esa-sceva/satcom-chunk-collection huggingface dataset:
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
## 3 Results
### Earth Rotation Parameters  
The quality of the estimated ERPs is evaluated by comparison with the IERS-14-C04 product [41], which is generated by combining four space geodetic techniques: GNSS, SLR, very long baseline interferometry (VLBI), and doppler orbitography radiopositioning integrated by satellite (DORIS). Figure 10 displays the error series of Xp, Yp and LOD  
Figure 8: Residual series of the N, E, and U components of the station coordinates for station 1868 after the Helmert transformation.  
Figure 9: Residual series of the N, E, and U components of the station coordinates for station 1879 after the Helmert transformation.  
with respect to the IERS-14-C04 products for the period in 2018, and Table 5 presents the corresponding statistical information. As shown in Figure 10, the error series of Xp, Yp and LOD are almost the same for the LAGEOS-only solutions and LAGEOS+Etalon solutions. Table 5 shows that the mean values for the error in the Xp component of the pole motion of the LAGEOS-only solutions and LAGEOS+Etalon solutions are 19.87 uas and 19.77 uas, respectively; the standard deviations are 263.37 uas and 263.36 uas, respectively. The mean values for the error in the Yp component of the pole motion in the LAGEOS-only solutions and the LAGEOS+Etalon solutions are 0.24 uas and 0.23 uas, respectively; the standard deviations of these two solutions are 259.08 uas and 259.86 uas, respectively. The mean values of the LOD in the LAGEOS-only solutions and LAGEOS+Etalon solutions are 18.25 uas/day and 17.69 uas/day, respectively; the standard deviation of the two solutions is 36.66 and 36.93 uas/day, respectively. After the observations of Etalon are added in 2018, the quality levels of Xp, Yp and LOD are slightly changed.  
Figure 11 shows the error series of Xp, Yp and LOD with respect to the IERS-14-C04 products for the period in 2019. Figure 11 clearly shows that, compared with the differences in LAGEOS-only solutions, the differences in Xp, Yp and LOD in the LAGEOS+Etalon solutions are smaller. Statistical information for Xp, Yp and LOD, such as maximum, minimum, mean and standard deviation, are given in Table 6. Table 6 shows that when the Etalon observations are added, the standard deviation of Xp is reduced from 179.97 uas to 167.64 uas and that of Yp is reduced from 270.45 uas to 231.90 uas. For LOD, the standard deviation of Xp is reduced from 2018 to 2018, the quality levels of Xp, Yp and LOD are slightly changed.  
deviation is reduced from 47.5 \(\upmu\)s/day to 45.13 \(\upmu\)s/day. Compared with the results from 2018, the quality levels of Xp, Yp and LOD are more affected by adding Etalon observations in 2019.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 4 Dynamic PN Modelling of the O&M of an Offshore Wind Turbine  
To simulate the O&M of an OWT, the following four PN models will be developed in this section:  
1. Operation Petri net (OPN)--for simulating the normal operation and periodic maintenance of an OWT. In the OPN, the design life of the turbine and the interval of the periodic maintenance will be defined.
2. System Petri net (SPN)--for simulating the degradation, the health state of the turbine subsystems over time, and the shutdown of the turbine due to failure.
3. Detection Petri net (DPN)--for simulating fault detection by the CMS.
4. Recovery and Maintenance Petri net (RMPN)--for simulating the process to prepare and conduct the maintenance when a subsystem fails, or a subsystem fault is detected by the CMS.  
The above four PNs communicate with each other and work synchronously as shown in Figure 3. After the design life of the OWT and the time interval of periodic maintenance are defined in the OPN, the natural degradation of the turbine subsystems will be simulated first by the SPN. The SPN results for individual subsystems will be fed into the DPN and RMPN. The fault detection results derived from the DPN, the information of the failure causing the shutdown of the turbine derived from the SPN, and the fault revealed during the \"advanced services\" derived from the OPN, will be fed into the RMPN. Then, based on the information received from the SPN and DPN, the RMPN will simulate the appropriate maintenance actions corresponding to the types of the faults and failure and their severities and consequences. Finally, the information derived from the RMPN will be used to update the corresponding information in the SPN. The simulation calculations of all PN models will be terminated when the design life of the wind turbine is reached.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 3 Experimental Area and Data Source  
In July 2023, a radial velocity validation experiment using the X-band weather radar was conducted at the Changsha Meteorological Radar Calibration Center using an RTK UAV. The UAV was used to execute constant velocity flights either toward or away from the radar along the radial direction, while the radar tracked it along its flight route. Synchronizedobservational experiments were conducted to obtain radar-measured values of the UAV velocity, and these values were subsequently compared with UAV velocity reference values derived from RTK information. Using the methodology proposed in this paper, the accuracy of X-band weather radar radial velocity measurements can be validated.
## 2 Friction Coefficient Estimation  
The friction coefficient estimation algorithm, which serves as the basis for speed planning, is proposed in this section. As shown in Figure 2, the proposed algorithm has four components. The first component is the tire force and effective radius estimation, in which the longitudinal, lateral, and vertical forces, as well as the effective radius of each tire are estimated. The second component is the calculation of the tire side-slip angles and slip ratios, which are calculated for each tire. The third component is the calculation of the friction coefficient, which produces the final estimated friction coefficient. The last component is torque injection [25; 26], which involves calculating the drive torque to be injected.  
In this study, an electric vehicle with four-wheel steering and independent four-wheel drive is considered. The structure of the vehicle used for estimating the friction coefficient is shown in Figure 3. The vertical tire force and side-slip angle of each tire can be calculated using this model. In this figure, \(l_{f}\) and \(l_{r}\) represent the distances from the front and rear axles, respectively, to the center of gravity (CG). \(l_{s}\) is equal to half of the front and rear wheel treads. \(v_{x}\) and \(v_{y}\) represent the longitudinal and lateral velocities, respectively, at the CG. \(\omega_{r}\) is the yaw rate. The subscripts \"\(fl\)\", \"\(fr\)\", \"\(rl\)\", and \"\(rr\)\" denote the values corresponding to the front left, front right, rear left, and rear right tires, respectively. \(F_{xi}\) and \(\alpha_{i}\) (\(i=fl\), \(fr\), \(rl\), and \(rr\)) are the longitudinal tire force and side-slip angle, respectively, of the corresponding tire. \(\delta_{f}\) and \(\delta_{r}\) are the steering angles of the front and rear wheels, respectively.  
Figure 1: Structure of a vehicle system including the proposed algorithm.  
Figure 2: Structure of the friction coefficient estimation algorithm.
## 4 Results and Discussion
### Dynamic Response of Different Drag Force in the Separation Region
Figure 11 shows the analysis of the specific displacement changes of typical atmospheric particulate matter PM\({}_{2.5}\), PM\({}_{10}\), and PM\({}_{100}\) under the same composite electric field and drag coefficient. Choosing the appropriate composite voltage, separation occurs between typical atmospheric particles. The larger the diameter of atmospheric particles, the greater the displacement of particles and the faster the separation speed. Particles with small diameters gradually separate out with time. Comparing Figure 11a,b, it can be seen that as the drag coefficient decreases, the displacement of atmospheric particulate matter increases significantly. The smaller the drag coefficient, the better the separation effect between atmospheric particles. The faster the particles with larger diameters are separated, the particles with smaller diameters will be separated slowly with the increase of time to achieve the purpose of separating atmospheric particles.
## 3 Results  
### Analysis of the Subfractions of Chokeberry Crude Extract Using LC-HRMS  
The crude extract of chokeberry pomace of the variety 'Nero' was fractionized by various analytical methods to provide a separation of the OPCs by their DP. In total, 59 subfractions were obtained, including trimers, tetramers, pentamers and hexamers. OPCs of these subfractions were characterized by using LC-HRMS. The characterization was based on the exact mass of the OPCs and by taking the product ions into account. Particularly, the fragment ions of the MS\({}^{2}\) experiments gave hints of the alleged structures. Thirteen structurally different OPCs as well as potential isomers of these were observed. A total of 58 analytes were detected (Table 1), including five trimers, four tetramers, two pentamers and one hexamer. Additionally, one dimer of interest was detected. A distinction between \(cis/trans\) isomers was not possible based on the MS data as well as the determination of the type of interflavan bond. The options are a bond between C4 \(\rightarrow\) C6 or between C4 \(\rightarrow\) C8 of the C-ring from one monomer and the D-ring of the second monomer. Overall, five isomers of the OPC with \(m/z\) 739 (dimer) were detected. The 17 analytes belonging to the group of trimers are three isomers with \(m/z\) 863, seven isomers with \(m/z\) 865, one single substance with \(m/z\) 879, three isomers with \(m/z\) 1025 and three isomers with \(m/z\) 1027. Five isomers with \(m/z\) 1151, seven isomers with \(m/z\) 1153, three isomers with \(m/z\) 1315 and three isomers of \(m/z\) 1167 were found and can be assigned to the tetramers. For the pentamers 11 analytes, six doubly charged isomers with \(m/z\) 720 and five with \(m/z\) 801 could be detected. In addition, six doubly charged isomers with \(m/z\) 864 which belong to hexamers were identified. The OPCs displayed in Figure 3 with \(m/z\) 1441, 1603 and 1729 were identified by the above-mentioned double-charged quasi-molecular ions [M-2H]\({}^{2-}\) with \(m/z\) 720, 801 and 864. [PERSON] et al. [11] showed that the OPCs with \(m/z\) 863, 865, 879, 1027, 1151, 1153 and 1441 are present in _Aronia melanocarpa_ and characterized the structures of these OPCs. Besides these, six new OPCs were detected in _Aronia \(\times\) prunifolia_ 'Nero' and could be characterized. The LC-HRMS data of the different identified OPCs are compiled in Table 1. Fragmentation spectra of the six new potential OPCs are shown for one of the isomers in Supporting Information Figures S2-S7. \begin{table}
\begin{tabular}{l l l l l l} \hline
**DP** & \begin{tabular}{l} **Postulated** \\ **Linkage** \\ \end{tabular}  & \begin{tabular}{l} **\(\mathbf{t_{g}}\) (min)** \\ *** _m/z_ **[M-H]**- \\ \end{tabular}
# Optimization of a Centrifugal Compressor Using the Design of Experiment Technique
## 3 Design of Experiment
### Response Surface Model  
After building the design space for each combination, the response (objective function) could be derived. The response surface is constructed as a surrogate model based on the most important effects. For instance if \(X\),\(Y\) and \(XZ\) combinations show the largest effects on the objective function, the estimated response surface [21] is defined as follows,  
\[\widehat{y}=\beta_{0}+\beta_{x}X+\beta_{y}Y+\beta_{xz}XZ \tag{5}\]  
in which \(\beta_{0}\) is the average of the response for all combinations, \(\beta_{x}\) is the \(E_{X}/2\) and so on. In this relation the \(X\),\(Y\) and \(XZ\) factors are varied in \([-1,+1]\) intervals which correspond to variations between low and high levels.  
To find the relationship between the objective function and factor values, the following second-order polynomial model is used, in which \(\{x_{1},...,x_{k}\}\) are the factors and \(\beta_{i}\)s are the coefficients and \(\epsilon\) is the approximation error.  
\[y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{i=1}^{k}\beta_{ii}x_{i}^{2}+ \sum_{i<j}\beta_{ij}x_{i}x_{j}+\epsilon \tag{6}\]  
Figure 2: Box-Behnken design space for three factors [20].  
The low order polynomial models may not be a reasonable approximation for entire design space; however they work quite well over a relatively small part of the domain. The method of least squares is used to estimate \(\beta\) coefficients in Equation (6). This method chooses \(\beta_{i}\)s coefficients so that the sum of the squares of the errors, \(L\), is minimized.  
\[L=\sum_{i=1}^{n}e_{i}^{2}=\sum_{i=1}^{n}\left(y_{i}-\beta_{0}-\sum_{j=1}^{k} \beta_{j}x_{ij}\right)^{2} \tag{7}\]
## 3 The GAGAN Framework: Evolutionary Optimization in GAN Architectures
### Genetic Operators in GAGAN
#### 3.2.2 Crossover Process  
Crossover is a genetic operator that combines the genetic information from two parent individuals to generate offspring potentially inheriting advantageous traits from both parents. In the GAGAN framework, a two-point crossover is employed, where two points along the weight vectors are randomly selected, and the segments between these points are swapped between the parents. This allows for the mixing of beneficial features from each parent, potentially creating offspring that outperform both parents individually.  
The crossover rate determines the probability of two offspring undergoing this two-point crossover, and is set at 0.5. This indicates that 50% of parent pairs will undergo the crossover operation. The two-point crossover strategy facilitates the exploration of new solutions by integrating successful attributes from different regions, thereby increasing  
Figure 5: The flow diagram for the GAGAN training process.  
the diversity of potential solutions. This enhanced exploration improves the likelihood of generating more effective discriminator weights, which in turn contributes to the improved overall performance of the GAN and the generation of high-quality synthetic images.  
In summary, the crossover rate controls the frequency of genetic exchange between parents to create new offspring, while the two-point crossover technique helps explore diverse solutions, enhancing GAGAN's ability to generate high-quality outputs.
# A Restoring Force Model for Prefabricated Concrete Shear Walls with Built-In Steel Sections
## 4 Results of the Low-Cycle Loading Test  
As mentioned earlier, a vertical load was applied first to reach the vertical pressure calculated by the axial pressure ratio defined in the design scheme. Then, a cyclic horizontal load was applied step by step, and the load and corresponding displacement were recorded. All the specimens showed similar failure patterns, which were bending-shear failure. The bending cracks developed obviously in the early and middle stages of loading, and cross oblique cracks appeared in the later stage.  
The yield load of the steel section, which was measured by a pre-mounted strain gauge on the steel sections (Figures 3 and 4), was taken as the yield load of the wall specimen, and the maximum load that can be reached in the process of horizontal loading was taken as the peak load. After passing the peak load, the displacement on the top of the specimen continued to increase gradually while the load decreased to 75% of the peak load, which is considered the failure load.  
The load and displacement at concrete cracking, at yielding, and the maximum load (peak load) and the corresponding displacements, as well as the load and displacement at failure are summarized in Table 3.  
The displacement ratio (horizontal displacement of the top of the wall/height of the specimen) increased with the load until the specimens failed. Due to the restraint effect of the steel sections, the specimens did not collapse.  
Under the same axial compression ratio ((vertical load applied to the top of the wall)/(measured concrete strength \(\times\) area of the wall section)), the cracking load and deformation of the specimens with different connection locations (bottom of the wall vs. 400 mm above the wall footing) are close to each other. With the increase of the axial compression load, the maximum lateral load resistance increases. Comparing the walls of XZW1 to 2, and 3 to 4, with a 10% increase of the axial compression load, the maximum lateral load resistance increased by about 25%.  
Comparing the wall specimens with different connection locations, the walls with a connection 400 mm above the footing have a slightly higher ultimate loading capacity and a higher ductility. This indicates that the connection at the worst location worked well.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## Inverse Perspective Transform (IPT)
### IPT Procedures  
Subsequently, to execute IPT, a Homography matrix is required to embody the relational transformation of coordinates between corresponding points within both the OI and horizontal plane converted images [30]. Given a point (x,y) in the OI coordinate system and a point (x',y') in the TI coordinate system, a geometric coordinate transformation from an arbitrary quadrangular ROI to a rectangle is undertaken by the following Equation (1).  
Figure 3: The procedure of finding four ROI points: (a) finding the four ROI points (yellow points) in a tunnel CCTV image and (b) flow chart.  
Figure 2: ROI configuration in a tunnel CCTV image: (a) top-view image; (b) tunnel CCTV image.  
\[\begin{bmatrix}\text{S}\begin{bmatrix}\text{x}^{\prime}\\ \text{y}^{\prime}\\ 1\end{bmatrix}=\text{H}\begin{bmatrix}\text{x}\\ \text{y}\\ 1\end{bmatrix}=\begin{bmatrix}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&1\end{bmatrix}\begin{bmatrix}\text{x}\\ \text{y}\\ 1\end{bmatrix}\end{bmatrix} \tag{1}\]  
In Equation (1), S is a scale factor which is required to scale between the two coordinate systems on GI and TI, respectively, and H represents the matrix in the form of a \(3\times 3\) matrix. In this context, matrix H is derivable by computer algorithms [31]. The \(h_{33}\) of the matrix H is normalized to 1 due to the homogeneity of the TI coordinates \((Sx^{\prime}\,,Syt,S)\), incorporating the scale factor S. With regard to other parameters, \(h_{11},h_{12},h_{21}\)_and_\(h_{22}\) govern rotation, scale, shearing and reflection, respectively, whereas \(h_{13},h_{23}\) regulate translation and \(h_{31},h_{32}\) underpin perspective transformation.  
A more detailed depiction of this IPT process is shown in Figure 4. First, the matrix between the blue rectangular box and the prescribed red ROI in Figure 4a is established. Then, the IPT with the composed matrix is applied to the cropped red ROI on OI, consequently resulting in the TI, as shown in Figure 4b. Through this process, the point (x,y) in the OI coordinate system shown in Figure 4a is at the same location as the point (x\({}^{\prime}\),y\({}^{\prime}\)) in the TI coordinate system shown in Figure 4b. The TI derived from this elaborate procedure guarantees a relatively constant object size regardless of the distance from the tunnel CCTV, thereby preserving the calculated vehicle object's velocity from the TI at an approximate constancy. Nonetheless, as IPT constitutes a geometric transformation, objects positioned farther from the tunnel's CCTV experience a reduction in their inherent resolution, resulting from their elongation.
# Detection of Thermal Strain in Steel Rails with BOTDA
## 4 Conclusions  
This laboratory test has verified the feasibility of using BOTDA in thermal-strain rail monitoring under a broad temperature range. Although the fiber jacket may cause a nonlinear temperature response of the Brillouin frequency shift, with its linear correspondence when glued on the rail, the jacketed fiber is suitable for thermal-strain sensing of the rail. Along the vertical position of the rail specimen, position independence of the thermal strain was found. Moreover, owing to a slightly different Brillouin frequency shift induced by the same amount of strain using different types of fiber, a calibration process is suggested to first be carried out in field applications.  
[PERSON] is the lead author of the article, in addition to performing the experiments using the BOTDA and the data analysis, and wrote the article; [PERSON], and [PERSON] helped to prepare the experiment; [PERSON] wrote portions of the article, as well as designed and performed the climatic testing; [PERSON] provided the technical support for the BOTDA unit and fiber A; and [PERSON] supported the design of the experiment, reviewed data analysis, and the writing of the paper.  
This research was funded by NRC of Bao/National Research Council Canada 150406, by NSERC of NSERC Discovery grant RGPIN-2015-06071 and by CRC of CRC chair fund 950-231352.  
The authors acknowledge funding support from the National Research Council Canada, NSERC, and the Canada Research Chair Program for the support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BOTDA & Brillouin Optical Time Domain Analyzer \\ CWR & Continuously Welded Rail \\ SHM & Structural Health Monitoring \\ DPP & Differential Pulse-Width Pair \\ BGS & Brillouin Gain Spectrum \\ \end{tabular}
###### Abstract
We evaluate the influence of noise, including the input and amplifier noise, on the QPSK-oriented all-optical matching system. We verify the effectiveness of the developed second-order noise-suppression structure at a transmission rate of 100 Gbaud. We also evaluate the ER, CR, quality (Q) factor, and logarithm of the bit error rate (BER) of the matching results. Compared with the system in [16], the optimized system demonstrates superior noise immunity while achieving reduced system complexity, fewer amplifier requirements, and lower power consumption. The remainder of this paper is organized as follows. Section 2 discusses the QPSK-oriented all-optical matching system reported in [16] and analyzes the noise in the system. Section 3 proposes a second-order noise structure and discusses its principles and functions. Section 4 validates and evaluates the performance improvement of the optimized system using the second-order noise-suppression structure through simulations using VPItransmission-Maker 8.5. Section 5 describes the limitations of the proposed scheme and discusses future challenges. Section 6 concludes this paper.