esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Molecular Docking Studies  
Two molecular docking techniques were used to characterize the binding of MAO-A to propenylbenzene derivatives: LeadFinder (LF) and AutoDock (AD). Each ligand and the protein were formatted separately, and calculations were performed. LF is a semi-empirical technique with high accuracy for generating ligand conformation algorithms. The results we obtain in this method are the binding energy and the predicted docking position of the ligand to the protein [32]. In contrast, AD exhibits ligand-protein integration, which exhibits larger spatial conformation surfaces and an accurate assessment of binding energies, which are most often used in drug design and discovery [33]. Modeling was performed  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compound** & **K\({}_{A}\)** & **K\({}_{A}\)** & **\(\times\)**1**\({}^{a}\)** & **All** & **AG** & **AS** & **Inhibitor** & **IC\({}_{50}\) (**1**\(\upmu\)**\(\upmu\)**\(\upmu\)**\  
affinity for actin, with \(\Delta\)G amounting to \(-31.18\) kJ/mol (AD) and \(-30.64\) kJ/mol (LF), and **5b** showed high \(\Delta\)G, amounting to \(-27.21\) kJ/mol (AD) and \(-32.94\) kJ/mol (LF). In the LF molecular modeling technique, the tested compounds interacted through H-bonds and \(\pi\)-cations. The highest number of H-bonds occurred for **5b** (Figure 4G), linking to the amino acids residues of GLY443, TYR407, THR435, and SER23, and for **1b** (Figure 4A) to TYR407, GLY443, THR435, and SER24. Compounds **3b**, **3c**, and **4b** (Figure 4D-F) bound to MAO-A in the active site with the residues of TYR407 and SER24. However, compounds **2b** and **2c**, in addition to H-bonds, bound to the enzyme in the active site via a \(\pi\)-cation (Figure 4B). Compound **2b** bound by H-bonds to the amino acid residues of ILE23, ARG51, and GLY49 and by \(\pi\)-cation to ARG51. Compound **2c**, in addition to the \(\pi\)-cation binding to the residue of ARG51, bound to THR435, ALA272, and AGR51.  
The results of molecular modeling using the AD technique were characterized by fewer H-bonds and the occurrence of \(\pi\)-\(\pi\) interactions. The \(\pi\)-\(\pi\) interactions occurred for compounds **2c** and **4b**. Compound **2c** is connected via a \(\pi\)-\(\pi\) interaction with TYR444 and TYR407 and by H-bond with GLY443 (Figure 3C). Compound **4b** bound by the \(\pi\)-\(\pi\) bond to the amino acid TYR47 and by H-bond with TYR69, ALA68, and CYS406 (Figure 3F). Compounds **1b**, **2b**, **3b**, **3c**, and **5b** were bound to the enzyme by H-bonds. Compound **1b** bound to MAO-A via an H-bond to the amino acid residues of SER24 and TYR407 (Figure 3A). Compound **3b** bound to MAO-A via an H-bond to CYS46, ALA68, and TYR69 (Figure 3D), and for **5b**, bonds with TYR407 and THR435 were calculated (Figure 3G). However, neither H-bond binding nor \(\pi\)-\(\pi\) interactions were recorded for compound **2b** (Figure 3B). Most of the compounds are bound with amino acid residues of TYR. Compounds hydrogen-bonded with TYR gave higher affinities for the enzyme.
## 4 Results
### Agent-Based Simulations of Global-Local Surveys
#### 4.2.3 Statistical Analysis and MCDA
Safety, scalability, relative land use, and citizen acceptance receive the highest weights in the overall assessments, followed by profitability. Reaction time, location dependency, and volatility receive the lowest weights. Each score is normalized proportionally to the sum of all existing scores. In this setting, the technological mix of 10% wind power and 90% photovoltaic power has the highest rank in the overall assessment (Figure 7). The option with 70% wind power and 30% photovoltaic power is the least popular. In addition to the absolute rankings of the decision alternatives, the decision makers can also see the contributions of each criterion to the overall assessment. The next step is to analyze the stability of these results and their robustness against small value/weight changes. For example, changing the weighting of a criterion does not affect the ranking of alternatives. If the weighting of the criterion \"profitability\" were increased, the assessment of the highest ranked alternative would improve, and the assessment of the lowest ranked alternative would deteriorate; reducing the weight would have the opposite effect. If the weighting of the criterion \"location dependency\" were increased, the assessment of the highest ranked alternative would deteriorate, and the assessment of the lowest ranked alternative would improve; reducing the weight would have the opposite effect. The \"scalability\" criterion has a high weighting; reducing the weighting would worsen the assessment of the best ranked alternative and improve the assessment of the worst-ranked alternative; the same applies to the criterion \"relative land use\". Changes in the weights of the criteria \"reaction time\", \"safety\", and \"citizen acceptance\" have little effect on the evaluation of the alternatives. The strongest effect when changing the weighting is observable for the criterion \"volatility\"; as the weighting increases, the differences in the assessments of the individual alternatives decrease. Figures 8 and 9 illustrate analyses of the criteria \"profitability\" and \"volatility\", respectively. Figure 7: Stacked bar chart of results of the MCDA. Therefore, the result is robust to changes in the weighting of individual criteria. However, changes in the weighting of the criterion \"volatility\" make the assessments of the decision alternatives less distinguishable. Sensitivity analyses can also be performed with respect to technological mixes. The decision alternatives are formed from certain percentage shares of wind power plants and photovoltaic plants. The higher the percentage of wind turbines in the technological mix, the worse the corresponding decision alternative is evaluated. The evaluation of the decision alternatives thus decreases with increasing weighting of the wind turbines. Visual support, such as that used in this study by means of a sophisticated MCDA tool, clearly helps decision makers in deciding between different technological mixes.
## 3 Proposal
### Data Refinement  
After the acquisition of data from the AEP and IHEPC datasets, data refinement is performed to eliminate missing and outlier variables and to normalize the acquired data. In the AEP dataset, a standard transformation technique is employed for converting the acquired data into a particular range. In the AEP dataset, the feature vectors range lies between 0 and 800, and by using the standard transformation technique the feature vectors range is transformed into \(-4\) and \(-6\). The mathematical expression of the standard transformation technique is defined in Equation (1):  
\[T_{standard}=(X-U)/S \tag{1}\]  
where \(S\) indicates standard deviation, \(X\) denotes actual acquired data, and \(U\) represents the mean. In addition, the IHEPC dataset comprises redundant, outlier, and null values, so a min-max scalar is applied to eliminate non-significant values and to bring the feature vectors into a particular range of values. In the IHEPC dataset, the feature vectors range lies between 0 to 250, and by using the min-max transformation technique, the feature vectors range is transformed into \(-2\) and \(-3\). The mathematical expression of the min-max transformation technique is defined in Equation (2):  
\[T_{min-max}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{2}\]  
where \(X_{max}\) and \(X_{min}\) indicates maximum and minimum values of the IHEPC dataset. A total of 2890 and 25,980 missing values are eliminated in the AEP and IHEPC datasets utilizing the pre-processing techniques. The refined data samples of AEP dataset and IHEPC dataset are presented in Figure 3.  
Figure 2: Data samples of (**a**) the AEP dataset and (**b**) the IHEPC dataset.
## 2 Model and Method
### Model and Method for the Decay Rate
#### 2.2.2 Spectral Transfer Matrix Method  
The STMM is used to solve the periodic track model. The short rail is divided into two spectral Timoshenko beam elements. The vertical extension spring representing the fastener is modeled with a two-node spectral spring elements. The model has 4 nodes and 3 spectral elements in total. The element division is shown in Figure 7.  
The spectral stiffness matrix of the periodic track model can be obtained by using the SEM. Thus, the relation between the spectral nodal forces and displacements of the periodic track model can be expressed as:  
\[\left[\begin{array}{cccc}\mathbf{S_{11}}&\mathbf{S_{12}}&\mathbf{S_{13}}& \mathbf{S_{14}}\\ \mathbf{S_{21}}&\mathbf{S_{22}}&\mathbf{S_{23}}&\mathbf{S_{24}}\\ \mathbf{S_{31}}&\mathbf{S_{32}}&\mathbf{S_{33}}&\mathbf{S_{34}}\\ \mathbf{S_{41}}&\mathbf{S_{42}}&\mathbf{S_{43}}&\mathbf{S_{44}}\\ \end{array}\right]\left(\begin{array}{c}\mathbf{U_{1}}\\ \mathbf{U_{2}}\\ \mathbf{U_{3}}\\ \mathbf{U_{4}}\\ \end{array}\right)=\left(\begin{array}{c}\mathbf{F_{1}}\\ \mathbf{F_{2}}\\ \mathbf{F_{3}}\\ \mathbf{F_{4}}\\ \end{array}\right) \tag{12}\]  
where \(\mathbf{U}_{i}\) is the spectral nodal displacement vector of node \(i\) (\(i\) = 1-4), and \(\mathbf{F}_{j}\) is the spectral nodal force vector of node \(j\) (\(j\) = 1-4). The spectral stiffness matrix of the model is divided into a \(4\times 4\) partitioned matrix according to the node number, while the submatrix is denoted by \(\mathbf{S}_{ij}\). To build the transfer  
Figure 6: The periodic track model.  
Figure 7: The element division of the periodic track model.  
where \(\text{Im}(k_{\text{x}})\) is the imaginary part of \(k_{\text{x}}\). The parameters of the Timoshenko-beam track model and the periodic track model are listed in Table 1. The vertical fastener stiffness \(k_{\text{v}}\) is obtained by fitting simulations to field measurements.
# X-ray Free Electron Laser Accelerator Lattice Design Using Laser-Assisted Bunch Compression
## 4 Bunch Compressors
### Second Bunch Compressor
#### 4.2.2 Determination of LABC Parameters  
The LABC modulation parameter \(A\) and the compression parameter \(B\) were calculated using the uncorrelated energy spread of the electron beam at BC2 exit \(\sigma_{\gamma,0}=0.170\), which was remarkably larger than that at BC1 exit used for the calculation of the laser modulator modulation amplitude \(A_{L}\) (Table 1). According to Equations (1) and (2), the LABC parameters of lattice 1 were calculated to be \(A=16.1\) and \(B=0.0927\).  
The theoretical prediction in [17] indicated that the ideal LABC operating point for MaRIE XFEL was represented by LABC parameters of \(A=13.8\) and \(B=0.0896\) for a compression ratio of \(C_{2}=6\) in BC2. The difference between the theoretical prediction and the Elegant simulation results should be considered according to two aspects. First, the LABC beam current profile feature was characterized by the product of \(A\) and \(B\), as the bunching factor in [40] suggested, which was provided by Equation (5).  
\[b_{n}=e^{-\frac{1}{2}B^{2}n^{2}}J_{n}(-ABn),\quad n\geq 1 \tag{5}\]  
The product of \(A\) and \(B\) from the Elegant simulation results was \(21\%\) greater than the theoretical prediction. Second, \(A\) and \(B\) from the Elegant simulation results deviated from the theoretical prediction also in terms of their individual numerical values.  
To clarify the reasons for this discrepancy, we should note that the theoretical prediction used an ideal formalism [40] that assumed the BC2 bunch compression capability would not be affected by the uncorrelated energy spread of the beam. In reality, the compression ratio achieved was consistently below the prediction by the ideal theory, i.e., the effective \(B\) value was below that provided in Equation (2). With the uncorrelated energy spread considered in the realistic bunch compression in BC2, a smaller effective \(B\) value, as compared to that derived from the Elegant simulations using Equation (2), was likely to result in the product of \(A\) and \(B\) being consistent with the critical condition of our desired LABC beam current profile feature.
## 3 miRNAs and Molecular Chaperones in Brain Tumors
### miRNAs in Brain Tumors
The oncogenic role of miR-335 was observed also in astrocytoma, a type of glioma deriving from astrocytes or astroglial precursors; it was overexpressed in astrocytoma cells, promoting their growth and invasiveness by targeting the tumor suppressor dishevealed-associated activator of morphogenesis 1 (Daam1), a member of the formin protein family acting downstream of Wnt signaling, and responsible for the regulation of cell polarization, migration, proliferation, and tissue morphogenesis during embryonic development [158]. On the contrary, miR-335 inhibition suppressed growth and induced apoptosis of astrocytoma cells in vitro and in vivo, suggesting its potential use as therapeutic target [158] (Table 2). The microRNA miR-224 level was found higher in meningioma tissues compared to normal brain and was positively correlated with advanced pathological grade [159]. miR-224 could be a promising therapeutic target for treating malignant meningiomas, since its downregulation in vitro suppressed cell growth and increased apoptosis through the activation of the ERG2-BAK-induced apoptosis pathway [159] (Table 2). Six miRNAs (miR-106a-5p, miR-219-5p, miR-375, miR-409-3p miR-197, and miR-224) were identified in serum from patients with meningioma [160]. The serum levels of miR-106a-5p, miR-219-5p, miR-375, and miR-409-3p were increased in meningioma patients compared to healthy controls and decrease after tumor removal. On the contrary, the serum levels of miR-197 and miR-224 were markedly decreased in meningioma patients but significantly increased in the postoperative samples of the same patients. Therefore, the use of this panel of miRNAs was suggested as potentially useful for the diagnosis and the evaluation of clinical outcomes during management of meningioma patients [160] (Table 2). Similarly, very recently the use of miRNAs profiling was proposed as novel tool to predict meningiomas recurrence, and improve patients' clinical management [161]. MicroRNA profiling in brain tumor biology has been conducted considering not only the miRNAs expressed in malignant tissues and in blood from patients, but also the miRNAs released in the cerebrospinal fluid (CSF). CSF is considered the ideal source of nervous tissue-specific miRNAs to use as diagnostic biomarkers for brain tumors, since it is in direct contact with the entire central nervous system, and has the advantage of containing fewer miRNAs than blood plasma or serum, which, flowing throughout the body, collect miRNAs generated by all tissues [162; 163; 164; 165; 166; 167]. Recently, a screening was conducted, using CSF samples from patients with glioblastoma, low-grade glioma, meningioma, and brain metastasis, and from nontumor patients as controls, with the aim of identifying specific CSF miRNA patterns that could differentiate brain tumors from one another [166]. CSF miRNA signatures were identified for all the cancer types studied, which revealed the potential of CSF miRNAs for diagnosis of brain tumors, especially in cases with borderline or uncertain imaging results [166] (Figure 1).
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 3 Results
### Analysis of Sudden Changes in Air Temperature

Table 3: _Cont._The results of our analysis of the abrupt change points of seasonal air temperature in the Yunnan Plateau lakes reveal considerable differences in air temperature change between different regions and during different seasons (Figure 6). For spring, the abrupt change point occurred in 2005 in northwest Yunnan, and in 2008 in central Yunnan; the summer abrupt change point occurred after 2005 in the northwest lakes, and mainly occurred after 2010 in the central lakes; the autumn abrupt change points occurred from 1998 to 2000; and the winter abrupt change points occurred in 1997. Consistent with our previous analyses, these findings indicate that the Yunnan Plateau lakes tend to be more sensitive to global warming during the colder seasons. In addition, we found that there was an abrupt change from cold to warm in the central Yunnan lakes in 1997, which was consistent with the time of the winter abrupt change point. On the basis of these findings, it can thus be concluded that the abrupt change point occurred during the winter phase is not a significant change in the winter phase. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lake**} & \multicolumn{3}{c}{**1980-1989**} & \multicolumn{3}{c}{**1990-1999**} & \multicolumn{3}{c}{**2000–2009**} & \multicolumn{3}{c}{**2010-2019**} \\ \cline{2-13}  & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** \\ \hline Chenghai & 30.11 & \(-\)15.99 & 11.51 & 29.17 & \(-\)6.67 & 11.31 & 28.38 & \(-\)7.09 & 11.26 & 30.75 & \(-\)12.32 & 12.30 \\ Danchi & 29.85 & \(-\)14.15 & 14 & 31.85 & \(-\)6.15 & 13.91 & 29.85 & \(-\)10.15 & 13.66 & 31.85 & \(-\)10.15 & 14.55 \\ Erhai & 33.34 & \(-\)5.56 & 16.45 & 33.72 & \(-\)2.87 & 16.23 & 33.31 & \(-\)0.47 & 16.19 & 33.34 & \(-\)2.16 & 17.19 \\ Fuxian & 32.20 & \(-\)6.92 & 15.16 & 32.23 & \(-\)3.47 & 15.07 & 29.97 & \(-\)3.98 & 14.82 & 31.15 & \(-\)6.61 & 15.72 \\ Lugu & 25.95 & \(-\)27.49 & 7.14 & 25.08 & \(-\)18.72 & 6.98 & 25.04 & \(-\)20.29 & 7.02 & 26.98 & \(-\)20.10 & 8.02 \\ Yangzonghai & 32.84 & \(-\)6.91 & 14.98 & 34.52 & \(-\)4.39 & 14.95 & 31.40 & \(-\)4.98 & 14.6 & 32.99 & \(-\)9.71 & 15.47 \\ Yilong & 34.23 & \(-\)3.41 & 17.43 & 33.47 & \(-\)1.34 & 17.22 & 32.65 & \(-\)1.64 & 17 & 33.18 & \(-\)8.35 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum, minimum, and average air temperatures (\({}^{\circ}\)C) of the Yunnan Plateau lakes per decade. Figure 5: Sudden change point analysis of the annual mean temperatures of lakes on the Yunnan Plateau from 1980 to 2019.  
that an abrupt change in the winter temperature is the main factor contributing to the abrupt change in average air temperature from cold to warm in 1997. Figure 6: Sudden change point analysis of the seasonal mean temperature of lakes on the Yunnan Plateau from 1980 to 2019.
# Flexural Buckling Failure of Rock Slopes: A Review of Influencing Factors and Analysis Methods
## 4 Analysis Methods for Flexural Buckling Failure
### Numerical Simulations
According to the UDEC simulation results, [PERSON] et al. [29] proposed some insightful views on the geological and mechanical conditions, the determination of critical failure state, and early signs for the buckling instability of a stratified rock slope. Later, taking the Xiaguiwa landslide in the upper Jinsha River, southeastern Tibetan Plateau, as an example, [PERSON] et al. [15] simulated the entire process from buckling deformation and failure to the formation of a landslide dam using the UDEC. Through discrete element modeling that accounted for the rheological properties of anisotropic greissic rocks, [PERSON] et al. [79] identified the critical orientation configuration between isotropy planes and slope faces that leads to buckling failure. Based on the PFC simulations of the buckling instability process of the Xinglangpo landslide, [PERSON] et al. [33] conducted an in-depth study of the micro-mechanical mechanism of the landslide. [PERSON] [80] employed the DDA method to simulate buckling deformation and ground subsidence phenomena in the lower slope area of the Chu-fen-er-shan landslide. By combining DEM and DDA, [PERSON] et al. [25,44] conducted a back-analysis of the buckling phenomenon of the Lavini di Marco area of Trento, Italy, and deeply investigated the effect of rock mass properties, slope geometry, and hydrological conditions on the buckling deformation behavior of stratified rock slopes. [PERSON] [81] proposed a stiffness reduction method to evaluate the flexural buckling stability of a stratified rock slope and simulated the entire process of buckling deformation and instability of a single rock layer using the DDA. Recently, by simulating the instability and evolution process of Malvern Hills in New Zealand using the NMM, [PERSON] et al. [32,69] explored the effect of soft interlayers and staggered joints on the buckling deformation and failure process of the stratified rock slope, confirming that NMM simulation can well reproduce the entire process from the progressive instability and failure to the run-out and accumulation of buckling landslides.
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 4 Structure, Types, and Applications of Optical Fibers
Pressure, temperature, and strain are the extensively investigated parameters, and, for the optical fiber sensors, the Bragg fiber grating sensors are the most widely studied technologies. However, in various applications, optical fiber-based sensors are expected to compete with other existing technologies like electronic-based systems. To get attention, since customers are already familiar with the current technologies, there is a need to demonstrate the superior qualities of optical fiber-based sensors over other contemporary methods. Usually, customers are not interested in the procedures involved in the detection. However, these clients only desire sensors with excellent performance at reasonable costs. Therefore, optical fiber-based sensors should be obtainable in the form of a system that includes signal detection and signal processing.
# Activation of Persulfate for Groundwater Remediation:
## 4 Factors Affecting the Application of Persulfate in ISCO
### Persulfate Dosage  
The dosage of persulfate is an important parameter affecting the effectiveness of a site remediation. A low dosage of persulfate may not achieve the intended remediation purpose, while an excessive dosage will not only result in higher remediation costs, but also cause damage to groundwater and soil [4; 39]. In laboratory-scale studies, it has been proven that the higher the concentration of persulfate, the more effective it is in degrading the contaminants [36; 55; 96; 125; 126; 127]. Due to the diffusion of persulfate in groundwater and the complex contaminants in actual spiked sites, however, the concentration of persulfate used in the laboratory may not be properly matched when applied to site applications. For instance, the concentrations of persulfate used in the laboratory typically ranged from 10 mmol/L to 100 mmol/L and can obtain a promising removal efficiency, whereas the concentrations of persulfate used in field applications need to reach 1000 mmol/L to meet the same removal efficiency [120]. For this reason, bench-scale studies or pilot-scale studies were needed using the site contaminated soils to evaluate the optimal persulfate concentration prior to field-scale site remediation. However, due to the different types of target pollutants and the varying severity of contamination at each site, the dosage of persulfate varies greatly from sites to sites. Few studies have unified the large amount of data and arrived at a reasonable range of values for the dosage of persulfate. Therefore, more in-depth research is still needed in this area.