esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Antioxidant Activity
_coronopifolia_ displayed the best chelating activity, reaching about 75% inhibition at the highest concentration tested. This was confirmed also by a comparison of the IC\({}_{50}\) values calculated for the extracts, which indicated that the chelating efficacy of _M. frucitulosa_ subsp. _coronopifolia_ (0.63 \(\pm\) 0.01 mg/mL) was approximately 2.5-fold higher than that of _M. frucitulosa_ subsp. _fruticulosa_ (1.49 \(\pm\) 0.01 mg/mL). The chelating activity of the extract of _M. frucitulosa_ subsp. _coronopifolia_ was close to that previously found for that of _M. incana_ subsp. _incana_ (IC\({}_{50}\) = 0.53 \(\pm\) 0.02 mg/mL), the most effective among those from the infraspecific taxa of _M. incana_[2,18]. The results of the antioxidant tests clearly indicate that the extracts of the two subspecies of _M. frucitulosa_ possess different antioxidant ability; in fact, the extract of _M. frucitulosa_ subsp. _fruticulosa_ exhibited higher radical scavenging activity than that of subsp. _coronopifolia_; conversely, the latter displayed better chelating properties than the former. Among plant secondary metabolites, polyphenols represent the most important group of antioxidant compounds. Flavonoids and phenolic acids, the largest classes of plant phenolics, have been shown to be effective antioxidants in several in vitro and in vivo investigations [21, 22, 23]. The higher radical scavenging properties of the extract of _M. frucitulosa_ subsp. _fruticulosa_ seem to be related to the greater content of flavonoids and phenolic acids detected by HPLC-PDA/ESI-MS analysis, both present in quantities about three times higher than those of subsp. _coronopifolia_. Regarding the chelating properties, which are greater for _M. frucitulosa_ subsp. _coronopifolia_, although the partial involvement of the phenolic constituents detected in the extracts cannot be ruled out, it is assumed that they may depend on other antioxidant phytochemicals.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 3 Simulation of Door Movement Obstruction
### Obstacle Detection Algorithm  
Changes in the current signal due to an applied load force were detected using the Daubechies wavelet with 10 vanishing moments (db10); this wavelet was applied to the current signal to decompose it into nine levels (Figure 5). Changes were detected using the level 9 detail coefficients (cD9). A change in the amplitude of cD9 at samples 60,000 and 80,000 can be observed in Figure 6. The figure also shows the DWT wavelet tree until level 9. Here, \(a\) represents the approximation coefficient and \(d\) represents the detail coefficient. The numeric value in the subscript denotes the level of decomposition.  
The current signal was decomposed into multiple levels by using the following mathematical equations given by [PERSON] et al. [26]. A discrete signal \(f[t]\) can be decomposed as  
\[f[t]=\sum_{k}A_{m0,n}\phi_{m0,n}[t]+\sum_{m=m0}^{m-1}\sum_{m}D_{m,n}\Psi_{m,n}[t] \tag{11}\]  
where \(\phi\) is the scaling function, \(\psi\) is the mother wavelet, \(A\) is the approximation coefficient, and \(D\) is the detail coefficient.  
Figure 4: The 105 N load force and motor current signal.  
For the discrete dyadic wavelet, in which positions and scales are based on powers of two, the scaling function is as follows:  
\[\phi_{m0,n}[t]=2^{m0/2}\phi\left(2^{m0}t-n\right) \tag{12}\]  
\(\phi_{m0,n}\) denotes the scaling function at a scale of \(2^{m0}\) shifted by \(n\).  
Figure 5: Wavelet tree and detailed coefficients at level 9.  
Figure 6: Norm at various decomposition levels.  
The wavelet function is also known as  
\[\Psi_{m,n}[t]=2^{m/2}\Psi(2^{m}t-n) \tag{13}\]  
where \(\Psi_{m,n}\) denotes the mother wavelet at a scale of \(2^{m}\) shifted by \(n\). In general, the inner product of the original signal and the scaling function yields the approximation coefficients \(A_{m0,n}\):  
\[A_{m0,n}=\int_{-\infty}^{\infty}f(t)\phi_{m0,n}(t)dt \tag{14}\]  
The discretized signal's approximation coefficients can be expressed as  
\[A_{(m+1),n}=\sum_{n=0}^{N}A_{m,n}\int\phi_{m,n}(t)\phi_{m+1,n}(t)dt=\sum A_{m,n }g[n] \tag{15}\]  
The approximation coefficients \(A_{m0,n}\) in the dyadic approach have a scale of \(2^{m0}\). The \(g[n]\) filter is a low-pass filter. Similarly, the detail coefficients \(D_{m,n}\) can be obtained in general by taking the signal's inner product and the complex conjugate of the wavelet function:  
\[D_{m,n}=\int_{-\infty}^{\infty}f(t)\Psi^{*}_{m,n}(t)dt \tag{16}\]  
The decomposed discretized signal's detail coefficients can be expressed as  
\[D_{(m+1),n}=\sum_{n=0}^{N}A_{m,n}\int\phi_{m.n}(t)\Psi_{m+1.n}(t)dt=\sum A_{m,n}h[n] \tag{17}\]  
\(D_{m,n}\) are the detail coefficients on a scale of \(2^{m0}\) in the dyadic approach. The \(h[n]\) filter is a high-pass filter.
## 3 Results and Discussion
### In Situ OES in Plasma-Catalytic Biogas Conversion  
To pinpoint crucial chemical entities present during the plasma-mediated catalytic co-conversion of carbon dioxide and methane, in situ optical emission spectroscopy (OES) was utilized, as shown in Figure 5. The plasma-driven biogas transformation, when conducted without the introduction of a catalyst, displayed the strongest emission intensity, characterized by numerous discrete spectral lines and two distinct spectral bands. These include the H\({}_{\alpha}\) line (656.3 nm, 3d\({}^{2}\)D \(\rightarrow\) 2p\({}^{2}\)P\({}^{0}\)), two atomic oxygen spectral lines (777.5 nm, 3s\({}^{5}\)S\({}^{0}\)\(\rightarrow\) 3p\({}^{5}\)P; 844.7 nm, 3s\({}^{3}\)S\({}^{0}\)\(\rightarrow\) 3p\({}^{3}\)P), a hydrogen molecular band (580-650 nm, d\({}^{3}\)I\({}_{\text{u}}\)\(\rightarrow\) a\({}^{3}\)Z\({}_{\text{g}}\)\({}^{\text{+}}\)), and a CO molecular band (450-580 nm, B\({}^{1}\)S\(\rightarrow\) A\({}^{1}\)I) [26; 27; 28; 29]. This observation suggests a significant production of H atoms and CO molecules within the plasma region. A noticeable reduction in emission intensity was observed upon the introduction of the catalyst, a phenomenon likely stemming from both the physical obstruction of light by the catalyst bed and the consumption of reactive plasma species by the catalyst's active sites [30].
# Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
## 2 Methodology and Validation
### Loading Elements under Different Heating Rates  
Two different cross-sections of columns were utilised: CHS (Circular Hollow Section) and hybrid (a section with four UHSS CHS welded to four plates of SS, stainless steel). These cross-sections are depicted in Figure 1. Additionally, five thermocouples were used for both cases, as specified by these experimental investigations [11; 12], see Figure 2.  
To achieve a more realistic simulation of the boundary conditions of the columns, upper and bottom end plates with a thickness of 50 mm were included in the model. In both cases, the upper plate was fixed, and the force Fz was applied to the moving bottom  
Figure 1: Geometry and thermocouples for: **(a)** CHS column; **(b)** Hybrid column.  
Figure 2: Experimental set up: **(a)** CHS column [11]; **(b)** Hybrid column [12].  
plate. The boundary conditions are depicted in Figure 3, following the main experimental setup used by [PERSON] et al. [11; 12].  
The verification of the fire resistance by testing establishes a failure criterion for the elements under compression. According to EN 1363-1 [15], columns will fail if the axial displacement (C) exceeds Equation (1) or when the rate of axial displacement (\(dC/dt\)) surpasses Equation (2). Here, \(H\) represents the length of the column.  
\[C=H/100 \tag{1}\]  
\[dC/dt=3H/1000 \tag{2}\]  
The fire rating used in Europe for columns is well established [16]. These types of elements may be rated according to R 15, R 20, R 30, \(\ldots\), and R 360. The use of higher heating rates (hydrocarbon fires) may not be sufficient to reach the lowest rating, unless the column is protected by any insulation material.  
According to both experimental campaigns, two different heating rates R5 (5 \({}^{\circ}\)C/min) and R20 (20 \({}^{\circ}\)C/min) were considered, with the temperature obtained from the five thermocouples placed on the column surface, see Figure 2. Furthermore, for this work, the ISO 834 [17] standard was included for the parametric analysis. Therefore, for validating the model with these experimental tests, simulations were conducted using GMNIA solution models, without the need for thermal analysis for the case of R5 and R20 heating rates, but with the required nonlinear thermal analysis for the case of the ISO 834 heating rate (no experimental results).
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 4 System Architecture  
### System Overview  
We divided the system proposed in this paper into four main parts: data collection, data processing, feature extraction, and active construction. The system flow is shown in Figure 4. In the data collection part, we selected multiple wall materials (concrete wall, plaster wall, wooden door, glass wall) and used a TP-LINK router and a commercial laptop device equipped with an Intel 5300 NIC to communicate in the experimental scenario with different wall materials: the router as the transmitter and the laptop as the receiver. The receiving device records and stores the raw CSI action signals. The data preprocessing part requires the raw CSI data to be processed and converted into Doppler shifts. First, outlier removal is performed on the raw CSI action data using the Hampel filter, and denoising is performed using a discrete wavelet transform. Then, the correlation extraction of CSI is performed using the OR-PCA algorithm to remove complex noise from other obstacles and walls. Finally, the subcarriers with significant waveforms are converted into Doppler shift maps. The feature extraction part first uses the K-Means algorithm to segment the central part of the action in the formed Doppler shift image and then extracts the HOG feature of the segmented image as the feature vector of the action, which is convenient for use in the human body detection and recognition stage. In the human detection and recognition stage, the G-SVM classifier optimized by the grid search algorithm is used to classify and identify the images after feature extraction, and finally output the human detection results.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 3 Kubernetes Architecture
### Control Plane Elements  
The control plane functions as the central nervous system of a Kubernetes cluster. It regulates the system's functionality by preserving the desired state, reacting to user inputs, and orchestrating resource allocation among nodes.  
* Kubernetes API Server: functions as the front-end interface for the cluster. All operations, whether initiated by users or automated, interface with the cluster through this RESTful API.
* etcd: a distributed key-value repository that maintains the complete cluster state, encompassing configuration, metadata, and status data. The consistency and availability are essential for the reliability of the cluster.
* Controller Manager: manages multiple controllers that oversee the status of cluster resources and align them with the intended state. The ReplicationController guarantees that the designated number of pod replicas is operational.
* Scheduler: tasked with allocating pods to nodes. It determines actions based on resource necessities, affinity regulations, and policy limitations, guaranteeing optimal workload allocation.  
These components uphold the cluster's intended state and continuously monitor for discrepancies to trigger corrective measures.
# Welding Image Data Augmentation Method Based on LRGAN Model
## 4 Experimental Results and Discussion
### The Optimal Number of Samples for LRGAN in Few-Shot Learning  
Based on the final selection of 1468 original welding images with lower similarity, 400, 300, and 200 original samples were respectively chosen for the training of LRGAN, with the same test set used for validation. The experimental results comparing different sample sizes are shown in Table 5.  
From Table 5, it can be concluded that as the number of original samples input into LRGAN decreases, each evaluation metric either increases or decreases to some extent. From 400 original samples to 200 original samples, PSNR and SSIM increased by 32 and 37 percentage points, respectively, while FID decreased by 42 percentage points. This indicates that the quantity of original input data directly affects the quality of the generated LRGAN samples and determines the similarity between the generated samples and the original samples. When comparing the 300-sample case with the 200-sample case, the  
Figure 17: Different models’ effectiveness 3D area visualization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Sample Quantity** & **PSNR** & **SSIM** & **FID** \\ \hline
400 & 63.30 & 59.98 & 72.20 \\
300 & 82.22 & 88.13 & 46.32 \\
200 & 84.03 & 82.33 & 41.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal sample size.  
values of PSNR, SSIM, and FID show minimal fluctuation. However, the SSIM value for the 300-sample case is closer to 1 (100%). Therefore, based on this generative model (LRGAN), the optimal small sample size is 300.
## 3 Results and Discussion
### BPNN-QPSO Network Model for Optimizing Laser Cladding Process Parameters
#### 3.2.2 Analysis of the Predictive Performance of BPNN-QPSO Model  
BPNN, BPNN-PSO, and BPNN-QPSO models predicted micro-hardness for the 5 coating groups, as listed in Table 3. As shown in Figure 11, it can be seen that the maximum errors of BPNN, BPNN-PSO, and BPNN-QPSO predictions were 14.47%, 7.24%, and 9.12%, respectively. The correlation coefficients calculated by Equation (13) are shown in Figure 12. The correlation coefficients of BPNN, BPNN-PSO, and BPNN-QPSO were 0.8179, 0.8363, and 0.9575, respectively. The results showed that the established BPNN-QPSO network model had better prediction results and the prediction results had a better correlation, which could be used to predict the micro-hardness of laser cladding Ti(C, N) ceramic coatings.  
Figure 11: Comparison diagram predicted and experimental micro-hardness of coatings: **(a,b)** BPNN; **(c,d)** BPNN-PSO; **(e,f)** BPNN-QPSO.  
Figure 10: Scatter plot of training values and experimental values of micro-hardness: **(a)** BPNN; **(b)** BPNN-PSO; **(c)** BPNN-QPSO.
# Structural Behaviour of Aluminium-Timber Composite Beams with Partial Shear Connections
###### Abstract  
In this paper, the short-term behaviour of innovative aluminium-timber composite beams was investigated. Laminated veneer lumber panels were attached to aluminium beams with screws. Recently conducted theoretical, experimental, and numerical investigations have focused on aluminium-timber composite beams with almost full shear connections. However, no experiments on aluminium-timber composite beams with partial shear connections have yet been conducted. For this reason, composite action in composite beams with different screw spacing was studied in this paper. Four-point bending tests were performed on aluminium-timber composite beams with different screw spacing to study their structural behaviour (ultimate load, mode of failure, load versus deflection response, load versus slip response, and short-term stiffness). The method used for steel-concrete composite beams with partial shear connection was adopted to estimate the load bearing capacity of the investigated aluminium-timber composite beams. The resistance to sagging bending of the aluminium-timber composite beams with partial shear connections from the theoretical analyses differed by 6-16% from the resistance in the laboratory tests. In addition, four 2D numerical models of the composite beams were developed. One model reflected the behaviour of the composite beam with full shear connection. The remaining models represented the composite beams with partial shear connections and were verified against the laboratory test results. Laminated veneer lumber was modelled as an orthotropic material and its failure was captured using the Hashin damage model. The resistance to sagging bending of the aluminium-timber composite beams with partial shear connections from the numerical analyses were only 3-6% lower than the one from the experiments.  
aluminium-timber composite beams; partial shear connection; aluminium; engineering wood products; screws; laminated veneer lumber (LVL); flexible connection; bending test; Hashin damage model; finite element method (FEM)
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 3 Simulation Results
### Simulation Results of Redundant Manipulator  
The UR5 manipulator was used to experimentally verify and examine the algorithm that is proposed in this study. The experimental findings demonstrate the effectiveness of the algorithm suggested in this paper in resolving the manipulator's route-tracking issue. In addition, the verification was conducted on a redundant manipulator to further confirm the efficacy and generalizability of the technique. The specific parameter settings for the experiment are detailed in Section 2.3.3.  
Since this was a verification experiment, it was only used to explore the path-tracking results in the speed-control mode. The path-tracking results of the redundant manipulator are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Energy Consumption**} & \multicolumn{1}{c}{**0.5 kg**} & \multicolumn{1}{c}{**1 kg**} & \multicolumn{1}{c}{**2 kg**} & \multicolumn{1}{c}{**3 kg**} & \multicolumn{1}{c}{**5 kg**} \\ \hline Position Control & w/o terminal reward & 4.44438 & 4.71427 & 5.27507 & 5.79426 & 6.92146 \\  & terminal reward & 5.01889 & 5.34258 & 5.95310 & 6.55227 & 7.76305 \\ Velocity Control & w/o terminal reward & 4.97465 & 5.38062 & 6.23886 & 6.95099 & 8.33596 \\  & terminal reward & 6.03735 & 6.37981 & 7.05696 & 7.75185 & 9.15828 \\ Traditional & Jacobian matrix & 8.95234 & 9.81593 & 10.8907 & 10.9133 & 13.3241 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of energy consumption.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Velocity Control**} & \multicolumn{2}{c}{**w/o Terminal Reward**} & \multicolumn{2}{c}{**Terminal Reward**} & \multirow{2}{*}{
\begin{tabular}{c} **Jacobian** \\ **Matrix** \\ \end{tabular} } \\ \cline{2-2} \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Interval**} & & \multicolumn{1}{c}{**Interval**} & & \\ \cline{2-2} \multicolumn{2}{c}{} & **1** & **5** & **10** & **1** & **5** & **10** \\ \hline Smoothness & 0.5751 & 0.3351 & 0.5925 & 0.0816 & 0.5561 & 0.4442 & 0.7159 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of track smoothness.  
Figure 9: Verification results of redundant-manipulator path tracking.  
This study also takes into account the sampling randomness of the deep-reinforcement-learning algorithm. Numerous trials were conducted using a variety of random seed settings in order to show the robustness of the methodology proposed. Figure 10 displays the experimental results' training-process curve. The X-axis represents the number of training sessions and the Y-axis represents the average return set in the reinforcement learning. It can be seen that the training process can still converge even if there are fluctuations.  
The training results under various random seed settings demonstrate that the generalization and stability of the method in this work are assured, and the experimental results demonstrate that it still had an excellent tracking effect on the redundant manipulator.
## 2 Materials and Methods
### Statistical Analysis  
Categorical variables are presented as frequencies. Normally distributed continuous variables are presented as mean \(\pm\) standard error (SE), whereas non-normally distributed continuous variables are presented as median (25 th, 75 th percentiles). Kolmogorov-Smirnov and Shapiro-Wilk tests confirmed data normality. For the comparison of AG changes before and after spirulina supplementation, a two-way time [PreSup, PreEx] \(\times\) intervention [PL, SPI]) repeated-measures analysis of variance was applied. The same analysis was used for the comparison of the AGs and TBARS kinetics for each alkane before and after exercise [time (PreEx, PostEx, 24 h, 48 h, 72 h)] \(\times\) intervention [PL, SPI]). Sphericity was checked by Mauchly's test, and Greenhouse-Geisser correction on the degrees of  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Compound** & **Retention Time** & **Relative** & **Chromatogram** \\  & & **(min)** & **Retention Time** & **Chromatogram** \\ \hline
1 & 2-Methylbutane & 6.028 & 0.218 & TIC 1  \\
2 & Pentane & 6.774 & 0.245 & TIC 1  \\
3 & 2-Methylpentane & 9.603 & 0.347 & \(m/z\) 71 \\
4 & 3-Methylpentane & 10.331 & 0.374 & TIC 1  \\
5 & Hexane & 11.119 & 0.402 & TIC 1  \\
6 & 2-Methylhexane & 14.928 & 0.540 & \(m/z\) 85 \\
7 & 3-Methylhexane & 15.498 & 0.561 & \(m/z\) 71 \\
8 & Heptane & 16.915 & 0.612 & TIC 1  \\
9 & Octane & 22.845 & 0.827 & \(m/z\) 85 \\
10 & 4-Methyloctane & 26.491 & 0.959 & \(m/z\) 85 \\
11 & Nonane & 28.400 & 1.028 & \(m/z\) 85 \\
12 & Decane & 33.499 & 1.212 & \(m/z\) 57 \\
13 & Undecane & 38.187 & 1.382 & \(m/z\) 57 \\
14 & Dodecane & 42.540 & 1.540 & \(m/z\) 57 \\
15 & Tetadecane & 50.962 & 1.844 & \(m/z\) 57 \\
16 & Chlorobenzene D5 (LS) & 27.631 & 1.000 & \(m/z\) 117 \\ \hline \end{tabular}
\end{table}
Table 2: Target straight-chain and mono-methylated alkanes.  
freedom was performed if necessary. In the cases where an interaction between time and condition was found, simple effects were investigated, and Bonferroni correction was used. GraphPad Prism 8.4.3 software was used for the calculation of the incremental area under the curve (iAUC) of the AGs and TBARSs. Pearson partial correlation coefficients were calculated between normally distributed variables. Statistical significance was tested at the \(p<0.05\) probability level for type I error \(p\)-values. Statistical analyses were performed with the use of Stata Statistical Software, Version 15 (StataCorp LP: College Station, TX, USA).
# Defect Prediction for Capacitive Equipment in Power System
## 5 Experiment Results and Discussion  
### Results of Defect Occurrence Prediction  
The defect occurrence prediction model is employed to predict whether a device will exhibit defects within a certain time threshold, which is a binary classification model with predicted outcomes of defect and normal. To assess the prediction performance of the model, four evaluation metrics are used. In our work, the dataset was partitioned into training and testing sets with a ratio of 7:3. Based on WOE encoding and one-hot encoding, we utilized the RF algorithm to construct a prediction model. Additionally, MLP, SVM, XGBoost, and linear classification algorithms were used for experimental comparison. The defect occurrence prediction results of the five models on the testing set, based on one-hot encoding, are shown in Table 5. Meanwhile, the prediction results of the models utilizing WOE encoding are presented in Table 6.  
The experimental results reveal that the performance of each algorithm is improved with WOE encoding. The accuracy of SVM, XGBoost, and linear classification improved by over 0.07, while MLP and RF improved by 0.02 and 0.04, respectively. These results underscore the superior performance of RF based on WOE, achieving an accuracy of 0.96. Consequently, the WOE_RF algorithm emerges as the optimal model for predicting the occurrence of defects in capacitive equipment.