esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification and Comparison of Results between This Study and Other Research  
### Verification and Comparison of Results between FEM and DCM  
The feasibility of the developed direct algorithmic process (DCM) is verified by numerical analysis with finite element analysis (FEM). The comparison of analytical and numerical  
Figure 3: Simulation results of a circular tunnel excavation calculated by finite element analysis. (**a**) the stress field, (**b**) the plastic zone, (**c**) the major principal stress, (**d**) the minor principal stress, (**e**) the initial stress, and (**f**) the total displacement.  
results, including the displacement/stress change of GRC and SCC in each excavation stage, and especially the influence of radial displacement and mobilized support pressure at the equilibrium point is investigated.  
Considering the influence of tunnel-advancing excavation, the unsupported span \(d\) (or unsupported distance) is usually used to consider the influence of the stress state. If the tunnel is excavated without considering the support system, it is clear that the in situ stress will drop rapidly to a plastic state. Therefore, under the isotropic stress condition, two unsupported spans were selected including (1) short unsupported span (\(\lambda_{d}\) = 0.4, \(d\) = 0.53 m) so that the surrounding rock stress falls in the elastic zone, and (2) long unsupported span (\(\lambda_{d}\) = 0.7, \(d\) = 1.37 m) so that the stress falls in the plastic zone.  
According to the results obtained by FEM and DCM for the support-ground interaction, the displacement/stress at the equilibrium state is different in the elastic region (Figure 4a) and the plastic region (Figure 4b). From the comparison results, it can be found that the mobilized support pressure in the elastic zone shows a relatively high value, while the radial displacement indicates a lower value, but the situation in the plastic zone shows the opposite. Furthermore, it must be noted that the trajectory of the support-ground interaction follows the ground reaction curve so, in this case, the interaction curve (IC) and the ground reaction curve (GRC) coincide with each other, i.e., the isotropic stress field.  
The mechanical behavior is demonstrated in this study for the displacement/stress continuity at the elastoplastic interface as shown in Figure 5, that is, the continuous connection between the interface (between the elastic and plastic regions). For the comparison of the distribution of displacement/stress around circular tunnel proximity, the radial displacement and the plastic zone radius of the ground under no support condition are larger than the support condition. In other words, the support installation in tunneling blocks the development of the plastic zone (Figure 5a). On the comparison between FEM and DCM in numerical analysis, the results obtained at the intrados of the tunnel are approximatively the same as shown in Table 2, and that around the tunnel proximity illustrates a consistent trend as shown in Figure 5.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 3 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of an Unpopular Video
### SASST Performance Evaluation: Numerical Analysis  
In this subsection, we compare SASST to SST, ST and Dyadic in terms of the average consumed bandwidth as a function of the SN request rate denoted as \(\lambda\). As we can see in Figure 4(a), SASST outperforms SST and ST in terms of the average bandwidth except for request rates smaller than 0.06, where the performances are very close to one other. Note here that the video size \(D=100\) and one slot \(s=1\).  
However, Dyadic requires a lower bandwidth than SASST. The maximum difference between them reaches 1.65 streams for an arrival rate equal to 1. In fact, having one or many requests per slot does not affect the performance in terms of the consumed bandwidth. Thus, SASST is very competitive in terms of the consumed bandwidth while being simpler than Dyadic with regard to the complexity of implementation.  
The bandwidth reduction of SASST, SST and Dyadic compared to ST is plotted in Figure 4(b). As we can see in this figure, SASST ranks second after Dyadic. At a high request rate (\(\lambda\geq 1\)), the bandwidth reduction values are almost equal to 50%, 39% and 18% for Dyadic, SASST and SST, respectively. Note that ST bandwidth is taken as reference.  
To obtain more insight into the optimal value of (\(n^{*}\)) with regard to the video size \(D\) using SASST, in Figure 6 we plot \(\frac{n^{*}}{17}\) for odd and even cases of \(n^{*}\) where \(D\in\{10,11,\ldots 1000\}\). As can be seen, as the video size increases, the storage node decides to open CS more frequently than if the video size is smaller. This could be explained by the rapid growth of  
Figure 5: Comparison of bandwidth and bandwidth reduction between SASST, SST, ST and Dyadic as a function of the request arrival rate \(\lambda\), respectively. (**a**) Bandwidth; (**b**) Bandwidth Reduction.  
cumulative FTS sizes that quickly reach the size of the video, which encourages opening a new CS rather than an FTS for the current late SN request. Indeed, serving late requests with a smaller FTS if a new CS is opened is better than serving the late requests with very long FTSs in terms of bandwidth minimization.
## 4 Mesh Independence and Validation of the CFD Simulation
### Validation
Despite this limitation, an attempt was done to compare qualitatively our CFD results with other researchers' results obtained either experimentally or numerically. [PERSON] et al. [25] have done experimental and numerical work using ANSYS Fluent on an ejector flow, focusing their work on the boundary layer separation inside the mixing section. They have measured the wall pressure along the ejector's mixing section up to the diffuser to validate their CFD results. Several turbulence models have been tested (k-epsilon standard, k-epsilon realizable, k-epsilon RNG, and k-omega SST) combined with two wall functions, i.e., the standard wall function (SWT) and enhanced wall function (EWT). To do the qualitative comparison mentioned above, the current CFD results are compared to the CFD and experimental results of [PERSON] et al. [25] which is presented in Figure 8. It should be noted that [PERSON] et al. have named the mixing section differently than what we have defined. They have given the term mixing section only for the converging mixing section while the constant area duct is simply termed the ejector throat. In the current work, however, the term mixing section encompasses both the converging section and the ejector throat. As can be seen in Figure 8, the trend of both results is nearly the same. There is a relatively sharp pressure drop near the ejector throat inlet which is caused by the secondary flow acceleration through a constricted hypothetical annular duct. This duct is formed by the primary jet flow and the wall of the ejector. In our CFD results, the shock train enters the ejector throat further, while in [PERSON] et al. 's work, the shock train strength has decreased  
Figure 8: Qualitative comparison of the non-dimensional wall pressure (\(P_{w}/P_{2}\)) along the mixing section up to the diffuser between [PERSON] et al.’s work (**a**) and the current work (turbulence model: transition SST; \(P_{0}=618\) kPa) (**b**). The left figure (**a**) is reprinted from the work of [PERSON] et al. [25] with the wall pressure (\(y\)-axis) is non-dimensionalized by the secondary inlet pressure of their work (\(P_{2}=3170\) Pa). before it enters the throat. The shock train inside the throat causes the hypothetical annular duct to restrict periodically along the \(x\)-direction. This is the reason why the wall pressure inside the eject throat fluctuates in the present work while it does not in the work of [PERSON] et al. Despite this difference, the general trend of the wall pressure inside the throat is the same, i.e., the pressure gradually increases until the flow enters the subsonic diffuser at a speed greater than or equal to sonic speed. Therefore, the flow expands sharply since, for a supersonic flow, a subsonic diffuser acts as a nozzle. At some point inside the diffuser, a shock appears (circled in black) which is indicated by a sharp increase in pressure. The Mach number contour plot for M \(\geq\) 1 is presented in Figure 9 to clarify the discussion while for the Mach number contour plot of [PERSON] et al., one can refer directly to their paper.
## 3 Method
### Light Compensation
#### 3.1.1 Image Gradient Metric  
In order to standardize the gradient metric, we set the acquired image as \(I(x,y)\) and the pixels of the image as \(P=\{P_{1},P_{2},\ldots,P_{n}\}\). Then, the gradient value of a pixel point \(P_{i}\) in the acquired image within a certain exposure time \(t_{p}\) is  
\[R(P_{i},t_{p},I)=\left\|\
abla I(P_{i},t_{p})\right\|^{2} \tag{1}\]  
where \(\
abla I=\frac{\partial I}{\partial x}i+\frac{\partial I}{\partial y}j\), the total image gradient value, is  
\[Wz=\sum_{P_{i}\in P}R(P_{i}) \tag{2}\]  
\[W_{p}=\left\{\begin{array}{c}\frac{1}{N}\log(\eta(\widetilde{R}(P_{i})-T)+1),R(P_{i})\geq T\\ 0,R(P_{i})<T\end{array}\right. \tag{3}\]  
The image gradient is normalized by Equation (3) to accelerate the convergence of the operation and to improve computational efficiency, where \(\widetilde{R}(\cdot)\) is the normalized pixel of the image. In this paper, a Gaussian function is used to simulate the influence of different  
Figure 3: A flowchart of the reflective region removal method based on the combination of image complementation and illumination compensation.  
brightness components on the image. By analyzing the influence factors of brightness with different weights, the brightness estimate \(B_{0}(x,y)\) of the gradient value is obtained. \(T\) is the activation threshold, and \(\eta\) is the weight coefficient to adjust the intensity.
## 4 Types of Catalysts Used in Transesterification
### Nano-Catalysts
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & **Operating Parameters** & & & \\ \multirow{2}{*}{**Catalysts**} & \multirow{2}{*}{**Synthesis**} & \multirow{2}{*}{**Size of**} & \multirow{2}{*}{**Feedstock Oil**} & \multirow{2}{*}{**Oil to**} & \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{**Temp.**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Biodiesel**} & \multirow{2}{*}{**Reference**} \\  & & & **(nm)** & & & & **Moalar** & & **Conc. (\%)** & & **(H)** & **(\%)** \\ \hline \hline \multicolumn{10}{c}{**Magnesium Oxide Catalyst**} \\ \hline MgO/MgAl2O4 & Combustion & 15.9 & Sunflower & 1:12 & 3 & 110 & 3 & 95 & [64] \\  & Co-precipitation & 7.86 & Waste cooking oil & 1:24 & 2 & 65 & 1 & 93.3 & [65] \\ MgO & - & 5.5 & Goat fat oil & 1:12 & 1 & 70 & 3 & 93.12 & [66] \\ MgO over & - & 25 & Moringa seed oil & 1:12 & 1 & 45 & 4 & 93.69 & [67] \\ cerium doped & & & & & & & & & \\ MCM-41 & Hydrothermal & 17.3 & Sunflower & 1:9 & 5 & 70 & 6 & 94.3 & [68] \\ nanocatalyst & & & & & & & & & \\ MgO/La2O3 & Co-precipitation & 21.1 & Sunflower & 1:18 & 3 & 50–65 & 5 & 97.7 & [69] \\ MgO/NaOH & Co-precipitation & 66.7 & Waste cooking oil & 1:6 & 3 & 50 & 6 & 97 & [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Nano-catalysts employed in biodiesel synthesis process.
### Traditional Machine Learning and Ensemble Learning  
Traditional machine learning has significantly progressed in the medical domain, particularly in analyzing clinical data and medical imaging. Traditional machine learning can effectively classify and predict outcomes based on complex datasets by employing algorithms such as decision trees, support vector machines, and artificial neural networks. These models can analyze vast amounts of data, identifying patterns that may not be apparent to human observers and enhancing diagnostic accuracy [12].  
Ensemble learning is an approach in machine learning that aims to improve predictive performance by combining multiple models, known as base learners, to create a single, more accurate model. This technique leverages the diversity of the individual models, which may have different strengths and weaknesses, to enhance overall performance. Ensemble learning methods can be broadly categorized into four main categories [24]:  
* Bagging: involves training multiple models independently on different subsets of the training data and then averaging their predictions, to reduce variance (e.g., random forest classifier).
* Boosting: sequentially trains models, where each new model focuses on correcting the errors made by the previous ones, thereby improving accuracy (e.g., XGBoost classifier).
* Stacking: combines multiple models by training a meta-learner to optimally weigh their predictions.
* Voting: aggregates the predictions of multiple base models (that can be ensemble models already) to make a final decision. Voting classifiers can be implemented in two main ways:Majority Voting: predicts the class with the majority vote from the base models. This approach is simple and works well when individual models are diverse and have similar levels of accuracy.
* Weighted Voting: averages the predicted probabilities from each base model and selects the class with the highest average probability, which is particularly effective when base models output reliable probabilities.
## 3 Random Field Generation
### Generation of Random Fields Using the Karhunen-Loeve Series Expansion Method  
The spatial variability of random soils is represented by a random field model. Several common models have been developed and used to generate random fields such as the covariance matrix decomposition method [44; 45; 46], the local average subdivision method [9; 47; 48] and the Karhunen-Loeve (K-L) series expansion method [27; 49; 50; 51]. This paper applies the following K-L series expansion method to generate the Gauss random field because of its efficiency and precision.  
\[\hat{H}_{i}(x)=\mu_{X_{i}}+\mathrm{s}_{X_{i}}\hat{H}_{i}{}^{D}(x)=\mu_{X_{i}}+ \mathrm{s}_{X_{i}}\sum_{j=1}^{M}\sqrt{\lambda_{j}}f_{j}(x)\chi_{ji} \tag{15}\]  
where \(\hat{H}_{i}(x)\) is the desired random field considering the mean value \(\mu_{X_{i}}\) and standard deviation \(\mathrm{s}_{X_{i}}\), \(i\) is the \(i\)-th random field, \(x\) is the spatial position, \(\hat{H}_{i}{}^{D}(x)\) is a standard Gaussian random field after discretizing the space, \(M\) is the number of series expansion terms of a random field, \(\lambda_{j}\) and \(f_{j}(x)\) are the \(j^{\mathrm{th}}\) eigenvalue and \(j^{\mathrm{th}}\) eigenfunction of spatial correlation model \(\rho\), respectively, \(\chi\) is a stochastic matrix of size \(M\times N\) considering the autocorrelation of the random field, \(\chi=\xi L^{\mathrm{T}}\), where \(\xi\) is a stochastic vector of size \(M\times N\) in standard Gaussian space, and \(L\) is the lower triangular matrix obtained by the Cholesky decomposition of matrix \(\rho\) in standard Gaussian space, and its size is \(M\times N\).
## 4 Experiments and Results  
### Data Preparation  
The analyzed research data were all of the bad-weather meteorological data of Yinchuan Hedong International Airport in 2019 provided by the Ningxia Air Traffic Control Branch. These data comprised meteorological radar echo charts, meteorological information, flight information, and controller call records. The research data include the bad-weather radar data of 2019, for which the data were refreshed every 6 min, giving a total of 15,840 data points. In 2019 there were 97,820 flights at Yinchuan Hedong International Airport. There are eight routes in the Yinchuan control sector: four approach routes and four departure routes. The call records of controllers in bad weather during 2019 were extracted, resulting in 20,460 pieces of data. Radar meteorological data include the date, time, range, and echo reflectivity. After radar data processing, radar echo reflectivity can be converted into information from the data to the data. The data is collected from the data, and the data is collected from the data. The data is collected from the data, and the data is collected from the data.  
mation about the severity of the bad weather. The types of bad weather can be determined according to the meteorological information for particular days, and information on wind speed, visibility, cloud base height, and other factors related to bad weather can be obtained according to the collected METAR messages.
## 7 Results and Discussion
### Performance Indices  
To evaluate the performance of the presented methodology scheme, two customised performance indices (\(\eta\) & \(\tilde{\varsigma}\)) based on the integral square error of the voltages (ISE) and the integral absolute error (IAE) of the voltages at different buses were calculated, both of which are used to quantify the performance of the proposed scheme. The higher the values of the ISE and IAE, the poorer the performance, and vice versa [44]. Mathematically, the performance indices ISE and IAE are given by Equations (20) and (21), respectively:  
\[\eta=\sum_{n=1}^{4}(V_{nom}-V_{n})^{2} \tag{14}\]  
\[\tilde{\varsigma}=\sum_{n=1}^{4}|V_{nom}-V_{n}| \tag{15}\]  
where \(V_{nom}\) represents the nominal bus voltage, \(n=n^{th}\) DG unit, and \(V_{n}\) represents the voltage of the \(n^{th}\) DG connected bus.  
To demonstrate the superiority of the proposed method, the following scenarios are used: base case (without DG and EV loads), WTGS-DG incorporated, and with both WTGS-DG and EV loads. In Table 3, the determined values for all three scenarios applying ISE and IAE are presented in tabular format.  
The effectiveness of the proposed methodology is further validated and highlighted by graphical representations of these two performance indices for each of the three scenarios obtained by employing both BWO and PSO. The IAE and ISE for all three scenarios obtained by using BWO are less than the values obtained using PSO, as shown in Figures 10 and 11.  
Figure 9: Voltage stability factors at 33 buses of modified test system using BWO and PSO.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ISE**} & \multicolumn{3}{c}{**IAE**} \\ \hline  & \(\mathbf{\eta_{1}}\) & \(\mathbf{\eta_{2}}\) & \(\mathbf{\eta_{3}}\) & \(\mathbf{\eta_{4}}\) & \(\mathbf{\eta}\) & \(\mathbf{\tilde{\varsigma}_{1}}\) & \(\mathbf{\tilde{\varsigma}_{2}}\) & \(\mathbf{\tilde{\varsigma}_{3}}\) & \(\mathbf{\tilde{\varsigma}_{4}}\) & \(\mathbf{\tilde{\varsigma}}\) \\ \hline Base Case & 0.0071 & 0.0074 & 0.0075 & 0.0069 & **0.0289** & 0.0841 & 0.0861 & 0.0867 & 0.0831 & **0.3400** \\ With DG & 0.0011 & 0.0017 & 0.0019 & 0.0003 & **0.0051** & 0.0338 & 0.0412 & 0.0436 & 0.0183 & **0.137** \\ With DG & 0 & 0 & 0.0078 & 0.5099 & **0.5177** & 0 & 0 & 0.0009 & 0.0071 & **0.008** \\ and EV loads & & & (* 10\({}^{-4}\)) & (* 10\({}^{-4}\)) & (* 10\({}^{-4}\)) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of indices.
## 2 Methods and Materials
### Correction for Lens Distortion
#### 2.4.4 Fatigue Crack Growth Data from Infrared Thermography
In our approach, the DIC/IRT pattern for correlation is based on the texture of the high emissivity paint. The benefit of this approach is that since the whole ROI is painted, the thermal measurements are not affected by low emissivity speckle pattern needed for conventional DIC, and the correlative analysis can be completed using only IRT images with little effort. Some image preparation is, however, required since the temperature data are obtained from a camera in a single precision floating point format, whereas the DIC software works with the 8-bit unsigned integer format. To convert the data to the format digestible by the DIC algorithm, IRT images are filtered by a conventional Gaussian filter with the degree of smoothing adjusted for the feature size. From experience, we set the standard deviation of the Gaussian kernel function at 6 for the cases considered here. The smoothed images were subtracted from the original images, unveiling the paint texture. Any values deviating from means for more than five standard deviations (measured for the distribution of intensity over the entire image) after filtering, were replaced by the five standard deviations limit, to reduce the influence of noise on rescaling of images when converting them to 8 bit format. To proceed with the DIC analysis, a virtual mask covering the crack was applied using the crack location data determined in Section 2.4.4. The IRT-based DIC processing yields the displacement values evolving during cyclic loading. By subtracting the initial image corresponding to the undeformed state from the sequence of images representing the cyclically deformed state, one obtains the local temperature increments in the reference and deformed configurations. The background image subtraction removed most of the texture visible in the raw images. Figure 12 shows a comparison of the raw image (a), reference image of undeformed sample (b) and the results of subtraction based on rigid body motion correction (c,e), and DIC-based motion correction (d,f). When deformations are small, the rigid body correction scheme might be sufficient (cf. (c,d)), while when the deformations become larger, the rigid body correction gives rise to the noisy texture pattern in the temperature field (e), whereas the IRT/DIC-based correction (f) still shows good results. Figure 12: _Cont._  
The approach for local regression is similar to the one used for the tensile tests. A reference image taken before the beginning of the test was subtracted from all images acquired during cyclic testing, and the ROI from the IRT/DIC analysis for the motion compensation was used. At each pixel position within the global ROI, a smaller circular region was selected with a radius of 10.5 pixels, and the data within that region were used to fit by a second-order polynomial function. The second-order spatial derivative of the temperature, as well as other spatial properties, can be extracted directly from the fitting function afterwards.