esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 2 Study Area  
Mei County, a subdivision of Baoji City, is geographically located between 33\({}^{\circ}\)59\({}^{\prime}\) and 34\({}^{\circ}\)19\({}^{\prime}\) N latitude and 107\({}^{\circ}\)39\({}^{\prime}\) and 108\({}^{\circ}\)00\({}^{\prime}\) E longitude (Figure 1), covering a total area of 863 km\({}^{2}\), which represents 4.74% of the total area of Baoji City District. Situated at the foot of the Taibai Mountains in the Qinling range, Mei County lies on the north bank of the Wei River and is part of the semi-hilly region on the northern slopes of the Qinling Mountains. Geologically, Mei County is part of the Mei County Shallow Depression, a secondary tectonic unit characterized by complex lithological formations and significant structural variation. The southern portion, particularly around Yingtou and Tanguyu towns, lies within the North Qinling Fold Belt, an active orogenic zone with intense tectonic deformation. The region's lithology, as shown in, comprises five distinct groups: unconsolidated deposits such as gravel, sand, and clay (Group 1); loess and loess-like soils (Group 2); granitic and gneissic rocks (Group 3); metamorphic sequences including marble and volcanic clastic rocks (Group 4); and highly foliated schists and quartzites (Group 5). These diverse lithological units exhibit varying mechanical properties and weathering behaviors, directly influencing slope stability and the development of landslide-prone areas. Mei County's hydrogeological setting is strongly influenced by its diverse soil types, including Regosols, Luvisols, Leptosols, Fluvisols, Cambisols, and Anthrosols. These soils vary in permeability and water retention, affecting how rainwater moves through the landscape. Less cohesive soils like Regosols and Fluvisols are more prone to erosion and saturation, which can trigger landslides, especially during heavy rainfall. In contrast, soils like Cambisols and Leptosols offer better drainage, reducing slope failure risk. Combined with the region's steep terrain and seasonal rains, these conditions contribute significantly to landslide susceptibility across the area.  
The region experiences a temperate continental monsoon climate, characterized by altitudes ranging from 442 to 3767 m, an annual frost-free period of 218 days, an average annual temperature of 12.9 \({}^{\circ}\)C, and mean annual precipitation of 609.5 mm. The terrain is elevated in the north and south, with a lower central area, featuring loess ridges and the northern Weinan loess tableland. The landscape is relatively flat, with fertile soils that are rich in organic matter, while the climate offers ample sunlight, abundant rainfall, and significant temperature variations between day and night.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### Effect of Chronological Characteristic on the Power System Reliability
#### 4.2.3 Temperature  
The availability of gas turbines depends on temperature sensitivity. A reliability evaluation was performed by considering the change in availability according to the temperature of G55 and G355 in RTS 2020 to confirm its influence. The data in Table 6 show that the gas turbine has a 100% output at 15 \({}^{\circ}\)C and changes by 0.6% per 1 \({}^{\circ}\)C [34]. The temperature was referenced in Washington, D.C., and provided by WWIS [35].  
Table 7 presents the reliability indices considering temperature. A significant change in the reliability indices was noted because G55 and G355 accounted for a large proportion of RTS 2020. These changes were observed because the monthly system availability varies dramatically. Overall, a reliability evaluation considering temperature may be required depending on the generation mix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**Temperature**} & \multirow{2}{*}{**Change (\%)**} & \multirow{2}{*}{**G55 (MW)**} & \multirow{2}{*}{**G355 (MW)**} \\  & (\({}^{\circ}\)C) & & & \\ \hline
1 & 0.65 & +8.6 & 59 & 385 \\
2 & 2.35 & +7.6 & 59 & 382 \\
3 & 6.80 & +4.9 & 57 & 372 \\
4 & 12.45 & +1.5 & 55 & 360 \\
5 & 17.30 & \(-\)1.4 & 54 & 350 \\
6 & 22.45 & \(-\)4.5 & 52 & 339 \\
7 & 24.85 & \(-\)5.9 & 51 & 334 \\
8 & 24.10 & \(-\)5.5 & 52 & 335 \\
9 & 19.90 & \(-\)2.9 & 53 & 344 \\
10 & 13.35 & +1.0 & 55 & 358 \\
11 & 8.00 & +4.2 & 57 & 369 \\
12 & 2.55 & +7.5 & 59 & 381 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Monthly capacity of gas turbines according to temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Generator**} & \multirow{2}{*}{**Maintenance**} & \multirow{2}{*}{**Maintenance**} & \multirow{2}{*}{**Derated Capacity**} \\  & **Index** & & **Start Week** & **Period (Weeks)** & **(MW)** \\ \hline  & g63–g64 & 1 & & \\  & g65–g68 & 4 & & \\ U355 & g69–g70 & 15 & 1 & 348 \\  & g71 & 45 & & \\  & g72 & 48 & & \\ \hline U400 & g73 & 12 & 6 & 354 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Indices**} & \multirow{2}{*}{**Base Case**} & \multirow{2}{*}{**Optimized Case**} & \multirow{2}{*}{**Derated Case**} \\  & & & \\ \hline LOLH & 0.001898 & 0.001898 & 0.011447 \\ (hour/year) & & & \\ \hline EUE & 0.23380 & 0.23380 & 1.510155 \\ (MWh/year) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for IEEE RTS 2020 reliability indices considering maintenance.
# Design and Implementation of Spring Cable Shaping Method Based on Fuzzy Control
## 4 Realization of Current Heating Shaping Control
### Realization of Temperature Control Algorithm for Spring Cable Shaping Based on Fuzzy Theory
#### 4.2.3 Output Clarity  
Table 2 lists 36 fuzzy relations among PLC output control current \(I\) and spring cable temperature deviation \(e_{1}\) and winding rod temperature deviation \(e_{2}\). However, it is not necessary to activate all fuzzy relations when calculating actual current \(I\). let \(e_{1}\) = 80 \({}^{\circ}\)C and \(e_{2}\) = 140 \({}^{\circ}\)C at a certain time. According to the fuzzy distribution of parameters, \(e_{1}\) belongs to between 60 \({}^{\circ}\)C and 90 \({}^{\circ}\)C, \(e_{2}\) belongs to be between 110 \({}^{\circ}\)C and 160 \({}^{\circ}\)C. According to formula (2), \(e_{1}\) = 80 \({}^{\circ}\)C is mapped to fuzzy subsets \(M\) and \(B\), and \(M\) (80) = 0.3, \(B\) (80) = 0.7; \(e_{2}\) = 140 \({}^{\circ}\)C is mapped to fuzzy subsets \(B\) and \(VB\), and \(B\) (140) = 0.2, \(VB\) (140) = 0.6; Four fuzzy rules \(VB_{(7)}\), \(VB_{(8)}\), \(VB_{(13)}\) and \(B_{(14)}\) in Table 2 are activated.  
If the activated fuzzy outputs are \(R_{1}\), \(R_{2}\), \(R_{3}\) and \(R_{4}\),  
\(R_{1}\) = (\(B\) (80) \(\wedge\)\(VB\) (140)) \(\cdot\)\(VB_{(7)}\) = (0.7 \(\wedge\) 0.6) \(\cdot\)\(VB_{(7)}\);  
\(R_{2}\) = (\(B\) (80) \(\wedge\)\(B\) (140)) \(\cdot\)\(VB_{(8)}\) = (0.7 \(\wedge\) 0.2) \(\cdot\)\(VB_{(8)}\) = 0.2 \(\cdot\)\(VB_{(8)}\);  
\(R_{3}\) = (\(M\) (80) \(\wedge\)\(VB\) (140)) \(\cdot\)\(VB_{(13)}\) = (0.3 \(\wedge\) 0.6) \(\cdot\)\(VB_{(13)}\) = 0.3 \(\cdot\)\(VB_{(13)}\);  
\(R_{4}\) = (\(M\) (80) \(\wedge\)\(B\) (140)) \(\cdot\)\(B_{(14)}\) = (0.3 \(\wedge\) 0.2) \(\cdot\)\(B_{(14)}\) = 0.2 \(\cdot\)\(B_{(14)}\).  
The fuzzy total output of current \(R=R_{1}\)\(\cup\)\(R_{2}\)\(\cup\)\(R_{3}\)\(\cup\)\(R_{4}\) = 0.6 \(\cdot\)\(VB_{(7)}\), so the accurate output value of current corresponds to membership function \(VB\) (\(\lambda\)) and membership degree is 0.6. The \(VB\) (\(x\)) function in formula (2) is 0.6 = \(x-12/20-12\), and the calculated PLC output control signal is 16.8 mA.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 3 The Proposed Deep Crack Segmentation Approach  
As stated in the previous sections, this paper proposes an end-to-end \(U^{2}\)-Net based pixel-level crack segmentation approach aiming to generate a pixel-level crack map. It consists of four phases, as shown in Figure 2, namely **(1) data acquisition phase**, which is responsible for collecting surface crack images of historical buildings; **(2) data preparation phase**, which is responsible for preparing and preprocessing the images for training; **(3) pixel-level map generation**, which is responsible for annotation of the primary dataset, and **(4) pixel-level crack segmentation phase**, which investigates three different U-Net architectures, namely Deep ResU-Net, ResU-Net++, and \(U^{2}\)-Net.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### The Temporal Convolutional Network (TCN)  
The TCN, as a new architecture based on convolutional neural networks (CNNs), can not only solve the problem of excessive time consumption by parallel computation but is also different from the CNN because the TCN is composed of causal convolution, dilated casual convolution, and residual block structure.
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 2 Methodology
### Real-Time Prediction: Walk Forward  
Walk forward (WF) is a real-time prediction method mainly used in the field of time series prediction, such as real-time trading strategies, to optimize the coefficient of a model in real time [21]. As shown in Figure 1, WF optimizes a predictive model using a previous dataset called the \"in-sample\" and then tests it on a small period of the subsequent dataset called the \"out-of-sample\". This step is repeated whenever the \"in-sample\" is extended to the future, and the outputs predicted in each step are sequentially stacked. In the final step, all the predicted values are evaluated at once.  
WF optimizes the model's structure or coefficients for the adaptability of the TBM to the future environment. The previous data information for the tunnel sections, where the TBM excavation is already completed, are regarded as the \"in-sample\". On the other hand, the sections ahead of the TBM face to predict the thrust are considered as the \"out-of-sample\" (real-time prediction sections).
## 2 Signal and System Models
### Signal Modulation for Dimming Control  
\(P_{\mathrm{T}}(t)\) is a pulse width modulated signal. PWM is a way to digitally encode analog signal levels [18]. PWM is used in motor drive circuits, dimmer control, power control, _etc._ The LED current is modulated by PWM signal to control its brightness changing the \"ON\" duration of the whole period, while the LED current is kept constant. During the \"OFF\" period, there is no signal transmitted. The duty cycle \(D\) of PWM signal is varied according to how often is the \"ON\" and the \"OFF\" periods. When the duty cycle is 100%, all the LED light is transmitted and the light is with its highest brightness. When the duty cycle is reduced, the LED light is blocked in the \"OFF\" duration. Therefore, the light is dimmed when the whole period of a PWM signal is taking into account. Figure 4a-c shows examples of three different PWM signals, _i.e._, with the duty cycle 10%, 50%, and 90%, respectively. This \"ON\"-\"OFF\" flashing condition does not affect what is seen as the human eyes fills in the gaps  
Figure 3: LEAs’ placement on the ceiling in a hexagonal lattice alignment.  
between the \"ON\" and \"OFF\" light pulses, provide that the pulse frequency is high enough (at least 200 Hz) to make it appear as a continuous light output [19]. When using a PWM signal, the total number of transmitted bits is reduced, which deteriorates the overall system performance. In order to maintain the communication quality in terms of the number of transmitted bits under dimming control scheme, the data rate of the modulating signal should be adjusted when the duty cycle of PWM dimming control signal is changed. Although the BER performance (as discussed later) during the \"ON\" duration of the PWM dimming control signal is not changed, taking into account the whole PWM period the number of transmitted bits is reduced. In order to keep the number of transmitted bits unchanged when the LED light is dimmed, the data rate should be increased as:  
\[R_{\mathrm{d}}TD=R_{\mathrm{o}}T,\] \[R_{\mathrm{d}}=\frac{R_{\mathrm{o}}}{D} \tag{3}\]  
where \(R_{\mathrm{o}}\) is the original data rate, \(R_{\mathrm{d}}\) is the data rate under dimming control, and \(T\) is the signal period.  
The main reason for the use of the PWM technique is that it is simple to implement, requiring only that the backlight can be switched on and off rapidly, and also it gives a large range of possible luminance values. This technique also does not result in a loss of efficiency. Under dimmed conditions, the LEDs are still operating at the same voltage and current as during full light output. In addition, PWM gives a more continuous level of light than other methods [19].
## 3 Results and Discussion
### Effect of Ultrasound Pretreatment on Total Curcuminoids Recovery of Fresh Turmeric  
The effects of UP to total curcuminoids recovery were summarized in Figure 2. Applied ultrasound may significantly increase the mass transfer by destroying the plant  
Figure 1: Influence of pretreatment on cell disintegration index (\(Z_{p}\)) of fresh turmeric. Means with different lowercase letters indicate significant differences (\(p<0.05\)). Ultrasound pretreatment (UP), Pulsed electric field pretreatment (PEF), Thermal pretreatment (TP), Enzyme pretreatment (EP).  
Figure 2: Effect of pretreatment on total curcuminoid recovery. Means with a different letter are significantly different (\(p<0.05\)).  
cells and decrease the size of materials [1]. Besides shock waves and cavitation effects, ultrasound may create a thermal effect [39].  
Ultrasound can increase extraction yield, but high ultrasound levels can also lead to a degradation of curcuminoid power exceeding a particular value [40]. In the present work, the UP effect was investigated at different specific energy inputs (W\({}_{\text{T}}\)): 40 kJ/kg (UP-40), 80 kJ/kg (UP-80) and 120 kJ/kg (UP-80).  
UP treatment did not lead to a significantly higher TC than the untreated sample (Figure 2) and TP. However, the disintegration level of UP-treatment was considerably higher than the untreated sample, shown as \(Z_{p}\) value (Figure 1). Moreover, UP-40 had the lowest W\({}_{\text{T}}\), but TC was 50% and 34% higher than UP-80 and UP-120 treatment, respectively. Even the amount of curcuminoids in UP-80 treatment was lower than the untreated sample. It is clearly shown that higher W\({}_{\text{T}}\) does not necessarily contribute to increasing the number of curcuminoids in the extract.  
In the report of [PERSON], [PERSON], [PERSON] and [PERSON] [40], the author showed that when the ultrasonic power is too high, the extracted compounds might degrade, and the yield may exhibit a slight decrease. It is also essential to understand that the ultrasound applying temperature cannot be controlled adequately at significantly high power levels [41]. In our study, the sample was applied UP-120 increased temperature than 20 \({}^{\circ}\)C at the end of the pretreatment (Table 1). It may lead to the degradation of curcuminoids because UP can create bubbles with a temperature of up to 5000 K, 1000 atm, and heating and cooling rate above 1010 K/s [42].  
As a result, destroying plant cells in the UP does not result in higher amounts of curcuminoids leaching into the extract solution during rapid extraction.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 1 Introduction
### Structure of LDPC Code  
The sparse parity check matrix can be used in LDPC code to define parity check sets. The term \"Sparse\" in a matrix with the \((n-k)\times n\) dimension denotes that there are fewer instances of \"1\" than there are of \"0\". There are more zeros than ones in \((n-k)\times n\) entries. The sparse parity check matrix, \((n,\mathrm{w}_{c},\mathrm{w}_{\mathrm{r}})\), is defined by three variables. \(n\) stands for the coded length, while \(\mathrm{w}_{\mathrm{r}}\) and \(\mathrm{w}_{\mathrm{c}}\) represent the number of ones in a row and a column, respectively. The \(-\mathrm{w}_{c}\) and \(w_{r}<<n\times(n-k)\) requirements must be met for a matrix to be referred to as low-density or sparse [15].  
Parity Check Matrix can be classified into two types: Regular and Irregular. Moreover, LDPC codes can be encoded and decoded using a variety of methods. The common objective of all systems is to simplify and speed up the encoding and decoding process. Linear Block Code \((\mathrm{n},\mathrm{k})\) is the foundation for the encoding method that is used most frequently. The message bits and parity bits that make up a Linear Block Code codeword can be described as follows: \(m\) is the message, and \(p\) is the parity vector.  
According to Linear Block Code's characteristics, \(\mathrm{C}\cdot H^{T}=0\). We obtain the equations for \(P_{n-k\times 1}\) in terms of message bits to satisfy this criterion via matrix multiplication. Therefore, after obtaining the values of \(P_{n-k\times 1}\), we may obtain the N-bit codeword.  
The bit-flipping algorithm and the sum-product method are used to decode LDPC codes. There are two categories of decoding: hard decision decoding and soft decision decoding. Decoding by bit-flipping corresponds to a hard-decision message-passing algorithm [16].  
A list of acronyms used in this paper is provided in Table 1.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### Joint User Pairing and Association Matrix  
Let us know a binary UE association matrix as \(\mathcal{A}^{U}\in\{0,1\}^{\mathcal{B}\times U\times N_{\mathcal{A}}^{u}\times \mathcal{A}}\), where \(a_{bu}^{mm}=1\) if a UE \(u\) is only associated with one base station and assigned to a PRB \(n\) on mini-slot \(\mathcal{U}\), and \(a_{bu}^{mm}=0\), otherwise. Let us express a binary UE coupling matrix \(\mathcal{X}^{U}\in\{0,1\}^{U\times U^{\prime}\times N_{\mathcal{A}}^{u}\times \mathcal{A}}\), where \(x_{uu}^{mm}=1\) if an eMBB UE \(u\) is paired with an uRLLC UE \(\hat{u}\) by superposition scheme, at the same BS, and \(x_{uu}^{mm}=0\), otherwise. \(\mathcal{O}_{bu}^{n}\) denotes the integer number of mini slots assigned to UE \(u\) when attached with BS by, if a UE is eMBB then \(\mathcal{O}_{bu}^{n}=\mathcal{M}\) (along TTI \(\mathcal{T}\)) as for a time slot; but if a UE is uRLLC so \(\mathcal{O}_{bu}^{n}\) is based on the paired eMBB UE.  
To formulate the user association, we define the association matrix \(\mathcal{A}^{U}\) as  
\[a_{bu}^{mm}=\left\{\begin{array}{ll}1,&\text{if UE $u$ is associated with BS $b$}\\ 0,&\text{otherwise.}\end{array}\right. \tag{1}\]  
The adoption of PD-NOMA scheme by the user \(u\) is represented by  
\[x_{uu}^{mm}=\left\{\begin{array}{ll}1,&\text{if user $u$ is superposited with $\hat{u}$are associated with the same BS },\\ 0,&\text{otherwise.}\end{array}\right. \tag{2}\]
# A Semi-Analytical Approach for the Linearized Vibration of
## 2 Analytical Approach for Nonlinear Governing Equations  
This study considers a clamped-clamped rectangular beam, as shown in Figure 1, where \(L\), \(b\), and \(h\) represent the length, width, and thickness of the beam, respectively, and \(P\) denotes the pressure load. The total displacements of a point along the \((x,z)\) coordinates are denoted as \((u,\,w)\). The equation of motion for bending vibrations, according to the Euler-Bernoulli beam theory, disregards the impact of \(x\)-axial displacement under pressure load [34]. The \(x\)-axial displacement components in the mid-plane are assumed to be zero. The von Karman strain and Hamilton's principle are utilized to derive the nonlinear static equilibrium equations and nonlinear governing equations of the beam. The basic assumptions made to derive the equation can be summarized as follows:  
* Assuming that the deformation of the beam obeys the hypothesis of the Bernoulli-Euler beam equation;
* Assuming that all strain components are small enough to satisfy [PERSON]'s law.  
The nonlinear strain-displacement relations can be expressed as  
\[\varepsilon_{x}=-z\frac{\partial^{2}w}{\partial x^{2}}+\frac{1}{2}(\frac{ \partial w}{\partial x})^{2} \tag{1}\]  
in which \(\varepsilon_{x}\) is the normal strain. Considering the thermal effect, the stress constitutive equations can be written as  
\[\sigma_{x}=E(\varepsilon_{x}-\alpha T) \tag{2}\]  
where \(\delta_{x}\) is the normal stress, \(E\) is the modulus of elasticity, \(\alpha\) is the coefficient of expansion, and \(\Delta T\) is the thermal load. And [PERSON]'s principle can be stated as  
\[\int_{t_{1}}^{t_{2}}{(\delta T-\delta U+\delta W)dt}=0 \tag{3}\]  
where \(\delta\) is the variational operator. \(\delta T\), \(\delta U\), and \(\delta W\) represent virtual kinetic energy, virtual strain energy, and virtual work, which can be written as  
\[\begin{array}{l}\delta T=\delta\int_{0}^{L}{\frac{1}{2}\rho A\dot{w}^{2} \mathrm{d}x}\\ \delta U=\delta\int_{0}^{L}\int_{-h/2}^{h/2}{\frac{1}{2}\sigma_{x}(\varepsilon _{x}-\alpha\Delta T)b\mathrm{d}z\mathrm{d}x}\\ \delta W=\delta\int_{0}^{L}{\frac{1}{2}\rho w-c\dot{w}\mathrm{d}x}\end{array} \tag{4}\]  
Figure 1: A clamped-clamped rectangular beam under pressure load.  
in which \(\rho\) is the density, \(A\) is the cross-sectional area, \(c\) is damping coefficient and \(P\) is the pressure load. The differential equation for nonlinear vibrations of a uniform beam with the effect of thermal load can be stated as  
\[\rho A\ddot{w}+c\ddot{w}-(\frac{3}{2}EAw^{\prime 2}+N_{x})w^{\prime\prime}+ EIw^{(4)}=P \tag{5}\]  
where \(I=bh^{3}/12\) is the cross-sectional moment of inertia. And \(N_{x}\) is the thermal stress resultant, which can be written as  
\[N_{x}=-\int_{-\frac{b}{2}}^{\frac{b}{2}}Eba\Delta Tdz=-EAa\Delta T \tag{6}\]
## 3 Results and Discussion
### Sample Preparation Optimization
Figure S1 illustrates the number of pesticides in rivers and sea waters that exhibit recoveries ranging from 70% to 120%. All pesticides exhibited %RSDr < 13% in both river and sea waters. The intra-laboratory repeatability, expressed as the relative standard deviation (RSD\({}_{\mathrm{wR}}\)%), was below 15%. It should be noted that the maximum acceptable threshold for all instances is 20%, and no pesticide exceeded this limit. Table 2 presents the method's recoveries, repeatability, and reproducibility for these compounds at three spiking levels. The use of matrix matched calibration curves alongside isotopically labeled internal standards (if available) effectively reduces calculation errors caused by ionization [29]. The matrix effect was investigated in marine waters, given its heightened significance resulting from the elevated salinity levels. Figure 2 depicts the matrix effect in the presence of pesticides. The matrix effect generally exhibited negative values (\(\mathrm{<}\mathrm{-}20\%\)), indicating moderate signal suppression for all studied pesticides while a stronger signal suppression was calculated for dimethoate (31% signal reduction). \begin{table}
\begin{tabular}{l c c c c} \hline
**Compound** & **MDL (ng L\({}^{-1}\))** & **MQL (ng L\({}^{-1}\))** & **Linearity Range (ng L\({}^{-1}\))** & **r\({}^{2}\)** \\ \hline Diuron & 3.0 & 10.0 & MOL–1000 & 0.9997 \\ Fluazifop-p-butyl & 2.0 & 6.0 & MOL–1000 & 0.9987 \\ Fluometuron & 30.0 & 90.0 & MOL–1000 & 0.9987 \\ Liuron & 8.0 & 23.0 & MOL–1000 & 0.9994 \\ Prometryn & 0.5 & 1.7 & MOL–500 & 0.9992 \\ S-metolachlor & 0.7 & 2.3 & MOL–500 & 0.9990 \\ Chlorantraniliprole & 1.0 & 3.3 & MOL–1000 & 0.9989 \\ Chlorpyrifos & 8.4 & 25.6 & MOL–1000 & 0.9991 \\ Dimethoate & 2.0 & 6.0 & MOL–500 & 0.9994 \\ Tebupirimfos & 9.0 & 30.0 & MOL–1000 & 0.9997 \\ Thiacloprid & 0.9 & 2.5 & MOL–500 & 0.9991 \\ Thiamethoxam & 3.0 & 10.0 & MOL–1000 & 0.9970 \\ Boscalid & 5.0 & 16.0 & MOL–500 & 0.9991 \\ Imazalil & 2.0 & 6.0 & MOL–500 & 0.9994 \\ Metalaxyl & 0.8 & 2.3 & MOL–500 & 0.9984 \\ Myelobutanil & 4.0 & 12.0 & MOL–1000 & 0.9987 \\ Acetamiprid & 0.9 & 2.5 & MOL–500 & 0.9997 \\ Fenpyroximate & 2.0 & 6.0 & MOL–500 & 0.9990 \\ \hline \end{tabular}
\end{table} 
Table 1: MDL, MQL, linearity range and correlation (r\({}^{2}\)) of the method.