esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigations of Pile Group Foundations under Different Pile Length Conditions
## 2 Bearing Principle  
Steep slope group pile foundations, often situated on steep slopes or riverbanks, exhibit distinct bearing mechanisms and deformation characteristics compared to common group pile foundations on flat ground. These characteristics include the steep slope effect on the loaded section and the embedding depth effect on the embedded section. Many scholars use the classic [PERSON] theory of lateral strength of piles to assess the bearing capacity of piles under lateral loads. This theory is simple and practical, requiring only simple parameters, making it suitable as a preliminary assessment tool. However, this theory is only applicable to small lateral displacements and relatively small lateral loads. Therefore, for the case of group pile bearing capacity discussed in this article, this theory does not apply.  
The stress situation is depicted in Figure 1. In Figure 1, the distance b1, from the plane of point O to the slope surface at the front side of the foundation, should not be less than 4d, where d represents the diameter of the pile. The value of 4d corresponds to four times the diameter of the pile. If b1 is less than 4d, the analysis plane at point O should be lowered by d1, ensuring that b1 is not less than 4d. By doing so, the bearing performance of the soil between the piles and the soil on the front side of the foundation within this range is not considered. The soil behind the foundation within this depth should act in the form of active earth pressure q\({}^{\prime}\) on the pile foundation. In the loaded section above the sliding surface, the pile body primarily experiences landslide thrust. Conversely, in the embedded section below the sliding surface, the pile body mainly encounters the foundation reaction force. The foundation reaction force increases as the piles get closer to the front slope face. Consequently, with the same length of each foundation pile, the back row of piles with excessive bearing performance can be made shorter. However, the length difference of the group pile foundation should not be too significant. Therefore, the pile length can be adjusted appropriately according to the slope conditions.
## 5 Experimental Analysis of Acoustic Propagation Characteristics about High-Energy Pulse Excitation
### Lift-Off Characteristic of High-Energy Pulse EMAT  
The induced eddy current and magnetic field in the skin layer of the steel plate are very sensitive to the change of lift-off distance. The magnetostrictive force is directly affected by the magnetic flux density in the steel plate. The Lorentz force is generated by the interaction of the magnetic field and the induced eddy current in the steel plate. It is necessary to analyze the effect of the Lamb wave excited about the lift-off distance in the steel plate. Acoustic propagation experiments were carried out at different lift-off distances, and the Lamb wave amplitude data were extracted at the lift-off distance from 0 mm to 4 mm. The normalized data was obtained by taking the logarithm of the Lamb wave data as the reference at the distance \(h=0\) mm, and the logarithm of the normalized data was obtained. Figure 13 shows the (\(h,\,\ln(P)\)) curves at different distances. \(P\) is the amplitude of the received ultrasonic wave.  
In Figure 13, as the lift-off distance h increases, the Lamb wave S0 mode and A0 mode signals gradually decay, and the attenuation rate of S0 is significantly lower than that of A0. When the lift-off distance \(h>2\) mm, the attenuation amplitude and noise amplitude of the Lamb wave A0 mode are in the same order of magnitude. The experimental result deviates from the expected result. However, when the lift-off distance \(h\leq 2\) mm, the Lamb wave signal is clear and easy to identify. The experimental results are consistent with the simulation expectation. Therefore, when the lift-off distance is small, the Lorentz force \(\mathbf{F_{L}}\) and the magnetostrictive force \(\mathbf{f_{M}}\) work together to make the particle vibrate periodically to excite Lamb waves in the steel plate. The Lorentz force \(\mathbf{F_{L}}\) decays faster than the magnetostrictive force \(\mathbf{f_{M}}\) with the lift-off distance increases. When the lift-off distance is larger, the Lorentz force transduction efficiency decays to negligible. The high-energy pulse electromagnetic acoustic transduction mechanism in the steel plate is mainly dominated by magnetostrictive force.  
Figure 12: Received Lamb at the distance \(h=0\) mm.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Rationale for Model Selection  
The Random Forest and the Gradient Boosting techniques were selected over other methods because they perform well on varieties of predictive tasks, and are robust methods in handling nonlinearity in the data with large samples. Gradient Boosting tends to outperform Random Forest in determining through ensemble learning the more accurate predictions, while Random Forests tend to be effective in minimizing overfitting. The resource utilization pattern observed in Apache Spark MLlib operators was complex enough to make these models preferred over simpler alternatives like Linear Regression. However, we included simpler models such as Linear Regression for baselines as a comparison to highlight the power of these advanced techniques.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 4 LCoS for Augmented Reality Displays
### Augmented Reality Head-Up Display  
Head-up display (HUD) was first developed for military aircraft. Nowadays, it is extended to automobile driving assistance. By projecting a virtual image onto the windshield, the driver does not need to head down to watch for the information displayed on the cockpit cluster. In so doing, the distraction could be minimized. A schematic of today's HUD is shown in Figure 14. By using a double mirror (M1 and M2) reflection and the windshield as the combiner, the displayed information can be integrated with the front scenery. In some cost-effective designs, an additional combiner is employed so that the HUD box works with multiple vehicle models. Current HUD systems are employing transmissive TFT-LCD panels, providing a limited FoV and a relatively short virtual image distance (VID). The typical FoV = 5\({}^{\circ}\)\(\times\) 3\({}^{\circ}\) and VID = 2 m still need to be improved keeping the compact package size [112]. Multiple studies suggest a preferred HUD information placement at 5\({}^{\circ}\) eccentric to the right and a FoV no larger than 20\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) for fast recognition [113, 114]. Display registration with the real-world objects is also highly recommended, requiring a variable VID with a wide range [112, 113, 115, 116]. The LCoS-integrated system could realize holographic AR-HUD with a larger FoV (>10\({}^{\circ}\)\(\times\) 5\({}^{\circ}\)) and a variable VID (>10 m) [93]. From the LCoS device aspect, the requirements in AR-HUD are somewhat different from that in AR-HMD: (1) the longer VID (>2 m) and the smaller FoV help relieve the burden on resolution; (2) the driving conditions with an intense daylight demand a much higher display luminance for keeping the high ambient contrast ratio [117]; (3) the LCoS device is expected to work in extreme environments with a wide temperature range [59, 118]. Among them, the thermal management challenge related to high luminance is a major concern.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 2 Design and Analysis of Rail-Mounted Automatic Welding Equipment
### Mechanical Design of the Equipment's Core Mechanisms
#### 2.1.1 Mechanical Design of the Rail and Base  
As shown in Figure 2, the track consists of two parallel assembled guide rails. These guide rails are constructed from multiple profiled supports and single-flanged guide rails, mechanically installed across the small assembly workpiece. The profiled supports and vertical supports within the guide rails form a ladder-shaped structure, enhancing the track's stability and strength to prevent deviation or deformation. The motor shaft drives the end gear to mesh with the rack and, in coordination with the rollers, moves the base mechanism along the single-flanged guide rail. The rollers have a circumferential groove along the axial direction that matches the edge of the single-flanged guide rail. The two engage with each other, providing precise guidance for the base and reducing frictional losses.  
Figure 1: Overview of track equipment.  
Figure 2: Partial diagram of the track and base.
## 4 Results
### Model Evaluation  
#### 4.3.1 Construction of Evaluation Metrics System  
To evaluate the performance of the prediction model, a multi-dimensional evaluation metric system is employed for a comprehensive evaluation. Specifically, the Root Mean Square Error (RMSE) is used to measure the degree of deviation between predicted and actual values, the Mean Absolute Percentage Error (MAPE) to reflect the relative accuracy of the prediction results, and the coefficient of determination (r\({}^{2}\)) to assess the model's ability to explain data variability. Among these three metrics, smaller values for RMSE and MAPE indicate smaller prediction errors. The r\({}^{2}\) value ranges from [0, 1], with higher values signifying better prediction accuracy and a better fit. Their calculation formulas are as follows.  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}{(y_{i}-\hat{y}_{i})}^{2}}{N}} \tag{4}\]  
\[MAPE=\frac{1}{N}{\sum\limits_{i=1}^{N}{\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}}} \tag{5}\]  
\[r^{2}=\frac{\sum\limits_{i=1}^{N}{(\hat{y}_{i}-\overline{y})}^{2}}{\sum \limits_{i=1}^{N}{(y_{i}-\hat{y}_{i})}^{2}} \tag{6}\]  
where \(N\) represents the number of samples, \(y_{i}\) represents the actual value, and \(\hat{y}_{i}\) represents the predicted value.
## 5 Discussion
### Concordia: Amplitude and Phase Scintillations
Figure 12 reports the variations in the \(H\) and \(Z\) components of the magnetic field recorded at the DMC ground magnetic station located at Concordia. As in the case of SNA, these two magnetic components help reveal the location of the DMC magnetic station with respect to the auroral electrojet current systems during the selected day. The station is always located inside the auroral oval, moving from 22:00 to around 09:00 UT underneath the auroral electrojet eastward and from 09:00 and 22:00 UT underneath the westward one. According to the values of the \(Z\) component, DMC magnetic station is underneath the bulk of the current only in the time interval from 15:00 to 20:00 UT, when the values of the \(Z\) component are close to zero. Finally, Figure 13 shows the isocontours of the ionospheric potential (red = positive; blue = negative potential) derived from SuperDARN measurements along with the location of measurements themselves (black squares) recorded in the two minutes following the time shown at the top of each map. In each map the projection to 350 km of altitude of all the \(\sigma_{\varphi}\) values, which have elevation angle \(\alpha\) = 30\({}^{\circ}\) (purple circles), recorded in the same time interval as for SuperDARN observations by the GNSS receiver at Concordia, are also shown. In all maps, the location of the GNSS receiver's field of view (purple circles) with respect to the configuration of the ionospheric potential cells suggests that amplitude scintillations occurred in the ionospheric region between the two convection cells. Such region is well-known to be characterized by fluid and magnetohydrodynamic turbulence in ionosphere or in magnetosphere, which map to the ionosphere [62; 93; 94], which is able to generate small-scale irregularities. In addition, inside the GNSS receiver's field of view (purple circles) SuperDARN recorded echoes back from the ionosphere, confirming the presence of irregularities whose spatial scale is well below the L-band Fresnel scale [47]. The higher level of phase scintillation than the amplitude scintillation at Concordia is due to the already mentioned detrending filtering issue [29]. [PERSON] et al. [29] report also an  
Figure 12: \(H\) and \(Z\) magnetic field components recorded at DMC magnetic station at Concordia on 8 September 2017.  
estimate of the plasma relative velocity ranging from 200 to 500 m/s, confirming the highly dynamic scenario in which phase and amplitude fluctuations were observed.
## 3 WOA-VMD Denoising Method
### Denoising Method  
Multi-scale permutation entropy (MPE) can be used to analyze signal mutation characteristics. The timing signal \(x=\{x_{1},x_{2}...,x_{L}\}\) carries out multi-scale coarser granulation  
Figure 1: Variational mode decomposition parameters optimization process by whale optimization algorithm.  
and reconstruction, and calculates the PE of signals at different scales. The steps are as follows [18]:  
1 Multi-scale coarsening of temporal signals.  
In this step, the time series is divided into different scales and averaged in each segment so as to obtain the coarse-grained time series \(Y^{s}{}_{t}\). The calculation formula is shown in Equations (14) and (15).  
\[y^{s}_{j}=\frac{1}{s}\sum\limits_{i=(j-1)s+1}^{js}x_{i}\quad 1\leq j\leq L \tag{14}\]  
\[Y^{s}_{t}=\left\{y^{s}_{t},y^{s}_{t+\tau},\cdots,y^{s}_{t+(m-1)\tau}\right\} \tag{15}\]  
where \(s\) is the scale factor; \(y^{s}_{j}\) is the time series; \(\tau\) is the delay; \(m\) is the embedding dimension; \(Y^{s}{}_{t}\) is the reconstructed sequence.  
2 Reconstruction of temporal signals.  
Reconstruct the \(Y^{s}{}_{t}\) ascending order to obtain the m! permutation. Moreover, the number of occurrences \(N_{1}\) and probability \(P^{s}{}_{l}\) of each permutation were calculated.  
\[y^{s}_{t+(j_{1}-1)\tau}\leq y^{s}_{t+(j_{2}-1)\tau}\leq\cdots\leq y^{s}_{t+(j_ {m}-1)\tau} \tag{16}\]  
\[P^{s}_{l}=\frac{N_{l}}{n/s-m+1} \tag{17}\]  
3 Calculation of permutation entropy at multiple scales  
The permutation entropy is calculated at different scale factor \(s\) and normalized to obtain multi-scale permutation entropy.  
\[h^{s}_{P}=H^{s}_{P}/\ln(m!)=\frac{-\sum\limits_{l=1}^{m!}P^{s}_{l}\ln P^{s}_{l }}{\ln(m!)} \tag{18}\]  
The improved algorithm is applied to decompose the measured signals, and the \(K\) components of IMF are obtained. The dominant frequency increases with the increase in \(K\) value. MPE values of different scales were calculated for each IMF, and noise components were identified by means. After removing the noise components, the IMF was reconstructed to obtain the denoised signal.
## 4 Results
### Machine Learning Approach  
The original, mature miRNAs (_n_ = 420) were first filtered using a supervised hybrid filter-wrapper approach as the feature-selection method. As the result, top 50 ranked miRNAs were identified, and an unsupervised machine learning approach using SOM was performed.  
The input data were presented in the 2 \(\times\) 2 feature output space, which consisted of four neurons in total. A total of 50 features of the data were clustered into 4 neurons. The mean distance was calculated based on the position of each neuron. Following that, additional subclustering was subsequently carried out, using HC on the 2 \(\times\) 2 feature output space to split the four neurons into two clusters, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**DEMi** & **Log2 FC** & _p_**-Value** & **Regulation** \\ \hline hsa-miR-941 & 0.752891 & 0.020286 & Up \\ \hline hsa-miR-1273c & 0.72872 & 0.016058 & Up \\ \hline hsa-miR-150-5p & 0.685625 & 0.049536 & Up \\ \hline hsa-miR-10a-5p & 0.597925 & 0.023684 & Up \\ \hline hsa-miR-9-5p & 0.563954 & 0.011416 & Up \\ \hline hsa-miR-1307-3p & \(-\)0.59803 & 0.024833 & Down \\ \hline hsa-miR-877-5p & \(-\)0.61671 & 0.048433 & Down \\ \hline hsa-miR-4433b-5p & \(-\)0.67039 & 0.036891 & Down \\ \hline hsa-miR-143-3p & \(-\)0.69404 & 0.048802 & Down \\ \hline hsa-miR-1296-5p & \(-\)0.85825 & 0.018019 & Down \\ \hline hsa-miR-5100 & \(-\)1.21725 & 0.046714 & Down \\ \hline hsa-miR-6501-5p & \(-\)1.34159 & 0.003957 & Down \\ \hline \end{tabular}
\end{table}
Table 1: DEMi candidates selected using edgeR.  
Figure 2 illustrates the subclusters of the four neurons generated in SOM. Of the four neurons, three had higher connectivity with one another (pink), which indicated that these neurons were located in the same cluster. In contrast, the remaining cluster contained only one neuron (black).  
Next, the result was visualized using PCA to produce a more interpretable view of the miRNA clustering. Figure 3A shows the distribution of the miRNAs in the two clusters, as extended from Figure 2. Figure 3B shows the distribution pattern of the samples, indicated by the pointing of the arrows that originate from the centre point. All of the AD samples were located on the right side of the plot, indicating higher values in these samples. Corresponding to the pattern of miRNA clustering in Figure 3A, the miRNAs in SOM cluster 1 (red dots) showed a similar distribution to the AD cohorts in Figure 3B. Hence, the member miRNAs in SOM cluster 1 in Figure 3A were identified, and 24 miRNAs were selected as DENi candidates.  
Figure 3: PCA plot of (**A**) clusters of top 50 ranked miRNAs based on SOM; (**B**) sample distribution corresponding to PCA: \(x\)-axis = PC1, \(y\)-axis = PC2.  
Figure 2: Clustering of SOM neurons using HC.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods
#### 3.3.5 Karaboga's Method
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# Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline
## 4 Simulation and Experimental test
### Dynamics Simulation  
To verify the independent adjustment device and in-pipe robot differential motion process, we established simulation model by [PERSON].  
First, a one-meter diameter experimental steel pipe was set. This pipe is provided with two gentle obstacles (up to 150 mm), located at the top and bottom left of the pipe, respectively. Each obstacle is 1.5 m long and two meters apart.  
Figure 12: Changes in support force (**a**) and support force position (**b**) with the length of the putter e when the center of gravity is at the center of the robot (j = 0.521 0.5 m) and changes in support force (**c**) and support force position (**d**) with the length of the putter e (the center of gravity is not at the center of the robot).  
The robot was in a stable posture depicted in Figure 6a at the beginning. Gravity and friction were set close to the reality. Driving robot forward based on the force in each crawler. Set soft contact between robot and pipe.  
By using two different ways, robot passed these two obstacles. The first was the traditional way of simultaneous expansion with three crawlers (red) and the second was the three-crawlers independent adjustment method (blue). As shown in Figure 13b, measured the pressure on the lower left crawler. Force on crawler in the independent adjustment device was more uniform and returned to stability faster.  
Next, a one-meter diameter straight pipe is used for another simulation, which is just the same as our real steel pipe experimental platform. Other settings of the robot were the same as before.  
Calculated by previous theory, the force provided by the motor (191.7 N) as the driving force. At the beginning, set the two crawler's force to increase or decrease by 1% as Figures 6b and 7a. Then, made the robot move forward until it stably runs. A virtual angle sensor was added to the robot to measure the rotation angle. In addition, the elapsed time was counted. After that, gradually increased the deviation of the force (PCT) and repeated the previous process. Maximum turning angle (' ) and the required time were obtained. As shown in Figure 14, with the increase of the force deviation, maximum stable angle and required time for stabilization gradually increased. Maximum turning angle robot could reach was 14. In addition, robot motion would be very unstable when the differential ratio exceeds 15%.  
Figure 14: Rotation process time and maximum achievable angle under different tripod differential force ratios.  
Figure 13: Independent adjustment analysis. (a) prototype in Adams; (b) feedback of the supporting force on one crawler in two ways.