esa-sceva/satcom-chunk-collection huggingface dataset:
# Courtyard Sound Field Characteristics by Bell Sounds in Han Chinese Buddhist Temples
## 3 Results and Discussion
### Height of Courtyard Walls  
Most urban Chinese Buddhist temples have courtyard walls. Field surveys revealed that temple courtyard walls are generally 2 to 3 m high--sufficient to block outside sights and noises, creating a quiet atmosphere for Buddhist religious practice. In this study, the simulation model was set to include wall heights of 0 m (no wall), 1.5 m, 3 m, 4.5 m, 6 m, and 7.5 m. For each courtyard, Figure 6a,b show the SPL(A) and its standard deviation, respectively. The first courtyard's SPL(A) was relatively stable with every 1.5 m increase in wall height, leading to an increase of less than 0.7 dBA in SPL(A). For the second through fourth courtyards, SPL(A) increased along with wall height; this was attributed to amplified sound reflection. For every 1.5 m increase in wall height, SPL(A) rose, on average, from about 0.7 to 1.7 dBA, 1 to 2 dBA, and 1.5 to 2.3 dBA in the second, third, and fourth courtyards, respectively. The standard deviation (STD) of the SPL(A) at sound receivers in each courtyard decreased significantly with increasing wall height. In the second, third, and fourth courtyards, the standard deviation of the SPL(A) with a wall height of 7.5 m was smaller by about 2.4 dBA, 2.1 dBA, and 3.1 dBA, respectively, compared to those without a wall. This indicates that increased courtyard wall height led to higher sound field uniformity, which may be attributed to higher courtyard walls increasing sound reflection.  
Figure 6: Diagrams of the effects of wall height on courtyard sound fields. (**a**) Variation in weighted SPL(A). (**b**) Variation in the standard deviation of SPL(A). (**c**) Variation in RT. (**d**) Variation in C\({}_{80}\).  
As shown in Figure 6c, courtyard RT rose with increasing wall height. The T\({}_{30\text{m}}\) of the first through fourth courtyards with a wall height of 7.5 m was higher by about 0.54 s (58%), 0.67 s (67%), 0.77 s (77%), and 0.83 s (73%), respectively, compared to those without a wall. EDT\({}_{\text{m}}\) increased by about 0.42 s (71%), 0.71 s (74%), 0.64 s (51%), and 1.29 s (108%), respectively. In addition, the results showed that after the wall height was greater than the height of the sound source, courtyard SPL(A) and RT no longer changed with increasing wall height. In the meantime, Figure 6d indicates that C\({}_{80}\) decreased with increased wall height. For the first through fourth courtyards, in the presence of a 7.5 m high wall, courtyard C\({}_{80}\) was lower by 4.89 dB, 5.35 dB, 6.93 dB, and 6.45 dB, respectively, than their counterparts in the absence of a wall. All decreasing values exceeded the JND. These results indicate that changes to temple courtyard wall height will affect courtyard sound fields.
## 2 Feature Extraction Method for Axis Motion Trajectories
### Axis Trajectory Signal Feature Extraction
#### 2.3.1 Feature Point Selection  
The curvature changes vary across different regions of the axis trajectory pattern. Therefore, the same feature point selection principle cannot be applied to the entire pattern. Specific feature point selection principles need to be applied to regions with relatively significant curvature changes to maximize the retention and highlighting of the pattern's distinguishing features. The process involves dividing the pattern into regions and selecting feature points based on the curvature of each region.  
1. Pattern region division  
The purified and preprocessed axis trajectory sample signal \(R=[r(1),r(2),\cdots,r(N)]\), where \(r(i)=(x_{\mathrm{s}}(i),y_{\mathrm{s}}(i))\) and \(i=1,2,\cdots N\), consists of a series of coordinates. If any point \(A=r(i)\) is selected, and a second point \(B=r(i+k)\) and a third point \(C=r(i+\frac{2k}{n})\) are selected, the vectors  
where \(\cdot\) represents the dot product, \(\alpha\) represents the angle between the vectors \(\overset{\rightarrow}{AB}\) and \(\overset{\rightarrow}{CB}\), and \(\left|\overset{\rightarrow}{AB}\right|\) represents the magnitude of the vector \(\overset{\rightarrow}{AB}\).  
By setting an angle threshold \(\beta\) and comparing the value of \(\alpha\) with \(\beta\), the type of point \(B\) can be determined. If \(\alpha>\beta\), the point is classified as a high curvature point; otherwise, it is classified as a general curvature point. By processing all coordinate points in the pattern in this manner, the pattern can be divided into two regions, designated as the high curvature region and the general curvature region, as shown in Figure 4.  
2. Feature Point Selection Principles  
For the entire axis trajectory pattern, the overall principle is to select feature points at equal intervals, but different intervals \(n\) are chosen for different regions. In the general curvature region, feature points are selected using a large interval principle, while in the high curvature region, a small interval principle is applied. After multiple parameter adjustments, the final intervals are determined as follows:  
\[\left\{\begin{array}{l}n=4\alpha<\beta\ (\text{general-curvature region})\\ n=8\alpha>\beta\ (\text{high-curvature region})\end{array}\right. \tag{4}\]  
Figure 5 shows the feature point selection results for a set of measured axis trajectory sample signals. It can be seen that the pattern regions are effectively divided, and there are clear differences in the feature point selection principles between the high curvature and general curvature regions.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
## 4 Simulation Studies
### Results for LOS Conditions  
For the above assumptions, we carried out simulation studies. Results obtained for LOS conditions are depicted in Figures 7 and 8. Graphs in Figure 7 present the SIRs versus the angle of beam separation for the various distances between the UE and gNodeB, obtained for the MPM and 3 GPP model. Figure 8 shows the corresponding cumulative distribution functions (CDFs) of SIR.  
As can be expected, an increase in \(\Delta\alpha\) reduces the DL interference between the reference beam (providing services to the UE) and the interference beam. However, the nature of the SIR graphs is not uniform. For \(\Delta\alpha\cong 15^{\circ}\), \(30^{\circ}\), and \(48^{\circ}\), there are local maxima. They are visible for both assumed channel models and result from considering side lobes in the realistic patterns of gNodeB beams. For the 3 GPP channel model, the magnitudes of local maxima are noticeably higher than in the MPM. High maxima in the 3 GPP model are  
Figure 8: Cumulative distribution functions (CDFs) of SIR for LOS conditions and selected distances obtained for the MPM and 3 GPP model.  
Figure 7: Signal-to-interference ratios (SIRs) versus angle of beam separation for line-of-sight (LOS) conditions and different UE–gNodeB distances obtained for the multi-elliptical propagation model (MPM) and 3 GPP model.  
caused by the significant difference between powers of direct and reflected multipath components, which is typical for the 3 GPP LOS channel models. However, for the remaining ranges of angular separation \(\Delta\alpha\), where local maxima are not present, the results obtained for the MPM and 3 GPP model are comparable. The CDFs of SIR (see Figure 8) illustrate that for 80% of the analyzed range of angular separation, the results obtained by both models are within 2 dB, with half of these results within 1 dB of difference. This comparison of the LOS scenario clearly indicates that estimation of intra-cell interference and SIR with the use of the MPM demonstrates accuracy comparable with the 3 GPP channel model.  
To estimate intra-cell interference and SIR, the MPM only needs a single simulation instance. In contrast, the 3 GPP statistical channel model requires computational power- and time-consuming Monte Carlo simulations. Taking the above facts into account, the MPM seems to be a reasonable alternative to the commonly used channel model of the 3 GPP, especially if obtained simulation results are comparable.
# Magnus-Forces Analysis of Pitched-Baseball Trajectories Using YOLOv3-Tiny Deep Learning Algorithm
###### Abstract  
This study analyzed the characteristics of pitched baseballs from TV broadcast videos to understand the effects of the Magnus force on a pitched-baseball trajectory using aerodynamic theory. Furthermore, an automatic measurement and analysis system for pitched-baseball trajectories, ball speeds, and spin rates was established, capturing the trajectory of the baseball thrown by the pitcher before the catcher catches it and analyzing its related dynamic parameters. The system consists of two parts: (1) capturing and detecting the pitched baseball in all frames of the video using the YOLOv3-tiny deep learning algorithm and automatically recording the coordinates of each detected baseball position; (2) automatically calculating the average speed and spin rate of the pitched baseball using aerodynamic theory. As the baseball thrown by the pitcher is fast, and live-action TV videos like sports and concerts are typically at least 24 fps or more, this study used YOLOv3-tiny algorithm to speed up the calculation. Finally, the system automatically presented pitching data on the screen, and the pitching information in the baseball game was easily obtained and recorded for further discussion. The system was tested using 30 videos of pitched baseballs and could effectively capture the baseball trajectories, throw points, catch points, and vertical displacements. Compared with the values from the TV broadcast, the average errors on the calculated ball speed and spin rate were 1.88% and 7.51%, respectively. Using the ratio of the spin rate and ball speed as a parameter to analyze the pitching state of the pitcher's four-seam fastball in the Nippon Professional Baseball and Major League Baseball matches, it was observed that when this ratio increased, the Magnus displacement of the ball increased, thereby decreasing its late break. Therefore, the developed system provides scientific pitching data to improve the performance of baseball pitchers.  
magnus force; pitched-baseball trajectory; YOLOv3-tiny deep learning algorithm; ball speed; spin rate Article
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 2 Materials and Methods
### Structure and Functionality
orbital plane number, satellite type/generation, satellite system, information about the LRA (size, shape, type of coating, number of retroreflectors in array, and dimension of a single retroreflector), and information about the orbit (semi-major axis, altitude, revolution period, inclination, and eccentricity)
* The table called \"satellite_tv\" (satellites' time variables) describes the time-dependent features of each satellite such as: SVN (identifier of the satellite, to which variables are referred), PRN (Pseudo Random Noise, which is different for each satellite in particular system and indicates the channel number of the satellite microwave signal transmission; however, the satellite can be associated with different PRN numbers during its lifetime), generation (chronological number of change for the particular satellite), time interval for the time variables, microwave antenna offset, and LRA offset in reference to the satellite center of mass (CoM). The application allows for creating many types of advanced and interactive plots. These features are ensured by using external JavaScript libraries (mainly Highcharts.js) dedicated for data-processing and data-visualizing. A complete list of the libraries and the terms of its licenses are attached in \"About\" subpage of the GOVUS web-application. To logically divide the service's functionality between tools, separate modules were prepared: (1) Plot Analyses; (2) Table List; (3) Station Statistics; (4) Satellite Statistics; (5) Report module; (6) Interactive map; (7) Tools; and (8) GOVUS SQL Explorer. Almost every dataset chosen by the user may be converted into the *.CSV format and downloaded. The dataset is ready to be reprocessed in more advanced external software. All plots can be freely downloaded in raster (*.png and *.jpeg) or vector (*.svg and *.pdf) file formats, thus the user may adjust selected plots to their needs. Figure 3: Partial scheme of the GOVUS database, used in the web application. Abbreviations used: PK, primary key; FK, foreign key, UNIQ, unique value; int, integer; double, double-precision floating-point number.
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
## 6 Conclusions  
We evaluated the performance of coded-GFDM using various forward error correction codes that include BCH, RS, and convolutional codes for an acoustic GFDM transceiver in shallow underwater channels. GFDM is one of the contender waveforms for 5G and its spectral efficiency and flexibility make it an ideal OFDM alternate in underwater networks. Simulation results have shown that BCH (31,6) and RS (15,3) codes are very effective for multicarrier communication in a shallow underwater acoustic channel in terms of their error performance. The convolutional codes though substantially improve the error performance of GFDM but not as much as shown by BCH and RS. Furthermore, all the codes that we tested give exceptionally good error rates up to a transmitter receiver distance of 2.5 km. Moreover, we tested the proposed system using two different equalizers: Matched Filter (MF) and Zero-Forcing (ZF). The performance of both the equalizers was quite similar with the ZF equalizer performing marginally better than MF. This work could be extended by using more powerful codes such as Turbo codes, LDPC codes, etc., and more complex equalizers such as MMSE.  
[PERSON] and [PERSON] conceived the idea and designed the system. [PERSON] and [PERSON] implemented the model, ran the simulations and wrote the manuscript. [PERSON] conceived and designed the UWA channel model used and contributed to the discussion section. [PERSON] and [PERSON] reviewed the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Spanish Ministry of Science and Innovation in the framework of the project \"NAUTILUS: Swarms of underwater autonomous vehicles guided by artificial intelligence: its time has come\" (PID2020-112502 RB/AEI/10.13039/501100011033). The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4300148 DSR01).  
Not applicable.  
The authors express their gratitude to the Escuela Tecnica Superior de Ingenieria de Telecomunicacion and the Instituto de Ingenieria Oceanica, Universidad de Malaga, Malaga, Spain. We would also like to thank Deanship of Scientific Research and College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.  
The authors declare no conflict of interest.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 4 Information Bottleneck Polar Decoders  
In this section, we show how the results of polar code construction using the information bottleneck method can also be used for coarsely quantized SC and SCL decoding [26]. Note that the lookup tables used for decoding in the following section are generated offline for a single design \(E_{b}/N_{0}\) and are used mismatched to the whole operating \(E_{b}/N_{0}\) range. First, decoding at a single building block is demonstrated, followed by demonstration of SCL decoding in Figure 2.
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 3 Electro-Thermal Integrated Energy System with \"P2G Function\"
### Solution Strategy  
Similar to the genetic algorithm, particle swarm optimization (PSO) is an iterative optimization algorithm developed by [PERSON] and [PERSON] (1955); [PERSON] and [PERSON] (1955). It is considered as realistic and powerful solution schemes to obtain the global or quasi-global optimums in power system optimization problems. Also, PSO has been successfully applied to a number of optimization problems. For the above model, the complex constraints of the nonlinear decision variables make it difficult to solve the model directly. PSO is developed in this section for the day-ahead dispatch model. The electrical decision variables including \(p_{i,t}^{\text{TU}}\), \(p_{i,t}^{\text{CHP}}\), \(p_{i,t}^{\text{WT},\text{SC}}\) and the thermal decision variables including \(h_{i,t}^{\text{CHP}}\), \(h_{i,t}^{\text{gas}}\), \(h_{i,t}^{\text{hydrogen}}\) are denoted by \(x\), Then the day-ahead dispatch model can be described in an abstract form:  
\[\min_{x}\{f(x):s.t.h(x)=0,g(x)\leq 0\} \tag{22}\]  
where \(f(x)\) denotes the objective function as shown in Equation (23), \(h(x)=0\) and \(g(x)\leq 0\) denote the constraints described in Equations (10)-(21). \[ \begin{array}{ll}f(x)&=\sum\limits_{t=1}^{T}\left[\sum C_{i,t}^{\text{TU}} \left(p_{i,t}^{\text{TU}}\right)+\sum C_{i,t}^{\text{CHP}}\left(p_{i,t}^{\text {CHP}},h_{i,t}^{\text{CHP}}\right)+\sum C_{i,t}^{\text{GB}}\left(h_{i,t}^{ \text{gas}}\right)\right]\\ &=\sum\limits_{t=1}^{T}\left[\sum\left(\lambda^{\text{TU}}\cdot p_{i,t}^{\text {TU}}\right)+\sum\left(\lambda^{\text{CHP}}_{\text{P}}\cdot p_{i,t}^{\text{ CHP}}+\lambda^{\text{CHP}}_{\text{h}}\cdot h_{i,t}^{\text{CHP}}\right)+\sum\left( \lambda^{\text{GB}}\cdot h_{i,t}^{\text{gas}}\right)\right]\end{array}  \tag{23}\]
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
###### Abstract
However, the lack of large enough tag data for model training and testing has become a key factor limiting the application of data-driven machine learning-based damage recognition methods in practical engineering. It is worth noting that the deep learning model has too many hidden layers and too many parameters, so training the deep neural network model from scratch requires a lot of data, higher computing and time cost. Furthermore, in some actual damage recognition cases, the availability of damage data is lower, so it is difficult to train an effective deep learning model. In order to solve the above-mentioned problems, this paper proposed a structural damage identification method based on fractal and RCPN. Using the fractal correlation dimension as the index of structural damage identification, combined with data fusion technology, RCPN was used for decision-level fusion to identify structural damage. Finally, the rest of the paper is organized as follows. The fundamental theories are described in Section 2. Section 3 proposed a structural damage identification method based on fractal dimension, data fusion and RCPN, and applied the proposed method into an ASCE benchmark structure damage identification in the Section 4. Section 5 presents the conclusion.
## 4 Results
### Rail Sound Power Level  
By substituting the calculation results of the rail mobility amplitude (Figure 11) and the DR (Figure 12) into Equation (22), the sound power level of the rail subjected to a harmonic point excitation can be obtained, as shown in Figure 13 (in the form of the one-third octave spectra).  
Below the center frequency of 800 Hz, the rail sound power level increases with the increase of frequency. A peak appears at the center frequency of 800 Hz with the value of 48 dB. In the frequency range of 1000-1600 Hz, the rail sound power level is within the range of 36-39 dB.  
The high rail mobility amplitude and the low DR will both lead to a significant rail sound power level. Figures 11 and 12 show that although the direct mid-span rail mobility amplitude is significant  
Figure 12: Decay rate.  
Figure 13: Sound power level of the rail subjected to a harmonic point excitation.  
near the frequency \(f_{\rm v}\), the DR also has a high value. As a result, no peak occurs in the curve of the rail sound power level near \(f_{\rm v}\). However, there is a peak at the center frequency of 800 Hz. As the DR and the mobility amplitude both decrease with the increase of frequency, the contributions of the second and the third items of Equation (22) to the rail sound power level are cancelled out by each other to a certain extent. Furthermore, the rail radiation ratio increases with the increase of frequency and reaches a peak near the center frequency of 800 Hz [2]. Thus, it is the main reason why the rail sound power level reaches a peak at the center frequency of 800 Hz.
## 2 The Principle and Components of CPFTA
### The Unit Cell of Transmitarray
To better understand the working mechanism of the cell, the current distributions on bottom E-shaped patch at the two resonant frequencies are analyzed in Figure 4. At 9.4 GHz, the resonance is mainly formed by bended arms. Compared with Figure 4b, the current path is longer, and the current intensity is higher in Figure 4a. Therefore, the cell has lower amplitude of \(R_{xx}\), as seen in Figure 3a. While at 10.8 GHz, the direction of currents is consistent. Figure 4b shows that the E-shaped patch works such as ordinary microstrip antenna. Figure 3: The reflection and transmission amplitudes of the unit cell under (a) _x_-polarized and (b) _y_-polarized incident waves. Simulated (c) AR and (d) TR under _x_-polarized incident waves. In addition, the current distributions of top anisotropic patch with phases of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) are simulated at 9.2 GHz. Figure 5 depicts that the direction of current changes along the left-hand rotation. Therefore, the transmitarray radiates left-handed circularly polarized waves. Figure 4: The current distributions on bottom E-shaped patch at (a) 9.4 GHz and (b) 10.8 GHz. Figure 5: The current distributions on top 248(distr73 nisotsiat.16 Tm JTI548 Td8 pistri9m)TJ/ff7541816 Tmff  
Figure 7a shows that the PCR is more than 95% in the range of 8.2-11.2 GHz with incident angle less than 30\({}^{\circ}\). The principle of cell can be analyzed in \(u\)-\(v\) coordinate. Suppose that the x-polarized incident waves are decomposed into \(u\)-polarized and \(v\)-polarized waves in same phase. When the incident waves are \(u\)-polarized, \(R_{uu}\) and \(R_{uu}\) represent co-polarized and cross-polarized reflection, respectively. Similarly, \(R_{uv}\) and \(R_{uv}\) represent co-polarized and cross-polarized reflection of \(v\)-polarized incident waves. Since the cell is symmetric along the \(u\)-\(v\) coordinate, the \(u\)-polarized and \(v\)-polarized incident waves will realize co-polarized total reflection, as shown in Figure 7b. Moreover, the phase difference between the co-polarized reflection of \(u\)-polarized and \(v\)-polarized waves are around 180\({}^{\circ}\). Therefore, the \(u\)-polarized and \(v\)-polarized reflected waves are combined into the \(y\)-polarized waves.