esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recent applications of DP and related machine learning interatomic potentials such as Gaussian Approximation Potentials (GAP), the Spectral Neighbor Analysis Potential (SNAP), and SchNet have demonstrated remarkable accuracy in predicting formation energies, phase stability, and thermal transport properties in materials [17; 18; 19]. In this study, we investigate the elastic behavior of LCO across a wide range of SOCs using both an empirical Buckingham potential and a DP model trained on ab initio data. The Buckingham potential has been one of the few available and widely adopted interatomic potentials for LCO in classical molecular simulations. Its popularity stems from analytical simplicity and low computational cost, which make it suitable for large-scale simulations. However, it is limited in its ability to accurately capture complex bonding environments, electronic effects, and structural evolution under diverse operating conditions. In contrast, machine learning-based potentials such as the DP model offer higher fidelity by learning interatomic interactions directly from quantum-mechanical data, making them more transferable across different chemistries and states of charge. The two models are systematically compared in terms of their ability to predict mechanical responses and capture stress evolution mechanisms. Our findings reveal that the DP model provides more consistent and accurate predictions, particularly at low SOCs where structural instabilities emerge. The study further identifies the increasing dominance of Co\({}^{4+}\)-O interactions as a key driver of localized stress concentrations, which may promote crack initiation. These results not only validate the robustness of machine learning-based potentials but also provide critical input parameters for multiscale models of electrode degradation, ultimately informing the design of more durable battery systems.
## 3 Test Results and Discussion
### Deflection  
The steel-reinforced concrete beams displayed similar behavior, experiencing failure at stresses of 20 kN and 21.8 kN, resulting in midspan deflections of 4.76 mm and 4.65 mm, respectively. Remarkably, the glass fiber-reinforced concrete beams exhibited remarkable strength, with ABGF-1 collapsing at 32 kN and ABGF-2 lasting until 40 kN. At the point of failure, midspan deflections measured 14.01 mm and 14.61 mm. The basalt fiber-reinforced concrete beams, performing between the steel- and GFRP-reinforced concrete beams, recorded failures at 26 kN (ABBF-1) and 30 kN (ABBF-2). The corresponding midspan deflections were 16.17 mm and 21.50 mm. Figure 4 illustrates the graphical implications of the maximum deflection observed in the sample specimens.  
In this study, concrete beams that were reinforced with BFRP bars were found to exhibit higher deflection. Figure 5 illustrates the deflection of the beams under a regular load of 20 kN, where the GFRP-reinforced concrete beams displayed deflections of 7.73 mm (a 62% increase compared to the steel-reinforced beams) and 7.44 mm (a 60% increase compared to the steel-reinforced beams). On the other hand, the basalt fiber-reinforced beams demonstrated deflections of 10.87 mm (a 127% increase compared to the steel-reinforced beams and a 40% increase compared to the GFRP bar-reinforced concrete beams) and 10.10 mm (a 117% increase compared to the steel-reinforced concrete beams and a 35.75% increase compared to the GFRP-reinforced concrete beams). These findings underscore the greater flexibility of basal fiber-reinforced beams compared to glass fiber-reinforced beams.  
Figure 4: Load vs. deflection curves.
## 4 Case Study Investigation
### Asphalt Mixture Monitoring System  
For the asphalt mixture mixing monitoring system, SBW series temperature transmitters were installed in the discharge bin of the mixing station and Mettler Toledo IND series load cells were installed in the silo screening system to monitor the quality and weight of the silo aggregates. The system recorded the monitored data and made a monitoring report (as shown in the Figure 13), and transmitted the monitoring data to the data management system through the 5G transmission channel. The construction site of the mixing station is shown in the Figure 14.  
Figure 11: Energy consumption and carbon emissions of equipment in the aggregate production stage.  
Figure 12: Energy consumption and carbon emissions in the production of mineral powder materials.
# A New Instrument Monitoring Method Based on Few-Shot Learning
## 2 Methods and Dataset
### Dataset  
In this paper, the dataset used for FLIMM consists of the training dataset, validation dataset, and test dataset. Among them, the training dataset contains 1015 images (1920 \(\times\) 1080 pixels), the validation dataset contains 435 images (1920 \(\times\) 1080 pixels), and the test dataset contains 390 images (1920 \(\times\) 1080 pixels). All images in the training dataset and validation dataset are simulation data that are generated by the FADGAM function of FLIMM, while the test dataset consists of 390 real experimental images which are shown in Table 1. In detail, among the 1015 training images of FLIMM, 1000 images were generated by the multi-digit simulation mode of FADGAM based on one image with blank screen background (1920 \(\times\) 1080 pixels, Figure 2a) and 10 digital figures (30 \(\times\) 50 pixels, Figure 2b) for pre-training, and the other 450 were generated by single-digit mode of FADGAM based on 16 real images augmented by the simulation mode for fine-tuning (Figure 2c).  
The dataset used for Pure YOLOv5 and Mask R-CNN contains 280 real images for the training dataset, 120 real images for the validation dataset, and 390 real images for the test dataset. In addition, all comparison methods (human detection, template matching, Pure YOLOv5, Mask R-CNN and FLIMM) use the exact same test dataset, and the test dataset contains all the same real data images without any augmentation that are directly captured from the actual experiments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Train** & **Validation** & **Test** \\ \hline Mask R-CNN & 280 (all real) & 120 (all real) & 390 (all real) \\ Pure YOLOv5 & 280 (all real) & 120 (all real) & 390 (all real) \\ FLIMM & 1015 (all simulation) & 435 (all simulation) & 390 (all real) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Summary.  
Figure 1: A flow chart of FLIMM. (**a**) Background Images. (**b**) Number Images. (**c**) Real images. (**d**) Simulated Images. (**e**) Detection results.  
Figure 2: The augmentation results comparison (**a**) Mosaic data augmentation. (**b**) FADGAM with multi-digit augmentation mode. (**c**) FADGAM with single-digit augmentation mode.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## 1 Introduction
The essence of an ensemble model is to assign weight factors to the known individual surrogate models and then sum the results of each model. The accuracy of each model affects the accuracy of the ensemble model; thus far, individual surrogate models are expected to have relatively high accuracy to meet the robustness and the predictive performance requirements. When high-precision individual models are obtained, the weight factors can be calculated by regression methods to improve computational efficiency and save computational cost, instead of other optimization algorithms or error metrics. Motivated by the regression idea, this paper proposes a novel ensemble modeling technique named the regularized least squares ensemble model (RLS-EM). Three individual surrogate models, KRG, RBF, and SVR, are used to develop the RLS-EM; the least squares algorithm with a regularization strategy and an augmentation strategy is used to calculate the weight factors. The regularization strategy and augmentation strategy help to solve the collinearity problem caused by the inherent interpolation properties of the KRG and RBF models, and the similar prediction values at some sample points. The augmentation strategy is carried out on the unexplored regions, which can help improve the accuracy of the surrogate models, while the regularization strategy helps to avoid the potential overfitting phenomenon. The RLS-EM aims to take advantage of the well-performing ensemble surrogate model to guarantee the robustness and accuracy for different problems from low to relatively high dimensions. The RLS-EM aims to take advantage of the well-performing ensemble surrogate model to guarantee the robustness and accuracy for different problems from low to relatively high dimensions. The remainder of this paper is organized as follows. In the next section, a brief introduction to the ensemble methods is presented. Then, the development of the proposed RLS-EM is described. Several numerical functions and an engineering application are tested in the following section. Finally, the conclusions are presented.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 2 Analysis of Traditional Interconnections in Space-Borne Microwave Systems
### Gold-Ribbon Wrap Bonding
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No** & **Characterization Quantity** & \begin{tabular}{c} **Parameter** \\ **Code** \\ \end{tabular} & \
\begin{tabular}{c} **Parameter Value (mm)** \\ \end{tabular} & \multicolumn{2}{c}{**Stress Value (MPa)**} &
\begin{tabular}{c} **Reflection** \\ **Loss (dB)** \\ \end{tabular} \\ \hline
1 &
\begin{tabular}{c} Drop between the inner core \\ and the substrate \\ \end{tabular} & \(g\) & 0.1 & 0.20 & 0.3 & 29.3 & 26.1 & 23.7 \\
2 &
\begin{tabular}{c} Length of the bottom of \\ bonding ribbon \\ \end{tabular} & \(L_{4}\) & 0.1 & 0.30 & 0.52 & 17.9 & 26.1 & 31.8 \\
3 &
\begin{tabular}{c} Half span of gold ribbon \\ \end{tabular} & \(P\) & 0.3 & 0.5 & 0.7 & 38.3 & 26.1 & 17.9 & \(-10\) dB \\
4 &
\begin{tabular}{c} Distance from the gold ribbon to \\ the edge of the substrate \\ \end{tabular} & \(L_{3}\) & 0.07 & 0.43 & 0.8 & 24.9 & 26.1 & 27.3 \\
5 &
\begin{tabular}{c} Width of gold ribbon \\ \end{tabular} & \(B\) & 0.25 & 0.35 & 0.5 & 23.1 & 26.1 & 28.8 \\
6 &
\begin{tabular}{c} Distance from the gold ribbon to \\ the end of the inner core \\ \end{tabular} & \(L_{1}\) & 0.95 & & & 26.1 & \\
7 &
\begin{tabular}{c} Distance from the seal to the \\ edge of the substrate \\ \end{tabular} & \(L_{2}\) & 0.12 & & & 26.1 & \\
8 &
\begin{tabular}{c} Thickness of the gold ribbon \\ \end{tabular} & \(T\) & 0.025 & & & 26.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters and analysis results of gold-ribbon wrap bonding. Figure 6: S-parameter of the above structures. (**a**) S11 of the above structures; (**b**) S21 of the above structures. and manually assembled after the coaxial seal is soldered. Therefore, additional processes of microstrip substrate assembly and soldering are created, which greatly hinders the automatic assembly of microwave modules and is not conducive to the mass production of microwave modules, as shown in Figure 9.
## 2 Periodic Haar Piecewise-Linear PHL Transform
### Two-Dimensional PHL Functions and Transform  
The 2D PHL transform can be formulated in the following way:  
1. Forward transform \[[\text{C}(N_{x},N_{y})]=[-\frac{1}{2^{k_{y}+1}}][\text{PHL}(N_{y})][\text{F} (N_{x},N_{y})][\text{PHL}(N_{x})]^{T}[-\frac{1}{2^{k_{x}+1}}]^{T}\] (10)
2. Inverse transform \[[\text{F}(N_{x},N_{y})]=[\text{IPHL}(N_{y})][\text{C}(N_{x},N_{y})][\text{IPHL }(N_{x})]^{T}\] (11) where \([\text{F}(N_{x},N_{y})]\)--matrix of 2D signal; \([\text{C}(N_{x},N_{y})]\)--matrix of coefficients (2D PHL spectrum); \([\text{PHL}(N_{y})],[\text{PHL}(N_{x})]\)--matrices of 1D PHL forward transform; \([\text{IPHL}(N_{y})],[\text{IPHL}(N_{x})]\)--matrices of 1D PHL inverse transform; \([-\frac{1}{2^{k_{y}+1}}],[-\frac{1}{2^{k_{x}+1}}]\)--diagonal matrices of normalization.  
The non-periodic Haar Piecewise-Linear Transforms have an order (\(N+1\)) while the PHL Transforms have an order (\(N\)). Due to this fact, PHL transforms can be applied in digital signal and image processing since the data usually has a dimension that is a power of 2.  
Figure 1: Set of PHL functions for N = 8.
## 8 Sensitivity Analysis  
Sensitivity analysis is a technique used to determine how different values of an independent variable affect a particular dependent variable under a given set of assumptions. While keeping all the other input variables constant, only a single input variable was changed, and the variation in the output was studied to reveal the trend that arises. The variation in the Fatigue Cracking Area with the different inputs is shown in Figure 10.  
As can be seen from Figure 10, variation in pavement thickness seems to have a seemingly minimal effect on fatigue crack areas contrary to the literature claiming that thinner pavements are more susceptible to this kind of distress. It is also evident that air voids and KESAL have a positive relationship with fatigue cracking in an asphalt pavement, meaning an increase in the input variable results in an increase in the output variable. On the contrary, asphalt content, average annual temperature, and \(\text{CAr}^{\prime}\) have a negative relationship with fatigue cracking. This is in line with the available literature and corroborates the relationship between the high value of \(\text{CAr}^{\prime}\) and a good quality of pavement.  
In this study, in order to gauge the significance of each individual input and to see inputs that are most influential to the output, the sensitivity index of each input variable is calculated. Sensitivity index is shown in its mathematical form in Equation (8):  
\[\text{S}=(\frac{\text{O}_{2}-\text{O}_{1}}{\text{I}_{2}-\text{I}_{1}})(\frac{ \text{I}_{\text{ave}}}{\text{O}_{\text{ave}}}) \tag{8}\]  
where  
\(\text{S}=\text{sensitivity index}\);  
\(\text{O}_{2}\) and \(\text{O}_{1}=\text{maximum and minimum ANN output}\);  
\(\text{I}_{2}\) and \(\text{I}_{1}=\text{maximum and minimum input variables}\);  
\(\text{I}_{\text{ave}}=\text{average of input variables}\);  
\(\text{O}_{\text{ave}}=\text{average of ANN output}\).  
In Figure 11, the SI chart illustrates the degree to which each factor influences the model's output. Notably, asphalt content is the most impactful parameter with the highest sensitivity index of close to 8, indicating significant changes in the model's output in response to even minor variations in asphalt content. On the other hand, \(\text{CAr}^{\prime}\) shows minimal impact, with an index around 2, suggesting it has the least influence on the model. Other parameters like Thickness, Air Voids, KESAL, and average annual temperature also vary in their sensitivity, with indices ranging from about 3 to 5. This visualization aids in identifying which factors are crucial for the model's performance, guiding targeted adjustments and prioritization in the modeling process.  
Figure 11: Sensitivity index.  
Figure 10: Sensitivity analysis, **(a)** fatigue crack vs. thickness, **(b)** fatigue crack vs. CA’z, **(c)** fatigue crack vs. KESAL, **(d)** fatigue crack vs. air void, **(e)** fatigue crack vs. annual temperature, **(f)** fatigue crack vs. asphalt content.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Entropy Analysis  
Information entropy is a statistical test that estimates the uncertainty and randomness in communication systems. It was first introduced by [PERSON] in 1948 [52]. In image encryption, entropy is used to measure the randomness of the distribution of pixels in the cipher image. A high entropy value indicates a better obfuscation of the plaintext and the [PERSON] of any discernible pattern. To evaluate the unpredictability and randomness of an encryption algorithm, researchers rely on two types of entropy tests, namely, global entropy and local entropy. Shannon entropy, also known as global entropy, calculates the pixel information for the full image, while local entropy measures the mean entropy of randomly selected non-overlapping blocks. Shannon entropy can be calculated as follows:  
\[H(m)=-\sum_{i=0}^{2^{n}-1}p(m_{i})\log_{2}[p(m_{i})], \tag{18}\]  
where \(n\) represents the number of bits used to represent the symbol \(p(m_{i})\), and \(p(m_{i})\) represents the probability of symbol \(m_{i}\). Entropy for an image can be calculated using Equation (18), where \(p(m_{i})\) represents normalized histogram counts for each intensity value in the image. Given that a pixel's maximum possible intensity value in an 8-bit representation is 256, and that the probability of a pixel value occurrence is \(\frac{1}{256}\), the ideal value of entropy for an image can be calculated using Equation (18), as follows:  
\[H_{ideal}=-\sum_{i=0}^{255}\frac{1}{256}\times\log_{2}\frac{1}{256}=8\]  
The entropy of a cipher image generated by an encryption algorithm should be as close as possible to the optimal entropy value of 8 to withstand brute force attacks.  
The local entropy is measured by computing the mean Shannon entropy of randomly selected non-overlapping blocks. Local entropy is considered more accurate, consistent, and efficient than global Shannon entropy [53]. Local entropy can be calculated as follows:  
\[H_{K,T_{B}}(S)=\sum_{i=1}^{K}\frac{H(S_{i})}{K}, \tag{19}\]  
where \(S\) is a set of non-overlapping blocks selected randomly, containing \(T_{B}\) pixels, and \(K\) represents the number of random blocks. \(H(S_{i})\) is the Shannon entropy (from Equation (18)) of the \(i\)th block.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.7 Support Vector Machine  
Support vector machine (SVM) is a new machine-learning method based on statistical theory and structural risk minimization criteria [33]. SVM takes the training error as the constraint condition of the optimization problem and takes the minimization of the confidence range as the optimization objective; that is, SVM is a learning method based on the structural risk minimization criterion. Since the solution of SVM is transformed into the solution of a quadratic programming problem, the solution of SVM is the globally unique optimal solution. SVM has many unique advantages in solving small sample, nonlinear, and high-dimensional pattern recognition problems and can be extended toother applications in machine learning, such as function fitting [34; 35; 36]. When the input space is linearly inseparable, the nonlinear mapping is completed by the kernel function of the support vector machine. Linear, polynomial, Gaussian radial basis kernel (RBF), and Sigmoid are the kernel functions used commonly [37]. The principles and characteristics are shown in Table 1.  
When SVM is used to establish a classification model, optimizing two parameters (gamma and C) can improve the accuracy of the model. The smaller the gamma value, the larger the diameter of the Gaussian kernel, the smoother the boundary of SVM, and the simpler the model, which tends to be underfitting. Parameter C is the penalty coefficient. Increasing C can improve the accuracy of the model, but it is easy to cause over-fitting. The smaller the C value is, the better the fault tolerance is, but it may lead to under-fitting. This paper uses the grid search method to find the best parameters, C and gamma.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Potentially Hydrocarbon Supporting Faults (F7-F10)
#### 4.2.4 Fault Stability Analysis  
The vertical sealing capacity of faults F7, F9, and F10 was evaluated using slip tendency and fracture stability. Slip tendency analysis indicates that the majority of F7 exhibits values lower than 0.2, signifying a substantial margin of safety from failure (Figure 11). F9 and F10 display a similar slip tendency pattern, with roughly half of these faults experiencing moderate stress (0.3 \(<\) Ts \(<\) 0.5), while the other half remains significantly distant from failure (Ts \(<\) 0.2) (Figure 11).  
In the context of fracture stability calculations, we employed low-friction fault rock parameters (cohesion C = 0.5 MPa; coefficient of internal friction \(\mu\) = 0.45) to estimate the fracture stability for the mentioned faults. The outcomes reveal notable differences in pore pressure increasing requirements to induce failure. Specifically, F7 demonstrates a considerably high threshold of 13.8 MPa (Figure 11), whereas F9 and F10 exhibit comparatively moderate pore pressure thresholds of 6.5 MPa and 5.4 MPa, respectively (Figure 11).  
By synthesizing the results from slip tendency and fracture stability analyses, it becomes evident that F7 remains in a stable stress state with a robust vertical sealing capacity. Conversely, F9 and F10 exhibit moderate levels of stable stress, translating to a moderate vertical sealing capacity within these faults.  
Figure 11: Stereoplots illustrating the slip tendency and Mohr diagrams illustrating the fracture stability for F7, F9, and F10. Black arrows show the increase in pore pressure needed to force the fault into failure.
## 2 Materials and Methods
### Fatty Acid Composition Analysis by Gas Chromatography-Flame Ionization Detector (GC-FID)  
The fatty acid composition of all edible fixed oils was determined as fatty acid methyl esters (FAME) using an Agilent 6890 series gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a flame ionization detector. Fatty acid methyl esters (FAME) of oils were prepared according to the procedure described by [PERSON] and [PERSON] [9]. Briefly, 10 mg of edible fixed oil samples were dissolved in 0.75 mL of n-hexane; then, 0.1 mL of 2 M potassium hydroxide in methanol was added, and the solution was mixed for 2 min in a vortex mixer (Velp Scientific, Usmate Velate, Italy), dried over anhydrous sodium sulfate and left for 5 min. After phase separation, 1 mL of the upper layer of n-hexane containing the fatty acid methyl esters was immediately injected into a DB-23 capillary column (60 m \(\times\) 0.25 mm i.d. 0.15 \(\upmu\)m film) [50%-Cyanopropyl-methylpolysiloxane] (Agilent Technologies, Catalogue No.: 122-2361). The analysis was split injection (split ratio 1:10), and it was performed according to [PERSON] et al. [10], with helium as a carrier gas, under the following column oven temperature program. The initial temperature was programmed at 100 \({}^{\circ}\)C for 0 min, raised from 100 to 150 \({}^{\circ}\)C by a rate of 10 \({}^{\circ}\)C min\({}^{-1}\), and held constant at 150 \({}^{\circ}\)C for 0 min. It was then raised from 150 to 195 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C min\({}^{-1}\) and held constant at 195 \({}^{\circ}\)C for 5 min. It was then raised from 195 to 210 \({}^{\circ}\)C at a rate of 1 \({}^{\circ}\)C min\({}^{-1}\) and held constant at 210 \({}^{\circ}\)C for 0 min and finally raised from 210 to 240 \({}^{\circ}\)C by a rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and held constant at 240 \({}^{\circ}\)C for 5 min. Injector and detector temperatures were maintained at 250 \({}^{\circ}\)C and 260 \({}^{\circ}\)C, respectively. The analysis time was 55.50 min. Hewlett-Packard Chem Station Software (ChemStation Rev. A.10.02) was used to calculate peak areas and retention times. Peak areas of FAME were normalized as the relative percentage of total methyl esters corresponding to their fatty acids.