esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Design and Evaluation of Square Reflector
## 3 Analysis and Evaluation of Simulation Results
### Static Analysis  
During the installation and adjustment of the space camera on the ground, the mirror will be affected by gravity, and it is necessary to analyze the degree of influence through simulation during the development process and take corresponding countermeasures [19; 20]. The static analysis is to analyze the deformation of the reflector assembly under the action of gravity load and temperature load. According to the temperature range given by the thermal control analyst, the temperature condition of the reflector assembly is loaded. In this paper, the temperature load of the reflector assembly is 15 \({}^{\circ}\)C\(\sim\)25 \({}^{\circ}\)C. The axial and radial directions of the reflector assembly will show different characteristics due to the influence of temperature. It is also necessary to apply a 5 \({}^{\circ}\)C temperature gradient load to the mirror assembly axially and radially.  
In this paper, the mirror assembly is loaded with a 1 g gravity load, forced displacement load, 15 \({}^{\circ}\)C\(\sim\)25 \({}^{\circ}\)C temperature load, 2 \({}^{\circ}\)C axial gradient load, and 2 \({}^{\circ}\)C radial gradient load. The results of the variation of the root mean square value of the reflector assembly shape in the spatial environment by simulation analysis are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Order** & **Frequency (Hz)** & **Mode Shape Description** \\ \hline
1 & 173.3 & The primary mirror rotates around the Z axis \\
2 & 186.9 & Primary mirror X-direction translation \\
3 & 204.4 & Primary mirror Y-direction translation \\
4 & 292.9 & The primary mirror rotates around the Y axis \\ \hline \end{tabular}
\end{table}
Table 7: Results of analysis of the four modes of the mirror assembly.  
\begin{table}
\begin{tabular}{c c c} \hline
**First-order mode shape** & **Second-order mode shape** \\ \hline
**Third-order mode shape** & **Fourth-order mode shape** \\ \hline \end{tabular}
\end{table}
Table 6: Four vibration modes of the mirror assembly.  
After completing the simulation analysis and calculation of the 1 g gravity load, forced displacement (5 \(\upmu\)m) load, \(20\pm 5\)\({}^{\circ}\)C temperature load, axial gradient 2 \({}^{\circ}\)C load, and radial gradient 2 \({}^{\circ}\)C load, the average value of the root mean square of the reflector assembly surface shape can be obtained by substituting the data results in Table 8 into Equation (3).  
\[\begin{array}{l}\sqrt{x^{2}+y^{2}+t_{(20\pm 2^{\circ})}^{2}+s_{(forcodidisplacement)}^{2 }+t_{(Axidoratial\,2^{\circ})}^{2}}\\ =\sqrt{0.0086^{2}+0.0106^{2}+0.00191^{2}+0.00144^{2}+0.00713^{2}}\lambda\\ =0.0156\lambda\end{array} \tag{3}\]  
The calculated mean value of the root mean square of the mirror surface shape is 0.0156\(\lambda\).
## 4 Privileged User Behavior Audit Subsystem
### Deep-Learning-Based Text Information Extraction
#### 4.2.2 Text Recognition Process  
The text recognition process aims to transform the text regions into editable characters (the argument \"text\" in the audit database). Considering that the text images belong to sequence-like objects, the Convolutional Recurrent Neural Network (CRNN) [23; 24], a combination of CNN and RNN, is adopted in this paper. The images generated during user operations often contain incomplete characters. Therefore, a pre-process is designed to segment the text regions and remove incomplete characters.  
Pre-process before text recognition: The pre-process modifies the text regions to uniformly formatted text regions of the same size and no incomplete characters. This process mainly contains three steps: 1. Segmenting the text regions based on the OSTU [25] algorithm, the segmentation in this step is according to the gap between characters; 2. Removing text regions whose size is below the threshold, these small text regions are considered incomplete words or characters; 3. Scaling all of the remaining text areas to a fixed size through the bi-linear sampling algorithm. The unified formatting of the text regions improves the accuracy of text recognition. This conclusion will be explained in Section 6.  
As is shown in Figure 3b: 1. A text region is converted to feature sequence by CNN-2, this CNN model is specially designed to divide the text region vertically into several parts, and each part corresponds to a feature sequence. 2. RNN-2 analyzes these sequences and predicts the characters to which each sequence may correspond, the output of RNN-2 is that each sequence corresponds to any character. 3. Connectionist Temporal Classification (CTC) [26] is used to transform the probability predicted by RNN-2 into the final character sequence.  
For instance, CNN-2 divides the text region into 12 parts with 12 feature sequences. RNN-2 predicts the characters to which these 12 parts may correspond, and the output result is the sequence \"-u-ss-er-ID-\" (\"-\" means RNN-2 predicts this part as blank). CTC transforms this sequence into \"userID\" by computing the probability.
# Factors Influencing the Stability of a Slope Containing a Coal Seam in a Goal
## 4 Analysis of the Influencing Factors
### Orthogonal Multifactor Simulation Test
#### 4.2.1 Range Analysis of the Slope Stability Coefficient  
The purpose of the range analysis is to calculate the range of the R values of the simulation results, corresponding to each column and level in the orthogonal table using mathematical statistics. Because the R of a factor is defined as the difference between the maximum and minimum value of the factor, the relative influence of the factor on the slope stability could be judged, according to the value of R. A large R indicates that the factor has a strong relative influence, so is a relatively main influencing factor. A small R indicates that this factor has a relatively weak influence and is a relatively minor factor [30]. During the range analysis, the total value (Tij) of the simulation results at each level of each factor should be first calculated, and then the average value (Kij) of the simulation results at each level of each factor should be calculated, according to Formula (4). Finally, the R value (Rij) of each factor can be obtained by Formula (5).  
\[K_{ij}=\frac{1}{N}T_{ij} \tag{4}\]  
\[R_{ij}=K_{ijmax}-K_{ijmin} \tag{5}\]  
According to the above formula and combined with the simulation results of the slope stability coefficient in Table 3, we calculated the \(R\) value of the slope stability, and the results are shown in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(H\) & \(W\) & \(i\) & \(T\) & \(R\) & \(c\) & \(\varphi\) & \(\alpha\) & **Simulation Result** \\ \hline  
\hline \end{tabular}
\end{table}
Table 10: Orthogonal table of a horizontal combination of the L\({}_{27}\)(3\({}^{8}\)) parameters and the calculated slope stability coefficients.  
According to the ranges listed in Table 11, the R values corresponding to the eight factors \(H\), \(W\), \(i\), \(T\), \(R\), \(c\), \(\varphi\), and \(\alpha\) were 1.21, 1.85, 0.83, 0.72, \(-\)0.05, \(-\)0.69, \(-\)0.71, and \(-\)0.57, respectively. The R values of the eight factors in descending order were \(\rm{R_{W}>R_{H}>R_{i}>R_{T}>R_{T}>R\varphi>R_{R}>R_{R}}\). That is, among the eight factors that impacted the slope stability that we analyzed, because the R value corresponding to \(W\) was the largest, we found that the change in the roadway width \(W\) had the strongest relative impact on the slope stability coefficient index. Ranked second was \(H\), followed by \(i\), \(T\), \(\varphi\), \(c\), \(\alpha\), and R, indicating that the level change of the last had the weakest relative influence on the slope stability coefficient. Therefore, we found that the ranking of the relative influence of each factor on the slope containing a coal seam in a goat was \(W>H>i>T>\varphi>c>\alpha>R\).
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 5 Results and Discussion
### PU Decision Matrix  
The PU Decision Matrix (DM) to be also used here consists of a list of the TV-UHF channels, and it allows us to identify the most occupied channels by a PU as well as to rate those channels with a preferred order. The decision is based on PU presence criteria [25]. Here, we obtained the probability of channels that contain a PU signal, and for this purpose, our DM is composed of information obtained form the channels sensed.  
For a visual representation of the DM, we plotted 32 channels numbered from 19 to 51 (channel 37 is not considered and is represented in yellow). The most likely channels to appear as used are in full blue colour; the coloured lines reduce its height according to the usage probability detected. Finally, we observe the less occupied channels in full white colour. By using this plot, we can estimate the amount of TVWS in the sensed area. The results are represented in a preferred order, where number 1 corresponds to the first option to be assigned as TVWS and number 6 correspond to the last option to be assigned as TVWS. Table 7 summarizes the DM output. For our purposes, we used channels with a PU occupation of less than 1% of the sensed times. This means that only 10 channels can be assigned when we find E-TVWS with our REM model, and these are 31.25% of the TV-UHF channels available in the Windsor-Essex region.  
\begin{table}
\begin{tabular}{c c c c} \cline{3-4}
**Ord** & \(P_{d}\) & \(P_{m}\) & \(PPV\) \\ \hline P1 & 37.15 & 13.67 & 92.52 \\ P2 & 42.42 & 6.45 & 93.39 \\ P3 & 47.17 & 4.34 & 94.02 \\ P4 & 41.22 & 3.22 & 93.21 \\ P5 & 49.37 & 3.67 & 94.27 \\ P6 & 56.17 & 0.96 & 94.92 \\ P7 & 42.38 & 10.31 & 93.38 \\ P8 & 50.82 & 3.09 & 94.42 \\ P9 & 50.16 & 3.38 & 94.35 \\ P10 & 45.85 & 4.44 & 93.85 \\ \hline \end{tabular}
\end{table}
Table 6: Confusion Matrix results for \(P_{fa}=3\%\) and \(P_{n}=97\%\).  
Figure 17: Confusion matrix.  
The elements considered to find E-TVWS are based on the collected readings (i.e., data of points P1 to P10) and on simulated readings (i.e., Scenario 1 and Scenario 2), and they are listed below as a part of a PU DM:  
* \(P_{d}\) calculated from collected readings.
* \(P_{dmax}\) calculated from Scenario 1.
* The value for \(\tau_{\overline{\
u}}\).
* The value of \(\alpha\).
* Preferred order to find E-TVWS.
# A Review of Road Design for Wind Farms in China
## 5 Research on Water and Soil Loss
### Prediction of Water and Soil Loss Intensity of the Wind Farm
#### 5.3.2 Prediction of Water and Soil Loss Intensity  
[PERSON] [37] obtained an empirical formula of soil erosion in different regions and land types through statistical analysis, as shown in (20) and (21). The predicted results are near to the actual results and are suitable for predicting the amount of soil erosion in western Liaoning. [PERSON] [46], [PERSON] [48], and [PERSON] [51] proposed empirical formulas for predicting the amount of soil loss in the wind farm area in different periods and the amount of new soil loss, as shown in (22) and (23), which provide strength prediction for water and soil loss during the road construction of the wind farm and provide a basis for the selection of prevention measures, the layout of the prevention measure system, the construction schedule, and the monitoring of water and soil conservation.  
Soil Erosion Models Applicable to Western Liaoning  
Coverage formula:  
\[E=\left\{\begin{array}{ll}506.87-5.22\text{C}&\text{S$<$5}\\ 2315.56-24.58\text{C}&\text{5$<$5$<$10}\\ 4614.4-51.59\text{C}&\text{10$<$5$<$15}\\ 9818.58-115.75\text{C}&\text{15$<$5$<$25}\end{array}\right. \tag{20}\]  
Parameter significance: C--forest and grass coverage; S--slope (\({}^{\circ}\)).  
Slope formula:  
\[E=62.95\text{S}^{1.53} \tag{21}\]  
Parameter significance: S--slope (\({}^{\circ}\)).  
The amount of soil loss in different periods is calculated according to the following formula:  
\[W=\sum_{j=1}^{2}\sum_{i=1}^{n}\left(F_{ji}M_{ji}T_{ji}\right) \tag{22}\]  
The newly increased soil loss is calculated according to the following formula:  
\[\Delta W=\sum_{j=1}^{2}\sum_{i=1}^{n}\left(F_{ji}\Delta M_{ji}T_{ji}\right) \tag{23}\]  
In the above formula:  
\(W\)--Soil loss (t);  
\(\Delta W\)--Increased soil loss (t);  
\(F_{ji}\)--The predicted area of a unit in a certain period (km\({}^{2}\)-a);  
\(M_{ji}\)--Soil erosion modulus of a unit in a certain period t/(km\({}^{2}\)-a);  
\(\Delta M_{ji}\)--New soil erosion modulus of a unit in a certain period, t/(km\({}^{2}\)-a). It is equal to the soil erosion modulus after disturbance minus the soil erosion modulus before disturbance. Only positive values are counted, and negative values are counted as 0;  
\(T_{ji}\)--Forecast time of a unit in a certain period;  
\(i\)--Prediction unit, \(i=1\), 2, 3,..., \(n\);  
\(j\)--Forecast period, \(j=1\), 2. The construction period includes construction preparation period and natural recovery period.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
###### Abstract  
This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km\({}^{2}\) in central Tunisia. In addition to satellite acquisitions, texture measurement samples were taken in several agricultural fields, characterized by a large range of clay contents (between 13% and 60%). For the period between July and August, various optical indicators of clay content Short-Wave Infrared (SWIR) bands and soil indices) were tested over bare soils. Satellite moisture products, derived from combined S-1 and S-2 data, were also tested as an indicator of soil texture. Algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content and a three-fold cross-validation is used to evaluate both approaches. The classifications with the best performance are achieved using the soil moisture indicator derived from combined S-1 and S-2 data, with overall accuracy (OA) of 63% and 65% for the SVM and RF classifications, respectively.  
Sentinel-1; Sentinel-2; Soil Moisture; Texture; Clay; SVM; Random Forest 20 May 2019; 11, 1520; doi:10.3390/rs11131520
## 3 Results and Discussion  
### Fluorescence Spectral Properties of Oils at Different Concentrations  
The Excitation-Emission-Matrix (EEM) fluorescence spectrum can be generated by collecting several emission spectra at series excitation wavelengths and combining them. It can clearly show the variation of fluorescence characteristics with the concentration, such as the number of peaks, peak location, peak intensities, and peak shapes. Take crude oil as an example. The EEM fluorescence spectra obtained at the concentrations of 1.2 ppm, 19.5 ppm, 78 ppm, 156 ppm, 312 ppm, 625 ppm are shown in Figure 1.  
Figure 1: The Excitation Emission Matrix (EEM) spectra obtained from crude oil at concentrations of 1.2 ppm, 19.5 ppm, 78 ppm, 156 ppm, 312 ppm, and 625 ppm.  
It was clear that the main fluorescence center was at the \(\lambda_{ex}/\lambda_{em}\) of 295 nm/342 nm in the low concentration of 1.2 ppm and the fluorescence characteristics of low-ring PAHs were exhibited. As the oil concentration increased, the fluorescence peak gradually moved toward the long wavelength and the fluorescence maxima located at \(\lambda_{ex}/\lambda_{em}\) of 275 nm/420 nm. When the concentration increased to 625 ppm, a new fluorescent peak appeared at the \(\lambda_{ex}/\lambda_{em}\) of 312 nm/420 nm and the other fluorescence peak was still located at the \(\lambda_{ex}/\lambda_{em}\) of 275 nm/420 nm, which represents the fluorescence characteristics of high-ring PAHs. The reason why the spectrum changes with concentration was that the oil sample contained a variety of aromatic compounds, which cause self-absorption, light attenuation, collision quenching, and molecular aggregation with increased concentration. The results demonstrate that the fluorescence spectrum of the oil has a \"red-shift\" behavior as the oil concentration increases, which needs to be considered in quantitative measurement.
# Motor Vector Control Based on Speed-Torque-Current Map
## 2 Electric Drive System Configuration and Main Parameters  
The configuration of the dual-motor drive system [16] is shown in Figure 1. The system consists of two drive motors (MG1 and MG2), one planetary gear, three clutches (C1, C2, and C3), one brake (B1), two gear pairs (Gear 1 and Gear 2), and one main reducer (Final Drive). This electric drive system can realize four working modes including MG1 driving alone (M1), MG2 driving alone (M2), torque coupling drive (M3), and speed coupling drive (M4), as shown in Figure 2. The vehicle working modes and the corresponding component status are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mode** & **MG1** & **MG2** & **C1** & **C2** & **C3** & **B** & **Power Flow** \\ \hline M1 & M & C & OFF & OFF & ON & OFF & Figure 2a \\ M2 & C & M & OFF & ON & OFF & ON & Figure 2b \\ M3 & M & M & OFF & ON & ON & ON & Figure 2c \\ M4 & M & M & ON & ON & OFF & OFF & Figure 2d \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle working modes and the corresponding component status. This electric drive system can realize four working modes: MG1 driving alone (M1), MG2 driving alone (M2), torque coupling drive (M3), and speed coupling drive (M4).  
The main parameters of the system are shown in Table 2.  
Figure 1: Electric drive system configuration. The system consists of two drive motors (MG1 and MG2), one planetary gear, three clutches (C1, C2, and C3), one brake (B1), two gear pairs (Gear 1 and Gear 2), and one main reducer (Final Drive).  
Figure 2: The power flow of different working modes. (**a**) MG1 drive alone; (**b**) MG2 drive alone; (**c**) Torque coupling drive; (**d**) Speed coupling drive.
###### Abstract
However, the tracking errors affected by the base vibration damping and the controller may become deficient due to modeling errors. The base vibrations should be suppressed during the visual servo task of EEF; it is a kind of dual-task control. [PERSON] et al. [27] firstly propose the pseudo-passive energy dissipation, the idea is to choose the PD gains in the joint controllers to transfer the energy from base to manipulator actuators. For the redundant manipulator, the reaction null-space control framework proposed by [PERSON] et al. in [28] is based on the coupling momentum. In an alternative way, the resolved solution based on the null-space of the redundant manipulator is studied by the author in [29], the damping term is added in the closed-loop by the successive null space projection. Recently, augmented projections based on coordinate transformation is proposed by [PERSON] in [30]. Different coordinate transformation methods are proposed by [PERSON] et al. in [31; 32], where the dynamic consistency is satisfied with the decoupling of joint space and operation space. Figure 1: The CMM and EMM series connection. The macro/micro manipulator is difficult to model. The joint of the EMM is flexible with backlash, and the joint stiffness in and out of the backlash differ. The dynamic model-based control, as mentioned before, is inadequate for this situation. However, the sensors of the manipulators will make it possible for precise deformation measurement. The six-axis force/torque sensor (FTS) is designed in [33], which can measure the three orthogonal forces and torques between EMM and CMM. The base force/torque sensing are discussed for different applications [34; 35; 36], but with a lack of macro/micro manipulator vibration suppression control. The contribution of this paper is to deal with the vibration problem of the MMMS with PBVS, the requirements of the control strategy are related to the robot's high speed and high precision operations with large payload. Firstly, the PBVS is performed effectively in cooperation with eye-in-hand camera and other types of sensors due to the multi-sensor data fusion. Secondly, the vibration control of the FBMS is studied by the FTS damping control and backlash compensation. This paper is organized as follows. Section 2 discusses the measurement of the manipulator payload motion. In Section 3, an implementation of a visual servo is given, including base vibration and backlash compensation. In Section 4, simulation results are discussed for the macro-micro manipulator systems. In Section 5, experimental implementation and evaluation of a laboratory's flexible base manipulator system is discussed. The paper ends with concluding remarks in Section 6.
## 3 Results and Discussion
### Energy Band-Gap of B-co-MP  
The energy band-gap (\(E_{g}\)), the number of carbon atoms per conjugation length (\(N\)), and the refractive index (\(n\)) all had an impact on the material's optical properties. The energy band-gap of B-co-MP in the THF was calculated from the absorption spectra before and after \(\gamma\)-irradiation, using the Tauc equation [27].  
\[\alpha(h\
u)=\frac{B\big{(}h\
u-E_{g}\big{)}^{r}}{h\
u} \tag{1}\]  
where \(\alpha\) is the absorption coefficient, \(h\
u\) is the incident photons energy, \(B\) is the constant, and \(r\) is an empirical power representing the electronic transition type. The value of \(r\) corresponds to \(1/2\) or \(3/2\) for the direct allowed or direct forbidden transitions, respectively. However, it took values of two and three for the indirect allowed and indirect forbidden transitions, respectively [23]. The studied samples showed a direct allowed transition by plotting \((\alpha h\
u)^{2}\) versus \(h\
u\) as demonstrated in Figure 4. The straight line's extrapolation obtained the \(E_{g}\) values to the \(h\
u\)-axis, and its values are mentioned in Table 1. It could be seen that the energy band-gap of the copolymer increased from 2.28 eV for the non-irradiated to 2.53 eV for the highest dose \(\gamma\)-irradiated samples. The expansion of the energy band-gap can be ascribed to bond breaking in the polymer caused by the interaction of \(\gamma\)-irradiation with a polymer, decreasing the effective conjugation length.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gamma Dose** & **Optical Band-Gap** & **Number of Carbon Atoms** & **Refractive Index (\(n\))** \\ (KGy) & **Energy (eV)** & **per Conjugated Length (N)** & **Refractive Index (\(n\))** \\ \hline
0 & 2.28 & \(\sim\) 8.0 & 2.62 \\
5 & 2.34 & \(\sim\)7.8 & 2.60 \\
10 & 2.4 & \(\sim\)7.6 & 2.58 \\
15 & 2.45 & \(\sim\)7.4 & 2.56 \\
20 & 2.53 & \(\sim\)7.0 & 2.54 \\ \hline \end{tabular}
\end{table}
Table 1: The variation of optical energy band-gap (\(E_{g}\)), the number of carbon atoms per conjugated length (\(N\)), and refractive index (\(n\)) for B-co-MP in THF at a concentration of 1 \(\mu\)M at different gamma doses.  
Figure 3: The main absorption peak wavelength (\(\lambda_{\max}\)) of the B-co-MP in THF versus gamma doses at different concentrations.