esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Audio Characterization and Recognition Tests
### SVM Classifier Training and Recognition  
#### 3.3.1 Identification of a Single Audio Type  
To verify the performance of the present feature vector TF in identifying a single audio type, the first step was to extract the TF features of 207 frames of data from A1-C4. The normal vibration needed to be identified separately in some cases, so three and four types of labels were added to create four sets of feature libraries, corresponding to SVM classifiers TF1 and TF2. In addition, the time domain features, \(d=(d_{1},d_{2},d_{3},d_{4},d_{5})\), corresponding to the classifiers CD1 and CD2, and the frequency band vector, T, corresponding to the classifiers CT1 and CT2, and the MFCC features proposed in the literature ([PERSON] et al., 2015), corresponding to the classifiers CM1 and CM2, were jointly compared. Next, the classifier was configured with the penalty parameter set to 1, the kernel function was a Gaussian kernel, and the decision function type was one-to-one. Then, the data of the feature library were divided into two parts in a ratio of 7:3, and the training set was used as the input vector of the classifier for training, and finally the training and test sets were cross-validated, and the average score was calculated ([PERSON] et al., 2015).  
The recognition rates of the classifiers are shown in Table 7. TF1 and TF2 had the highest total recognition accuracies of 99.6% and 98.6%, respectively, while the recognition accuracy for CD2 was only 88%, due to its inability to recognize normal vibrations. CM1 and CM2 had better recognition rates for mechanical vibration and PD, but lower recognition accuracy for background noise, at 89.4% and 92.4%, respectively. CT1 and CT2 had the lowest total recognition accuracy, with some of the PDs being misclassified as vibrations, and failing to recognize normal vibrations. Therefore, compared to time domain features, MFCC features, and frequency band energy features, the proposed TF features had the best recognition performance and could recognize background noise, normal vibration, abnormal vibration, and PD in SVM.
## 3 Design Procedure of Control Scheme
### Design of S-Fotsms  
To synchronously achieve fast convergence of both the position error and synchronization error along with the robustness rejects the effects of the lumped uncertain terms, the S-FotsMS is designed by using the cross-coupling error (Equation (9)) as follows:  
\[S_{S-FotsMC}=\ddot{e}_{\alpha}^{c}+\Gamma_{2}\big{|}\ddot{e}_{\alpha}^{c} \big{|}^{\gamma_{2}}\text{sgn}\big{(}\ddot{e}_{\alpha}^{c}\big{)}+\Gamma_{1} \big{|}\dot{e}_{\alpha}^{\big{|}^{\gamma_{1}}}\text{sgn}(e_{\alpha}^{c}) \tag{12}\]  
where \(S_{S-FotsMC}=\left[\begin{array}{cc}S_{S-FotsMC1},&\ldots,&S_{S-FotsMC3}\\ \end{array}\right]^{T}\in R^{3\times 1}\) are the S-FotsMS, \(\gamma_{1},\gamma_{2},\Gamma_{1},\Gamma_{2}\) are the positive constants, which are chosen as in References [47; 58; 59]. \(\Gamma_{1}\) and \(\Gamma_{2}\) can be selected such that the polynomial \(p^{2}+\Gamma_{2}p+\Gamma_{1}\) is Hurwitz, i.e., the eigenvalue of the polynomial are all in the left-half side of the complex plane. \(\gamma_{1}\) and \(\gamma_{2}\) can be determined based on the following conditions: \(0<\gamma_{1}<1\), and \(\gamma_{2}=\frac{2\gamma_{1}}{1+\gamma_{1}}\).  
From the second derivative of the cross-coupling error in Equations (11) and (12), we obtain  
\[S_{S-FotsMC}=\ddot{e}_{\alpha}+\lambda\dot{\Delta}e^{s}+\Gamma_{2}\big{|} \dot{e}_{\alpha}^{c}\big{|}^{\gamma_{2}}\text{sgn}\big{(}\ddot{e}_{\alpha}^{c} \big{)}+\Gamma_{1}\big{|}\dot{e}_{\alpha}^{\big{|}^{\gamma_{1}}}\text{sgn}(e_{ \alpha}^{c}) \tag{13}\]  
**Remark 1**.: _In the design of the control laws (15)-(17), the design procedure is based on the assumption that the bounded value of \(G\) in Assumption 1 can be obtained in advance. However, this parameter is difficult to be obtained in advance in practical engineering applications. In the next work, we use a neural network or an adaptive control law to estimate the bounded value of \(G\) in Assumption 1. Therefore, our controller can provide high performance without the requirement of the bounded value of \(G\) in Assumption 1._  
**Remark 2**.: _The proposed control method could be applied to not only the joint position tracking control for parallel Robot. manipulators but also other mechanical systems such as the position synchronization control of multiple motion axes, mobile Robot.s, serial Robot. manipulators._
# Pipe Spatter Detection and Grinding Robot
## 2 SGR Design
### Work-Head Module  
Figure 1(a) shows the configuration of the work-head module, which contains a vision camera for spatter detection through image recognition, a grinding tool for spatter removal, a proximity sensor for accurate absolute positioning of the spatters, a controller in charge of the grinding tool's rpm and head-elevation control, and a monitoring camera for estimating the final quality after grinding.  
Because spatter detection through image recognition operates in dark conditions inside a small pipe, a high-resolution five-megapixel camera with a screen resolution of 2592 \(\times\) 1944@15 fps was used. The captured images were processed by an image recognition algorithm, and spatter recognition and positioning were performed. The results of the image recognition include information on the direction and location of the spatters in terms of circumferential coordinates. The head is rotated to the corresponding coordinates to check the accuracy of the height information transmitted to the spatter-grinding tool. The correct height can be confirmed if the head elevation is lowered until the detection is completed by the proximity sensor. This removes spatters only during the grinding process and helps protect the base material on the pipe surface.  
The components of the working head-driving mechanism are shown in Figure 1(b). The working tool was designed to rotate because spatters may be attached to the inner surface of the pipe in the circumferential direction. The work-head module rotation axis serves to rotate the grinding tool based on the detected position and direction of the spatters. Moreover, the head-elevation axis was designed with a rack and pinion structure to move the grinding tool to the detected spatter position. Because rotation and elevation must be simulta  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Contents**} & \multicolumn{2}{c}{**Size (cm)**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Vertical payload (kg)**} & \multirow{2}{*}{**Horizontal Payload (kg)**} & \multirow{2}{*}{**Driving Distance (m)**} \\ \cline{2-2} \cline{6-6}  & & & & & 16 \\ Dimensions & 100 & 18–25 & 9.8 & (wheel angle of 30\({}^{\circ}\), 252 diameters) & 6 & 10 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Robot specifications.  
Figure 1: Pipe spatter detection and grinding robot.  
neously performed during grinding, the head-elevation motor output and head-rotation axis were designed as an integrated structure using bearings on the same axis.
## 3 Simulation Model  
### Geometries of the Projectile  
In this study, various types of projectiles were employed in the penetration analysis. The projectile having an ogive nose geometry shown in Figure 5, was selected as the base geometry. The geometry of the projectile was divided into two parts: a body and a nose. The basic features are shown in Figure 5. The diameter of the projectile is 2a, and the value is 7.1 mm. The length of the body is L\({}_{\mathrm{b}}\), and the value is 59.3 mm. The ogive nose is defined with the caliber-radius-head (CRH) which is R/2a. When CRH is 3, the nose length (L\({}_{\mathrm{n}}\)) is 11.8 mm.  
Figure 4: Schematic figure describing the free surface effect.  
Even if the geometries were changed, a and \(\rm L_{b}\) were fixed. Only the length and geometry of the nose were modified. The projectiles with other geometries were compared to the aforementioned one. The conical geometry has the nose length (\(\rm L_{b}\)) of a. The blunt geometry has the spherical geometry of the nose end, and the radius of the nose is a. For the blunt geometry, the CRH value is 0.5a. In addition, it was compared to a cylinder that has no nose geometry. The projectile having an ogive nose with CRH values of 1, 3, and 5 were also examined. The truncated geometry in Figure 6 has a half-height of the nose length of the projectile with an ogive nose with a CRH value of 3. In this paper, the projectiles with an ogive nose with CRH values of 1, 3, and 5 are noted as CRH=1, CRH = 3, and CRH = 5, respectively.  
The projectile material was vacuum-arc-remelted (VAR) 4340 steel [1]. The density of the material was 8000 kg/m\({}^{3}\). The stress-strain curve of the VAR 4340 steel is shown in Figure 7. The initial yield stress of the material in the quasi-static condition is 1430 MPa. When the projectile hits the target, the strain rate of the projectile is nearly \(10^{5}\) s\({}^{-1}\), which is very high value comparing with the quasi-static condition. As a result of the high strain rate, the strain rate dependent material properties shown in Figure 7 should be employed. In this work, the material property shown in Figure 7 was employed in ABAQUS/Explicit v6.13 using table type data. The reference strain rate was \(10^{-4}\) s\({}^{-1}\). The maximum strain rate in this work was \(4\times 10^{5}\) occurred in the normal impact of the cylinder geometry.  
Figure 5: Geometry of the projectile having an ogive nose geometry.  
Figure 6: Compared geometries of the projectile.
# Passively Q-Switched Operation of a Tm,Ho:LuVO\({}_{4}\) Laser with a Graphene Saturable Absorber
## 4 Conclusions  
In conclusion, we have experimentally demonstrated a Tm,Ho:LuVO\({}_{4}\) laser under the PQS mode operation with a graphene SA for the first time. A pulse energy of 40.4 \(\upmu\)J and an average output power of 1034 mW were acquired at 2057.03 nm. In addition, the beam quality factors (\(M_{x}^{2}=1.19\) and \(M_{y}^{2}=1.19\)) were obtained at about 1-W average output power from the PQS Tm,Ho:LuVO\({}_{4}\) laser.  
**Author Contributions:** In this paper, [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents, materials and analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (No. 61378029, 61775053, 51572053 and 51777046), the Science Foundation for Outstanding Youths of Heilongjiang Province (JC2016016), the Science Foundation for Youths of Heilongjiang Province (QC2017078), and the Program for Equipment Pre-research Field Funds (No. 6140414040116 CB01012).  
**Conflicts of Interest:** We declare that this article does not have any conflicts of interest because the author's order is sorted by actual contribution for the paper.  
Figure 6: The output wavelengths of the Tm,Ho:LuVO\({}_{4}\) laser in CW and PQS mode Operations.  
Figure 7: 2D and 3D output beam profiles of Tm,Ho:LuVO\({}_{4}\) laser.
## 6 Conclusions  
In this article, we propose a novel approach for the analysis of human-caused fires using dimensionality reduction techniques including PCA, FA, and OOB predictor importance in Keelung city, Taiwan. By analyzing various aspects, valuable insights can be gained to enhance fire prevention and risk assessment strategies. Key findings are summarized as follows:  
The multifaceted process that incorporates the application of a GIS includes geographical information about the area for various attribute data, which are digitized and input into the GIS as layers. We gathered historical fire data from the past 10 years, including the location, frequency, and type of fires. To investigate human-caused fires using PCA, FA, and OOB in Keelung City, Taiwan, this study collected ten potential influencing factors related to fire incidents. These factors encompass considerations such as building density, aging structures, aging population, and industrial patterns. The results of this study suggest a strong correlation between human-caused fires in Keelung City and areas characterized by a high proportion of buildings over 40 years old. Furthermore, significant correlations are observed, indicating an increased occurrence of fire incidents associated with the proportion of the tertiary sector of the economy and the proportion of the elderly population.  
Based on the findings of this study, the following strategies are recommended to reduce the frequency of fire incidents, particularly in areas with the highest fire frequency: building safety upgrades, industrial oversight, and elderly population safety. It is hoped that the analytical results of this study can serve as a reference for relevant departments in effectively reducing fire incidents in the future.  
Design and composing the initial draft, C.-Y.K.; data curation and investigation, H.-C.L.; visualization, Y.-T.T.; methodology, writing and editing, C.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
Not applicable.  
Relevant data related to this study can be obtained by contacting the corresponding author (C.-Y.L.) upon request. The data are not publicly available due to privacy.  
All authors of this article declare no conflicts of interest.
## 2 Experimental Procedure  
### The Design of Shield Configurations  
The sketch of shield configurations is shown in Figure 1. The three configurations contained the same two bumpers but different rear walls, which were Al 7075-T651 plate in Configuration A (Con. A), B\({}_{4}\)C/UHMW-PE composite plate in Configuration B (Con. B) and Kevlar/B\({}_{4}\)C/ Kevlar/Al 7075-T651 composite plate in Configuration C (Con. C). The two bumpers were 3 mm-thick Al 7075-T651 plates with a spacing of 100 mm, and the spacing between bumper II and the rear wall was 80 mm. Detailed parameters of the rear wall are provided in Table 1. The size of all of the bumpers was 200 \(\times\) 200 \(\times\) 3 mm. The B\({}_{4}\)C/UHMW-PE composite plate, as well as the composite plate in Configuration C, was clamped in a square frame, as represented in Figure 2. The density of the Al 7075-T651, B\({}_{4}\)C, UHMW-PE and Kevlar plates were 2.804, 2.516, 0.98 and 1.65 g/cm\({}^{3}\), respectively. The woven pattern for UHMWPE was a cross-ply layup with an angle of 90\({}^{\circ}\). The matrix of the Kevlar was orthogonal-braided and the fiber percentage was about 70%. It is worth mentioning that the woven pattern and molding process of UHMW-PE affects the mechanical properties of the material. The molding process technology of UHMW-PE is mature and the performance of different samples varies by a small amount, which affects the ballistic result slightly. The projectiles used were Al 7075-T651 cylinder--17.2 mm long by 12.6 mm in diameter.  
Figure 1: A sketch of the shield configurations.
## 4 Validation and Discussion
The LISA-LPSD algorithm's MSE is about 77% of that of the LPSD algorithm, while the adaptive sine multi-taper algorithm's MSE is roughly 59% of that of the LISA-LPSD algorithm, reflecting a 41% reduction, as shown in Table 2 under 'Noise Metric Experiment: Adaptive Sine Multi-Taper vs. Others'. Figure 11: Performance evaluation of algorithms under noise metric experiment. (**a**) Squared Bias, Variance, and MSE before and after improvements for LPSD, LISA-LPSD, and Adaptive Sine Algorithm; (**b**) Comparison of computation time for LPSD, Adaptive Sine Multi-Taper, Original Adaptive Sine Multi-Taper, and LISA-LPSD Algorithms. Figure 10: Comparison of computation time for LPSD, LISA-LPSD, and Adaptive Sine Algorithm before and after improvements under noise metric experiment. (**a**) Noise spectrum spectral density comparison; (**b**) Taper number comparison before and after improvements for adaptive sine multi-taper algorithm. As depicted in Figure 11b, for the longest processing time of 5 h, the improved adaptive sine algorithm's execution time is reduced to approximately 33% of that of the original algorithm, reflecting a reduction of 67%, 14% of that of the LPSD algorithm, indicating a reduction of 86%, and 70% of that of the LISA-LPSD algorithm, corresponding to a reduction of 30%, as shown in Table 1 under 'Noise Metric Experiment: Adaptive Sine Multi-Taper vs. Others'. Both experimental simulations demonstrate a significant reduction in MSE for the improved adaptive sine multi-taper algorithm compared to the original version, the LPSD algorithm, and the LISA-LPSD algorithm. The algorithm's complexity is substantially streamlined, leading to notable improvements in computational efficiency and speed. Specifically, as shown in Table 2, the MSE relative to the LISA-LPSD algorithm decreases by 40% to 80%. For signals with sharp characteristics, such as sine noise, the improved adaptive sine multi-taper algorithm outperforms others, with experimental results aligning closely with prior simulations. Under stable conditions, the bias decreases by approximately 40%, and the execution time is reduced to 65% to 70% of that of the original algorithms, exhibiting minimal fluctuations and stable performance.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 3 Integrated Prediction Method Based on the LSTM-SVR Model
### Verification and Analysis of the Integration Model  
To verify the prediction performance of the proposed integration method, short-term predictions were made on chaotic time series data. During the experiment, the predicted data length was 150. The network parameters of the SVR method were set to \(\varepsilon=0.01\) and C = 20, and the Gaussian radial basis function was adopted. The parameters of the LSTM model were set as follows: the number of hidden layer units was 200, the maximum number of iterations was 100, the initial learning rate was 0.005, Adam was selected as the optimization function, and the weight coefficient of the fusion method was 0.5. The prediction results of the above two methods are shown in Figure 5, and Figure 6 shows the prediction result of the integration method.  
The prediction effects of three prediction methods, including the LSTM prediction model, the SVR method, and the integration of LSTM and SVR, on the chaotic time series are compared in Figures 5 and 6 and Table 1. Figure 5 shows that the prediction ability of the SVR model is higher than that of LSTM in the case of small samples; the RMSE of LSTM and SVR is 0.0419 and 0.0097, respectively, and \(R^{2}\) is 0.8905 and 0.9579, respectively. Figures 5 and 6 show that the prediction effect of the integration of LSTM and SVR is better than both individually. It is known from Table 1 that the RMSE of the integration method drops to 0.0071, the MAPE reaches 1.54%, the SDAPE is 0.7141, the RWI is the closest to 1, and the corresponding \(R^{2}\) is also improved. In summary, the integration of LSTM and SVR is effective for small sample predictions.
## 5 Performance Evaluation
### Dataset Collection  
The CIC-IDS-2017 public dataset, an open dataset collected by the Canadian Institute for Cybersecurity Research, is used in the experiments for analysis, including raw network traffic in PCAP format and labeled feature data in CSV format collected by simulating realistic attack scenarios. The dataset collects normal traffic and attack traffic of 15 types, including DoS, DDoS, Heartbleed, Web attacks, Infiltration, and Botnets. We remove the very small and uncommon types of attacks, such as FTP-Parator, SSH-Parato, Infiltration, and Heartbleed, and select five common types of attacks with sufficient data volume: Dos, DDoS, PortScan, Bot, and Web Attacks. In addition, there is a severe data imbalance problem between benign and attack traffic. To construct a fair classifier, we adjust the ratio between benign and attack data to 10:7 by randomly filtering out benign data. The composition of the dataset is shown in Table 5.
## 2 INS/GNSS Concepts
### Global Navigation Satellite System (GNSS)  
The Global Navigation Satellite System (GNSS) is a technology that allows for the determination of position, velocity, and time anywhere on Earth using satellite signals. GNSS includes various satellite constellations, such as the United States' GPS, Russia's GLONASS, the European Union's Galileo, and China's BeiDou [33; 34]. These systems work by transmitting radio signals from satellites in orbit around the Earth. GNSS receivers on the Earth's surface capture these signals and use the differences in arrival times to calculate the receiver's position. GNSS is widely used in applications such as terrestrial, aerial, and maritime navigation, precision agriculture, surveying, and environmental monitoring [35]. Modern GNSS receivers can utilize multiple constellations simultaneously, enhancing the accuracy and robustness of measurements.  
Despite these advancements, GNSS is still subject to several types of errors that can affect measurement accuracy. The main errors include satellite ephemeris errors (inaccuracies in the transmitted orbital information), satellite clock errors, ionospheric delays (caused by the dispersion of GNSS signals in the ionosphere), tropospheric delays (due to the refraction of signals in the troposphere), multipath effects (signals reflected off surfaces before reaching the receiver), and receiver measurement errors [34].
###### Abstract
In most of the proposals, the AI model used is trained with the signal from the fault-free machine, to later use a sample of the signal that contains some type of failure caused on purpose (so the type of failure is known in advance). In these conditions, diagnosis is relatively easy, but due to the related costs, in the wind industry it would be very difficult to proceed in this way, so only the signal during the WT operation is available. However, if we only have the signal from the electric generator of TWs during its operation, the diagnosis process is complicated, since, in this case, and assuming that there are few faults or the faults are in an incipient state, the data are unbalanced. Since the efficiency of generative adversarial networks (GANs) is so high that it is difficult to distinguish between real and synthetic data, samples obtained by GANs could be used to compensate for unbalanced datasets. Considering what has been stated previously, the main objectives of this work are to investigate the detection and diagnosis of faults of the SCIG installed in an operating WT (on which there are very few field studies), using electrical current signal and GANs, which is a methodology little explored thus far. The rest of this investigation is organized as follows: in Section Two, a brief conceptual analysis is made on the use of the current signal and GANs for fault detection and diagnosis in WTs. Section Three includes the methods and materials. In Section Four, the results obtained when applying the proposed methodology are shown and discussed. Conclusions and recommendations are included in Section Five.
## 3 Experiments and Results  
This section presents the experiments that were carried out. First, we estimate the EV by applying supervised non-linear regression techniques. Second, the statistical results are shown in terms of several figures of merit. Third, using the best fitting models, we evaluated the feature capabilities to predict the EV. Finally, a trend analysis was carried out to determine the behavior of the variables with respect to the EV.