esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion on the Effectiveness of HPO Algorithm in Optimizing KNN and SRU  
In addition to the techniques applied to raw data outliers, the parameters of the KNN and SRU models are another factor that has a strong impact on prediction performance. By comparing the performance of the optimized model and the non-optimized model, the effectiveness of the HPO algorithm in optimizing KNN and SRU parameters is verified. Three improvement percentage indicators between the optimization method (better performance) and the non-optimization method (poor performance) are given in Table 6, including \(P_{RMSE}\), \(P_{MAE}\) and \(P_{R2}\). As can be seen from Table 6, compared with the HPO-KNN-SRU (only optimizing the KNN), the HPO-KNN-SRU can still achieve very small improvements in RMSE, MAE, and R2 when the SRU has been optimized for grid search. This further verified the effectiveness of the HPO intelligent optimization algorithm in improving the prediction performance of the SRU model. Meanwhile, the HPO-KNN-SRU (only optimizing the KNN) has improved to varying degrees in \(P_{RMSE}\), \(P_{MAE}\), and \(P_{R2}\) compared to the KNN-SRU, which verifies the effectiveness of HPO parameter optimization technology in improving KNN outlier processing.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 2 Finite Element Model Building and Verification  
### Finite Element Model and Simulation  
This study includes the simulation and analysis of prestressed concrete T-beam fire resistance using finite element software ABAQUS. The finite element model reference is from the fire resistance test in the literature [10], and the model dimensions and finite element model are shown in Figures 1 and 2. The method of model calculation is heat transfer analysis and thermal-force coupling analysis: first, the temperature field of a prestressed concrete T-beam is solved in the heat transfer analysis step according to ISO-834 international standard temperature rise curve. Then, the temperature field analysis results obtained in the heat transfer analysis step are imported into the calculation model of fire resistance analysis as predefined fields, and then the thermal-force coupling analysis is performed on the model. Finally, the fire resistance of the model is obtained. The temperature field calculation model and the calculation model of fire resistance analysis in the finite element simulation calculations are described in detail below.
## Appendix B Correction of Inertia Equations  
The inertia equations are expressed in the system of coordinates attached to the current centroid of the system. However, it has been shown that the equations should rather be expressed in the system of coordinates attached to either the theoretical centroid of the system or to the geometrical center of the container if no balancing constraints are considered.  
With the simplified model of the INTELSAT-III considered as a benchmark in this thesis, the inertia should then be expressed in the system of coordinates with its origin being the center of mass of the empty module and denoted \(O_{sat}x_{sat}y_{sat}z_{sat}\), as illustrated in Figure 19.  
Figure 19: Simplified model of the INTELSAT-III satellite module.In both figures, the functionally incompatible components are highlighted in orange and blue for the heat constraints and orange for the electromagnetic constraint. Moreover, the redder the components, the heaviest their corresponding masses.  
Algorithm A1 gives the pseudo code of the swap operator in the case where the swap operator is used to improve the objective function \(f_{obj}\) to minimize. It can be summarized as follows: if the current iteration corresponds to \(t_{swap}\), the swap operator is called. If the objective function is improved and if the corresponding constraint violation \(CV_{swap}\) of the \(N_{Cst}\) constraints' functions (\(\mathbf{g}_{k}\) for \(k\in\{1,...,N_{cst}\}\)) remains lower than or equal to the previous constraint violation \(CV_{NoSuap}\) times the relaxation factor \(r\), then the swap between the two components occurs.  
```
Input:\(t_{swap}\), \(r\), \(\mathbf{p}\) Output: new vector of positions \(\mathbf{p}\) if\(i\) is \(t_{swap}\)then for\(i=0\) to \(N\)do for\(j=i+1\) to \(N\)do \(\mathbf{x}_{p,swap}\leftarrow\mathbf{x}_{p}\) with \(\mathbf{x}_{p,i}\) and \(\mathbf{x}_{p,j}\) exchanged. \(CV_{Susp}\gets 0\) \(CV_{NoSusup}\gets 0\) for\(k=0\) to \(N_{k}\)do \(CV_{Susp}=CV_{Susp}+\mathbf{g}_{k}(\mathbf{x}_{p,swap})\) #Constraint violation if the swap occurs \(CV_{NoSuap}=CV_{NoSusp}+\mathbf{g}_{k}(\mathbf{x}_{p})\) #Constraint violation if not endfor if\(f_{obj}(\mathbf{x}_{p,swap})<f_{obj}(\mathbf{x}_{p})\)and\(CV_{Susp}\leq r\times CV_{NoSuap}\)then \(\mathbf{x}_{p}\leftarrow\mathbf{x}_{p,swap}\) endif endfor endfor  Update \(t_{swap}\) endif
```  
**Algorithm A1** The Swap Operator
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Male Reproductive Cells: Leydig Cells and Sperm Cells  
_M. oleifera_ leaf extracts (10, 50, 100, 250, 500 and 1000 ug/mL) on TM3 Leydig cells increased the levels of testosterone under stimulatory conditions of hCG. However, the testosterone increase was seen only in 500 and 1000 ug/mL concentrations under basal conditions [22]. In addition, _M. oleifera_ also demonstrated its antioxidative effects by increasing the glutathione concentration in the cells exposed to 250 ug/mL of the extract [22].  
_M. oleifera_ leaf extracts were also observed on sperm cells with varying concentrations (0.625; 6.25; 62.5; 625 ug/mL). The findings indicated that _M. oleifera_ inhibited the formation of sperm intracellular ROS at 62.5 and 625 ug/mL, reduced the percentage of sperm with DNA fragmentation and increased the percentage of incapacitated and intact acrosome spermatozoa at 625 ug/mL [73].  
Effects of _M. oleifera_ Leaf Extracts on Male Reproductive System Constituents following Exposure to Male Infertility Risk Factors  
Table 1 summarises the effect of the _M. oleifera_ on the male reproductive system following exposure to various infertility risk factors.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Male Infertility** & **Oxidative Stress** & **Hormonal Levels** & **Sperm Parameters** & **Gene Expression** & **Testicular Histology Examination** & **Authors** \\
**Risk Factors** & **Parameters** & & & & & \(\uparrow\) the normal morphology & \\  & & & & & and number of the tubular & \\  & & & & \(\uparrow\) sperm quality (gperm & epithelial cells, germinal \\  & & & concentration with intact & Sertoli cells, spermatatognia, & \\ Heat stress & \(\uparrow\) TAC, & \(\downarrow\) GST & \(\uparrow\) Testosterone levels & \(\uparrow\) secrosome, total sperm & \(\uparrow\) secromodes, last spermatids & \\  & & & & output, motility & & \\  & & & and viability). & & & \\  & & & & & \(\downarrow\) Leydig cells and Sertoli \\  & & & & \(\uparrow\) Epididymal sperm count & & \\ Electromagnetic radiations & \(\downarrow\) MDA, \(\uparrow\) SOD and & \(\uparrow\) Serum & & \(\downarrow\) sperm defects (pyriform & \\  & & & & head, detached head, coiled & \\  & & & tails and multiple & & \(\uparrow\) the number of \\  & & & & & \(\downarrow\) Leydig cells. \\  & & & & & \\ \end{tabular}
\end{table}
Table 1: Effects of _M. oleifera_ leaves on damage induced by male infertility risk factors in the male reproductive system.
# Improving the Accuracy of Predicting Bank Depositor's Behavior Using a Decision Tree
## 2 Materials and Methods
### Data-Driven Models  
There is a myriad of data-driven models to address classification problems. Some of the most widely adopted are grounded in statistical methods, such as logistic regression. Nevertheless, Artificial Intelligence (AI) has emerged to take advantage of the large amount of data available in today's world which is flooded with data. AI-based methods include evolutionary algorithms [16], modern optimization [17], genetic algorithms [18] and nature-inspired algorithms [19], among others. Such approaches can be applied to several different contexts, such as online learning, scheduling, multi-objective optimization, vehicle routing and medicine classifiers, among others, e.g., [20; 21].  
The complexity associated with AI-based approaches to data-driven problems and subsequent solutions still has an important drawback to overcome: since most managers do not have advanced algorithm backgrounds, they tend to be reluctant to adopt such approaches [4]. Typically, this occurs because AI methods are black-box, i.e., one cannot directly understand how the model makes a decision, whereas in comparison, one can easily understand an equation representing a logistic regression [22]. However, decision tree algorithms can be complex due to the training process that leads to the actual tree itself based on the Gini index coefficient (CART trees) or the entropy (C4.5) [23]. Thus, decision trees can usually achieve better performance than traditional logistic regression [5].  
In this experiment, a decision tree classifier was used to improve the accuracy. Implementation was performed by a J48 data mining classifier to find the best results in terms of three important comparison metrics. As previously stated, the decision tree is a model that can be read and explained by humans.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Dataset Attribute Description** & **Type** & **Detail** \\ \hline
14 & Previous & Numeric & 0–7 \\ \hline
The classification is due to the fact that the decision tree is being used to perform simple computations [24]. It is an appropriate model to predict nominal results that are the same as the prediction output of yes or no. It is one of the most famous and powerful classification algorithms of the decision tree [25]. J48 is an open-source java implementation of the C4.5 algorithm [23] used in this paper.
## 4 Results and Discussion
For example, Resnet50 has an accuracy of 63.6% in category 0 and 69.2% in category 3; Fast-MPN-COV has an accuracy of 90.9% in category 0 and 84.6% in category 3; after the gated fusing operation, the average accuracy of PFN method in this 0-th pest class is up to 100%; the accuracy in the 3-th category was 100%. It can be found that the fused module makes the identification difference of diverse individual modules or methods for fine-grained targets gradually reduced, thereby improving the overall accuracy. In addition, take some test samples (Geometridae of class 49, Classis Nymphial-dae of class 120) as examples. This paper compares the recognition results under the four models of ResNet50, NTS-net, Fast-MPN-COV, and the fusion model. As shown in Figures 9 and 10, the red boxes represent the images that the model has misidentified, and the rest represent the images that are correctly identified. It can be seen that compared with other models and a single fine-grained model, the accuracy of the model recognition after fusion is greatly improved, and the reliability of the above results is also verified on the side. In a word, the results of the method indicate that the model has the advantages of high accuracy, less time of processing, fewer parameters of model, stable operation, and fast convergence, which means that the improved model of us can effectively solve the problem of fine-grained pest recognition. Meanwhile, the small amount of model parameters means that the model is lightweight, which makes the model run effectively on other devices. In practical applications, the model can be directly deployed in various devices (such as unmanned robots and drones) to improve the ability to automatically recognizing pests, or the image can be transmitted through the device (e.g., various IoT cameras and smartphones) to the cloud server, in which the model deployed to provide fast and accurate identification services. Even if farmers do not have professional biological knowledge of pests, they can accurately know the category and obtain the corresponding  
Figure 8: Accuracy of each type of the Resnet50, NTS-net, Fast-MPN-COV, and PFNT. management and prevention strategies, which will effectively guarantee the production and quality safety of the entire plant.
# Safety Evaluation of Crossing Tunnel Engineering: A Case Study
## 3 Analysis of Highway Tunnel Safety
### Establishment of Numerical Calculation Model
#### 3.2.1 Geometric Model and Calculation Assumptions  
In this paper, the numerical simulation model is established using the finite element numerical analysis software Midas GTS. This software is currently widely utilized in various geotechnical fields, including the design and evaluation of tunnel excavations and support systems, slope stability analysis, and foundation pit excavations. It enables the simulation of three-dimensional strata, tunnels, slopes, and foundation pit excavations, allowing for a comprehensive analysis of the entire construction process.  
Figure 3 illustrates a three-dimensional geometric model diagram generated via the software. The \(X\)-axis represents the horizontal direction, the \(Y\)-axis corresponds to the tunnel axis direction, and the \(Z\)-axis represents the vertical upward direction. The single-lane highway tunnel being excavated has a span of 11.8 m and dimensions of 176 m in the X direction, 100 m in the \(Z\) direction, and 160 m in the Y direction. This model is specifically employed to simulate the intersection between the highway tunnel and the railway tunnel. The numerical model comprises 19,071 nodes and 101,888 elements, providing an accurate representation of the system under consideration. These elements and nodes form the basis for conducting the numerical analysis and evaluating the behavior and response of the tunnels during the construction process.
## 3 Impulse Detection Methodology Using Deep Learning Flexible Boundary Regression
### Preprocessing to Extract the Shape Signal  
The preprocessing step works to extract the global shape of the impulse at the macro-level, corresponding to the length of the sampled signal. Normally, the concept of object detection is used in an image processing technique where the object is smooth, continuous, and mostly homogenous inside the point area of the object. With this condition, the convolution neural network considers the boundary of an object to be composed of an edge and blob patterns, which have a sudden change between their different colors. However, the AE signal collected from the leak detection testbed always includes environmental noise and contains many small troughs and peaks. If these troughs and peaks are smaller than the grid size generated by the deep learning detector, the object detection algorithm cannot give a satisfying result. To mitigate the undesired variations and unexpected instantaneous frequency values produced by the remaining amount of small noise, the preprocessing step helps obtain the overall shape of the signal with little random noise. Figure 3 presents the preprocessing step of the algorithm.  
In the first step, the AE signal sample is segmented into non-overlapping frames and rectified to obtain the positive part. Then, in each window, the root means square (RMS) is calculated to form the lower rate RMS signal. Next, the lower rate signal is expanded to its original size by scaling the time axis using cubic interpolation and antialiasing. The cubic interpolation method performs piecewise cubic Hermite interpolation based on the values at neighboring grid points. It seeks to match only the first-order derivatives at the data points with those in the intervals before and after. For a set of data points \((x_{i},y_{i})\), \(i=1..n\), the cubic Hermite interpolant at any point \(x\), with \(x_{i}<x<x_{i+1}\), takes the form:  
\[P(x)=\frac{3 cs^{2}-2s^{3}}{c^{3}}y_{i+1}+\frac{c^{3}-3 cs^{2}+2s^{3}}{c^{3}}y_{ i}+\frac{s^{2}(s-c)}{c^{2}}d_{i+1}+\frac{s(s-c)^{2}}{c^{2}}d_{i}, \tag{1}\]  
where  
\[\begin{array}{rl}c=x_{i+1}-x_{i}&\delta_{i}=\frac{y_{i+1}-y_{i}}{c_{i}}\\ d_{i}=P^{\prime}(x_{i})&s=x-x_{i}.\end{array}\]  
An instance based on piecewise cubic Hermite interpolation is shape-preserving piecewise cubic, which preserves the shape of the data since the resulting interpolated function has a continuous derivative. Shape-preserving piecewise cubic interpolation has a similar formula to that of piecewise cubic Hermite interpolation, but it differs in the component of the first-order derivatives \(d_{i}\). The output vector value is a weighted average of points taken from at least the nearest four neighborhoods; this interpolation ensures that the value of the interpolant is located within a range of local points.  
Figure 3: Preprocessing process of the acoustic emission (AE) signal.
## 4 Materials and Methods
### Protein Identification and Functional Annotation  
The MS raw data was analyzed with Proteome Discoverer V1.4 software (Thermo Fisher Scientific, Waltham, MA, USA) using the Mascot search engine to search against the masson pine database, including 17,323 sequences from Iso-Seq constructed as described above. The search parameters were set as follows: precursor mass tolerance was 15 p.p.m., fragment tolerance was 20 m.m.u., the dynamic modifications were oxidation (M) and iTRAQ labeling (K, Y, and N-term), the static modification was carbamidomethyl (C), and up to two missed cleavages were allowed. Proteins with only fold change > 1.2 (the average of all comparison group ratios) and false discovery rate (FDR) < 0.05 were defined as differentially expressed proteins (DEPs). All proteins identified were functionally annotated and classified by GO, KOG, and KEGG databases. DEPs were analyzed using the GO and KEGG databases to identify significantly enriched functional categories and metabolic pathways.
## 5 The General Model of an ADR Based on Photodiodes (PDs)
### The Incidence Angle Model for the Photodiodes in the ADR  
\(\psi_{k}\) is defined as the angle between the direction of the incoming signal \(\mathbf{r}\) (from the mirror to the receiver) and the normal vector \(\mathbf{n}_{k}\) of the photodiode as follows:  
\[\cos(\psi_{k})=\frac{\mathbf{m}_{k}}{|\mathbf{r}||\mathbf{n}_{k}|}, \tag{29}\]  
where \(\mathbf{r}\) is the vector of the incoming signal to the PD, and \(\mathbf{n}_{k}\) is the normal vector of the photodiode \(k\).  
The direction of the incoming signal \(\mathbf{r}\) is obtained as the vector from the point of reflection on the mirror (flat or convex) to the receiver. If the mirror is at \((x_{ij},y_{ij},z_{ij})\) and the receiver is at \((x_{r},y_{r},z_{r})\), then  
\[\mathbf{r}=(x_{r}-x_{ij},y_{r}-y_{ij},z_{r}-z_{ij}), \tag{30}\]  
The power received by the photodiode \(k\) of the ADR from a flat or convex mirror depends on the angle of incidence \(\psi_{k}\), the effective area \(A_{\text{PD}_{k}}\), and the parameters of the VLC channel. The power received by the photodiode \(k\), \(P_{r,\text{PD}_{k}}\), is expressed as follows:  
\[P_{r,\text{PD}_{k}}=P_{s}\frac{A_{\text{PD}_{k}}}{d_{ij}^{2}}\cos^{m}(\theta_{ ij})R(\theta_{ij})T_{ij}(\lambda)H_{ij}(t)\cos(\psi_{k}), \tag{31}\]  
where \(A_{\text{PD}_{k}}\) is the effective area of the photodiode \(k\).  
To integrate all the signals the ADR receives, it combines the signals received from multiple photodiodes (PDs). The way they are combined can be through direct summation or a signal selection algorithm (such as a maximum selection scheme or signal combination). For example, in the equal gain combining scheme, the total power received by the ADR, \(P_{r}^{\text{ADR}}\), can be expressed as the sum of the power received by each photodiode:  
\[P_{r}^{\text{ADR}}(t)=\sum_{k=1}^{N_{\text{PD}}}P_{r,\text{PD}_{k}}. \tag{32}\]By integrating all the contributions from each photodiode and considering the angular and distance dependence, the total power received by the ADR from a flat mirror \((i,j)\) is expressed as  
\[P_{r,\text{flat}}^{\text{ADR}}(t)=\sum_{k=1}^{N_{\text{PD}}}\sum_{i=1}^{M}\sum_{ j=1}^{N}\Bigg{(}P_{s}\frac{A_{\text{PD}_{k}}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})R( \theta_{ij})T_{ij}(\lambda)H_{ij}(t)\cos(\psi_{k})\Bigg{)}. \tag{33}\]  
Similarly, the power received in the ADR from convex mirrors, considering all the photodiodes, is expressed as  
\[P_{r,\text{convex}}^{\text{ADR}}(t)=\sum_{k=1}^{N_{\text{PD}}}\sum_{i=1}^{M} \sum_{j=1}^{N}\Bigg{(}P_{s}\frac{A_{\text{PD}_{k}}}{d_{ij}^{2}}\cos^{m}( \theta_{ij})F_{\text{conv}}(R_{ij})R(\theta_{ij})T_{ij}(\lambda)H_{ij}(t)\cos (\psi_{k})\Bigg{)}. \tag{34}\]
## 3 Methodology and Data
### Analysis Method--Questionnaire  
The pre-questionnaires were distributed to 35 executives (Senior Level: 14, Medium Level: 19, and others: 2) selected from the project database of the SMEs Agency. Each item was measured by using the five-point Likert scale. A total number of 34 valid pre-questionnaires were retrieved. The test results of reliability and validity reached a good level. In the formal stage, the selection of SMEs' respondents abided by the principle of a random sample selected from the databases of SMEs promotion organizations, such as the National Association of Small and Medium Enterprises, Corporate Synergy Development Center, and Taipei Computer Association. The participants had at least 6 months' experience in the company and an entry- to mid-level position. A total of 364 questionnaires were distributed in both hard copy and email. Subsequently, 260 valid questionnaires were retrieved, which showed a valid response rate of 71.43% and reached the acceptable level [48]. To better understand the composition of questionnaire participants and associated responses, the information is divided by industry, into services and non-services, and by age of company into various clusters. The statistical data of questionnaire distribution and return are shown in Table A3 in Appendix A, and the sample descriptive statistics are shown in Table A4 in Appendix A.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Context** & **Hypotheses** \\ \hline \multirow{4}{*}{Internal users} & The performance expectancy (H1a)/effort expectancy (H1b)/user habit (H1c) have a significant impact on the intention of internal users to adopt broadband mobile applications \\ \hline \multirow{4}{*}{Technology} & The relative advantage (H2a)/compatibility (H2b)/trialability (H2c)/observability (H2d) have a significant impact on the SMEs’ adoption of broadband mobile applications. \\ \hline \multirow{4}{*}{Organization} & The data security (H3a)/top management support (H3b)/employees’ knowledge (H3c)/absorptive capability (H3d) have a significant impact on the SMEs’ adoption of broadband mobile applications. \\ \hline \multirow{4}{*}{Environment} & The competitive pressure (H4a)/business partner (H4b)/external support (H4c)/government’s support (H4d) have a significant impact on the SMEs’ adoption of broadband mobile applications. \\ \cline{1-1}  & The intention of internal users (H5) have a significant impact on the SMEs’ adoption of broadband mobile applications. \\ \hline \multirow{4}{*}{Behavioral Intention} & The intention of internal users (H5) have a significant impact on the SMEs’ adoption of broadband mobile applications. \\ \cline{1-1}  & \\ \end{tabular}
\end{table}
Table 2: Research hypotheses.