esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Identification of Recent Developments and Possible Solutions
### Compressor Solutions
The injection can take place at various compression grades (point of injection) and allows to provide different functions such as sealing, lubrication and lowering the vapor temperature during compression [63]. The point of injection and amount of the injected liquid is a matter of optimization [64]. If all the liquid is carried along with the vapor, it is defined as wet compression [34]. [PERSON] and [PERSON] (1990) [35] stated that the liquid injection makes the compressor operation smoother and more silent than dry compression. However, especially for operation as wet compression, the isentropic efficiencies obtained are often comparatively low due to the complexity of the compression process, causing various challenges in the design and optimization of the compressor and the injection system [65; 66].
## 2 Computational Model
### Numerical Validation  
Two airfoils are considered. One is the S809 airfoil, which has the maximum relative thickness of 21%, and the maximum relative camber (of 0.95%) is specifically used in the NREL Phase VI wind turbine. The other is the NACA 0012 airfoil. Figure 1a shows an O-type computational domain with a diameter 50 times as long as the airfoil chord length (\(c\)). It contains a smaller circular domain surrounding the airfoil, as shown in Figure 1b, which has a 3\(c\) diameter and can rotate around the quarter chord position. After a grid independence study, they, respectively, have the sizes of 553 \(\times\) 78 and 553 \(\times\) 199, with a total mesh number of 153,181, which is much larger than that given by [PERSON] et al. [37]. At least 25 grid cells, in vertical the direction, are used to resolve the boundary layer, and the first cell height is set to make the dimensionless normal distance from wall less than 1.  
The pitching motion of the airfoil is defined by:  
\[\alpha(t)=\alpha_{m}+\alpha_{A}\sin(2\pi ft) \tag{10}\]where \(\alpha_{m}\), \(\alpha_{A}\), and \(f\) are the mean angle of attack, the amplitude, and the frequency of oscillation, respectively. The reduced frequency is defined as:  
\[\omega_{red}=\frac{\pi fc}{V_{\infty}} \tag{11}\]  
where \(V_{\infty}\) denotes the free-stream velocity.  
A turbulence intensity of 0.1%, which is frequently used in the low turbulence wind-tunnel experiment [40, 41], is adopted at the inlet and outlet boundary, and the no-slip condition is used on the airfoil surfaces. The physical time step size is set as 5 \(\times\) 10\({}^{-4}\) s. The correlation pitching parameters are listed in Table 1. Figure 2 shows lift coefficient hysteresis loops of S809 airfoil by numerical and experimental [19] methods in a clean air flow. It was found that the iteration number has great effect on the result, and a number of 80 is an appropriate choice to achieve the balance between accuracy and efficiency. The moment coefficient loop with an iteration number of 80 is shown in Figure 2, and the good agreement with the experimental result is a further support for the choice.  
Figure 1: Computational field and mesh: (**a**) the blue and red semicircle edges are the velocity-inlet and pressure-outlet boundaries, respectively; (**b**) the green circle edge is the interface between the stationary domain and the rotating domain.  
Figure 2: Numerical and experimental data of time-varying aerodynamic coefficients: (**a**) lift coefficient loops; (**b**) moment coefficient loops.  
Figure 3 displays the streamwise velocity distributions of air in the wake of a circular cylinder at three Stokes numbers of 0.98, 6.5, and 19.7. In these three cases, comparisons between numerical results and those from a gas-solid experiment [42] are good, indicating the used DPM is reliable.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 3 Methods
### Spectral Separability  
In order to measure the separability of the NDVI and the DSM layers of different types of vegetation present in the study area (cork oak, shrubs, and grass) and, consequently, their importance for the classification provided by the original spectral bands (R-G-NIR), we implemented the M-statistic, defined originally by [PERSON] and modified as follows:  
\[M=\frac{\left|\mu_{1}-\mu_{2}\right|}{(\sigma_{1}+\sigma_{2})} \tag{1}\]  
where \(\mu_{1}\) is the mean value for class 1, \(\mu_{2}\) is the mean value for class 2, \(\sigma_{1}\) is the standard deviation for class 1, and \(\sigma_{1}\) is the standard deviation for class 2. M-statistic expresses the difference in \(\mu\) (the signal) normalized by the sum of their \(\sigma\) (that can be interpreted as the noise) (i.e., the separation between two samples by class distribution). Larger \(\sigma\) values lead to more overlap of the two considered bands, and therefore to less separability. If M \(<\) 1, classes significantly overlap and the ability to separate (i.e., to discriminate) the regions is poor, while if M \(>\) 1, class means are well separated and the regions are relatively easy to discriminate.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Performance Evaluation Metrics
#### 4.1.3 Average End-to-End Latency (\(\bar{\mathcal{T}}\))  
The average end-to-end latency per node per data-gathering cycle (\(\bar{\mathcal{T}}\)) can be calculated as given in Equation (21). Latency is a measure of delay taken to send a packet from a source to a destination. In centralized and hybrid architectures of SDVNs, data packets are originated in the nodes (OBUs or RSUs), and are sent to the control server node. A low latency value is desirable.  
\[\bar{\mathcal{T}}\ =\ \frac{1}{\mathcal{K}N}\sum_{m=1}^{\mathcal{K}}\sum_{i=1}^{ N}\left(T_{ri}-T_{si}\right) \tag{21}\]  
In Equation (21), \(T_{ri}\) is the receiving timestamp of the packet transmitted by the \(i^{th}\) node at the destination, whereas \(T_{si}\) is the sending timestamp of the packet by the \(i^{th}\) source node. Usually, end-to-end delay is calculated among data packets transmitted from one vehicle to another (between two data plane units). However, the endpoints for calculating latency in this research are different, and packets consist of metadata.
## 3 Experimental Results
### Results of EU-TIRADS Classification System  
Random Forest, J48 DT, Ada Boost, and SVM models were used with the EU-TIRADS system. The findings revealed that J48 DT yielded the top accuracy rate with 99.7853%. Table 2 shows the success rates for all classifiers. While Table 3 presents the confusion matrix generated for the J48 DT classifier, Table 4 demonstrates the evaluation metrics for J48 DT.  
\begin{table}
\begin{tabular}{c c} \hline
**Classifier** & **Accuracy (\%)** \\ \hline Random Forest & 99.6933 \\ \hline J48 DT & **99.7853** \\ \hline Ada Boost & 97.4241 \\ \hline SVM & 99.6933 \\ \hline \end{tabular}
\end{table}
Table 2: Comparative results for the classifiers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Class “0”** & **Class “1”** \\ \hline \multirow{3}{*}{**FNA**} & **Class “0”** & 1246 & 1 \\ \cline{2-4}  & **Class “1”** & 6 & 2008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for J48 DT.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 5 Results and Discussion
### Selection of Multi-Layer Feature Distillation  
Since the LSTM model learns knowledge from the Crossformer model through multi-layer feature distillation, selecting which decoder layer outputs to use as intermediate features for learning becomes crucial. To address this issue, this study trains the model using combinations of intermediate features from different layers to identify the optimal intermediate feature distillation combination.  
The Crossformer model consists of four hidden layers, with the output of the fourth hidden layer serving as the final prediction result. Therefore, when combining intermediate features, this study only considers the outputs of the first three hidden layers, resulting in seven possible combinations (for example, CLMFD(23) indicates using the outputs of the second and third hidden layers as intermediate features).  
To ensure the reliability and stability of the experimental results, all experiments in this study were conducted with multiple independent trials (each group of experiments repeated five times), and the final results were averaged. As shown in Table 4, among the seven combinations, CLMFD(23) performs the best, indicating that the outputs of the second and third hidden layers contain more representative feature information. Therefore, this study selects the outputs of the second and third hidden layers as intermediate features for distillation with the student model.
## 2 Materials and Methods
### Methodology
#### 2.3.5 Effectiveness Assessment  
The negative log-likelihood (NLL) value is an important metric for evaluating statistical models, based on the probability (likelihood) of the model generating observed data to measure the goodness of fit of the data in model fitting [44]. The calculation of the NLL value is based on the maximum likelihood estimation (MLE), where the likelihood function is defined as the probability of observing data \(X\) given parameter \(\theta\), as shown in Equation (9):  
\[L(\theta;X)=\prod_{i=1}^{n}f(x_{t};\theta) \tag{9}\]  
where \(\theta\) represents the model parameter. Since the product operation may lead to numerical underflow, we typically take the logarithm of the likelihood function to convert it into a sum form, i.e., the log-likelihood function. In maximum likelihood estimation (MLE), our goal is to find the parameters that maximize the likelihood function. Similarly, we can achieve this by minimizing the negative log-likelihood (NLL) function, as shown in Equation (10):  
\[\left\{\begin{aligned} logL(\theta;X)=\sum_{i=1}^{n}logf(x_{t}; \theta)\\ NLL(\theta;X)=-\sum_{i=1}^{n}logf(x_{t};\theta)\end{aligned}\right. \tag{10}\]  
In different models, a lower NLL value indicates that the model's parameter settings are more likely to generate the observed data. However, when we adopt more complex models (such as non-stationary extreme value models), due to having more parameters, there might be a phenomenon of overfitting, which also leads to a lower NLL value, but this does not mean that the model performs better. Therefore, we introduce evaluation criteria that penalize complexity, namely, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), as shown in Equations (11) and (12):  
\[AIC=2k-2 ln(L) \tag{11}\]  
\[BIC=ln(n)k-2 ln(L) \tag{12}\]  
where \(k\) is the number of parameters in the model, \(n\) is the number of data points, and \(L\) is the maximum likelihood estimate of the model (the value of the likelihood function for the observed data under the optimal parameters).  
The foundation of AIC is the concept of information entropy, aimed at minimizing the information loss between the true distribution and the model distribution. It focuses on finding the model that fits the data best while penalizing an increase in the number of model parameters. BIC is a model selection criterion based on the likelihood function, derived from Bayesian principles. It imposes a stricter penalty on the number of parameters than AIC and places more emphasis on finding the model most likely to generate the observed data from the true process [45; 46].
## 2 Materials and Methods
### Crack Localization
Generally, spatial information generated using image stitching has a high resolution that allows for the identification of cracks. However, the resulting spatial information does not include the Geotags of each original image used in the stitching process. Geotags refers to the geographical location information contained in the images. During the stitching process, the images are transformed and combined to create a new image. In this process, there is a typical loss of individual metadata for each image, including Geotags [42]. In this study, the spatial information layer merged based on the Point Cloud technique, which contains position information but may have difficulty identifying cracks, with the spatial information layer based on the image stitching technique, which has a high resolution, enabling crack identification but lacks position information. By combining the layers via layer merging, we performed crack location estimation of the building's exterior. Additionally, to validate the accuracy of the crack location information estimated using the proposed methodology, we compared and analyzed the estimated crack positions with the crack position information obtained using field measurements.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 1 Introduction
The results showed that when time-domain signals were used, their classification ability was lower than their prediction ability. Compared with SVM, neural network has strong generalization ability, so it is widely used in fault diagnosis. [PERSON] [20] proposed a self-organizing map (SOM) neural network, which utilizes various features in the signal and the characteristics of the internal representation of its spatial organization for self-organization and self-learning, and then hierarchical clustering. A gear fault diagnosis method based on HHTand SOM neural network is proposed by [PERSON] [21], which used EMD to decompose gear vibration signals to obtain several IMFs, and selected the energy percentage of the first six IMFs as the input vector of SOM neural network for fault identification. The analysis results show that this method can effectively identify gear fault types. The SOM-radial basis function (RBF) neural network to detect and analyze the fault of induction motor is used by [PERSON] [22]. The results show that the method can not only detect electrical and mechanical faults, but also estimate the degree of fault. Based on the studies in the above literatures, due to the problems of mode mixing and endpoint effect in EMD and LMD methods, VMD was adopted for signal decomposition in this paper.At the same time, VMD is difficult to determine the penalty factor and the number of modal decomposition in the decomposition process, so the instantaneous frequency mean change of each component of the fault signal after VMD decomposition is put forward as an evaluation index, so as to determine the number of modal decomposition in the VMD process. At the same time, in order to highlight the characteristics of the signal, the kurtosis value is used as the characteristic parameter of the signal to extract, and a gear fault diagnosis based on the kurtosis criterion VMD and SOM neural network is proposed.
## 2 Simulation Models
### Experimental Validation of the Radiant Floor Model
However, during the experiments, the difference in water temperature between the inlet and the outlet was small (as an example, when the inlet temperature was 40 \({}^{\circ}\)C, the outlet was 39.4 \({}^{\circ}\)C, with small variations, during the test), so the temperature on the entire surface was very homogeneous. Therefore, the temperature distribution problem, for this case, was minimal. On these grounds, an average experimental soil temperature was obtained to compare with that of the model. Hence, the a priori limitation of the model, which only uses one temperature for the entire radiant floor surface, is irrelevant to its validation. The flooring surface temperatures found with the numerical model for water inflow temperatures of 35 \({}^{\circ}\)C and 40 \({}^{\circ}\)C fitted the experimental measurements very closely, with RMS errors of only 0.33 \({}^{\circ}\)C and 0.27 \({}^{\circ}\)C, respectively. The numerical and experimental values agreed less closely at a water inflow temperature of 45 \({}^{\circ}\)C, particularly in the early time steps, although the RMS error was only 0.75 \({}^{\circ}\)C. The less narrow fit for the inflow water temperature at 45 \({}^{\circ}\)C may be attributable to the use of an adiabatic boundary condition for the bottom side of the flooring. The downward losses in the flooring, which were greater for the inflow water at 45 \({}^{\circ}\)C than for the other values, were not be accounted for by the model. In light of the aforementioned findings, the accuracy of the numerical model was deemed to be valid for the present purposes. Figure 5: Experimental validation of the numerical model for radiant flooring.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Experimental Grouping and Data Collection  
A total of 10 experimental setups were used, with each setup undergoing multiple impact tests on the testing platform until the equipment was damaged. After each experiment, the equipment status was checked, and acceleration curves were recorded. Multiple sets of acceleration curves collected from each setup were used as training data. To ensure data diversity and representativeness, the following grouping design was implemented:  
* 3 setups with incrementally increasing acceleration, but with different step sizes.
* 3 setups with relatively constant peak acceleration values.
* 4 setups with randomly varying acceleration within the range of 20,000 g to 90,000 g. Acceleration peaks under different impact conditions were recorded, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Experiment Number** & **Peak Acceleration Value (Unit: g)** \\ \hline
01 & [24,439, 38,164, 50,529, 61,137, 72,144, 85,871] \\
02 & [32,518, 41,486, 52,299, 62,395, 74,480, 82,090] \\
03 & [46,702, 701, 513, 36, 8175, 64,646, 710,725, 783, 883, 788] \\
04 & [63,097, 61,659, 60,010, 61,211, 63,038, 60,193, 60,078, 6297, 61,125, 61,269] \\
05 & [65,681, 66,628, 65,624, 68,690, 71,801, 70,041, 69,895, 70,766] \\
06 & [79,280, 75, 749, 783, 841, 803, 76,744] \\
07 & [31,368, 733, 25, 79,391, 35,725, 43, 639, 88,706] \\
08 & [48,370, 51,866, 76,331, 31,079, 58,324, 237, 494, 8063, 57,906, 39,087, 87,732] \\
09 & [37,443, 56,419, 33,818, 79,596, 27,284, 76,450, 47,402, 88,521] \\
10 & [25,139, 65,885, 34,232, 71,183, 46,534, 59,143, 86,526] \\ \hline \end{tabular}
\end{table}
Table 6: Experimental Peak Acceleration.  
Figure 3: (a) Shock Test Table Model; (b) Acceleration Curves.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions
### Adsorption Capacity of \(\upalpha\)-Tocpheryl Acetate
#### 3.4.1 Correlation of Adsorption Capacity and Total Mesopore Volume  
Based on the assumption of monomolecular adsorption of \(\alpha\)-tocopherol acetate into the pores, a linear regression analysis was carried out. The total BJH mesopore surface area explained the adsorption capacity by 88.8% (\(r^{2}_{adj}=0.888\)), with \(r=0.949\), which underlines the strong positive linear connection between these parameters (Figure 5). Consequently, knowledge of the total mesopore area, depending on the size of the adsorptive, is of huge importance to predict the adsorption process.  
When considering the standard deviations, the homogeneity to heterogeneity of the biochars' particle size distribution becomes obvious. The more homogeneous the particle size distribution appears (Figure 1), the lower the standard deviation of the relative adsorption of \(\alpha\)-tocopherol acetate. Biochars D and E, which have the largest total mesopore volume and highest adsorption capacities, show a heterogeneous particle size distribution due to woody fragments. Biochars D and E also show powdery particles; however, biochars B and C contain more powder and smaller particles, but a smaller total mesopore volume and adsorption capacity. The impact of biochars having a larger amount of small particles, with an assumed smaller diffusion path length for the adsorptive, was not confirmed. Generally, these differences in the adsorption capacity of \(\alpha\)-tocopherol acetate into the pores of biochars underline the impact of pore volume, which is affected by sorption kinetics [46]. We conclude that, in this study, the influence of the biomass on the pore  
Figure 4: Theoretical (\(n=2\)) and experimental adsorption capacities and their dependence on the biochars (\(n=3\)). The experimental adsorption capacities were determined based on the weight of the biochar, as well as on the dry matter weight of the biochar. Different small letters (abcde) indicate significant differences between biochars for adsorption capacity, determined by One-Way ANOVA and [PERSON]’s post hoc test (\(p<0.05\)), even if the normality test and/or the equal variance test failed (\(p>0.05\)).  
volume was rather small, whereas the influence of processing seems to have been large. Thus, to gain a comprehensive understanding of the sorption kinetics, more information about biochar and its processing is mandatory.
# The Protection Role of Cysteine for Cu-5 Zn-5 Al-1 Sn Alloy Corrosion in 3.5 wt.% NaCl Solution
## 3 Results  
### Potentiodynamic Polarization  
Figure 1 presents Tafel polarization curves for the Cu5 Zn5 Al1 Sn alloy exposed to 3.5 wt.% NaCl solution with and without the Cys inhibitor. The trend of polarization curves suggests that a low amount addition of cysteine (\(10^{-4}\) M, \(10^{-5}\) M) in 3.5 wt.% NaCl solution moves the curves to the lower current region in the anodic potential route compared with the blank solution. Nevertheless, the higher concentration (\(10^{-3}\) M, \(10^{-2}\) M) of the inhibitor in solution causes a shift of the curves to a more cathodic potential region and decreases the corrosion current meaningfully. Consequently, cysteine influences both the cathodic and anodic reactions. Hence, cysteine is shown to act as a mixed-type inhibitor to the Cu5 Zn5 Al1 Sn alloy in 3.5 wt.% NaCl and to retard majorly cathodic corrosion reactions [22; 23; 24; 25]. The corrosion current density (Icorr) and corrosion potential (Ecorr) were calculated by fitting the experimental results with Gamry Echem Analyst software 5.61 and listed in Table 1. The inhibition efficiency (\(\upmu\%\)) was also obtained by Equation (1) using parameters listed in Table 1[26].  
\[\upmu\%=\frac{\mathrm{Icorr-Icorr(inh)}}{\mathrm{Icorr}}\times 100\% \tag{1}\]  
Figure 1: Potentiodynamic polarization curves for the Cu5 Zn5 Al1 Sn alloy after 60 min immersion in 3.5 wt.% NaCl in the absence (Blank) and presence of different concentrations of cysteine (\(10^{-5}\) to \(10^{-2}\)).  
In this equation Icorr (inh) and Icorr are the current densities of the working electrode in the 3.5 wt.% NaCl solution with and without cysteine, respectively.  
The Icorr is inversely related to the inhibitor concentration, i.e., an increase in inhibitor concentration results in a lower value of Icorr. This is probably due to the improved protection of the Cu5 Zn5 Al1 Sn alloy by cysteine molecules. Therefore, the mitigation of the Cu5 Zn5 Al1 Sn alloy deterioration in 3.5 wt.% NaCl solution may associated with the development of a protective layer from the adsorbed inhibitor species on the alloy surface [27]. The adsorption layer of cysteine molecules on the alloy surface can hinder the movement of corrosive species, resulting in a reduction in corrosion rate [2; 23; 24; 28; 29].  
It is evident from Table 1 that both \(\beta\)c and \(\beta\)a are varied on the addition of the inhibitor. From this behavior, it is suggested that both anodic and cathodic reactions are retarded by the protective layer formed by inhibitor molecules and the alloy/electrolyte interface [3; 23; 29]. In addition, \(\beta\)c values are greater than \(\beta\)a values at all inhibitor concentrations and the variation in \(\beta\)c with the inhibitor concentration is greater than the variations in \(\beta\)a. Both the results give the same evidence that cysteine is more efficient in inhibition of cathodic reactions than that of anodic reactions [3; 30]. The efficiency of cysteine increases with its concentration and reaches a maximum value of 97.2% at the concentration \(10^{-2}\) M.