esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 2 Methods
### Recurrent Neural Network  
The recurrent neural network (RNN) is a recently developed architecture in which connections between nodes form a directed graph along a temporal sequence, which allows it to exhibit temporal dynamic behavior [3]. RNN is similar to DNN, but it also includes a memory of previous results. Our RNN model used two layers of long short-term memory (LSTM) as shown in Figure 4 and Table S3 in the Supplementary Material. Because LSTM was responsible for the dramatic advancement in speech recognition [37], we anticipated a similar performance gain in seismic recognition.  
Figure 3: The CNN architecture used in this study. Five convolutional layers each contain 50 filters and a MaxPool layer to downsample the amount of contained data. The convolutional and flattening layers condense the original 1251 samples to 650 samples containing filtered features. These are introduced to a neural network with four hidden layers and one batch normalization (B.N) operation. The model produces four values, indicating the probability of each vehicle class.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE
### Classification Based on Energy Trading Structure
#### 5.1.3 Federated Power Plant  
FPP offers a unique way to connect a wide range of distributed energy sources and controllable loads to form an integrated self-healing network of energy resources. Away from traditional electricity infrastructure, FPP represents a paradigm shift in which independent and distributed energy generation and storage assets are integrated to form a network of energy resources logically managed as a \"single\" plant. The rich diversity of such energy sources and their cooperative mode of operation enable FPP to reliably deliver energy whenever needed; this is in tandem with the sustainability agenda of the smart grid vision. From a utility perspective, these distributed generation components imply that FPP inherently holds some DR capabilities that not only reduce dependence on the main grid during energy shortfalls due to peak demand or maintenance activities, but can also improve network resilience. FPP is particularly suitable for small communities, educational campuses and small-to-mid-sized industrial facilities. The FPP may be equipped with a controller to enforce reactive demand regulation by disconnecting household loads or choosing from available generation sources to meet the current load of the community according to the aggregate load profiles of the houses [79]. In such cases, the controller determines from which generator company to purchase power based on the current shortfall and the price. It is necessary at this point to clarify some common misconceptions about MG and FPP. Whereas the former can be deployed per household to serve local loads within its geographical boundaries, following grid events, the latter pushes those boundaries by interconnecting various assets (including MGs) to form a single network of energy resources. In other words, an MG is made up of at least one energy source and load, while FPP aggregates geographically-dispersed DERs of various types and sizes into a single portfolio that can be administered as a single power plant. FPP operators are therefore known as aggregators, and FPP and MG can co-exist in a TE system.
## 2 Materials and Methods
### Data Selection and Statistical Analysis  
The trace elements data (As, Cu, Pb, and Zn) from the \"Chilean regulations on metal-polluted soils: The need to advance from adapting foreign laws towards developing sovereign legislation\" paper by [PERSON] et al. [13] were analyzed. One hundred and forty-seven and thirty-five topsoil samples (0-20 cm) from the Aconcagua and Casablanca Rivers, respectively, were selected. In the Aconcagua River basin, 127 samples were collected from various locations, including mining areas, and only 20 topsoil samples were from agricultural areas with no mining activity. The Casablanca River basin samples were sampled from fruit and vegetable agriculture areas without mining activity.  
The Spearman correlation coefficient is a nonparametric measure of the strength and direction of the association between two ranked variables, and the database in this study is not normally distributed. Principal component analysis (PCA) simplifies multidimensional data while retaining the underlying trends and patterns of the original dataset. PCA can identify relationships among the analyzed geochemical patterns, providing insight into the characteristics of the environment. Cluster analysis is a statistical technique used to group a set of objects, observations, or variables into clusters (groups), such that the objects in the same cluster are more similar to each other than to those in other clusters. Correlation, principal component analysis, and cluster analysis are commonly used in various fields, including soil contamination studies, to identify patterns, classify data, and make data-driven decisions.  
The kriging method using ArcGIS v.10.5 software was applied to show the spatial distribution of soil trace element concentrations. The software allows the user to locate each observed concentration geographically and to generate a map of hotspots to compare the distribution of different elements in the studied area [1]. This functionality was used to present the concentrations of As, Cu, Pb, and Zn using the ordinary Kriging method in ArcGIS version 10.5. In this study, several theoretical variogram functions, including linear, exponential, and spherical models, were evaluated. Table S3 presents the variogram parameters used for kriging modeling. The ranges were divided into 25 th, 50 th, 75 th, 90 th, and 95 th percentiles using IBM SPSS Statistics v.21 software [18].
## 3 Experiments and Evaluation
### Dataset, Parameters and Metrics  
In this work, the point cloud obtained by tilt photography in an area of Xi'An is selected as the experimental data, as shown in Figure 4a. The spatial resolution of the original tilt aerial camera film is about 1.5 cm, and the density of the generated point cloud is randomly re-sampled to about 400/m\({}^{2}\), and an average point distance of 5 cm. The area of the test region is about 17,500 m\({}^{2}\), and manual vector maps are produced as reference data, as shown in Figure 4b. The properties of the dataset and statistical information of the reference can be found in Table 1. The parameters used in the proposed approaches are described in Table 2. The grid size and projection density should be adaptive with the average point distance. The thresholds \(Th_{dis}\) and \(Th_{trp}\) reflect the precision and smooth of the point clouds. The rest of the parameters are used to separate the bounding boundary from the courtyard.  
The evaluation metrics include three parts: the precision of the wall corner location via the Roof Mean Square Errors, as well as the completeness (comp), correctness (Corr),  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Point Density**} & \multirow{2}{*}{**Area Size**} & \multicolumn{3}{c}{**Ground Truth**} \\ \cline{3-5}  & & & **Corners** & **Edges** & **Polygons** \\ \hline Xi’An & 400 pts/m\({}^{2}\) & 17,500 m\({}^{2}\) & 371 & 350 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of the dataset and reference.  
Figure 4: Overall view of the input point cloud and reference vector map. (**a**) Input color point clouds. (**b**) Manual reference vector map.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Grid Size**} & \multirow{2}{*}{**Density**} & \multicolumn{3}{c}{**RANSAC**} & \multicolumn{3}{c}{**Roof Extraction**} \\ \cline{3-6}  & & & \(Th\_dis\) & \(Th\_nv\) & DEM Hei & **Planarity** & **Perc** \\ \hline Para & 0.1 m & 50 & 0.2 m & 10\({}^{\circ}\) & 2 m & 0.9 & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters used in the experiments.  
and quality (Qua) indexes regarding the wall corners, lines, and polygons [36]. All of the considered elements are described below:  
\[RMSE=\sqrt{\frac{\sum_{n\in U}\mathit{dis}(pt_{ref},pt_{res})^{2}}{n}} \tag{6}\] \[Comp=\frac{\|TP\|}{\|TP\|+\|FN\|}\] \[Corr=\frac{\|TP\|}{\|TP\|+\|FP\|}\] \[Qua=\frac{\|TP\|}{\|TP\|+\|FN\|+\|FP\|}\]  
where \(U\) is the set of wall corners, and \(\mathit{dis}(pt_{ref},pt_{res})\) is the 2D Euclidean distance between the reference corner points and the detected results. \(\|TP\|\) (True Positive) is the number of objects which exist in both the reference and results, \(\|FP\|\) (False Positive) is the number of objects not found in the reference, and \(\|FN\|\) (False Positive) is the number of objects not found in the results.
## 3 Results and Discussion
** From the data reported in Table 4 and from the relative distribution of Figure 5, it is possible to infer that the process B proceeds in the same way as for process A, but the two steps described above start and finish at different times. Indeed, Stage I of Sample 5 goes from 0 to 6 h, unlike Sample 4, in which it continues up to 9 h, while Stage II occurs at the time of about 8 h compared to 12 h for process A. Moreover, for Sample 5, it is possible to hypothesize a third stage between 9 and 12 h: at this moment, the trend previously observed in Stage I, i.e., an oxidation of the alcoholic groups to carbonyls with simultaneous loss of CO2, occurs. In Table 4 and Figure 5 are also reported the NMR data obtained for the Sample 6_8 h, in which the same \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process B** & \(\mathbf{C_{aliph}}\) **(\%)** & \(\mathbf{C_{X}}\) **(\%)** & \(\mathbf{C_{unsat}}\) **(\%)** & \(\mathbf{C_{cub}}\) **(\%)** \\ \hline
**Sample 0** & \(54\pm 1\) & \(32\pm 3\) & \(9\pm 1\) & \(5\pm 1\) \\
**Sample 3** & \(56\pm 1\) & \(31\pm 1\) & \(8\pm 1\) & \(5\pm 1\) \\
**Sample 5_3 h** & \(60\pm 1\) & \(25\pm 2\) & \(8\pm 1\) & \(7\pm 1\) \\
**Sample 5_6 h** & \(63\pm 1\) & \(21\pm 1\) & \(10\pm 1\) & \(6\pm 1\) \\
**Sample 5_9 h** & \(53\pm 1\) & \(33\pm 1\) & \(8\pm 1\) & \(7\pm 1\) \\
**Sample 5_12 h** & \(56\pm 1\) & \(32\pm 2\) & \(6\pm 1\) & \(6\pm 1\) \\
**Sample 6_8 h** & \(64\pm 2\) & \(24\pm 2\) & \(6\pm 1\) & \(6\pm 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Integral values, in percentage, of \(\mathbf{C_{aliph}}\), \(\mathbf{C_{X}}\), \(\mathbf{C_{unsat}}\), and \(\mathbf{C_{cub}}\), calculated for samples of process B and comparison with Sample 0 and Sample 3. Figure 5: Relative distribution, expressed as percentage (%), of the carbon groups \(\mathbf{C_{aliph}}\), \(\mathbf{C_{X}}\), \(\mathbf{C_{unsat}}\), and \(\mathbf{C_{cub}}\) for process B samples and comparison with the Sample 0 and Sample 3.  
concentrations of Sample 5 are used but the reaction proceeds continuously for 8 h. As can be seen from Table 4 and Figure 4, in Sample 6_8 h, there is a slight increase in the carbon atoms bound to the heteroatoms compared to the situation observed at 6 h. A decrease in the aliphatic content at 8 h is not observed, but a decrease in the unsaturation content is appreciable and suggests that this process (Stage II) begins with the oxidation of the double bonds and that it only subsequently proceeds with the oxidation of the saturated chains, as highlighted in Sample 5_9 h.
## 5 Experimental Evaluation
### Experimental Setup  
Our proposed approach MPPEngine is implemented by extending jGenProg in the Astor [11; 12; 13] workspace. As we evaluated our approach MPPEngine against jGenProg, we used the same Defects4j [14] benchmark that was used to evaluate jGenProg in the Astor [11; 12; 13] workspace. The Defects4j [14] benchmark contains 6 subjects in total, but 2 of the subjects do not come with JUnit test cases. Therefore, we conducted our experiments on 4 subject programs (Chart, Lang, Math, and Time) with 224 bugs from the Defects4j benchmark, as mentioned in Table 1.  
In Astor, the fault localization tool used is GZoltar [24], and it comes with the Ochiai algorithm [25]. As we extended jGenProg in Astor to implement MPPEngine, we used the same in our approach as well. We ran experiments on a virtual machine with a Ubuntu OS, Intel Core-i5 processor, CPU with @3.50 GHz, and RAM of 4 GB. For all the experiments, the time limit was set to 3 h (i.e., 180 min), the total generation set to 10,000 generations in maximum, and sampling size to 20 (each sampled subset will have 20 test cases). In other words, MPPEngine tries to generate a valid patch within 3 h of the time limit or within 10,000 generations running the test suite in subsets each with 20 test cases. Also to reduce randomness, we ran all the experiments 3 times and took the average value for the results.
# Internalization of Phospholipid-Coated Gold Nanoparticles
## 1 Introduction
Accordingly, in this work, we sought to answer the following three questions; (1) Do these PCAuNPs internalize by clathrin-mediated endocytosis, (2) Is this PCAuNP endocytosis cell type dependent, and (3) What happens to these PCAuNPs with time? Using image cross correlation spectroscopy analysis of laser scanning confocal microscopy images, we explored the uptake of PCAuNPs in two cell types, C2C12 and A549 cells. The A549 cell line was chosen because it is derived from type II alveolar epithelial cells that are known to process lipid coated material, which is why we chose these cells in our previous work [51]. The C2C12 cell line was chosen for comparison with the A549 cells because it is a mouse myoblast cell line that is used to study differentiation and biochemical pathways. Both cells lines are well characterized and widely used [53]. Moreover, they grow as monolayers and are flat, and therefore, they are easy to use in confocal microscopy studies. We first incubated cells with fluorescent PCAuNPs followed by fixation and immunofluorescence labeling of antibodies specific for markers of the clathrin-mediated endocytic pathway. We observed that PCAuNPs were internalized and processed by clathrin-mediated endocytosis with similar uptake pathways in both cell types; however, the extent of aggregation was far greater in A549 cells.
## 4 Experimental Verification
### Validation of Data Augmentation Effects  
In order to verify the effectiveness of the GRW-DBA data augmentation method proposed in this paper, the three classic methods of sampling [47], GAN [28], DBA [34], and our method were used to augment dataset 1, and the augmented data were used to train the ANN model. As a test set, the prediction accuracy of the four trained models was evaluated. At the same time, the ANN model using 70% of the source data as the training set was used as a blank control. Figure 8 shows the model prediction accuracy obtained by the four augmentation methods.  
The smaller the RMSE and MAE index values, the larger the R\({}^{2}\) index value, and the better the prediction effect of the model. As shown in Figure 8, the prediction accuracy of the model trained using the augmented data obtained by GRW-DBA proposed in this paper is better than that of GAN [28] and other comparable models. At the same time, compared with the blank group obtained by using the source data training, the prediction accuracy of the model is greatly improved. It shows that the augmented data obtained by the GRW-DBA method is more in line with the distribution of the source data, and at the same time, the prediction accuracy of the ANN model under the condition of small data volume is effectively improved.
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
## 3 Effect of Working Parameters on the Cooling Performance of the Blade
### Influence of Temperature Ratio of Steam to Mainstream
From Figure 15d, it can be seen that the average dimensionless temperature of both air-cooled and steam-cooled blade shows an increasing trend with the increase of temperature ratio of steam to mainstream, but the steam-cooled blade is about 1.86-2.58% lower compared with air-cooled blade at the same steam to mainstream temperature, and the average is about 2.20% lower.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 1 Introduction
### Comparison Between Numerical Simulation and Experimental Results
#### 1.6.1 Site Horizontal Acceleration  
Figure 8 compares the acceleration time histories at typical measurement points obtained from numerical simulations and experimental tests. The results indicate that the acceleration time histories and amplification factor distributions from numerical calculations generally match well with the experimental data.  
However, slight differences in peak values are observed. In the numerical model, the shear wave velocities of soil layers were determined using the distance between accelerometers and the time difference in pulse wave arrival. The distances were based on initial modeling data. Changes in these distances may occur during the static centrifuge loading phase and under seismic conditions. These changes could lead to deviations in the calculated shear wave velocities. This is considered a primary reason for the minor discrepancies between the peak site accelerations in the numerical results and the experimental results.
## 4 Results
### Model Performance Metrics  
Three classifiers were trained on the preprocessed dataset: logistic regression (L2-penalized), Random Forest, and XGBoost. Hyperparameters for Random Forest and XGBoost were optimized via 5-fold cross-validation using F1 score as the optimization metric. Performance metrics on the test set (n = 79) are listed in Table 6.  
For **Random Forest**, we tuned n_estimators (\(\in\{100,150,\ldots,500\}\)), max_depth (\(\in\{5,10,\ldots,20\}\)), and min_samples_split (\(\in\{2,3,\ldots,10\}\)). The optimal parameters were n_estimators=300, max_depth=10, and min_samples_split=5. For **XGBoost**, we varied learning_rate (\(\in\{0.01,0.1,0.3\}\)), n_estimators (\(\in\{100,200,300,400\}\)), and max_depth (\(\in\{3,5,\ldots,10\}\)), yielding optimal values of learning_rate=0.1, n_estimators=200, and max_depth=5.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Before** & **After** \\ \hline Pass & 271 & 271 \\ Fail & 124 & 271 \\ \hline \end{tabular}
\end{table}
Table 5: Class distribution before and after SMOTE.  
To assess the impact of intermediate grades, we repeated the experiments after excluding G1 and G2 from the feature set. Table 7 shows the performance results for this scenario.  
While logistic regression yielded numerically higher accuracy and AUC than Random Forest and XGBoost, statistical analysis using the Tukey HSD test (see Table 5) revealed that these differences were not statistically significant. Therefore, rather than claiming numerical superiority, we emphasize that logistic regression achieves comparable predictive performance to more complex models. Given its simplicity and interpretability, logistic regression is preferable according to the principle of parsimony, particularly in educational contexts where transparency is highly valued.  
The confusion matrix for logistic regression, illustrating the model's classification outcomes on the test set, is shown in Figure 1. The corresponding ROC curve, presented in Figure 2, demonstrates the high discriminative power of the model
# Field and Numerical Investigation of Taihu Resort Cut Slope Failure in Suzhou, China
## 4 Methods
### Hydraulic Model  
The finite element code, Midas GTS, is used to investigate the failure of the Taihu Resort's cut slope in Suzhou. In this study, the slope model was simulated based on actual on-site measurements and mechanical property data. The numerical calculation enables us to measure the volume of infiltration water. The soil-water retention curve (SWRC) is used to determine the volumetric water content curve, which has the capability to describe the changes in the pore-water pressure. Therefore, in order to predict or estimate the volume of infiltration water for soil and rock slopes, a fitting formula obtained from field or laboratory tests of water retention was developed by [PERSON] (1980) [62]. The model was applied to this study as follows:  
\[S=\frac{\theta_{w}-\theta_{r}}{\theta_{s}-\theta_{r}}=\left(\frac{1}{1+\left( a\times h\right)^{n}}\right)^{m}\left\{\begin{matrix}\theta_{(h)}=\theta_{s}\\ h\geq 0\end{matrix}\right. \tag{3}\]  
Figure 7: Ground surface displacement monitoring data from August 2012 to July 2013 (refer to Figure 2 for point locations).  
\[k_{w}=k_{s}\frac{\left\{1-\left(a\times h^{(n-1)}\right)\left[1+\left(a\times h \right)^{n}\right]^{-m}\right\}^{2}}{\left\{\left(1+\left(a\times h\right)^{n} \right)\right\}^{\frac{m}{2}}} \tag{4}\]  
\[m=1-\frac{1}{n} \tag{5}\]  
where \(S\) is the saturation, \(\theta_{w}\) is the volumetric water content, and \(\theta\)\(r\) and \(\theta\)\(s\) represent residual and saturated volumetric water content, respectively. \(h\) is the pressure head, \(a\), \(n\), and \(m\) are fitting parameters, and \(k_{s}\) and \(k_{w}\) represent the saturated coefficient of permeability and the coefficient of hydraulic conductivity, respectively.  
Based on the van Genuchten model, a typical soil-water retention curve (SWRC) is illustrated in Figure 8.
# Improving Particle Swarm Optimization Analysis Using Differential Models
## Unified Model for PSO
### Evolutionary Behavior Analysis of PSO Algorithm
Defining the Lyapunov function:  
\[V(\mathbf{y})=\mathbf{y}^{T}\mathbf{P}\mathbf{y} \tag{11}\]  
The derivation formula is:  
\[V^{\prime}(\mathbf{y})=-\mathbf{y}^{T}\mathbf{Q}\mathbf{y} \tag{12}\]  
where \(\mathbf{P}\) is a positive definite matrix, \(\mathbf{Q}\) is a positive definite symmetric matrix and satisfies the following Lyapunov equation:  
\[\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}=-\mathbf{Q} \tag{13}\]  
The convergence performance of the system can be obtained from Equation (14):  
\[\delta=-\frac{V^{\prime}(\mathbf{y})}{V(\mathbf{y})} \tag{14}\]  
The smaller \(V(\mathbf{y})\) is and the larger the absolute value of \(V^{\prime}(\mathbf{y})\) is, the larger \(\delta\) will be, and the faster the system will converge; on the contrary, the smaller \(\delta\) will be, the slower the corresponding system will converge [23; 24]. Further, integrating the above equation, we get Equation (15):  
\[-\int_{0}^{t}\delta dt=\int_{0}^{t}\frac{V^{\prime}(\mathbf{y})}{V(\mathbf{y})}dt=ln \frac{V(\mathbf{y})}{V(\mathbf{y}_{0})} \tag{15}\]Thus, Equation (16):  
\[V(\mathbf{y})=V(\mathbf{y}_{0})e^{-\int_{0}^{t}\eta dt} \tag{16}\]  
and Equation (17):  
\[\delta_{min}=\min_{\mathbf{y}}\biggl{\{}-\frac{V^{\prime}(\mathbf{y})}{V(\mathbf{y})}\biggr{\}} \tag{17}\]  
Since Equation (17) is a constant, there is Equation (18):  
\[V(\mathbf{y})\leqq V(\mathbf{y}_{0})e^{-\int_{0}^{t}\delta_{min}dt}=V(\mathbf{y}_{0})e^{- \delta_{min}t} \tag{18}\]  
This shows that once \(\delta\) is determined, an upper bound on the convergence of \(V(\mathbf{y})\) over time can be determined, and Equation (19) is:  
\[\delta_{min}=\min_{\mathbf{y}}\biggl{\{}-\frac{V^{\prime}(\mathbf{x})}{V(\mathbf{x})} \biggr{\}}=\min_{\mathbf{y}}\biggl{\{}\frac{y^{\mathsf{T}}Q_{\mathbf{y}}}{y^{\mathsf{ T}}P_{\mathbf{y}}}\biggr{\}}=\min_{\mathbf{y}}\{\mathbf{y}^{\mathsf{T}}Q_{\mathbf{y}},\;\mathbf{y}^{ \mathsf{T}}P_{\mathbf{y}}=1\} \tag{19}\]  
When \(\eta,\omega\), and \(\varphi\) are constants, the system (4) is a linear constant system; then, there is Equation (20):  
\[\delta_{min}=\lambda_{min}\Bigl{(}\mathbf{Q}\mathbf{P}^{-1}\Bigr{)} \tag{20}\]  
where \(\lambda_{min}(\cdot)\) represents the minimum eigenvalue of \((\cdot)\).