esa-sceva/satcom-chunk-collection huggingface dataset:
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 5 Conclusions  
In this paper, a novel predictive method of SOH is addressed based on the charging data considering real application requirement. Three feature vectors, i.e., \(E_{s}\), \(A_{h}\), and \(t\), are analyzed and chosen as characteristic variables, which are set to be the input of the proposed SVM model. The grid search method is employed to search the optimal kernel parameters of SVM model.  
The partial charge data are considered to achieve the SOH estimation, where the voltage range of 3.95 V to 4.0 V is adopted given that it can satisfactorily obtain accurate predictions. The experiment results validate that the proposed method can efficiently predict the SOH online with less than 2% error. However, in actual application, sampling error, temperature and other factors could have an important impact on estimation precision and thus future study can be focused on them along with different voltage region validation.  
In addition, battery pack SOH estimation should also be the research focus based on the proposed algorithm considering the computation intensity and data storage limitation. Furthermore, future research should focus on different lithium-ion batteries' SOH estimation, since they can exhibit different degradation mechanism and electrical performance.  
[PERSON] and [PERSON] conceived this paper, carried out the battery experiment and completed the paper writing. [PERSON] discussed the SVM algorithm and revised the paper. [PERSON] conducted the SOH estimation and instruct the whole paper. [PERSON] provided some valuable suggestions.  
This research received no external funding.  
The work was supported by National Key R&D Program of China (Grant No. 2018 YFB0104000). National Natural Science Foundation of China (Grant No. 61763021); the Innovation Team Program of Kunming University of Science and Technology (Grant No. 14078368); and the Scientific Research Start-up Funding of Kunming University of Science and Technology (Grant No. 14078337). The authors would like to thank them for their support and assistance. Most importantly, the authors would like to thank the reviewers for their comments and suggestions.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Procedure  
Each Taekwondo athlete was individually evaluated at the Movement Analysis Laboratory of the Sports and Health Centre at the Universidad Autonoma de Chile. The process began with an individual interview, followed by anthropometric measurements and an sEMG assessment. The warm-up, led by a high-ranking Taekwondo instructor and former national coach, included joint mobility, dynamic flexibility exercises with directional movements, technical drills without knee extension, and assisted attack and defense simulations. It concluded with three sets of 12 front kicks and roundhouse (_bandal chagui_) kicks on an impact shield. After a five-minute active rest (self-directed flexibility exercises), the athletes performed 15 maximum-intensity roundhouse (bandal chagui) kicks, striking an impact shield held by an expert who provided verbal encouragement to ensure optimal performance. Kicks were executed at a self-paced rhythm, with short pauses between each attempt to allow athletes to reset their stance and ensure proper execution.  
The acquired sEMG signal was processed digitally using an algorithm implemented in Matlab R2016a software (MathWorks Inc., MA, USA). Initially, the signal was corrected by offset subtraction using a detrending function. The signal then underwent filtering with fourth-order low-pass and high-pass Butterworth filters, with cut-off frequencies set at 10 Hz and 400 Hz, following methodologies used in previous studies with Taekwondo athletes [14; 19]. The root mean square (RMS) of the EMG signal was calculated using a sliding window method, with a window width of 20 samples and a one-sample overlap. The amplitude was normalized relative to the MVIC, and each electromyographic burst was segmented to determine the peak amplitude for each muscle analyzed. The co-contraction index was calculated as follows [27]:  
\[\textit{Co-contraction Index}=\frac{\sum_{i=1}^{N}\min\bigl{(}EMG_{agonist}(i), EMG_{antagonist}(i)\bigr{)}}{\sum_{i=1}^{N}\bigl{(}EMG_{agonist}(i)+EMG_{antagonist}(i)\bigr{)}}\]  
The \(EMG_{agonist}(i)\) and \(EMG_{antagonist}(i)\) represent a specific repetition of the kick, denoted as \(i\). \(N\) is the total number of repetitions considered in the analysis. A higher co-contraction index indicates greater simultaneous muscle activation.
# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## Methodology
### Implementation of Multi-Attribute Utility Theory
After determining mean values and standard deviations of the assigned grades, a weight of importance for a specific attribute can be calculated by:  
\[\mathrm{w_{i}}=\frac{\mathrm{QV_{C,i}}}{\sum_{\mathrm{i=1}}^{n}\mathrm{QV_{C,i }}}\,, \tag{8}\]  
where  
\(\mathrm{w_{i}}\)--weight of importance for Attribute \(i\)  
The sum of all weight values for all attributes is equal to 1:  
\[\sum_{\mathrm{i=1}}^{n}\mathrm{w_{i}}=1. \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Attribute** & & & & \\
**Alternatives (m)** & **QV\({}_{\mathbf{C1}}\)** & **QV\({}_{\mathbf{C2}}\)** & **QV\({}_{\mathbf{C3}}\)** & **QV\({}_{\mathbf{C4}}\)** & **QV\({}_{\mathbf{C5}}\)** \\ \hline \(\mathrm{S_{1}}\) & \(\overline{\mathrm{U}_{1}}\)(\(\mathrm{S_{1}}\)) & \(\overline{\mathrm{U}_{2}}\)(\(\mathrm{S_{1}}\)) & \(\overline{\mathrm{U}_{3}}\)(\(\mathrm{S_{1}}\)) & \(\overline{\mathrm{U}_{4}}\)(\(\mathrm{S_{1}}\)) & \(\overline{\mathrm{U}_{5}}\)(\(\mathrm{S_{1}}\)) \\ \(\mathrm{S_{2}}\) & \(\overline{\mathrm{U}_{1}}\)(\(\mathrm{S_{2}}\)) & \(\overline{\mathrm{U}_{2}}\)(\(\mathrm{S_{2 Finally, the overall utility function values U(S) for each alternative are calculated by combining the calculated utility functions (Equation (6)) and the weight of importance (Equation (8)):  
\[\text{U}\big{(}\text{S}_{\text{j}}\big{)}=\sum_{\text{i}=1}^{\text{n}}\text{w}_ {\text{i}}\cdot\overline{\text{U}_{\text{i}}}\big{(}\text{S}_{\text{j}}\big{)}, \tag{10}\]  
where  
\(\text{U}\big{(}\text{S}_{\text{j}}\big{)}\)--the overall utility function value for Alternative \(j\)  
After the overall utility function is calculated for each alternative, the development of a final ranking list is completed as follows. A classification list is developed for five (5) categories ranging from an embankment in a very poor to very good condition. The appropriate MAUT condition for each category is shown in Table 4. The list is color-coded in order to be easily comprehensible for railway infrastructure managers. A higher value of overall utility function for a specific alternative reflects a larger number of visible as well as deeper (GPR) anomalies, larger ballast depth, lower ballast quality, and more tampering activities per year.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Feature Selection Methods
#### 2.3.5 Validation of Gene Signature Using External Dataset  
To validate the three-gene signature, an RNA-Seq experiment, GSE158444, was selected [30]. This dataset comprises transcriptome response to heat stress in 148 samples of the Arabidopsis. Counts were downloaded from GEO and 10-fold cross-validation random forest was performed using randomForest in R, as explained in Section 2.3.4.
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Materials and Methods  
### Preparation of Materials  
This study investigated the performance of the WEB-modified bitumen at different dosage levels (0% to 10%) when mixed into virgin bitumen. The general information of the virgin bitumen binder used in this experiment is shown in Table 1.  
The penetration grade of the bitumen binder was 60/70 (pen 60/70). The WEB used in this experiment was produced by the Shunfeng Company. The WEBs were colored white on the outside and black inside (Figure 1a). Figure 1b shows how the WEBs were ground using a grinding machine. The aperture of the filter was 2 mm. The WEB diameter after grinding was less than 2 mm (Figure 1b,c). The WEBs were then heated in an oven at 135 \({}^{\circ}\)C for 5 min and then mixed with the pen 60/70 bitumen binder. The WEB and virgin bitumen binder mixture was stirred homogeneously by a high-speed shearing machine (13,000 RPM) at a temperature of 180 \({}^{\circ}\)C \(\pm\) 5 \({}^{\circ}\)C for 60 min. Figure 1d shows the modification of the WEB and virgin bitumen binder. The test results of previous research revealed that WEBs over 10% had poor workability and storage stability, which means that if the dosages of the WEB additives exceeded 10%, then the WEB-modified bitumen cannot meet the construction requirements [19]. The dosages of WEB were set as 0%, 2%, 4%, 6%, 8% and 10% by the mass of modified bitumen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Item** & **Result** & **Specification** \\ \hline Penetration (25 \({}^{\circ}\)C), 0.1 mm & 63.4 & ASTM D5 \\ Penetration Index & \(-\)1.35 & ASTM C1125-89 \\ Softening point (R\&B), \({}^{\circ}\)C & 49.0 & ASTM D36 \\ Ductility (15 \({}^{\circ}\)C), cm & \(>\)150 & ASTM D113 \\ Ductility (10 \({}^{\circ}\)C), cm & 31.5 & ASTM D113 \\ Kinematic Viscosity (60 \({}^{\circ}\)C), Pa \({}^{\star}\)s & 208 & ASTM D445 \\ Wax content, \% & \(<\)2.1 & T-0615 \\ Solubility in Trichlorethylene & \(>\)99.5 & ASTM D2042 \\ Flash point, \({}^{\circ}\)C & 335 & AASHTO T 79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of pen 60/70 bitumen.  
Figure 1: Preparation of the wasted express bag (WEB)-modified bitumen. (**a**) wasted express bags; (**b**) grinding machine; (**c**) wasted express bags (after grinding); (**d**) modification process.
## 2 Materials and Methods
### Joining Process  
For the joining process, a custom joining device was used (see Figure 3). This device is equipped with an infrared spot type 600.5100.1 (Optron Infrared Systems GmbH, Garbsen, Germany) and a pneumatic piston type ADN-80-25-A-P-A (Festo SE & Co. KG, Esslingen, Germany). The infrared spot has a maximum power output of 150 watts and is focused to a nominal spot diameter of 10 mm at a focus distance of 50 mm, and is used to locally heat the CFRT component above the melting point of the matrix. Despite the nominal focus diameter, the actual irradiated area is not sharply limited and exceeds the nominal spot diameter of 10 mm. Consequently, a mask with a circular opening of 12 mm is used to sharply define the irradiated area. The pneumatic piston is used to press the pin structure into the CFRT sheet and to reconsolidate the CFRT sheet during the cooling stage after the joining process. The piston diameter is 80 mm resulting in a maximum force of 3000 N at an operation pressure of 0.6 MPa. To control the joining speed, a choke valve is equipped, which allows to adjust the piston velocity between approx. 0.01 m/s and 0.1 m/s. A polyetherehterketone (PEEK) insert is utilized to thermally insulate the CFRT sample from the steel positioning device that would otherwise lead to excess heat loss at the bottom of the sample.  
Figure 2: Schematic illustration of the pin extrusion process and the essential tool components.  
The joining process is conducted in the following three steps:  
First, the sample is placed in the positioning device and is heated in such way, that the matrix is molten through the entire thickness of the sample. The heating parameters have been identified in previous investigations [16] and are directly taken over.  
Second, the positioning tool is placed under the pneumatic piston and the metal component is placed on top of the CFRT sample while a form-fit in the positioning device allows proper concentrical alignment of the pin structure with the heated zone in the CFRT component. Detailed measurements of the samples used can be seen in Figure 4.  
Third, the pin is pressed into the CFRT component via the pneumatic piston. During the cooling stage, the pressure remains applied onto sample, to allow a complete reconsolidation of the laminate. After the cooling stage, which was defined to approximately 60 s in this study, the pressure is removed and the joined sample is removed from the positioning device.  
The heating as well as joining parameters in this study were identical with the settings in [16], where a good reconsolidation quality without matrix damage due to overheating could be achieved. The settings are summarized in Table 2.  
Figure 4: Sample dimensions and test setup for single lap shear tests.  
Figure 3: Schematic illustration of the joining device.
## 4 Case Study
### Data Pre-Processing  
Three data pre-processing techniques have been applied using the Weka software tool. Since the values of the attributes in the PID have different scales, the values were normalized to range between 0 and 1. Then, the data was standardized to have a mean value of 0 and a standard deviation of 1. This is needed to train the data with algorithms like Naive Bayes and Logistic Regression that are based on Gaussian distribution. After that, the missing values were replaced with the average value of the related attribute. By completing the pre-processing step, it becomes ready to be trained using different techniques. The dataset is split into the training and testing sets using 10 fold cross validation.
# Numerical Simulations of Novel Conning Designs for Future Super-Large Wind Turbines
## 3 Results and Discussions
### Special Coned Configurations: C4S0, C4S1, C4S2, C4, C-4S0, C-4S1, C-4 S2 and C-4  
#### 3.2.1 Overall Force Performance  
Configuration C-4S2 gives the lowest thrust among the cases listed in Table 3, i.e., 7.30% lower than the baseline rotor. C-4S2 had the largest blade tip offset among the upwind configurations. Although C-4S2 reduced the torque by 1.96%, it still had the largest torque-to-thrust ratio \(QT\) due to the large reduction of thrust. The downwind counterpart C4S2 produced the lowest \(Q\) and the lowest \(QT\), which was unfavorable. The upwind configuration surpassed its downwind counterpart, as also revealed in the pairs of C4 and C-4, C4S0 and C-4S0. For these pairs, the upwind coned rotors had a smaller \(T\), a larger \(Q\) and a higher \(QT\) than their downwind coned counterparts. However, for the pairs C4S1 and C-4S1, the downwind configuration C4S1 had a higher \(QT\). Interestingly, the blade tip of the downwind configuration C4S1 was pointing upwind, which may be the reason why C4S1 had a higher \(QT\). Lastly, it should also be noted that the radial velocity component is omitted in the traditional BEM method. Therefore, the same results will be obtained for an upwind configuration and its downwind counterpart, such as C4S2 and C-4S2, which is clearly in contrast with reality. In Table 3, it may be seen that the thrust discrepancy between C4S2 and C-4S2 reached nearly 8%. The torque discrepancy was approximately 5%. These results reveal that the inaccuracies of traditional BEM methods are not negligible, making the conclusions from [9; 10; 11; 12; 13; 14] in Section 1 disputable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Straight** & **C4S2** & **C4** & **C4S0** & **C4S1** & **C-4S1** & **C-4S0** & **C-4** & **C-4S2** \\ \hline \(T\)(KN) & 1046.06 & 1051.37 & 1061.10 & 1054.99 & 1032.59 & 1040.55 & 1031.22 & 1005.89 & 969.73 \\ \(\delta T\) & 0\% & 0.51\% & 1.44\% & 0.85\% & \(-\)1.29\% & \(-\)0.53\% & \(-\)1.42\% & \(-\)3.84\% & \(-\)7.30\% \\ \hline \(Q\)(KNM) & 7283.11 & 7055.26 & 7197.34 & 7261.64 & 7254.33 & 7250.16 & 7280.18 & 7242.09 & 7140.14 \\ \(\delta Q\) & 0\% & \(-\)3.13\% & \(-\)1.18\% & \(-\)0.29\% & \(-\)0.40\% & \(-\)0.45\% & \(-\)0.04\% & \(-\)0.56\% & \(-\)1.96\% \\ \hline \(QT\)(m) & 6.96 & 6.71 & 6.78 & 6.88 & 7.03 & 6.97 & 7.06 & 7.20 & 7.36 \\ \(\delta QT\) & 0\% & \(-\)3.62\% & \(-\)2.58\% & \(-\)1.14\% & 0.90\% & 0.07\% & 1.40\% & 3.41\% & 5.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thrust and torque of different configurations.  
Figure 10: Streamlines and pressure on \(r=77.59\) m normal-cut plane: (**a**) upwind coning of \(T_{trans}=5/R\), \(C_{conv}=-4\); (**b**) straight baseline; (**c**) downwind coning of \(T_{trans}=5/R\), \(C_{conv}=4\); (**d**) comparisons of streamlines near stagnation points.
# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 4 Results and Analysis
### Evaluation Indicators  
In order to objectively evaluate the performance of the high-speed slope rockfall detection model, P, R, mAP, F1, and the number of parameters are selected as the experimental evaluation indexes.  
Precision: Precision measures how many of the positive samples predicted by the model are truly positive. It is calculated as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) (True Positive) represents correctly identified positive samples in the test and \(FP\) (False Positive) indicates samples that were incorrectly predicted as positive.  
Recall: Recall measures the proportion of all actual positive samples correctly identified as positive. It is calculated as follows:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{10}\]  
where \(FN\) (False Negative) indicates positive samples incorrectly identified as negative.  
\begin{table}
\begin{tabular}{c c} \hline
**Environmental Parameter** & **Value** \\ \hline Operating system & Ubuntu 24.04 LTS \\ Deep learning framework & Pytorch \\ Programming language & Python3.8 \\ CPU & Intel(R) Core(TM) i9-10940X \\ GPU & RTX 2080 \\ RAM & 251 \\ \hline \end{tabular}
\end{table}
Table 1: Configuration and training environment.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyper-Parameterization** & **Value** \\ \hline Learning rate & 0.01 \\ Image size & 640 x 640 \\ Momentum & 0.937 \\ Optimizer & SGD \\ Batch size & 16 \\ Epochs & 300 \\ Weight decay & 0.0005 \\ Workers & 4 \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparametric configuration.  
mAP: The mAP is the most commonly used aggregate performance metric for target detection tasks and represents the average AP across all categories. AP (Average Precision) is the average precision over a single category. The calculations are expressed as follows:  
\[AP=\int_{0}^{1}p(r)dr \tag{11}\]  
where \(p(r)\) is a functional relationship between precision and recall derived by calculating the area under the precision-recall curve. mAP then calculates the average AP across all categories. Common threshold settings are [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95. It is calculated as follows:  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{12}\]  
where \(N\) is the number of datasets per category and \(AP_{i}\) is the average precision per category.  
The F1 score is a reconciled average of precision and recall used to balance the two. It is calculated as follows:  
\[F1 Score=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{13}\]  
The F1 score provides a tradeoff between precision and recall, a metric that is especially important when there is a large difference between the two.
## 4 Case Study
### BLCC Creation for Hotel A
#### 4.2.2 Bottom Level: \(g\)-Chart  
Figure 6 shows the review-based \(g\)-charts for performance monitoring drawn for Hotel A. To exclude the influence of attribute importance and support the apples-to-apples comparison of the attributes, the \(g\)-charts consider only the reviews that mention each attribute and plot complaint intervals, that is, the number of non-negative reviews between two consecutive negative reviews. The complaint interval can be a measure of attribute performance.  
Figure 5: Review-based \(p\)-chart for Hotel A. The red squares and the numbers below them indicate the existence of special-cause variation and the corresponding rule number, respectively. The rule numbers 1 and 2 indicate an out-of-control subgroup and a long-term bias, respectively.  
In the proposed \(g\)-chart, the CL represents the median estimate of the complaint interval, while the UCL and LCL represent the expected range of complaint intervals under common-cause variations only. Plots above the UCL and those in a row above the CL are regarded as positive signals of decreased complaints or improved performance. For instance, Figure 6c reveals that there were special-cause variations in the performance of room condition, leading to an increased level for a certain period. Identifying the assignable root cause(s) responsible for this variation can offer valuable insights for quality enhancement, such as the factors that contribute to customer satisfaction with RC. In the  
Figure 6: Review-based \(g\)-chart for Hotel A. The red squares and the numbers below them indicate the existence of special-cause variation and the corresponding rule number, respectively. The rule numbers 1 and 2 indicate an out-of-control subgroup and a long-term bias, respectively.  
case of Figure 6c, reviews during that period frequently mentioned the antique atmosphere and cleanliness of the rooms. Notably, there were fewer complaints regarding cleanliness compared to other periods.  
A benefit of the proposed \(g\)-chart is that it enables the comparison of the performance of multiple attributes using CLs. For example, the CL of RC is 4.89, which implies that the median interval between two complaints is 4.89 reviews; in other words, complaints appear every 4.89 reviews on average. On the other hand, the CL of DF is 11.10, implying that complaints appear every 12.10 reviews on average. Thus, DF shows a better performance than RC with respect to the complaint level. Figure 6h compares the CL values of the seven attributes. It reveals that the performances of LS, DF, and SS were relatively high, whereas those of FC, BC, RC, and FA were relatively low.
# Active Sonar Target Classification Method Based on Fisher's Dictionary Learning
## 3 Active Sonar Target Classification Method Based on [PERSON]'s Dictionary Learning
### Fisher Discriminant Model and Solution Method
#### 3.1.2 FDDL Model-Solving  
The Solution of Cross-Iteration  
In order to solve the FDDL model, we use the method of cross-iteration, and update the sparse coefficient and dictionary alternately. First, training signal A is used as the initial learning dictionary; then the learning dictionary and sparse coefficient are alternately fixed, and the sparse coefficient and dictionary are updated until the specified number of iterations is reached or the conditions are met. The specific solution steps are as follows.  
Step 1: Once the dictionary D is fixed, solve for the sparse coefficient X.  
When dictionary D is fixed, \(J_{(D,X)}\) is a sparse coding problem. When calculating \(X_{i}\), \(X_{j}(i\
eq j)\) is fixed. The model is simplified as follows:  
\[\min_{X_{i}}\{r(A_{i},D,X_{i})+\lambda_{1}\|X_{i}\|_{1}+\lambda_{2}f_{i}(X_{i})\} \tag{7}\]  
\[f_{i}(X_{i})=\|X_{i}-M_{i}\|_{F}^{2}-\sum_{k=1}^{K}\|M_{k}-M\|_{F}^{2}+\eta\|X _{i}\|_{F}^{2} \tag{8}\]  
where \(M_{k}\) and \(M\) are the mean column vector matrices of all of the categories. \(r(A_{i},D,X_{i})+\lambda_{2}f_{i}(X_{i})\) is a strictly convex optimization, and is differentiable to \(X_{i}\). In order to solve Equation (7), we use the iterative projection method [22].  
Step 2: Once the sparse coefficient \(X\) is fixed, solve for dictionary \(D\).  
When the sparse coefficient \(X\) is fixed, the method of updating dictionary \(D\) is the same as above; when updating the sub-dictionary \(D_{i}\), \(D_{j}(i\
eq j)\) is fixed. The model can be simplified as follows:  
\[\min_{D_{i}}\{\left\|A_{i}-D_{i}X_{i}-\sum_{j=1\atop j\
eq i}^{k}D_{j}X_{j} \right\|_{F}^{2}+\left\|A-D_{i}X_{i}\right\|_{F}^{2}+\sum_{j=1\atop j\
eq i}^ {k}\left\|D_{j}X_{j}^{i}\right\|_{F}^{2}\}st.\ \|d_{i}\|_{2}=1,l=1,\ldots,p_{i} \tag{9}\]  
In order to solve Equation (9) as a quadratic planning problem, we solve each column of the dictionary items.