esa-sceva/satcom-chunk-collection huggingface dataset:
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 4 Materials and Methods
### Grape Sampling, Winemaking, and Wine Analysis
In absence of commercial standards, compounds with the same chromophore were used: stilbenes using \(t\)-resveratrol; flavanols using catechchin, epigallocatechin, and procyanidin B1; flavonols using isorhannetin-3-\(O\)-glucose, kaempfer-3-\(O\)-glucose, myricetin, quercetin-3-\(O\)-galactoside, quercetin-3-\(O\)-glucose, quercetin-3-\(O\)-glucuron, and syringe-3-\(O\)-glucose; hydroxybenzoic acids using gallic acid; hydroxycinnamic acid derivatives using caffeic and \(p\)-coumaric acids, and anthocyanins using malvidin-3-\(O\)-glucose (Sigma-Aldrich, St. Louis, MO, USA; Fluka, Buchs, Germany; Extrasynthese, Genay, France). The total contents of each phenolic group were obtained as the sum of their respective individual compounds. For VOC extraction, 1 mL of wine per sample was transferred to a 10 mL headspace screw cap vial and subjected to headspace solid-phase microextraction (HS-SPME). A 65 uM PDMS/DVB fiber (Supelco, Bellefonte, PA, USA) was used for this analysis. Pre-incubation and extraction were carried out at 50 \({}^{\circ}\)C for 10 and 20 min, respectively, followed by desorption for 1 min at 250 \({}^{\circ}\)C in the splitless mode. The VOCs trapped on the fiber were analyzed by GC-MS using an autosampler COMBI PAL CTC Analytics (Zwingen, Switzerland), a 6890 N GC Agilent Technologies (Santa Clara, CA, USA), and a 5975B Inert XL MSD Agilent equipped with an Agilent J&W Scientific DB-5 fused silica capillary column (5%-phenyl-95%-dimethylpolysiloxane as stationary phase, 60 m length, 0.25 mm i.d., and 1 um thickness film). Open temperature conditions were 40 \({}^{\circ}\)C for 2 min, 5 \({}^{\circ}\)C min\({}^{-1}\) ramp up to 250 \({}^{\circ}\)C, followed by isothermal hold at 250 \({}^{\circ}\)C for 5 min. Helium was used as the carrier gas at 1.4 mL min\({}^{-1}\) constant flow. \(m/z\) detection was obtained by an Agilent mass spectrometer operating in the EI mode (ionization energy of 70 eV; source temperature 230 \({}^{\circ}\)C), for which data were acquired in the scanning mode (mass range \(m/z\) 35-220). Chromatograms and spectra were recorded and processed using Enhanced ChemStation software for GC-MS (Agilent). Compound identification was based on the comparison between the MS for each putative compound with those of the NIST 2005 Mass Spectral library, as well as the match to a GC retention time and Mass Spectra custom library generated using commercially available compounds.
## 3 Calculation Results and Analysis
### Analysis of Calculation Results in Case 3
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Vertical**} & \multicolumn{2}{c}{**Bridge Pier 1**} & \multicolumn{2}{c}{**Bridge Pier 2**} & \multicolumn{2}{c}{**Bridge Pier 3**} & \multicolumn{2}{c}{**Bridge Pier 4**} \\
**Settlement** & **Top** & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** \\
**displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** \\
**/cm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** \\ \hline
10 & 3.044 & 1.443 & 2.985 & 2.064 & 2.670 & 2.370 & 2.400 & 1.216 \\
20 & 4.304 & 1.878 & 4.189 & 2.478 & 3.578 & 2.723 & 3.073 & 1.701 \\
30 & 5.270 & 2.161 & 5.099 & 2.656 & 4.170 & 2.639 & 3.429 & 2.012 \\
40 & 5.887 & 2.260 & 5.663 & 2.531 & 4.391 & 2.053 & 3.408 & 2.129 \\
50 & 6.348 & 2.288 & 6.072 & 2.287 & 4.441 & 1.231 & 3.210 & 2.145 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Horizontal displacement of bridge pier. Figure 11: The inclination of each bridge pier under different settlement amounts.
## 2 Materials and Methods
### Validation of the Analytical Method  
The established HPLC assay was verified with respect to linearity, range, limit of detection (LOD), limit of quantification (LOQ), accuracy (recovery), and precision tests according to the International Conference on Harmonisation guidance [26]. Linearity of the response for the analysis of compounds **1-18** was evaluated based on the coefficient of determination (\(r^{2}\)) of the calibration curve prepared at different concentrations (0.31-20 \(\upmu\)g/mL for compounds **7**, **8**, **13**, **14**, and **18**; 0.78-50 \(\upmu\)g/mL for compounds **1-3**, **5**, **6**, **9**, **11**, **12**, and **15-17**; 1.25-80 \(\upmu\)g/mL for compound **4**; and 1.56-100 \(\upmu\)g/mL for compound **10**).  
Accuracy was assessed by conducting extraction recovery tests using the standard addition method.  
The precision of the assay was evaluated by plotting the relative standard deviation (RSD) of each parameter, such as repeatability and intra- and inter-day precisions. In other words, the repeatability was evaluated by the RSD value of the retention time and peak area of each marker component after six repeated measurements of a standard solution.  
Intra- and inter-day precisions were assessed by calculating the RSD value based on the data measured on one day and on three consecutive days, respectively.  
System suitability in the established assay was verified by confirming parameters such as the capacity factor (\(k^{\prime}\)), selectivity factor (\(\alpha\)), resolution (\(Rs\)), number of theoretical plates (\(N\)), and tailing factor (\(Tf\)) values [27].
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Analysis of Fiber Bragg Gratings
#### 2.2.1 Coupled-Mode Theory (CMT)-Based Analysis Method
The elements of the matrix \(M_{U,i}\) are derived from the CMT as  
\[M_{i,11}(f)=\frac{\gamma\cosh(\gamma\cdot\delta L_{i})+j(\Delta\beta/2)\sinh( \gamma\cdot\delta L_{i})}{\gamma}\exp(j\beta_{0}\cdot\delta L_{i}) \tag{11}\]  
\[M_{i,12}(f)=\frac{j\kappa\sinh(\gamma\cdot\delta L_{i})}{\gamma}\exp(-j\beta_{0 }\cdot\delta L_{i}) \tag{12}\]  
\[M_{i,21}(f)=\frac{j\kappa^{*}\sinh(\gamma\cdot\delta L_{i})}{\gamma}\exp(j \beta_{0}\cdot\delta L_{i}) \tag{13}\]  
\[M_{i,22}(f)=\frac{\gamma\cosh(\gamma\cdot\delta L_{i})-j(\Delta\beta/2)\sinh( \gamma\cdot\delta L_{i})}{\gamma}\exp(-j\beta_{0}\cdot\delta L_{i}) \tag{14}\]  
Eventually, the total grating response is obtained by multiplying the matrices of the different layers in the appropriate order:  
\[[M_{FBG}]=[M_{U,1}]\cdot[M_{U,2}]\cdot\ldots\cdot[M_{U,i}]\cdot\ldots\cdot[M_ {U,N-1}]\cdot[M_{U,N}] \tag{15}\]  
The grating's reflection and transmission spectral responses are obtained as:  
\[H_{R}(f)=\left.\frac{E^{-}(z=0,f)}{E^{+}(z=0,f)}\right|_{E^{-}(z=L,f)=0}=\frac {M_{FBG,21}(f)}{M_{FBG,11}(f)} \tag{16}\]  
\[H_{T}(f)=\left.\frac{E^{+}(z=L,f)}{E^{+}(z=0,f)}\right|_{E^{-}(z=L,f)=0}=\frac {1}{M_{FBG,11}(f)} \tag{17}\]  
The number of layers (\(N\)) selected to implement the piecewise calculation is established by the required accuracy. However, it is important to note that the section lengths must accomplish that \(\delta L_{i}\gg\Lambda_{\max}\), with \(\Lambda_{\max}\) the longer period found along the grating. Otherwise, the approximations considered in the CMT to attain Equations (7) and (8) will start to fail [6; 27]. This analysis method delivers an accurate outcome for the broad majority of FBGs of practical interest, i.e., those containing an arbitrary apodization profile, arbitrary period profile and even those including phase shifts, with relatively low computational time. For gratings incorporating phase shifts in their period, also known as phase-shifted gratings [6], a phase-shift matrix \([M_{ps,j}]=\left[ \begin{array}{cc}\exp\{j\phi_{p}/2\}&0\end{array} ; \begin{array} []{cc}0&\exp\{-j\phi_{p}/2\}\end{array} \right]\) is to be inserted in the adequate location in Equation (15), where \(\phi_{p}\) is the shift in the phase of the refractive index modulation [6]. Yet, CMT-TMM is not valid for the analysis of specific grating profiles including superimposed FBGs or gratings based on superstructures.
## 2 Proposed Segmentation Methodology  
The segmentation procedure proposed in the present study includes three steps: (1) neighborhood definition; (2) attribute computation; (3) clustering of points. One should note that the proposed methodology focuses on planar segmentation since most of the man-made structures consist of planar patches. As the first step, the neighborhood definition adopted in this study considers both the three-dimensional proximity between points and the shapes of surfaces, in order to increase the homogeneity among neighbors. Point attributes, then, are computed using the neighboring points identified as based on the neighborhood definition.  
Subsequently, an attribute vector with two parameters is acquired and utilized to reduce the dimensions of the attribute space. Afterwards, clustering of points on the same plane is carried out while considering, simultaneously, the similarity in the attribute space and the proximity in the object space. These three segmentation-procedural steps will be explained in detail in the following subsections.
## 4 Methodology
### Optimization Model Formulation
#### 4.2.2 Parameters  
We have parameters indicating machine capacities, setup times, and batch-dependant processing times. We would consider the filtering-stage processing time as minimal; for modelling purposes, we treat them independently, but essentially, once assigned both machines, they operate as one. The \(\beta_{ob}\) matrix or parameters, in particular, indicate SKU format admissibility on bottling lines, due to real production-environment constraints.  
* \(p_{o}^{F}\): Filtering time for order \(o\).
* \(p_{o}^{S}\): Bottling time for order \(o\).
* \(CT\): Time required to reconfigure line.
* \(M\): A sufficiently large constant (Big M).
* \(Q_{f}^{F}\): Flow rate of filtration unit \(f\).
* \(Q_{b}^{B}\): Flow rate of bottling unit \(b\).
* \(\beta_{ob}:\begin{cases}1&\text{if bottling unit $b$ can process order $o$}\\ 0&\text{otherwise}\end{cases}\)  
Every order has a vector of attributes that will be considered for the calculation of the sequence-dependent setup times. Specifically, they include the volume of the order to be processed \(V_{o}\) and the respective SKU requirements. We have also specified setup times for each task to be performed in the aggregated setup time.  
* \(V_{o}\): Volume of order \(o\).
* **Batch Attributes:** BottleSize\({}_{o}\); Color\({}_{o}\); Complexity\({}_{o}\); Height\({}_{o}\); Diameter\({}_{o}\); FilterReq\({}_{o}\); Corking\({}_{o}\); Packaging\({}_{o}\).
* **Setup Time Components:*
* \(W_{3}\): Base wash time.
* \(W_{4}\): Wash time if wine color changes.
* \(W_{5}\): Sanitation time if complexity changes.
* \(st_{\text{bottle}}\): Time for bottle format changeover.
* \(st_{\text{height}}\): Time for height adjustment.
* \(st_{\text{diameter}}\): Time for diameter adjustment.
* \(st_{\text{filter}}\): Time for filter changeover.
* \(st_{\text{corking}}\): Time for corking system change.
* \(st_{\text{packaging}}\): Time for packaging format change.
* \(st_{\text{label}}\): Time for label change.  
In the following expression, the bottleneck of the production setup is highlighted. Due to operational constraints, the production line operates at the minimal flow rate between both machines. So the processing time would initially consider the volume over the flow rate.  
\[Q_{l} =\min\left(Q_{f},Q_{b}\right), \forall l\in L\] \[p_{o}^{F} =p_{o}^{B}=\frac{V_{o}}{Q_{l}}, \forall i\in I,\forall l\in L\]
## Abbreviations  
The following abbreviations are used in this manuscript:  
ANFIS  
Adaptive Neuro-Fuzzy Inference System  
AR  
Accept Ratio  
BH-VQA  
Blind High Frame Rate Video Quality Assessment  
CRF  
Constant Rate Factor  
FCM  
Fuzzy C-Means  
FIS  
Fuzzy Inference System  
FPS  
Frames per Second  
FRQM  
Frame Rate Quality Metric  
FSIM  
Feature Similarity Index  
GSTI  
Gradient-based Spatio-Temporal Index  
HFR  
High Frame Rate  
MOS  
Mean Opinion Score  
RMSE  
Root Mean Squared Error  
MS-SSIM  
Multi-Scale Structural Similarity  
PCC  
Pearson Correlation Coefficient  
PSNR  
Peak Signal-to-Noise Ratio  
QoE  
Quality of Experience  
RoI  
Range of Influence  
RR
Reject Ratio  
SAR  
Synthetic Aperture Radar  
SD  
Standard Deviation  
SF  
Squash Factor  
SI  
Spatial Information  
SpEED  
Spatio-Temporal Entropic Differencing  
SROCC  
Spearman Rank-Order Correlation Coefficient  
SSCQE  
Single-Stimulus Continuous Quality Evaluation  
SSIM  
Structural Similarity Index  
ST-RRED  
Spatio-Temporal Reduced Reference Entropy Difference  
TI  
Temporal Information  
VQA  
Video Quality Assessment  
VMAF  
Video Multi-method Assessment Fusion
## 2 Data and Methods
### Reference Data
#### 2.1.2 ASCAT Data  
The Advanced Scatterometer (ASCAT) is carried on the meteorological operational (MetOp) platforms that constitute the space segment of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System (EPS). It is a real aperture C-band VV-polarized radar with incidence angles ranging from 25\({}^{\circ}\) to 65\({}^{\circ}\). It is well calibrated using transponders with an absolute calibration precision better than 0.25 dB [19]. The ASCAT level-1B 12.5 km \(\sigma^{0}\) product produced by EUMETSAT from January 2014 to December 2016 was used. The ASCAT \(\sigma^{0}\) measurements influenced by non-wave phenomena were rejected based on the following principles: (1) the default filling data and the abnormal data flagged as bad-quality were excluded; (2) the measurements contaminated by rainfall were eliminated (the rainfall information was provided by ECMWF); and (3) the measurements contaminated by land/island were eliminated.  
Then, the \(\sigma^{0}\) measurements of ASCAT were collocated with the buoy observations of sea surface winds obtained from the National Data Buoy Center (NDBC) with the following criteria: time separation within 30 min and spatial separation of less than 25 km. If one buoy datum matches several ASCAT \(\sigma^{0}\) data, we used the one with the closest distance. Finally, 3,502,466 matching pairs were obtained. The buoys were all located in waters more than 50 km away from land and 150 m deep. The buoy wind speeds sampled at 2-5 m heights above the sea surface were converted to 10 m neutral wind speeds based on the logarithmic marine boundary layer assumption proposed by [PERSON] and [PERSON] [20] after eliminating low-quality observations.
## 2 Materials and Methods
### Data
#### 2.1.3 Data Range  
The data range for this study was confined within the fourth ring road of Shenyang. The selection of Shenyang City was primarily due to the following reasons:  
1. Representation: As the capital of Liaoning Province, Shenyang is highly representative of the second-hand housing market in Northeast China. It is an influential economic, cultural, and transportation hub in the region, making it a significant area for study.
2. Market size: Shenyang boasts a large population, a positive population growth rate, a relatively developed economy, and a substantial second-hand housing market. Studying this market in Shenyang provides comprehensive data, facilitating in-depth analysis and research.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time/Area**} & **Tiexi** & **Heping** & **Shenhe** & **Hunnan** & **Dadong** & **Huanggu** & **Yuhong** & **Sujiatum** & **Shenbei New** & \multirow{2}{*}{**Total**} \\  & **District** & **District** & **District** & **District** & **District** & **District** & **District** & **District** & **District** & **District** \\  & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** & **/Unit** \\ \hline
2018–2019 & 1160 & 1199 & 772 & 2662 & 443 & 380 & 1096 & 165 & 553 & 8430 \\
2019–2020 & 2374 & 2367 & 1840 & 4474 & 1008 & 1436 & 3198 & 514 & 1831 & 19,042 \\
2020–2021 & 2673 & 3048 & 1685 & 4601 & 1280 & 2216 & 3368 & 750 & 1881 & 21,502 \\
2021–2022 & 1938 & 1999 & 643 & 3374 & 869 & 1364 & 2112 & 408 & 1497 & 14,204 \\
2022–2023 & 3310 & 3649 & 955 & 5775 & 1231 & 2726 & 3154 & 665 & 2989 & 24,454 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of transaction data of second-hand houses in Shenyang City and its regions.  
Figure 1: Trend of transaction volume of second-hand houses in Shenyang and its regions.  
(3) Data availability: Accurate data related to the secondary market are more readily available in Shenyang, which is crucial for conducting both quantitative and qualitative analyses.  
The algorithm results are specifically distributed within the fourth ring road of Shenyang. There is a notable disparity in development levels between various districts within Shenyang and the main urban areas. This study focuses on Tiesi District, Heping District, Shenhe District, Human District, Dandong District, Huanggu District, Yuhong District, Sujiatum District, and Shenbei New District. Regions with 0-100 second-hand housing transactions over the past five years are considered inactive second-hand housing market areas and are excluded from the study. This exclusion ensures that the study is more targeted and focuses on analyzing the market situation and characteristics of the major regions, thus providing more precise and targeted research findings. It also avoids confusion and inaccuracies in the results due to differences between primary and secondary urban areas.
# Cytoprotective Effect of Acid-Hydrolyzed Sesame Leaves against Oxidative Stress in HepG2 Cells
###### Abstract  
In this study, we investigated the cytoprotective effects of acid-treated sesame leaf extracts against oxidative damage in HepG2 cells. Treatment with 0.1 M HCl of sesame leaves significantly increased their verbascoside content (4.398 g/100 g) compared to non-acid-treated leaves (3.950 g/100 g). Acid-treated sesame leaf extract (ASLE) showed no cytotoxicity in HepG2 cells. ASLE conferred a greater cytoprotective effect against oxidative insult than a methanol extract of sesame leaves (SLE), verbascoside, and a vehicle control group. ASLE treatment also significantly inhibited reactive oxygen species generation in response to oxidative stress. Treatment with _tert_-butyl hydroperoxide increased malondialdehydedehyde (MDA) levels, and depleted reduced glutathione (GSH). However, ASLE treatment significantly ameliorates this MDA and GSH depletion. Moreover, ASLE increased the activities of antioxidant enzymes including catalase, glutathione reductase, and glutathione peroxidase. Phenolic compounds in ASLE and SLE were characterized using UPLC-Q-TOF/MS analysis. A total of 29 iridoid and phenol compounds were tentatively identified in ASLE, and 27 compounds were observed in SLE. These results suggest that acid treatment of sesame leaves enhances the protective effects of their extract against oxidative stress by modulating antioxidant enzymes in HepG2 cells.  
Keywords:seame leaves; verbascoside; acid hydrolysis; HepG2 cells; oxidative stress +
Footnote †: journal: Applied Sciences
## 2 Methods
### Entropy Method  
The entropy weighting method is an objective weighting method, the basic idea of which is to determine the objective weight according to the variability of the indicator. It is based on the principle that the smaller the variability of an indicator, the less information it reflects, and the lower the corresponding weight should be. The process to generate comprehensive agricultural investment climate evaluation grade according to entropy method included the following steps.  
Step 1: Data selection  
Set the number of indicators as \(m\) and number of samples as \(n\). \(X_{ij}\) is the value of \(j\)th indicator of the \(i\)th sample, \(i=1,2,3,\ldots,n;j=1,2,3,\ldots,m\).  
Step 2: Data standardization processing  
In cases where the units of measurement and direction of the indicators are not uniform, the data need to be standardized, and to avoid meaningless logarithms for the entropy values, a smaller order of magnitude of real numbers can be added to each 0.00 value, e.g., 0.01 or 0.0001.  
For positive indicators (a higher value indicates a better performance):  
\[X^{\prime}=\ \frac{X_{ij}-Min\Big{(}X_{ij}\Big{)}}{Max\Big{(}X_{ij}\Big{)}- Min\Big{(}X_{ij}\Big{)}} \tag{5}\]  
For negative indicators (a lower value indicates a better performance):  
\[X^{\prime}=\ \frac{Max(X_{ij})-X_{ij}}{Max\Big{(}X_{ij}\Big{)}- Min\Big{(}X_{ij}\Big{)}} \tag{6}\]  
Step 3: Weight calculation of each sample  
Calculate the weight of the \(i\)th sample under indicator \(j\):  
\[P_{ij}=\frac{X_{ij}}{\sum_{i=1}^{n}X_{ij}} \tag{7}\]  
Step 4: Entropy value calculation of the indicators  
Calculation of the entropy value of indicator \(j\):  
\[e_{j}=-k\sum_{i=1}^{n}\Big{(}p_{ij}*\ln\Big{(}p_{ij}\Big{)}\Big{)} \tag{8}\]In this formula, \(K=\frac{1}{\ln(n)}\), and \(n\) is the number of samples.  
Step 5: Calculation of the variance factor for indicator \(j\)  
The information utility value of an indicator depends on the difference between the information entropy \(e_{j}\) and 1 for that indicator, and its value directly affects the weighting. The greater the information utility value, the greater the importance to the evaluation and the greater the weighting.  
\[d_{j}=1-e_{j} \tag{9}\]  
Step 6: Weight calculation of each indicator  
The essence of estimating indicator weights using the entropy method is to use the coefficient of variation of the information for that indicator; the higher the coefficient of variation, the more important it is for evaluation.  
\[w_{j}=\frac{d_{j}}{\sum_{j=1}^{m}d_{j}} \tag{10}\]  
Step 7: Composite score calculation for each sample  
\[z_{i}=\sum\
olimits_{j=1}^{m}w_{j}x_{ij} \tag{11}\]