esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Unified Frequency Model for Multi-Machine Systems
### Parameter Design Based on Monte Carlo Algorithm  
The design process of the controller parameters is as follows:  
1. The perturbation block \(\Delta i\) reflecting uncertainty and the corresponding weight function \(W_{i}(s)\) are introduced into the unified frequency model, and then the virtual  
Figure 10: The reconstructed standard \(M\)-\(\Delta\) model.  
perturbation block \(\Delta f\) and sensitivity weight function \(W_{S}\)(s) reflecting robust performance are inserted.
2. Separate the uncertainty of the model constructed in (1) and reconstruct the model as an extended \(M\)-\(\Delta\) model.
3. Set frequency sweep points [\(\omega_{1}\), \(\omega_{2}\),..., \(\omega_{\Omega}\)] rad/s according to the frequency range to be studied. This paper focuses on the control performance of the system during ultra-low-frequency oscillation, so the frequency sweep range is set between 0.01 and 0.1 Hz.
4. According to the possible operating parameter range of the system, at each frequency point \(s=j\omega\), the nominal value and the corresponding weight function of each generator are calculated.
5. Set the parameter search space of the series correction controller of the hydroelectric unit and the active power additional controller of the PV generator and give their upper and lower limits.
6. Using the Monte Carlo algorithm, a set of controller parameters for hydropower and PV generators is randomly generated in the search space of control parameters input in (5).
7. According to the frequency sweep range set in (3), bring each frequency sweep point into the constructed extended \(M\)-\(\Delta\) model, calculate the structural singular value and judge whether it is less than or equal to 1, and if the requirements are met, this set of controller parameters is indicated and the system can meet the robust performance requirements; otherwise, return to (6) and regenerate new parameters for calculation.
## 3 Results and Discussion
### Catalytic Cleft and Sub Site Predictions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Enzyme**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**PDB Code**} & \multirow{2}{*}{**Resolution**} & \multicolumn{2}{c}{**Glt1**} & \multicolumn{2}{c}{**Glt3**} & \multicolumn{2}{c}{**Glt20**} \\ \cline{4-10}  & & & & **(Å)** & & **RMSD** & **Atom Pairs** & **RMSD** & **Atom Pairs** & **RMSD** & **Atom Pairs** \\ \hline \(\beta\)-1,3-glucanosyl transferase & & & & & & & & & \\ (Bgt17A) & _Rhizomov mideni_ & 4 WTP & 1.3 & 1.143 & 176 & 1.059 & 140 & 1.214 & 152 \\ \hline endo-\(\beta\)-1,3-glucanase/allergen & _Musat_ & & & & & & & & \\ (Mus a 5) & _Atumina_ & & & & & & & & \\ \hline endo-\(\beta\)-1,3-glucanase (Bgl32) & _Hordeum vulgare_ & 1 GHS & 2.30 & 1.148 & 87 & 1.151 & 81 & 1.311 & 98 \\ \hline endo-\(\beta\)-1,3-1,4-glucanase II & & & & & & & & & \\ (EII)/lichenase II \(\beta\)-glucosidase & _Hordeum vulgare_ & 1 AQ0 & 2.00 & 1.141 & 88 & 1.100 & 81 & 1.194 & 100 \\ \hline endo-\(\beta\)-1,3-glucanase (GluB20-2, GluB20-2) & _Solanum tuberosum_ & 3 UR8 & 1.26 & 1.153 & 96 & 1.222 & 72 & 1.296 & 103 \\ \hline \(\beta\)-1,3-glucanase (Glu1, Glu2, Hev b 2) & _Hera brasiliensis_ & 4 HPG & 2.54 & 1.183 & 94 & 1.207 & 93 & 1.243 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the refined models Glt1, Glt3, Glt7 and Glt20 with the crystallographic structures available in family GH17 (see Figure 5). situated 2.9 and 3.8 A, respectively, from the OH- groups of the sugars, which may not be close enough for the interaction. The putative subsite \(-4\) is clearest in Glt20 due to the shape of the cleft and presence of potential hydrogen bonding residues, which are situated in the loops \(\beta 1\)-\(\alpha 1\), \(\beta 2\)-\(\alpha 2\) and the Arg94 in the \(\alpha 3\)-helix (Figure 7). The main conformational changes between the enzymes are found in the loops located at the end of the catalytic cleft, corresponding to \(\beta 1\)-\(\alpha 1\) involved in the putative subsite \(-4\). The different conformation of loop \(\beta 1\)-\(\alpha 1\) in Glt1 and Glt3, compared to Glt20, may be one reason for observed differences in substrate binding, as subsite \(-4\) binds the terminal residue in the non-reducing end of the donor, while in Glt20, the substrate can extend beyond this binding site. The residues involved in the two disulphide bonds (Cys15-Cys43 and Cys218-Cys264) found in the \(Rm\)Bgt17A structure [14], and conserved in other fungal transglycosales [14], are not present in the proteobacterial enzymes studied in the present work. Particularly interesting is the Cys15-Cys43 disulphide bond, which could affect the flexibility of the loops \(\beta 1\)-\(\alpha 1\) and \(\beta 2\)-\(\alpha 2\). According to the models, both loops are important to determine the interactions in the putative subsite \(-4\) in Glt1, Glt3 and Glt20. In subsite \(-3\), only the Trp65 is conserved (Trp87 in Glt1, Glt3 and Glt20).
# Octacalcium Phosphate Bone Substitute (Bontree(r)): From Basic Research to Clinical Case Study
## 3 Results  
### Basic Research  
#### 3.1.1 Material Characterization  
Figure 1A shows the FE-SEM micrographs of two different granule sizes of Bontree(r). At low magnification, both Bontree(r) granules had irregular shapes with clean and rough surface morphologies, and no cracks or defects were found on any Bontree(r) granule surface. High-magnification FE-SEM images clearly showed that micron-sized crystals uniformly covered the surface of the Bontree(r) granules. There were no significant differences between the surface qualities and morphologies of the two Bontree(r) granules.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**PMH**} & \multirow{2}{*}{**Procedure**} & \multicolumn{2}{c}{**Position of**} & \multirow{2}{*}{**ISQ (B/L)**} & \multirow{2}{*}{**Implant**} \\  & & & & & **Implant Placement** & & **Prognosis** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{77} & \multirow{3}{*}{Male} & \multirow{3}{*}{HTN} & Bone augmentation of & \#44i & 78/84 & Success \\  & & & & peri-implant defects & \#45i & 80/79 & Success \\  & & & & & \#46i & 85/85 & Success \\  & & & & & \#24i & 75/76 & Success \\
2 & 69 & Male & HTNCVD & Vertical ridge augmentation & \#26i & 71/71 & Success \\  & & & & & \#27i & 75/73 & Success \\
3 & 63 & Male & HTNDM & Sinus and ridge augmentation & \#16i & 62/75 & Success \\  & & & & & \#17i & 61/71 & Success \\ \hline \multicolumn{7}{c}{PMH, past medical history; ISQ (B/L), implant stability quotient (buccal/lingual); HTN, hypertension; CVD, cardiovascular disease; DM, diabetes mellitus.} \\ \end{tabular}
\end{table}
Table 1: Characteristics of the patients.  
Figure 1: **(A)** Representative low- and high-magnification FE-SEM images of the Bontree® granules: Bontree® 0.5-granule and Bontree® 1.0-granule refer to Bontree® granules with diameters of 0.5 mm and 1.0 mm, respectively. **(B)** The XRD spectra of the Bontree® 0.5-granule and Bontree® 1.0-granule. The OCP and HA peaks are indicated by * and *, respectively. FE-SEM, field-emission scanning electron microscopy; XRD, X-ray diffraction; OCP, octac calcium phosphate; HA, hydroxyapatite.  
Figure 1B shows the XRD patterns of Bontree(r) 0.5-granule and Bontree(r) 1.0-granule. XRD was conducted to examine the presence of OCP and HA in Bontree(r) grafts. The presence of diffraction peaks at 20 = 10.8\({}^{\circ}\), 26.3\({}^{\circ}\), 31.8\({}^{\circ}\), 32.2\({}^{\circ}\), 32.9\({}^{\circ}\), 39.8\({}^{\circ}\), 46.6\({}^{\circ}\), 49.3\({}^{\circ}\), and 53.2\({}^{\circ}\) indicated the formation of apatite (HA), while the strongest intensity of the OCP diffraction peak (100) was observed at 4.7\({}^{\circ}\) in both samples [15,16]. Phase analysis using XRD showed that OCP is the main constituent of Bontree(r), regardless of size.
## 2 Materials and Methods  
Due to the random interference of Rayleigh backscattering (RBS) generated by detecting pulsed light at different positions on the optical fiber, the intensity of RBS fluctuates randomly. As mentioned above, due to the influence of the fading effect, the RBS intensity in some regions of the optical fiber is weak, the phase demodulation results of these regions cannot be effectively identified, and these regions change over time, resulting in a high rate of false positives [23]. At the same time, \(\Phi\)-OTDR has a fast response and the characteristic of high sensitivity, which causes a huge pressure on data storage and computing [24, 25, 26]. All these problems hindered the application of \(\Phi\)-OTDR system in practical environments, and most of the research does not aim at solving the above problems at the same time.  
In this study, a method based on CEEMDAN-KL and AMNBP optimized the performance of a \(\Phi\)-OTDR system. The overall diagram of signal collection using \(\Phi\)-OTDR and events identification with the method is shown in Figure 1.  
Figure 1: Schematic of the process used. \(\Phi\)-OTDR: Phase-sensitive optical time-domain reflectometry, CEEMDAN: Complete ensemble empirical mode decomposition with adaptive noise KL: Kullback Liebler, MNBP: Moving neighbor binary pattern.  
In practical environments, the fiber (green color in Figure 1) is usually buried in the soil. The event produces vibration, which causes a phase change in the RBS at the corresponding positions of the fiber. Furthermore, the target signals and reference signals are both collected using the \(\Phi\)-OTDR system (black and red curves in Figure 1). Since the original signals are easily disturbed by strong noise, the characteristics of the target signals are difficult to distinguish. Given this situation, the key features of signals can be enhanced using CEEMDAN-KL. It reduces the interference at any individual position or point during the location and recognition. Through the AMNBP algorithm with adaptive adjustment, the coding sequence improves the calculation efficiency and provides outstanding differentiation that allows for identifying the target event.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design  
### Satellite-Based IoT (SIoT)  
#### 2.1.1 Advantages of Satellite Communications  
Satellite communications have high accuracy and low delay [7], so they play an important role in the fields of navigation, time service, measurement, and communication. Satellite communications can also provide services in emergency, service delivery, distribution, and the energy Internet, which means satellites have great potential in the future development of IoT networks and next-generation communication networks [8].
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 2 Method
### Noise Model of Intensity Accumulation  
Before comparing different methods of binary CGHs, we need to find a model to quantitatively describe the dependence of the speckle noise on the number of intensity accumulation. Because this is a statistical problem and needs complicated calculation, we intend not to derive the exact solution. By contrast, we only build an empirical formula according to the simulation data. The candidate formula comes from an analogous speckle phenomenon, i.e., the summation of multiple speckled images in laser projection [25; 26]. This postulation is reasonable because the holographic recording is similar to the step of light from a micro displayer to the entrance pupil of the projector; the holographic reconstruction is comparable to the procedure of light from the exit pupil of the projector to the screen. In short, the speckle contrast of the displayed image is expressed as  
\[C=\sqrt{\frac{N+K+1}{KN}}, \tag{8}\]  
where \(N\) is the number of summation, and \(K\) is the ratio of imaging resolution (the minimum imaging spot size) over the speckle size. In our problem, i.e., the intensity accumulation of CGHs, \(K\) depends on object distance, the pixel pitch, and the hologram size. According to our simulation condition, \(K\) ranges from 15 to 25 normally. However, direct calculation of \(K\) is difficult because it is also affected by the distribution of object function and the processing method. Finally, by assuming that all the noise in reconstruction comes from speckle, the PSNR in this model is expressed as  
\[PSNR_{C}=20\log_{10}\frac{S}{C}, \tag{9}\]  
where \(S\) is the peak signal, which depends on the distribution of the objective function and the object processing method. It should be noted that Equation (8) is originally derived under the assumption that the signal is uniform. In practice, the signal, i.e., the original object, contains different brightness in various locations. Therefore, we can only find the mean values of \(S\) and \(K\) from simulation or experimental data.
# 3-D Point Cloud Registration Using Convolutional Neural Networks
## 4 The Proposed Descriptors for 3-D Point Clouds
### Point Cloud Data Set Preparation  
In order to generate the data set, several 3-D point clouds with different orientations of an object need to be collected. Nevertheless, it is not suitable to collect a large number of 3-D point clouds of an object when using a 3-D scanner to capture these point cloud data sets. Therefore, the data set can be obtained by randomly rotating and downsampling the model point cloud of the object. Particularly, the box grid filter algorithm provided by Matlab is used to downsample the rotated model point cloud to low-resolution point clouds with the different number of points and distribution characteristics. Based on this approach, the created point clouds can simulate disparities among point clouds that would exist in real 3-D point clouds captured by a 3-D camera. The principal difference between two kinds of data sets of an object is that each rotation angle with respect to an axis is randomly initialized with 80 different values in the range from \(-180^{\circ}\) to \(180^{\circ}\) for the first data set whereas the second data set is generated based on 80 random values of each rotation angle in the range from \(-15^{\circ}\) to \(15^{\circ}\). Totally, there are 3 rotation angles with respect to \(x\), \(y\), and \(z\) axis that results in generating \(80^{3}\) = 512,000 point clouds with different orientations. Each data set is divided into 3 subsets including training set (392,000 samples), validation set (60,000 samples) and test set (60,000 samples). Algorithm 1 shows procedures step by step in order to generate these data sets.  
```
Input: Model point cloud \(\mathbf{P}\); the number of random values of each rotation angle \(n_{ra}\); range of uncertainty \(r_{u}\); 1: Randomly initialize rotation angles \((\psi,\theta,\phi)\) with \(n_{ra}\) values for each rotation angle; 2:for\(i=1\) to \(n_{ra}\)do for\(j=1\) to \(n_{ra}\)do for\(k=1\) to \(n_{ra}\)do - Rotate the model point cloud, \(\mathbf{P}^{\prime}=rot_{(\psi,\theta,\phi)}(\mathbf{P})\); - Downsample the point cloud, \(\mathbf{Q}_{l}\Leftarrow\textit{downsample}(\mathbf{P}^{\prime})\); - Record the data point cloud \(\mathbf{Q}_{l}\) and \((\psi,\theta,\phi)\) as a sample;  end for  end for  end for Output: Data point clouds \(\mathbf{Q}_{l}\).
```  
**Algorithm 1** Data set generation  
Figure 3: A comprehensive block diagram of the proposed registration architecture.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 3 U-Shaped Roadway Model and Reflection Path Validity
### Valid Mirror Points
#### 3.1.3 Reflecting Surfaces Boundary Evaluation  
For the U-shaped roadway, electromagnetic waves passing through the boundary of the reflection plane may be launched by bypassing and other phenomena. To determine whether a reflection point exists in a reflection plane, the extent of the boundary of the reflection plane must be determined. As reflection planes \(m_{7}\) and \(m_{8}\) are U-shaped, electromagnetic waves passing through the boundary of these planes are prone to bypass and other phenomena. Using reflection plane \(m_{8}\) as an example, we split it into rectangular planes similar to several other reflecting surfaces, planes \(M_{1}\), \(M_{2}\), and \(M_{3}\), and verify that the reflection point is located in the plane to determine the scope of the boundary for each rectangular plane. If the reflection point is within the range of any one of its planes, the reflection point is valid. According to the principle of the mirror image method, it is known that the mirror point is then also valid.  
Figure 4 shows a two-dimensional schematic of the reflection plane \(m_{8}\) of the U-shaped mine roadway after it was split into three rectangular planes, \(M_{1}\), \(M_{2}\), and \(M_{3}\). Let the coordinates of the reflection point be H(\(x_{h},y_{h},z_{h}\)); when point \(H\) is in the rectangular plane \(M_{1}\), it should satisfy \(0<x_{h}<a\) and \(0\leq y_{h}<L_{1}\); if it is in the plane \(M_{2}\), it should satisfy \(a\leq x_{h}\leq(L_{2}\)--\(a)\) and \((L_{1}\)--\(c)<y_{h}<L_{1}\); and if it is in the plane \(M_{3}\), it should satisfy \((L_{2}\)--\(a)<x_{h}<L_{2}\) and \(0\leq y_{h}<L_{3}\). Similarly, the same method can be used for the other reflection planes. The method used for validating the boundary range of the reflection plane is consistent with this method.  
Figure 4: Two-dimensional segmentation of the plane \(m_{8}\).
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Analysis of Fiber Bragg Gratings  
The analysis of FBGs provides the reflection \(H_{R}(f)\) and transmission \(H_{T}(f)\) spectral responses obtained from a particular grating structure, \(n_{FBG}(z)\). In this section, we describe analytically the Coupled Mode Theory and Multilayer methods for the analysis of FBGs.
## 3 Results and Discussion
### Plasma-Erosion Analysis
The oxygen and nitrogen atmospheric plasmas, however, can readily chemically affect the polymer surface of the organic materials, rather than that of the inorganic solid dielectrics. Thus, the polymers can be easily etched away by the _reactive oxygen and nitrogen species_ (RONS) in the atmospheric-pressure plasma discharge, while the oxide materials such as silica (SiO\({}_{2}\)) and aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) are quite stable against the RONS. Therefore, the chemical reactions rather than the physical sputtering exerted the effects on the surface degradation.
_Article_  
On a New Type of Combined Solar-Thermal/Cold  
Gas Propulsion System Used for LEO Satellite's  
Attitude Control  
[PERSON] 1, [PERSON] 1, [PERSON] 2,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1  
1 National Research and Development Institute for Gas Turbines COMOTI, 220D Iuliu Maniu, 061126 Bucharest, Romania; [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (V.S.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (T.T.); [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (A.R.)
2 Faculty of Aerospace Engineering, Politehnica University of Bucharest, 1-7 Polizu Street, 011061 Bucharest, Romania
* Correspondence: [EMAIL_ADDRESS]  
Received: 15 September 2020; Accepted: 12 October 2020; Published: 15 October 2020  
This paper presents the development, construction and testing of a new type of solar-thermal propulsion system which can be used for low earth orbit (LEO) satellites. Currently, the vast majority of LEO satellites are fitted with a cold gas propulsion system. Although such a propulsion system is preferred, the service duration of an LEO satellite is limited by the amount of cold gas they carry onboard. In the case of the new type of solar-thermal propulsion system proposed in this paper, the cold gas is first transferred from the main tank in a cylindrical service tank/buffer tank which is placed in the focal line of a concave mirror. After the gas is heated by the solar light focused on the service tank by the concave mirror, it expands by opening the appropriate solenoid valve for the satellite's attitude control. In this way the service duration of LEO satellite on orbit can increase by 2.5 times compared with a classic cold gas propulsion system. This is due to the propellant's internal energy increase by the focused solar light. This paper also presents the results achieved by carrying out tests for the hot gas propulsion system in a controlled environment.  
solar energy; solar-thermal; cold gas; propulsion system; LEO satellites
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 3 Results and Discussion
### Parametric Analysis  
#### 3.2.1 Effect of Water Velocity  
Based on the developed numerical model, the effect of water velocity on the PV/T performance was analyzed. The environmental conditions involved in this analysis were a solar irradiance of 1000 W/m\({}^{2}\), an outdoor temperature of 35 \({}^{\circ}\)C, and a wind speed of 1 m/s. The fluid velocity as a flow control parameter had a significant effect on the cooling of the PV unit. Hence, Figure 4 investigates the PV temperature evolution as function of water inlet temperature for different water velocities of 0.010 m/s, 0.015 m/s, 0.02 m/s, 0.025 m/s, 0.030 m/s, and 0.035 m/s. As result, it is depicted that by increasing water velocity from 0.010 m/s to 0.035 m/s, a drop in the PV temperature was observed in the four cases, where the water film thickness was 3 mm, 5 mm, 7 mm, and 10 mm. In addition, a minimum value of PV operating temperature about 27.7 \({}^{\circ}\)C was observed for an inlet water temperature of 20 \({}^{\circ}\)C, water thickness of 7 mm, and highest velocity of 0.035 m/s. This underlines the effective cooling of the PV cells, where a significant heat is extracted from the PV back side while increasing the water velocity.
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings  
#### 3.5.1 Scaffold Size  
Except for three papers [28; 30; 34], all other reviewed papers reported the size of the ceramic scaffolds. Table 3 shows the geometrical parameters of the ceramic scaffolds.  
Comparing the scaffold size and the primary outcome, some studies observed a relationship, while others did not [26; 32]. [PERSON] et al. (2021) [26] used ceramic scaffolds measuring 5 mm \(\times\) 5 mm \(\times\) 5 mm, yielding specific results, including the formation of soft calluses and low bone density at the periphery of the poly(lactic-co-glycolic acid)/\(\beta\)-tricalcium phosphate-bone morphogenetic protein (PLGA/TCP-BMP) structures. On the other hand, scaffolds with a diameter of 3 cm yielded different results, forming bone tissue similar to that of native bone.  
Therefore, [PERSON] et al. (2021) [26] also concluded that smaller scaffolds (e.g., 5 mm long \(\times\) 5 mm high \(\times\) 5 mm thickness) tend to show specific results in small areas, such as forming irregular bone layers. In comparison, larger scaffolds (e.g., 54.80 mm \(\times\) 34.4 mm \(\times\) 11.58 mm) demonstrated long-term results, including complete bone maturation and extensive integration, in a study conducted by [PERSON] et al. (2022) [32].