esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Potential of the Data and First Results
### Profiles  
#### 5.2.1 Composite Profiles from Multiple Systems  
Figure 9 shows an example of atmospheric profile measurements for temperature, \(T\), and wind speed, \(U\), from different systems, i.e., AWS-ice at 1 m agl, 2 m agl and 4 m agl; AWS-FMI (only \(U\)) at 29 m asl; Bebop2 Met (only \(T\)) from 0 m agl to 350 m agl; SUMO from 40 m agl to 1800 m agl; and LIDAR from roughly 200 m agl to 450 m agl. The displayed AWS and LIDAR data represent time-averaged data for the time period indicated in the legend, whereas the UAV data correspond to one single ascent. The Bebop2 Met \(T\) data is bin-averaged with 10 m increments, while for the SUMO data, the bins are 25 m, and the LIDAR data points are separated by roughly 24 m. It also has to be noted that we used three different scales for the y-axis to increase the level of detail in the SL towards the surface.  
During the morning of 26 February, the ABL was stably stratified with a surface-based inversion reaching up to about 300 m, as well as several smaller, but also sharp inversions further above. All three systems matched very well with temperature differences in the range of 0.5 \({}^{\circ}\)C, which could have  
Figure 9: Combined temperature (**a**) and wind speed (**b**) profiles based on AWS-ice AWS-Finnish Meteorological Institute (FMI) (only \(U\)), Bebop2 Met (\(T\)), Small Unmanned Meteorological Observer (SUMO) and LIDAR (\(U\)) observations from 26 February between 05:20 and 06:20 UTC. The AWS and LIDAR data represent averaged profiles for the periods indicated in the legend.  
been caused by differences in the sampling times and differences in the time and spatial averaging procedures applied to the data.  
The profile of the horizontal wind speed has a gap from 4 m agl to 40 m agl, since no reliable estimates from the Bebop2 Met UAV could be computed due to a significant cross-wind component acting on the multicopter and the lack of LIDAR data with sufficient CNR. The SUMO data, however, indicated the existence of an LLJ with a peak velocity of about 7.5 m s\({}^{-1}\), located just below 100 m, which also corresponds well to the notable decrease in the vertical temperature gradient observed at this level. At the levels between the 200 m to 500 m, where LIDAR data was available, and in the vicinity of the 29-m wind measurement at AWS-FMI, the agreement between the observations within 1 m s\({}^{-1}\) was fairly good, given the differences in the observation and data processing principles.
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 3 Method  
### TC Center Location  
[PERSON] et al. [29] noted that the divergence and/or the curl of scatterometer wind stress near the TC core show distinct signatures which are useful for identifying TC center location. That is, two (positive) local maxima and two (negative) local minima appear symmetrically near the TC core, as shown in Figure 2. Consequently, one can take the intersection of the two lines constructed separately by the local maxima (blue line) and the local minima (red line) as the TC center. In practice, the TC center locations derived from both divergence and curl are of similar accuracy compared to the collocated best-track data [29]. As such, the wind stress curl of HSCAT wind data is hereafter used to identify the TC center location.  
Although the conditions for the presence of such distinct signatures in Figure 2 have not been examined thoroughly, we found that these signatures are rather prominent after checking the divergence and curl patterns for dozens of TC acquisitions. Consequently, the above technique is applied to the 32 HSCAT and 9 ASCAT acquisitions of TCs in Figure 1, as well as the collocated ECMWF forecast winds. The mean and standard deviation (SD) values of the difference between the identified HSCAT TC center and the interpolated best-track positions are about 28.8 km (approximately one pixel of the HSCAT grid data) and 16.1 km, respectively, which are generally smaller than those described by [PERSON] et al. [13]. The latter shows a mean difference between the HSCAT TC center and the best-track position larger than three HSCAT 25-km pixels (~75 km). On the other hand, the mean and SD values of the difference between the best-track position and the TC center depicted by ECMWF winds are about 34.9 km and 19.2 km, respectively, demonstrating that HSCAT winds result in closer TC center location to the best-track than ECMWF forecast winds. For the 9 ASCAT acquisitions, the mean difference between the best-track position and the ASCAT-derived (ECMWF-derived) TC center is about 24.9 km (37.9 km), which is consistent with the above results.
## Materials and Methods
### Measurement of Toluene Dynamic Adsorption Capacity  
Adsorption capacity of ZIF-8 samples toward toluene was measured using breakthrough curves obtained under different experimental conditions. The breakthrough curves are the concentration-time profile, which are expressed in term of \(C/C_{0}\) as a function of time (where \(C\) is effluent and \(C_{0}\) is influent concentration of the toluene). A known concentration of toluene was passed through the fixed bed of the adsorbent, and the concentration of toluene at effluent of the reactor was measured. The concentration of toluene in the feed was controlled at 1000 ppmv by using a 0.02 L\(\cdot\)min\({}^{-1}\) flow rate of the carrier gas (\(Q\)). The amount of adsorbent (\(M\)) in each experiment was 0.05 g, which was packed into the quartz tube. The time of the breakthrough adsorption \(t_{b}\) (min) and time of the equilibrium adsorption \(t_{e}\) (min) were determined, while the ratio of influent and effluent toluene concentrations (\(C/C_{0}\)) were equal to 0.05 and 0.95, respectively. Breakthrough capacity was measured using the following equation [24; 42; 43]:  
\[q=\frac{QC_{0}}{M}t_{e}-\frac{Q}{M}\int_{0}^{t_{e}}C.dt_{e} \tag{1}\]  
where \(q\) is the breakthrough capacity (mg\(\cdot\)g\({}^{-1}\)), which is indeed the total mass of toluene adsorbed on ZIF-8 (\(M\)). In Equation (1) the first term is the total toluene in the influent gas relative to the mass of ZIF-8 in the duration of \(t_{e}\) and the second term is the total mass of toluene in the effluent gas.  
The total adsorption percentage of toluene (\(A\)) was calculated using the following equation [44]:  
\[A=\frac{s}{x}\times 100 \tag{2}\]  
where \(s\) is the adsorbed toluene (mg) on ZIF-8 and \(x\) is the total amount of toluene in influent (mg) in the duration of \(t_{e}\).
## 4 Performance Analysis
### Overhead Estimation
#### 4.2.1 Average Link Estimation  
The overhead and signaling required for allowing the proposed scheme to properly function are linearly related to the average NLE \(N=P_{1}+2\cdot P_{2}\). This metric can be evaluated as the probability that exactly one path is examined which is equal to \(P_{1}=\pi_{1}\text{Pr}[\gamma_{1}\geq\gamma_{\text{th}}]+\pi_{2}\text{Pr}[ \gamma_{2}\geq\gamma_{\text{th}}]\) plus the probability that both paths are examined \(P_{2}=\pi_{1}\text{Pr}[\gamma_{1}<\gamma_{\text{th}}]+\pi_{2}\text{Pr}[\gamma_ {2}<\gamma_{\text{th}}]\). Substituting the corresponding CDF expressions in these definitions yields the following expression  
\[N=\pi_{1}(1+F_{\gamma_{1}}(\gamma_{\text{th}}))+\pi_{2}(1+F_{\gamma_{2}}( \gamma_{\text{th}})). \tag{28}\]  
From the above equation, it can be concluded that the NLE increases as \(\gamma_{\text{th}}\) increases until it reaches its maximum value which is 2, i.e., both links are always examined before selecting the one that offers the maximum SNR.
## 2 Overview of Machine Learning Principles
### Overview of Machine Learning Models
#### 2.1.5 Support Vector Machine and Support Vector Regression  
The support vector machine (SVM) [36] is a classifier, while its modified version, support vector regression (SVR), is used for regression tasks.  
In SVM, support vectors, which are the closest data points of each class to each other, are assigned after mapping the data into hyperspace using a kernel function and then classifying them. Therefore, a subset created by the support vectors from the input data to minimize the distance between the input data points and hyperplane is used for the classification.  
SVR uses real-valued data as inputs and produces real-valued outputs for prediction tasks, instead of binary outputs [37]. The mapping procedure of SVR is the same as that of SVM, and the projection of data into a higher dimension makes the classification and prediction of non-linear data possible.  
Different kernel functions, such as quadratic, polynomial, linear, and radial basis functions, can be used in both SVM and SVR.
## 3 Operating Characteristics Analysis and Parameter Design  
### Operating Characteristics  
#### 3.1.1 Inverter Output Voltage  
According to Figure 4, the following relationship must be satisfied between duty cycles \(D\), \(D_{1}\), and \(D_{2}\)  
\[\left\{\begin{array}{l}D_{1}\leq D\\ D_{2}\leq 1-D\end{array}\right. \tag{2}\]  
According to the analysis of the operating modes of the proposed inverter, the voltage expression of the inductor \(L\) is  
\[u_{L}=\left\{\begin{array}{l}U_{dc}\quad,0<t\leq t_{2}\\ -U_{C}\quad,t_{2}<t\leq T\end{array}\right. \tag{3}\]  
In the stable state, \(L\) should satisfy the volt-second-balance principle and satisfy the following expression,  
\[U_{dc}DT-U_{C}(1-D)T=0 \tag{4}\]  
By solving Equation (4), the expressions for \(U_{C}\) and \(U_{b}\) can be obtained as follows,  
\[U_{C}=\frac{DU_{dc}}{(1-D)} \tag{5}\]  
\[U_{b}=\frac{U_{dc}}{(1-D)} \tag{6}\]  
From the analysis of Figure 4, the Fourier series expansion of \(u_{1}-u_{4}\) is  
\[u_{1}=(D-D_{1})U_{b}+\frac{\sqrt{2}U_{b}}{\pi}\sum_{n=1}^{\infty}[\frac{\sqrt {1-\cos 2n\pi(D-D_{1})}}{n}\sin(n\omega_{0}t+\varphi_{1n})] \tag{7}\]  
\[u_{2}=D_{1}U_{b}+\frac{\sqrt{2}U_{b}}{\pi}\sum_{n=1}^{\infty}[\frac{\sqrt{1-\[u_{3}=D_{2}U_{b}+\frac{\sqrt{2}U_{b}}{\pi}\sum_{n=1}^{\infty}\left[\frac{\sqrt{1- \cos 2n\pi D_{2}}}{n}\sin(n\omega_{0}t+\varphi_{3n})\right] \tag{9}\]  
\[u_{4}=(1-D-D_{2})U_{b}+\frac{\sqrt{2}U_{b}}{\pi}\sum_{n=1}^{\infty}\left[\frac{ \sqrt{1-\cos 2n\pi(l-D-D_{2})}}{n}\sin(n\omega_{0}t+\varphi_{4n})\right] \tag{10}\]  
where \(\omega_{0}\) is the operating angular frequency of the proposed inverter, \(n\) is the harmonic order, and \(\varphi_{1n}\)-\(\varphi_{4n}\) is the phase angle of the Fourier series expansion. As each output of the proposed inverter is connected to a resonant compensation network, which has good filtering characteristics, the system mainly transmits the fundamental power. Furthermore, the expression for the fundamental RMS of \(u_{1}\)-\(u_{4}\) is  
\[U_{1}=\frac{U_{dc}\sqrt{1-\cos 2\pi(D-D_{1})}}{\pi(1-D)} \tag{11}\]  
\[U_{2}=\frac{U_{dc}\sqrt{1-\cos 2\pi D_{1}}}{\pi(1-D)} \tag{12}\]  
\[U_{3}=\frac{U_{dc}\sqrt{1-\cos 2\pi D_{2}}}{\pi(1-D)} \tag{13}\]  
\[U_{4}=\frac{U_{dc}\sqrt{1-\cos 2\pi(1-D_{1}-D_{2})}}{\pi(1-D)} \tag{14}\]  
From Equations (11)-(14), it can be seen that when \(U_{dc}\) is determined, the control of each output can be achieved by adjusting the corresponding duty cycle.
## 6 Discussion
### The Petrogenesis of the Jabal Sayid and Dayheen Granitoids  
#### 6.2.1 Subduction-Related Granitoids  
During an orogenic cycle, which encompasses the development of a mountain belt, the character of magmas can change as different tectonic processes occur. Regionally, this cycle is characterized by a transition in the nature of magmatism. It starts with typical subduction-related calc-alkaline compositions, followed by late-orogenic calc-alkaline rocks, and finally culminates in post-collisional peralkaline suites. As mentioned above, the Dayheen granodiorite-diorite association exhibits characteristics consistent with I-type granitoids. These characteristics include the following: high LREE/HREE ratios (7.47, on average) with nearly flat patterns for HREEs; small negative Eu anomalies; HFSE (Nb, Ta, and Ti) depletion; and Sr and Ba enrichment. This implies that these rocks were possibly derived from the partial melting of a subducted oceanic crust, with subsequent interaction with fluids and/or melts from the subduction zone. Geochemically, these granites yield lower Rb/Zr than that of S-type granitoids (Figure 12a, [36]). Moreover, they have lower (Na\({}_{2}\)O + K\({}_{2}\)O)/CaO and Zr + Nb + Ce + Y than A-type granitoids (Figure 12b, [35]). The positioning of the Dayheen granodiorite-diorite rocks in the low-K mafic rocks field (Figure 12d) further supports their classification as I-type granitoids (Figure 12c, [37]). The Dayheen granodiorite-diorite association, as indicated by Figure 12e, is characterized by being Sr- and Ba-rich and Rb-poor. This implies that the generation of these magmas occurred in a subduction-like setting [38]. Additionally, the CaO/(MgO + FeO\({}_{\text{total}}\)) vs. Al\({}_{2}\)O\({}_{3}\)/(MgO + FeO\({}_{\text{total}}\)) diagram (Figure 12f) provides insights into the probable magma sources for the considered granitoids. The diagram suggests that most of the granodiorite-diorite association was developed by the melting of a mainly metabasaltic source. The composition of the lower crust in the AS, where the study areas are located, is consistent with the nature of this source.
# CFD Research for Air Bearing with Gradient-Depth Recesses
## 5 Conclusions  
In order to reduce the influence of the traction force of air tubes and improve the rotational stiffness, we propose the technical concept of an air bearing without an air tube. The research in this article includes theoretical modeling, performance optimization, simulation analysis, and final comparative verification for an air bearing with gradient-depth recesses. The detailed results are as follows:  
1. To avoid mechanical crawling and accuracy reduction from the air tube of the air bearing, we put forward a new structure of an air bearing without an air tube.
2. The mechanical performance model is established. The analysis results indicate that the air bearing without an air tube needs higher rotational stiffness \(K_{\theta}\) to ensure the moving distance and linear accuracy.
3. The result of the theoretical analysis indicates that the gradient depth of recesses could improve the guideway's rotational stiffness, which is verified by CFD investigations of AFVM.
4. The comparison shows that gradient recesses could reduce the straightness error by improving the rotational stiffness. The straightness error caused by rotational torque is always smaller than 3 \(\upmu\)m, less than half of the original structure. The final actual error is even smaller because of the air film homogenization effect.  
**Author Contributions:** Conceptualization, Z.W.; methodology, Y.C.; software, H.G.; validation, Y.C.; writing--original draft preparation, Z.W. and H.Q.; writing--review and editing, H.Q.; funding acquisition, Z.W. and Z.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52205042, and the Young Elite Scientists Sponsorship Program by CAST, grant number 2022 QNRC001.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** Numerical calculation was supported by Beijing Engineering Research Center of Precision Measurement Technology and Instruments. The authors would like to thank all the reviewers who participated in the review.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 4 Multi-Scale Convolutional Neural Networks for Anchor Bolt Corrosion Recognition  
### CNN for 1D Signals  
A CNN is generally used in the fields of computer vision, image recognition, and so on. In dimensional data processing, a 1D-CNN can analyze numbers of fixed length, especially when the data feature is not highly correlated with the location of the data. However, the corresponding characteristics can be obtained effectively. In order to prevent anchor bolt failure caused by corrosion and provide early fault diagnosis, an AI-based intelligent decision-making mechanism can be developed [18]. Unlike traditional machine learning algorithms, a convolutional neural network (CNN) architecture offers effective solutions for classification problems with its powerful self-learning capabilities and state-of-the-art performance. The CNN is an important algorithm in the field of deep learning and operates through convolutional layers, pooling layers, and fully connected layers. In the convolutional layer, neurons gather data from different input regions and share the same set of weights. In assuming that the input image matrix is denoted as \(X\), the convolutional kernel matrix as \(W\), the activation function as f, the bias matrix as b, and the output pixel matrix as \(Y\), the calculation formula for the convolutional layer is as follows:  
\[Y=f(X\otimes W+b) \tag{16}\]  
where \(\otimes\) represents the convolution operation. In practice, the bias term b is obtained through training. The size of the output feature map depends on the size of the filter and the stride. For instance, when the convolutional kernel size is \(F\times F\) and the stride is \(S\), for an input image of size \(H\times H\), the output size is \(W\times W\).  
The pooling layer is used to reduce the resolution of the previous feature maps and can operate based on methods like maximum or average pooling. If an input of size \(W\times W\) is fed into the pooling layer, the output size is determined by the following formula:  
\[W^{\prime}=(W-F)/S+1 \tag{17}\]  
Figure 7: Schematic of guided wave propagation: (A) wave transmission; (B) wave reflections at boundaries 2 and 1; (C) waves are reflected from boundaries 3 and 2; (D) wave reflections at boundaries 3 and 1; (E) wave transmission; (F) wave reflections at boundaries 3 and 4; (G) wave reflections from borders 2 and 3; and (H) wave reflections from borders 2 and 4.  
Convolutional neural networks (CNNs) typically consist of multiple fully connected layers at the top, similar to feed-forward neural networks, aiming to extract global features from the input. The last layer is a softmax classifier used to estimate the posterior probability of the input sample across \(K\) different classes.  
In summary, a CNN extracts features and performs pattern learning through operations such as convolution, pooling, and fully connected layers. It holds a significant position in the field of deep learning and has achieved remarkable results in areas such as time-series data recognition.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Circular Trajectory Placement with a Radius of 100 m
#### 4.1.3 Hybrid  
The results of source localization using a hybrid approach that integrates RSSI, AOA, and EOA are shown in Figures 21-23.  
As shown in Figure 22, the localization errors using the HYBRID method are uniformly small across the entire area and significantly lower than those observed with RSSI or AOA alone. This indicates that combining distance- and direction-based features enhances localization stability even in NLoS environments. Figure 23 confirms that 90% of the sources were localized within approximately 8.7 m, with a steep CDF slope and minimal outliers.  
Figure 21: Optimized UAV placements for HYBRID localization integrating RSSI, AOA, and EOA features. Placement reflects weighting of multidimensional signal characteristics.  
Figure 20: CDF of localization errors with AOA-based localization. Compared to RSSI, error convergence is faster with fewer large outliers.  
This result suggests that the hybrid integration of RSSI, AOA, and EOA reduces uncertainty and compensates for the weaknesses of individual signal features. These findings extend the previous works by [PERSON] [10], who demonstrated the benefit of combining RSSI and AOA, and [PERSON] [8], who improved RSSI-based localization accuracy through UAV trajectory optimization. The present study combines these two directions and further improves robustness by including EOA and jointly optimizing signal feature selection and UAV path design.
# Nondestructive Tomographic Imaging of Rust with Rapid THz Time-Domain Spectroscopy
## 4 Conclusions  
In this study, we conducted rapid nondestructive testing on rusted metal using a high-speed THz-TDS spectroscopy system at a rate of 100 Hz/pixel. The rusted area was clearly identified in the TOF imaging in the presence of the paint layer, which is advantageous over conventional NDT imaging techniques that are frequently obscured when there is lift-off in the protective layer. In addition, the THz reflection images enable us to distinguish between the lightly and heavily rusted area in contrast to the photographic images. Via Fourier transformation of the phase-sensitive signals, we obtained frequency-selective imaging in the range 0.2-1.3 THz. Overall, a higher contrast was achieved with the higher frequency component. In addition, we monitored the spread of rust in a steel plate when the steel was submerged in NaCl and acidic solution (a mixture of hydrogen peroxide and acetic acid). We found that in the early stages, the decrease in the THz reflection signal was governed by the high-frequency components, whereas the contrast in the lower frequency range developed for longer periods. In other words, the absorption peak reached 0.8 THz as hematite was formed over a long period of time. THz response on a ferrous oxide pallet (combined with the Raman spectroscopy results) revealed the dynamic transition between the different oxide types, i.e., from lepidocrotic to hematite. Consequently, the rapid imaging technique was found to be a very powerful tool for the nondestructive inspection of rust in metals, including their dynamic behaviors.  
Conceptualization, Y.H.A.; formal analysis, H.S.K., S.Y.B. and Y.H.A.; investigation, H.S.K. and S.Y.B., J.W.L. and Y.H.A.; resources, J.K.; writing--original draft, H.S.K. and Y.H.A.; writing--review and editing, H.S.K., J.W.L., J.K. and Y.H.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Midcareer Researcher Program (2020R1A2C1005735) and Basic Science Research Program (2021R1A6A1A1004950) through a National Research Foundation grant funded by the Korea Government, and by the Human Resources Program in Energy Technology (20184030202220) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.