esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Position Control  
The gap opening was detected via the inductive probes during closed-loop position control (Figure 10). The setpoint value of the position control was selected to be smaller than the value of the maximum gap bridgeability. The linear actuators pulled the sheet 2 from zero gap to the specified setpoint value of the gap at the start of the welding process. If the inductive probes detected deviations, it was compensated by the linear actuators. Both gap sizes were set independently of each other. The gap measured at the beginning of the sheet by inductive probe IP1 was assigned to linear actuator LA1 and inductive probe IP3 was assigned to linear actuator LA2 for the gap size at the end of the sheet.  
The gap size was put to 0.2 mm as setpoint value \(X_{set}\) which is equal to approximately 75% of the focal diameter and the gap had been bridgeable for the laser welding process without discontinuities. Figure 11a shows the results for the gap width based on inductive probe measurements at three positions. The gap was adjusted to the setpoint value directly at the process start, had reached 0.2 mm quickly and could be considered constant during the welding process. Deviations, e.g., at approximately 1.7 s, 7.6 s and 13.5 s were well compensated. The maximum gap size reached was 0.203 mm at the position of IP2. Contraction of the sheets occurred during solidification and cooling of the weld led to a decrease in gap dimension as noticeable for IP1 and IP2. The position controller started compensating the deviation by reducing forces for LC1 with increasing process times (see Figure 11b). LC2 showed the largest deviation, indicating that there were further variances between the two controlled positions at LC1 (associated to IP1) and LC2 (associated to IP3) that could no longer be fully compensated due to the already solidified weld.  
Figure 11: Closed-loop position control (\(v_{\mathrm{w}}\) = 1 m/min, \(P_{\mathrm{L}}\) = 0.4 kW): (**a**) Gap measurement via inductive probes; (**b**) Force measurement via load cells.  
Figure 10: Closed-loop position control.  
The forces during position control showed comparable values to the rigid clamping even if the increase in force is steeper for load cell LC1 at the process start and for load cell LC2 after approx. 13.5 s. Maximum tensile forces of 133 N and compressive forces of \(-105\) N acted during the welding process.  
The control of the gap was thus successfully demonstrated and allowed the setting of nearly constant gap sizes during welding while avoiding the occurrence of any seam discontinuities. The control loop had a sufficient stability and did not tend to overshoot.
## 5 Explanation of the Prediction Results
### Analysis of Risk Factors  
We calculated Chi-squared to determine significant distinguishing features between patients with gastric cancer and patients with atrophic gastritis. Among 13 features, categorical variables such as sex, alcohol habit, smoking status, smoking duration, frequency of smoking, family cancer history, income, and exercise were selected. Additionally, the number of endoscopic screenings and BMI were transformed into categorical values and added. For the number of endoscopic screening tests, if it was more than one, then the outcome was transformed to yes, otherwise it was transformed to no. For BMI, the values were transformed to underweight, normal weight, and overweight. As Table 4 indicates, sex, smoking status, and smoking duration were significantly distinct. Differently from our expectation, regular exercise and number of endoscopic screenings were not related to gastric cancer incidence.  
This interpretation has some limitations. First, although _H. pylori_ infection is a well-known risk factor of gastric cancer, we did not consider the patients' infection status. As the purpose of our study was to identify risk factors of the patients' lifestyle, we considered demographic and environmental factors. Second, our findings that sex, smoking status, and smoking duration are influential factors were based on the 93 actual gastric cancer patients predicted as gastric cancer patients. Therefore, the dataset is insufficient to generalize the findings. Despite these limitations, our study provides an improved understanding of the risk factors of progression of atrophic gastritis to gastric cancer.
## 2 Theoretical Models  
### Tining Jitter  
Timing jitter in a mode-locked laser is defined as the short-term jitter of the pulse train's envelope. From the perspective of the frequency domain, timing jitter is equivalent to the repetition rate's frequency noise. The timing jitter of an optical pulse train could be introduced by various noise sources, e.g., intra-cavity amplified spontaneous emission (\(ASE\)), cavity's non-zero dispersion, intensity fluctuation, and recover time of the saturable absorber. The very first theoretical models depicting mode-locked lasers' timing jitter is developed by [PERSON] and [PERSON] [40]. Through applying soliton perturbation theory to solve the perturbed master equation, the pulse train's timing jitter, governed by various noise sources are determined analytically. For soliton-pulse lasers, the timing jitter's power spectral density (PSD) from the direct coupling of intra-cavity's \(ASE\) could be obtained as follows:  
\[S_{\Delta t}^{ASE,QL}(f)=\frac{D_{T}}{\left(2\pi f\right)^{2}}\]  
\[D_{T}=\frac{\pi^{2}\tau^{2}}{6E_{p}}\Theta\frac{2g}{T_{rt}}hv_{0}\]  
where \(f\) is the Fourier frequency; \(D_{T}\) is the diffusion constants of \(ASE\)-induced group velocity for soliton pulses; \(\tau\) is pulsewidth; \(E_{p}\) is the pulse energy; \(\Theta\) is the excess spontaneous emission factor; \(g\) is the gain of the cavity; \(T_{rt}\) is the round-trip time of the optical pulse in the cavity; \(hv_{0}\) is the single pulse energy. For soliton-pulse lasers, the center frequency fluctuation can also introduce timing jitter via non-zero intra-cavity dispersion. This is called Gorden-Haus timing jitter. The PSD of Gorden-Haus timing jitter can be obtained as:  
\[S_{\Delta t}^{GH}(f)=\frac{4D^{2}f_{rep}^{2}D_{\omega}}{(2\pi f)^{2}\Big{[}{(2 \pi f)}^{2}+\tau_{\omega c}^{-2}\Big{]}}\]  
\[D_{\omega}=\frac{2}{3E_{p}\tau^{2}}\Theta\frac{2g}{T_{tr}}h\upsilon_{0}\]  
\[\frac{1}{\tau_{\omega c}}=\frac{4}{3}\frac{g}{T_{rt}\Delta f_{g}^{2}\tau^{2}}\]  
where \(D\) is half of the net cavity dispersion; \(f_{rep}\) is the repetition rate of the optical pulse train; \(D_{\omega}\) is the diffusion constants of center frequency changes; \(\tau_{\omega c}\) is the decay time for frequency perturbation; \(\Delta f_{g}\) is the gain bandwidth in the unit of frequency. It should be noted that the above expressions only hold for soliton-pulse lasers. For stretched-pulse lasers, more detailed perturbation formalism was developed by [PERSON] and [PERSON] in 199
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 2 Materials and Methods
### Satellite Image Processing
For shallow to medium depths (\(<\)10 m), a greater layer weight was placed on surface reflectance values, and for deeper depths (\(>\)10 m), a greater weight was placed on bottom reflectance values. The lower portions of water leaving radiance are contributed by the bottom reflectance at greater depths [19,22]. Upon completing the benthic composition classification, an accuracy assessment (confusion matrix) was conducted using the remaining field validation points.
## 3 Comparison of the Models
### Sensing and Scattering of EGWs  
Figures 13-16 show the response of the piezoelectric sensor to guided waves excited by the actuator for the considered models. The first two are obtained for an intact structure, while Figures 15 and 16 are calculated for a waveguide with a T-shaped stringer adhesively attached to its upper surface \(x_{3}=H\) (geometric parameters of this obstacle and adhesive are provided in Figure 3, while their mechanical properties are summarized in Table 4). As in the previous cases, the models are in qualitative agreement with each other at lower frequencies. When, however, \(\bar{\varphi}(f)\) is quantitatively compared, acceptable results for the waveguide \([90^{\circ},0^{\circ}]_{2}\)s are obtained in a rather narrow frequency range (below 20 kHz) while for the \([0^{\circ},90^{\circ}]_{2}\)s laminate it is wider (below 200 kHz) (zoomed parts in Figures 13 and 14). If the scattering by the stringer is additionally considered, the coincidence of the homogenized models with the multi-layered laminate model is mainly qualitative, but not quantitative. Here again, the LWHM-model predicts the dynamics of the laminate more accurately than the SLWH-model. The values and local maxima of the voltage \(\bar{\varphi}(f)\) measured via the sensor are shown in Figures 15 and 16 (up to 400 kHz for \([0^{\circ},90^{\circ}]_{2}\)s and \([90^{\circ},0^{\circ}]_{2}\)s waveguides).  
Figure 14: Voltage \(\phi(f)\) measured at the sensor on the surface of multi-layered waveguide \([90^{\circ},0^{\circ}]_{2}\)s (thick solid line), and waveguides with effective properties estimated according to the SLWH-model (thin solid line) and the LWHM-model (dash-dotted line).  
Figure 13: Voltage \(\bar{\varphi}(f)\) measured at the sensor on the surface of multi-layered waveguide \([0^{\circ},90^{\circ}]_{2}\)s (thick solid line), and waveguides with effective properties estimated according to the SLWH-model (thin solid line) and the LWHM-model (dash-dotted line).
## 3 Results
### The Results of the FEM Simulation
#### 3.1.1 The Results of the Normal Case  
Based on the FEM model as shown in Figure 5, the magnetic fields measured by tracking receivers, shown as Figure 6, are the radiated magnetic fields of the transmitting coil on the line ((0, 3, \(-\)4.5), (0, 3, 4.5)) and the line ((0, 3.5, \(-\)4.5), (0, 3.5, 4.5)) in meters. It can be found that \(y\)-axis components of the magnetic field \(B_{y}(z)\) follow the characteristics of origin symmetry and the \(z\)-axis components of the magnetic field \(B_{z}(z)\) follow the characteristics of axisymmetric, which are consistent with theoretical analysis.  
References [28; 29; 30] use orthogonal coils to obtain \(B_{y}(z)\) and \(B_{z}(z)\) at the same time. The zero magnetic field point of \(B_{y}(z)\) and the maximum magnetic field point of \(B_{z}(z)\) are used to locate the PIG. However, the numerical integration of \(B_{y}(z)\) yields \(integrationB_{y}\), which has similar characteristics as \(B_{z}(z)\). The maximum values of \(B_{z}(z)\) and \(integrationB_{y}\) correspond to the same \(z\)-axis coordinates. For the coordinate \(z\) tracking of PIG, \(B_{z}(z)\) can  
Figure 5: The 3-D FEM model of the dual tracking system. The gray cylinder represents the pipeline, the yellow one represents the transmitting coil, the black one represents the magnetic core and the dual blue points represent the dual tracking receivers.  
be completely replaced by \(integrationB_{y}\) in the MFSI method. The effective information obtained by the two orthogonal sensors is the same. A single-axis receiving coil can be used to achieve the same effect as the orthogonal coils. Therefore, the \(z\)-axis coil can be omitted. Furthermore, the MFSI tracking algorithm reduces the complexity of the traditional tracking coil system.  
According to (8) in the 4-D tracking algorithm, the distance between the PIG transmitter and the tracking receivers can be calculated as in Table 1. In the table, the tracking error of the DOF \(z\) is \(-0.9\) cm, which is much lower than the error of 14.3 cm in [30] and the error of 40 cm in [27]. At the same time, the tracking error of the DOF \(y\) is \(-1.88\) cm, which is also much lower than the error of 20 cm in [30]. In short, the tracking accuracy of the MFSI algorithm is ten times that of traditional ELF-AGMs in the normal case.
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 3 Results
### Effect of Bacteriophage Infection on Host Virulence  
Considering the prospective use of bacteriophages as antibacterial agents, the effect of bacteriophage vB_SauM-515A1 on the expression of host virulence factors was evaluated. Different virulence factor genes (\(n\) = 72) were found in the genome of the SA515 strain, including numerous genes of adherence, serine and cysteine proteases, staphylokinase, capsule, and type VII secretion system (Table S3). Among the found virulence factors, 17 toxin genes were annotated (hemolysin alpha, delta, gamma, enterotoxin A, exotoxins [_set21, set30-set34, set36-set40_]).  
Transcriptional data analysis revealed eleven virulence factor-related DEGs (Table 2). At the early stage of infection, three downregulated virulence factor genes were observed: staphylocoagulase (_caa_), triacylglycerglycerol lipase (_lip_), and one exotoxin (_set14_). At the late phage-infection stage, the upregulated virulence genes included three toxin genes (hemolysin gamma and exotoxin), Type VII secretion system genes (_essA_, _esxA_), and immune evasion factors (_scn_, _sbi_).
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 3 The Proposed Method Description
### Sparse Representation of Signal  
Sparse representation of signal is the core of compressed sensing. According to the characteristics of the aforementioned echo model, discrete cosine basis (DCT) is used to sparsely represent the echo. However, the fixed sparse base is not completely suitable for many specific signals, which will lead to poor effect of reconstructed signal. To address this problem, we propose a sparse base optimization algorithm based on transfer learning, which converts fixed sparse base into adaptive sparse base. The objective function of the algorithm is as follows:  
\[\min_{\psi,\ \ a}\|\psi x-a\|_{F}^{2}+\alpha\|a\|_{1}+\beta\|\psi-\psi_{0}\|_{F}^ {2}\,\ \ \text{s.t.}\ \ \psi^{T}\psi=I. \tag{8}\]  
where \(\|\ \|_{F}^{2}\) is \(l_{F}\) norm, \(I\) is the identity matrix, and \(\alpha\) and \(\beta\) are regularization parameters. The first term of the objective function is the loss term of data, the second term is the sparse constraint term, and the third term is the regularization term of transfer learning. We solve the problem (8) through the following two steps:  
Step 1. Update sparse representation coefficient \(a\): sparse base \(\psi^{(k-1)}\) is fixed and we minimize:  
\[\min_{a}\|\psi^{(k-1)}x-a\|_{F}^{2}+\alpha\|a\|_{1} \tag{9}\]  
The above optimization problem has the following solution:  
\[a^{(k)}=S_{\alpha/2}\Big{[}\psi^{(k-1)}x\Big{]} \tag{10}\]  
where \(S_{\alpha/2}(x)=\text{sign}(x)\cdot\max(|x|-\alpha/2\,\ 0)\) is the soft thresholding operator.  
Step 2. Update sparse base \(\psi\): with the sparse representation \(a^{(k)}\) fixed, we minimize the corresponding sparse base transformation subproblem:  
\[\min_{\psi}\|\psi x-a^{(k)}\|_{F}^{2}+\beta\|\psi-\psi_{0}\|_{F}^{2},\ \text{s.t.}\ \ \psi^{T}\psi=I. \tag{11}\]  
Equation (11) can be expressed in the form of matrix trace:  
\[\|\psi x\|_{F}^{2}+\|a^{(k)}\|_{F}^{2}-2\text{tr}\Big{[}(\psi x)^{T}a^{(k)} \Big{]}+\beta\Big{\{}\|\psi\|_{F}^{2}+\|\psi_{0}\|_{F}^{2}-2\text{tr}\Big{(} \psi^{T}\psi_{0}\Big{)}\Big{\}} \tag{12}\]  
Because \(\psi\) is orthogonal, Equation (11) can be equivalent to:  
\[\max_{\psi}\text{tr}\Big{[}x^{T}\psi^{T}a^{(k)}\Big{]}+\beta\text{tr}\Big{(} \psi^{T}\psi_{0}\Big{)} \tag{13}\]  
According to Theorem 4 in [21] and the property of matrix trace \(\text{tr}\Big{[}x^{T}\psi^{T}a^{(k)}\Big{]}=\text{tr}\Big{[}\psi^{T}a^{(k)}x ^{T}\Big{]}\), we perform the SVD for \(a^{(k)}x^{T}+\beta\psi_{0}\) to get \(U\sum V^{T}\). Accordingly, we can obtain the unique solution of the above problem:  
\[\psi^{(k)}=UV^{T} \tag{14}\]  
Then, we can solve the problem by iterating the above update steps alternately. Algorithm 1 shows the description of the sparse base optimization algorithm based on transfer learning.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 1: CWRU Bearing Dataset
#### 4.2.6 Visualization and Analysis with Small Sample Sizes  
To further analyze the feature extraction performance of the model with small samples sizes, this experiment utilized the t-distributed stochastic neighbor embedding (t-SNE) technique to visualize the FD results of ResNet-18, EfficientNetV2-S, MPNet, and the proposed model on dataset A. This approach also allowed for the visualization of the clustering and separation of the extracted fault features after dimensionality reduction.  
The visualization results are presented in Figure 11. The separation of fault features extracted by MPNet and EfficientNetV2-S for samples with fault labels 2, 4, 5, 8, and 9 was insufficiently clear, which could lead to misclassification of fault samples. In contrast, ResNet-18 demonstrated better clustering and separation of features than the previous two models, but the clustering centers for fault labels 4 and 5 were relatively close. Although the proposed model yielded a small number of misclassifications, the clustering and separation of fault features across different samples were satisfactory, highlighting its excellent feature extraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Accuracy/\%**} \\ \cline{2-5}  & **A/(Train10/Test70)** & **B/(Train20/Test70)** & **C/(Train30/Test70)** & **Average** \\ \hline EfficientNetV2-S & 91.74 \(\pm\) 1.14 & 97.74 \(\pm\) 0.43 & 98.54 \(\pm\) 0.25 & 96.01 \\ ResNet-18 & 98.00 \(\pm\) 0.42 & 98.74 \(\pm\) 0.16 & 98.91 \(\pm\) 0.15 & 98.55 \\ RepViT & 85.40 \(\pm\) 1.32 & 94.63 \(\pm\) 0.89 & 97.86 \(\pm\) 0.45 & 92.63 \\ SqueezeNet & 81.97 \(\pm\) 1.55 & 88.71 \(\pm\) 0.82 & 92.71 \(\pm\) 0.55 & 87.80 \\ MPNet & 92.00 \(\pm\) 0.92 & 97.34 \(\pm\) 0.48 & 98.57 \(\pm\) 0.18 & 95.97 \\ MobileNetV3 & 83.60 \(\pm\) 1.5 & 93.86 \(\pm\) 0.76 & 95.40 \(\pm\) 0.44 & 90.95 \\ HFDF-EffNetV2 (Ours) & 98.09 \(\pm\) 0.52 & 98.91 \(\pm\) 0.17 & 99.09 \(\pm\) 0.12 & 98.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental results for different models with different training-sample sizes in Case 1.
## 3 Results
### Meta Groups of Factors  
Another way of understanding the importance, especially the interaction of different F.G., is to plot the variables along multiple axes to judge their distributions. This kind of distribution may also allow a grouping. Figure 3 shows a multidimensional projection of the metals in relation to the amount of MIV per F.G. Instead of a two-dimensional coordinate system (x, y), this figure has a six-dimensional coordinate system. The six dimensions are the respective F.G. Land Cover, Geology, Dams, Climate, Runoff, and Position. This selection of F.G. was determined experimentally, and it resulted in the projection in which the silhouettes of the groups were smoothest. The background coloring is based on the Kmeans grouping into the four meta groups, Cluster 1 to Cluster 4 (C1-C4). The grouping indicated the effect of each F.G. on the classification of the respective metal. The affected metals of C1, Al, and As had relatively similar importance of Land Cover and Geology. Group C2, which affected Co, Mn, and V, was defined by the importance of the F.G. Geology and Runoff. The largest group was C3 affecting Cd, Co, Cu, Pb, Sn, and Zn. These metals were grouped by the effect of the F.G. Dams, Land Cover, and Geology. Group C4, which affected Cr and Hg, was mainly defined by Runoff and Climate. These results highlight that some metals have similar dependencies on F.G. and complexes of F.G., making it possible to group them based on their main factors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Al** & **As** & **Cd** & **Co** & **Cr** & **Cu** & **Hg** & **Mn** & **Pb** & **Sn** & **V** & **Zn** & **All** \\ \hline Geology & 4 & 2 & 1 & 7 & 1 & 1 & - & 5 & 1 & 4 & 5 & 2 & 33 \\ Dams & 1 & 2 & 5 & - & - & 6 & 1 & 1 & 5 & 5 & - & 5 & 31 \\ Land Cover & 4 & 6 & 4 & 1 & - & 3 & 3 & 1 & 3 & 2 & 2 & 2 & 31 \\ Runoff & - & - & - & 5 & 4 & 2 & 4 & 5 & - & 2 & 1 & 2 & 25 \\ Position & 2 & 2 & 2 & 1 & 2 & 2 & 1 & 2 & 2 & 2 & 3 & 1 & 22 \\ Soil & 3 & 2 & 2 & 1 & 3 & 2 & 2 & - & - & - & 2 & 1 & 22 \\ Climate & - & - & - & 1 & 5 & 1 & 4 & 1 & 3 & 1 & 3 & 1 & 20 \\ Terrain & 2 & 4 & 2 & 1 & - & - & 2 & 1 & 3 & 1 & 1 & 1 & 18 \\ Hydrology & 1 & 1 & 1 & 2 & 2 & 1 & 2 & 2 & - & 1 & 1 & 2 & 16 \\ Water Balance & 2 & 1 & 1 & 1 & 3 & - & 1 & 1 & 2 & 2 & 1 & 1 & 16 \\ Population & - & - & 2 & - & - & 2 & - & - & - & - & - & 4 \\ Channel & 1 & - & - & - & - & - & - & - & - & 1 & - & 2 \\ All & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The distribution of the 20 MIV of all 12 models into the 12 F.G.
## 2 Materials and Methods
### Statistical Analysis  
The analysis was performed using RStudio (version 2023.06.2). The Shapiro-Wilk test of normality was applied to assess the normal distribution of the dependent variables. The homogeneity of variances was verified using Levene's test. Independent samples t-tests were initially performed to compare continuous variables between the groups, including weekly training hours, years of practice, mean peak EMG values, and body compositionmetrics. To examine the trends in peak EMG activity over a series of 15 continuous roundhouse (_bandal chagui_) kicks, linear regression analyses were conducted for each muscle. This approach allowed for the estimation of the slope and intercept for the EMG data. A mixed-effects model was employed to account for both fixed and random effects. Fixed effects included experience level, number of kicks, and a body composition component derived through principal component analysis (PCA), which combined fat mass and muscle mass into a single continuous variable. Given the strong inverse correlation between these two metrics, PCA was employed to reduce dimensionality and avoid multicollinearity. The resulting component scores (representing the relative balance between fat and muscle mass) were used as predictors in the mixed-effects models to examine their influence on EMG activity across different muscles. Random effects were incorporated to control for variability between individual participants and enhance the model's robustness. The explained variance was calculated for the fixed effects alone and for the complete model, which combined both fixed and random effects. Marginal R\({}^{2}\) m and conditional R\({}^{2}\) c were estimated to quantify the proportion of variance explained by the fixed effects and by both fixed and random effects, respectively. Statistical significance was defined as \(p<0.05\).