esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-to-Plasma Momentum Transfer
###### Abstract  
The momentum of light in a plasma and the momentum transfer from light to plasma is calculated for a uniform plane of light incident into a uniform plasma. At low irradiance, the Minkowski and Abraham expressions for photon momentum are shown to be equivalent. We evaluate relativistic electron motion at a high irradiance for a plasma and show that most light momentum is transferred to the electrons associated with motion parallel to the light propagation at an irradiance corresponding to the reduced vector potential \(a_{\rm o}\approx 3.7\) (reduced irradiance \(l\lambda^{2}\approx 2\times 10^{19}\) W cm\({}^{-2}\)um\({}^{2}\)). Our results show that to ensure the maximum momentum transfer from photons to electrons in motion parallel to the \(\mathbf{k}\)-direction for fixed laser pulse energy, the laser focusing should be adjusted to achieve \(a_{\rm o}\approx 3.7\), even if tighter focusing, and thus higher \(a_{\rm o}\) values, are possible.  
plasma; light; momentum +
Footnote †: journal: Applied
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 4 Test Observations, Results and Analysis
### Cyclic Test
#### 4.3.1 Test Results
The other loops of specimens in groups TC and WA had lots of details that cannot be listed and were also stable, demonstrating a superior performance except for specimen WA-P-50-52-H, in which a clear pinching phenomenon was identified due to the excessively wide plates. Figure 10d shows that specimen WB-P-75-40-H had inferior hysteresis loops with a pinch phenomenon in the middle (also called a \"bow-shaped curve\"). In this study, that was attributed to the detachment of the T-stub flange and inserted plates, which was caused by the bolt shrinkage failure during cyclic loading. The hysteresis loops of other specimens in group WB were not full either, and specimen WB-P-75-52-H in particular suffered a serious pinch effect. Overall, when the width of inserted plates was within 46 mm, the elastic-plastic performance and energy consumption capacity of specimens could be significantly enhanced as expected, but when it continued to increase to 52 mm, exceeding the reasonable range of plate width, the reverse effect of hindering energy consumption occurred. Since plates that are too wide limit the deformation space of flange, this is equivalent to the traditional T-stub connection without plates being inserted.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 2 Datasets
### XCO\({}_{2}\) Enhancements from FLEXPART Model  
We use the Lagrangian particle dispersion model, FLEXPART (FLEXible PARTTicle dispersion model) [27], to simulate anthropogenic and biospheric XCO\({}_{2}\) enhancements at OCO-2 measurement locations. We follow the approach used by [PERSON] et al. [13], where the localized GOSAT CO\({}_{2}\) enhancements are compared to the inventory-based CO\({}_{2}\) estimates using the FLEXPART model outputs. We use the JRA-55 meteorology provided at 60 vertical levels and interpolated to a horizontal resolution of 1.25 degrees. As there are about 85 000 OCO-2 data points per day, for computational reasons, we aggregate OCO-2 data points into one-second averages, taken separately for OCO-2 footprints 1-4 and 5-8 (\(\leq\)12 soundings in one set, 2 sets per second). The sampling corresponds to the OCO-2 data version V7r.  
As input information for anthropogenic emissions, we use the high-resolution ADIAC (Open-Data Inventory for Anthropogenic Carbon dioxide) inventory [28]. In the ODIAC inventory, the anthropogenic CO\({}_{2}\) emissions are estimated at 1 km \(\times\) 1 km resolution given the power plant emissions (intensity and geographical location) and satellite-observed nightlights. In the FLEXPART model simulations, from each aggregated OCO-2 data point, ten thousand virtual particles are released and transported three days backward in time with the three-dimensional wind field using parameterizations for turbulence and convection.
## 1 Introduction
They enable many new useful engineering applications, especially when it comes to the realization of unmanned aerial vehicles (UAVs). For example, UAVs could autonomously perform inventory in warehouses. For this purpose, parallel guides can be attached to the floor or ceiling for navigation between shelves. In addition, UAVs could provide an inspection of rail systems efficiently when a current flows through them. Figure 1: Navigation using a one-axis coil [15]. Figure 2: Principle of a three-axis coil. Another interesting topic is the autonomous performance of inspection and assembly work around power line systems. This is exactly what was investigated in the Aerial-Core ([[https://aerial-core.eu/](https://aerial-core.eu/)]([https://aerial-core.eu/](https://aerial-core.eu/)), accessed on 06.04.2021) project. One of the goals of the project was to develop a technology that will enable a UAV to perform (Figure 3) the placement of distance separators between power lines. This is usually performed by workers who are exposed to very high electric and magnetic fields. Moreover, such a task requires a highly skilled helicopter pilot to keep the helicopter close to the power lines while a worker performs the installation. Therefore, the main objective of this work is to derive the equations for determining the position and orientation of a UAV moving through the magnetic field of two parallel conductors, thus enabling precise navigation of a UAV in close proximity to the power lines. We assumed that a UAV is equipped with visual and other sensors that are used both for navigating farther away from the lines and for approaching the lines.
## 4 Analysis and Results
### Model Fit  
Since the structural research model needs to be tested with the present collected data for the model fit, this study uses the maximum likelihood method. Scholars proposed several criteria to examine the model fit from different perspectives, such as [PERSON]; [PERSON] and [PERSON]; [PERSON] and [PERSON] [50; 51; 52]. A total of 194 papers from American Psychological Association Journals were collected and reviewed from 1998 to 2006. By comparing confirmatory factor analysis (CFA), the recommended criteria and standard for model fit have been proposed, such as \(\chi^{2}\), df, \(\chi^{2}\)/df ratio, GFI, AGFI, RMSEA, SRMR, CFI and TLI(NNFI), etc. The basic assumption of structural equation model is that the covariance matrix of model and sample should be the same. However, since SEM is the big sample analysis, it is easy for \(p\) value less than 0.05, which make failure for hypothesis rejection. The research model fit would not have a good result. Therefore, according to [PERSON] [53], the Bootstrap method was adopted to do the modification.  
Table 5 displays sorts of model fits indicators with the suggested thresholds. Most of the results reach acceptable levels, according to [PERSON] [51]. Only \(\chi^{2}\) does not come out as the satisfied result, which should be less than 3. Since \(\chi^{2}\) is very sensitive to a large number of samples, we need to calculate the ratio of \(\chi^{2}\) over its degree of freedom. [PERSON] [54] suggested implementing the combined rules for the model fit criteria. That would make the type I error controlled better.
## 3 Experimental Results
### Experimental Results
#### 3.3.1 Experiments with the Parameter \(N_{l}\)
\begin{table}
\begin{tabular}{c c c c c} \hline
**DATASET** & **INNC (\(F=1.5\))** & **INNC (\(F=2.0\))** & **INNC (\(F=4.0\))** & **INNC (\(F=8.0\))** \\ \hline ABALONE & 4.49 & 4.43 & **4.33** & 4.66 \\ \hline AIRFOIL} & 0.002 & 0.002 & **0.002** & 0.002 \\ \hline BASEBALL & **48.31** & 50.38 & 48.42 & 48.52 \\ \hline BK & 0.06 & 0.21 & 0.07 & **0.02** \\ \hline BL & 0.003 & 0.004 & **0.002** & 0.003 \\ \hline CONCRETE & **0.005** & 0.005 & 0.005 & 0.005 \\ \hline DEE & **0.23** & 0.23 & 0.23 & 0.23 \\ \hline HO & **0.01** & 0.01 & 0.01 & 0.01 \\ \hline HOUSING & 16.91 & 16.34 & **16.01** & 16.96 \\ \hline FY & 0.042 & 0.043 & **0.042** & 0.043 \\ \hline LASER & 0.005 & 0.005 & 0.005 & **0.004** \\ \hline LW & **0.011** & 0.012 & 0.012 & 0.012 \\ \hline MORTGAGE & 0.027 & 0.025 & 0.026 & **0.024** \\ \hline MUNDIAL & 0.035 & **0.033** & 0.034 & 0.23 \\ \hline PL & **0.022** & 0.022 & 0.022 & 0.022 \\ \hline QUAKE & **0.036** & 0.037 & 0.04 & 0.036 \\ \hline REALESTATE & 72.40 & 71.16 & 70.99 & **70.76** \\ \hline SN & 0.024 & 0.024 & **0.023** & 0.024 \\ \hline TREASURY & 0.07 & 0.068 & **0.066** & 0.068 \\ \hline TZ & **0.029** & 0.031 & 0.03 & 0.031 \\ \hline VE & 0.032 & 0.03 & **0.025** & 0.028 \\ \hline
**AVERAGE** & **6.80** & **6.81** & **6.69** & **6.75** \\ \hline \end{tabular}
\end{table} 
Table 9: Experimental results using a variety of values for the parameter \(F\) with application on the the regression datasets. The bold notation marks the method with the lowest test error.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 4 Discussion  
This paper proposes a novel deep learning method for seismic signal denoising based on the time-frequency domain representation of the signal. The proposed method utilizes a generative adversarial network (GAN) to separate the seismic signal and noise in the time-frequency domain. The generator network takes the real and imaginary parts of the short-time Fourier transform (STFT) of the seismic signal as input and outputs two time-frequency masked signals to separate the seismic signal and noise. The discriminator  
Figure 10: Noise residual comparison for ChinArray X1 Network. (**a**–**c**) represent the noise signals separated by DeepDenoiser, ARDU, and the method proposed in this paper, respectively. In (**a**), the S-wave and weak P-wave phases can be clearly observed, indicating that DeepDenoiser damaged the effective signal during the denoising process. In (**b**,**c**), there are no obvious seismic phases, suggesting that both ARDU and the proposed method cause less damage to the effective signal.  
network consists of a feature extraction module and a discrimination module. The feature extraction module adaptively extracts signal features and calculates perceptual losses, while the discrimination module determines whether the input signal is a true signal or noise. Compared to existing deep learning methods, the proposed method employs multiple loss functions to comprehensively evaluate the denoising effectiveness, achieving better denoising performance, especially for low SNR signals. To demonstrate the effectiveness of the proposed method, this paper conducts qualitative and quantitative analyses of the denoising results on synthetic and real seismic data. The experimental results demonstrate that compared to DeepDenoiser and ARDU, the proposed method can more effectively suppress noise while preserving the true signal.  
Formal analysis, M.W. and J.Z.; methodology, M.W.; writing, M.W.; review and editing, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDB42020304) and the National Natural Science Foundation of China (grant No. 42074059).  
Not applicable.  
Data Availability StatementData source: STEAD ref. [38]. ChinArray X1 Network waveform data were provided by the International Earthquake Science Data Center (Doi:10.11998/IESDC).  
The authors declare no conflicts of interest.
# Solution to the Inverse Problem of the Angular Manipulator Kinematics with Six Degrees of Freedom
## 4 Discussion
For point K, this is an unfolding spiral, and for point E, this is the collapsing spiral with centers on a vertical line passing through point D. The initial and final positions of the output link during such a motion are shown in Figure 9a,b, respectively. Thus, when implementing the algorithm, expressions (9) and (15) or (16) are constructed in the time cycle from 0 to t1. The time functions of all six link rotation angles are calculated (see Figure 12a,b). After that, the direct kinematics problem is calculated for the obtained functions. There are no special features here. This is a classical problem usingthe Denavit-Hartenberg matrices, which is essential for independent verification of the proposed algorithms built on other principles. The results of such calculations will be the time functions of the coordinates of points K, E, D. These are the functions shown in Figure 9c. The total discrepancy in the coordinates of these points did not exceed \(1.10^{-11}\) mm. The meaning of the given example is also that in the initial position, Expression (3) degenerates for point K, as well as in the final position for point E. Thus, the obtained results confirm the consistency of the algorithm from this side as well. It is clear that when conducting a large number of such numerical experiments, all expressions have to be checked.
## 3 Analysis Method  
### Pattern Recognition Algorithm  
To identify the damage mechanisms in the reactive specimens a pattern recognition algorithm was employed for data classification. Pattern recognition is under the machine learning field and has two major types: unsupervised and supervised. When a background history for a data set is available or the data set has labeled classes, supervised pattern recognition is employed. If there are no labeled classes available, unsupervised pattern recognition can be utilized to identify the potential patterns in the data set based on the selected features.  
An agglomerative hierarchical clustering algorithm [25] was employed for classifying the AE data into subsets. The clustering procedure is chronologically illustrated in Figure 3. The first step in the pattern recognition was deriving the frequency-based features for the signals. AE signals were transferred into the frequency domain using the Fast Fourier Transform (FFT). The FFT amplitude spectra were determined for each AE signal and the frequency domain was divided into ten frequency ranges. Then, signal energy in each frequency range was calculated and normalized to the total energy of the signal. These ten signal energies are the signal features. For example, in Figure 3a, the area of the hatched region is the energy of the signal in the frequency band from 200 kHz to 250 kHz. This value is normalized to the total energy of the signal, which is the entire area under the FFT spectrum. Principle component analysis (PCA) was used to reduce redundancy in the data. In this analysis, the original data is projected to the new orthogonal coordinates having high variation. An input for PCA is a matrix with the number of columns and rows equal to the feature numbers and the number of hits. Then a variance-covariance matrix for the features was calculated. The coefficients and variance of a specific principle component were calculated by eigenvalue analysis of the variance-covariance matrix. The principal components were selected in a way that represented more than 90% of the entire data. The principal components of the original features were selected as the input features for the pattern recognition algorithm. The algorithm initially calculated the Euclidian distance between the resulting data from PCA analysis. The result was a proximity matrix that contained distances between the original objects (data).  
The objects were initially linked together according to calculated distances in the previous step and [PERSON]'s method. [PERSON]'s method is based on calculating the total within-cluster sum of squares of the data resulting from combining the clusters [25]. In each level, the data was merged into a binary linkage and the clusters were again merged into new clusters according to the [PERSON]'s method. This procedure was continued to form a single cluster which includes all data. The number of the cluster was determined according to the developed dendrogram and the height of each link with respect to the adjacent links [26].
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 3 Strain Transfer Ratio Experiment
### Experimental Data Analysis  
The average strain transfer rates directly drawn from three groups of experimental data are shown as Figures 16-18:  
In traditional investigation, strain measured by FBG is considered to be equal to the cantilever beam theoretical strain. This is not accurate. Actually, the fiber is above the surface of the cantilever beam, which cause the measured strain to be higher than the theoretical strain. As to say, the average  
Figure 16: The unresolved average strain transfer rate of the FBG (1570 nm).  
Figure 17: The unresolved average strain transfer rate of the FBG (1525 nm).  
Figure 18: The unresolved average strain transfer rate of the FBG (1520 nm).  
strain transfer rate must not be greater than 100%. Hence, it can be concluded that the height of fiber above the surface of the cantilever beam has more influence on strain measurement than the average strain transfer rate.  
To measure the average strain transfer rate of FBG accurately, the impact of \(\alpha_{L}\) must be eliminated. The diameter of the bare polyimide FBG is about 130 \(\upmu\)m, so the radius of it is 65 \(\upmu\)m. Because the thickness of the bonding layer was uneven, a thickness gauge was used for measurements along the axial direction of the fiber. After multiple-time measurement, the mean value of adhesive thickness was found to be 50 \(\upmu\)m. \(\alpha_{L}\) can be calculated by the following equation:  
\[\alpha_{L}=1+2\times\frac{0.065+0.05}{3.5}=1.067\]  
Table 1 shows the average strain transfer rates from three groups of experiments, theoretical average strain transfer rates calculated from Equation (11), and relative errors.  
According to experimental results, the measured average strain transfer rates exceeded 96%, and the relative error with theoretical average strain transfer rate was below 1%. With consideration of experimental operation errors, it can be concluded that the average strain transfer model proposed in this paper is able to produce results that matches the measured results.
## 2 Discrete Element Parameter Model and Parameter Calibration of Soil-Soil Interaction
### Simulation Test
#### 2.5.3 Box-Behnken Design Test
Table 8 reveals that, when considering pairwise interactions among the three significant factors, the influence on the heap angle decreases in the following order: GE, GA, and then AE. A detailed analysis of the interaction response surfaces is presented in Figure 6a,c,e. From Figure 6a, it can be observed that, when the soil-steel static friction coefficient (E) remains constant, the heap angle initially decreases and then increases with an increase in the soil JKR surface energy (G), and this change trend is very apparent. When the soil JKR surface energy (G) remains constant, the heap angle also initially decreases and then increases with an increase in the soil-steel static friction coefficient (E), albeit with a slightly diminished change trend compared to the variation in soil JKR surface energy (G). From Figure 6c,e, it can be observed that when one factor remains constant at a certain level, the other factor exhibits a slow increasing trend. Figure 6: GE interaction analysis diagram of response surface. (**a**) GE Response surface, (**b**) GE Contour line, (**c**) AG Response surface, (**d**) AG Contour line, (**e**) AE Response surface, (**f**) AE Contour line. Contour plots are curves formed on the base surface by identical factor values on the surface (Figure 6b,d,f). The closer the contour profile is to a perfect circle, the smaller the interaction between the two factors. Conversely, the closer the contour profile is to an ellipse, the greater the interaction between the factors. In the instance of an interaction, one factor exerts different effects at different levels on another factor. Using the optimization module within Design-Expert software and setting the measured pile angle value of 35.55\({}^{\circ}\) as the target, a close set of solutions was obtained, consisting of the following parameters: JKR surface energy of the soil (G) = 5.85 J\(\cdot\)m\({}^{-2}\), restitution coefficient among soil particles (A) = 0.65, and static friction coefficient between soil and steel (E) = 0.5.
## 5 Summary and Conclusions  
This study was aimed mainly at the ill-conditioned problem in medium-long baseline RTK. The height component is highly correlated with troposphere parameters and was unstable in the medium-long baseline RTK solution. For the BDS system, due to a lack of variation for BDS GEO satellites and nonhomogeneous satellite distribution, the height component was more difficult to distinguish from the troposphere zenith delay compared with the GPS system. As a result, the performance of BDS medium-long baseline RTK was worse than that of GPS, particularly for the height direction. Even for the GPS/BDS-combined RTK, although the geometric structure is evidently improved, due to the lack of variation in geometry structure, the convergence time of ambiguities for the BDS GEO satellite was significantly longer than that of satellites with other orbit types, which may result in lower precision in height direction in the initial period.  
To reduce the ill-conditioned problem and alleviate the negative effects of BDS GEO satellites, the GPT2-constrained RTK model was used and evaluated. In the case of 15 baselines at the medium latitude of China, with a variance of 4 \(\times\) 4 cm\({}^{2}\), the results show that the ambiguity fixed rate of BDS was improved by approximately 7.5%, whereas only a slight improvement was observed for GPS/BDS when the GPT2-constrained RTK model was used. Compared with the traditional RTK model, the STD of the height component was reduced by an average of 52.4% and 34.0% for BDS-only and GPS/BDS-combined situations, respectively. In terms of RMS error, the average performance was improved by 40.0% and 19.8% for the BDS and GPS/BDS-combined situations, respectively. It should be noted that this method could improve the performance of medium-length RTK only to a certain degree. For the bad satellite geometric structure, it is also difficult to acquire satisfying results. Meanwhile, the accurate a priori tropospheric information is difficult to obtain in some conditions (e.g., long baselines, large elevation differences, or bad weather conditions). As a result, this method is applied to baselines with medium length. In addition, a larger variance is usually proposed to ensure reliability.
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 2 Materials and Methods
### Face Detection  
In any face recognition program, automatic face detection in input images is crucial. If not executed correctly, it can hinder further analysis. Several popular face detection algorithms include:  
- Kawulok-Szymanek Algorithm: This method employs the Hough transform to detect image ellipses. The results are then verified using a support vector machine [58].  
- Deformable Part Model (DPM) Algorithm: this utilizes the Histogram of Oriented Gradients (HOG) for face detection [59].  
- Viola-Jones Algorithm: this approach involves feature selection and AdaBoost classification [60].  
- Zhu-Ramanan Algorithm: this creates local models characterizing facial structure, leveraging biometric features regardless of head angle [4].  
- Maximum Margin Object Detection Algorithm (MMOD): using a support vector machine, MMOD employs face classification based on the HOG descriptor [61].