esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of an InfraRed Pyroelectric Detector Performances in THz Range Using the Terajet Effect
## 3 Results and Discussions  
The detection measurements were performed with and without the cube particle with different emitted power. The measurements (Figure 3) show a linear dependence of the voltage (U) with the power at \(\lambda\) = 1.5 mm (this value gives the maximum output power from the BWO). The method for measuring the NEP was similar to that described in Reference [40]. The idea of this configuration is to exploit a dielectric PTFE particle that has a wavelength-scale size to concentrate the radiating THz waves into the sensitive area of PD with a diffraction-limited value. This allows increasing the intensity of signal to be measured, such that the signal-to-noise ratio (SNR) can be improved.  
Figure 2: (**a**) Sketch description of the cube with the plane wave and the propagation direction used in the simulations, (**b**) Electric field intensity along the \(xz\) direction versus the \(z\) direction for the case of two refractive index (\(n=1.4\) and \(2.1\)) and (**c**) the terajet formation from the cube with 2-wavelength dimensions by the finite integral technique realized in commercial software—the CST Microwave Studio.  
Figure 4 shows the measured photoresponse without the cube (black) and with the cube (red). To demonstrate the sensitivity enhancement, the detector was excited by the source at its lowest power idem when the signal from the receiver (without a particle) was at the noise level. Then, we placed the particle and looked at the increase in the signal and the noise level. The measured amplitude with the cube was 6.1 \(\pm\) 0.06 mV, which was 5.6 dB higher than that without the enhancer. The standard error was calculated from eight distinct measurements and a Student's (t) coefficient [51] of 2.132, corresponding to a 90% two-sided confidence interval. The standard deviations of the amplitude were measured for the cases without and with the PTFE particle, respectively. These results indicate that the proposed technique can enhance the detected THz intensity of 5.6 dB at 0.2 THz for MG-33 PD. Similar results were obtained for the MG-30 sensor, which has the same internal design as the MG-33 but differs only in standard 1203.15-1 package. The obtained gain is below the theoretical one because of the uncertainties in the setup such the dimension of the active area of the PD, and the distance from the optical window to the active element of the PD was fixed by the manufacturer and unknown. Furthermore, we did not verify the refractive index of particle material experimentally. From a practical point of view, it is quite difficult to perform a precise relative positioning between the cuboid and detector to ensure that the detector is located just at the point of maximum electric field generated by the cuboid. However, knowing the gain obtained experimentally, we can obtain an estimate of the real distance between the cuboid and the detector by looking at Figure 2b.
# Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
## 2 Related Theoretical Foundations
The hierarchical steps of the network structure layer are as follows: first, all nodes with node degree \(k=1\) are recursively removed from the network and assigned an integer value of \(KS=1\). This process is then repeated until only nodes with node degree \(k>1\) are left on the network. Subsequently, we remove all nodes with node degree \(k=2\) and assign them integer values \(KS=2\). This process is repeated until only nodes with node degree \(k\geqslant 3\) are left on the network, and so on until the network is deleted, at which point all nodes of the network are assigned one of the \(k\) shells. The k-shell decomposition method layers the network structure from the perspective of topology location and describes the core degree of different nodes in the network. The greater the value of KS in the node belt, the closer it is to the core level of the network, and vice versa. **Definition 4**. : _Network constraint coefficient (abbreviated as NCC)._  
The network constraint coefficient is derived from the structural hole theory proposed by [PERSON] [29], which is described as the tightness distance of a node connected directly or indirectly to other nodes. The value of the network constraint coefficient is negatively related to the extent of the structural hole. However, this concept only considers the nearest neighbors of a given node; therefore, it cannot find some important bridging nodes in complex networks. \[NCC_{i}=\sum_{j}NCC_{ij}=\sum_{j}\left(p_{ij}+\sum_{q}p_{iq}p_{qj}\right)^{2}, i\
eq q\
eq j \tag{3}\]  
\[p_{ij}=\frac{Z_{ij}}{\sum_{q}z_{iq}} \tag{4}\]  
where \(p_{ij}\) is the proportion of \(i\)'s resources devoted to \(j\), and the variable \(z_{iq}\) represents the strength of the contact between \(i\) and \(j\), with \(p_{ij}\) being the direct contact and \(\sum_{q}p_{iq}p_{qj}\) being the indirect contact. Thus, the constraint \(NCC_{ij}\) for a linked node varies in the interval [0, 1] to the extent to which resources of \(i\) are used directly or indirectly for \(j\). The network constraint coefficient represents the information advantage and control advantage of nodes in the network structure. The lower the network constraint coefficient, the more structural holes are occupied by nodes, so it plays a more important role in the network structure. Figure 1: Diagram of k-shell decomposition process.
# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods
### Model-Based Methods  
The model-based method assumes the statistical models of technical and biological variability and noise distribution, and interpolates by estimating the parameters of the distribution, such as sclmpute, SAVER, SAVER-X, BISCUIT, VIPER, and bayNorm. The availability links, programming language, whether a package is available, year of presentation, operative system, distribution compatibility and reference of the representative methods based on model see Table 2.  
1. sclmpute reduces the dimension of gene expression matrix by principal component analysis (PCA) and uses spectral clustering. The hierarchical model of Gamma distribution and Normal distribution is established for the gene expression in each cell subpopulation, the Gamma distribution can explain \"dropout\" and the Normal distribution represents the real gene expression level. Then, the Gamma-Normal mixed model is used to estimate which values are affected by dropout, then the dropout values are subsequently imputed by non-negative least square (NNLS) regression using the most similar cells in its neighbors, as shown in Figure 5. The specific workflow is as follows [4]: 1. The count matrix is normalized according to the library size of each sample (cell), and the logarithmic transformation is performed to prevent the effect of outliers. 2. Cell sub-population and outliers are detected using spectral clustering. 3. Dropout values were identified using a Gamma-Normal mixed model. 4. Information from the same genes is borrowed from similar cells to impute in the dropout values. Figure 4: The four classification of imputation method for the detailed division of Bayesian and non-Bayesian methods. Figure 5: The framework of sclmpute method [4]. * 2. SAVER assumes that the count of each gene in each cell follows the Possion-Gamma mixed distribution. Specifically, the technical noise in the gene expression signal is approximated by the Possion distribution, while the Gamma distribution explains the uncertainty in the real expression. The method does not specify a gamma prior, but instead uses the expression of other genes as the predictors, by using a Poisson Lasso regression to estimate a few prior parameters in the empirical Bayes-like approach. Once the prior parameters are estimated, the SAVER outputs the posterior distribution of the true expression that quantifies the estimated uncertainty, and the posterior mean is used as the expression value for the SAVER recovery, for more detail, please refer to Ref. [26]. We give a simple framework as shown in Figure 6.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Transformer Grouping and Fault Types  
To conduct fault analysis and classification, transformers were grouped into two categories based on their characteristics and operational status.
###### Abstract  
This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.  
groundwater; remote sensing; GIS; random forest; Boosted Regression Tree; logistic regression +
Footnote †: journal: Accepted: 26 September 2019; Published: 30 September 2019
## 2 Background
#### Ground Control Station (GCS)  
A ground control station is the command post that offers the necessary elements to the human for the remote control of UAVs. A GCS is designed for flight planning and monitoring, as well as the real-time processing of UAV-environment interactions. A GCS is equipped with a Human-Machine Interface that conducts the planning of the missions, display and control consoles, a real-time video stream, an on-board computer, telemetry instrumentation, a data communication platform, and links with the UAVs via antennas [30].
## 5 Discussion
### Nutrient Export Estimation  
The nutrient (N and P) export estimation under three different scenarios indicates that the change in LULC types and areas affects parameters in the biophysical table, leading to different nitrogen and phosphorus export. The contribution of LULC type under three scenarios indicates that the highest total nitrogen and phosphorus exports occur on paddy fields. In contrast, the lowest total nitrogen export occurs on water bodies, and the lowest total phosphorus export occurs on miscellaneous land. However, the highest average nitrogen and phosphorus export mostly appeared on field crops and perennial trees/orchards. These findings agree with [76], who indicated that the highest nitrogen and phosphorus exports occurred on cultivated land. There was very little nitrogen and phosphorus export on forest land, water areas, and unused land. This is similar to the result of [77], who analyzed nutrient load and delivery from different scenarios and found the most significant load rate per unit area and low retention efficiency of cultivated crops. Furthermore, agriculture was the leading cause of nutrient release in the watershed under the different LULC scenarios. These findings are similar to those of [78], who found that cropland and agroforestry influenced roughly 90% of the nutrients exported, while water bodies were identified as sinks.  
Moreover, as a runoff proxy, the annual rainfall is not a sensitive factor for estimating nutrients, as suggested by [61] and the previous study of [62]. The best fit of the sixth-order polynomial equation between annual rainfall and nutrient (N and P) export under three different scenarios in this study reconfirm the relationship as mentioned earlier. In this study, the R\({}^{2}\) value of the relations between annual rainfall and nitrogen export varies from 0.4920 to 0.9638, while the R\({}^{2}\) value of the relations between annual rainfall and phosphorus export diverges from 0.8834 to 0.9615.
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Optical Cavity Boundary Layer Control
#### 4.1.2 Slotted Plate Experiment Results  
When the upper and lower wall panels of the optical cavity were replaced with slotted panels, the chemical reaction platform ran normally. The pressure distribution along the optical cavity under different pumping volumes is shown in Figure 8. The results in the figure show that the pressure distribution in the optical cavity was different from that in the solid plate state after using the slotted plate and boundary layer suction. In the flow direction, the pressure distribution in the optical cavity first increased and then decreased. This indicates that the thickness of the boundary layer in the back part of the optical cavity decreased, the area of the effective channel increased, and the expansion of the airflow in the back part of the optical cavity accelerated. In the first half of the optical cavity, the pressure distribution gradually increased, which may have owed to heat released from the chemical reaction. The highest pressure point in the pressure distribution of the different pumping volumes appeared at the third point, and the pressure distribution decreased with increasing pumping volume. A slotted plate can effectively control the development of a boundary layer in an optical cavity.  
Table 3 lists the chemical reaction platform power at different pumping rates, where N\({}_{\text{D}}\) is the design power. Within 2-8% units, the power increases with an increase in the  
Figure 8: Distribution of cavity pressure at varying pumping rates with a slotted plate.  
Figure 7: Distribution of cavity pressure during independent SCRP operation.  
pumping volume. When the pumping capacity continued to increase to 6%, the power began to decrease. This demonstrates that the boundary layer control effect was more apparent when the pumping volume was properly increased within a small range; however, if the pumping volume exceeds a certain threshold, the chemical reaction platform power will be affected by the reduction in effective airflow.
## 5 Experimental Results and Discussions  
This section presents the experimental results of the proposed methodology for dynamic UAV task allocation, path planning, and energy management, utilizing the Adaptive Perturbation PSO with Local Search (APSO-LS) algorithm within a rolling horizon framework. The simulations, conducted using the provided MATLAB 2024a implementation, evaluate the algorithm's performance across multiple stages with dynamically evolving task demands. We analyze the convergence behavior, solution quality, and practical implications based on the generated figures, focusing on makespan minimization and energy efficiency.
## 5 Results
### Cyber Attack Scenarios  
In the simulation, a DoS attack, an FDIA, and a combination of both attacks were implemented on the wind turbine generator control system. In all case studies, if the communication signal violated the time sampling bounds of 50 \(\upmu\)s or if the DC-link voltage fluctuated greater than 5% of the nominal voltage, the controller then checked if the specific  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Nominal power—\(P_{nom}\) & 10 MVA \\ Line-to-line nominal voltage—\(V_{l\_1~{}nom}\) & 575 \(v_{rms}\) \\ Nominal rotor voltage—\(V_{r\_nom}\) & 1975 \(v_{rms}\) \\ Nominal frequency—\(P_{nom}\) & 60 Hz \\ Stator resistance—\(R_{s}\) & 0.023 p.u. \\ Stator inductance—\(L_{ls}\) & 0.18 p.u. \\ Rotor resistance—\(R_{r}\) & 0.016 p.u. \\ Rotor inductance—\(L_{lr}\) & 0.16 p.u. \\ Mutual inductance—\(L_{m}\) & 2.9 p.u. \\ Inertia constant—\(H\) & 0.685 s \\ Friction factor—\(F_{\mu}\) & 0.01 p.u. \\ Number of pole pairs—\(P_{\mu}\) & 3 \\ RSC PWM frequency—\(PWM_{freq\_-rsc}\) & 1620 Hz \\ GSC PWM frequency—\(PWM_{freq\_-gsc}\) & 2700 Hz \\ DC-link nominal voltage—\(V_{dc\_nom}\) & 1150 v \\ DC-link capacitor—\(C_{dc\_link}\) & 10 mF \\ Nominal mechanical power—\(P_{mcc\_nom}\) & 9 MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of the grid-connected wind farm.  
Figure 12: PI controller block diagram.  
set points changed more than 5% of their original values. If this was found to be the case, the specified DDPG agent would be commanded on.  
In the first scenario, the PWM signal to the rotor-side converter will be hit with a DoS attack in the form of a jamming attack. The signal to the converter will be extremely delayed, with the jamming signal overwhelming the communication lines of the controllers, effectively disabling them. In the second and third scenarios, the set points for the current and voltage d-q parameters will be adjusted with a scaling attack, with the scaling value being \(-2\), thus inverting the control signal. This will cause an increase or decrease in the set-point values, causing over-voltage or under-voltage conditions. Figures 13 and 14 depict the performance of the conventional PI controllers (not mitigation controllers) under a DoS attack on the rotor-side controller PWM signal and an FDIA attack on the rotor voltage set points. These figures clearly show the need for a mitigation strategy when cyber attacks occur on wind turbine generator control systems.
## 3 Main Technical Research  
### Research on Point Cloud Registration and Thickening Method Combining ORB-SLAM3 and NDT Technology  
Addressing the issue of the sparseness of target object point clouds acquired by single-frame LiDAR that fails to meet the detection requirements of small-volume obstacles in high-speed rail tunnels, this study introduces a point cloud registration and densification method integrating Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping 3 (ORB-SLAM3) [31] and Normal Distributions Transform (NDT) techniques. This method, by computing the transformation matrix between adjacent frames, aligns multi-frame consecutive point cloud data within a certain time window to the coordinate system of the real-time frame. As a result, real-time point cloud registration and multi-frame accumulation are realized, enhancing the point cloud density and enabling the reliable detection of small-volume obstacles in high-speed rail tunnels.
## 2 Ensemble Neural Network and Signal Graph-Based SEI
### The Proposed Ensemble Neural Network
#### 2.2.3 Training  
The ENN was trained in two steps: local classifier supervision and global classifier supervision. In the first step, the outputs from the sequence classifier and the graph classifier were supervised by the loss functions at the same time. Hence, the total loss function \(L_{step1}\) in this step was the sum of \(L_{S}\) and \(L_{G}\). The purpose was to enable the sequence/graph feature extractor and classifier to extract and classify corresponding features.  
\[L_{step1}=L_{S}+L_{G} \tag{14}\]  
The second step started after the training passed 90% of the whole epoch. In this step, the total loss function \(L_{step2}\) include \(L_{S}\), \(L_{G}\), and \(L_{E}\). Over the training in the first step, the sequence feature extractor and graph feature extractor of the ENN had been able to extract feature; on this basis, a good ensemble classifier was obtained just over a fine adjustment.  
\[L_{step2}=L_{S}+L_{G}+L_{E} \tag{15}\]  
To summarize, the ENN first used a sequence feature extractor and a graph feature extractor to extract sequence and graph features, respectively, and then fused the two features into an ensemble feature. During training, the first step was to supervise the outputs from the sequence classifier and the graph classifier to make the sequence feature extractor and the graph feature extractor able to extract corresponding features effectively. In the second step, the output from the ensemble classifier was also supervised, and the network was fine-tuned on the basis of the first step, obtaining the optimum SEI performance.
## 5 Evaluation Metrics and Results
### Results for the Second Test Scenario  
In this test scenario, the designed exams include both the C++ coding experiments, MCQ, and true & false questions. In this scenario, the exam includes different types of questions that may exist in an exam. As explained before, after collecting the raw data and performing feature extraction. The preprocessed data are trained using the mentioned AI algorithms, and the results are calculated as shown in Figure 14 and Table 6.  
As presented in Figure 14 and Table 6, the LightGBM algorithm gives the best results compared to the other AI algorithms. Furthermore, the results collected from exams include both types of static and VRL-based questions, which are comparable to that of exams that include only VRL-based questions. There is a relatively small difference between results in Tables 5 and 6 due to the nature of static questions, which results in degradation in the five metrics for the KNN algorithm. However, the results of the five metrics show an enhancement for the LightGBM, XGBoost, and Random forest algorithms, as these algorithms use ensemble techniques, which work well for different types of datasets than in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms/Metrics** & **Accuracy** & **Precision** & **TPR** & **F1-Score** & **AUROC** \\ \hline KNN & 0.70 & 0.59 & 0.51 & 0.55 & 0.73 \\ SVC & 0.72 & 0.77 & 0.33 & 0.46 & 0.81 \\ Random Forest & 0.86 & 0.87 & 0.73 & 0.79 & 0.93 \\ Logistic Regression & 0.74 & 0.73 & 0.43 & 0.54 & 0.81 \\ XGBoost & 0.87 & 0.88 & 0.77 & 0.82 & 0.943 \\ LightGBM & 0.88 & 0.88 & 0.78 & 0.83 & 0.945 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A detailed description of the used machine learning algorithms and the metrics used to test these models for exams, including the VRL-based questions.  
From the results in Table 6, it is noticed that the LightGBM algorithm is the best AI algorithm relative to XGBoost, Logistic Regression, Random Forest, SVC, and KNN algorithms, where it reaches an accuracy of 90%, while other reaches an accuracy of 88%, 77%, 87%, 73%, and 68%, respectively. Furthermore, LightGBM achieved the highest value of AUROC, which equals 95.6% relative to its competitors AI algorithms. As a consequence, the results collected for two scenarios, as presented in Tables 5 and 6, showed that the LightGBM is better than its competitor's algorithms. Therefore, it is better to use the LightGBM algorithm in training the preprocessed data for all types of exams.