esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Drag Coefficient Variation by Ball Type  
Figure 5 shows the drag characteristic curves of each soccer ball obtained from the wind-tunnel tests. We positioned T1 and T2 soccer balls with two panel orientations (A and B) (Figure 4) and measured the drag coefficient of each soccer ball at wind velocities ranging from 7 m/s to 30 m/s. The drag characteristic curves represent the mean values over three trials for each soccer ball. For each ball, the Pearson product-moment correlation coefficient for the results of the three trials was r = 0.95 (\(p<0.01\)), suggesting that there was no significant difference between trials. In the case of the four-panel T1 with no grooves, it was observed that changing the panel orientation caused a significant change in the drag exerted on the ball. However, in the case of the T2 ball with new grooves introduced on the surface of the panel, changing the panel orientation caused slight change in the drag exerted on the ball. These drag coefficient results suggest that introducing a shallow groove into the surface of the ball is an effective way to minimize the variation in aerodynamic force due to changes in the position of the ball.  
Figure 4: Soccer balls used in this experiment and their panel orientations.  
The relationship between wind velocity and drag coefficient is illustrated. T1_A indicates the drag coefficient on the Type 1 soccer ball positioned at orientation A, and T1_B indicates the same at orientation B. Likewise for the drag coefficient on the Type 2 soccer ball (T2_A, T2_B)  
We found no significant difference in the critical Reynolds number (\(Re_{c}\)) of each soccer ball under the different panel orientations. For the T1 soccer ball, the critical Reynolds number was \(\sim\)\(2.4\times 10^{5}\) (\(Cd\approx 0.16\)) under orientation A (T1_A) and \(\sim\)\(2.4\times 10^{5}\) (\(Cd\approx 0.16\)) under orientation B (T1_B). For the T2 soccer ball, the critical Reynolds number was \(\sim\)\(2.6\times 10^{5}\) (\(Cd\approx 0.15\)) under orientation A (T2_A) and \(\sim\)\(2.5\times 10^{5}\) (\(Cd\approx 0.15\)) under orientation B (T2_B). It can be observed that the difference was insignificant. However, in the case of T1, the drag coefficient values varied significantly depending on the orientation of the ball in the supercritical region (above \(Re=2.5\times 10^{5}\)) where the wind velocity is higher. The fact that the air resistance acting on the T1 soccer ball (T1_A & T1_B) varies greatly in this high-speed segment (at wind velocities of 20 to 30 m/s) suggests that changes in the orientation of the T1 soccer ball could cause significant changes in its flight. In contrast, the T2 ball (T2_A & T2_B) with grooves on the surface of the ball exhibited little orientation-dependent variation in aerodynamic force even at high velocity (Figure 5), which suggests that this ball will follow a more stable flight trajectory with less orientation-dependent variation over the course of its flight.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 2 Materials and Methods
### Signal Processing on Radar Signals  
Due to its ultra-wideband (UWB) nature, the FMCW radar is able to resolve temporally the reflections and, therefore, it is able to isolate the Doppler signals (i.e., phase shift) caused by the cardiopulmonary activity of the driver from the unwanted phase shifts present in the car generated by the vibrations of objects (e.g., dashboard, driving mirror, wipers) and/or other passengers.  
The first step is to individuate the range bin wherein the subject's back lies. Considering that each range bin (i.e., range resolution) is 20 cm and that the seat is about 10 cm thick, the vital signs information can be retrieved extracting the phase shift from the FFT's complex samples which belong to the first range bin.  
The second step is to extract and optimize the Doppler (phase) signal. In fact, static reflectors (e.g., seat, static body parts of the driver) in the range bin yield phase distortion. In order to overcome this issue, the linear demodulation technique, described in [23], is applied to recover phase signal.  
In the car and with the radar positioned to the back of the seat, the Doppler signal is mainly a contribution of two sources: (1) The displacement of the back surface due to the cardiopulmonary industry activity; (2) the vibration of the subject's back due to the car movement. An estimate of the latter vibration is measured by the accelerometer. The result of the linear demodulation is a signal that can be fairly considered directly proportional to the Doppler signal. Since the two sources are non-Gaussian distributed, statistically independent, and linearly combined, the independent component analysis (ICA) algorithm can be used to separate the two sources. The ICA takes as input the linear demodulation output and the acceleration in the z direction and provides two outputs: (1) The phase shift caused by the vibration; (2) the phase shift due to the vital signs. The latter can be further processed by Wavelet Transform to separate the heartbeat from the respiration and to remove the noise.
## 3 Results and Discussion  
### Probability Characteristics of Extreme Pressure Coefficients  
#### 3.1.1 Extreme Value Distribution Type  
Three extreme value probability models are used to determine the probability distribution of the sample extreme value of wind load coefficient and the quantiles, which are Extreme value type I distribution (Gumbel distribution), Extreme value type II distribution (Frechet distribution), and Extreme value type III distribution (Weibull distribution). In this study, the model is described as Gumbel distribution when the shape parameter is 0, and as the GEV distribution when the shape parameter is not 0. The specific expressions of the three extreme value distributions are shown in Table 1.
## 2 Materials and Methods
### Performance Evaluation  
Our study rigorously assessed the impact of integrating both quantitative and textual data on the predictive accuracy of mortality in ICU patients through the application of five widely acknowledged evaluation metrics in binary-classification tasks. These metrics serve as key indicators of model effectiveness and precision:  
* _Precision (PPV)_: Denoting the accuracy of positive predictions, precision is calculated as the ratio of true positives (_TP_s) to the sum of true positives (_TP_s) and false positives (_FP_s). \[Precision=PPV=\frac{TP}{TP+FP}\] (1)
* _Recall_ (sensitivity, _TP_R): Recall quantifies the model's ability to correctly identify positive cases and is calculated as the ratio of true positives (_TP_s) to the sum of true positives (_TP_s) and false negatives (_FN_s). \[Recall=TPR=\frac{TP}{TP+FN}\] (2)
* _F1-score:_ This is a comprehensive metric combining precision and recall, calculated as the harmonic mean of the two, offering a balanced measure of overall performance. \[F1-score=\frac{2*Precision*Recall}{Precision+Recall}\] (3)
* _Accuracy. Accuracy_ assesses the overall correctness of predictions and is calculated as the ratio of the sum of true positives (_TP_s) and true negatives (_TN_s) to the total number of samples in the test dataset. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\] (4)
* AUROC (Area Under the Receiver Operating Characteristic Curve): AUROC is a common diagnostic metric that provides a comprehensive measure of classifier performance by plotting the relationship between the true-positive rate (TPR) and the false-positive rate (FPR) across various classification thresholds.  
By employing these evaluation metrics, this study aimed to provide a thorough and standardized evaluation of predictive models that integrates both quantitative and text data, specifically in the context of mortality prediction for ICU patients.
## 3 Experiments and Analyses
### Image Quality Comparison
Figure 7: Fused Images of “Lab” by Different Methods, (**a**,**b**) are source images; (**c**–**j**) are fused images of LE, DWT, DT-CWT, CVT, NSCT, SR-DCT, SR-KSVD, and proposed SR-SCC respectively. Figure 10: Difference Images of “Lab” by Different Methods, (**a**–**h**) are differences images between source image in Figure 7a and fused images of LE, DWT, DT-CWT, CVT, NSCT, SR-DCT, SR-KSVD, and proposed SR-SCC in Figure 7c–j respectively, (**i**–**p**) are differences images between source image in Figure 7b and corresponding fused images in Figure 7c–j respectively. Figure 9: Difference Images of “Leopard” by Different Methods, (**a**–**h**) are differences images between source image in Figure 6a and fused images of LE, DWT, DT-CWT, CVT, NSCT, SR-DCT, SR-KSVD, and proposed SR-SCC in Figure 6c–j respectively, (**i**–**p**) are differences images between source image in Figure 6b and corresponding fused images in Figure 6c–j respectively. Objective metrics of multi-focus \"leopard\", \"lab\", and \"bottle\" fusion experiments are shown in Tables 2-4 respectively to evaluate the quality of fused images. * **Multi-focus \"leopard\" fusion:** The proposed method SR-SCC achieves the largest value in MI and VIF. LE obtains the largest value in EI index, but it makes inaccurate decision in detecting the focused region. SR-KSVD shows great performance in \(Q^{AB/F}\), and the result of proposed method is only 0.0002 smaller than SR-KSVD. According to the quality of visual image, the accuracy of focused region, and objective evaluations, the proposed method does a better job than the rest of the methods. * **Multi-focus \"lab\" fusion:** The proposed method SR-SCC achieves the largest value in \(Q^{AB/F}\) and VIF. DWT obtains the largest value of EI index, but it cannot distinguish the correct focused areas. SR-KSVD has the best performance in MI. The proposed method and SR-KSVD show great performance of visual effect in focused area, distinguishing focused area, and objective evaluation. Compared with SR-KSVD, the proposed method dramatically reduces computation costs in dictionary construction. So the proposed method has the best overall performance among all comparison methods. * **Multi-focus \"bottle\" fusion:** DWT obtains the largest value in EI, but it does not get an accurate focused area. The proposed method achieves SR-SCC with the largest values of the rest of the objective evaluations. So the proposed method has the best overall performance compared with other methods in the \"bottle\" scene. Figure 11: Difference Images of “Bottle” by Different Methods, (**a–h**) are differences images between source image in Figure 8a and fused images of LE, DWT, DT-CWT, CVI, N3 CT, SR-DCT, SR-KSVD, and proposed SR-SCC in Figure 8c–j respectively, (**i–p**) are differences images between source image in Figure 8b and corresponding fused images in Figure 8c–j respectively.
## 4 Establishing the Data Fusion Kalman Filter Equations
### Data Acquisition Equipment  
To ensure the accuracy of time synchronization during the experiment, we have set up a data acquisition system, which consists of a time synchronization module and a data acquisition module as is shown in Figure 8. During the experiment, this system continuously sends 1 PPS synchronization signals via the serial port and simultaneously receives raw measurement data from the SRINSs. This data is then timestamped with millisecond precision, aiming at the preprocess of time alignment in subsequent data analysis.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 3 Experiment
### Filtering Effect and Comparison
#### 3.2.2 Low-Density Random Value Impulse Noise Experiment
From the objective evaluation criteria, the peak signal-to-noise ratio (_PSNR_) and structural similarity (_SSIM_) calculation for each filtering method are shown in Table 2, and the comparison figure is shown in Figure 15.  
The larger the _PSNR_, the closer the _SSIM_ value is to 1, and the better the image filtering quality is. According to the above figure, in the processing of low-density impulse noise and from the perspective of _PSNR_, the _PSNR_ of the filtering method in this paper was always the highest, followed by adaptive median filtering and mean filtering. With increases in noise density, the _PSNR_ of the switching median filter decreases faster. At 15% noise density, the _PSNR_ of the switching median filter is lower than that of the median filtering method. From the perspective of _SSIM_, the structural similarity of the filtering method in this paper is still the highest, and that of median filtering is second. With increases in noise density, the _SSIM_ of the proposed filtering method decreases to some extent, but it is still higher than median filtering. The _SSIM_ of the median filter does not change much with increases in noise density (staying above 90%) and has a high degree of similarity to the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise** & **Evaluation** & **Mean Filtering** & **Median Filtering** & **Switching** & **Adaptive** & **Filtering** & **Method in** \\
**Density** & **Criteria** & **Mean Filtering** & **Mean Filtering** & **Mean Filtering** & **This Paper** \\ \hline
5\% & _PSNR_ & 33.8915 & 38.8005 & 42.8376 & 42.4698 & 43.2589 \\  & _SSIM_ & 0.7750 & 0.9182 & 0.8422 & 0.9053 & 0.9756 \\ \hline
10\% & _PSNR_ & 33.8915 & 38.4237 & 39.8674 & 40.8705 & 41.5853 \\  & _SSIM_ & 0.7228 & 0.9106 & 0.6193 & 0.7789 & 0.9510 \\ \hline
15\% & _PSNR_ & 33.8915 & 38.0209 & 37.7253 & 39.1448 & 40.0889 \\  & _SSIM_ & 0.6790 & 0.8995 & 0.4470 & 0.6381 & 0.9210 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results with low-density random value impulse noise. Figure 15: Performance comparison graph of several filtering algorithms. original image. However, the _SSIM_ of switching median filtering, adaptive filtering, and mean filtering decreases greatly with increases in noise density. The _SSIM_ of the adaptive median filter at 15% noise density is lower than that of the mean filter. The switching median filter decreases the fastest, and structural similarity is even less than 0.5 at 15% noise density. To sum up, under subjective and objective evaluation criteria, the filtering method proposed in this paper has a better denoising effect than other filtering methods for low-density random value impulse noise.
## 5 Discussion
### Similarities and Differences
The results showed that post-processing methods are essential to improve the accuracy and rationality of meteorological and hydrological runoff predictions. This phenomenon may be because the post-processing method removes the negative values from the prediction results and ensures that all data are positive, which controls the output error very well. Since the ensemble model can combine the advantages of several single models, the ensemble model performs better for runoff forecasting. Therefore, the QM-EMD-Elman model can significantly improve the precision of monthly runoff predictions, and its runoff series prediction results are meaningful.
## 3 Exact Statistical Analysis of \(\gamma_{e}\)
### Ergodic Capacity  
The ergodic capacity is defined by \(\bar{C}=\mathbb{E}[\log_{2}(1+c\gamma_{e})]\) as in [7; 10], where \(c=1\) for HD and \(c=e/(2\pi)\) for IM/DD. By employing a part-by-part integration method, we can write it in terms of the \(F_{\gamma_{e}}(x)\) as  
\[\bar{C}=\frac{c}{\ln(2)}\int_{0}^{\infty}\frac{1-F_{\gamma_{e}}(x)}{1+cx}dx. \tag{19}\]
## 2 Integrated Navigation System of SINS/DVL/MCP/PS
### Error Propagation Model of SINS
The error propagation process of the SINS can be described as follows [4]:  
\[\dot{\mathbf{\phi}}=\mathbf{\phi}\times\mathbf{\omega}_{in}^{n}+\delta\mathbf{\omega}_{in}^{n} -\mathbf{C}_{b}^{n}\mathbf{\varepsilon}^{b} \tag{4}\]  
\[\dot{\delta\dot{V}}^{n}=-\mathbf{\phi}\times\dot{\mathbf{\rho}}^{n}+(2\delta\mathbf{\omega} _{ie}^{n}+\delta\mathbf{\omega}_{ie}^{n})\times\mathbf{V}^{n}+(2\mathbf{\omega}_{ie}^{n}+ \mathbf{\omega}_{ie}^{n})\times\delta\mathbf{V}^{n}+\mathbf{C}_{b}^{n}\mathbf{\
abla}^{b} \tag{5}\]  
\[\delta\dot{L}=\frac{\delta V_{N}^{n}}{R_{M}+h}-\delta h\frac{V_{N}^{n}}{(R_{M }+h)^{2}},\delta\dot{\lambda}=\frac{\delta V_{N}^{n}\text{sec}L}{R_{N}+h}- \delta h\frac{V_{N}^{n}\text{sec}L}{(R_{N}+h)^{2}}+\delta L\frac{V_{N}^{n} \text{sec}L\text{tan}L}{R_{N}+h},\delta\dot{h}=\delta V_{U}^{n} \tag{6}\]  
where \(\mathbf{\phi}\) denotes the misalignment defined as the Euler angle describing the rotation relationship between the calculated navigation frame \(n^{\prime}\) and the ideal navigation frame \(n\), \(\delta\mathbf{V}^{n}\), \(\delta\ L\), \(\delta\lambda\), and \(\delta h\) denote the errors of the velocity, latitude, longitude and height, \(\delta\mathbf{A}\), such as \(\delta\mathbf{\omega}_{ie}^{n}\), denotes the error of vector \(\mathbf{A}\), and \(\mathbf{\varepsilon}^{b}\) and \(\mathbf{\
abla}^{b}\) are the errors of gyroscopes and accelerometers projected in frame \(b\). In Equations (4)-(6), the error of gravity is ignored. The precision of inertial sensors is the key element to determine the precision of the SINS; generally, the errors in the sensors can be classified as constant and random errors. Selecting a suitable model to describe inertial sensors is a challenge; different sensors have different characteristics. In engineering, the one-order (or high-order) Markov process and white noise are used to propagate random noise. In this paper, the main target is to pretreat the DVL measurement, and white Gaussian noise is selected to simplify the discussion. Thus, the error propagation models of gyroscopes and accelerometers can be constructed as follows:  
\[\dot{\mathbf{\varepsilon}}^{b}=0,\dot{\mathbf{\
abla}}^{b}=0 \tag{7}\]  
The statistical characteristics of the random noise of gyroscopes and accelerometers, denoted as \(\mathbf{w}_{g}^{b}\) and \(\mathbf{w}_{a}^{b}\), are as follows:  
\[\mathbf{w}_{g}^{b}\sim\left(0,\mathbf{Q}_{g}^{b}\right),\mathbf{w}_{a}^{b}\sim\left(0,Q_{ a}^{b}\right) \tag{8}\]  
where \(\mathbf{Q}_{g}^{b}\) and \(\mathbf{Q}_{a}^{b}\) are the variance matrices of white noise corresponding to \(\mathbf{w}_{g}^{b}\) and \(\mathbf{w}_{a}^{b}\), respectively.
## 4 Conclusions
This research was jointly supported by the National Development and Reform Commission, China (18S-1926), the Shanghai Science and Technology Committee, China (21692195400), the Pudong New Area Science and Technology Development Fund (PKJ2021-C11), National Natural Science Foundation of China, China (41503119), the Shanghai Environmental Monitoring Center (HT2022162A1), and the International Partnership Program of Chinese Academy of Sciences (131211 KYSB20180002). Not applicable. **Data Availability Statement:** Satellite observed level 2 XCO\({}_{2}\) from OCO-2 (version 10r) and OCO-3 (version 10r) were obtained from the Goddard Earth Sciences Data and Information Services Centre (disc.gsfc.nasa.gov, accessed on 10 January 2022). Satellite observed level 2 XCO\({}_{2}\) (V02.98) and XCH\({}_{4}\) (V02.96) from GOSAT were obtained from GOSAT Data Archive Service (data2_gosat.nies.go.jp, accessed on 10 January 2022). Satellite observed level 2 XCO\({}_{2}\), XCH\({}_{4}\) and XCO from GOSAT-2 were obtained from GOSAT-2 Data Archive Service ([[https://prdct.gosat-2.nies.go.jp](https://prdct.gosat-2.nies.go.jp)]([https://prdct.gosat-2.nies.go.jp](https://prdct.gosat-2.nies.go.jp)), accessed on 1 March 2022). Offline level 2 XCH\({}_{4}\) from Sentinel-5P (S5-P) were acquired from Sentinel-5P Presentations Data Hub (s5 pub.copernicus.eu/dhus, accessed on 10 January 2022). The EM27/SUNFTIR and G2301 spectrometer data at Dunhuang, China is available in the Science Data Bank (DOI: [[http://www.doi.org/10.11922/sciencedb.01233](http://www.doi.org/10.11922/sciencedb.01233)]([http://www.doi.org/10.11922/sciencedb.01233](http://www.doi.org/10.11922/sciencedb.01233))) ([PERSON] and [PERSON], 2021). We thank [PERSON] and [PERSON] from Karlsruhe Institute of Technology (KIT), Germany for their support and guidance of PROFFAST. The authors declare no conflict of interest.
## 2 Methods
### Mathematical Formulation
#### 2.2.2 Dams with a High Groundwater Level
\(\Delta h_{0}\) can be obtained by equating Equation (23) with Equation (24): \[\Delta h_{0}=\frac{a_{1}\zeta\Delta h_{1}}{a_{1}\zeta Ch(a_{1}L_{0})+sh(a_{1} L_{0})}.\] (25) The seepage gradient of the foundation at \(x=0\) is calculated as: \[J_{x=0}=a_{1}\Delta h_{1}\frac{a_{1}\zeta Ch(a_{1}L_{0})+1}{a_{1}\zeta Ch(a_{1} L_{0})}.\] (26) The equivalent length of the blanket can be expressed as: \[L_{e}=\frac{\Delta h_{1}}{J_{x=0}}=\frac{a_{1}\zeta T+th(a_{1}L_{0})}{a_{1}+a _{1}^{2}\zeta Ch(a_{1}L_{0})}.\] (27)\(\Delta h_{1}\) can also be approximately given as:  
\[\Delta h_{1}=H_{1}-\left[1+\frac{H_{1}-H_{2}}{(L_{\epsilon}+L^{\prime})tan\gamma} \right]h_{1}, \tag{28}\]  
where \(L^{\prime}=L_{1}+0.44T\). Consequently, the seepage flux per unit width \(q_{1}\) of the foundation passing through the vertical section at the end of the blanket is derived as:  
\[q_{1}=\overline{K_{x}}TJ_{x=0}=\sum_{i=1}^{n}l_{i}K_{i}a_{1}\bigg{\{}H_{1}- \left[1+\frac{H_{1}-H_{2}}{(L_{\epsilon}+L^{\prime})tan\gamma}\right]h_{1} \bigg{\}}\frac{a_{1}\zeta Tth(a_{1}L_{0})+1}{a_{1}\zeta T+th(a_{1}L_{0})}. \tag{29}\]  
(2) The seepage flux \(q_{c}\) per unit width through the slope wall  
Assuming that the head loss of the slope wall is linear, the seepage flux \(q_{1}\) through the _ith_ section of the slope wall can be computed as:  
\[q_{i}=\left\{ \begin{array}{ll}K_{c}\frac{dx}{cos\gamma}\frac{\Delta h_{1}+ \frac{H_{1}-\Delta h_{1}-h_{1}}{h_{1}}xtan\gamma}{\delta}&\left(0<x\leq\frac{ h_{1}}{tan\gamma}\right)\\ K_{c}\frac{dx}{cos\gamma}\frac{H_{1}-\frac{tan\gamma}{cos\gamma}}{\delta}& \left(\frac{h_{1}}{tan\gamma}\right)<x\leq\frac{H_{1}}{tan\gamma}\end{array}  \right.. \tag{30}\]  
The total seepage flux of the slope wall per unit width can be calculated by integrating and combining Equation (30):  
\[q_{c}=\frac{K_{c}}{28 sin\gamma}\Big{[}(\Delta h_{1}-H_{1})h_{1}+H_{1}^{2} \Big{]}. \tag{31}\]  
Under three-dimensional conditions, the front view of the slope wall is simplified as shown in Figure 2B. The total seepage flux \(Q_{c}\) of the slope wall is written as:  
\[Q_{c}=\frac{K_{c}B_{1}}{28 sin\gamma}\Big{[}H_{1}^{2}-H_{1}h_{1}+h_{1}\Delta h _{1}\Big{]}+\frac{K_{c}(m_{1}+m_{2})}{68 sin^{2}\gamma}\Big{[}h_{1}^{2}\Delta h _{1}-H_{1}h_{1}^{2}+H_{1}^{3}\Big{]}. \tag{32}\]  
(3) The seepage flux \(q_{2}\) per unit width through Section II is calculated as:  
\[q_{2}=\frac{K_{b}(h_{2}^{2}-H_{2}^{2})}{2L_{1}}+\frac{\overline{K_{x}}(h_{1}- H_{2})T}{L_{1}+0.44T}, \tag{33}\]  
where \(K_{b}\) denotes the permeability coefficient of the dam body. Since the seepage flux through Section I is equal to that through Section II, the continuity condition at the interface is written as:  
\[q_{2}=q_{c}+q_{1}. \tag{34}\]  
\(h_{1}\) can be derived by trial calculations of Equation (34); then, the total seepage flux \(Q_{total}\) can be obtained by substituting \(h_{1}\) into Equation (35):  
\[Q_{total}=q_{1}B_{t}+Q_{c}, \tag{35}\]  
where \(B_{t}\) is the width of the horizontal blanket.