esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Multiple Interleaved Bi-Directional DC/DC Converter with Current Ripple Optimization
## 3 The Model of Variable Multiplicity Interleaving Bidirectional DC/DC Power Converter
### The Simulation of the Variable Multiplicity Interleaving Bidirectional DC/DC Power Converter  
Based on the analysis of the previous chapters, the simulation model is built, and then the parameters in Table 2 are brought into the model. The simulation model is operated to extract the current ripples of different parallel numbers when the duty cycle is 0.25, 0.33, 0.40, and 0.5, which are shown in Figures 13-16 (In each Figure, a is the current ripples in four parallel multiplicities; b is the current ripples in five parallel multiplicities; c is the current ripples in six parallel multiplicities.). Figure 13 shows the output current ripples when the duty cycle is 0.25, and the current ripple is the smallest when the parallel number is four; Figure 14 shows the output current ripples when the duty cycle is 0.33, and the current ripple is the smallest when the parallel number is six; Figure 15 shows the output current ripples when the duty cycle is 0.4, and the current ripple is the smallest when the parallel number is five; Figure 16 shows the output current ripples when the duty cycle is 0.50, and the current ripple is the smallest when the parallel number is six. The above simulation results are consistent with the conclusion in Table 1, which is obtained from the previous analysis, this is when the duty cycle is different, the number of parallel multiplicities with the smallest current ripples is different, and so the DC/DC converter needs to flexibly choose to turn on or turn off some parallel branches according to the current ripple optimization law.  
Figure 12: The Bode diagram of the corrected system.  
Figure 13: Output current ripples of DC-DC converter with 0.25 duty cycle under the 6A output current. (**a**) The current ripples in four parallel multiplicities; (**b**) The current ripples in five parallel multiplicities; (**c**) The current ripples in six parallel multiplicities.  
Figure 16: Output current ripples of DC-DC converter with 0.50 duty cycle under the 6A output current. (**a**) The current ripples in four parallel multiplicities; (**b**) The current ripples in five parallel multiplicities; (**c**) The current ripples in six parallel multiplicities.  
Figure 14: Output current ripples of DC-DC converter with 0.33 duty cycle under the 6A output current. (**a**) The current ripples in four parallel multiplicities; (**b**) The current ripples in five parallel multiplicities; (**c**) The current ripples in six parallel multiplicities.  
Figure 15: Output current ripples of DC-DC converter with 0.40 duty cycle under the 6A output current. (**a**) The current ripples in four parallel multiplicities; (**b**) The current ripples in five parallel multiplicities; (**c**) The current ripples in six parallel multiplicities.
# Research on Railway Emergency Resources Scheduling Model under Multiple Uncertainties
## 2 Model Description and Establishment
### Fuzzy Credibility Constraint Programming  
The form of fuzzy credibility constraint programming is as follows:  
\[\text{max}f=\sum\limits_{j=1}^{n}c_{j}x_{j} \tag{14}\]  
The constraint condition is shown as follows:  
\[Cr(\sum\limits_{j=1}^{n}a_{ij}x_{ij}\leq\widetilde{A})\geq\lambda_{i},\ i=1,2, \ldots,m \tag{15}\]  
\[x_{j}\geq 0,j=1,2,\ldots,n \tag{16}\]  
where \(c_{j}\) and \(a_{ij}\) are real number parameters. \(\widetilde{A}\) represents fuzzy numbers, \(x_{j}\) represents the decision variables, \(Cr\) is the credibility measure, \(\lambda_{i}\) is the credibility confidence level.  
Suppose \(\xi\) is the triangular fuzzy variable \((a,b,c)\), \(a\),\(b\), and \(c\) are all real numbers greater than 0, \(a\) is the lower limit of the fuzzy number \(\xi\), \(c\) is the upper limit of the fuzzy number \(\xi\), and \(b\) is the possible value of the fuzzy number \(\xi\). Then, the expression of the membership function \(\mu_{\widetilde{A}}(x)\) is shown as follows:  
\[\mu_{\widetilde{A}}(x)=\left\{\begin{array}{ll}0&(x\leq a)\\ \frac{x-a}{b-a}&(a\leq x\leq b)\\ \frac{c-x}{c-b}&(b\leq x\leq c)\\ 0&(x\leq c)\end{array}\right. \tag{17}\]  
Then, based on this membership function, the credibility of \(x\leq\xi\) is expressed as follows:  
\[Cr(x\leq\xi)=\left\{\begin{array}{ll}1&(x\leq a)\\ \frac{2b-a-x}{2(b-a)}&(a\leq x\leq b)\\ \frac{x-c}{2(b-a)}&(b\leq x\leq c)\\ 0&(c\leq x)\end{array}\right. \tag{18}\]  
Using \(U\) instead of \(\sum\limits_{j=1}^{n}\text{a}_{ij}x_{j}\), the constraints of the model can be transformed into:  
\[Cr\Big{\{}U\leq\widetilde{A}\Big{\}}\geq\lambda_{i},\ i=1,2,\ldots,m \tag{19}\]The model can be converted as follows:  
\[\max f=\sum_{j=1}^{n}c_{j}x_{j} \tag{20}\]  
The constraint condition is shown as follows:  
\[Cr\Big{\{}U\leq\widetilde{A}\Big{\}}\geq\lambda_{i},\,i=1,2,\ldots,m \tag{21}\]  
\[x_{j}\geq 0,\,j=1,2,\ldots,m \tag{22}\]  
The general confidence level should take a value greater than 0.5; therefore, based on the above definition of confidence, \(\lambda_{i}\) will be expressed as  
\[1\geq\mu_{A}(\widetilde{x}_{i})\geq\lambda_{i}\geq 0.5 \tag{23}\]  
\[\frac{2b-a-U}{2(b-a)}\geq\lambda_{i}\to U\leq b+(1-2\lambda_{i})(b-a) \tag{24}\]  
The above constraints are substituted into the model to find the optimal solution. From the above model, it can be seen that the fuzzy credibility-constrained programming model is transformed into a linear planning model, which can be solved according to the solution method of the linear programming mode.
## 2 Platform Scheduling Model  
The heterogeneous signal processing platform scheduling is greatly affected by the correlation between components, and the communication between heterogeneous processors, so its model mainly includes the task model and the target platform hardware model. A reasonable and effective scheduling model can greatly improve the performance of the target platform [24].
# Tire Bubble Defect Detection Using Incremental Learning
## 3 Experimental Results
### Results Obtained by the YOLOv3 SPP Architecture  
Spatial Pyramid Pooling (SPP) [14] is a pooling layer, using 1 \(\times\) 1, 5 \(\times\) 5, 9 \(\times\) 9, and 13 \(\times\) 13 maximum pooling methods to achieve multi-scale fusions verification including local and global features. In this experiment, we added an SPP layer to the YOLOv3 backbone network to improve the detection accuracy at the cost of higher computing resource requirements.  
Table 5 shows the detection results after adding an SPP structure to the YOLOv3 architecture to achieve local and global feature fusions. The results show that both tread and sidewall samples have higher detection accuracies than those obtained by the YOLOv3 architecture. For both the sidewall and the tread, after adding the SPP structure, the detector extracts more feature information than the YOLOv3 architecture in the feature extraction stage, which effectively increases the reception range of the backbone feature and significantly improves the detection rate. Adding the incremental YOLO architecture further increases the detection rate by approximately 6%.
## 4 Results
### Turbulent Component of the Pressure  
Pressure fluctuations remain a critical area of study, primarily due to the computational challenges associated with performing LES for these types of flows. This study assessed the twoPhaseEulerFoam solver's capability in simulating the pressure's turbulent component. When coupled with the k-OmegaSST turbulence model, the solver cannot simulate pressure fluctuations, highlighting a significant limitation of solvers using Reynolds-Averaged Navier-Stokes (RANS) turbulence models.  
If this type of turbulence model, RANS, is applied, regardless of the numerical schemes for computing time, pressure, and velocity evolution in time, we can easily find out that results in terms of fluctuations will remain unchanged, i.e., are not captured by the model because the numerical simulation reaches a semi-steady state. Experimental studies express the fluctuating dynamic pressures by a coefficient, \(C_{p^{\prime}}\), defined in Equation (10), where \(\sigma\) represents the standard deviation of the pressure. Since the numerically calculated pressures with RANS are steady, the resulting standard deviation, \(\sigma\), equals zero.  
This shortcoming of the RANS turbulence model was overcome by applying the empirically deduced Equation (11) proposed by [PERSON] et al. [3].  
\[C_{p^{\prime}}=\frac{\sigma}{\Phi\,\
icefrac{{V_{2}^{2}}}{{2g}}} \tag{10}\]  
\[C_{p^{\prime}}=10\left(\frac{u^{\prime}}{U_{j}}\right)^{2} \tag{11}\]  
As the turbulence model calculates the turbulent kinetic energy \(k\), which is in itself a measure of the turbulent fluctuating parameters of the flow, one can compute the root mean square of the velocity fluctuations, denoted as \(u^{\prime}\), from the turbulent kinetic energy using Equation (12).  
\[u^{\prime}=\sqrt{\frac{2k}{3}} \tag{12}\]  
Figure 11 presents the fluctuating dynamic pressure coefficient, \(C_{p^{\prime}}\), as a function of \(r/D_{i}\). In a simulation involving a 7.4 m/s jet in a plunge pool with a water depth of 0.8 m, the pressure fluctuations estimated through Equation (11) aligned closely with the experimental data within the jet stagnation point. Discrepancies between numerical and experimental data were minimal.  
However, as the measuring point moves away from the jet stagnation point (\(r/D_{i}>0.69\)), numerical simulations predicted more intense pressure fluctuations than experimentally observed. Conversely, at distances farther from the jet centerline (\(r/D_{i}>2.80\)), experimental data revealed higher fluctuating dynamic pressure coefficients than numerical estimates.
# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 6 Experimental Results and Discussion
### Result of Visual Sentiment Classification with the Fine-Tuned pre-Trained Models
#### 6.4.1 Weight Regularization  
To reduce the overfitting of the models to the training data and to achieve performance improvement, we implemented the weight regularization (L2 regularization) concept. The weight regularizer was added to the dense layer with 512 units, which was placed just before the final dense layer with 2 units. It was implemented with the Keras weight regularization API, which helped add a penalty for the weights. The Keras API provided three different weight regularizations, namely the L1 regularization, which generates the sum of absolute weights, the sum of squared weights was generated by L2, and the combination of the sum of the absolute and squared weights was provided by the L1 and L2 regularizers. The L2 regularization technique was implemented with the help of the Keras library by summing up the squared weights of the model. We evaluated the model's performance using various regularization parameter settings. We performed the grid search through different values of regularization parameter values of L2 regularization to find the value that gives higher accuracy with the model.  
From Figure 16, we were able to conclude that the VGG-19 model achieved its highest accuracy of 75.21% when the value of the weight regularization parameter was \(10^{-6}\). The training and testing accuracies of the model reached their lowest values when the value of the weight regularization parameter reached \(10^{-1}\).  
Figure 16 and Figure 17 depict the classification accuracies achieved by choosing different values for the weight regularization parameter. From the plots, we were able to conclude that the DenseNet-121 model had its best value for testing accuracy, 89.26%, when the value of the weight regularization parameter was \(10^{-2}\). The Resnet50V2 model achieved its highest accuracy of 75.12% when the value of the weight regularization parameter was \(10^{-6}\). The training and testing accuracies for both the models reached their lowest values when the value of the weight regularization parameter reached \(10^{-1}\).  
Figure 16: Accuracy plot of the grid search of regularization parameter (VGG-19 model).
## 3 Interfacial Phenomena between Solid-State Electrolytes and Electrodes
### Buffer Layers  
In principle, either the electrode or the electrolyte can be coated with a buffer layer to stabilize the electrode/electrolyte interface. Oxide materials are known as good candidates to be used as a buffer layer at the electrode/electrolyte interface in SSBs [276]. The materials choices for buffer layers include, Al\({}_{2}\)O\({}_{3}\)[277], ZrO\({}_{2}\)[249; 278; 279], ZnO [88], LiNbO\({}_{3}\)[27; 280], Li\({}_{2}\)CO\({}_{3}\)[281; 282], Li\({}_{3}\)BO\({}_{3}\)[238], Li\({}_{3}\)PO\({}_{4}\)[283; 284; 285], LiAlO\({}_{2}\)[286], Li\({}_{2}\)SiO\({}_{3}\)[287], LTO [288] and Li\({}_{2}\)MoO\({}_{4}\)[289]. The most commonly used deposition techniques for coating buffer layers are sputtering, ALD and PLD. As several reviews [290; 291; 292] about the effects of the buffer layers on stabilizing the electrode/electrolyte interface can be found, this study will focus on recent progress of buffer layers introduced in SSBs. The buffer layers introduced at the electrolyte/electrode interface will also be discussed in terms of two aspects: the electrolyte/cathode interface and the electrolyte/anode interface.
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 2 Sensor Configuration Setup  
The tri-directional force measurement system under study has been developed for being installed in a customized test rig for the shroud contact force measurement during blade forced response tests, as shown in Figure 2.  
The force measurement system consists of three strain gauge based uniaxial force sensors HBM U9C (0.5 kN) assembled in a tripod configuration having three mutually orthogonal branches, where each branch will include a force sensor attached to a contact reference block on one end and collar link on the other. In the proposed test rig, the blade shrouds will be in contact with the contact reference block that is especially designed with a contact surface centrally aligned with the shroud contact surface. The three branches will be fixed to the test rig frame with the help of three collar links. As a whole, two of these force measurement systems based on the tripod sensor configuration will be installed in the test rig for each blade shroud end.  
A single force sensor of this type is compact and can measure both tensile and compressive forces. Because of its high stiffness and high natural frequency, it can measure static as well as dynamic forces applied in axial direction with good accuracy and minimum crosstalk. Due to the design of the configuration and the nature of the contact forces being 3D point forces, it is assumed that the bending moment is negligible.  
For the calibration of the force measurement system, the tripod sensor configuration was fitted on a two walled frame with bolted base, as shown in Figure 3a, with the following configuration: Sensor \(1//x\)-axis, Sensor \(2//y\)-axis, Sensor \(3//z\)-axis). Each force sensor was connected to a cube instead of the contact reference block to be used in the proposed test rig. This was done in order to facilitate the application of forces in the axial direction of the sensors by using deadweights and wire-pulley systems during the calibration. The other end of the force sensor was screwed in collar link which was then connected to a wall. Two different types of collar links were used and tested (Figure 3b), i.e., locknut collar and necked collar, in order to assess their effect on the mechanical coupling of the three branches, and therefore on the measurement accuracy. The locknut collar link was assembled using a standard M5 bolt screwed in an extended locknut whereas the necked collar consisted of a slender section machined in cylinder attachment to cater for the lateral component of the force and allow bending.  
Figure 2: Blade shrouds in contact with force measurement system in the proposed test rig.
## 2 Materials and Methods
### Heart Rate Variability (HRV)
#### Statistical Analysis  
JAMOVI(r) version 2.3.21 for Windows (Sydney, Australia) was used for statistical analysis. The variables were presented with means and standard deviations to describe muscle oxygenation, HRV data, and VAS pain at the three measurements: Points (1 and 2) without fatigue and point 3 measurements with fatigue. First, a normality test was performed through the Shapiro-Wilk test. Then, an analysis of variance (ANOVA) was applied to establish differences between conditions; a post hoc was performed with the Bonferroni test. A partial eta squared test (\(\mathrm{\eta^{2}_{P}}\)) was also performed considering values of <0.01, between 0.01 and 0.059, and >0.138, classifying as small, medium, and large effect sizes, respectively [38]. The variables that showed a significant difference were then used in a multiple linear regression analysis to identify the influence of HRV (independent variable) on muscle oxygen consumption variables (dependent variables). The condition (without fatigue or with fatigue) was included as a factor that modulates the multiple regression. The regression model was visualized using a residual analysis plot to identify outliers. Additionally, a Pearson correlation analysis was applied between VAS pain and muscle oxygen consumption variables. A \(p\)-value of <0.05 was considered statistically significant in all tests. The reliability analysis included the calculation of the typical error of the estimate (TEE) (change in \(\mathrm{SD}\div\sqrt{2}\)), which represents the noise of the signal for each variable. Furthermore, we analyzed the intraclass correlation coefficient (ICC; 1). Values below 0.5 indicate poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values above 0.9 indicate excellent reliability [39]. The coefficient of variation percentage (CV) was also estimated based on the TEE [(\(\mathrm{SD}\div\mathrm{mean}\)) \(\times\) 100], and the Minimum Detectable Change (MDC) was calculated as 1.96 \(\times\) TEE \(\times\)\(\sqrt{2}\)[40].
## 2 Comprehensive Evaluation Method for AC/DC Hybrid Power Network Planning
### Evaluation Index System for AC/DC Hybrid Microgrid Planning
#### 2.1.4 The Environmental Protection Indicators  
Compared with traditional thermal power generation, DG emits a small amount of polluted gas. It is environmentally friendly. \(E\) is defined as the amount of polluted gases emitted by the system [23; 24].  
\[e_{k}=\sum\limits_{g=1}^{T_{U}}{P_{TRg}e_{TRgk}}+\sum\limits_{i=1}^{M}{\delta_ {ai}P_{fa}S_{DGa}e_{DGak}}, \tag{9}\]  
\[E=8760\sum\limits_{t=1}^{T}{\sum\limits_{k=1}^{B}{\omega_{k}e_{k}}}. \tag{10}\]  
In Equations (9) and (10), \(E_{K}\) is the \(k\) pollutant discharge. \(T_{U}\) is the number of traditional generators in the system. \(M\) is the total number of load nodes. \(T\) is the planned life year. \(t\) is the current year. \(B\) is the total number of polluted gases, \(P_{TRg}\) is the active output of the \(g\)-th traditional generator, \(e_{TRgk}\) is the amount of the \(k\) polluted gases released by the \(g\)-th traditional generator when it emits unit active power. \(\delta_{ai}=1\) represents installing DG of type \(a\) at the \(i\)-th load node. While \(\delta_{ai}=0\) indicates not to install. \(p_{fa}\) is the power factor of \(a\)-type DG. \(S_{DGa}\) is the output of \(a\)-type DG. \(e_{DGak}\) represents the amount of \(k\) polluted gas released by \(a\)-type DG when it emits unit active power. \(A\) is the total number of DG types. \(\omega_{k}\) is the weighting factor of the \(k\)-th pollution gas. \(\omega_{k}\) should satisfy Equation (11).  
\[\left\{\begin{array}{l}\sum\limits_{k=1}^{B}{\omega_{k}=1}\\ 0\leq\omega_{k}\leq 1\end{array}\right.. \tag{11}\]  
Table 1 shows the comparison of pollutant emission data between conventional power generation and part of DG [24]. As can be seen from the table, the emissions of pollutants from photovoltaic power generation and wind power generation are zero. Compared with the traditional generation mode, the emission of pollutant gas from distributed generation is lower.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 2 Temporal Convolutional Neural Networks  
This Section aims at presenting TempCNN models. We first briefly review the theory of neural networks and CNNs. We then present the principles of temporal convolutions. We finally introduce the general form of the TempCNN architecture studied in Section 4.
## 2 Materials and Methods
### Software Design and Architecture
#### 2.4.6 DSS Output Evaluation  
The developed DSS produces two main outputs: the model that best fit the experimental data and the geographical distribution map displaying areas of suitability or application of control measures. To evaluate the prediction accuracy of the outputs, users are requested to conduct additional investigations in natural conditions and use the model (if fitted with data from the laboratory) to mimic the field behavior or vice versa. When the output of the DSS is a map, ground scouting is necessary to confirm the projections.  
**3. Case Study: Using the DSS to Fit Time-Dose-Mortality Data to Mathematical Expressions and Mapping the Potential Zone of Efficacy of Fungal-Based Biopesticides in the Killing of Insect Pests**  
The case study consisted of using the DSS features to map the potential zone of efficacy for the virulence of the biopesticide _Metarhizium anisopliae_ isolate ICIPE 62 against mustard aphids.  
Fungi are the most widespread entomopathogenic organisms in terrestrial ecosystems [32; 33], gaining many interest for their potential use as biopesticides, due to their ability to infect and kill insects [32; 34; 35]. Their efficacies were demonstrated both in laboratories and in the field, leading to a worldwide increasing interest in the development of EPF-derived products for commercialization. They are, however, highly influenced by biotic (e.g., pest dynamics) and abiotic (e.g., sunlight, rainfall, temperature, humidity) factors. The platform is used to fit the time- and temperature-dependent mortality data to temperature-dependent virulence mathematical expressions. Thereafter, the virulence equation that best fitted the data is spatially projected to map the areas of the potential efficacy of the biopesticide in controlling the targeted pest.  
Data on the mortality (dependent parameter) of mustard aphids caused by the biopesticide _Metarhizium anisopliae_ isolate ICIPE 62 were obtained in the laboratory at five different temperatures (independent parameter): 10, 15, 20, 25, and 30 \({}^{\circ}\)C. Many isolates of _Metarhizium anisopliae_ were reported to have high pathogenicity against several insect pests [36; 37; 38]. _Lipaphs pseudobrassicae_, commonly known as turnip aphid or mustard aphid, is a pest that can feed on many types of crops; it was recorded from host plants belonging to over 10 families including Brassicaceae, Cucurbitaceae, and Solanaceae [39]. This pest is globally distributed with the highest level of infestation occurring in Africa, while it is also present in Europe, Asia, and America [40]. The optimal temperature for its development is reported to be around 20 \({}^{\circ}\)C [41]. The virulence map was produced for Kenya and Cameroon after completing the fitting process. The spatial evaluation of the generated map was made by comparing the known locations of successful field application of the biopesticide in controlling targeted insect pests to the predicted level of virulence. Illustrations are done below in an interactive way through the features of the designed tool.