esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Static Position Estimation  
For the static position estimation experiment, ten positions with known ground truths are chosen. The receiver unit is placed at those positions, and the receiver positions are estimated with the localization system. Figure 17 shows the results for those positions, whereby the estimation is performed with a PF and M = 1000 particles. While [PERSON] et al. [4] report root-mean-square errors between 1 mm and 2 mm for their original method, the errors in Figure 17 are an order of magnitude higher. The data processing capabilities of the hardware are inferior to the signal analyzer in [4]. However, it is compact and inexpensive. Another reason for the degraded performance is the strong reflections in the tank. The points in proximity to the tank walls, e.g., 1, 2, 3 and 9, show systematic biases due to reflections, whereas results for points further away are more accurate, e.g., Points 3 and 4. Despite their biases, all measurements show a small variance. This emphasizes our assumption that the deviations mainly result from the reflections and are, thus, caused by the characteristics of our test basin, which is significantly smaller than the test tank used in [4].  
Figure 16: Underwater range sensor model and RSS measurements.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Summary of Comparative Study  
To recapitulate the comparative analysis, in scenario 1 with the Two-Ray propagation model and routing protocols states OLSR has better performance than AODV and DSDV. As per scenario 2, the Log-Distance model outperforms other propagation models in terms of delay and packet delivery, whereas FRIIS and Two-Ray give better performance in terms of throughput, average routing goodput, and MAC/PHY overhead. In scenario 3, Log-Distance has less delay, however, Two-Ray and FRIIS outperform in terms of packet delivery, throughput, average goodput, and overhead. It is to be noted that although Nakagami is not the best propagation model, it gives the most stable performance. Further, FRISS and Two-Ray are deterministic propagation models, whereas Nakagami and Log-Distance are probabilistic models. Thus, using models like FRISS and Two-Ray may not be feasible for varying scenarios as the results are misleading. For real-time scenarios, models like Nakagami and Log-Distance can provide an accurate result. A more appropriate model can be a combination of Nakagami (fading model) and Log-Distance (path-loss propagation  
Figure 9: Performance Analysis of Routing Protocols varying Number of Nodes for Realistic Scenario simulated for 60 s. (**a**) Number of Nodes vs. End-to-End Delay; (**b**) Number of Nodes vs. Packet Delivery Ratio; (**c**) Number of Nodes vs. Throughput; (**d**) Number of Nodes vs. AverageRoutingGoodput; (**e**) Number of Nodes vs. MAC/PHY Overhead.  
model). As per analysis 4, OLSR performs better in terms of delay, throughput, packet delivery, goodput, and overhead compared to AODV and DSDV. The comparative analysis 5, for scenario 5, depicts OLSR to perform better than the other compared protocols in terms of all performance metrics. To sum up, OLSR is the best routing protocol than the compared ones for a VANET scenario due to its multi-point relay mechanism for a more efficient, organized, reliable, and scalable routing. Whereas, the preferred propagation model is Log-Distance and Nakagami rather than FRIIS or Two-Ray, as it shows the most accurate representation and better performance.
## Appendix A  
This appendix gives the derivation of the maximum likelihood estimator of the frequency rate for a linear chirp signal.  
Still taking Equations (1) and (2) as the received signal model, in which the phase model is defined in Equation (11).  
\[\theta(t)=\theta_{0}+2\pi\left(\frac{1}{2}f_{1}t^{2}\right), \tag{12}\]  
which contains only the frequency rate and the initial phase.  
The ML estimates of the signal parameters are still those values that simultaneously maximize the conditional joint probability density of the observation vector, conditioned on the signal parameters, which is formulated as Equation (12).  
\[\left(\hat{A},\hat{\theta}_{0},\hat{f}_{1}\right)=\max\Biggl{\{}\left(\frac{ \pi N_{0}}{T_{s}}\right)^{-N}\exp\left[-\sum_{i=-N/2}^{(N/2)-1}|\widetilde{r}_ {i}-\widetilde{s}_{i}|^{2}\right]\Biggr{\}}, \tag{13}\]  
Following the similar derivation process shown in [12], the ML estimation for signal amplitude and initial phase are separately shown in Equations (14) and (15).  
\[\hat{\theta}_{0}=arg\Biggl{[}\sum_{i}\overline{r}_{i}e^{-j2\pi\times\frac{1}{2 }f_{1}t_{i}^{2}}\Biggr{]}, \tag{14}\]  
\[\hat{A}=\frac{1}{N}\Bigl{|}\overline{r}_{i}e^{-j2\pi\times\frac{1}{2}f_{1}t_{i }^{2}}\Bigr{|}, \tag{15}\]  
The maximum likelihood estimation for instantaneous frequency rate is proved to be Equation (16).  
\[\left(\hat{f}_{1}\right)_{ML}=\underset{\hat{f}}{\operatorname{argmax}} \Biggl{|}\sum_{i=-N/2}^{N/2-1}\biggl{[}\overline{r}_{i}\times e^{-j2\pi\times \frac{1}{2}\hat{f}_{i}^{2}}\biggr{]}\Biggr{|}^{2} \tag{16}\]  
which is similar to the periodogram estimation.
## 3 Validation of the Numerical Method  
### Validation of the Numerical Wave Tank  
In order to validate the wave generation equations and the artificial damping effect, we first built a numerical wave tank with wavelength \(\lambda\) = 4 m and wave amplitude \(a\) = 0.048 m, \(V_{0}\) = 0.28 m/s, in which the foil was not placed. From the numerical model, we then obtained free surface time series at different positions (2 m, 6 m) and compared the wave amplitude versus time with analytic results, and the waveform at \(t\) = 20 s of the NWT, as shown in Figure 4, in which \"ana. simu\" means the analytic simulation process and \"num. simu\" means the numerical simulation process proposed in this paper.  
Figure 4: _Cont._  
As shown in Figure 4a,b, the numerical wave amplitudes keep steady and their time series matches the analytic one. In Figure 4c, wave amplitude then drops in the artificial damping zone. Based on Figure 4, the wave generation equations and artificial damping in this paper are valid and the boundaries of the NWT do not affect the waveform at the foil's position. In addition, the longer the distance between the left boundary of the NWT and the measured position, the longer the time to form a stable waveform and the larger the difference between numerical and analytic waveforms. This phenomenon means wave energy dissipates during its propagation because of the viscous effect of water. This will occur in physical experiments as well. However, the wave amplitudes remains constant at the certain location of the NWT in the time domain.
# Developing Machine Learning-Based Models for Railway Inspection
## 3 Feature-Based Classification Method
### Wavelet Feature Extraction  
As in all challenging machine learning applications, the quality of the representation of the data input is a key factor for building high-performance models. In this work, the acceleration data of both sides of the track was sampled with a given frequency of 464 Hz as mentioned above. The data was transformed by signal processing in the data acquisition system mounted on the rail inspection car. After the exploration of acceleration data, we believe it is necessary to perform the wavelet analysis in order to augment the data by generating new features. Wavelet analysis is a signal processing technique that has emerged over the last 30 years as an alternative to traditional signal processing methods, like the Fourier transform. The wavelet transform [23; 24] can extract local information in the time-domain and frequency-domain. Discrete wavelet transform (DWT) is a common method of wavelet analysis, which decomposes low/high-frequency components from the original signals. The following describes the foundation of the standard discrete wavelet transform.  
In DWT, a discrete signal can be expressed using a scaling function and a wavelet function [24]. Using the Haar scaling function, and the Daubechies wavelet function, the signal \(f[n]\) can be expressed in Equation (1).  
\[f[n]=\frac{1}{\sqrt{M}}\sum_{k=0}^{\infty}W_{\varnothing}[j_{0},k]\varnothing_ {j_{0},k}[n]+\frac{1}{\sqrt{M}}\sum_{j=0}^{\infty}\sum_{k}^{M}W_{\psi}[j,k] \psi_{j,k}[n], \tag{1}\]  
where \(f[n],\ \phi_{j_{0},k}[n],\ \text{and}\ \psi_{j,k}[n]\) are discrete functions defined in \([0,M-1]\), with a total of \(M\) points. By taking the inner product to obtain the wavelet coefficients: approximation coefficients \(\left(W_{\psi}[j,k]\right)\) and detailed coefficients \(\left(W_{\psi}[j,k]\right)\) are given in Equations (2) and (3), respectively.  
\[W_{\psi}[j_{0},k]=\frac{1}{\sqrt{M}}\sum_{n=1}^{M}f[n]\phi_{j_{0},k}[n], \tag{2}\]  
\[W_{\psi}[j,k]=\ \frac{1}{\sqrt{M}}f[n]\psi_{j,k}[n]j\geq j_{0}, \tag{3}\]  
In this study, we generate the wavelet features based on Equations (2) and (3). The computed wavelet features will be added on to the original data set as the new features for the model-building step.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 5 Experimental Verification
### The Analysis Results of MOMEDA  
The obtained vibration signal is analyzed by the MOMEDA method, and the obtained analysis result is shown in Figure 9. Figure 9a is a time-domain graph of the analysis result, and Figure 9b is an envelope spectrum graph of the analysis result. It can be seen that the number of effective pulses in the envelope spectrum of the original signal processed by MOMEDA is increased, and the extracted fault frequency is 162.4 Hz. The experimental results were compared by permutation entropy and the fault energy ratio. The results of the comparison between the original experimental signal and the signal processed by MOMEDA are shown in Table 3. It can be seen from the results in Table 3 that permutation entropy of the experimental signal is reduced after MOMEDA processing, which proves that the regularity of the signal increases, and the fault energy ratio of the signal also increases.
## 2 Materials and Methods
### Data Processing for Damage Assessment  
Ultrasonic signals were registered automatically in the monitoring mode. The first signal was registered at the beginning of the push-out test, i.e., for the force equal to 100 N. This signal was next  
Figure 4: Excitation signal.  
Figure 5: Digital photograph of the left adhesive joint: (**a**) RGB photograph; (**b**) magenta component; (**c**) binary image.  
considered as a reference signal. All other signals were considered as signals relating to successive states of mechanical degradation. The idea of the evaluation of damage growth proposed in this study is to monitor the changes in amplitude and phase shift of signals measured during subsequent phases of degradation of the adhesive joint. To quantify the agreement between two signals, the Sprague and Gears (S&G) metric was applied [35; 36; 37]. Originally, this metric was developed for comparing measured and calculated transient response histories. In the present study, the Sprague and Gears metric was applied to quantify discrepancies between the reference signal and other registered signals.  
The S&G metric considers both the magnitude and the phase shift aspects. The magnitude error factor (\(M\)-error) and the phase error factor (\(P\)-error) are given by the following relations [35; 36; 37]  
\[M=\sqrt{\frac{\psi_{mm}}{\psi_{rr}}}-1 \tag{2}\]  
\[P=\frac{1}{\pi}\arccos\left(\frac{\psi_{rm}}{\sqrt{\psi_{rr}\psi_{mm}}}\right) \tag{3}\]  
where  
\[\psi_{rr}=\frac{1}{(t_{2}-t_{1})}\int_{t_{1}}^{t_{2}}r^{2}(t)dt;\psi_{mm}= \frac{1}{(t_{2}-t_{1})}\int_{t_{1}}^{t_{2}}m^{2}(t)dt;\psi_{rm}=\frac{1}{(t_{2 }-t_{1})}\int_{t_{1}}^{t_{2}}r(t)m(t)dt \tag{4}\]  
In Equation (4), \(r(t)\) denotes a reference signal and \(m(t)\) is a signal acquired during the monitoring of damage growth. For discretized time histories, the time integrals (4) can be calculated as  
\[\psi_{rr}=\frac{1}{N}\sum_{i=1}^{N}r_{i}^{2};\;\psi_{mm}=\frac{1}{N}\sum_{i=1} ^{N}m_{i}^{2};\;\psi_{rm}=\frac{1}{N}\sum_{i=1}^{N}r_{i}m_{i} \tag{5}\]  
where \(r_{i}\) and \(m_{i}\) are the discretized values of signals \(r(t)\) and \(m(t)\), respectively and \(N\) denotes the number of samples. The characteristic features of both \(M\) and \(P\) factors are that the magnitude error is insensitive to phase discrepancies and the phase error is insensitive to magnitude discrepancies. The comprehensive error (C-error) is defined as  
\[C=\sqrt{M^{2}+P^{2}} \tag{6}\]  
and it represents both the magnitude and phase errors.
## 3 Results and Discussion
### Microstructural Characterization Results  
The main focus of this study is to increase the efficiency of the Vo-CAP process, which is presented to the literature as a novel SPD method, through optimization studies. Furthermore, another main objective is to make the process more applicable by eliminating the fasteners that cause major time losses and process difficulties during SPD processes, thanks to the conical geometry used for the design of the Vo-CAP die, and to pioneer its use within the industry. Therefore, microstructure investigations were limited to OM analysis only.  
Optical Microscopy Characterization  
OM images were taken to gain a better understanding of the mechanical tests that were carried out to see the effects of this Vo-CAP process on the material properties and to observe the possible particle orientations. In Figure 21, the obtained OM images are illustrated for annealed and Vo-CAP-processed workpieces. In Figure 21a, a 500x-zoomed OM image is given for annealed AA6082. When the image is examined, it can be said the secondary phases were distributed homogeneously thanks to the annealing process. But the grain boundaries could not be specified by only OM images. But, after the Vo-CAP process was conducted again, the grain boundaries could not be observed. This meant that the expected grain size reductions resulting from Vo-CAP processing could not be studied using OM images. However, it is clear that the grains were oriented as shown in Figure 21b. This is one of the key factors explaining the enhancement of mechanical properties after the Vo-CAP process. The primary focus of this study is on die design, optimization, and experimental feasibility and applicability, as opposed to the effect of the process on the grains. It is evident that, owing to the unavailability of detailed information from the OM images obtained, the observation of particle shrinkage and orientation was deemed sufficient to demonstrate the effect of the process on the material's particles. Consequently, OM images were employed, and no additional detailed microstructural analysis was deemed necessary in this study.
## 4 Results and Discussion
### Comparison Experiments and Results Analysis
#### 4.3.2 Comparison of Various Ensemble Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} } & \multicolumn{6}{c}{**Model States**} \\ \cline{3-8}  & & \begin{tabular}{c} **Great** \\ **Satisfaction** \\ \end{tabular} & \begin{tabular}{c} **Satisfaction** \\ \end{tabular} & \begin{tabular}{c} **Neutral** \\ \end{tabular} & \begin{tabular}{c} **Disappointment** \\ \end{tabular} & \
\begin{tabular}{c} **Great** \\ **Disappointment** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ \end{tabular} \\ \hline \multirow{3}{*}{MOGWO} & Accuracy & 0.9760 & 0.9600 & 0.9700 & 0.9580 & 0.9720 & 0.9672 \\  & Precision & 0.9783 & 0.8846 & 0.9126 & 0.8692 & 0.9574 & 0.9204 \\  & Recall & 0.9000 & 0.9200 & 0.9400 & 0.9300 & 0.9000 & 0.9180 \\  & F1 & 0.9375 & 0.9020 & 0.9261 & 0.8986 & 0.9278 & 0.9184 \\ \hline \multirow{3}{*}{MOPSO} & Accuracy & 0.9600 & 0.9520 & 0.9460 & 0.9360 & 0.9660 & 0.9520 \\  & Precision & 0.9348 & 0.8800 & 0.8411 & 0.8091 & 0.9560 & 0.8842 \\  & Recall & 0.8600 & 0.8800 & 0.9000 & 0.8900 & 0.8700 & 0.8800 \\  & F1 & 0.8958 & 0.8800 & 0.8696 & 0.8476 & 0.9110 & 0.8808 \\ \hline \multirow{3}{*}{NSGAII} & Accuracy & 0.9600 & 0.9420 & 0.9500 & 0.9380 & 0.9620 & 0.9504 \\  & Precision & 0.9167 & 0.8318 & 0.8713 & 0.8350 & 0.9355 & 0.8780 \\ \cline{1-1}  & Recall & 0.8800 & 0.8900 & 0.8800 & 0.8600 & 0.8700 & 0.8760 \\ \cline{1-1}  & F1 & 0.8980 & 0.8599 & 0.8756 & 0.8473 & 0.9016 & 0.8765 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different ensemble models. Figure 4: The iterative curve of different multi-objective optimization algorithms.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Performance Results
#### 4.2.4 Results on VIPeR DataSet  
The VIPeR [25] contains 1264 pedestrian images taken from arbitrary viewpoints under varied illumination conditions. We randomly split the data set into two halves, 316 for training and the remaining 316 for testing. This procedure will be repeated 10 times to obtain an average result. As this dataset is too small to train the proposed network directly, we first pre-trained the model on the dataset of Market-1501 [24] and CUHK-03. Then, the fine-tune strategy was exploited to use the 316 training identities. The learning rate was set as \(1\times 10^{-2}\) for the first \(4\times 10^{3}\) epochs. After that, it decreased to \(1\times 10^{-3}\). The momentum was set as 0.9, and the weight decay was \(5\times 10^{-3}\). The experimental results are tabulated in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rank = 1** & **mAP** \\ \hline [PERSON] et al. [24] & 34.40 & 14.09 \\ PersonNet [42] & 37.21 & 18.57 \\ LOMO+XQDA [34] & 43.37 & 22.22 \\ kLFDA [28] & 44.37 & 23.14 \\ CycleGAN (baseL) [43] & 45.60 & 19.10 \\ Hybrid [44] & 48.15 & 29.94 \\ End-2-End CAN [45] & 48.24 & 24.43 \\ MVLDML+ [31] & 68.38 & 41.84 \\ PT [46] & 87.70 & 68.90 \\ NRMT [47] & 87.80 & 71.70 \\ B-SNR+GDS-H [48] & 89.30 & 72.50 \\ MEB-Net [49] & 89.90 & 76.00 \\ PEFA [50] & 91.20 & 76.80 \\ SPReID [51] & 92.50 & 81.30 \\ MSFANet [8] & 92.90 & 82.30 \\ \hline Ours & 93.84 & 78.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on Market-1501.  
We compared our method with the existing methods (like MLF [27], kLFDA [28], LOMO+ XQDA [34], FPNN [23], IDLA [29], DeepFeature [53], DeepList [54], and TJAIDL [52]). From Table 5, our method gives the best rank@1 matching rate.
## 2 Field Experiment and WRF Model Setup
### WRF Model Setup  
Radar observations can provide the fine vertical structure and temporal evolution of clouds but are insufficient for investigating the mechanisms of cloud formation and their interactions with environment. Therefore, a numerical simulation based on the WRF model was conducted to interpret the effect of the thermodynamic environment and vertical wind shear in cloud evolution. The model domain is shown in Figure 1a. The outer domain (D01) has a resolution of 9 km and the nested domain 2 (D02) has a resolution of 3 km. The inner domain (D03) has 697 \(\times\) 535 grids with a resolution of 1 km and 33 stretched vertical levels topped at 50 hPa. Most of the Tian Shan Mountains are within the inner domain. The National Centers for Environmental Prediction (NCEP) Reanalysis data, which have a resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), were used to provide the initial and boundary conditions every 6 h. The physical choices are listed in Table 1. Since nested domains 2 and 3 were both convection-permitting, the cumulus scheme was turned off [6]. This configuration was established based on sensitivity tests carried out using different choices of physical schemes. The simulation ran from 6 UTC on 26 July 2019, to 18 UTC on 27 July 2019--i.e., 36 h in total; therefore, there was enough spin-up time for the convection initiation and cloud formation. The simulation was evaluated by comparing the modeled results to the radar and rain gauge measurements (see details in Section 3.2.1). In general, the model had a good performance in simulating the cloud and precipitation, and the correlation coefficient between the observed and modeled precipitation rate was as high as 0.907, providing us with the confidence to use the simulation to investigate the impact of vertical wind shear on cloud formation.
## 3 Experiments and Performance Measurements  
### Experimental Setup  
The experimental setup used to evaluate our M2M gateway is introduced as follows. The RESTful M2M gateway software has been installed on an industrial computer NISE-3600 (Intel(r) Core(tm) (Intel, CA, USA) 2.7 G with 2 \(\times\) DDR3 8 G DDR RAM, Intel(r) GbE LAN port). The installed software includes the H2 memory database, which is used to store the data tables, and also the open-source Apache Tomcat v.6.0.25 has been installed, which is used to deploy various web services required in the data processing. Around the RESTful M2M gateway, and one Siemens and two Delta PLC devices from different boiler rooms and heating exchange stations are connected via the COM1, COM2, and COM3 serial ports. And the data collection time interval is 5 s. The test environment for the RESTful M2M gateway is shown in Figure 6.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Domain Finite Difference Scheme and Boundary Conditions  
In wave equation forward modeling, at least three forward simulations are required in each iteration to obtain the gradient operator. Therefore, the correctness of the forward numerical simulation plays an important role in the final inversion result. In the two-dimensional Cartesian coordinate system, the frequency domain acoustic wave equation of a homogeneous isotropic medium is shown as follows [29]:  
\[\
abla^{2}P(x,z,w)+\frac{w^{2}}{v^{2}}p(x,z,w)=-S(w), \tag{1}\]  
where \(\
abla^{2}P\)\((x,z,w)\) is the Laplace operator, \(p\) is the wavefield value of each point in the medium, \(w\) is the angular frequency, \(v\) is the medium velocity, \(S(w)\) is the source term.  
Due to the small range and scale of detection in tunnel space, the optimized nine-point difference scheme proposed by [PERSON] et al. [30] can meet the requirements of difference accuracy. The difference form of the acoustic wave equation is shown in Equation (2) and the diagram of the difference scheme is shown in Figure 1.  
\[\Delta x=\Delta z=\Delta \tag{2}\] \[\frac{w^{2}}{v^{2}}\left(\begin{array}{c}cP_{m,n}+d(P_{m+1,n}+P_{ m,n-1}+P_{m,n+1}+P_{m,n-1})+\\ e(P_{m+1,n+1}+P_{m-1,n+1}+P_{m+1,n-1}+P_{m-1,n-1})\end{array}\right)=-S(w)\]  
Compared with ground seismic exploration, the scale of research in the tunnel environment is relatively small; at the same time, the wavelength of low frequency information is longer. Thus, it is necessary to increase the boundary thickness to reduce the boundary reflection, which greatly increases the calculation amount. For the above problems, the Complex Frequency-Shifted Perfectly Matched Layer (CFS-PML) is used to enhance the absorption of boundary reflection [31,32]. Its basic form is as follows:  
\[S_{x}(x)=\beta_{x}(x)+\frac{d_{x}}{\partial_{x}(x)+iw}, \tag{3}\]  
\[\partial_{x}(x)=\partial_{0}\Big{(}1-\Big{(}\frac{x}{L}\Big{)}\Big{)}, \tag{4}\]  
\[\beta_{x}(x)=1+(\beta_{0}-1)(\frac{x}{L})^{2}, \tag{5}\]  
\[d_{x}(x)=d_{0}(\frac{x}{L})^{2}, \tag{6}\]  
where \(d\) directly acts on the matching layer and enables the medium to decay in the form of exponential so that PML can obtain a better absorption effect. \(\partial\)'s function is to suppress the converted wave generated at the boundary of the medium perfectly matched layer by the generated light wave. \(\beta\) indirectly increases the absorption capacity of PML by increasing the incident angle of the light wave.  
Figure 1: Diagram of the nine-point difference scheme.