esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Distribution Characteristics of Backflow Proppants in Tight Sandstone  
As an important oil and gas reservoir, the fracturing effect of tight sandstone is directly related to the efficiency of oil and gas resource extraction [15]. The settling speeds and distribution characteristics of proppants play crucial roles in the formation, expansion,and oil and gas migration of fractures during the fracturing process [16; 17]. Therefore, based on existing practical case data, we conducted in-depth research on the distribution characteristics of backflow proppants in tight sandstone. Firstly, in the experimental results of foreign researchers, the single-particle settling velocity under unconstrained conditions is as follows:  
\[V_{ss}=\frac{\frac{\varrho_{s}-\rho_{l}}{\rho_{l}}gd^{2}}{C_{1}\frac{\mu_{s}}{ \rho}+\sqrt{0.75C_{2}\frac{\varrho_{s}-\rho_{l}}{\rho_{l}}}gd^{3}} \tag{1}\]  
\(V_{ss}\)--the settlement velocity under unconstrained conditions, m/s;  
\(g\)--the gravitational acceleration, m/s\({}^{2}\);  
\(\mu_{s}\)--the viscosity of the fluid in the fracturing fluid, Pa-s;  
\(d\)--the diameter of the proppant particles, m;  
\(\rho_{s}\)--the densities of the proppant, kg/m\({}^{3}\);  
\(\rho_{l}\)--the densities of fracturing fluid, kg/m\({}^{3}\);  
\(C_{1}\)--a constant related to the properties of the supporting agent particles, \(C_{1}\) = 18;  
\(C_{2}\)--the asymptotic value of the drag coefficient. When the surface of the proppant particles is smooth, \(C_{1}\) = 0.4. When the proppant particles are natural sand particles with an uneven surface, \(C_{2}\) = 1.  
[PERSON] and [PERSON] studied the relationship between crack width and proppant settlement velocity through experimental methods and obtained the calculation formula for the proppant settlement velocity model [18]:  
\[\text{When }\frac{d}{W}<0.9,\ \frac{v_{s1}}{v_{ss}}=1-0.16\mu_{s}^{0.28}\frac{d}{W} \tag{2}\]  
\[\text{When }\frac{d}{W}\geq 0.9,\ \frac{v_{s1}}{v_{ss}}=8.26e^{-0.0061\mu_{s}}(1- \frac{d}{W}) \tag{3}\]  
\(W\)--the width of the crack, m;  
\(v_{s1}\)--proppant settling rate, m/s;  
\(\mu_{s}\)--the viscosity of the fluid in the fracturing fluid, mPa-s.
## 6 Conclusions  
The CVQKD protocols are based on Gaussian modulation, and powerful post-processing is needed to maximize the extractable valuable information from the correlated raw data. The physical layer solutions for the reconciliation of Gaussian variables require tomography that is intractable in a practical CVQKD scenario. The reconciliation is also possible in the level of the logical layer by a classical authenticated communication channel, and by traditional algorithmical tools. The multidimensional approaches were developed for this purpose, however the use of complex multidimensional calculations is also not desirable in a practical scenario. The proposed scalar reconciliation eliminates the use of multidimensional spherical space along with the dimensional boundaries. The scalar reconciliation process neither requires any physical-layer tomography, and only standard operations and calculations needed in the level of raw data. The method provides an easy implementation to maximize the extractable valuable binary information from the correlated raw data to significantly boost up the key rates and to improve the distance ranges of CVQKD.  
Available online at www.mdpi.com/2076-3417/8/1/87/s1, Table S1: Summary of the notations.  
**Acknowledgments:** This work was partially supported by the GOP-1.1.1-11-2012-0092 project sponsored by the EU and European Structural Fund, by the Hungarian Scientific Research Fund--OTKA K-112125, by the COST Action MP1006, and by the National Research Development and Innovation Office of Hungary (Project No. 2017-1.2.1-NKP-2017-00001).  
[PERSON] designed the protocol and wrote the manuscript. [PERSON] and [PERSON] analyzed the results. All authors reviewed the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments
### Experimental Setup  
In the experiments, the original dataset is randomly divided into training set and test set in the ratio of 7:3. Among them, 25% of the training set is used for cross-validation. The data augmentation strategy is used during the training process to improve the generalization ability of the model. The optimizer is chosen to be stochastic gradient descent (SGD) with the momentum factor set to 0.9. The initial learning rate is 0.001 and is dynamically adjusted by applying the inverse decay strategy in eight rounds of training. The experimental environment consists of SitOnHoly dual NVIDIA 2080 Ti GPUs (each with 11 GB of VRAM), 64 GB of RAM, and uses the PyTorch 2.1.0 deep learning framework.
## 3 Experiment Results and Analysis
To compute the PSNR, the mean squared error between the transmitted and received images is given by [39; 40]:  
\[\text{MSE}=\frac{\sum\limits_{m=1}^{H}\sum\limits_{n=1}^{N}\left[I_{\text{Tx}} (m,n)-I_{Rx}(m,n)\right]^{2}}{N_{\text{column}}\times N_{\text{row}}}, \tag{9}\]  
where \(I_{\text{Tx}}(m,n)\) and \(I_{\text{Rx}}(m,n)\) are the intensity levels within the ROI of transmitted and received images of size height (\(H\)) \(\times\) width (\(W\)), and \(N_{\text{column}}\) and \(N_{\text{row}}\) are the number of columns and rows of the images, respectively. The PSNR is then given as:  
\[\text{PSNR}=10\log_{10}(\frac{R^{2}}{\text{MSE}}), \tag{10}\]  
where \(R\) is the maximum span of input data (e.g., in the current scheme, the input image has an 8-bit unsigned integer data type; therefore, \(R=255\)). Figure 11a shows the performance of the OCC link in terms of PSNR with respect to \(L\) for different values of SS. As shown, PSNR increases with SS and decreases with the link span. This is due to the fact that the images of captured Tx at higher SS are more saturated, compared with those captured at lower SS (see Figure 9). The PSNR values of \(\sim\)14 and \(\sim\)16 dB are measured for SS values of 200 and 800 \(\upmu\)s, respectively, for \(L\) of 100 cm, increasing by 4 and 3 dB for \(L\) of 20 cm for the same SS values, respectively.
## 4 Experimental Results and Evaluation
### Experiment C
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method/ Problem**} & \multirow{2}{*}{**Complexity**} & \multicolumn{2}{c}{**ABC with Pareto**} & \multicolumn{2}{c}{**Computation Runtimes**} \\ \cline{3-6}  & & **Broken Space** & **Profit** & **Average Runtimes (second)** & **Standard** \\  & & **(M\({}^{2}\))** & **(USD)** & **(USD)** & **Deviation** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{\(100\%\)} & 0.550806 & 1038 & & \\  & & 0.531041 & 990 & & \\  & & 0.56424 & 1055 & & \\  & & 0.589491 & 1356 & & \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{\(100\%\)} & 0.533096 & 4809 & & \\  & & 0.547748 & 4992 & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{\(100\%\)} & 0.412006 & 3310 & & \\  & & 0.410012 & 3302 & 111.62 & 4.86 \\  & & 0.432743 & 3400 & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{\(100\%\)} & 0.492349 & 2193 & & \\  & & 0.509934 & 2354 & 112.99 & 2.53 \\  & & 0.500023 & 2312 & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{\(100\%\)} & 0.573422 & 1203 & & \\  & & 0.579743 & 1219 & 116.79 & 2.10 \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{\(50\%\)} & 0.316389 & 3201 & & \\  & & 0.314329 & 3007 & & \\  & & 0.323278 & 3219 & & \\  & & 0.316194 & 3198 & & \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{\(50\%\)} & 0.3104 & 2845 & & \\  & & 0.310001 & 2703 & & \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{\(50\%\)} & 0.421 & 3200 & & \\  & & 0.38891 & 2704 & & \\  & & 0.42109 & 3432 & & \\  & & 0.39234 & 3102 & & \\ \hline \multirow{4}{*}{9} & \multirow{4}{*}{\(50\%\)} & 0.352539 & 2500 & & \\  & & 0.412403 & 2583 & 115.59 & 4.19 \\  & & 0.321111 & 2397 & & \\ \hline \multirow{4}{*}{10} & \multirow{4}{*}{\(50\%\)} & 0.312853 & 2735 & & \\  & & 0.32222 & 3205 & 116.59 & 6.03 \\  & & 0.31532 & 3134 & & \\ \hline \multirow{4}{*}{11} & \multirow{4}{*}{\(25\%\)} & 0.200343 & 1237 & & \\  & & 0.200098 & 1200 & 119.19 & 4.01 \\ \hline \multirow{4}{*}{12} & \multirow{4}{*}{\(25\%\)} & 0.322469 & 1290 & & \\  & & 0.212 & 1228 & 111.39 & 5.82 \\  & & 0.332034 & 1434 & & \\ \hline \multirow{4}{*}{13} & \multirow{4}{*}{\(25\%\)} & 0.270933 & 1625 & & \\  & & 0.289043 & 1627 & & \\  & & 0.262393 & 1555 & & \\  & & 0.264829 & 1587 & & \\ \hline \multirow{4}{*}{14} & \multirow{4}{*}{\(25\%\)} & 0.312032 & 890 & & \\  & & 0.273421 & 850 & & \\ \hline \multirow{4}{*}{15} & \multirow{4}{*}{\(25\%\)} & 0.17402 & 1520 & 115.79 & 8.01 \\  & & & Average & 114.94 & 4.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The Pareto optimal solutions and computational runtimes of the ABC algorithm with Pareto-based approach.
## 3 Experimental Setup for the TD-BOCDA Sensor System  
Figure 2 shows that the experimental setup of the TD-BOCDA sensor system was composed of a closed optical fiber loop including a sensing optical fiber. Optical light wave is generated from a distributed feedback (DFB) laser diode (LD) of 15 dBm output. This light is split by a 3 dB coupler into two separate directions for the probe and the pump. The probe light is modulated by a phase modulator (PM) of LiNbO3. This causes the phase of the light to be moved into one of two states between 0 and 180, according to a pseudo random bit sequence (PRBS) code pattern with a length of 215-1. This pattern length was chosen to be longer than the length of the sensing fiber, so that only one correlation peak would produce SBS gain in the fiber. After phase modulation, the probe is again modulated  
Figure 1: Schematic of an optical fiber embedded composite cylinder.  
by a single sideband modulator (SSBM), using a sinusoidal waveform of around 11 Gbps. This causes the peak frequency of the probe light wave to be down-shifted. Thus, the probe and pump are generated from the same light source, but as a result, their frequencies are different. This frequency difference is crucial for SBS gain caused by the correlation between the probe and the pump. The difference that gives the best gain is called the Brillouin frequency. After two modulators, the probe is inserted into a polarization scrambler that removes the polarization dependency of the SBS gain.  
The pump is also phase-modulated using the same PRBS pattern as that for the probe, but with bit delay, which is called the time differential, between them. Delayed bits, similar to the time differential between the pump and the probe, cause the position of the correlation peak to move proportionally, and the number of delayed bits decides the measurement position. This is controlled by the computer system. This proposed TD-BOCDA sensor technique makes the traditionally used optical delay unnecessary. The modulated pump is inserted into the chopper and optical amplifier. This chopper is used for SBS gain detection using a lock-in amplifier (LIA). The pump is propagated into the sensing fiber after passing through a circulator. This circulator blocks the propagation of the probe traveling to the pump, and further passes the probe towards the optical receiver. The pump passes through the sensing fiber, and is blocked by the isolator in front of the polarization scrambler. Thus, the circulator and isolator are positioned at the two ends of the sensing fiber.
# Maximizing the Capacity of Edge Networks with Multicasting
## 2 Related Work
### Influence of Caching Scheme on the Capacity  
Recent studies show that a pre-caching scheme positively influences the network's performance [22]. The authors of [23] analyzed the impacts of caching schemes on the wireless content distribution networks and two schemes were considered. The first is the nearest caching node scheme that responds to a user request by caching the content at the nearest node. The second is the transparent en route caching scheme that responds to a user request via an intermediate node on the route path caching the content. The authors of [24] investigated the capacity of an information-centric network when the cached content's lifetime is limited. [PERSON] et al. [25; 26] studied the scaling law of a cache-enabled wireless network and concluded that, even if the number of nodes increases, the system's capacity could remain unchanged under certain cache constraints. In the paper [27], the authors took the multicast routing and content caching into account. They concluded that the use of multicast transmission technology can significantly save bandwidth if each task request is processed from a local caching and computing site.
# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 1 Introduction
However, this methodology is also linked to the judgement of experts (senior decision-maker and the junior team) to rank the possible causes of failure and does not take into account the time-variable. The Decision-Making Trial and Evaluation Laboratory (DAMATEL) method determines the risk priority of failure modes based on the severity of an effect and on the direct and indirect relationships [14]. The lack of mathematical rigour in the formulation of the RPN and its subjectivity in calculating the three parameters, O, S, and D, is studied with the Fusion FMEA method based on 2-tuple linguistic information and interval probability to analyse the failure modes, taking into account heterogeneous information, rather than single type information [30]. In general, the authors presented focused on three main limits of the traditional FMEA/FMECA that are:  
* First is how the RPN index is calculated; many researchers have focused on determining a replicable method to identify different weights to be assigned to various criteria;
* Secondly, many scholars have focused on finding a method that would allow the proper evaluation of alternatives in the case of linguistic variables and, therefore, in cases of uncertainty;
* Finally, in recent years, the academic world has tried to solve another critical shortcoming of the traditional FMEA: the lack of some factors, first and foremost, the cost. Therefore, many methods involve the use of several factors. In this research paper, presented a new methodology to overcome FMEA's shortcomings is presented, including:  
* The absence of weights on O, S, and D factors;
* The lack of economic factors;
* The absence of a scientific basis in the RPN calculation formula and many duplicates in RPN results. The combined Entropy Best Evaluation Distance Dynamic FMECA (EN-B-ED Dynamic FMECA) can be used to prevent a failure in a process or machine. This particular methodology permits one to obtain a more objective rank and to overcome the limits of the traditional FMEA/FMECA and to have a trend over the time of faults. In fact, when machinery is broken down into its functional units, its main components are identified. The various failure modes must be identified for each component, and, for each failure mode, the possible causes must be analysed. The factors for each cause of failure are evaluated. The paper is organised as follows: In Section 2, the methodology is proposed. In Section 3, the case study is presented and discussed, while, in Section 4, the conclusions and new directions are presented.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 4 Nonconvex Nonlinear Transformation for Tensor Completion
### Convergence  
The global convergence under the PAM algorithm framework has been demonstrated in [27]. Here, we validate both the theoretical and practical convergence of the proposed algorithm by recording the data from each iteration of the algorithm, as depicted in Figure 2. The graph illustrates that the PSNR value consistently increases with increasing iterations, while SSIM approaches 1. After 180 iterations, the values stabilize, indicating algorithm convergence. We have also verified this through numerical experiments, as shown in Section 5.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 5 Theoretical Analysis  
The proposed decoding algorithm is theoretically analyzed to explain the existence of a partial H-matrix that results in almost the same decoding performance as the full H-matrix. It will be shown that the theoretical prediction of the required number of iterations is consistent with the simulation results. The partial H-matrix can be determined by looking into the number of iterations. To calculate the number of iterations required for a specific SNR theoretically, we estimate how the BER of the decoded outputs changes according to decoding iterations. The LLR distribution obtained by the internal message tracking technique, which is called density evolution in [16], is used to estimate the BER of the decoded outputs. The distribution of the LLR values over all variable nodes is investigated in each iteration. The SPA is assumed for this analysis, as the internal steps of the algorithm can be described in mathematically closed forms.
## 5 Longitudinal Dynamics Vehicle Model
### Track Height Profile  
The rail grade must be considered as it is important to correctly reproduce the weight force component when climbing up an inclined path. As depicted in Figure 7, the track profile is defined using the following coordinates:  
* \(x_{T}\), the horizontal coordinate of the track profile;
* \(z_{T}\), the vertical coordinate of the track profile;
* \(s_{T}\), the travelled distance of the track profile, i.e., the curvilinear abscissa following the track.  
The \(x_{T}\) coordinate is considered as the independent variable while \(z_{T}\) and \(s_{T}\) are \(x_{T}\) dependent as follows:  
\[z_{T}=z_{T}(x_{T}) \tag{13}\]  
\[s_{T}(x_{T})=\int_{0}^{x_{T}}\frac{dz}{dx}dx \tag{14}\]  
These quantities were tabled and stored in a MATLAB workspace to be used in Simulink look-up tables. Figure 7 shows the relationship between cited quantities for the k-th element of the array of data. In fact, these relationships are important since they allow the evaluation of motion for a wheel-rail contact point. When considering the front wheelset, the contact point vertical displacement and velocity are defined as follows:  
\[z_{cF}=z_{T}(x_{F}) \tag{15}\]  
Figure 7: Track profile definition.  
\[\dot{z}_{CF}=\left.\frac{\partial z_{T}}{\partial x_{T}}\right|_{x_{F}}\dot{x}_{F} \tag{16}\]  
The rail grade \(\alpha\) is given as follows:  
\[\alpha_{F}(x_{F})=\arctan\left(\frac{\partial z_{T}}{\partial x_{T}}\right) \bigg{|}_{x_{F}} \tag{17}\]  
Considering the typical slopes of high-speed lines, which do not exceed a few percentage points, the arctangent can be approximated with its argument without introducing significant errors. The same procedure was adopted for the rear wheelset.
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 2 Star Catalog Partitioning
### Star Catalog Preprocessing  
A star catalog is a database containing information such as the number, right ascension, declination, and brightness of stars. Generally, the brightness of a star is indicated by its apparent magnitude, which relates to the irradiance and the distance from Earth. The irradiance of Vega is denoted as \(E_{0}=2.96\times 10^{-14}\) W/mm\({}^{2}\). Defining the apparent magnitude of Vega as zero, the irradiance of the star with apparent magnitude \(m\) is given by the following:  
\[E_{m}=E_{0}.2.512^{-m} \tag{1}\]  
It is evident from Formula (1) that, with apparent magnitude increases, the irradiance of the star decreases by a factor of 2.512, and a higher apparent magnitude indicates lower irradiance.  
Figure 1 shows the distribution of the right ascension and declination of stars in the star catalog supplied by the National Astronomical Observatory of the Chinese Academy of Sciences, which includes 77,189 stars with an apparent magnitude of up to 10, detailing their number, right ascension, declination, and brightness.  
As shown in Figure 1, there are a large number of stars in the star catalog. However, stars of high apparent magnitude or with small angular distance will reduce the identification speed and rate during star identification. Therefore, star catalog preprocessing is necessary before it is used.  
Due to the limitation of the detection sensitivity, stars in the star catalog with brightness below the detection sensitivity cannot be detected during the imaging process of the star sensor. Thus, it is necessary to eliminate stars whose magnitude exceeds the detection sensitivity of the star sensor. Moreover, to ensure the position accuracy of stars in the star image, the energy of the stars is dispersed across multiple pixels of the image sensor by a defocusing technique. If two stars in the star catalog are too close in angular distance, the two stars may be merged into a single star on the image sensor, thus degrading position accuracy and affecting the identification rate. Therefore, it is crucial to eliminate double stars with angular distances that are too small.  
Figure 1: The distribution of the right ascension and declination of stars in the star catalog.
## 2 Modeling
### Mathematical Models and Correlations  
Precise correlations of momentum and heat transfer are crucial in modeling the PCTCS due to their significant influence on simulation accuracy. Within various sections of the PCTCS, the refrigerant can exist in a single-phase or a two-phase state. For analysis  
Figure 3: Pressure-enthalpy (P–h) diagram of the simplified PCTCS.  
Figure 2: Schematic diagram of the simplified PCTCS.  
purposes, we define the thermodynamic quality, denoted by x, as a criterion to discern the flow state, as expressed in Equation (1):  
\[\mathrm{x}=\frac{\mathrm{h}{-}\mathrm{h}_{\mathrm{I}}}{\mathrm{h}_{\mathrm{g}}- \mathrm{h}_{\mathrm{I}}} \tag{1}\]  
In this equation, h symbolizes the enthalpy of the refrigerant, while hg and hl represent the enthalpies of the refrigerant in the saturated gas and liquid phases, respectively. When the refrigerant is in a two-phase state, the thermodynamic quality x ranges from 0 to 1, whereas in a purely liquid phase, x equals or is less than 0.  
The total pressure drop across each section of the PCTCS (Pdt) is computed as the sum of the frictional pressure drop (Pdf), gravitational pressure drop (Pdg), and accelerational pressure drop (Pda), as shown in Equation (2):  
\[\mathrm{Pdt}=\mathrm{Pdf}+\mathrm{Pdg}+\mathrm{Pda} \tag{2}\]  
The accelerational pressure drop stems from velocity changes due to alterations in vapor quality and void fraction. Therefore, for refrigerants devoid of phase change, Pda can be disregarded. In the following sections, we present detailed descriptions of the mathematical models of each component, along with the associated equations.