esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Grade Prediction Method Based on DNNs
### Pearson Correlation Coefficient, Definitions of Distance and Thought on the KNN Algorithm
#### 5.1.2 Euclidean Distance  
Euclidean distance [39] is the most common and intuitive distance measurement method, usually used to describe the distance between two points in a 2D space. The Euclidean distance between the two points \(a(x_{11},x_{12},\cdots x_{1n})\) and \(b(x_{21},x_{22},\cdots x_{2n})\) in an n-dimensional space is expressed, as shown in Formula (3).  
\[d_{ab}=\sqrt{\sum_{k=1}^{n}(x_{1k}-x_{2k})^{2}} \tag{3}\]  
The Pearson correlation coefficient is used to propose an improved Euclidean distance measurement method. Assume that the n + 1 dimension is the dimension that must be calculated, it is weighted as per the correlation coefficient. Its expression is shown in Formula (4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Indicator Name** & **Mathematical Meaning** \\ \hline
1 & MAE & \(\frac{1}{n}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})\) \\
2 & MSE & \(\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\) \\
3 & MAPE & \(\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})/y_{i}|\times 100\%\) \\
4 & PUDA & \(\frac{1}{n-1}\sum_{i=1}^{n-1}\{(\hat{y}_{i+1}-\hat{y}_{i})\times(y_{i+1}-y_{i} )\geq 0?1:0\}\times\) \\  & & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mathematical meaning of each model indicator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Indicator Name** & **LR** & **RF** & **BPNN** & **DNN** \\ \hline
1 & Train MAE & 0.1364 & 0.0601 & 0.0738 & 0.0743 \\
2 & Train MSE & 0.0289 & 0.0059 & 0.0099 & 0.0092 \\
3 & Train MAPE & 21.55 & 31.70 & 353.1 & 35.68 \\
4 & Train PUDA & 58.52 & 93.56 & 76.24 & 85.57 \\
5 & Test MAE & 0.1499 & 0.1217 & 0.2036 & 0.0865 \\
6 & Test MSE & 0.0319 & 0.0229 & 0.0966 & 0.0115 \\
7 & Test MAPE & 22.60 & 19.45 & 33.31 & 34.59 \\
8 & Test PUDA & 52.38 & 50.00 & 59.09 & 80.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of each model indicator.  
\[d_{ab}=\sqrt{\sum_{k=1}^{n}\rho_{X_{k}X_{n+1}}(x_{1k}-x_{2k})^{2}} \tag{4}\]
# Targeting Type II Toxin-Antitoxin Systems as Antibacterial Strategies
## 4 Toxicity to Eukaryotic Cells  
Although TA _loci_ do not occur in eukaryotic genomes, most toxins of TA systems are endoribonucleases that cleave mRNAs irrespective of their origin, be that prokaryotic or eukaryotic. Therefore, before using these toxins in potential antibacterial strategies, it is important to consider possible cytotoxicity and side effects on human cells. Unfortunately, only a few studies have addressed this issue to date. Notably, toxin cytotoxicity was exploited in one study that employed an engineered version of the MazF toxin to reduce solid tumors in mice [87]. Similarly, the VapC toxin from a TA system of _M. tuberculosis_ demonstrated pro-apoptotic activity in human cancer cells, regardless of the  
Figure 4: Engineered CcdB toxin as a _V. cholerae_-specific antimicrobial agent. pLASMID—carrier plasmid molecule containing engineered genes of the _ccdAB_ TA system. (**a**) In _V. cholerae_ cells, by producing ToxRS and SetR transcription factors, transcription of the antitoxin gene is repressed, and expression of engineered toxin genes is activated, leading to cell death; (**b**) in other bacterial cells (lacking both transcription factors), the antitoxin gene is preferentially expressed.  
expression system used. In another study, [PERSON] and colleagues found that the MazF toxin dosage is a critical factor in determining its activity and cytotoxicity in eukaryotic cells [88; 89]. The above examples show that mammalian cells are sensitive to the ribonuclease activity of toxins. However, the ability of bacterial TA-system toxins to penetrate eukaryotic cells is currently unknown, and strategies that specifically target pathogens or utilize toxins that lack targets in human cells might prevent any deleterious effects.
## 5 Analysis of Calculation Examples
### Case Calculation and Analysis of E-Commerce Logistics Alliance of Forest Products
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters (CNY)** & **Reference Value of 20 GP** & **Reference Value of 40 GP** \\  & **Container Truck** & **Container Truck** \\ \hline Empty driving distance \(D_{U}\) & [200, 206, 210, 218] & [200, 206, 210, 218] \\ Loaded driving distance \(D_{L}\) & [172, 178, 182, 190] & [172, 178, 182, 190] \\ Empty-load fuel cost \(O_{U}\) & [2.52, 2.73, 2.94, 3.15] & [3.02, 3.27, 3.52, 3.78] \\ Full-load fuel cost \(O_{F}\) & [3.78, 4.09, 4.41, 4.72] & [4.53, 4.91, 5.29, 5.66] \\ Expressway toll fee \(H\) & [2.05, 2.10, 2.15, 2.20] & [2.05, 2.10, 2.15, 2.20] \\ Annual departure time \(n\) & [150, 157, 163, 170] & [150, 157, 163, 170] \\ Vehicle insurance premium \(C\) & 9000 & 9000 \\ Annual vehicle maintenance cost \(M\) & 3000 & 3000 \\ Driver’s annual salary \(S\) & 80,000 & 80,000 \\ Vehicle purchase cost \(B\) & 400,000 & 400,000 \\ Loading and unloading cost \(L\) & 3 & 3 \\ \hline \end{tabular}
\end{table} 
Table 2: Relevant parameters and parameter values for cost accounting. According to the organizational structure of sub-alliances in the e-commerce logistics alliance of forest products, we compare the changing trends of the total costs and the total profits of three e-commerce logistics enterprises of forest products when the enterprises transport separately, when two of the e-commerce logistics enterprises of forest products form an alliance for transportation, and when all three of the e-commerce logistics enterprises of forest products participate in the same alliance for transportation. The sum of cost and profit is expressed by trapezoidal fuzzy numbers, and the trend of change of the score is consistent, so it is only plotted with its lower bound value as a reference, and the trend of change is shown in Figure 5.  
It can be seen that the three e-commerce logistics enterprises of forest products, namely A, B, and C, have gradually developed from individual transportation to two-by-two cooperation and ultimately formed a grand alliance for the cooperation of the three e-commerce logistics enterprises of forest products, whose cooperation profit has increased, and the cooperation profit of the grand alliance is much larger than that of the three e-commerce logistics enterprises of forest products when they transported individually. Therefore, each e-commerce logistics enterprise of forest products is inclined to cooperative transportation. However, how to fairly and reasonably distribute the cooperative profit of alliance has become an important issue to be solved at present.
## 3 Experiments and Results
### Analysis of the Results  
Comparing the experimental results in Figure 11 in this paper with those in Figure 10 in Reference [25], the maximum comparison deviation of the horizontal straightness of our system and theirs is within 2.8 \(\upmu\)m and 1.5 \(\upmu\)m, respectively. The maximum comparison deviation of the vertical straightness is within 3.6 \(\upmu\)m and 1.16 \(\upmu\)m, respectively. The maximum comparison deviation of the roll is within 4.3\({}^{\prime\prime}\) and 1.01\({}^{\prime\prime}\), respectively. The maximum comparison deviation of the pitch is within 1.9\({}^{\prime\prime}\) and 0.89\({}^{\prime\prime}\), respectively. The maximum comparison deviation of the yaw is within 2.8\({}^{\prime\prime}\) and 0.89\({}^{\prime\prime}\), respectively.  
Compared with their system, our system has a larger measurement error. There are three main aspects that affect the measurement accuracy for the provided system, which include beam drift, crosstalk error, and installation and manufacturing errors. Their system analyzed these three errors. According to the above discussion and experiments, the beam drift has been effectively corrected and compensated to improve the measurement accuracy of the system. In theory, crosstalk errors, manufacturing errors, and installation errors can be compensated by ray tracing to establish a mathematical model. Owing to the limitations in length, we will analyze these errors and establish a compensation model in future works.
## 1 Introduction
[PERSON] and [PERSON] [27] predicted that, in _Ophrys_, the biosynthesis of alkenes would follow the biosynthetic pathway for alkanes [28], but with the addition of an extra desaturation step, potentially achieved by stearoyl-acyl carrier protein desaturases (SAD). Later, three putative SAD genes (SAD1-SAD3) were isolated [29]. Transgenic expression and in vitro enzyme assays revealed SAD2 to be a functional desaturase capable of introducing 18:1 \(\Delta^{9}\) and 16:1 \(\Delta^{4}\) fatty acid intermediates, from which it was hypothesized that (_Z_)-9 alkenes and (_Z_)-12 alkenes are built. Three additional putative SAD genes (SAD4-SAD6) were also identified from an _O. splegodes_ transcriptome [30]. In _O. splegodes_ and _O. exaltata_, SAD1 and SAD2 expression levels were shown to be significantly correlated with (_Z_)-9 and (_Z_)-12-alkene production, while high SAD5 expression was correlated with the (_Z_)-7-alkene production unique to _O. exaltata_[31]. In vitro enzyme activity studies further showed that a putative housekeeping desaturase, SAD3, catalyses the general reactions of stearate to oleate (18:0-ACP to 18:1 \(\Delta^{9}\)-ACP), and palmitate to palmitate (16:0-ACP to 16:1 \(\Delta^{9}\)-ACP), whereas SAD5 is a specialized 16:0 \(\Delta^{9}\)-ACP enzyme [32]. Subsequent elongation of a 16:1 \(\Delta^{9}\)-ACP to a 26:1 \(\Delta^{19}\)-coenzyme A precursor, followed by decarbonylation, would yield the (_Z_)-7 alkene (25:1 \(\Delta^{7}\)) that characterizes _O. exaltata_. In _O. speculum_, the pollinator attractants were also identified as carboxylic acid derivatives [17]. The most attractive compounds from both floral extracts and females of the scoliid wasp pollinator _Campsoscolia ciliata_ were (\(\omega\)-1)-hydroxy- and -oxo acids. However, it is noteworthy that the pseudo-copopulation rates in field bioassay experiments more than doubled when aldehydes such as (_Z_)-9-octadecenal and octadecanal, together with the esters ethyl linoleicate and ethyl oleate, were added to the dummy female [17]. The phylogenetic relationships within _Ophreys_ are currently under debate [33; 34; 35; 36; 37], with some phylogenetic analyses indicating that the _Argogorytes_-pollinated _O. insecifera_ group represents a basal taxon, while the latest studies place the _O. fusca_ complex, including _O. iricolor_, as ancestral [36; 37]. All studies agree that wasp pollination is ancestral to bee pollination in _Ophrys_. To obtain a broader understanding of the chemical details of semiochemicals in the wasp-pollinated _O. insecifera_, and sex pheromone candidates in its pollinator _A. fargeii_, we used GC-EAD, GC-MS, microderivatisation reactions, and organic synthesis to identify EAD-active compounds. These semiochemicals were compared with previously identified pollinator attractants from the bee-pollinated _O. sphegodes_ and wasp-pollinated _O. speculum_, and biosynthetic relationships within _Ophrys_ were proposed.
## 3 Results and Discussion
### Biochar Characterization
#### 3.1.2 Elemental Analysis and Molar Ratios
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Yield \%**} & \multicolumn{4}{c}{**Ultimate Analysis**} & \multicolumn{4}{c}{**Proximate Analysis**} & \multicolumn{4}{c}{**Molar Ratio**} \\ \cline{3-11}  & & \multicolumn{1}{c}{**C \%**} & \multicolumn{1}{c}{**H \%**} & \multicolumn{1}{c}{**N \%**} & \multicolumn{1}{c}{**O \% (*)**} & \multicolumn{1}{c}{**Moisture \%**} & \multicolumn{1}{c}{**Ash \%**} & \multicolumn{1}{c}{**H/C**} & \multicolumn{1}{c}{**O/C**} & \multicolumn{1}{c}{**(O+N)/C**} \\ \hline
**BC300** & 43 & 63.65 (4.44) & 3.82 (0.33) & 1.01 (0.06) & 18.78 & 4.47 & 12.74 & 0.72 & 0.22 & 0.24 \\  & & & & (4.83) & (0.26) & (0.02) & (0.01) & (0.07) & (0.07) \\ \hline
**BC400** & 38 & 64.95 (0.84) & 2.96 (0.04) & 0.89 (0.01) & 17.04 & 3.80 & 14.16 & 0.55 & 0.20 & 0.22 \\  & & & & (0.80) & (0.12) & (0.12) & (0.01) & (0.01) & (0.01) \\ \hline
**BC500** & 35 & 70.75 (1.24) & 2.33 (0.06) & 0.86 (0.01) & 9.85 & 3.20 & 16.21 & 0.40 & 0.10 & 0.12 \\  & & & & (1.29) & (0.09) & (0.20) & (0.00) & (0.01) & (0.02) \\ \hline
**BC600** & 33 & 74.76 (0.88) & 1.60 (0.04) & 0.71 (0.00) & 7.13 & 2.43 & 17.8 & 0.26 & 0.05 & 0.06 \\  & & & & (1.94) & (0.10) & (0.04) & (0.00) & (0.01) & (0.01) \\ \hline
**BC700** & 32 & 72.76 (1.95) & 0.90 (0.01) & 0.65 (0.01) & 5.32 & 2.87 & 18.37 & 0.14 & 0.08 & 0.09 \\  & & & & (0.91) & (0.05) & (0.18) & (0.01) & (0.02) & (0.02) \\ \hline \hline \end{tabular} (\(\uparrow\)) Oxygen determined by difference (100% - (C + H + N + ash \(\times\))). All analyses were conducted in triplicate (\(n\) = 3), values between brackets represent the standard deviation.  
\end{table} 
Table 2: Chemical characteristics of biochar samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Mass \%** & **C** & **O** & **Mg** & **Al** & **Si** & **S** & **Cl** & **K** & **Ca** & **O/C** \\ \hline
**Sample** & & & & & & & & & & \\ \hline
**BC300** & 57.00 & 32.72 & 0.86 & 0.24 & 1.71 & 0.63 & 1.83 & 3.47 & 1.54 & 0.57 \\
**BC400** & 59.62 & 29.70 & 0.26 & - & 1.42 & 0.53 & 2.48 & 4.56 & 1.43 & 0.50 \\
**BC500** & 66.41 & 15.52 & 0.97 & - & 1.21 & 0.89 & 5.43 & 6.80 & 2.77 & 0.23 \\
**BC600** & 69.19 & 11.88 & 0.94 & - & 1.21 & 0.75 & 5.84 & 7.04 & 3.16 & 0.17 \\
**BC700** & 69.32 & 12.47 & 1.05 & - & 1.18 & 0.79 & 5.34 & 6.34 & 3.52 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elemental composition of biochar samples.
## 2 Materials and Methods
### Numerical Studies
#### 2.3.3 Numerical Strain Evaluation  
The resulting distributions for the radial strains \(\varepsilon_{r}\) and vertical strains \(\varepsilon_{z}\) from the numerical models were evaluated at the designated cross sections shown in Figure 5b.  
Figure 6: **(a)** Normal force distribution within the cylindrical sample with cross sectional area \(A_{cross}\) resulting from the applied loading in terms of prescribed displacement \(u_{z}\). **(b)** Flow chart diagram describing the process of determining the value \(u_{z}\) to yield the required axial stress state \(\sigma_{z,req}\) (in this study, 10 MPa).  
The vertical strains \(\varepsilon_{z}\) were computed for pairs of vertically aligned circumferential mesh nodes at cross sections H25 and H75 (\(P_{n,0(H25)}\) and \(P_{n,0(H75)}\)) from the relative change in vertical distance between these nodes. In total, 56 circumferential mesh nodes \(n\) were present in each of the considered cross sections. Figure 7a shows the distribution of these mesh nodes exemplary for cross section H75 besides the considered distances and length changes for the determination of the vertical strains \(\varepsilon_{z}\). Referring to this figure, \(\varepsilon_{z,n}\), corresponding to the vertical strain between a single pair of mesh nodes, can be calculated acc. to Equation (8).  
\[\varepsilon_{z,n}=\frac{\Delta l_{z}}{l_{z,0}} \tag{8}\]  
The determination of the radial strains \(\varepsilon_{r}\) around the cylindrical sample was carried out for each circumferential mesh node with orientation \(\theta_{n}\) of each cross section individually. The absolute value for the radial strain corresponding to a single node \(n\) computes as  
\[\varepsilon_{r,n}=\left|\frac{r_{0}-r_{def,n}}{r_{0}}\right|, \tag{9}\]  
where \(r_{0}\) equals the initial radius of the sample and \(r_{def,n}\) is computed as the horizontal distance between the deformed \(n\)th circumferential mesh node and the shifted center point \(M\) of the individual cross section. Figure 7b displays the considered point displacements used to derive the deformed distance \(r_{def,n}\) for a cross sectional mesh node oriented at \(\theta_{n}\) and located at the sample circumference.
## 3 Experimental Results and Discussion
### Young's Modulus  
#### 3.3.1 Initial Young's Modulus  
The initial elastic modulus is taken as the slope of the best-fit line through the stress-strain curve between 0% and 30% of the peak stress and is provided in Table 7. Specifically, the values of the Young's modulus fluctuated between 2350.86 MPa and 4239.79 MPa, with a mean value of 3359.53 MPa and standard deviation of 611.00 MPa.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen No.** & **Strength (MPa)** & **Critical Strain (mm/mm)** \\ \hline
1 & 15.65 & 0.0057 \\
2 & 14.96 & 0.0064 \\
3 & 16.70 & 0.0052 \\
4 & 15.02 & 0.0051 \\
5 & 15.76 & 0.0065 \\
6 & 14.09 & 0.0075 \\
7 & 15.30 & 0.0075 \\
8 & 17.65 & 0.0073 \\
9 & 16.11 & 0.0057 \\
10 & 15.91 & 0.0061 \\
11 & 14.44 & 0.0077 \\
12 & 15.13 & 0.0068 \\ Mean & 15.23 & 0.0064 \\ STD. & 1.83 & 0.0037 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Strengths of CA specimens.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specimen No.** & **Young’s Modulus (MPa)** \\ \hline
1 & 4100.23 \\
2 & 3316.96 \\
3 & 4135.15 \\
4 & 4239.79 \\
5 & 3365.67 \\
6 & 2477.80 \\
7 & 2761.34 \\
8 & 3186.02 \\
9 & 3579.87 \\
10 & 3799.22 \\
11 & 2350.86 \\
12 & 3001.41 \\ Mean & 3359.53 \\ STD & 611.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Young’s Modulus of the 12 specimens.
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 3 LBTM Operation  
Following the 2016 version of the the ETSI EN 302-208-1 standard [27], we assume a maximum interrogation time (IT) of 4 s, and minimal waiting time between two consecutive ITs of at least 100 ms. The access to the channel [28] operates as follows:  
1. Before occupying the channel, a reader probes the channel during a listen period of 5 ms. Henceforth, this period is termed as _listening time_ (LT).
2. If the channel is free during this time, the reader can start the interrogation process.  
If the channel is busy, it waits for a contention period between 0 and 5 ms selected at random (in 11 discrete values).
4. It repeats the random time until the channel is free, and then it starts the interrogation.  
The LBTM protocol uses this same structure, and the single difference appears in point 3, where a back-off time of duration \(\Delta_{j}\) LT periods (including the first LT) is inserted before the contention periods, where \(j\) indicates the particular traffic class, being \(j=1,\ldots,C\), with \(C\) being the total number of classes. Note that the back-off period has a fixed length for each class. This way, when several readers of different classes are waiting for the channel, it is more likely that a high-priority one occupies it after it becomes idle. Moreover, after the corresponding back-off period, readers access the channel with the same priority. This way, starvation of the lower priority ones is avoided.  
Finally, an additional reason for keeping the LBTM protocol simple is to facilitate its implementation in actual readers already supporting LBT [29]. With LBTM, a reader would require a single additional configuration parameter (the \(\Delta\) associated with its traffic). Here, we assume that each reader is associated exclusively with a fixed class, although possible variations where the reader can autodetect the underlying traffic class dynamically could also be considered. We describe an online possibility in Section 7. Note that different readers may have the same underlying traffic and can be assigned the same \(\Delta\).  
An example of the operation of LBTM is shown in Figure 1, for a \(C=2\) classes configuration with 3 readers. When the channel is idle, regardless of its class, the reader just uses the channel (Reader 1 in the first row of figure). If the channel is busy, a short back-off time (low \(\Delta\)) is used for the high-priority traffic class (middle row in Figure 1), whereas the low-priority traffic shown below has a long back-off time (high \(\Delta\)). As can be seen, back-off periods delay the access of the low-priority reader which--instead of starting its interrogation process earlier--has to delay it until after the high-priority reader finishes.
## 2 Methodology and Data
### Satellite Data
#### 2.1.3 GF-5 Data  
The GF-5 satellite was launched on 9 May 2018 from the Taiyuan Satellite Launch Center using a Long March 4C rocket. It is China's first hyperspectral comprehensive observation satellite. For the GF-5 series, the AHSI has a spectral resolution of about 10 nm in the SWIR band and 5 nm in the VNIR band. There are 150 spectral bands in the visible and near-infrared (VNIR) regions and 180 bands in the short-wave infrared (SWIR) regions, totaling 330 bands. The data are available from China Centre for Resources Satellite Data and Application.
## 2 Materials and Methods
### Identification of Flavonoids in SJFE  
Rutin (a quercetin glycoside), quercetin, kaempfer, and isorhamnetin in SJFE were quantified using HPLC. SJFE (20 mg) was dissolved in methanol (10 mL) and sonicated for 1 h (Powersonic 520, HWASIN Technology, Republic of Korea) and then filtered through a 0.45 \(\upmu\)m membrane filter (Sartorius, Gottingen, Germany). An aliquot (5 \(\upmu\)L) of the filtrate was injected into the column. The authentic compound of rutin (5 \(\upmu\)g) was dissolved in methanol (10 mL), and an aliquot (5 \(\upmu\)L) of the resulting solution was injected into the column. The conditions of chromatography were as follows: pump, Agilent Tech 1260 Infinity II (Agilent Technology, Santa Clara, CA, USA); column; YMC-Pack Pro, C18, 150 \(\times\) 4.6 mm, 5 \(\upmu\)m, 12 nm (YMC, Kyoto, Japan); mobile phases, 0.2% formic acid in DW (A) and acetonitrile (B); gradient of mobile phases, 0-8 min, (A) 82%, 8-10 min, (A) 75%, 10-20 min, (A) 70%, 20-25 min, (A) 64%, 25-30 min, (A) 60%, 30-31 min, (A) 5%, 31-40 min, (A) 5%; flow rate, 1.0 mL/min; and detection with a diode array detector at 254 nm.  
For the analysis of quercetin, kaempfer, and isorhamnetin, the SJFE (300 mg) was dissolved in 10 mL of a hydrolyzing solution (a mixture of 100 mL ethanol, 40 mL DW, and 16 mL HCl) and reacted at 90 \({}^{\circ}\)C for 1 h. The reaction was filtered through a 0.45 \(\upmu\)m filter. The 3 authentic compounds (quercetin 5.06 \(\upmu\)g, kaempfer 4.50 \(\upmu\)g, and isorhamnetin 2.39 \(\upmu\)g each) were dissolved in 50 \(\upmu\)L of methanol. Aliquots (5 \(\upmu\)L) of the filtrate or each solution were injected into the column. The conditions of chromatography were the same as those above except for the following: column, YMC-Triart, C18, 250 \(\times\) 4.6 mm, 5 \(\upmu\)m (YMC, Kyoto, Japan); mobile phase; acetonitrile containing 0.1% phosphoric acid (A) and DW containing 0.1% phosphoric acid (B); the gradient of the mobile phase, 0-35 min, (A) 40%; detection with a diode array detector at 370 nm; and temperature, 35 \({}^{\circ}\)C.