esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Consideration  
### Ctle  
The equalization value of conventional CTLEs can be increased or decreased by varying the capacitor (\(C_{\mathrm{S}}\)) and resistor (\(R_{\mathrm{S}}\)), illustrated in the schematic in Figure 2. The peaking frequency is directly correlated with this equalization value and affects the bandwidth \(f_{\mathrm{w}}\). Consequently, disassociating the equalization value and \(f_{\mathrm{w}}\) in the circuit is key. The source-degenerated Gm-cell is chosen such that the gain is degenerated through \(R_{\mathrm{S}}\), ensuring sufficient linearity in the system (Figure 2). A zero can also be introduced by combining \(C_{\mathrm{S}}\) and \(R_{\mathrm{S}}\)[8; 16].  
Equation (1) presents the transfer function of the single-ended CTLE.  
\[\mathrm{H}\left(\mathrm{S}\right)=\frac{G_{M}R_{L}}{1+\frac{G_{M}R_{\mathrm{S}} }{2}}\times\frac{1+\frac{S}{\omega_{z}}}{(1+\frac{S}{\omega_{pl}})(1+\frac{S}{ \omega_{p2}})} \tag{1}\]  
where \(\omega_{z}\)=\(\frac{1}{R_{L}C_{z}}\), \(\omega_{pl}\)=\(\frac{1+\frac{G_{M}R_{\mathrm{S}}}{R_{\mathrm{S}}C_{z}}}{R_{\mathrm{S}}C_{z}}\), and \(\omega_{p2}\)= \(\frac{1}{R_{L}C_{L}}\) are the zero and two poles of the CTLE, respectively (Figure 3).  
Figure 2: Schematic of Conventional CTLE.  
To attain the peak gain at a higher frequency, the CTLE must be designed such that the pole frequency is higher than the zero frequency [17; 18; 19]. The peaking gain also depends on the dominant pole and zero. The frequencies depend on \(G_{\rm M}\) of the differential pair and of the degeneration \(C_{\rm S}\) and \(R_{\rm S}\), as presented in Equation (2).  
\[\text{Maximum boost peaking}=\frac{\omega_{p1}}{\omega_{z}}=1+G_{M}R_{\rm s}. \tag{2}\]  
In the low-frequency range, CTLE operates similarly to a differential amplifier with source degeneration, and their gains are therefore equal in this range:  
\[\text{DC gain}=\frac{g_{m}R_{L}}{1+\frac{G_{\rm M}R_{\rm s}}{2}} \tag{3}\]  
In the high-frequency range, \(C_{\rm S}\) shorts the pins of the CTLE, and the circuit operates similarly to a differential amplifier without source degeneration. Therefore, the peak gain of the CTLE is as follows:  
\[\text{peak gain}=g_{m}R_{L} \tag{4}\]  
Figure 2 reveals that if \(V_{bias}\) varies, the current flow through transistors \(M_{1}\) and \(M_{2}\) varies. However, the output current at nodes \(V_{out1}\) and \(V_{out2}\) changes drastically, and the dominant poles and zero also change slightly. To achieve constant current flow at nodes \(V_{out1}\) and \(V_{out2}\) while enabling variation in \(V_{bias}\), a two-stage differential input pair CTLE with fixed degeneration \(C_{\rm S}\) and \(R_{\rm S}\) is proposed.
## 1 Introduction
[29] introduced the engineering decision-making of microseismic data acquisition equipment to improve the information content of microseismic monitoring in the geodynamic process of underground hard ore mining. In summary, for the study of fault activation monitoring, most scholars have focused on the problem of rock burst induced by fault activation, but ignored the fault displacement and stress changes in the process of fault activation. Few scholars have proposed analyzing a fault activation slip's spatial and temporal evolution characteristics through the field monitoring of fault displacement and stress. Based on the background of fault activation in the footwall of the main orebody induced by deep continuous mining in Shizishan mining area, Yuxi City, Yunnan Province, China, this paper designs a monitoring system suitable for fault activation and slip in this mining area in view of fault activation and slip. The displacement and stress of the fault are monitored by the multipoint displacement meter, bolt stress meter, and borehole stress meter. Through the field measurement of fault activation and slip, the stress and displacement evolution laws of the fault group activation process in the footwall of the main orebody during deep continuous mining are analyzed, and the spatiotemporal evolution characteristics of fault activation are discussed. The research results are significant for the research of fault activation monitoring methods caused by deep mining and the prevention and control of related disasters caused by fault activation.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 2 Numerical Model
### Mesh Independence Study and Validation  
The numerical results were validated using experimental data published in [11], considering the same wave height, rectangular buoy, and numerical wave tank. The effective density of the rectangular floating buoy considered is 500 kg/m\({}^{3}\). A mesh independence study was carried out by plotting the dimensionless vertical displacement (\(z/d\)) as a function of the dimensionless time (\(t/T\)) obtained for meshes comprising 107,686, 115,660, 140,320, and 126,676 cells; see Figure 3a.  
In Figure 3, \(z\) represents the center position of the buoy at time \(t\), \(d\) is the water tank depth, and \(T\) is the wave time period. Meshes comprising 140,320 and 126,676 cells generate the same displacement, which is slightly smaller than that obtained with the 115,660 and 107,686 cell meshes; see Table 2 for information at the maximum displacement, i.e., at  
Figure 3: Mesh independence study of the rectangular buoy of density 500 kg/m\({}^{3}\). (a) Dimensionless vertical displacement (\(z/d\)) of the buoy plotted with respect to dimensionless time (\(t/T\)). (b) Comparison between numerical and experimental displacements at a wave height of 0.04 m.  
\(t=1.49\) s, corresponding to \(t/T=1.241\). Although all meshes can be used for the simulation work due to there being little difference in the displacement obtained with each of them, the mesh comprising 115,660 cells was selected in subsequent simulations, as this mesh provides a good compromise between simulation time and accuracy.  
The simulated data points obtained with the selected mesh were further compared with the non-dimensional experimental vertical displacement of the rectangular buoy (\(z/d\)); see Figure 3b. The simulation results show between a 5 and 12% absolute error in vertical displacement when compared to the measured data, validating the numerical results. It should be noted that a time step of 0.001s provides numerical results in good agreement with the experimental data, so no time-step sensitivity analysis was carried out.
## Materials and Methods
### Image Feature Extraction
#### 2.4.2 Gray-Level Co-Occurrence Matrix (GLCM)  
Gray-level Co-occurrence Matrix (GLCM) is a statistical method proposed by [PERSON] et al. [29] in the 1970s. It assumes that the texture features in the image can be described using the positional relationship between different pixels. GLCM can count the frequency distribution of two grayscale values in a specified space, and express the contrast and variance of pixels in the image. Definition of the gray value of any point (\(x\), \(y\)) in the point image is (\(g1\), \(g2\)), and different coordinates in the image have different gray values. Assuming that there are \(k\) types of gray value levels, the number of gray value combinations at any point (\(x\), \(y\)) is \(k^{2}\). The number of occurrences of each gray value in the image is counted, and the probability \(P(g1,g2)\) of the corresponding gray value is calculated. The probability \(P\) is arranged in a square matrix, which is the gray level co-occurrence matrix. Suppose there is another point (\(x+a\), \(y+b\)) in the figure, usually let (\(a\), \(b\)) be (1, 0), (1, 1), (0, 1), (-1, 1) and other positive or negative values, then the joint gray level co-occurrence matrix of the two pixels in the image in four directions can be calculated. Therefore, the GLCM of the image contains the complex texture information such as the direction, spacing distance, and difference between the pixels, so it can be used to analyze the local features in the image and their arrangement rules.  
HOG can express edge information such as gradient information and direction density of pixels in an image, while GLCM can obtain texture information such as the direction, separation distance, difference amplitude and the difference between image pixels. Therefore, HOG and GLCM are used for image feature extraction, and the feature vectors are added as the final feature vector of the image.
## 2 Materials and Methods
### Field Experiment and Sample Collection
#### 2.1.3 Plant Material and Agronomic Practices  
The early-maturing 'Abaca' soybean variety (Saatbau) was used. This cultivar was selected due to its short vegetation cycle, high fat content (~22.7%), and moderate protein levels (~36.2%), which make it particularly suitable for temperate Central European agroclimatic conditions. Additionally, 'Abaca' has demonstrated reliable performance under drought-prone summer periods and offers favorable processing characteristics for both full-fat soy and oil-extracted products. Soybeans were sown at a density of 70 seeds/m\({}^{2}\) using a disc seeder (Agro Masz SN300, Strzelce Male, Poland) (row spacing: 24 cm; depth: 2.5-4.5 cm). Fertilization (P + K + Ca, Mg, S) was applied uniformly (350 kg/ha annually). Herbicide regimes varied annually, with increasing reliance on mechanical weeding (P1) versus conventional chemical protection (P2).  
P1 (semi-organic): post-emergence weed control was carried out mechanically twice per growing season using a rotary narrow. Herbicides were used only in pre-emergence stages and limited post-emergence applications, aligned with semi-organic practice standards.  
P2 (conventional): Weed control relied exclusively on chemical herbicides, with both pre-emergence and multiple post-emergence applications. No mechanical interventions were applied in this system.  
In both systems, metribuzin and S-metolachlor were used for pre-emergence control (replaced in 2024 by petoxamide + clomazone due to regulatory changes). Post-emergence treatments included bentazon + imazamox as needed (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Treatment**} & **Pre-Emergence** & **Post-Emergence** & **Mechanical** & **Sowing Date** & **Harvest Date** \\  & & **Herbicides (g ha\({}^{-1}\))** & **Herbicides (g ha\({}^{-1}\))** & **Herbicides (g ha\({}^{-1}\))** & **Weeding** & **Sowing Date** & **Harvest Date** \\ \hline \multirow{4}{*}{2021} & \multirow{2}{*}{P1} & Metribuzin 200 + & \multirow{2}{*}{None} & \multirow{2}{*}{2\(\times\)} & \multirow{2}{*}{11 May} & \multirow{2}{*}{30 October} \\  & & S-metolachlor 960 & & & & \\ \cline{1-1} \cline{3-6}  & \multirow{2}{*}{P2} & Metribuzin 200 + & \multirow{2}{*}{None} & \multirow{2}{*}{None} & \multirow{2}{*}{11 May} & \multirow{2}{*}{30 October} \\  & & S-metolachlor 960 & & & & \\ \hline \multirow{4}{*}{2022} & \multirow{2}{*}{P1} & Metribuzin 200 + & \multirow{2}{*}{1\(\times\) Bentazon 600 + } & \multirow{2}{*}{2\(\times\)} & \multirow{2}{*}{05 May} & \multirow{2}{*}{1 October} \\  & & S-metolachlor 960 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of agronomic practices for soybean cv. ‘Abaca’ cultivation from 2021 to 2024, including herbicide regimes, mechanical weeding, and key dates.  
Figure 1: Distributions of total precipitation and average temperature values during the study years.
## 3 Results
### Statistical Analysis  
The frequency distribution of the dataset is presented in Figure 3, making use of a histogram plot. Histograms are plots that provide a visual representation of the distribution of the dataset, showing shape, center, and spread of the data. The frequency distribution and summary of statistical information of the dataset used in this research are shown in Figure 3 and Table 1, respectively.  
As shown in Figure 3 and Table 1, the values of input variables, i.e., WOB, RPM, TOR, and FLOW, vary from 1.09 up to 43.31 klb, 109.36 up to 151.77 rev/min, 0.01 up to 19.00 klb-ft, and 594.86 up to 803.32 gpm, respectively. The values of response variables, i.e., ROP and MSE vary from 1.29 up to 32.12 ft/hr and 0.61 up to 3750.44 kpsi, respectively.  
Figure 3: Distribution of dataset: (**a**) WOB; (**b**) RPM; (**c**) TOR; (**d**) FLOW; (**e**) ROP; (**f**) MSE.  
As shown in Figure 4, from the depth of 17,245.00 up to 17,344.50 ft, a decrease occurs in ROP value and an increase in MSE value, i.e., indicating an inefficient drilling process. For this depth interval, the mean values of ROP and MSE are approximately 5.48 ft/h and 41.24 kpsi, respectively. This fact occurs due to an increase in WOB value, which indicates an operation beyond the foundering point, and a decrease in flow rate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Coded Factor** & \
\begin{tabular}{c} **Minimum** \\ **Value** \\ \end{tabular} & **Mean Value** &
\begin{tabular}{c} **Maximum** \\ **Value** \\ \end{tabular} \\ \hline WOB klb & X\({}_{1}\) & 1.09 & 18.27 & 43.31 \\ RPM rev/min & X\({}_{2}\) & 109.36 & 137.99 & 151.77 \\ TOR klb-ft & X\({}_{3}\) & 0.01 & 7.62 & 19.00 \\ FLOW gpm & X\({}_{4}\) & 594.86 & 735.05 & 803.32 \\ ROP ft/h & Y\({}_{1}\) & 1.29 & 13.00 & 32.12 \\ MSE kpsi & Y\({}_{2}\) & 0.61 & 462.01 & 3750.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of statistical information.  
Figure 4: Input and response variables for a specific carbonate rock reservoir interval drilled.
## 2 Materials and Methods
### Inulin Analysis
#### 2.2.3 HPLC-ELSD Analysis  
A Waters 1525 series liquid chromatographic system was coupled with an Evaporative Light Scattering Detector (ELSD, Alltech 3300, Nicholasville, KY, USA). A PrevailTM Carbohydrate ES column (250 mm \(\times\) 4.6 mm, 5 um, Grace, Columbia, MD, USA) was used. The sample was pre-filtered with a 0.45 um membrane before injection. The volume of the sample injected was 20 mL (filling the loop completely). The chromatographic separation was achieved with a mobile phase of acetonitrile-water (75:25, \(v/v\)). The flow rate was set at 1.0 mL/min, and the temperature of the column was set at 35 \({}^{\circ}\)C. The evaporation temperature of the ELSD was set at 45 \({}^{\circ}\)C with a gas flow of 2.0 L/min. Peak areas were used for quantitative analysis. D-fructose with purity of 99% (Aladdin, Shanghai, China) was used as standard. Calibration curves were obtained by preparing five concentrations of fructose in water ranging from 0.20 to 0.60 mg/mL.
## 4 Discussion
### Loading Rate  
The results of the SHPB test indicate that the dynamic peak stress of specimen increases with the loading rate, regardless of the appearance of the joint, the angles of base plane and sawteeth of the joint, and the type of failure type of the specimen. The rate of increase of the dynamic peak stress typically decreases as the loading rate increases. When the jointed specimen is crushed to failure, the applied load is dissipated by the whole specimen. The DSLR value is moderately negatively correlated with the loading rate: increasing the loading rate reduces the efficiency of increase of the dynamic peak stress. If the specimen is broken by fracture, then the applied load is mostly dissipated by a small number of fracture surfaces, so the rate of increase of the dynamic peak stress is lower than that of a specimen under crushing failure. The concentration of stress on a few fracture surfaces, where it is affected by the angles \(\beta\) and \(\alpha\), causes variation in the relationship between the DSLR and loading rate. Therefore, the results of ANOVA indicate that both \(\beta\) and \(\alpha\) angles affect the dynamic peak stress of a jointed specimen that fails by fracture. The joint across the whole specimen in the direction of the base plane causes \(\beta\) to affect the dynamic peak stress of the jointed specimen when slide failure occurs. The stresses on both sides of the triangles of sawteeth that are caused by the applied load vary greatly, accounting for the fact that the influence of \(\alpha\) on the dynamic peak stress of a jointed specimen is insignificant. The dynamic peak stress of the jointed specimen increases with loading rate, but the rate of increase is significantly lower than for specimens under fracture failure or crushing failure, and the DSLR varies considerably with the loading rate, so ANOVA and simple regression cannot quantify these relationships.
## 3 Results and Discussion  
### Chemical Composition of SBP and SBP-CO\({}_{2}\)  
Fractionated extraction approaches, including SFE-CO\({}_{2}\) and complementary techniques, are increasingly employed in biorefinery processes to selectively isolate lipophilic and polar bioactive fractions from fruit and berry pomace, whereas the residual biomass--despite being rich in fiber and protein--often receives less attention in terms of vaporization [26; 27]. In this study the proximate composition of SBP and SBP residues after SFE-CO\({}_{2}\) is shown in Table 1. The main constituents of SBP are TDF followed by protein and fat content. [PERSON] et al. reported a similar chemical composition of the SBP except for the lipid content (20.78% d.m.), which was 66% higher than in the analyzed SBP [10]. The lipid content in the SBP can vary from 1.8 to 29.1%, depending on the amount of seed, pulp and peel fractions in the raw material. The observed differences in the chemical composition of the pomace may depend on climatic growing conditions, berry size, degree of maturity,variety characteristics, storage conditions and processing methods [28]. Application of SFE-CO\({}_{2}\) resulted in an 85.7% reduction in the lipid content of the SBP-CO\({}_{2}\) after extraction. In this regard, the protein content increased by 20.1% and the TDF content increased by 10%. In another study, SFE-CO\({}_{2}\) extraction of black current pomace not only resulted in a nearly complete defatting of the material, leaving less than 0.8% residual lipids, but also led to a significant 30% increase in protein content [29]. The efficiency of SFE-CO\({}_{2}\) depends on several extraction parameters (pressure, time, temperature, sample particle size, total solvent type and amount). Therefore, the process should be optimized for each individual plant material before its application for industrial use [30].  
The SDF/IDF ratio is important for the nutritional value and technological properties of the pomace, such as water retention capacity and viscosity. From a nutritional point of view, the SDF/IDF ratio should be close to 1:2-1:3 [15; 16]. Considering that the SDF/IDF ratio obtained during the study (1:12 and 1:9.5) does not correspond to the recommended one, additional processing of the SBP is required to increase the SDF content.
# Source Rock Prediction Using Well Log and Seismic Data:
## 4 Results  
### Abundance of Organic Matter in Source Rocks  
The formation of oil and gas is inseparable from the organic matter in the rock, and only when the organic matter reaches a certain level can an effective source rock be formed [25]. Therefore, the abundance of organic matter is an important basis for evaluating the hydrocarbon generation intensity of the source rock. In this study, the TOC and hydrocarbon generation potential of rock pyrolysis (S\({}_{1}\) + S\({}_{2}\)) are used to evaluate the abundance of organic matter [6; 7; 26]. We referred to the standards of SY/T5735-2019, a geochemical evaluation method for source rocks issued by the China National Energy Administration when classifying the source rocks. According to the geochemical data of the source rock samples from 10 wells in the Cote d'Ivoire Basin, the TOC distribution of Lower-Cretaceous-Albian-stage source rocks ranges from 0.47 wt.% to 3.08 wt.%, with an average valus of about 2.63 wt.%. Among them, 32% of the samples have a TOC value between 0.5 and 1 wt.%, indicating that they are medium source rocks. A TOC value in the range of 1-2 wt.% indicates a good source rock, and 125 samples account for 41% of the total. The content of high-quality source rocks is high, with 77 samples accounting for a quarter of the total, and the overall abundance of organic matter is high. The results of the hydrocarbon generation potential S\({}_{1}\) + S\({}_{2}\) show that the data points of the Lower-Cretaceous-Albian-stage source rocks S\({}_{1}\) + S\({}_{2}\) are mainly distributed between 0.2 and 10.0 mg/g, with an average value of 4.06 mg/g, indicating good hydrocarbon generation potential (Figure 2). Among them, the mean TOC of the well located in the shallow water to deep-water area of the basin is about 1.1 wt.%, the abundance of organic matter is high, the mean of S\({}_{1}\) + S\({}_{2}\) is 6.3 mg/g;the hydrocarbon generation potential is good. The wells at the edge of the basin shelf have a mean TOC of about 1.5 wt.%, with a high abundance of organic matter, but an S\({}_{1}\) + S\({}_{2}\) value of 3.3 mg/g, indicating average hydrocarbon generation potential. In summary, the source rocks in the shallow-deep-water area of the study area belong to the high-quality source rocks with good hydrocarbon generation potential, while the source rocks near the shelf constitute medium-good source rocks with average hydrocarbon generation potential.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 4 The Security of the Public-XOR-Key Scheme  
We described the complex QKD network scheme with multiple endpoints and analyzed its advantages in Section 3. Compared with the re-encryption scheme that does not announce the XOR value, our scheme can improve the actual performance and simplify the complexity of the system. In this section, we analyze our scheme from the security perspective. We analyze the security of the scheme by building the threat model of adversaries and strictly demonstrate its security under the threat model. As described in Section 3.2, there is always a chain between any two endpoints in all kinds of networks. So, we can use the chain QKD network as an example and build the security model. In addition, by comparing the security of our scheme with that of the re-encryption scheme, we prove that our scheme has the same security with that of the re-encryption scheme under the same assumption of adversary's capacity, while our scheme achieves simplification in implementation.