esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### General Characteristics of Subjects According to Anxiety Due to COVID-19  
Table 2 shows the differences (chi-square test results) by the general characteristics of adults who experienced anxiety due to COVID-19 and adults who did not experience it. Among 210,606 adults in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{2}{c}{**Experienced Anxiety Due to COVID-19**} & \multicolumn{1}{c}{\(p\)*****} \\ \cline{2-4}  & **No (n = 170,838)** & **Yes (n = 39,768)** & \\ \hline Gender & & & \(<\)0.001 \\ Male & 80,923 (84.9) & 14,364 (15.1) & \\ Female & 89,915 (78.0) & 25,404 (22.0) & \\ Basic living security recipient status & & & \(<\)0.001 \\ Current recipient & 6211 (75.3) & 2034 (24.7) & \\ Former recipient & 1117 (77.4) & 327 (22.6) & \\ Non-recipient & 163,477 (81.4) & 37,399 (18.6) & \\ Subjective health status & & & \(<\)0.001 \\ Very good & 17,044 (82.5) & 3622 (17.5) & \\ Good & 66,882 (84.0) & 12,748 (16.0) & \\ Average & 66,441 (81.6) & 14,995 (18.4) & \\ Bad & 16,983 (72.4) & 6469(27.6) & \\ Very bad & 3483 (64.3) & 1933 (35.7) & \\ Lifetime smoker & & & \\ Non-smoker & 109,581 (79.4) & 28,430 (20.6) & \\ Smoker & 58,152 (84.3) & 10,846 (15.7) & \\ Lifetime drinker & & & \(<\)0.001 \\ Yes & 132,359 (83.2) & 26,734 (16.8) & \\ No & 38,468 (74.7) & 13,033(25.3) & \\ Mean number of days having a breakfast per week & & & \(<\)0.001 \\
5–7 times per week & 112,065 (78.4) & 30,885 (21.6) & \\
3–4 times per week & 14,007 (85.3) & 2416 (14.7) & \\
1–2 times per week & 11,076 (87.5) & 1576 (12.5) & \\ Rarely & 33,688 (87.3) & 4891 (12.7) & \\ Weight control experience & & & \(<\)0.001 \\ Try to lose weight & 70,903 (82.6) & 14,970 (17.4) & \\ Try to maintain weight & 27,313 (82.1) & 5959(17.9) & \\ Try to gain weight & 8236 (80.6) & 1984 (19.4) & \\ Never tried & 64,369 (79.3) & 16,850 (20.7) & \\ Subjective stress level & & & \(<\)0.001 \\ Very much & 4728 (74.5) & 1621 (25.5) & \\ Much & 32,432 (80.1) & 8055 (19.9) & \\ A little & 90,998 (82.6) & 19,108 (17.4) & \\ Rarely & 42,641 (79.5) & 10,970(20.5) & \\ Depression experience & & & \(<\)0.001 \\ Yes & 9145 (77.1) & 2712 (22.9) & \\ No & 161,658 (81.4) & 37,049 (18.6) & \\ Annual influenza vaccination & & & \(<\)0.001 \\ Yes & 95,310 (77.0) & 28,524 (23.0) & \\ No & 75,192 (87.0) & 11,200 (13.0) & \\ Whether knowing own blood pressure level & & & \(<\)0.001 \\ Yes & 109,850 (80.8) & 26,093(19.2) & \\ No & 60,520 (81.7) & 13,570 (18.3) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The general characteristics of adults who experienced anxiety due to COVID-19: univariate analysis.
## 5 Discussions
### Results of This Research  
We chose to use the traditional forms of KNN, LR, and RF because they are classic algorithms in ML. The bibliographic study showed that these algorithms have many improved versions, but they can also be complex or have potential disadvantages. Focusing on these algorithms, a clear comparison served as a basis for understanding their applicability.  
The data analyzed in the experimental evaluation of this study were highly imbalanced, with 93% of the attributes belonging to the majority class and only 7% belonging to the minority class (positive biopsy cases). The CI with a 95% confidence level ranges between 9.23 and 16.22, while the the IB is 12.73. ML algorithms applied to the imbalanced data had difficulties detecting the minority class. One of the effects of imbalanced learning is that oversampling methods are more stable than other sampling methods (undersampling and hybrid sampling), while undersampling has is the least stable [24].  
Evaluation of the imbalanced learning techniques applied to the dataset revealed that the use of oversampling techniques increased the performance of the models, proving to be inspired choices. KNN2 and KNN3 with undersampling initially predicted 7% of the minority class; after the application of sampling techniques, 86 % of the majority class was predicted. LR improved from 0% to 93% with the SMOTE technique. Even for RF, which worked well even before applying the techniques, some metrics peaked. Decreasing the dimensionality of the dataset did not produce major changes, but some obtained metric values increased.  
Based on the research that we performed, it can be concluded that the relationship between the minority and majority class is important in capturing each region in an imbalanced dataset while taking into consideration the sensitivity of ML in general and Neural Networks (NNs) in particular, which are more effective at learning the minority class with a focus near the borderline.
## 1 Introduction
This experimental study presents the development of a global slamming force model to predict wave-induced loads on offshore wind jacket structures, utilizing statistical analyses of experimental data from the WaveSlam project [33]. A recent study incorporated site-specific meteorological and oceanographic parameters into the analysis process, including wave data simulated via WAVEWATCH III (version 4.18), with uncertain parameters accounted for in the input data. Long-term load distribution parameters were localized to determine the platform's Probability of Failure (PoF) due to extreme wave loading. Additionally, the structural damage rate was evaluated based on the most recent underwater inspection conducted in the oilfield under study [34]. Despite the extensive body of research, a notable gap remains: no studies have explicitly applied six distinct analytical methods--including three linear analyses, two nonlinear analyses, and spectral fatigue analysis--to evaluate the structural integrity of jacket-type offshore platforms based on in situ data. In Mexico, no research has yet examined structural behavior under different loading conditions, such as hurricanes, earthquakes, and fatigue, using a drilling platform installed at a 50 m water depth. Therefore, the primary contribution of this study is to propose a methodology for conducting six on-site analyses (static operation and storm analysis, dynamic storm analysis, strength-level seismic analysis, ductility-level seismic analysis, ultimate wave resistance analysis, and spectral fatigue analysis) to determine the structural integrity of jacket-type offshore drilling platforms in Mexican waters.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 3 Methods
### Evaluation Criteria  
In this study, three parameters were selected as evaluation metrics to assess the model's performance: Mean Absolute Error (_MAE_), Mean Square Error (_MSE_), and Root Mean Square Error (_RMSE_). The smaller these values, the less the entire trajectory deviates from the actual situation. The specific calculation formulas for the _MAE_ and _RMSE_ evaluation metrics are shown in Equations (14) and (15), respectively, while _MSE_ is defined in Equation (9).  
Figure 7: Illustration of the Sliding Window.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y}_{i}\right| \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{15}\]  
In the above equations, \(y_{i}\) represents the true value of the sample, \(\hat{y}_{i}\) denotes the predicted value of the sample, and \(N\) represents the number of samples in the sequence.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### Overview of WBAN
#### 2.1.1 Architecture  
In [40], communication devices in a hospital are classified into three categories, defining three communication models. In communication tier 1, each sensor node of the WBAN collects data and sends them to the WBAN Coordinator. In communication tier 2, the WBAN forwards data for a next hop, which can be a WBAN, a medical display coordinator, a nursing station coordinator, or a cellular device. Finally, communication tier 2 forwards to tier 3 communication devices which can be a 3G tower, a nurse device, an application server, or any cloud servers through the Internet. Othertaxonomy used for WBAN architecture talks about intra-WBAN, extra-WBAN and beyond-WBAN communications to designate almost the same communication models.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 4 Link-Building Planning  
The link-building planning algorithm is the core of the onboard centralized ISL-building planning scheme. In this section, within the scheme of the onboard centralized ISL-building planning, and on the basis of the visibility calculation, the link-building constraints, modeling, and onboard inter-plane link-building algorithm are discussed.
## 4 Simulation Model and Results  
Simulation analysis was carried out to verify the working of the proposed system. In this work, the theoretical analysis of the proposed HMLI system for output current tracking and capacitor voltage balancing and leakage current minimization is discussed in detail. A computer program generated on the basis of the algorithm (shown in Figure 4) is used in the controller design. Simulation was carried out on a MATLAB R2017a with a 64-bit Intel(R) Core \({}^{\mathrm{TM}}\) i7-4770 CPU operating at 3.40 GHz. Simulations were done on the basis of the presented theoretical analysis and the results of the simulations were subsequently presented.
## 3 Methodology and Theoretical Background
### Fault Assessment in Selected LSHT Machines
#### 3.2.1 Unbalance, Misalignment, and Eccentricity  
Unbalances and misalignments are typically treated as eccentricity in FE simulations, where the eccentricity is quantified based on the variations in the airgap length.  
The airgap length (\(\delta\)) in the absence of eccentricity is expressed as follows:  
\[\delta(\theta_{\mathrm{s}})=\delta_{0} \tag{1}\]  
where \(\theta_{\mathrm{s}}\) is the coordinate angle (mechanical degrees) referenced in the stator frame, and \(\delta_{0}\) is the original faultless airgap length.  
When static eccentricity is considered, the airgap length can be expressed as a function of \(\theta_{\mathrm{s}}\), and is given by the following:  
\[\delta(\theta_{\mathrm{s}})=\delta_{0}[1+\Delta_{\mathrm{se}}\cos(\theta_{ \mathrm{s}}+\theta_{\mathrm{s0}})] \tag{2}\]  
where \(\Delta_{\mathrm{se}}\) represents the static eccentricity severity, ranging from 0 to 1. It is defined as the ratio of the eccentricity length to the original airgap length. The angle \(\theta_{\mathrm{s0}}\) is an auxiliary coordinate which allows for adjusting the position of maximum airgap length.  
In turn, when dynamic eccentricity is considered, the airgap length can be expressed as a function of both \(\theta_{\mathrm{s}}\) and the rotor angular position \(\theta_{\mathrm{r}}\), as described by the following:  
\[\delta(\theta_{\mathrm{s}},\theta_{\mathrm{r}})=\delta_{0}[1+\Delta_{\mathrm{ de}}\cos(\theta_{\mathrm{s}}+\theta_{\mathrm{r}}+\theta_{\mathrm{s0}})] \tag{3}\]  
where \(\Delta_{\mathrm{de}}\) is the dynamic eccentricity severity, also ranging from 0 to 1. In turn, \(\theta_{\mathrm{r}}\) is a function of rotor speed and time, resulting in a dynamic variation in the airgap.  
Lastly, for mixed eccentricity, the airgap length can be described combining (2) and (3) as follows:  
\[\delta(\theta_{\rm s},\theta_{\rm r})=\delta_{0}[1+\Delta_{\rm se}\cos(\theta_{ \rm s}+\theta_{\rm s0})+\Delta_{\rm de}\cos(\theta_{\rm s}+\theta_{\rm r}+\theta _{\rm s1})] \tag{4}\]  
Implementing eccentricity in an FE model can be challenging if the quality of the mesh and the time step are not properly considered. The present work implements static eccentricity by devising a static translation vector that acts on the stationary parts of the model. The magnitude of this vector is given by \(\delta_{0}\Delta_{\rm se}\). For the dynamic eccentricity, all moving parts of the model are translated dynamically according to a rotating vector of magnitude \(\delta_{0}\Delta_{\rm de}\). Mixed eccentricity takes into account the combination of both translating vectors.  
In this paper, and following suggested values in the literature, moderate to severe eccentricity (up to 50% of the airgap length) is evaluated, considering the following conditions:  
* **Static eccentricity** with two degrees: \(\Delta_{\rm se}=0.25\) and \(\Delta_{\rm de}=0.5\)
* **Dynamic eccentricity** with two degrees: \(\Delta_{\rm de}=0.25\) and \(\Delta_{\rm de}=0.5\)
* **Mixed eccentricity** with one degree: \(\Delta_{\rm se}=0.25\) and \(\Delta_{\rm de}=0.25\)
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 4 Parametric Study
### Vehicle Speed  
Next, we will take a look at the effect of vehicle speed on the accuracy of the proposed framework. The first immediate impact of the driving speed on the proposed framework is its effect on the driving frequency and the scale it corresponds to. As such, it is likely to alter the scale at which we take a horizontal section of the WCM. The shift in the scale corresponding to the driving frequency with the vehicle speed is demonstrated in the wavelet coefficient maps depicted in Figure 13 for different vehicle speeds. Recalling the procedure to select the scale where a horizontal section of the WCM will be taken, we first need to identify a range of scales which is sufficiently lower than the scale corresponding to the driving frequency and higher than the scales corresponding to the vehicle and the bridge frequencies. As shown in Figure 13 and also in Figure 10 for a speed of 2 m/s, which is not repeated here for brevity, this range is quite wide for lower vehicle speeds  
Figure 11: Wavelet coefficients at scale 600 for different damage locations and amounts.  
Figure 12: Wavelet coefficients at scale 600 for 20% damage at different locations for.  
but gets narrower as the vehicle speed increases because the scale corresponding to the driving frequency decreases with increasing vehicle speed. Also depicted in the figure are the suitable ranges of scales for each vehicle speed to select the scale that will be used for damage detection and location. Accordingly, we selected the scales 600, 240, 240, and 185 for vehicle speeds of 2 m/s, 5 m/s, 7.5 m/s, and 15 m/s, respectively, as indicated by the dashed, red lines in Figure 13.  
The variation of wavelet coefficients with the location of the vehicle on the bridge for different vehicle speeds at the respective scales are shown in Figure 14. The damage is located between \(x=10\) m and \(x=11\) m and the damage level varies between 20% and 80%. Figure 14 along with Figure 13 indicate that the accuracy of the proposed framework in detecting and locating damage decreases significantly with an increase in the vehicle speed.  
Figure 13: Wavelet coefficient maps for different vehicle speeds.  
This can be attributed to the fact that, the interaction between the driving frequency, bridge frequency and the vehicle frequency increases as the vehicle speed increases, i.e., when the driving frequency decreases. This interaction make it difficult to find a scale that is dominated by the static response, which is critical for the success of the proposed framework. Further, the dynamic response of the bridge is amplified significantly with an increase in the vehicle speed. As such, capturing the static response of the bridge becomes more difficult as the vehicle speed increases due to the increased dynamic response and the interaction between the driving, bridge and vehicle frequencies. Recalling that the proposed method owes its success in estimating the static displacements of the bridge, lower vehicle speeds lead to a much improved success rate in detecting and locating damage.
# Calculation and Dressing Simulation of the Profile of the Form Grinding Wheel for Modified ZI Worms
## 6 Conclusions  
This study derived the grinding wheel profile tooth surface equation based on the form grinding principle. Using MATLAB, it solved the ZI worm's end section and the grinding wheel's axial profiles, supporting grinding wheel design and manufacture.  
A software for ZI worm form grinding wheel profiles was developed. The grinding process simulation with VERICUT verified the calculation method's practical applicability.  
Through data extraction, gear grinding tests, and tooth profile accuracy detection, the effectiveness of the grinding wheel profile dressing was confirmed. Thus, the algorithm is suitable for calculating ZI worm form grinding wheel profiles.  
Future experimental research can be carried out from the following three aspects:  
1. Multi-parameter Experimental Exploration  
Study the influence of the combinations of different worm parameters and grinding wheel parameters on the machining accuracy. Set up multiple groups of experiments, where some parameters are changed in each group. Monitor the changes in indicators, such as the tooth profile accuracy and tooth pitch error of the worm after machining and construct a parameter accuracy correlation model to provide a basis for the selection of parameters in actual production.  
2. Process Parameter Optimization Experiment  
Conduct experiments on the grinding process parameters. Use the single factor experimental method to determine the relatively optimal value range of each parameter. Then, by using orthogonal or response surface experimental designs, explore the influence of the interaction of multiple parameters on the machining quality of the worm. Establish an optimization model to obtain the optimal combination of process parameters.  
3. Experimental Verification under Different Working Conditions  
Simulate different working conditions in actual production, such as varying machining environment temperatures, humidity levels, and equipment accuracy grades, and carry out worm grinding experiments. Compare the machining results under different working conditions with the ideal state, analyze the influence of working condition factors on the machining accuracy and the wear of the grinding wheel, and provide a reference for controlling working conditions and the selecting equipment in actual production.  
Conceptualization, J.S. and J.X.; data curation, J.X.; funding acquisition, J.S.; methodology, J.S. and J.X.; investigation, J.S. and J.X.; writing--original draft, J.S. and J.X.; supervision, J.X.; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52375052), the Natural Science Foundation of Henan Province (No. 242300421183), the Key R&D Program of Henan Province (No. 241111230300), and the Frontier Exploration Project of Longmen Laboratory (No. LMQYTSKT027).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
## Appendix C Comparison of BEM Methods  
This appendix details the differences in the results obtained with the three different BEM implementations. The three methods comprise azimuthal averaging (averaged momentum theory, AMT), element-wise formulation (differential momentum theory, DMT) and the weighted solution between the two (weighted momentum theory, WMT), which takes the AMT solution for the root and gradually blends it into the DMT solution at the tip (see [PERSON] [16] for more details about the methods). In the main paper, the weighted approach is used for the BEM+Wagner method.  
The differences in the different BEM methods only apply for non-uniform inflow conditions, where lift and thus induced velocity varies over the azimuth. Steady lift distribution is not affected by the methods. Figure 14 shows the difference in 1P hub loads among the three different methods, with the weighted method being denoted as BEM+Wagner like in the rest of the paper. Additionally, UPM results are shown as the mid-fidelity reference. Wagner results without induced velocity are also shown for comparison.  
As [PERSON] [16] already pointed out, the annular BEM (AMT) over-predicts the 1P hub loads. In fact, the induced velocity effects exerted by the harmonic lift distribution are completely averaged out, giving the same results as without the induced velocity model (as it can be observed by comparing it to the Wagner method). Using azimuthally varying induction (DMT) by using a purely element-wise BEM iteration under-predicts the loads, on the other hand. The weighted BEM, which basically blends the two solutions in the radial direction using the local radius, achieves better correlation with most 1P-Load components (except for \(M_{\text{J}\varphi}\)). The results presented in Figure 14 for the quasi-steady 1P hub loads can be transferred to the hub load transfer functions, with the weighted BEM showing the best correlation, and AMT over- and DMT under-predicting most unsteady loads.  
Figure 15 presents the performance of the three BEM methods with regard to whirl flutter stability in a plot similar to Figure 12. Despite predicting the aerodynamic loads better than the two other BEM formulations, the BEM+Wagner results are not always closer to the mid-fidelity reference (see middle row in Figure 15). On the other hand, the AMT (like the Wagner method without inflow) for some configurations (e.g., mid and short pylons) gives an even more stable system than Houbolt/Reed, and the DMT shows higher sensitivity to the system parameters compared with the reference (see dark-blue bars in the middle row).  
Figure A4: Comparison of 1P hub loads due to disc pitch of different BEM formulations.  
Figure A5: Comparison of whirl flutter stability predictions of different BEM formulations.