esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Design of Key Components
### High-Temperature and Ultrahigh-Pressure Simulation Test Cabin
#### 3.1.3 Analysis and Calculation of Wall Thickness
\(n_{b}\) is the blasting safety factor of the cabin, which should be greater than or equal to 2.2 when calculated from tensile test data. Considering the economy of equipment  
Figure 6: Failure process for nonideal plastic materials. manufacturing and the safety of use, the blasting safety factor of the rock cabin designed in this paper is 2.5. As the total length of drill pipe cabin is 5 m, to avoid the instability of equipment caused by large axial deformation during operation, its blasting safety factor is further increased to 3. Therefore, the wall thickness calculation formula can be derived as follows [39]. \[\ln(K)=\frac{P_{b}}{\frac{2}{\sqrt{3}}R_{P0.2}(2-\frac{R_{P0.2}}{R_{m}})}=\ln( \frac{R_{i}+\delta}{R_{i}}) \tag{7}\]  
where \(R_{i}\) is the inner radius, mm and \(\delta\) is the wall thickness, mm. Therefore,  
\[\delta=\frac{D_{i}}{2}\left[\exp(\frac{\sqrt{3}n_{b}P(D)}{2\phi R_{P0.2}(2- \frac{R_{P0.2}}{R_{m}})})-1\right] \tag{8}\]  
where \(D_{i}\) is the inner diameter, mm. The wall thickness required for a rock cabin with an inner diameter of 500 mm was calculated with Equation (8) as follows:  
\[\delta=\frac{D_{i}}{2}\left[\exp(\frac{\sqrt{3}n_{b}P(D)}{2\phi R_{P0.2}(2- \frac{R_{P0.2}}{R_{m}})})-1\right]=\frac{500}{2}\left[\exp(\frac{\sqrt{3}\times 2.5\times 140\times 1.08}{2\times 0.9\times 960\times(2-\frac{960}{1070})})-1 \right]=102.49\text{mm}\]  
The wall thickness required for a drill pipe cabin with an inner diameter of 150 mm was calculated as follows:  
\[p_{s}=\frac{2}{\sqrt{3}}R_{p0.2}\ln(K) \tag{2}\]0  
Therefore, when 1.08 times the maximum working pressure was taken as the design pressure and blasting failure was adopted as the design criterion, the wall thickness of the rock cabin was 102.49 mm and that of the drill pipe cabin was 38.27 mm. In actual working conditions, the influence of added thickness and manufacturing deviations should be considered, so the thickness of rock cabin should be increased to 107.5 mm and that of drill cabin was rounded up to 40 mm. Therefore, the inner diameter of the rock cabin is 500 mm and that the outer diameter is 715 mm. The inside diameter of the drill pipe cabin is 150 mm, and the outside diameter is 230 mm. In the calculations of wall thickness for each section of the cabin, the thermal load and pressure on the test cabin were considered simultaneously. Theoretically, the calculated wall thickness can bear an ultrahigh pressure of 140 MPa and a temperature of 150 \({}^{\circ}\)C at the same time.
## 3 Case Study
### Fake Ship License Plate Judgment  
#### 3.3.1 Fake Ship License Plate Discovery  
Based on ship behavior feature graph analysis, we focus on the abnormal behavior of double trajectories of ships in specific areas. As shown in Figure 14, taking \"Xinhong 998\" ship chart node in the upstream section of the Yangtze River as an example for analysis, this ship is a sea ship with static chart characteristics, and in the time range of '21 March 2021 00:00:00' to '21 March 2021 03:16:14', the ship dynamically reports ports to call at Jiangyin, Diagang, and Niutoushan ports. Checking the AIS trajectory of this ship, it is found that one is the inland trajectory of the Yangtze River, and the inland trajectory is consistent with the reported path. The other is the sea navigation trajectory, and there is no matching sea reporting information, which means that some ships have multiple abnormal trajectories. The abnormal trajectory is the sea section, so it can be inferred that this ship has the risk of being licensed by the inland ship for sea navigation.
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 4 Results
### Window Size Analysis  
In order to explore the performance of the network with varying window sizes, we ran experiments with window sizes ranging from 20 to 200. More specifically, the chosen window sizes were [20, 50, 100, 150, 200]. As for the experiment itself, we used the proposed solution, performed 10 training sessions on the original train/test split of the WISDM dataset with different initializations for each window size, and report the average and standard deviation of the accuracy. The standard deviations of the accuracy for each window size were found to range from 0.1% to 0.25% and thus were excluded from the graph, since their impact is negligible.  
In Figure 8, the rate of increase of the accuracy for each range of values is clear. The higher increase rate can be found in the range from 20 to 50, which explains the choice of 50 as the window size for the faster implementation, since it provides a favorable efficiency to performance trade-off. The average accuracy continues to increase in all the ranges, but as we approach 200, the rate of increase is lower. This is demonstrated in the minor difference (0.36%) between the performance of the model with the window size of 150 and the one with the window size of 200, compared to the 2.12% difference between the ones with window sizes of 50 and 100.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Calibration
#### 2.2.4 Calibration Procedure  
Step one: Choose the appropriate radial basis function. Because radial basis function interpolation with shape parameters is better, this paper uses Multi-Quadric function (MQ function) with \(\phi(r)=\left(r^{2}+\beta^{2}\right)^{1/2}\) and Gaussian function with \(\phi(r)=e^{-\frac{r^{2}}{\rho^{2}}}\) as the radial basis function to calibrate the camera, and \(\beta\) is the shape parameter.  
Step two: The control points are adaptively selected based on the given data set using the Kmeans clustering method [16]. The radial basis function is an interpolation method of high-dimensional scattered data. The approximation and stability of the function are closely related to the distribution of control points. Therefore, the selection of control points is a very important part of the calibration method and directly affects the measurement accuracy. Thus the control points selected by Kmeans clustering method can represent the distribution characteristics of the data set.  
Step three: Coordinate normalization. The radial basis functions chosen in this paper have shape parameters, and the choice of shape parameters depends on the distribution of the points in the coordinate system. In order to avoid the influence of the scale factor of the optical imaging system in terms of the coordinate values, the image coordinate and the world coordinate of each point must be normalized. The normalized data set is used for subsequent steps.  
1. Normalization of the image coordinate. According to the affine transformation, the image coordinates \(x_{i}\)\(i=1,\cdots,N\) are normalized by \(u_{i}=\tau^{-1}(Ax_{i}+\alpha)\). \(\tau\), \(A\), and \(\alpha\) are normalized parameters which can be solved by Choleski decomposition method.
2. Normalization of the world coordinate. According to the affine transformation, the world coordinates \(w_{i}\)\(i=1,\cdots,N\) are normalized by \(q_{i}=\rho^{-1}(Bw_{i}+b)\). \(\rho\), \(B\) and \(b\) are normalized parameters which can be solved by Choleski decomposition method.  
Step four: Solve camera matrix \(H_{cam}\). Calculate the calibration matrix M according to Equation (12), and then use the singular value decomposition method to solve the camera matrix \(H_{cam}\).  
Step five: Put the image coordinate \(x\) of any point and the solved camera matrix \(H_{cam}\) in Equation (7), the corresponding line coordinate can be fitted. Since the normalized coordinate values will change the coordinate values of the line space, the line coordinate obtained by using the interpolation should be converted to the original values according to Equation (14).  
\[l^{(2)}=\left(\begin{array}{cc}B&0\\ \rho^{-1}[b]_{x}B&\rho^{-1}det(B)B^{-T}\end{array}\right)l^{(1)} \tag{14}\]  
Step six: Repeat the fifth step until the line coordinate values of all points in the data set are fitted.
## 2 Materials and Methods
### Analytical Model to Determine Aerodynamic Forces  
The simplified analytical model assumes that components from two types of loads act simultaneously on the blade during rotor operation. The first is the wind load generating \(F_{xW}\) and \(F_{yW}\) components on the blade. The second is a load with components \(F_{xD}\) and \(F_{yD}\) resulting from the air drag the blade is subjected to during rotor rotation. Under the assumption of the simplified analytical model, at each angular position of the rotor \(\alpha\), the blade is subjected to the resultant load components according to Equation (1).  
\[\overset{\rightarrow}{F}\ =\ \overset{\rightarrow}{F_{W}}\ +\ \overset{ \rightarrow}{F_{D}} \tag{1}\]  
The values of the forces \(F_{x}\) and \(F_{y}\) are influenced by the rotor's peripheral speed and wind speed (Figure 1); hence, the formulas for components \(F_{xD}\) and \(F_{yD}\) were derived from the resistance provided by the rotating blade with planetary motion, given by Equations (2) and (3).  
\[F_{xD}\ =\ 0.5\cdotp\cdot{V_{0}}^{2}\cdot A_{ref}\cdot C_{xD} \tag{2}\]  
\[F_{yD}\ =\ 0.5\cdotp\cdot{V_{0}}^{2}\cdot A_{ref}\cdot C_{yD} \tag{3}\]  
The force components \(F_{xW}\) and \(F_{yW}\) related to wind load are given by Equations (4) and (5).  
\[F_{xW}\ =\ 0.5\cdotp\cdotp W^{2}\cdot A_{ref}\cdot C_{x} \tag{4}\]  
\[F_{yW}\ =\ 0.5\cdotp\cdotp W^{2}\cdot A_{ref}\cdot C_{y} \tag{5}\]  
Finally, the total force components \(F_{x}\) and \(F_{y}\) were obtained as the difference of the pair of blade forces acting on the blade as given by Equations (6) and (7).  
\[F_{x}\ =\ F_{xW}+F_{xD}\cos\alpha+F_{yD}\sin\alpha \tag{6}\]  
\[F_{y}\ =\ F_{yW}-F_{xD}\sin\alpha+F_{yD}\cos\alpha \tag{7}\]  
The use of a simplified analytical model in the calculation of the total load acting on the blade requires the determination of aerodynamic coefficients, which depend on the geometry of the blade. On the basis of data on the values of the \(P_{x}\) and \(P_{y}\) forces from wind tunnel tests conducted on a sectional model of a dual-coherent blade [30], the values of the aerodynamic coefficients of the drag force \(C_{x}\) and the lift force \(C_{y}\) were determined. The coefficient of aerodynamic drag \(C_{x}\) and the coefficient of the lateral aerodynamic force \(C_{y}\) are given by Equation (8).  
\[C_{x}\ =\ \frac{2P_{x}}{\rho W^{2}\cdot A_{ref}};\ C_{y}\ =\ \frac{2P_{y}}{\rho W^{2} \cdot A_{ref}} \tag{8}\]  
where \(P_{x}\)--aerodynamic drag [N]; \(P_{y}\)--lift force [N]; \(W\)--the average wind speed [m/s]; \(\rho\)--the air density; \(A_{ref}\)--the effective area of the blade.
# The Effect of Cu (II) on Swelling and Shrinkage Characteristics of Sodium Bentonite in Landfills
## 3 Results
### Shrinkage  
The shrinkage of samples was calculated by Equation (2),  
\[\delta_{si}=\frac{Z_{i}-Z_{0}}{h_{0}}\times 100 \tag{2}\]  
where, \(\delta_{si}\) was the rate of shrinkage at a certain time, \(Z_{i}\) and \(Z_{0}\) were shrinkages of two adjacent moments. The shrinkage curve was illustrated in Figure 5. In which, the time-dependent shrinkage rate and mass loss were plotted, respectively.  
As shown in Figure 5, with the increase of Cu (II) concentration, the shrinkage of bentonite decreased, and the required testing time decreased as well. Therefore, the shrinkage curves of bentonite can be divided into uniform, deceleration, and stabilization stages. The maximum shrinkage of the bentonite sample was 1.108 mm. The shrinkage rate was 5.54%. Though the swell and shrinkage will not happen simultaneously, a similar tendency is observed. The reason for the less shrinkage in this experiment may be caused by the constraint on the top and bottom sides of the sample in the process of saturation. Therefore, the adsorbed water by bentonite in a fixed volume was limited, leading to small shrinkage. The change of the shrinkage corroded by different Cu (II) concentrations reveals that the hydrophilicity decreased, eventually causing the decay in the swell and shrinkage performance.  
The mass variation curves show similar behavior as the shrinkage curve, which indicated that the temperature and humidity conditions were well controlled during the experiment. Among the four groups of samples, the loss rate of mass was slightly different before 100 h due to the different shrinkage rates. Once the loss rate of water and absorption rate of the samples reaches a dynamic balance status, the mass variation curves tend to be stable.  
The swell and shrinkage processes of clay usually show the same behavior, so the dose-response model can also be applicable. In this case, the logarithmic coordinate was used, and the shrinkage curves were replotted in Figure 6, and the correlation coefficient and undetermined coefficient were shown in Table 4.  
Figure 5: (**a**) Shrinkage curve (**b**) mass loss curve.  
Comparing with Figure 6, it can be observed that the shrinkage rate of bentonite samples was high at the initial shrinkage stage. However, the dose-response model was relatively stable at this stage, which could result in a relatively low estimation of the correlation coefficient. And also, the bentonite usually has very strong shrinkage. Therefore, the dose-response model will not be applicable in unconstrained conditions or the clay of great swelling and shrinkage performance. While with the consideration of constraints and weak swelling and shrinkage, the swelling and shrinkage variation curve of bentonite tended to be \"S\" type, and the dose-response model will be suitable.
## 5 Conclusions  
In this paper, a numerical simulation model was presented to study the disengaging process of a Cu-based wet multi-disc clutch, considering the hydrodynamic lubrication, the asperity contact, the heat transfer, the spline resistance, and the impact between the piston and clutch hub. The influence of ATF temperature on the disengaging dynamic characteristics of a six-friction-pair wet clutch was discussed. The conclusions were summarized as follows:  
1. The friction pair gaps first increased slowly, then increased rapidly, finally stabilized after fluctuation during the disengaging process. When the first friction pair gap stabilized, the disengaging process was completed. The increase of ATF temperature  
Figure 6: Variations of torques in the disengaging process with different ATF temperatures.  
(from 60 \({}^{\circ}\)C to 140 \({}^{\circ}\)C) significantly shortened the disengaging duration (shortened by 55.1% from 0.245 s to 0.11 s), but its influence became weaker and weaker.
2. The disengaged friction pair gaps decreased from the first friction pair to the sixth friction pair in sequence. With the increase of ATF temperature, the NUC first decreased then increased slightly, indicating that the disengaging uniformity first increased then decreased slightly. The disengaged friction pair gaps were distributed most uniformly when the ATF temperature was 80 \({}^{\circ}\)C.
3. The disengaging process of a wet multi-disc clutch included the boundary, mixed, and hydrodynamic lubrication stages in sequence. The increase of ATF temperature accelerated the change of lubrication status between friction pairs, shortened the duration of the boundary lubrication stage slightly, and lessened the duration of the mixed and hydrodynamic lubrication stages significantly in the disengaging process.
4. The contact torque decreased in the disengaging process, and the increase of ATF temperature significantly promoted its decrease (promoted by 91.2%). The hydrodynamic torque first increased sharply, then decreased significantly, and finally increased slowly after fluctuation during the disengaging process, and was dramatically reduced by the increase of ATF temperature (reduced by 94.7%).  
Conceptualization, B.M. and M.C.; Data curation, L.Z.; Formal analysis, B.M. and L.Y.; Funding acquisition, B.M. and M.C.; Investigation, L.Z.; Methodology, L.Z.; Project administration, M.C.; Software, L.Z.; Supervision, B.M.; Validation, L.Z.; Visualization, L.Z.; Writing--original draft, L.Z.; Writing--review & editing, L.Z., L.Y. and Q.W. 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 (Grant No. 52175037, No. 51975047 and No. 51805289).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Navigation Model Based on GNSS
### Model Linearization  
In the navigation system composed of Equations (1) and (3), since the navigation models are all nonlinear models, hence, we discretize the state equation and measure equation, and then linearizes it as follows:  
\[\begin{cases}\mathbf{X}_{k+1}=\mathbf{F}_{k+1,k}\mathbf{X}_{k}+\mathbf{w}_{k}\\ \mathbf{Z}_{k}=\mathbf{H}_{k}\mathbf{X}_{k}+v_{k}\end{cases} \tag{4}\]  
where \(\mathbf{X}_{k}\in\mathbf{R}^{6\times 1}\) is the state vector. \(\mathbf{Z}_{k}\in\mathbf{R}^{m\times 1}\) is the measurement vector. \(m\) is the number of visible navigation satellites. \(\mathbf{F}_{k+1,k}\in\mathbf{R}^{6\times 6}\) is the state one-step transition matrix, the expression is as shownin Formula (5). \(\mathbf{H}_{k+1,k}\in R^{m\times 6}\) is the measurement matrix, the expression is as shown in Formula (5). \(\mathbf{w}_{k}\in\mathbf{R}^{6\times 1}\) is the system noise vector, and \(\mathbf{v}_{k}\in\mathbf{R}^{m\times 1}\) is the measurement noise vector. Both of them are zero-mean Gaussian white noise vector sequences, and they are not correlated with each other, that is, satisfying: \(\begin{cases}\text{E}[\mathbf{w}_{k}]=0,&\text{E}[\mathbf{w}_{k}\mathbf{w}_{j}^{\text{T}}]= \mathbf{Q}_{k}\delta_{kj}\\ \text{E}[\mathbf{v}_{k}]=0,&\text{E}[\mathbf{v}_{k}\mathbf{v}_{j}^{\text{T}}]=\mathbf{R}_{k} \delta_{kj}\quad\mathbf{Q}_{k},\mathbf{R}_{k}\text{ is the system noise covariance matrix and the}\\ \text{E}[\mathbf{w}_{k}\mathbf{v}_{j}^{\text{T}}]=0\end{cases}\)  
measurement noise covariance matrix respectively.  
\[\begin{split}&\mathbf{F}_{k+1,k}=\
abla_{\mathbf{X}_{k}}\mathbf{\tilde{f}}( \mathbf{X}_{k})^{\text{T}}=\frac{\partial\left(\mathbf{X}+\mathbf{f}\left(\mathbf{X},k\right)^ {\text{T}}\right)}{\partial\mathbf{X}}|_{\mathbf{X}=\mathbf{\hat{X}}_{k}}=\mathbf{I}_{6\times 6 }+\frac{\partial\mathbf{f}\left(\mathbf{X}\right)}{\partial\mathbf{X}}|_{\mathbf{X}=\mathbf{\hat{ X}}_{k}}T\\ &\mathbf{H}_{k}=\
abla_{x}h(\mathbf{X}_{k})^{\text{T}}=\frac{\partial\mathbf{h} (\mathbf{X})}{\partial\mathbf{X}}|_{\mathbf{X}=\mathbf{\hat{X}}_{k}}\end{split} \tag{5}\]  
where \(T\) is the sample interval.
# Alginate-Chitosan Microencapsulated Cells for Improving CD34+
## 3 Results and Discussion  
### Fabrication of a Durable Double-Layered Microcapsule  
A double-layered alginate-chitosan microcapsule was fabricated to encapsulate CD34+ progenitor cells (Figure 1A,B). The coating is intended to protect the cells from immune cell intrusion and, at the same time, to be permeable enough to allow nutrient and gas exchange or paracrine secretion (Figure 1A). The core is alginate-entrapped cells, and the outer layer consists of chitosan cross-linked with glutaraldehyde (Figure 1B,D). The glutaraldehyde cross-linked chitosan coating improved alginate microcapsule stability (Figure 1C). Based on microscopic observations, the alginate-chitosan microcapsules retained its spherical shape for at least 12 days in culture media. The DNA analysis was conducted to confirm no cell leakage from microcapsules into media. Non-encapsulated cells and fresh media were used as positive and negative controls of DNA detection, respectively. DNA concentration was zero for microcapsule-soaked media (\(n=3\)) and fresh media, whereas DNA concentration of positive control (\(10^{6}\) cells) was 26.3 ng/\(\mu\)L. Chelating agents (i.e., phosphate, citrate, lactate) and non-gelling cations (i.e., Na+, Mg\({}^{2+}\)) contained in the media are known to destabilize calcium alginate gel [23]. Non-coated calcium alginate microcapsules typically break down after 3-day immersion in common saline [19]. Our experimental data suggested the alginate-chitosan microcapsules were able to provide a physical barrier in the employed culture condition.  
Next, the cytotoxicity effect of alginate-chitosan microcapsules was analyzed by an indirect MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay [24] after microcapsules were immersed in media for 3 days. There was no significant difference of cell viabilities cultured in media with or without microcapsules (Figure 1E). This result suggests that the microcapsules do not pose a cytotoxic effect on surrounding cells.
## 3 Experiments
### Experiment on the Volve Model  
The field Volve data used in this paper are from the open-source code of [PERSON] et al. [19]. The Volve field shown in Figure 14a is located in offshore Norway and is a clastic reservoir. There are 1300 labeled traces in this dataset, which were generated using the data augmentation method in [19] based on the statistical characteristics of the well position log data shown in Figure 14b. We randomly selected 750 traces as the training set and the remaining 550 traces were set to be the validation set. We used the single true well log data to test the performance of the two networks. Unlike the previous two datasets, the kernel size was set as 80 to adapt the 160 time sampling points of the Volve model. The number of epochs was set to 500, and the other hyperparameters and network structure were set to be the same as in the former two models. Similarly, when the validation loss is minimal during the 500 epochs, the corresponding \(\sigma_{pre}\) = 0.829 and \(\sigma_{rec}\) = 0.828, then we can calculate that the optimal weight for impedance prediction and seismic data reconstruction as 0.499:0.501. Figure 15 shows the predicted results of impedance prediction by the single-task model (blue) and the multi-task model (green) under the optimal weight. We can see that the impedance predicted by the multi-task model under the optimal weight matches the true impedance (red) better than the single-task model, especially in the time range between 40 and 50 ms. The blue and green dotted lines in Figure 15 represent residuals between the true impedance and the impedance predicted by the two networks. The MSE of the impedance predicted by the multi-task model under the approximate optimal weight is 0.0239, whereas the MSE predicted by the single-task model is 0.0352. In addition, the PCC between the ground truth and the values predicted by the multi-task model and the single-task model are 0.872 and 0.832, respectively. The above two metrics prove the superiority of the multi-task model under the optimal weight. It is worth noting that the test MSE and PCC in reference [19] are 0.0255 and 0.82, which is comparable with our single-task model, but is slightly inferior to our multi-task model. The Volve field data test shows that the proposed method in this paper improves the accuracy of impedance prediction on real seismic data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR (dB)** & **Single-Task** & **Multi-Task (0.28:0.72)** \\ \hline
0 & 0.1300 & 0.2163 \\
5 & 0.0295 & 0.0199 \\
15 & 0.0128 & 0.0068 \\
25 & 0.0120 & 0.0063 \\
35 & 0.0119 & 0.0063 \\
45 & 0.0119 & 0.0063 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MSEs between the prediction of different SNR data and the true impedance of the Overthrust model