esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Polycarboxylate Admixture on the Performance of Fluorogypsum-Based Self-Leveling Material
## 3 Results and Discussion
### Xrd  
Figure 5 presents the XRD patterns of FSLM3 and FSLM5 at different ages. The dominant crystalline compounds identified are gypsum and anhydrite, accompanied by portlandite and calcium fluoride traces, indicating a limited degree of hydration in FSLM. The characteristic peaks of gypsum gradually intensify as hydration progresses, while the characteristic peaks of anhydrite gradually weaken. This trend is particularly noticeable from 1 d to 3 d, indicating that hydration primarily occurs during the early stage. The slower hydration observed in the later stage may be attributed to water evaporation under natural maintenance conditions. Furthermore, an increase in PCE doping results in a decrease in the diffraction peak intensity of anhydrite (e.g., d = 1.32 A, d = 2.83 A, d = 1.74 A) and an enhancement of the diffraction peak of gypsum (d = 7.49 A), particularly in the early stage. These findings demonstrate that the hydration of fluorogypsum in FSLM is predominantly concentrated in the early stage, and the presence of PCE doping influences the hydration products, suggesting that increased doping levels inhibit the hydration of fluorogypsum.  
Figure 4: Influence of PCE on the strength of FSLM: (**a**) Flexural strength, (**b**) Compressive strength.
## 4 Model Development
### Data Analysis  
The purpose of the data analysis was to examine the data quality. The identified factors (variables) with brief descriptions are given in Table 6, while Table 7 gives the basic statistics of the collected data, which shows the quality of the data collected and identifies any spurious data, if any. The table shows that there were a variety of school types, sizes, number of classes, etc., which allowed for developing a very realistic model that reflects many cases and conditions.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{**Definition**} \\ \hline \multirow{4}{*}{City} & Location of the school in Eastern Province. Data were collected from nine different cities and they were defined in the NN as a number: \\  & \(\text{Dammamm}=1\), \(\text{Khobar}=2\), \(\text{Thooba}=3\), \(\text{Dhahran}=4\), \(\text{Qatef}=5\), \\  & \(\text{Naiyrah}=6\), \(\text{Kafje}=7\), \(\text{Abaaq}=8\), \(\text{Qaryah}=9\) \\ \multirow{2}{*}{Number of floors} & Number of floors of the school \\  & Total built area—all floors (sqm) & Total built area of the school; if the school had three floors and each floor area was 1000 sqm, then the total area = 3000 sqm \\ Total roof area (sqm) & Total roof area = the area of one floor \\  & Type of school included: \\  & Type of school (four different \\ school types considered) & 2 High school \\  & 3 Intermediate school \\  & 4 Elementary school \\ Number of students & Total number of students enrolled \\ Number of staff & Academic and non-academic staff \\ Age of building & Age of building \\ Number of classrooms & Number of classrooms \\ Total air-conditioned area (sqm) & Only area covered by AC \\ AC capacity & Capacity of used AC \\ \hline \hline \end{tabular}
\end{table}
Table 6: Identified factors (variables).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{**Minimum**} & \multicolumn{1}{c}{**Maximum**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Deviation**} \\ \hline Number of floors & 1 & 3 & 2.6 & 0.59 \\ Total built area—all floors (sqm) & 361 & 6900 & 4165.6 & 1902.66 \\ Total roof area (sqm) & 361 & 3078 & 1524.47 & 649.15 \\ Number of students & 10 & 1259 & 381.1 & 217.57 \\ Number of staff & 10 & 76 & 28.18 & 13.01 \\ Age of building & 11 & 58 & 32.82 & 9.35 \\ Number of classrooms & 2 & 40 & 14.26 & 6.12 \\ Total air-conditioned area (sqm) & 117 & 2340 & 817.71 & 354.6 \\ AC Capacity in ton of refrigeration (TR) & 38 & 250 & 160.12 & 55.82 \\ Annual energy consumption (kWh/year) & 99,275 & 683,192 & 410,968.1 & 143,405.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Basic statistics of the collected data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & **Number** & **Total Built** & **Total Root** & **Type** & **Number** & **Age of** & **Number of** & **Total** & **AC** & **Energy** \\  & **of** & **Area**—All** & **Area** & **of** & **of** & **of** & **Building** & **Classrooms** & **Aza**-**(**sqm) & **(TR)** & **Cap
## 2 Materials and Methods
### Scorecard Model
#### 2.3.2 Variable Binning  
Variable binning is the piecewise discretization of continuous feature variables. The chi-square binning method is used to bin the continuous features as follows:  
1. Set the initial discrete intervals of a feature, and calculate the chi-square value of each pair of adjacent intervals. The chi-square value is calculated as (11): \[X^{2}=\sum_{i=1}^{n}\sum_{j=1}^{2}\frac{\left(A_{ij}-E_{ij}\right)^{2}}{E_{ij}}\] (11) \(A_{ij}\) represents the number of samples of class \(j\) in the \(i\)th interval of the feature. \(E_{ij}=\frac{N_{i}}{N}\times C_{j}\), where \(N\) is the total number of samples, \(N_{i}\) represents the number of samples in the \(i\)th interval, and \(C_{j}\) is the number of samples of class \(j\) in all samples.
2. Merge the adjacent intervals if they have similar chi-distributions on the labels; i.e., the chi-square value of the intervals is less than the set threshold.  
Repeat 1 and 2 until the number of bins reaches the set value.  
The values of continuous variables change frequently and irregularly, while the small changes may not greatly impact the vibration stability score. Therefore, chi-square binning is performed on the features to convert the continuous values into discrete intervals. When the values of a feature fall in the same interval, the effects on the score are considered to be consistent.
## 2 Methods
### Data Processing
#### 2.4.2 Time-Frequency Analysis  
Convolutional neural networks are good at processing the input data of two-dimensional images. The one-dimensional time-domain signal provides a single feature, which needs to be analyzed in time-frequency to enhance the dimensionality of the data. The commonly used time-frequency analyses are short-time Fourier transform (STFT) [48], S-transform [49], and wavelet transform. The rest of the methods are deficient compared to the wavelet transform. The short-time Fourier transform has a fixed window size, which cannot balance the frequency and time resolution needs. Due to the kernel function property of the S-transform, the time-frequency window is long and thin in the high-frequency band, which makes the frequency domain resolution error in the high-frequency range smaller [50]. The wavelet transform has good time-frequency localization performance, which solves the window fixation problem and improves the flexibility of the resolution. Therefore, we chose the wavelet transform for time-frequency analysis in this paper.  
For \(f(t)\)\(\in\) L\({}^{2}\)(R), its continuous wavelet transform can be expressed as follows:  
\[WT(a,b)=\frac{1}{\sqrt{a}}f(t)\times\psi^{*}\bigg{(}\frac{t-b}{a}\bigg{)}dt \tag{12}\]  
where the basis function of the wavelet transform is:  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t-b}{a}\bigg{)} \tag{13}\]  
\(\psi(t)\) is the wavelet function, a is the scale factor, and b is the translation factor. The result of scaling and shifting the wavelet function is \(\psi_{a,b}(t)\) and normalized with \(1/\sqrt{a}\). Changing the wavelet's frequency and stretching the wavelet's length allows us to obtain wavelet coefficients at different frequencies.  
The original 1 \(\times\) 400-dimensional time-domain signal is compressed by wavelet transform into a 64 \(\times\) 64 \(\times\) 3 time-frequency image. Using the complex Morlet wavelet close to the original signal as the wavelet function performs the wavelet transform of the LSPIT signal of the multi-defective piles [51]. Figure 9 is the wavelet time-frequency image of 6 multi-defect piles. The rightmost small histogram is a legend, and the color represents the numerical value. For example, the first figure in Figure 9 shows that the main frequency component of the defect type 1 signal is less than 1 \(\times\) 10\({}^{4}\) Hz. The lowest frequency is yellow, and the specific frequency corresponds to its ordinate. The value of this frequency is about 0.75. Therefore, the size and color of the frequency components can judge the differences and characteristics of the six multi-defect piles.
# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 4 Experiments
### Limitations  
The proposed phishing detection system using a GNN and an SVM yields promising results; however, some limitations must be addressed:  
* The dataset primarily includes phishing and genuine URLs from publicly available sources, which may not fully capture new phishing patterns, zero-day assaults, or region-specific phishing campaigns.
* Our model relies on handmade lexical, content, and domain features, which may be disguised by phishing websites.  
Figure 5: The boundaries of the machine learning model decision making in detecting phishing and legitimate websites.  
* While evaluated on a public dataset, the model's applicability to other domains or multilingual phishing sites warrants additional examination.
* GNN-based models can be difficult to interpret in security-critical applications. More research is needed to improve the explainability for incident response.
## 4 Proposed Hybrid FVIMDE Algorithm
### Exploration and Exploitation Behavior
#### 4.6.3 Balancing Exploration and Exploitation  
The Hybrid FVIMDE Algorithm is designed to strike an optimal balance between exploration and exploitation. The DE phase illustrates that the algorithm does not get trapped in local optima by maintaining population diversity and searching broadly across the search space. In contrast, the FVIM phase capitalizes on the information gathered during the DE phase to focus on refining the best solutions. By transitioning from global exploration to local exploitation, the algorithm effectively navigates the trade-off between exploring new areas and exploiting known good solutions, thereby enhancing its ability to find the global optimum in complex optimization problems.
## 3 Numerical Model and Analysis Method  
### Numerical Model  
The deformation behavior of the crossing section of the Huyuan Road Tunnel and Luzhou Road Tunnel can be effectively obtained by means of numerical simulation, and the construction conditions can be changed as required. According to the construction process of the Huayuan Road Tunnel, a 3-D numerical model was established to simulate the asymmetric construction process of the crossing section with the help of the ABAQUS finite element platform, and the deformation behaviors of the tunnel structure and supporting pile were analyzed. The foundation pit of Huayuan Road was 33.2 m in width and 39.4 m in length with a slope excavation depth of 5 m and a pit excavation depth of 13.4 m. The pits on the two sides of Luzhou Road around the connecting section were both 25.0 m in width with an excavation depth of 10.9 m. The numerical model and mesh generation are shown in Figure 5. The numerical model constituted 75,969 elements and 100,728 nodes. The models of the supporting pile, the frame structure of the tunnel and the surrounding soil were all established by an eight-node brick element. The bottom of the numerical model was fixed at x, y and z directions, and the lateral sides of the model were fixed at x and y directions. The free field was adopted to deal with the model boundary.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### Superpixel Segmentation  
Superpixel segmentation approaches have gained popularity in recent years as these approaches have several benefits. Using superpixels, the computational complexity can be drastically reduced by computing features on more meaningful regions rather than acting on each individual pixel in HSI [43]. Simple Linear Iterative Clustering (SLIC) [44] is one of the most popular gradient-ascent-based superpixel segmentation approaches, where an initially defined tentative set of cluster points are iteratively refined using a gradient-ascent method until some convergence criteria are met. This algorithm has lower computational complexity as it applies the \(k\)-means method locally. The algorithm includes four key steps that can be summarized as follows.  
The first step is cluster center initialization. Let the input HSI be denoted as \(H^{b}\equiv\{h_{1}^{b},h_{2}^{b},\ldots,h_{N}^{b}\}\) with \(N\) pixels, where \(\{h_{i}^{b}\}\) represents the value at the \(i\)th pixel for the \(b\)th spectral band and \(i=1,2,\ldots N\); \(b=1,2,\ldots B\). \(B\) is the total number of spectral bands. Each pixel can be labeled as \(A_{i}=[h_{i},r_{i},u_{i}]\), where \(h_{i}^{T}=[h_{1},h_{2},\ldots,h_{B}]^{T}\) is the spectral vector and \([r_{i},u_{i}]^{T}\) is the position vector. The \(K\) number of initial cluster centers \(\mathcal{C}_{j}=[h_{j},r_{j},u_{j}]^{T}\) are sampled on a regular \(Q\times Q\) (\(Q=\sqrt{\frac{N}{K}}\)) grid and are, thus, equally spaced apart [45].  
The next step is the cluster assignment step, where each pixel is assigned to the nearby cluster center based on the computed distance measure \(D\). Distance is computed within a \(2Q\times 2Q\) window around the cluster center. The distance between the cluster center \(\mathcal{C}_{j}\) and pixel \(A_{i}\) is calculated as follows (Equation (1)):  
\[D=D_{spectral}+\frac{w}{Q}D_{spatial} \tag{1}\]  
where \(w\) is the weighting factor between spectral and spatial features. The spectral and spatial distance between pixel \(i\) and \(j\) are represented as in Equations (2) and (3) below.  
\[D_{spectral}=\sqrt{\sum_{b=1}^{B}\left(h_{i}^{b}-h_{j}^{b}\right)^{2}} \tag{2}\]  
where \(D_{spectral}\) is the measure of homogeneity within the superpixels.  
\[D_{spatial}=\sqrt{(r_{i}-r_{j})^{2}+(u_{i}-u_{j})^{2}} \tag{3}\]  
where \((r_{i},u_{i})\) denotes the location of pixel \(i\) in the superpixels. The spatial distance \(D_{spatial}\) ensures regularity and compactness in the generated superpixels.  
In the third step, the cluster centers are updated with the mean value of all pixels belonging to the same cluster. The second and third steps are iteratively repeated until convergence is achieved.  
In the final step, post-processing is performed to enforce connectivity by reassigning disjoint pixels to nearby superpixels.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 2 Related Work
### ResNet
Consequently, the neural network only needs to learn the difference between the input and output, ensuring stable gradients and facilitating smoother training, even as the network deepens. \[F(x)=H(ReLU(H(x))), \tag{1}\]  
\[R(x)=H(ReLU(H(x))+x). \tag{2}\]  
Equation (1) represents the formulation of a conventional neural network structure. This equation extracts features from input \(x\) through the convolution operation \(H(x)\). Next, the ReLU function introduces nonlinearity by converting negative values to 0 and keeping positive values unchanged. Finally, another \(H\) function is applied to extract the features and compute the final output. Equation (2) represents the formulation of the ResNet. This equation extracts features from input \(x\) using the convolution operation--\(H(x)\). The ReLU function then introduces nonlinearity to calculate the activation values. Subsequently, the previous computation result is added to input \(x\). This is referred to as a residual connection, allowing for the explicit learning of the residuals between the input and output. This residual connection alleviates the vanishing gradient problem as the network deepens and enables more accurate predictions. Thus, ResNet enhances the network performance by explicitly learning the difference between the input and output through residual connections. The residual connection stabilizes the gradients and facilitates smoother training. In addition, it improves the representation capability of the network by learning the residuals between the input and output. Thus, ResNet can construct deeper networks than traditional neural network structures and exhibits high accuracy and performance in various vision tasks such as image segmentation, object detection, and classification [16; 17; 18; 19].
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 3 Optimization Problem  
In this section, an optimization problem is defined to minimize the MSE between the true and estimated likelihood probabilities. To obtain the estimated likelihood probability, a data-based likelihood probability was first defined and then calculated based on the detected data symbols. Subsequently, the estimated likelihood probability was obtained by linearly combining the pilot- and data-based likelihood probabilities. The goal of the optimization problem is to determine the optimal combination ratio for this linear combination.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Descriptions** \\ \hline \(\mathbf{H}\in\mathbb{R}^{2N_{r}\times 2N_{t}}\) & wireless channel matrix \\ \(\mathbf{s}[n]\in\mathbb{R}^{2N_{t}}\) & transmitted symbol at time \(n\) \\ \(\mathbf{s}_{k}\in\mathbb{R}^{2N_{t}}\) & candidate transmitted symbol where \(k\in\mathcal{K}\) \\ \(\hat{k}[n]\in\mathcal{K}\) & detected symbol
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 4 Case Studies
### Google's DeepMind AlphaCode Transforms Code Generation and Problem Solving  
DeepMind's AlphaCode has garnered attention for its ability to generate high-quality code capable of solving intricate programming problems, often at or above human-competitive levels. Trained on a large corpus of competitive programming tasks and associated solutions, AlphaCode can interpret natural language problem statements and generate optimized, maintainable code.  
Its performance in competitive programming contests is particularly notable, with a 65% success rate in solving attempted problems [12]. AlphaCode's integration into development workflows enables rapid prototyping and brainstorming, cutting down the time from conceptualization to a working prototype by nearly 40%. This efficiency gain frees developers to concentrate on refining solutions, exploring multiple design approaches, and tackling more creative tasks.  
Developer feedback indicates a 25% increase in productivity when leveraging AlphaCode for initial code generation. Moreover, it offers an excellent learning resource, exposing teams to diverse coding strategies and best practices. In essence, AlphaCode exemplifies AI's capability not only to automate coding tasks but also to augment human problem-solving skills and foster innovation.
# Bioactive Glass as a Nanoporous Drug Delivery System for Teicoplanin
## 2 Materials and Methods  
### Synthesis of B-Doped Mesoporous Bioactive Glass  
BG was synthesized [10] using tetraethoxysilane (TEOS), ethanol, and water under acid catalysis with various amounts of added calcium and phosphate ions. The detailed experimental steps were described in our previous study [18; 19]. Calcium nitrate tetrahydrate (Ca(NO\({}_{3}\))\({}_{2}\)) and diammonium hydrogen phosphate ((NH\({}_{4}\))\({}_{2}\)HPO\({}_{4}\)) were added with the atom ratio of Si:Ca:P = 60:34:6. The formula of BG was modified according to 58S bioglass with 60 mol% SiO\({}_{2}\), 36 mol% CaO, and 4 mol% P\({}_{2}\)O\({}_{5}\)[20]. Trimethyl borate (C\({}_{3}\)H\({}_{9}\)BO\({}_{3}\)) was added to the solution to produce boric acid for B doping. B-doped BG was made with an Si/B molar ratio of 10:1 for BG. The B-doped samples BG_B75, BG_B50, and BG_B25 were sequence reduced to 75%, 50%, and 25% of BG_B100, respectively. Calcium nitrate was used as the calcium source, and the nitrate was removed to form the mesoporous structure. Samples were placed in a muffle furnace that started at room temperature and rose to 500 \({}^{\circ}\)C after 2.5 h. Samples were sintered at 500 \({}^{\circ}\)C for 6 h. At the end of the sintering process, the furnace was turned off until room temperature was reached, and samples were taken out for washing with DI water twice. Finally, the samples were dried at 60 \({}^{\circ}\)C overnight to remove the residual water and then ground.
## 3 Results and Discussion
### Multivariate Statistical Analysis of Volatile Components and Amino Acids  
Figure 6 illustrates the model cross-validation results of the flavor substances after 200 substitution tests. In model of the flavor substances, R\({}^{2}\)X = 0.862, R\({}^{2}\)Y = 0.984, and Q\({}^{2}\) = 0.957, which were all \(>\)0.5 and indicated that the model was stable and reliable. The intersection of the regression line of Q\({}^{2}\) and the \(Y\)-axis was on the negative half-axis, indicating that the model was effective without overfitting. It could be used to reflect the flavor substance. The O2 PLS-DA boxplots of the flavor substances in the samples are shown in Figure 6a. The differences among the various products could be effectively analyzed using O2 PLS-DA. DC-3 and DC-4 were positioned in the first quadrant, DC-6 resided in the second quadrant, DC-2 and DC-5 were located in the third quadrant, and DC-7 is found in the fourth quadrant. Although DC-1, DC-2, DC-4, and DC-7 were not in the same quadrant, they are related.  
Through O2 PLS-DA, a variable-importance-in-projection (VIP) value could be obtained for each metabolite. The higher the VIP value, the greater the contribution of the substance to distinguish the groups. Thus, we usually took the VIP value as one of the important indicators when selecting different metabolites [47; 48]. We found that the VIP values of twenty-seven bacterial genera and fifty-six volatile compounds were greater than one.  
We selected the flavor substances which had both VIP \(>\) 1 and OAV/TAV \(>\) 1 to establish the correlation analysis with microorganisms, including one amino acid (Glu) and twenty-two volatile components (ethanol, 3-methyl-1-butanol, (R,R)-2,3-butanediol, linalool, phenylethyll alcohol, 1-ocen-3-ol, isobutyraldehyde, isoamyl acetate, benzaldehyde, phenylacetaldehyde, acetaldehyde, isovaleric acid, ethyl acetate, ethyl isobutyrate, ethyl isovalerate, isoamyl acetate, ethyl phenylacetate, ethyl benzoate, diethyl phthalate, ethyl caprylate, and 2-pentylfuran).