esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Scale Damage Detection Results Using pNDE
### Ultrasonic C-Scan for Micro-Scale Damage Detection  
Figure 3 shows pNDE C-scan images in the X-Y plane parallel to the top surface of the composite sample at various depths. As illustrated in Figure 3a, the pNDE C-scan image clearly represented the X-shaped notches on the tested CFRP sample. The width of the X-shape notch shown on the C-scan matched well with that measured in the SEM image, indicating the maximum width of the notch was approximately 270 \(\upmu\)m, as shown in Figure 3b. In addition, the tow orientation and the woven structures of the carbon fiber fabrics were visible in the reconstructed images. The photoacoustic signal profile across two grid lines on the surface of the material was extracted along the solid blue line shown in Figure 3a. Based on the extracted grid line profiles, the lateral resolution of the system is estimated to be approximately 60 \(\upmu\)m, corresponding to the smaller full width at half maximum (FWHM) of the line spread function in Figure 3c.  
Figure 4a-e shows the typical pNDE C-scans in the horizontal cross-sections (X-Y plane) that were parallel to the top surface of the composite structure at different depths. The C-scan presentation provided the top view of the locations and sizes of the defects featured in the tested CFRP composites. In the C-scan images of Figure 4a,b, the X-shape notch was clearly visible. Only partial micro-scale matrix cracks can be visualized in Figure 4a,b because of the scanning plane (X-Y plane) is not strictly parallelized to the surface of the composite plate. In Figure 4e, the dense and highly distributed micro-scale matrix cracks, which lead to potential delamination, were detected. Due to the distribution of microscale matrix cracks throughout the entire layer in the composites, the ultrasonic signals were dispersed. Therefore, carbon fiber fabric yarns were not observed in this layer. In Figure 4f, the photoacoustic maximum-amplitude image projected from the top view of the CFRP composite indicated the complex microstructure inside the sample. Both the surface notches and embedded matrix cracks were shown in this image. The 3D image of the detected composite structure with micro-scale notch damage is shown in Figure 4g.  
Figure 3: X-Y plane image. (**a**) pNDE X-Y plane image; the ‘X’ marking is highlighted. (**b**) SEM image; the width of the ‘X’ marking is calculated. (**c**) Full width at half maximum (FWHM) measurements along the blue line on the pNDE image, which are taken as the lateral resolution of the image.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 3 Results  
### Bioinformatics Analysis and Antisense Strategy  
_In silico_ analysis of the \(F5\) exon 13 sequence showed that the donor and acceptor splice sites, defining the FV-short specific intron, have relatively high consensus scores of 83% and 88%, respectively (Figure 1). Two MAOs masking these splice sites were designed (Figure 1) and predicted to have no other targets in the human transcriptome. Interrogation of the Ensembl database revealed no common genetic variants in the vicinity of the FV-short splice sites that could potentially weaken the interaction of the MAOs with the target \(F5\) pre-mRNA.
## 2 Materials and Methods
### Multivariate Data Analysis
#### 2.3.3 Model Validation  
Table 2 shows the number of data sets used for the model development and verification. The accuracy of each developed PLS model was evaluated by means of the coefficient of determination for calibration (\(\text{R}_{\text{c}}^{2}\)), root mean square error of calibration (RMSEC), coefficient of determination for validation (\(\text{R}_{\text{v}}^{2}\)), root mean square error of validation (RMSEV), and optimal factor (F). RMSE was calculated according to Equations (4)-(6) [27]. The overall predictive accuracy was determined based on the total accuracy of the respective South Korean and Chinese rice predictions. The sensitivity and specificity were calculated using the predictive accuracy results for each rice sample.  
\[\text{RMSEC}=\sqrt{\frac{\sum_{i=1}^{I_{c}}\left(\hat{y}_{i}-y_{i}\right)^{2}}{ I_{c}-1}} \tag{4}\]  
\[\text{RMSEV}=\sqrt{\frac{\sum_{i=1}^{I_{p}}\left(\hat{y}_{i}-y_{i}-\text{bias} \right)^{2}}{I_{p}-1}} \tag{5}\]  
\[\text{bias}=\frac{\sum_{i=1}^{I_{p}}\left(\hat{y}_{i}-y_{i}\right)}{I_{p}} \tag{6}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) denote the predicted and measured value of the \(l\)th sample, \(I_{c}\) is the number of samples in the calibration set, and \(I_{p}\) represents the number of samples in the prediction set.  
Unscrambler X (v.10.4, CAMO Software, Oslo, Norway) was used for the PLS model development, validation, and spectrum pre-treatment. MATLAB (Ver. 2020, MathWorks, Natick, MA, USA) was used to obtain the PLS image and extract the spectrum data from the acquired HSIs.
## 5 Results
### Assessment Parameters  
A set of combined quantitative metrics was applied to thoroughly evaluate the performance of the suggested Hierarchical Federated Learning (HFL) framework. These measurements were selected to measure the predictive value of the model as well as its privacy retention, particularly within the multi-institutional healthcare data landscape. Each of the metrics and their definition, relevance, and mathematical formulation are presented below.  
1. Accuracy  
One of the most basic measures of the performance of a classification model is accuracy. It is the proportion of the number of actual instances (both positive and negative) and the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate & 0.01 \\ Batch size & 64 \\ Local training epochs & 5 \\ Global communication rounds & 100 \\ Optimizer & Adam \\ Weight decay & 0.0001 \\ Dropout rate & 0.3 \\ Privacy budget (\(\epsilon\)) & 1.0, 3.0 \\ Noise scale (\(\sigma\)) & 0.5, 1.0 \\ Clipping norm & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings used in experiments.  
number of predictions. Reliability in terms of diagnosis and forecasting outcomes requires high precision in the context of healthcare.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{11}\]  
where True Positive = TP, True Negative = TN, False Positive = FP, and False Negative = FN. To show the stability and consistency of the model, accuracy was reported as mean \(\pm\) standard deviation across many independent training runs.  
2. Privacy Leakage  
Privacy leakage quantifies the extent to which sensitive data (e.g., identity of the patient, diagnosis) can be deduced by observing gradients or shared parameters in the training process. Reduced leakage means more privacy preservation. The precise formulations can be different depending on the technique, but a normalized approximation goes as follows:  
\[Privacy\ Leakage=\mathbb{E}_{x\sim D}\big{[}max_{y\in Y}Pr(y|\theta(x))\big{]} \tag{12}\]  
The following leakage measure was evaluated with gradient inversion techniques and attack simulations. The offered HFL model demonstrated the least amount of leakage, demonstrating the advantage of uniting DP and SMPC in its hierarchical structure.  
3. Training Time Reduction  
The decrease in the overall training time compared to the baseline FLBM-IoT model was measured as a way to evaluate computational efficiency. This measure indicates the real-life viability of HFL implementation in healthcare networks.  
\[Training\ Time\ Reduction(\%)=\frac{T_{baseline}-T_{HFL}}{T_{baseline}}\times 100 \tag{13}\]  
The proposed model demonstrated a 15% reduction in training time, attributed to its reduced communication and efficient regional aggregation.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 7 Discussion of the Results  
In this section we discuss the limitations of our study and also compare our beamforming PMS design to the other designs known in the literature.
# Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
## 3 Methodology
### Artificial Neural Networks (ANN)  
Artificial neural networks are inspired by the human brain's biological structure. A simple ANN consists of an input layer, a hidden layer, and an output layer [25]. It mimics the behavior of the human brain to solve complex data-driven problems. The neural units are fed with input data that are processed via hidden layers to produce the desired output. ANNs are utilized by various studies across many fields with proven results in both supervised learning and unsupervised learning problems such as classification and regression real-time problem. In a simple ANN model, some inputs with corresponding multiple weight values are added with bias values along with a threshold that is defined by the activation functions to predict the output.  
Depending on the problem, whether a classification or a regression problem, the activation function for ANN that makes a decision is defined by the rectified linear unit (ReLu) function, the hyperbolic tangent (Tanh) function, or the Sigmoid function. The ReLu function ensures the output is not less than zero, \(f(a)=\max(0,a)\), while the Tanh function finds the hyperbolic output. Here, \(f(a)=\)tanh(\(a\)), and the Sigmoid function is defined as \(f(a)=\frac{1}{\left(1+e^{-1+\epsilon}\right)}\).  
In this work, a fully connected ANN with two layers and 15 neurons at each layer is utilized to predict the cutting force value as an output.
# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 4 Fractal Feature Analysis of Microseismic Waveform Signals
### Fractal Box Dimension Analysis  
Three groups of signals were randomly selected from 50 rock fracture signals and 50 rock blasting signals. The selected signals were processed by fractal processing, and the corresponding linear regression equations were obtained. According to the linear regression equations, the fractal box dimensions (\(D_{\delta_{1}\times\delta_{2}}\)) and constant (\(b\)) were obtained. MATLAB software was used to calculate the parameters according to the above method, and the results are shown in Figures 20-25.  
Figure 19: Fractal characteristics of microseismic waveform.  
Figure 21: Fractal box dimension of no. 2 rock fracture event.  
Figure 20: Fractal box dimension of no. 1 rock fracture event.  
Figure 23: Fractal box dimension of no. 1 rock blasting event.  
Figure 22: Fractal box dimension of no. 3 rock fracture event.  
By comparing Figures 20-25 for the rock blasting and the rock fracture signals, it can be observed that the fractal box dimension of the signal without noise reduction is different from that of the signal with noise reduction; however, the difference is small. In addition, it was confirmed that the noise signal collected with the target signal demonstrates a  
Figure 24: Fractal box dimension of no. 2 rock blasting event.  
Figure 25: Fractal box dimension of no. 3 rock blasting event.  
significant influence on the characteristic value of the signal. Therefore, it is necessary to denoise the original signal to reduce the impact of noise in the subsequent analysis.  
Through a comparative analysis of Figures 20-25, it was observed that the fractal box dimension of the rock fracture signal after noise reduction was significantly different from that of the rock blasting signal after noise reduction. To clarify the difference in the points of the fractal box dimensions of the two types of signals, the fractal box dimensions of the selected rock fracture and rock blasting signals were calculated. The calculation results are shown in Figure 26.  
According to an analysis of Figure 26, it was observed that the fractal box dimension of nearly 90% of the rock fracture signals was below 1.1. The fractal box dimension of nearly 70% of the rock blasting signals was more than 1.25. In addition, there was an uncertain interval of the fractal box dimension (i.e., interval of 1.1-1.25). After a comprehensive analysis, it was suggested that the fractal box dimension 1.15 should be selected as the fractal box dimension dividing line to distinguish the two types of signals. The accuracy of the rock fracture signal event judgment reached 88.89%, and the accuracy of the rock blasting event judgment reached 95.74%.
###### Abstract  
Quality of service in urban surface transit has a great impact on sustainable urban mobility, with travel time being one of the most important indicators of service reliability. But urban surface transit is prone to many disturbance factors causing travel time discrepancy and variability, making transit less reliable for passengers. We conducted research in the City of Zagreb on a single tram line by splitting it into constant-frequency segments. The first phase was modeling minimum segment travel times to base the indicators and predictors upon, and the second phase was establishing correlation matrices between the predictors and travel time using Pearson correlation coefficients and significance. Variance inflation factors were used to check for collinearities. While predictors belonging to the transport supply irregularity group did not have an impact, the ones belonging to the disturbance factors group showed correlation, with six of them being significant. This research in rarely represented tram transit determined the most significant disturbance factors rich in traffic context that can be used to develop travel time prediction models.  
disturbance factor; minimum travel time; service reliability; travel time discrepancy; travel time variability; urban surface transit +
Footnote †: journal: Applied sciences
## 2 Dual-Orthogonal Induction MEMS 3D Electric Field Sensor  
### Sensor Structure Design and Operating Principle  
The structure of the proposed MEMS 3D electric field sensor with a dual-orthogonal induction configuration is illustrated in Figure 1. The sensor consists of the sensing electrodes, sensing chips, internal signal processing circuits, and a housing. The sensing electrodes are arranged in a dual orthogonal differential configuration, with two pairs of electrodes aligned along the X- and Y-axes, while an additional height difference is introduced along the Z-axis to ensure measurement orthogonality in three dimensions.  
According to [PERSON]'s theorem, the relationship between the three spatial electric field components \(E_{x}\), \(E_{y}\), and \(E_{z}\) and the induced charges on the sensing electrodes can be expressed as follows:  
\[E_{x}=\frac{Q_{2}-Q_{1}}{\varepsilon(S_{1}+S_{2})} \tag{1}\]  
\[E_{y}=\frac{Q_{4}-Q_{3}}{\varepsilon(S_{3}+S_{4})} \tag{2}\]  
\[E_{z}=\frac{Q_{1}+Q_{2}-(Q_{3}+Q_{4})}{\varepsilon(S_{1}+S_{2}+S_{3}+S_{4})} \tag{3}\]  
where \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\) are the induced charges on the four sensing electrodes when subjected to an electric field in the respective directions. \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) denote the effective sensing areas of the corresponding electrodes, and \(\varepsilon\) is the permittivity of the surrounding medium.  
As observed from Equations (1)-(3), the symmetrical electrode pairs along the X- and Y-axes ensure that the induced electric field signals in each respective direction are equal in magnitude but opposite in polarity. This configuration enables differential amplification to achieve the decoupling of the lateral electric field components. Along the \(Z\)-axis, the height difference between the electrode pairs ensures that the induced electric field signals are also equal in magnitude but opposite in direction, thereby enhancing the measurement accuracy of the \(Z\)-axis electric field component.  
The sensor measures the external electric field based on the principle of charge induction. The sensor's sensing electrodes are electrically connected to the surface of the sensing chips via conductive traces. The working principle of the sensitive structure is illustrated in Figure 2. When the external electric field \(E_{0}\) is applied to the sensing electrodes, a periodic excitation signal modulates the system, causing the grounded shielding electrodes to induce a cyclic shielding effect on the positive and negative sensing electrode combs. This modulation generates an alternating induced signal that is directly proportional to the intensity of the external electric field. The induced signal is then amplified and converted into a voltage signal through the internal signal processing circuit, thereby enabling the accurate measurement of the external electric field.  
Figure 1: Structure of the dual-orthogonal inductive MEMS 3D electric field sensor.
# Calculation of Reasonable Tension Value for Longitudinal Connecting Reinforcement of CRTSII
## 3 Calculation Model and Experimental Verification  
### Colesive Zone Model  
The cohesive zone model (CZM) was introduced by [PERSON] and [PERSON] in 1960 and 1962, respectively [9; 10; 11]. CZM has been used for the fracture process analysis of most materials because  
Figure 4: Tensioning sequence of longitudinal reinforcement.  
the CZM overcomes the inconsistency of the crack tip stress in classical fracture mechanics, and can simply characterize the initiation and propagation of cracks [12; 13; 14; 15; 16; 17; 18].  
In this paper, the CZM was used to simulate the bonding characteristics between the track slab and the mortar layer. It was assumed that the relationship between the stress of each tiny region of the cohesive zone and the open displacement (\(\tau\), \(\delta\)) is defined as a bilinear relationship, as shown in Figure 5. This model is suitable for the numerical simulation of the finite element method.  
The model is divided into three stages. Stage 1: Elastic stage--the ultimate bonding strength of the interface is \(\tau_{f}\), and the corresponding displacement is \(\delta_{1}\). The interface stress increases linearly before it reaches the ultimate bonding strength \(\tau_{f}\). Stage 2: Softening stage--the interface stress decreases linearly when the relative displacement is greater than \(\delta_{1}\). The relative displacement reaches \(\delta_{f}\) when the bonding strength drops to zero. Stage 3: Failure stage--the model starts to fail when the relative displacement is greater than \(\delta_{f}\). \(G_{f}\) is defined as the fracture energy and its value is the area enclosed by the triangle.  
The maximum stress criterion is used to describe the initial damage of the interface element. The damage begins when any nominal stress ratio reaches 1.  
\[\max\left\{\frac{\langle\tau_{n}\rangle}{\tau_{n}^{0}},\frac{\tau_{s}}{\tau_{ 0}^{0}},\frac{\tau_{t}}{\tau_{t}^{0}}\right\}=1, \tag{1}\]  
where \(\tau_{n}^{0}\), \(\tau_{s}^{0}\), and \(\tau_{t}^{0}\) respectively represent the bonding strength of the normal stress, the first shear stress, and the second shear stress. \(\tau_{n}\), \(\tau_{s}\), and \(\tau_{t}\) respectively represent the normal stress and two shear stresses in the mixed stress mode. The brackets \(<\!>\) means that the compressive stress does not contribute to the initial failure of the structure.  
The damage value \(D\), proposed by [PERSON] and [PERSON] [19], is defined as  
\[D=\frac{\delta_{m}^{f}(\delta_{m}^{\max}-\delta_{m}^{0})}{\delta_{m}^{\max}( \delta_{m}^{f}-\delta_{m}^{0})}, \tag{2}\]  
where \(\delta_{m}^{0}\) is the effective displacement of the element node when the failure starts, \(\delta_{m}^{f}\) is the effective displacement of the node when the damage value \(D\) reaches 1, and \(\delta_{m}^{\max}\) is the maximum displacement of nodes in the load history. 0 \(<\!D\!<\) 1 means that the interface element is in the softening area and, when the \(D\) value is 1, it means that the interface element is broken.
# Evaluating Biosedimentation for Strength Improvement in Acidic Soil
## 3 Results and Discussions
### Micro-Mechanical Results
#### 3.3.3 SEM-EDX Results  
The coating rate of the interfacial of clay particles rose when the SEM sample was collected closer to the anode cell, indicating that the precipitated quantity of calcium carbonate (CaCO\({}_{3}\)) covered the interfacial of the clay particles. Following the coating rate, the edges of the clay layers sharpened and grew more interlocked. Figure 12 illustrates the calcium carbonate (CaCO\({}_{3}\)) coating dispersion in the mapping picture for D60. Figure 13c, at a distance of 50 mm from the cathode cell for D45, shows that the soil particles got smaller as the amount of calcium carbonate (CaCO\({}_{3}\)) precipitated decreased. Furthermore, as demonstrated in Figures 13a and 12d for D45 and D60, respectively, the quantity of calcium carbonate (CaCO\({}_{3}\)) precipitated increased as the treatment distance between the cathode and anode cells decreased. To explain this, upon getting further away from the anode and the direction of ion migration within the soil specimen during the treatment process, the range of the EK effect decreased [37; 38].  
Figure 11: XRD results and peaks for both untreated soil (N) and the treated distance of 450 mm (D45).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Compound** & **(Unit)** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **CaCO\({}_{3}\)** & **TiO\({}_{2}\)** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **Cl** & **Others** \\ \hline N & (\%) & 27.88 & 63.46 & - & - & - & 1.6 & - & 7.06 \\ D45-1 & (\%) & 28.78 & 62.97 & 1.18 & 0.35 & - & - & - & 6.72 \\ D45-2 & (\%) & 31.08 & 58.05 & 0.71 & - & - & 2.47 & - & 7.69 \\ D45-3 & (\%) & 31.02 & 61.22 & 0.12 & 0.56 & - & - & - & 7.08 \\ D60 & (\%) & 29.14 & 58.24 & 0.83 & - & - & - & - & 11.79 \\ \hline \end{tabular}
\end{table}
Table 8: Chemical composition of untreated soil (N) and treated distances of 450 mm (D45) and 600 mm (D60) after treatment.