esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Semantic Segmentation of an Oblique Photography 3D Model
#### 3.1.3 Model Training  
The process of 3D semantic segmentation involves several key parameters, including the k-neighborhood number and energy function coefficient \(\gamma\). During our model training, we adjusted training accuracy by dividing the relative influences of these two parameters as follows:  
1. Influence of the k value of k-neighborhoods on classification accuracy. With \(\gamma\) set to a fixed value of 0.2, the empirical results of training with different pairs of k values are shown in Table 2. The effects on the classification accuracy and mean F1 score were also investigated. According to a comprehensive analysis, when the k value was between 6 and 12, the training classification accuracy and F1 score obtained good results (Figure 13). As a result, the empirical value for k in this study was set to 10.
2. The influence of the energy functions coefficient \(\gamma\) on classification accuracy. With k set to a fixed value of 10, Table 3 presents the training results using different pairs of \(\gamma\) values. Their effects on classification accuracy and mean F1 scores were investigated. According to a comprehensive analysis, a \(\gamma\) value between 0.4 and 0.6 delivered both training classification accuracy and an F1 score that obtained good results (Figure 14). As a result, the empirical value for \(\gamma\) in this study was set to 0.5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline K values & 4 & 6 & 8 & 12 & 16 & 20 & 30 \\ Classification accuracy & 0.9553 & 0.9561 & 0.9583 & 0.9565 & 0.9519 & 0.9460 & 0.9460 \\ Mean F1 score & 0.8796 & 0.8774 & 0.8875 & 0.8912 & 0.8354 & 0.8737 & 0.8737 \\ \hline \end{tabular}
\end{table}
Table 2: Results of classification accuracy and F1 scores under different neighborhood k values.  
Figure 13: Variation of classification accuracy and mean F1 scores with changes in the value of k: (**A**) variation in training classification accuracy; (**B**) variation in mean F1 scores.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Gamma (\(\gamma\)) values & 0.01 & 0.1 & 0.4 & 0.6 & 1.0 & 2 & 6 \\ Classification accuracy & 0.9535 & 0.9553 & 0.9566 & 0.9544 & 0.9471 & 0.9351 & 0.8844 \\ Mean F1 score & 0.8700 & 0.8556 & 0.8907 & 0.8873 & 0.8278 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 3: Results of classification accuracy and F1 scores under different energy function coefficients (\(\gamma\) values).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DACs & Digital to Analog Converter \\ FoM & Figure of Merit \\ LEO & Low Earth Orbit \\ MMIC & Monolithic Microwave Integrated Circuit \\ NF & Noise figure \\ RMS & Root Mean Square \\ SATCOM & Satellite Communication \\ SNR & Signal-to-Noise Ratio \\ VGA & Variable Gain Amplifier \\ VM & Vector Modulator \\ VSPS & Vector-Sum Phase Shifter \\ \end{tabular}
## 3 Three-Dimensional Finite Element Model of Composite Foundations
### Displacement Distribution  
Similarly, to investigate the vertical displacement diffusion characteristics of friction piles and end-bearing piles in a composite foundation, taking a pile length of 20 m as an example, the vertical displacements of the central pile and the soil between the piles were extracted from the calculation results. The vertical displacement curves of the pile and soil in the direction of the soil were plotted separately. Figure 9 shows the distribution of the vertical displacement of the pile and soil under an upper load, and Figure 10 shows the distribution of the vertical displacement of the soil for two types of piles with soil elastic moduli of 50 MPa or 100 MPa.  
Figure 8: Influence of soil elastic modulus on stress distribution of two types of piles: (**a**) stress distribution of friction piles; (**b**) stress distribution of end-bearing piles.  
Figure 9: Distribution of displacement in piles and soil under upper load: (**a**) pile displacement; (**b**) soil displacement.  
From Figure 9, it can be observed that the vertical displacement of the central pile generally decreased with increasing depth. However, after surpassing a critical depth, there was a slight increase in the vertical displacement of the pile. It is noteworthy that the vertical displacement above the critical depth was negatively correlated with the elastic modulus of the pile body, meaning that a larger elastic modulus resulted in less vertical displacement. Conversely, the vertical displacement below the critical depth was positively correlated with the elastic modulus of the pile body, indicating that a larger elastic modulus leads to more vertical displacement. Lastly, from a depth of approximately 2 m above the pile base to the pile base itself, there was a significant reduction in the vertical displacement of the pile. The trend in the vertical displacement in the soil between piles was opposite to that for the pile body.  
From Figure 10, it is evident that, under the same conditions, smaller values of the soil elastic modulus resulted in larger vertical displacements of the pile body. Due to their end-bearing effect, the stiffness of the end-bearing piles was greater, resulting in smaller deformations. Consequently, the coordinated deformation between the pile and the soil between the piles was relatively small.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 3 Principle and Improvement of Defect Detection Algorithm
### Data Augmentation and Improvement
#### 3.4.1 Frequency Domain Filtering  
To reduce the impact of noise on image feature information, filter processing is applied to the image. Since filtering images in the spatial domain can easily result in the loss of edge features, while filtering in the frequency domain can preserve the edge information well, this paper adopts frequency domain filtering. The image is transformed from the spatial domain to the frequency domain for processing. The former refers to an image composed of image pixels, while the latter describes the distribution of image pixel values by spatial frequency and maps them out in the form of a spectrum. The transformation of the image domain can be achieved through a Fourier transform. After processing the image in the frequency domain, the processed image can be transformed back to the spatial domain using a Fourier inverse transform, which ensures the preservation of the information.  
From a \(M\times N\) two-dimensional image \(f(x,y)\), a two-dimensional discrete signal is collected. \((x,y)\) represents the real variable of the discrete signal, while \((u,v)\) works as its frequency; thus, the Fourier transform is represented as follows:  
\[\begin{split}& F_{(u,v)=}\frac{1}{\sqrt{MN}}\sum\limits_{x=0}^{M-1} \sum\limits_{y=0}^{N-1}f(x,y)\\ &\exp\left[-j2\pi\left(\frac{ux}{M}+\frac{vy}{N}\right)\right] \\ &(u=0,1,\ldots,M-1;v=0,1,\ldots,N-1)\\ & F_{(u,v)}=\frac{1}{\sqrt{MN}}\sum\limits_{v=0}^{M-1}\sum\limits _{y=0}^{N-1}f(x,y)\\ &\exp\left[j2\pi\left(\frac{ux}{M}+\frac{vy}{N}\right)\right]\\ &(u=0,1,\ldots,M-1;v=0,1,\ldots,N-1)\end{split} \tag{4}\]  
Among these formulae, \(exp[-j2\pi(ux/M+vy/N)]\) is the forward transformation kernel and \(exp[j2\pi(ux/M+vy/N)]\) represents the inverse transformation kernel.  
As shown in the frequency domain diagram in Figure 12b, the bright central position belongs to the low frequency region, which features gray-scale transformation and belongs to a flat region in the spatial domain. The darker side parts belong to the high frequency region, which represents the edge and noise in the spatial domain. In order to retain the edge information while suppressing the noise, a Butterworth low-pass filter was adopted, and its transfer function is as follows:  
\[(u,v)=\frac{1}{1+\left[\frac{D(uv)}{D_{0}}\right]^{2n}
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 2 Dynamic Model for Moving Platform
### Yaw Error Motion  
A single-axis linear stage consists of a moving platform mounted on a pair of LM Guides. The platform is driven by a linear actuator, as illustrated in Figure 2. The system may be characterized by the center of mass of the platform, the center of stiffness of the LM Guides, and the driving force offset with respect to the geometric center of the platform. In this system, the ratio of platform length (\(l\)) in the motion direction to the width (\(w\)) is very important for drive stability. Typically, \(l/w>1\) is required, and 1.6 or higher is recommended [1].  
When the driving force (\(F\)) offset is significant, the yaw motion of the platform is induced by its inertia, reaction forces (\(R_{a}\) to \(R_{d}\)), and friction forces (\(\mu R_{a}\) to \(\mu R_{d}\)), leading to possible jamming.
## 1 Introduction  
Hypoxemia is doomed to the common terminal pathway of multiple pathologies: clinicians are fearful of silent hypoxemia in the post COVID-19 era [1,2]. The intradialytic hypoxemia has been well recognized as a validated clinical predictor for inflammation, higher requirements of erythropoietin, and poor clinical outcomes [3, 4, 5]. Furthermore, coronary artery diseases (CAD) and obstructive sleep apnea (OSA) are closely related to hypoxia and are particularly common in patients with maintenance hemodialysis (MHD); it is no wonder that cardiovascular (CV) mortality remains the leading cause of death [3, 4, 5, 6]. OSA is a disease that manifests with restriction of the airflow through the upper airways during sleep, leading to the development of hypertensive CV diseases, CAD, and cardiac arrhythmia [7]. Profound OSA patients are more prone to have apnea predominance, earlier arousals, rather shorter hypopnea duration, and significant desaturation, despite a similar apnea duration during sleeping [8]. Indeed, OSA is the most prevalent form of sleep-related breathing disorders, which is characterized by repetitive episodes of hypoxia, contributing to extremely high mortality rates, secondary to associated CV and metabolic risks [3].  
In addition to conventional vital signs (body temperature, pulse rate, respiration rate, and blood pressure), studies have been increasing rapidly in the application of non-invasive screening systems to improve patient safety and predict clinical events in the high risk population [9]. Sudden cardiac death, intricately involved in heart rate variability, is highly prevalent among HD patients; there is, therefore, a need to develop a smart wearable device that has a microchip light emitting diodes (LED) sensor to detect pulse oximeter saturation (SpO2) [10, 11, 12]. In the post COVID-19 era, that has seen a high incidence of sudden death due to silent hypoxia, clinical assessments that reflect respiratory function would more likely relate to adverse outcomes, particularly in a burden of end-stage renal disease [1, 2, 3]. Given that OSA is a common chronic respiratory disorder resulting in raised morbidity and mortality, OSA characterized by frequent episodes of hypopneas, associated with intermittent hypoxemia, is now being recognized as a potential major risk factor of OSA-related death [13]. Despite previous documented information, the association between pre-dialytic SpO2 level, OSA and mortality risks needs be determined. Based on the above scenario, our hypothesis is to provide a combined evaluation of systemic oxygenation and underlying OSA severity for both all-cause and CV death. With respect to the clinical practicality and accessibility in a prognostic investigation, our research team used the Epworth Sleepiness Scale (ESS) score system for detecting severe OSA syndrome and our smart wearable LED oximetry device for pre-dialytic SpO2 in the MHD population-based study.
## 5 Results
### Panel Fixed-Effects Analysis
Second, excessive regulatory interference may distort market mechanisms, suppress technological innovation, and weaken environmental enforcement ([PERSON], 2018), thereby blurring the decoupling relationship between urban economic growth and urban carbon emissions intensity. These findings highlight the critical importance of maintaining equilibrium between governmental and market forces. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Urban Carbon Emissions Intensity** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline Green public procurement & \(-1.658\) *** & \(-2.036\) *** & \(-1.904\) *** & \(-1.799\) *** & \(-1.593\) *** & \(-1.546\) *** & \(-1.548\) *** \\  & (0.572) & (0.420) & (0.414) & (0.412) & (0.409) & (0.402) & (0.402) \\ Economic development & & \(-4.084\) *** & \(-4.243\) *** & \(-4.538\) *** & \(-4.619\) *** & \(-4.239\) *** & \(-4.233\) *** \\  & & (0.070) & (0.071) & (0.089) & (0.089) & (0.089) & (0.098) & (0.098) \\ Industrial structure & & & \(-2.694\) *** & \(-2.065\) *** & \(-2.354\) *** & \(-1.544\) *** & \(-1.531\) *** \\  & & & & (0.308) & (0.327) & (0.326) & (0.334) & (0.334) \\ Urban population & & & & & 8.440 *** & \(7.623\) *** & \(6.231\) *** & \(6.188\) *** \\  & & & & & (1.542) & (1.51) & (1.516) & (1.516) \\ Total retail sales & & & & & & \(-1.402\) *** & \(-1.180\) *** & \(-1.177\) *** \\  & & & & & & (0.205) & (0.203) & (0.203) \\ Government intervention & & & & & & & 3.394 *** & 3.420 *** \\  & & & & & & (0.391) & (0.391) \\ Energy structure & & & & & & & \(-1.080\) \\  & & & & & & & (0.804) \\ Constant & 99.059 *** & \(-2.946\) ** & \(-2.962\) ** & \(-14.478\) *** & \(-12.610\) *** & \(-10.314\) *** & \(-10.212\) *** \\  & (5.479) & (1.364) & (1.342) & (2.492) & (2.519) & (2.493) & (2.494) \\ N & 2565 & 2565 & 2565 & 2565 & 2565 & 2565 & 2565 \\ R-squared & 0.317 & 0.595 & 0.608 & 0.613 & 0.621 & 0.633 & 0.633 \\ \hline \hline \end{tabular} Note: Standard errors are in parentheses. All variables are mean-centered. **\(p<0.05\), ***\(p<0.01\).  
\end{table} 
Table 2: Panel fixed-effect regression results.
## 4 Results and Discussion
### Evaluation Metrics
#### 4.4.2 Loss Function  
A loss function is defined as a function that represents an event of more than one variable, illustrating certain costs corresponding to the event. In order to mitigate the loss function, optimization is used.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 6 Discussion  
### Estimations of Global Terrestrial ET  
The multiyear average of annual global terrestrial ET (511.9 \(\pm\) 43.4 mm yr\({}^{-1}\)) from 2013 to 2016 simulated by the BEPS model in this study is within the range (453.7 \(\pm\) 5.2  
Figure 11: Variations of (**a**) PPFD, (**b**) R\({}_{\rm n}\), (**d**) g\({}_{\rm sw}\), and (**e**) T for a sunlit and a shaded leaf in the overstory and understory canopies; (**c**) LAI and (**f**) canopy-level T for overstory and understory canopies with \(\Omega\), when the effective LAI for the overstory and understory canopies are set as a constant of 2.0 and 0.2, respectively, which are common in a typical conifer forest (Case III). The values of the environmental variables are the same as those in Figure 8. The red open and filled diamonds represent the overstory sunlit and shaded components, respectively; the blue open and filled circles represent the understory sunlit and shaded components, respectively; the crosses represent the total of the four components.  
to 697.3 \(\pm\) 10.4 mm yr\({}^{-1}\)) of those estimated by four remote sensings (RS) based physical models, two machine learning methods (i.e., model tree ensemble and random forest), and 14 land surface models (LSM) (Figure 12) [1]. For the model tree ensemble (MTE) method, eddy covariance (EC) measured ET was integrated with satellite remote sensing and surface meteorological data to estimate global terrestrial ET monthly from 1982 to 2008 at 0.5\({}^{\circ}\) spatial resolution [60]. For the random forest (RF) algorithm, a set of independent regression trees was generated by randomly selecting training samples automatically [61]. The RF global terrestrial ET product has a temporal resolution of half-hourly for the period of 2001-2014 at 0.5 spatial resolution [62]. The 14 LSM modeled ET products were from Trends and Drivers of the Regional Scale Sources and Sinks of Carbon Dioxide (TRENDY) project, including CABLE, CLASS-CTEM, CLM45, DLEM, ISAM, JSBACH, JULES, LPJ-GUESS, LPJ-wsl, LPX-BERN, O-CN, ORCHIDEE, ORCHIDEE-MICT and VISIT [1]. The global terrestrial ET results from the S3 simulation of TRENDY v6 with changing CO\({}_{2}\), climate, and land use for the period of 1982-2011 were used for comparison in this study. The interannual variability of global terrestrial ET from 1982 to 2011 is small (<13.0 mm yr\({}^{-1}\)) [1]. Thus, the magnitude of global terrestrial ET simulated by the BEPS model in this study is reasonable although its period is different from that in [PERSON] et al. [1].
# An Alternative Procedure for the Description of Seismic Intensity Parameter-Based Damage Potential
## 2 Methods
### Multiple Regression Analysis
In both methods, the selection criterion is the \(p\)-value, which is defined in [25] as \"the smallest level of significance that would lead to rejection of the null hypothesis H\({}_{0}\) with the given data\" and was assigned the value of 5% in this study. Following the statistical analysis of the initial set of models (Model Set 1: contains models 1.1, 1.2, and 1.3), a novel alternative approach is introduced (Model Set 2: includes models 2.1, 2.2, and 2.3) to highlight further the significance of each explanatory variable (IM) in predicting a specified Damage Index. This is achieved through the application of an importance factor to each of the selected Intensity Measures by raising each IM to a power related to the previously calculated rank correlation coefficient (\(\rho_{\rm IM_{i}}\)) between the IM and the respective DI. By exponentiating all IMs relating to their respective rank correlation with a specific DI, a transformation of the selected IMs can be obtained, leading to a new set of independent variables for the regression model. The general form of the proposed expression and the associated exponent (c\({}_{i}\), where i = 1, \(\ldots\), n) of each IM is shown in Equations (6a) and (6b), respectively. \[\rm DI=b_{1}\cdot IM_{1}{}^{c_{1}}+b_{2}\cdot IM_{2}{}^{c_{2}}+\cdots+b_{n}\cdot IM _{n}{}^{c_{n}}+e \tag{6a}\]  
\[\rm c_{i}=\frac{\rho_{IM_{i}}-\rho_{lim}}{1-\rho_{lim}} \tag{6b}\]  
Equation (6b) presents the general expression of the exponent for each IM that has been previously selected purely based on its rank correlation with the dependent variable (DI). Here, \(\rho_{\rm lim}\) denotes the lower limit (threshold) of the rank correlation coefficient that all selected IMs must satisfy, which in this study was assigned the value of 90%. Any IMs that did not exceed this threshold were discarded in any of the three studied DIs. The exponent results in values within the range [0, 1], with a value of 1 indicating perfect correlation of the corresponding IM with the predicted DI. The higher the rank correlation between an IM and a DI, the closer the modified IM is to its original value. Table 4 lists all examined regression methods and the general form of the expressions produced by each method.
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis
### Analysis of Forecast Results  
The prediction curves of the SARIMA-NAR combination model 1 constructed according to the linear and nonlinear component combination method and the SARIMA-NAR combination model 2 constructed according to the MSE weight combination method are shown in Figure 8. It can be seen that, compared to the SARIMA-NAR combined model 2, the resulting curve of SARIMA-NAR combined model 1 is closer to the actual value curve, and the effect of fitting and prediction is better.  
In addition, in Figure 9, the absolute error (AE) is used to evaluate the basic errors of the SARIMA model, the NAR model, and the two combined models mentioned in the article in monthly units. The absolute error of the NAR model was the largest, and the SARIMA-NAR combined model 1 was the smallest; the absolute error of the two combined models was much smaller than that of a single model. The calculation formula of absolute error is shown in Formula (14).  
\[e_{AE}=|y_{t}-\hat{y}_{t}| \tag{14}\]  
Figure 8: Prediction results of the combined model.  
Figure 9: Absolute error area chart.
## 2 Vein Pattern Identification System
### Human Recognition Process Based on Vein Pattern Matching
#### 2.4.2. Matching in the Presence of a Rigid Transformation  
The rigid transformation corresponds to the case where the hand of the query vein pattern, including the candidate retrieved in the database, is in exactly the same position and orientation. For each triangle, the shape (triangles sides' lengths and angles) and areas are preserved.  
The algorithm for matching two sets of triangles, one from the template and the other from the query (in Figure 16), proceeds as follows:  
Figure 14. Registration process.  
Figure 15. Triangulation on the set of minutia points close to the centroid: (**a**) determination of the centroid of vein pattern image; (**b**) calculation of the distance between minutia points and centroid; (**c**) selected ten closing minutia points; (**d**) local convex hull from the selected minutia set; and (**e**) forming triangles from the selected minutia set.  
1. Obtain the set of triangles for the template and the query vein pattern using a triangulation process. These sets are shown in Figure 16a,g, respectively.
2. Sort the triangles of each set in ascending order, as shown in Figure 16b,f, respectively.
3. Reorient each triangle in the list in a standardized position where the longest length is taken as the base of the triangle and the length is decreasing starting from the base in a clockwise direction. The list of triangles in the standardized position for the template and the query are shown in Figure 16c,e, respectively.
4. Perform a run length technique, which is similar to the \"list-matching algorithm\", by searching for the longest string of matching triangles between the triangles in the template and the query list. A circular shift of triangles in the query list is applied upon searching for the matching triangle in the template list. The matching criterion between each pair of triangles, say triangle it_th_ and _jth_, is according to \[\
icefrac{{\psi_{\phi}}}{{\psi_{\xi}}}^{2}=\frac{{\|F_{i}-F_{j}\|}^{2}}{{\|F_{i }\|}^{2}}\times 100<\xi\,\] (5) where F\({}_{i}\) = \(F_{i}\) (\(d_{1}\), \(d_{2}\), \(d_{3}\), \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(A\)) is the feature vector of the i_th_ triangle defined in Figure 17, with \(d_{1}\), \(d_{2}\) and \(d_{3}\) being the lengths of the triangle sides. \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are the angles and \(A\) is the area of the triangle;\(\xi\) is some threshold value.
5. Declare the match on the longest string (\(N\)) of triangles that yields the minimum averaged error of \(\frac{{\sum_{i}\
icefrac{{\psi_{\xi}}}{{\psi_{\xi}}}^{2}}}{N}\).  
Figure 16: Matching in the case of rigid transformation. (**a**) Set of triangles of vein pattern minutiae of the template. (**b**) Sorted (**a**) in ascending order. (**c**) Reorientation of (**b**) in a normalized position. (**g**) Set of triangles of vein pattern minutiae of the unknown. (**f**) Sorted (**g**) in ascending order. (**e**) Reorientation of (**f**) in a normalized position. (**d**) Matching triangles between (**c**) and (**e**).