esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Approach
### Wedge Detection Algorithm
#### 2.3.2 Detection of the Wedge Position  
In order to detect the positions of the turbulence wedges, a cross-correlation of the thermogram with the wedge template is used:  
\[C[x]=\sum_{p}\sum_{q}\iota_{p,q}\cdot\tau_{p-y_{\text{NT}}(x),q-x}, \tag{4}\]  
where \(\tau\) denotes the template and \(\iota\) denotes the thermogram section with the same size of the template. The variable \(p\) is the row index of each image matrix, while \(q\) is the column index. In accordance with the natural occurrence of turbulence wedges on a rotor blade, the template's \(y\)-position equals the \(y\)-position \(y_{\text{NT}}(x)\) of the natural transition line, leading to a single cross-correlation result \(C[x]\) for each \(x\)-position. The process of the detection of the wedges' positions is exemplified in Figure 4 using a simulated thermogram with three wedges, shown in Figure 4a.  
Figure 3: Example of a turbulence wedge template with height \(h^{\prime}\), width \(w^{\prime}\) and angle \(\alpha^{\prime}\), which determines the position of the tip.  
A local minimum in the results of the cross-correlation indicates a good agreement between the thermogram and the template. To enable the detection of turbulence wedges with different, yet unknown sizes, the cross-correlation calculation is repeated for \(J\in\mathbb{N}\) templates with different wedge heights and widths. Each template size corresponds to a different curve in the cross-correlation results shown in Figure 4b. For each \(x\)-position, the number of local minima (marked as dots) in the cross-correlation results is counted and normalized by the total number of studied templates, which results in a relative frequency of minima for each \(x\)-position, see Figure 4c. If the relative frequency is larger than 35% at a certain \(x\)-position, which is an empirically based value, it is considered to be a likely position for a wedge. The so determined \(x\)-positions \(x_{i},i\in\{1,\ldots,N\}\), correspond to the middle of the wedges' bases and thus to the wanted \(x\)-position of each turbulence wedge.  
In addition, at each of the found position \(x_{i}\), the minimum of all cross-correlation values is determined. The associated template \(\tau_{0}\) with the height \(h_{0,i}\) and a width \(w_{0,i}\) has the best fit to the underlying wedge. The found wedge positions \(x_{i}\) as well as the best fitting template \(\tau_{0,i}\) for each \(x_{i}\) position with \((h_{0,i},w_{0,i})\) are needed for the precise determination of the wedges' areas, which is the next step of the algorithm.
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 4 Experiments
### Comparison Experiment  
To evaluate the methodological efficacy, we benchmarked our framework against eight contemporary multi-view unsupervised feature selection architectures: ASVW [14], CGMV-FS [12], CRV-DGL [34], NSGL [35], TLR [36], CvLP-DGL [25], CCSFS [11], and CDMvFS [15]. This comparator set captures key methodological dimensions in multi-view learning, including representation learning paradigms and discriminative feature selection frameworks. To define a baseline performance level in the raw feature space, we further apply k-means algorithm [37] using concatenated multi-view features without feature selection, providing a non-parametric baseline that quantifies clustering capability prior to dimensionality reduction.  
Tables 2 and 3 provide a systematic comparison of unsupervised multi-view feature selection methodologies across six datasets, where optimal and suboptimal performances are highlighted using boldface and underlined formatting, respectively. Empirical findings substantiate the sustained dominance of the CGMvFS architecture over contemporary methodologies under all evaluative scenarios. Specifically, CGMvFS achieves significant performance advantages in NMI, establishing new performance benchmarks across all six experimental datasets. Regarding ACC metrics, CGMvFS obtains optimal performance on four out of six datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **Handwritten** & **WebKB** & **MSRCV1** & **ORL** & **Outdoor Scene** & **Yale** \\ \hline
1 & FCCS (76) & view1 (1703) & HOG (576) & View 1 (4096) & GIST (512) & Intensity (4096) \\
2 & KAR (64) & view2 (230) & CMT (24) & View 2 (3304) & HOG (432) & LBP (3304) \\
3 & FAC (216) & view3 (230) & GIST (512) & View 3 (6750) & LBP (256) & GABOR (6075) \\
4 & PA (240) & – & CENTRIST & – & GABOR (48) & – \\
5 & MOR (6) & – & LBP (256) & – & – & – \\
6 & ZER (47) & – & – & – & – & – \\ \hline Instance & 2000 & 203 & 210 & 400 & 2688 & 165 \\ Class & 10 & 4 & 7 & 40 & 8 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of datasets.  
As depicted in Figures 2 and 3, the performance of CGMvFS's performance monotonically increases with the number of selected features until reaching a plateau beyond 150 features. This convergence pattern suggests diminishing marginal returns from additional features, empirically validating both the method's effectiveness and its resistance to feature space over-complexity.
# Hydrophobic Fluorinated Porous Organic Frameworks for Enhanced Adsorption of Nerve Agents
## 3 Results and Discussion
### Stability  
The thermal stability of C-1 and C-1-F was tested by TGA. As shown in Figure 7, both materials had excellent thermal stability and almost no weight loss was observed at temperatures below 400 \({}^{\circ}\)C. However, with increasing temperature (\(>\)400 \({}^{\circ}\)C), a gradual weight loss was observed, and the porous skeleton collapsed at about 650 \({}^{\circ}\)C.  
To further investigate the effect of humidity on the stability of C-1 and C-1-F, both materials were kept in a saturated standard salt solution environment with controlled relative humidity of 33%, 53%, 65%, 75%, 85%, and 97% for 24 h. As the relative humidity increased, the intensity of the characteristic IR peaks of the aldehyde and amino groups of C-1 at 1690 and 3300-3500 cm\({}^{-1}\), respectively, gradually increased, indicating the decomposition of the C-1 skeleton (Figure 8a). By contrast, the characteristic peaks of C-1-F remained almost unchanged (Figure 8b), suggesting that the stability of C-1-F was improved by fluorine doping.  
Figure 7: TGA curves of C-1 and C-1-F.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
## Materials and Methods
### Kendall's Tau Coefficient  
The Kendall's tau (\(\tau\)) is a rank correlation coefficient that measures the statistical association between ranked variables. The ranked variables should have ordered and numbered data samples. It is a non-parametric test as it does not rely on any assumptions with respect to the distributions of variables.  
For any two variables \(x\) (\(x_{1},x_{2},\ldots,\)\(x_{n}\)) and \(y\) (\(y_{1},y_{2},\ldots,\)\(y_{n}\)), the calculation of the coefficient is carried out by counting the number of concordant and discordant pairs, \(\left\{(x_{i},y_{i}),\)\(\left(x_{j},y_{j})\right\}\), out of all the possible pair combinations, \(\left(\begin{array}{c}n\\ 2\end{array}\right)\). A pair is concordant if (\(x_{i}<x_{j}\)) and (\(y_{i}<y_{j}\)) or (\(x_{i}>x_{j}\)) and  
Figure 3: The steps of the detrended fluctuation analysis (DFA) method: (**a**) a long-range correlated series (\(\alpha=0.6\)); (**b**) the integrated series and its best linear fits at \(n=100\) samples; (**c**) the detrended series at \(n=100\) samples; (**d**) the fluctuation function versus the box size.  
(\(y_{i}>y_{j}\)). On the other hand, a discordant pair has either (\(x_{i}<x_{j}\)) and (\(y_{i}>y_{j}\)) or (\(x_{i}>x_{j}\)) and (\(y_{i}<y_{j}\)). Any pairs with (\(x_{i}=x_{j}\)) or (\(y_{i}=y_{j}\)) are not considered in calculating the coefficient. The [PERSON]'s tau coefficient can be computed using Equation (3).  
\[\tau=\frac{\#\textit{of concordant pairs}\ -\ \#\textit{of discordant pairs}}{n(n-1)/2} \tag{3}\]  
The [PERSON]'s tau values vary between \(-1\) and \(+1\). The positive value (\(0<\tau<1\)) of the coefficient indicates a simultaneous increase in the rank of both variables. The negatively signed coefficients refer to a rank increase in one variable and rank decrease in the other one. A [PERSON]'s tau close to zero shows no association between the two variables. Three time series with their calculated [PERSON]'s tau coefficients are shown in Figure 4.
## 3 Method Implementation and Application
### Definition of a Landscape Subdivision Criterion and Identification of Symbols or Shapes
#### 3.2.6 Subdivision by Items of Interest  
Since none of the previous criterion fully responded to the requirements, we defined a \"combined\" criterion: we proceeded without considering a univocal criterion on the whole region, but selecting the shapes that best met the various requirements among the symbols identified by the different criteria for each location.  
Table 8 sums up the elements that best meet the previous criteria and the corresponding representative shapes. They are (Figure 8):  
* The statue of the [PERSON] for the province of Pescara Popoli.
* The emblem (coat of arms) of the province of L'Aquila, of medieval origin, is an eagle and coincides with the symbolic animal of the Velino--Sirente area and, by extension, of Marsica too.
* The sailing boat (or simply a sail) for the coastal area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & **Score** & **Judgement** \\ \hline Number of identified areas & 4 & Adequate \\ Shape suitability & Medium-Low & Only partially adequate \\ Shape/area consistency & 8 & Adequate \\ \hline \hline \end{tabular}
\end{table}
Table 7: Assessment of the criterion: subdivision by fauna.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Description**} & \multicolumn{2}{c}{**Judgements**} \\ \cline{2-5}
**Criterion** & **Area** & \# & **Symbol** & **Renowned** & **Shape/Area** \\  & & & & **Shape** & **Consistency** \\ \hline History & Pescara Popoli & A3 & Capestrano & 9 & 10 \\ Province & L’Aquila province & B1 & Province emblem & 9 & 9 \\ Geography & Coasts & C3 & Sail & 9 & 9 \\ Fauna & Velino—Sirente Regional park & E4 & Eagle & 9 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final criteria for the subdivision in areas (subdivision by items of interest).  
Figure 7: Subdivision by fauna.  
These were judged as symbols able to respond adequately to the judgement criteria (a representative form that is sufficiently famous and identified with the location) as well as suitable for use as the basis of new types of pylon (i.e., the shape is adequately streamlined).  
To sum up, the multiple criteria proposed revealed a series of symbolic elements representative of certain areas, but the compliance with the requirements proved only partial in all cases. The final criterion, based on the most generic discriminating element possible, summarizes and identifies the most representative and performant shapes in accordance with the previous criteria.
## 3 Influence Factors of the UAV-LDV System
### Testing Distance
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & \multicolumn{8}{c}{**First-Order Natural Frequency (Hz)**} \\ \cline{2-10}  & \multirow{2}{*}{**Numerical Simulation**} & \multicolumn{4}{c}{**Fixed Test**} & \multicolumn{4}{c}{**Mobile Test**} \\ \cline{3-10}  & & **1 m** & **Error** & **10 m** & **Error** & **1 m** & **Error** & **10 m** & **Error** \\ \hline Condition 1 & 101.00 & 101.43 & 0.43\% & 101.27 & 0.27\% & 104.34 & 3.30\% & 106.15 & 5.10\% \\ Condition 2 & 66.11 & 65.13 & \(-\)1.48\% & 64.47 & \(-\)2.48\% & 69.64 & 5.34\% & 70.48 & 6.61\% \\ Condition 3 & 75.37 & 72.04 & \(-\)4.42\% & 71.86 & \(-\)4.66\% & 74.07 & \(-\)1.72\% & 80.67 & 7.03\% \\ Condition 4 & 15.34 & 15.10 & \(-\)1.56\% & 14.09 & \(-\)8.15\% & 15.92 & 3.78\% & 13.36 & \(-\)12.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of first-order natural frequency results. Figure 8: Comparison diagrams of SSD. Note: The results of each test under the same condition are listed in the same row.
## 5 Denoising Experiments
### Simulation Results
It can be seen from Figures 6 and 7 that the algorithm proposed in this paper performs better in preserving the detailed features of ECG signals after noise reduction. The guidance signal constructed by this method can contain most of the morphological characteristics of the original ECG signals. However, the algorithm proposed in this paper can filter the electrode interference noises very well while retaining the original morphological characteristics of the P wave and T wave. However, the reconstructed ECG signals offset the original signals and many stair-case artifacts, which cannot retain the detailed features from the other filtering methods. The noise reduction differences among these algorithms are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Wavelet** & **Average** & **TV** & **BP-ADMM** \\ \hline mitdb/105 & 5.476 & 2.4294 & 4.9172 & 6.3297 \\ mitdb/106 & 5.5671 & 2.433 & 5.4115 & 5.8936 \\ mitdb/107 & 11.3446 & 3.2118 & 11.6603 & 11.6715 \\ mitdb/108 & 4.4446 & 2.2665 & 4.2473 & 4.8901 \\ mitdb/109 & 7.798 & 2.8677 & 7.6668 & 8.1662 \\ mitdb/111 & 3.0188 & 1.8348 & 2.7679 & 3.6756 \\ mitdb/112 & 11.7158 & 3.2256 & 11.432 & 12.0141 \\ mitdb/113 & 6.647 & 2.7342 & 6.4469 & 6.7013 \\ mitdb/114 & 2.9079 & 1.739 & 2.5118 & 3.5163 \\ mitdb/115 & 7.785 & 2.8554 & 7.5518 & 7.9067 \\ mitdb/116 & 13.2353 & 3.3241 & 13.0733 & 13.6521 \\ mitdb/117 & 11.2684 & 3.1911 & 10.6218 & 11.8165 \\ mitdb/118 & 12.1504 & 3.2713 & 11.8001 & 13.1671 \\ mitdb/119 & 12.4043 & 3.2694 & 11.8452 & 12.9172 \\ mitdb/200 & 6.3196 & 2.6159 & 6.0043 & 6.7027 \\ mitdb/201 & 3.6453 & 1.9371 & 3.2885 & 4.0273 \\ mitdb/202 & 2.9598 & 1.7462 & 2.4286 & 3.7509 \\ mitdb/203 & 6.0959 & 2.6427 & 5.8747 & 6.9253 \\ mitdb/205 & 5.8739 & 2.4731 & 5.322 & 6.0851 \\ mitdb/207 & 5.0551 & 2.405 & 4.8458 & 5.6361 \\ mitdb/208 & 7.4163 & 2.821 & 7.4943 & 8.1552 \\ mitdb/209 & 3.6206 & 1.9473 & 3.2982 & 4.7622 \\ mitdb/210 & 3.5107 & 2.0053 & 3.2245 & 4.1386 \\ mitdb/212 & 4.753 & 2.2869 & 4.5983 & 5.126 \\ mitdb/231 & 3.9913 & 2.0872 & 3.6602 & 4.5625 \\ mitdb/232 & 3.0207 & 1.7841 & 2.2933 & 3.6549 \\ mitdb/233 & 8.7804 & 2.9848 & 5.5975 & 9.2172 \\ mitdb/234 & 4.0129 & 2.1281 & 3.6201 & 4.5516 \\ Mean SNR & 6.6006 & 2.5185 & 6.3037 & 7.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SNR comparison results. Figure 5: ECG signal added noise with SNR = 10 dB. Figure 6: ECG signal added noise with SNR = 20 dB. Figure 7: Denoising and reconstruction details.
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 3 Results
### Establishment of Reliability Model  
The loading and unloading truss robot for CNC punch is a part of the automatic intelligent production line of sheet metal. The design of integrating X and U axis in a single beam is adopted. Its main components include beam, clamp, end tooling, hydraulic lifting platform, raw material car, electric cabinet, pneumatic system and suction cup, etc. Its structure diagram is shown in Figure 2.  
The working principle of the loading and unloading truss robot for CNC punch is as follows: the movement of the raw material car is controlled, the sheet material to be processed is sent to the set position, and then the operation of the X and Z axis motor is controlled by the electric control system. The loading mechanism moves in the direction of X axis and Z axis at the same time and moves to the raw material car. The sheet material is sucked up with the sucker by the pneumatic control system, and then the sheet material is sent to the CNC punch for processing, and the loading is completed. At the same time, the clamp is controlled, moving in the direction of U-axis by the electric control system, the processed sheet material is picked up and sent to the moving brush platform, and then the finished sheet material on the brush platform is blocked by the stop teeth on the hydraulic lifting platform to complete the blanking. Loading and unloading are carried out at the same time without interference. According to the component relationship of the loading and unloading truss robot for CNC punch, it is divided into mechanical system (beam, clamp, end tool, hydraulic lifting platform, raw material car, etc.), pneumatic system (suction cup) and electrical control system (electric cabinet).  
Then, the basic reliability block diagram of the loading and unloading truss robot of the CNC punch is established, as shown in Figure 3. The basic reliability block diagram of the loading and unloading truss robot for CNC punch includes the mechanical system block diagram (M), the pneumatic system block diagram (P) and the electrical control system (E), in which the letter behind each system indicates the code of the system. As long as one system fails, the loading and unloading truss robot for CNC punch will fail. Therefore, the reliability model of each system is a series model.  
Figure 3: The basic reliability block diagram of the loading and unloading truss robot for CNC punch.  
Figure 2: Schematic diagram of the structure of the loading and unloading truss robot for CNC punch.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 4 Experimental Analysis
### Results
However, not only do these simple algorithms enable the operation of the receiver but they also provide very positive results in terms of positioning error, as is shown in both plots of Figure 4. Note that in both cases, the deviation from the true coordinates is of 10 m at worst, which is a remarkably promising performance for devices with these characteristics, i.e., low-cost mass-market receivers. Lastly, Table 1 offers a summary covering the most remarkable aspects of the receiver performance, where the 3-Dimensional Root-Mean-Square (3D RMS) error has been used to combine the individual E, N, and U deviations and offer an overall result. It is clear from the information presented in Table 1 that the PI beamformer slightly outperforms the PREW beamformer, offering a higher satellite availability and, therefore, a smaller 3D RMS error, as one additional satellite is used with the PI algorithm to compute the position. Nevertheless, despite this minor difference between the two techniques, the results presented in this work confirm that the deployment of multiple antennas in mass-market GNSS devices with restricted capabilities is indeed feasible, and it can significantly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Sat. Availability** & **Avg. C/N0 [dB-Hz]** & **3D RMS [m]** \\ \hline Only GPS signals & 9/9 & 46.164 & 1.216 \\ \hline PI beamformer & 8/9 & 36.697 & 4.154 \\ \hline PREW beamformer & 7/9 & 39.308 & 5.487 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the relevant metrics obtained in each experiment. Figure 4: Error in the positioning solution after applying (**a**) PI beamformer; (**b**) PREW beamformer. improve the performance of the receiver in hostile scenarios, where undesired contributions are a great threat.
# The Effects of Loaded Plyometries and Short Sprints in U19 Male Soccer Players in Tunisia
###### Abstract  
We investigated adding 8 weeks of bi-weekly loaded glyometric and short sprints (LPaSS) training into training for under 19 (U19) soccer players. An experimental group (EG, n = 18, age: 17.5 \(\pm\) 0.58 years, body mass: 67.4 \(\pm\) 4.37 kg, height: 1.76 \(\pm\) 0.05 m, body fat: 11.4 \(\pm\) 1.55%), and a control group (CG, n = 16, age: 17.5 \(\pm\) 0.58 years, body mass: 68.7 \(\pm\) 3.65 kg, height: 1.78 \(\pm\) 0.03 m, body fat: 11.6 \(\pm\) 1.14%) participated. The pre- and postintervention measures were: the squat-jump (S); the countermovement-jump with arm swing (CMJA); the five jump test (STT); 10 m and 30 m sprint; the ability to change direction (sprint with 90\({}^{\circ}\) turns (S90\({}^{\circ}\)) and sprinting 9-3-6-3-9 m, involving running both backwards and forwards (SBF); repeated sprint ability (RSA), and balance (Y-balance test). The EG experienced superior jump (\(p<\) 0.001; d\({}_{\text{range}}\): 1.69-1.89), sprint (\(p<\) 0.001; d\({}_{\text{range}}\): 1.82-2.56), 590\({}^{\circ}\) (\(p<\) 0.001; d\({}_{\text{range}}\): 1.64-2.25), RSA (\(p<\) 0.001; d\({}_{\text{range}}\): 3.90-4.17), and balance (\(p<\) 0.001; d\({}_{\text{range}}\): 1.11-2.54) improvement. Comparatively, the pre- to postchanges in the CG ranged from d = 0.36 (dynamic balance) to d = 1.00 (10 m sprint). Therefore, bi-weekly LPaSS training improves athletic performance in young soccer players, particularly RSA.  
stretch-shortening cycle; sprint training; glyometric training; additional load; soccer training; periodization +
Footnote †: journal: Applied Sciences
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 3 Methods and Datasets
### Evaluation Metrics  
Different evaluation metrics were used to evaluate the performance of the machine learning models applied in this work. The metrics used in this work are the coefficient of determination (\(R^{2}\)) score, mean squared error (MSE), and root mean squared error (RMSE).  
\(R^{2}\) score is a statistical performance metric that can be used to determine the proportion of variance in an output variable that can be explained or predicted by an input variable in  
Figure 3: (**a**) architecture of the unidirectional LSTM-AE (**b**) architecture of the bidirectional LSTM-AE.  
a regression model. It can be used to measure how well a regression model predicts the output variable. The following equation formulates the \(R^{2}\) score [36].  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\bar{y }_{i})^{2}}, \tag{7}\]  
where \(\hat{y}_{i}\) represents the predicted value, \(y_{i}\) is the actual value, and \(\overline{y}_{i}\) is the mean of all the actual values. An \(R^{2}\) value ranges between 0 and 1; where 0 means that the model explains or predicts 0% of the relationship between the output and input variables, a value of 1 indicates that the model predicts 100% of the relationship, a value of 0.5 indicates that the model predicts 50% of the relationship, and so on.  
\(MSE\) is a commonly used metric to measure the error of a model. The objective of the training process in numerical predictions is to minimize the \(MSE\), as the smaller the \(MSE\), the lower the error; therefore, the better the model. It is calculated using the following equation [37].  
\[MSE=\frac{1}{n}*\sum(y_{i}-\hat{y}_{i}), \tag{8}\]  
where \(\hat{y}_{i}\) represents the predicted values while \(y_{i}\) are the actual values.  
\(RMSE\) is calculated using the following equation, where \(\hat{y}_{i}\) represents the predicted values and \(y_{i}\) are actual values. It is a good measure to estimate the standard deviation of an observed value from the actual value, thereby allowing insights into the efficacy of the model being evaluated.  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{y}_{i}-y_{i}\right)^{2}}{n}}. \tag{9}\]