.76 & 0.22–2.89 \\ \hline P & 665 & 637 & 157–2148 & 2359 & 2171 & 198–5294 \\ \hline Pb & 33.2 & 30.2 & 12.5–101 & 0.80 & 0.83 & 0.30–1.61 \\ \hline Rb & 15.3 & 13.7 & 7.6–38.4 & 18.9 & 14.8 & 2.47–39.8 \\ \hline Sb & 0.17 & 0.14 & 0.024–0.47 & 0.037 & 0.029 & 0.011–0.195 \\ \hline Se & 6.04 & 5.08 & 0.003–21.6 & 0.29 & 0.19 & 0.001–0.91 \\ \hline Sn & 0.66 & 0.49 & 0.020–2.83 & 1.43 & 1.46 & 0.39–2.75 \\ \hline Th & 4.30 & 4.11 & 2.45–12.4 & 0.034 & 0.039 & 0.008–0.109 \\ \hline Ti & 758 & 567 & 85.9–2199 & 6.96 & 4.11 & 1.54–28.9 \\ \hline Tl & 0.08 & 0.09 & 0.009–0.21 & 0.018 & 0.007 & 0.001–0.205 \\ \hline U & 1.06 & 0.86 & 0.57–3.40 & 0.031 & 0.026 & 0.013–0.100 \\ \hline V & 112 & 107 & 36.0–185 & 0.40 & 0.19 & 0.004–1.31 \\ \hline Zn & 133 & 127 & 36.4–340 & 61.1 & 55.5 & 12.4–249 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic statistics of the soil and plant samples.