esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Principle of IEMD and ARMA  
Based on the IEMD, a hybrid method for structural modal parameter identification is proposed, its identification process is shown in Figure 2; first, using IEMD to address two groups of acceleration response signals, to obtain two sets of IMFs, with one group as the reference signal and the obtained cross-spectral density function corresponding to every IMF; then, using Fourier inverse transformation to obtain the cross-correlation function, then, using NExT to obtain the free vibration response; finally, ARMA is used to identify the modal parameters of the structure.  
ARMA was used to extract the modal parameters of the structure, assuming the cross-correlation function was expressed by \(R_{j}\), which was obtained using the inverse Fourier transform of the \(j\)th cross-spectral density function and taken as the free vibration response of the structure \(x_{j}\).  
According to literature [11], the GREEN function of the ARMA model can be used to solve the frequency and damping ratio of the system, while the residue can be used to obtain the structural mode shapes. Assuming the excitation point is at the point \(q\), and the  
Figure 2: Diagram of using IEMD and ARMA to identify structural parameters.  
\(k\)th residue corresponding to the transfer function \(H_{pq}\)(s) of the response at point \(p\) as \(A_{kpq}\), then the residue can be obtained from the following equation:  
\[A_{kpq}=\lim\limits_{z\to z_{k}}H_{pq}(z)(z-k)=\frac{\sum\limits_{k=0}^{zN}b_{k}z ^{-k}}{\sum\limits_{k=0}^{zN}a_{k}z^{-k}}(z-z_{k})\Bigg{|}_{z=z_{k}} \tag{9}\]  
Thereby, the vibration pattern vector can be obtained by processing the residues obtained from all response measurement points. If a structure contains \(n\) response measurement points, it is necessary to determine the corresponding measurement points of the maximum absolute value of the remaining number of \(n\) corresponding modes of the same order. If the measurement point is \(m\), the normalized complex mode shape vector corresponding to the \(k\)th mode is:  
\[\{\phi_{k}\}=\left[A_{k1q}\ A_{k2q}\ \cdots\ \ A_{k\text{knq}}\right]^{\text{T}} /A_{kmq} \tag{10}\]
# Quadcopter Adaptive Trajectory Tracking Control:
## 2 Preliminaries and Mathematical Model
\begin{table}
\begin{tabular}{c c c} \hline
**Definition** & **Symbol** & **Unit** \\ \hline Mass of whole UAV & \(m\) & kg \\ Gravitational acceleration & \(g\) & m/s\({}^{2}\) \\ Arm length & \(l\) & m \\ Thrust coefficient & \(b\) & N-s\({}^{2}\)/rad\({}^{2}\) \\ Drag coefficient & \(d\) & N-s\({}^{2}\)/rad\({}^{2}\) \\ Inertial moment about \(x\)-axis & \(I_{xx}\) & kg-m\({}^{2}\) \\ Inertial moment about \(y\)-axis & \(I_{yy}\) & kg-m\({}^{2}\) \\ Inertial moment about \(z\)-axis & \(I_{zz}\) & kg-m\({}^{2}\) \\ Drag moment coefficient & \(\mu_{i}\) & s\({}^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The quadcopter parameters. \begin{table}
\begin{tabular}{c c c} \hline
**Definition** & **Symbol** & **Unit** \\ \hline Position in Earth frame & \(x,y,z\) & m \\ Position in Body frame & \(x_{B},y_{B},z_{B}\) & m \\ Roll, Pitch, Yaw angles & \(\phi,\theta,\psi\) & rad \\ Rotor speed & \(\Omega_{1},\Omega_{2},\Omega_{3},\Omega_{4}\) & rad/s \\ Thrust force & \(f_{1},f_{2},f_{3},f_{4}\) & N \\ \hline \end{tabular}
\end{table} 
Table 2: The quadcopter variables. \[a_{1} =\frac{I_{yy}-I_{zz}}{I_{xx}}\qquad a_{2}=\frac{I_{zz}-I_{xx}}{I_{ yy}}\qquad a_{3}=\frac{I_{xx}-I_{yy}}{I_{zz}} \tag{10}\] \[b_{1} =\frac{l}{I_{xx}}\qquad b_{2}=\frac{l}{I_{yy}}\qquad b_{3}=\frac{1 }{I_{zz}}\]  
**Assumption 1**. : _It is assumed that this considered system does not flip in the air and lift force \(U_{1}\) is positive when the quadcopter is in operation._  
\[\frac{-\pi}{2}<\phi<\frac{\pi}{2},\quad\frac{-\pi}{2}<\theta<\frac{\pi}{2}, \quad U_{1}>0 \tag{11}\]  
**Assumption 2**. : _The considered system is required to track a reference trajectory vector \([x_{d}(t),y_{d}(t),z_{d}(t)]^{T}\) which is differentiable up to four times. Especially, the second derivatives of \(z_{d}(t)\), in particular, must satisfy the following condition [32] to ensure that a quadcopter is not allowed to land faster than it freely fall under gravity with a strictly positive constant \(k_{0}\):_  
\[\inf\left(m\ddot{z}_{d}(t)+mg\right)\geq k_{0} \tag{12}\]  
Now, the control objective is to design an adaptive backstepping controller to steer the quadcopter to asymptotically track the desired trajectory reference \(x_{d}\), \(y_{d}\), and \(z_{d}\), despite the presence of system model uncertainties. Moreover, the stability of the entire system is analysed and established by using [PERSON]'s direct method when combining suitable parameter estimators and control law based on certainty equivalence principle and backstepping technique [33], respectively.
## 3 Numerical Activity
A trapezoidal, cubic polynomial, V-degree motion profile, and finally, a polynomial that optimized the time derivative of acceleration (jerk) were generated as input data for a defined linear trajectory of 0.5 m.  
The system's response was analyzed by comparing the different amplitudes of oscillation of the pendulum, determining which law of motion could minimize sloshing. The most common law of motion is the trapezoidal one, typical of mechanisms driven by electric motors, considering a maximum speed equal to 0.5 m/s, setting starting and ending points as waypoints. Third- and fifth-degree polynomial laws and optimized jerk polynomial laws were also used to test the pendulum's behavior. In Figure 7, the velocity and jerk laws for the four laws of motion tested are reported. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Expressions** & **Values** \\ \hline Liquid density (water) & \(\rho\) & \(1000\) kg/m\({}^{3}\) \\ \hline Damping rate for first mode * & \(\xi_{m,1}\) & \(1.841\) \\ \hline Free-surface height & H & \(0.039\) m \\ \hline Flask radius & r & \(0.0205\) m \\ \hline Natural pulsation, \(\omega_{n}\) & \(\sqrt{\frac{\xi}{L_{n}}}\) & \(29.7\) rad/s \\ \hline Stiffness coefficient, \(k\) & \(m_{n}*\omega_{n}^{2}\) & \(11.3\) N/m \\ \hline Damping coefficient, \(\sigma\) & \(2*\omega_{n}*\xi*m_{n}\) & \(0.023\) Ns/m \\ \hline Slosh mass, \(m_{n}\) & \(m_{light}\left[\frac{d\tanh(2\xi_{n}h/d)}{\xi_{n}\left(\xi_{n}^{2}-1\right)h}\right]\) & \(0.0132\) kg \\ \hline Pendulum length, \(L_{n}\) & \(\frac{d}{2\xi_{n}\tanh(2\xi_{n}h/d)}\) & \(0.011\) m \\ \hline Pendulum hinge location, \(H_{n}\) & \(\frac{h}{2}-\frac{d}{2\xi_{n}}\left[\tanh(\xi_{n}h/d)-\frac{1}{\sinh(2\xi_{n}h/ d)}\right]\) & \(-0.0015\) m \\ \hline \hline \end{tabular}  
* Parameter evaluated from Bessel functions for the first mode [19].  
\end{table} 
Table 3: Geometric and physical parameters of the equivalent pendulum. Figure 6: Multibody model of the test-rig in the Matlab’s SimScape environment environment. From the literature, the influence of the discontinuities in the accelerations on the onset of sloshing in manipulating fluids is evident. From the graph reported in Figure 7b, it is evident that unlike the jerk-optimized law of motion, the other laws of motion always present a discontinuity. To verify this deduction, it was decided to test the behavior of the fluid by subjecting the cart to the optimized law and the fifth-degree polynomial law of motion, where the initial and final conditions of the acceleration were controllable.
## 3 Result and Discussion
### Spatial Distributions Characteristic of the FEGLD
It clearly shows that the electron density remains at 2.2 \(\times\) 10\({}^{15}\) cm\({}^{-3}\), almost without changes in the axial direction. The gas temperature remains at 320 K in the range of 1-12 mm, and reaches maxima of 340 K at 12 mm which is near the liquid surface. The increase in gas temperature near the liquid is also shown by [PERSON] et al. [55] who attributed it to the fast vibration-to-vibration (V-V) process of O\({}_{2}\)-H\({}_{2}\)O and N\({}_{2}\)-H\({}_{2}\)O and, at the same time, H\({}_{2}\)O's higher vibration-to-translation (V-T) rate compared to O\({}_{2}\) and N\({}_{2}\), which means that as the H\({}_{2}\)O concentration increases, additional gas heating occurs. [PERSON] et al. [56] found that the temperature will be elevated while water vapor concentration increases. Moreover, since the inelastic collisions between electrons and heavy particles induce the plasma chemistry [57], which could be have potential applications in the treatment of living human and animal tissue [58], for instance, the high electron density plasma with approximately room temperature at AP is expected to meet temperature-sensitive needs.
## 3 Results and Discussions
### FTIR Analysis  
Figure 7 reports the FTIR spectrum of the Co\({}_{3}\)O\({}_{4}\) NPs in the spectral range of 400-4000 cm\({}^{-1}\). The major bands at 3128 cm\({}^{-1}\), 1624 cm\({}^{-1}\), and 1399 cm\({}^{-1}\) can be attributed to the hydroxyl, carbonyl, and functional groups of C-N, respectively [71]. The I.R. spectrum displays two strong bands due to vibration \(\upsilon\)(Co-O) modes at \(\sim\)667 cm\({}^{-1}\) and \(\sim\)577 cm\({}^{-1}\), which originate from the fingerprint stretching vibrations of the cobalt-oxygen bond of the Co\({}_{3}\)O\({}_{4}\) spinel oxide [59]. More precisely, the first band at 577 cm\({}^{-1}\) is a characteristic of OB\({}_{3}\) vibration in the spinel lattice, with B signifying the Co\({}^{3+}\) in the octahedral site (O-Co), while the 667 cm\({}^{-1}\) band is attributed to the ABO\({}_{3}\) vibration where A denotes the Co\({}^{2+}\) in a tetrahedral hole (Co\({}^{2+}\) Co\({}^{3+}\)O\({}_{3}\)). In addition, some representative peaks of aromatic rings centered at about 1624 cm\({}^{-1}\) and 1399 cm\({}^{-1}\) could be attributed to the presence of hydroxyl group (O-H) or C-O stretching of alcohols and carboxylic acids due to the absorption of moisture by the nanoparticles [72]. The peak around 3128 cm\({}^{-1}\) is the distinctive peak of the hydroxyl group of polyphenic compounds. These results were consistent with other reports in which the FTIR spectra revealed the same characteristic peaks of Co\({}_{3}\)O\({}_{4}\) NPs [61].  
Figure 6: Raman spectrum of Co\({}_{3}\)O\({}_{4}\) NPs.  
Figure 7: FTIR spectrum of Co\({}_{3}\)O\({}_{4}\) NPs.
## 3 Results and Discussions
### Analysis of Monitoring Results of the Hollow Effect  
The tunneling blasting hollow effect refers to the phenomenon that the cavity formed in the excavated area causes the blasting vibration velocity above the ground surface to be higher than the vibration velocity of the surface above the unexcavated region. This phenomenon of blasting vibration propagation increases the impact of blasting vibration on the ground. The paper uses the intelligent automatic blasting vibration monitoring system for blasting vibration to study the hollow effect on the environment caused by tunneling.  
The N5 above the metacarpal surface was selected as the central point, and four measuring lines were set radially (eight points in every line, total 33 monitoring points), with the 45\({}^{\circ}\) between each measuring line, as shown in Figure 10. The line of the tunnel axis is the focus of the tests, the location of monitoring points (N14-N17) is above the tunnel, and there is a facing surface, while the location of monitoring points (N10-N13) is a solid rock body with no facing surface. Figure 11a shows that the peak blast velocity on the excavated area's surface is significantly higher than that of the unexcavated area. Therefore, the control of the blasting vibration effect should depend on the excavated area and the vibration monitoring in the upper part of the excavation area should be successful.  
Figure 10: The arrangement of test points in N-Station.  
Figure 9: Variation of blasting vibration on the surrounding rock of the foundation pit.  
Monitoring points are densely arranged on the surface near the blasting source to study the propagation law of blasting vibration by the hollow effect. The regional propagation characteristics of blasting vibration on the surface are studied with averaging multiple experiments. As illustrated in Figure 11b, the closer the blasting vibration frequency is to the palm face, the greater the frequency, the higher the density of the contours, and the faster the frequency decays, while the farther away from the source of the blasting, the sparser the shapes and the lower the frequency of the blasting vibration. The contours in the excavated area of the tunnel are depressed toward the source of the blast, indicating that the frequency in the tunnel part is relatively large. Therefore, the hollow effect affects the attenuation of the blasting vibration frequency, leading to an immense blasting vibration speed.  
The predicted and measured values of Peak Velocity-Z and Master Frequency have the same spatial and temporal distribution characteristics, and the overall error is within 20%. Model-Hollow can effectively reflect the vibration and frequency characteristics of the measuring points in the excavation space, which indicates that the established model and its monitoring system can effectively predict the vibration propagation law.
# High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
## 4 Conclusions  
High-speed micro-groove formation by angled irradiation was investigated using a single-mode CW fiber laser, and the formation mechanism of the micro-grooves was discussed using the results of high-speed camera observation. The main conclusions of this study are as follows.  
1. Use of angled laser irradiation combined with high-speed scanning of the laser beam can result in formation of a micro-groove on the reflection side of the laser irradiation.
2. A relatively deep groove more than 30 \(\upmu\)m in depth was obtained on the surface of mild steel at a laser power of \(P\) = 200 W, a scanning velocity of \(V_{s}\) = 2 m/s, and an irradiation angle of \(\theta_{i}\) = 45\({}^{\circ}\), whereas no noteworthy groove was formed by perpendicular irradiation.
3. Many spatters on the reflection side and the flow of molten metal on the incident side were observed using a high-speed camera. The groove formed on the reflection side immediately after laser irradiation and the subsequent flow and solidification of the molten metal on the incident side are thought to be important factors for micro-groove formation.
4. Only shallow grooves were formed on the surface of aluminum alloy, even during high-speed processing by angled laser irradiation. It is necessary to optimize the laser irradiation conditions according to the thermophysical properties of the material.  
Conceptualization, T.S., H.I., and Y.O.; Validation, T.S. and C.K.; Investigation, T.S., H.I. and C.K.; Writing-original draft preparation, T.S.; Writing-review and editing, Y.O.; Supervision, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Methodology
### Model Construction  
As mentioned in Figure 1, two models, ANN and GPR, are utilized for the diagnosis of component health and system performance, respectively. The reason for choosing these models is that they are established techniques, easy, and have been universally applied in the field of fault diagnosis for a long time.  
ANN and GPR are employed for the diagnosis of component health and the estimation of system health, respectively. ANN is widely used for data-driven diagnostics, since it can map various input variables onto the output data such as the health degradation. The structure of ANN is composed of an input layer, one or more hidden layers, and an output layer, as shown in Figure 4[22]. Each layer contains neurons (nodes) and weights. In this paper, the set of selected features \(F\) with dimension \(N_{f}\) from the motor signal is used as the input and the degradation level of the components \(H\) with dimension \(N_{C}\), which is the number of components, is given for the output. The ANN is trained using the data of \(F\) and \(H\), acquired for various fault conditions, to determine the optimum weights that represent the nonlinear relationship between the input and output. To enhance the accuracy of ANN, the number of nodes in hidden layers are optimized through k-fold cross-validation.  
The GPR is used to estimate the system health \(S\) based on the fault severity \(H\) of the components, which is identified by ANN. Denoting \(H\) and \(S\) by \(\mathbf{x}\) with the dimension as the number of components and \(f(\mathbf{x})\), respectively, the GPR is represented by the global function \(m(\mathbf{x})\) and the covariance function \(K(\mathbf{x},\mathbf{x})\) as follows [23; 24; 25]:  
\[f(\mathbf{x})\,\sim GP[m(\mathbf{x}),K(\mathbf{x},\mathbf{x})] \tag{15}\]  
Figure 4: Schematic diagram of artificial neural network.  
where \(GP[\cdot]\) represents the Gaussian process. Generally, the covariance function consists of two parts, \(K\big{(}x_{i},x_{j}\big{)}=k_{f}\big{(}x_{i},\ x_{j}\big{)}+k_{n}\big{(}x_{i},\ x_{j} \big{)}\), where \(k_{f}\big{(}x_{i},\ x_{j}\big{)}\) is the functional part, while \(k_{n}\big{(}x_{i},\ x_{j}\big{)}\) is the noise part, which are given as follows:  
\[k_{f}\big{(}x_{i},\ x_{j}\big{)}=\sigma_{f}^{2}exp\Bigg{\{}-\frac{\big{(}x_{i} -x_{j}\big{)}^{2}}{2l^{2}}\Bigg{\}} \tag{16}\]  
\[k_{n}\big{(}x_{i},\ x_{j}\big{)}=\sigma_{n}^{2} \tag{17}\]  
where \(\sigma_{f}^{2}\), \(h\), and \(\sigma_{n}^{2}\) are hyperparameters associated with the scaling factor, length-scale, and noise variance that need to be estimated. Generally, these hyperparameters are optimized by maximizing the log-likelihood function, giving the training dataset, \(\{(x_{i},\ y_{i})|i=1,2,\ \ldots,N\}\). The advantage of using GPR is that it cannot only represent the nonlinear relationship between input \(H\) and output \(S\), but also provide the distribution of the function to quantify the uncertainty of the model or measurement by means of the confidence interval.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 4 Simulation Results and Discussion of C-FSS  
Ansys HFSS R1 2021 software is used for simulations of the proposed model. These simulated results are used to examine the structure, showing that it could be used in LTE-band communication devices for filtering and isolating frequencies.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Remote Sensing  
Currently, amateur drone technology has been used to get high resolution imagery data of remote areas, islands, mountaintops and coastlines. UAV technology bridges the gap between airborne, spaceborne and ground-based remote sensing data. Low cost and lightweight characteristics of UAVs support quality observation with high temporal and spatial resolutions. The remote sensing capabilities of UAVs can support in disease detection, water quality monitoring, drought monitoring, oil and gas, yield estimates, hydrological modeling, biodiversity conservation, geological disaster survey, terrain survey, forest mapping, and crop monitoring. This technology can also be used for crowd sourced mapping and creation of 3D environmental maps and has become an active part of archaeology and cartography. Affordable drones can help to provide latest data that can fit land planning budget without relying on outdated mapping sources. UAVs applied to remote sensing for different applications are shown is shown in Figure 10.
# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 5 Experimental Results
Specifically, 24 subsequent CPIs were considered, each of length 512 OFDM symbols and overlapped by 256 symbols, for an overall observation time of approximately 7 s. The results clearly showed the advantage of the AB-STAP scheme compared to the full array scheme, both in terms of accuracy and stability of the estimation, especially in the case of a limited sample support. The results reported in this section prove the capabilities of the AB-STAP strategy against a real clutter scenario and its advantages over the full array scheme. The AB-STAP represents a suitable and convenient solution for target detection and DOA estimation in mobile passive radar equipped with multiple channels on receive.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 2 Evaluation of the Frequency Spectrum
### Comparison with Standard Algorithm  
To assess the performance of the optimized algorithm with respect to the traditional one, i.e., the one non-optimized for real data, the number of multiplications and sums (\(N_{\text{oper}}\)) was considered as a figure of merit. Indeed, the number of operations affects both the computation time and the power consumption, thus it should be minimized, definitely. As in modern microcontroller architecture, e.g., Advanced RISC Machine (ARM), the sum and the multiplication required a single operation to be performed, provided that the data is in fixed-point notation [29], it makes sense to consider their sum. Figure 2 shows the number of operation for different \(N_{s}\), in case of the traditional DEFT algorithm (dashed-dot line) and the optimized one for real data (continuous line). Even though step 4 introduces an overhead with respect to the evaluation of a \(N_{s}/2\) DEFT, the latter outperforms the former whatever \(N_{s}\) is, with a percentage reduction varying from 29% (\(N_{s}=2^{8}\)) up to 39.4% in the best case, i.e., \(N_{s}=2^{16}\).
## 3 Results and Discussion
### Study of the Degree of Grinding and Squeeze Pressure on the Residual Oil Content of Seed Cake  
At the next stage of research, we determined the residual oil content, depending on the screw pressing pressure and the degree of sunflower seed grinding. Residual oil content is an important indicator of the efficiency of the pressing process and the quality of the obtained oil. If the initial oil content of seeds before pressing was 45%, then with the beginning of pressing and the gradual increase of pressure inside the pressing chamber, the cake oil content decreases significantly (\(p<0.05\)). At that, the most significant reduction (\(p<0.05\)) of seed cake oil content was recorded when seeds were ground with the use of a plate with a diameter of holes at 4 mm. It should be noted that grinding through 2 mm and 3 mm plates and further pressing does not lead to the desired oil yield (residual oil cake fat content is high) (Figure 14). It is known that a low residual oil content indicates high oil extraction efficiency and good quality of the oil product, as most of the oil has been removed from the raw material. On the contrary, a high residual oil content may indicate an inefficient pressing process or equipment problems [31; 32].  
In a screw press, the seeds are moved along a helical screw, and under the influence of the screw rotation, the pressure increases along the length of the press. This causes compression and crushing of the raw material, which results in the release of oil from the seed. The screw, as it rotates, grabs the sunflower seed coming from the feed hopper and pushes it forward, increasing the pressure in the raw material from the feed area to the matrix. The pressurized seeds are enclosed between the moving surfaces (base and side walls of the screw channel) and the stationary inner surface of the housing (cylinder) [33; 34]. On the other hand, due to the increased pressure in the molding head (matrix), a counterflow (backflow) also occurs, which can be considered as a flow of material in the opposite direction from the molding head to the loading zone [35; 36].  
In typical oil extraction from oilseeds, the yield is limited by the fact that the applied mechanical pressure has two opposing effects: On the one hand, the pressure acts as a driving force for oil extraction. On the other hand, under the influence of pressure, the compaction of the oilseed material leads to a reduction in the oil flow and, thus, reduces the pressing capacity [37; 38].