esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review
### Hyperparameter Optimization
[32] investigated the use of grid search and gradient boosting machine for hyperparameter optimization of RF and SVM classifiers. They used 20 software defect datasets to explore the impacts of tuning in cross-project defect prediction (CPDP) and within-project defect prediction (WPDP). Their overall results showed that the prediction performance improved when hyperparameter optimization was used. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Datasets** & **Optimization Methods** & **Classifiers** & **Ensembles** \\ \hline
[29] & Eclipse JDT Core, Eclipse PDE UI, Equinox, Mylyn, Lucene & opt-aiNet & KNN, SVM, NB, DT, LDA, RF, AdaBoost & Yes \\  & JM11, PC51, Prop-12, Prop-22, Prop-32, Prop-42, Prop-52, Camel 1.22, Xalan 2.52, & Grid search & NB, KNN, LR, NN, PLS, DA, Rule-based, DT, SVM, Bagging, Boosting & Yes \\
[5] & Xalan 2.62, Platform 2.03, Platform 2.13, Platform 3.03, Debug 3.44, SWT 3.44, JDT5, Mylyn5, PDE5 & Grid search, Random search, Genetic algorithm, Differential evolution & NB, KNN, LR, NN, PLS, DA, Rule-based, DT, SVM, Bagging, Boosting & Yes \\
[6] & Eclipse JDT Core, Eclipse PDE UI, Equinox, Mylyn, Lucene & Grid search & KNN, SVM & No \\  & artV0, and V1, antV2, camelV0, camelV1, ivy, jeditV1, jeditV2, log4j, lucene, poiV0, poiV1, synapse, velocity, xercesV0, xercesV1 & Differential evolution & Where-based Learner, DT, RF & Yes \\  & ar1, ar3, ar4, ar5, ar6, cm1, jm1, kc1, kc2, kc3, pc1, pc2, pc3, pc4, pc5, Eclipse JDT Core, Eclipse PDE UI, Equinox framework, Lucene, Mylyn & Grid search & RF, SVM & No \\ \hline \hline This work & 21 Defect Datasets & Grid Search & Ada, RF, ET, GB, HGB, XGB, CAT & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between prior studies that used hyperparameter optimization.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### Methods
#### 2.6.3 Correlation Analysis  
In conducting the correlation analysis of SIF, SM, and VPD anomalies for July, August, September, and October 2022, we selected the anomalies of Layer 1's soil moisture (SM1) as representative of soil moisture anomalies. The computation not only required temporal consistency across the indices but also spatial consistency. Therefore, we performed bilinear interpolation resampling on SM anomaly and VPD anomaly data to adjust their original resolutions to match the resolution of the SIF images. Specifically, for each pixel position in the target image, we identified its four nearest neighboring pixels in the original image. Firstly, we linearly interpolated between two adjacent pixels horizontally to obtain two temporary values. Then, we linearly interpolated vertically between these two temporary values to obtain the final target pixel value.  
Upon achieving uniform spatial resolution across these three datasets, we proceeded to calculate Pearson correlation coefficients between SIF anomalies and anomalies of SM and VPD values. This analysis aimed to assess the interdependencies among these variables and provide insights into their relationships over the specified months of July, August, September, and October 2022.
###### Abstract  
Dynamic model updating based on finite element method (FEM) has been widely investigated for structural damage identification, especially for static structures. Despite the substantial advances in this method, the key issue still needs to be addressed to boost its efficiency in practical applications. This paper introduces the updating idea into crack identification for rotating rotors, which has been rarely addressed in the literature. To address the problem, a novel Kriging surrogate model-based FEM updating method is proposed for the breathing crack identification of rotors by using the super-harmonic nonlinear characteristics. In this method, the breathing crack induced nonlinear characteristics from two locations of the rotors are harnessed instead of the traditional linear damage features for more sensitive and accurate breathing crack identification. Moreover, a FEM of a two-disc rotor-bearing system with a response-dependent breathing crack is established, which is partly validated by experiments. In addition, the associated breathing crack induced nonlinear characteristics are investigated and used to construct the objective function of Kriging surrogate model. Finally, the feasibility and the effectiveness of the proposed method are verified by numerical experiments with Gaussian white noise contamination. Results demonstrate that the proposed method is effective, accurate, and robust for breathing crack identification in rotors and is promising for practical engineering applications.  
c 2019 9 2428 10.3390/app9122428
## 4 Results and Analysis  
### Results  
In our experiment, a total of 126 operators participated. However, data for 17 of these participants were unavailable due to self-occlusion, wherein 9 participants fully obstructed the view with their arm while 8 others partially obstructed the view with their finger during the injection process. Therefore, we analyzed data from a total of 109 participants. The results of our analysis are presented in Figure 18.  
The prediction performance of the YOLOv7-based test method proposed in this study was evaluated by comparing the predicted values with the ground truth. The ground  
Figure 18: Results of statistical analysis. (**a**) Bland–Altman plot between ground truth measurements and predictions. (**b**) Significance analysis  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threshold (\%)** & **95** & **96** & **97** & **98** \\ \hline Standard Deviation (SD) & 0.507 & 0.484 & 0.506 & 0.544 \\ Mean Absolute Error (MAE) & 0.358 & 0.330 & 0.406 & 0.539 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing the results of different thresholds.  
truth data were analyzed frame by frame by an expert observer to ascertain the moment of skin puncture. As shown in the Bland-Altman plot in Figure 18a, the red dashed lines in the plot indicate the upper and lower permissible limits of agreement (LOA) for the measured values and the average difference between the measured values between the two methods. The LOA range from 1.1406 [95% CIs: 0.9828 to 1.2984] s to \(-\)0.7600 [95% CIs: \(-\)0.9178 to \(-\)0.6022] s. The mean difference in measurements between the methods was 0.1903 [95% confidence intervals (CIs): 0.09829 to 0.2824] s, with a fixed error (one sample \(t\)-test assuming zero population mean, \(p\)-value = 0.0001). Figure 18a shows that 104 of the 109 data are within the acceptable range of measured values, which indicates a high level of correlation between the two values. Furthermore, the average preparation time is 2.264 s and the mean error is 0.330 s. Figure 18b shows that there is no statistically significant difference (\(p=0.25\)) between the predicted values and the ground truth, with a significance level of \(\alpha=0.05\). This result provides evidence to support the validity and reliability of the proposed method.
# Petrophysics Parameter Inversion and Its Application Based on the Transient Electromagnetic Method
## 3 Numerical Simulation  
To implement the inversion theory of rock physical parameters based on the transient electromagnetic (TEM) method, a series of 1D layered geoelectric models with varying porosities and water saturations are first constructed. Forward modeling is then conducted to simulate the corresponding geoelectric responses. Subsequently, resistivity inversion is performed, followed by the derivation of rock physical parameters, such as water saturation and porosity, based on the inverted resistivity.
## 5 Structural Hybrid of Reviewed Converters
### Multilevel Bidirectional DC-DC Converter  
Due to the ability of multilevel converters to reduce power losses, the voltage stress of switches, and the total harmonic distortion (_THD_) of output signals [141], they have been widely developed and tested in various applications to achieve beneficial points, such as good efficiency and low and optimal cost. The study by [142] presented a new multilevel boost-buck \(DC\)-\(DC\) converter that can be used when similar grounding is needed at both sides of the converter. The topology is comprised of a back-to-back connection of \(2n\) diode-clamped or level active converter legs. The efficiency of this structure for the _MLC_ is higher at a higher number of levels.  
The study presented by [PERSON] focused on the elimination of the imbalance in \(DC\) capacitor voltage, which has been the major problem of diode-clamped _MLIs_ with >3 levels. The study proposed the connection of the voltage-balancing circuit based on the buck-boost chopper to the \(DC\)-link of diode-clamped _MLIs_; this was considered effective in addressing the issues of line faults, disturbances, and transients, even though the cost and system complexity are increased by adding such a circuit to the system [143]. Considering that the _HBMLC_ topology demands the use of separate voltage sources, it is suitable for EVs and plug-in hybrid _EVs_ as these systems are dependent on a high number of batteries. The other interesting feature of the _MLI_ is that operations can proceed in a bidirectional mode, which allows the charging and discharging of the batteries [141, 144]. The neutral point diode-clamped (_NPC_) dual active bridge (_DAB_) converter was designed in [145], and the study applied a capacitor voltage balancing method in order to facilitate voltage balancing. The two _NPC_ legs, as shown in Figure 35, can generate five-level voltages across the primary winding of the medium frequency transformer. The balancing methods are dependent on the direction of power flow through the _DAB_ converter, but in this research the authors suggested a voltage balancing controller that is independent of power flow direction and does not require adjustments of active voltage vectors via the modulator. They concluded that the proposed voltage balancing approach is very and globally effective under all operating conditions of the converter [145].  
Figure 34: Circuit diagram of full-bridge _LLC_ resonant converter [140].
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 4 Evaluation
### Simulation in Deterministic Simulator
Therefore, R16 removes the BIER header and restores the packet of the protected flow to its original format (Figure 16). The decapsulation router is the end of the B-REP alternative path. The behavior described above can be observed on the output obtained from the OMNeT ++ console listed in Table 6. We may see that at the time 56 dims, the source host starts sending packets of the protected data flow intended to the host H3. Packets follow the shortest route via routers: R1 \(\rightarrow\) R6 \(\rightarrow\) R11 \(\rightarrow\) R16 \(\rightarrow\) H3 (Table 6, green lines). At 60 dims, we simulate the failure of the R6 router. R1 detects its unavailability using the BFD connection failure detection mechanism. Therefore, the R1 becomes the Source router S and starts using the B-REP pre-calculated alternative path which goes through R1 \(\rightarrow\) R5 \(\rightarrow\) R10 \(\rightarrow\) R15 \(\rightarrow\) R16 \(\rightarrow\) R10 (Table 6, blue lines). At the same time, all R6 neighbors are starting the OSPF update process. Until the OSPF R1 process completes and will provide actualized routing information, R1 will insert the pre-calculated path into the bit-string field of packet's header. According to obtained simulation results, packets were successfully delivered using the B-REP algorithm around the failure router (Table 6, blue lines). At 70 dims, the router R6 is restored (all interfaces go UP), which means the Source router detects reconnection with R6 and disables B-REP mechanism. Packets are routed via original route as before (Table 6, black lines).
## 4 Validation of the U-Smile Method
### Imbalanced Binary Classification  
In the second study, we introduced and validated the three-level approach of the U-smile method for imbalanced binary classification [7]. In addition to subclass-specific (Level 1) and class-specific net (Level 2) coefficients, a weighted overall level (Level 3) was defined. Level 3 aggregates net coefficients of Level 2 with weights proportional to class sizes, ensuring fair representation of both outcome classes in the overall assessment. Also, we scaled the point size in the U-smile plots of the BA and RB coefficients according to the I coefficient. This adjustment integrated the magnitude of prediction changes (BA and RB coefficients) and the number of prediction changes (I coefficient) in a single visual summary.  
The U-smile method was tested on the same dataset as in the first study under seven predefined levels of class imbalance (1%, 10%, 30%, 50%, 70%, 90%, and 99% of the event class). Informative and non-informative variables, both real and generated, were evaluated at each imbalance level in logistic regression models.  
Across the entire imbalance range, the U-smile method consistently distinguished informative variables from non-informative ones and remained robust and stable. Additionally, at high and extreme imbalance levels, the U-smile method detected both improvement in prediction in the minority class and reduction in overfitting to the majority class.  
Compared to traditional metrics such as \(\Delta\)AUC, \(\Delta\)F1-score, \(\Delta\)MCC, \(\Delta\)BS, and BSS, the U-smile method was more sensitive to subtle but meaningful effects of informative predictors, especially at higher levels of class imbalance, where it outperformed these measures. Similar to the first study, the U-smile method did not produce any false-positive or false-negative results for generated data. Table S3 shows a comparison of quantitative results of selected variable selection strategies for an informative variable (ST depression) and a non-informative variable (glucose) for models derived from the training dataset across imbalances ranging from 1% to 99% in the event class (adapted from Ref. [7]).
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### Model Evaluation Index
#### 3.2.1 Sensitivity  
Sensitivity refers to the proportion of all samples that are actually the main classification target (positive), which is predicted to be the main classification target. If the true event is a fall (positive), the model predicts a fall as well. Sensitivity is defined as the ratio of the two  
\[S=\frac{TP}{TP+FN}\times 100\% \tag{21}\]
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 2 Related Works
A classification method combining ML and DL, with a CNN-RNN-based DL model, gained an accuracy of 99.71%. Regarding implementation costs associated with WT algorithms, all cases demonstrate efforts to reduce the data volume input into complex DL models, either through signal decomposition or feature extraction. In the field of traditional ML, various signal and data processing techniques have been integrated with lightweight classifiers. The binary SVM classifier is particularly favored for its creation of models where the decision function is a simple linear function with coefficients equal to the number of features being classified. Linear and nonlinear kernels such as RBF and Polynomial have been commonly employed in classifications, with the latter two incurring higher computational costs compared to the linear kernel. Studies utilizing SVM-based feature classification techniques have employed signal decomposition methods in time-domain subbands, including DWT [22], Hilbert-Huang Transform (HHT) with Empirical Wavelet Transform (EWT) [23], Discrete Cosine Transform (DCT) [24], and Conditional Mutual Information Maximization (CMIM) feature extraction [25], achieving high accuracy levels of 95.6%, 100%, 97%, and 99.83%, respectively. In [26], the SVM approach was compared with algorithms like K-nearest neighbors (KNN) and Linear Discriminant Analysis (LDA) in the classification of third-order tensors obtained from HHT and WT, represented by Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD), achieving performance exceeding 98%. In [27], an approach based on KNN classified histograms of Multilevel Local Patterns (MLP) obtained through Empirical Mode Decomposition (EMD) and Intrinsic Mode Functions (IMF) was used, yielding an accuracy of 98.67%. Finally, [28] applied a five-level EWT to decompose EEG signals, utilizing time-frequency features in real-time and further processed classification outcomes, achieving a mean accuracy of 99.88%. ML algorithms, unlike DL models, necessitate manual feature extraction. Nonetheless, results indicate that these procedures do not significantly impair model performance. Furthermore, controlling the number of input features allows for the creation of simpler models with a small number of representative examples per class. Overall, these models, due to their low data volume requirements for training, better fulfill the computational cost reduction needs.
## 3 Results and Discussion
### Integration of Renewable Energy Sources and Hybrid Microgrid Configurations  
The integration of RES into microgrids is a fundamental enabler for developing sustainable and low-emission energy systems. However, the variability and unpredictability of renewable generation, combined with the increasing complexity of hybrid AC/DC microgrid architectures, introduce considerable technical and operational challenges. This section reviews the latest research and technological developments aimed at addressing these issues.
# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
## 3 Experiment
### Experimental Conditions  
The grinding wheel experiment was conducted on the 3 MK1440 high-precision external cylindrical automatic grinding machine, as depicted in Figure 7. The AE detection system employed in the experiment comprises an AE acquisition card, PXR15 AE sensor, PXPA3 preamplifier, and a PC computer. The material of the experimental grinding wheel is ceramic brown corundum, featuring a diameter of 550 mm and a width of 55 mm. The specific experimental grinding parameters encompass a grinding wheel linear velocity of 15 m/s and a grinding feed speed of 100 m/min. Following the grinding process, a portable surface roughness measuring instrument is employed to assess the surface roughness of the workpiece.  
During the grinding experiment, a node was designated for every 500 m of cumulative grinding, resulting in a total of 10 nodes. Following the grinding completion at each node, the surface morphology of both the grinding wheel and workpiece was carefully observed. The surface roughness of the workpiece after grinding was measured using a portable surface roughness gauge. The wear status of the grinding wheel after completing the grinding at each node was comprehensively analyzed and evaluated by referencing the alterations in the time-domain waveform of the acoustic emission signal.  
Figure 6: Flowchart of the AO-CNN-LSTM.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 3 Experimental Results and Analysis  
### Introduction to the Dataset  
Most of the ship targets are non-cooperative targets, and it is difficult to build an HRRP database of the targets from the actual measurement data. In this paper, we use CAD3D software to build ten kinds of 1:1 ship target models and import them into CST electromagnetic simulation software. The structural parameters of ten kinds of ship targets are shown in Table 1. CST simulation parameters are set as follows: azimuth angle is 0\({}^{\circ}\)-360\({}^{\circ}\), pitch angle is 90\({}^{\circ}\), angle step is 1\({}^{\circ}\); radar centre frequency is 3 GHz, bandwidth is 150 MHz, polarization mode includes vertical polarization and horizontal polarization, frequency sampling points are 360, the default optimal grid profiling size of the software is used, and ray tracing algorithm is selected for the solution. Eventually, the HRRP data of 360 azimuths for ten types of ship targets are simulated.  
The lack of training samples can lead to overfitting of the model, and the number of HRRP simulated with CST is far from sufficient for the proposed model, which needs to be expanded. In this paper, the original data are expanded by adding the clutter conforming to the K distribution in three times by the size of the signal-to-clutter ratio of 10 dB, and the data are expanded by a factor of three at each signal to clutter ratio. The HRRP of the ten-class ship target under the 10 dB signal-to-clutter ratio condition is shown in Figure 4. The HRRP of a class ten ship target under 10 dB signal-to-clutter ratio for both polarizations is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ship Number** & **Length (m)** & **Width (m)** \\ \hline
1 & 182.8 & 24.1 \\
2 & 153.8 & 20.4 \\
3 & 162.9 & 21.4 \\
4 & 99.6 & 15.2 \\
5 & 332.8 & 76.4 \\
6 & 337.2 & 77.2 \\
7 & 17.1 & 3.6 \\
8 & 143.4 & 15.2 \\
9 & 17.6 & 4.5 \\
10 & 16.3 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of ten types of ship targets.  
The model parameters are set: the total number of training epochs is 300, the initial learning rate is 0.03, and the learning rate becomes one-fourth of the original one for every 80 iterations during the training process. The dimensions of the hidden layers are all set to 128. The optimizer is chosen to be Stochastic Gradient Descent (SGD) with a batch size of 32. The sliding window length of the preprocessing is \(d=48\) and the sliding distance  
Figure 4: HRRP of class ten ship targets. (**a–j**) represent ships one–ten.  
\(l=24\). The kernel function used for Support Vector Machines (SVM) is the Radial Basis Function. The role of Principal component analysis (PCA) is to reduce the input data to two dimensions.
## 1 Introduction
The temporal information of the hand between video frames is captured, the motion trajectory is obtained, and, finally, the hand action categories in consecutive video frames are output. We evaluate our approach on the first-person hand motion benchmark datasets FPHA [1] and H2O [2]. We summarize the contributions as follows:  
(1) We introduce a unified framework for action recognition and pose estimation, enabling prediction of each frame hand pose and object category information, and, at the same time, it is able to output the hand movement categories in consecutive video frames. (2) We design two interconnected modules to capture the spatial and temporal information of the hand in the video, namely the Hand Pose Estimation Module (HPEM) and Action Recognition Module (ARM). On one hand, HPEM is used to capture the spatial position of the hand in a single-frame RGB image. The pose estimator through a double residual fusion structure is combined with a spatial detail feature extraction component. The key points of the hand in each image frame are further estimated to obtain spatial information. On the other hand, ARM is used to aggregate the features of consecutive video frames in the temporal dimension and obtain the complete motion trajectory of the hand and ultimately obtain the action classes by the action classifiers. (3) Comprehensive evaluations of two first-person benchmarks (FPHA, H2O) demonstrate state-of-the-art performance.