esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Spectral Feature Extraction Methods  
Spectral feature extraction is a widely used technique in speaker recognition, with MFCCs being one of the most common features. [PERSON] et al. summarized common views on the role of DNNs in speech modeling, emphasizing the importance of spectral features in deep learning [8]. [PERSON] and [PERSON] conducted a detailed comparison of multiple parametric representations and proposed the use of MFCCs in continuous speech recognition. Their method converts speech signals into feature vectors, making the subsequent recognition process more efficient and accurate [9].  
[PERSON] and [PERSON] extensively covered the basics of speech recognition, including spectral feature extraction methods, in their work. They delved into techniques like linear predictive cepstral coefficients (LPCCs) and MFCCs, comparing their performance in various speech recognition tasks [10].  
[PERSON] et al. introduced an algorithm combining MFCC and dynamic time warping (DTW) for speech recognition, effectively improving recognition efficiency by leveraging MFCC's feature extraction capabilities and DTW's time alignment [11].  
[PERSON] discussed the application of MFCCs in music modeling, showing that MFCCs are effective across a range of audio processing tasks, improving the accuracy and stability of feature extraction [12].  
[PERSON] and [PERSON] introduced power normalized cepstral coefficients (PNCCs), a new spectral feature extraction method that offers better robustness in noisy environments [13]. [PERSON] et al. proposed a line spectrum frequency distance measurement method based on one-way inner product for speech feature extraction. This new method improves the accuracy and stability of feature extraction while effectively handling background noise [14]. [PERSON] et al. researched the principles of MFCC and LPCC parameter extraction, and the methods for extracting differential cepstral parameters. Their study demonstrated that using MFCC, LPCC, and their first- and second-order differences in combination with the K-means algorithm and a three-layer backpropagation neural network can significantly improve recognition rates. They also confirmed that MFCC parameters offer better robustness than LPCC [15].
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 4 System Design
### Training and Classification  
As the distribution of the wavelet variance when there is human motion is different from that of static scenario, the Gaussian Mixture Model (GMM) is an appropriate classifier. In this GMM, there are two Gaussian models, one is static model and the other is human motion model. The moving data of different volunteers in different moving patterns and the data collected in the static scenario construct the training data. The GMM only need to be trained once, and it can be used in different scenarios without being re-trained. As a result, after a trained GMM is generated, the intrusion detection system is unsupervised. In the training phase, the vectors of wavelet variance of different scales and the ground truth are utilized to train the GMM. In the classification phase, the inputs are only the vectors of wavelet variance, while the outputs are the detection results whether there exists human motion.  
Figure 4: The distribution of wavelet variance when there is human motion and static.  
Figure 3: Wavelet coefficient of Channel State Information (CSI) when people move.  
In the end of classification, a post-processing procedure is added to improve the detection accuracy. In this procedure, it is assumed that a person cannot appear and disappear suddenly. As a result, an additional window beyond the detection window is utilized to reduce the detection mistakes. For example, 0 and 1 represent static and intrusion, respectively. If the detection result is 11011 in this additional window, we can consider there always exists intrusion in this window. The cost of this procedure is the time delay in detection, but the detection accuracy can be higher.
# Offloading of Atomic Tasks in Satellite Networks:
## 2 Related Work
[37] proposes an algorithm called JOBCA to obtain a feasible solution to the original problem by iteratively solving the urban road and winding road problems. [PERSON] et al. [38] investigates task offloading from a matching perspective, aiming to optimize the total network latency. [PERSON] et al. [39] proposes a load-aware MEC offloading method. To improve task offloading timeliness and maximize revenue in the spatial information networks with time-varying characteristics, we propose a secondary task offloading and caching model based on maximum flow allocation. Firstly, the model calculates the maximum flow that can circulate on the time-varying network. Moreover, the satellite nodes in the network calculate the task cache based on the high-orbit satellite atoms and transfer the tasks that need to be uploaded to the high-orbit satellite based on the maximum flow limit. When the satellite task load is high, the task will be offloaded and the execution results are cached. When a high-orbiting satellite exceeds the computational tasks that can be loaded, the tasks are offloaded from the high-orbiting satellite to a low-orbiting or medium-orbiting satellite. Meanwhile, the high-orbiting satellite can quickly complete the task computation and response, improving the overall resource utilization.
## 5 Experiment Results and Discussion
### Scenario 2: Loading Rate at 0.3 MPa/s
#### 5.2.1 Results on X-Direction Strain Dynamics
Through the detailed analyses on the failure process of every point, the strain ranges of failure at point A, B, and C were derived. Basically, it can be inferred that, when the loading speed is at 0.3 MPa/s, the failure strain value of sandstone is roughly estimated to be between 0.0048 and 0.093 versus the projection between 0.004 and 0.011 at the loading speed of 0.1 MPa/s (as shown above). It can be seen that little disparity exists between these estimated strain values, which may indicate that the magnitude of the failure strain value of sandstone will not be largely affected when loading speed is changed from 0.1 MPa/s to 0.3 MPa/s (which are at low loading rates).
## 4 Numerical Simulations  
### Simulation Setup  
The flow field of multi-rotor aircraft can be simulated by CFD with ANSYS 18 to obtain the aerodynamic interference between rotors. For simulation setup, the Spalart-Allmaras (S-A) model was selected with the Pressure-Implicit with Splitting of Operators (PISO) algorithm and the Semi-Implicit Method. Additionally, second-order upwind is applied to obtain the momentum, energy equation, and turbulent viscosity. The mesh distribution with 6.87 million cells is shown in Figure 8.  
Figure 8: Mesh distribution: (**a**) Computational domain; (**b**) mesh details.  
Figure 7: Hover efficiency \(\eta\) at different wind speeds.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
###### Abstract  
The purpose of this study was to investigate the characteristics of the blade tip excitation forces represented as the rotordynamic coefficients (stiffness and damping coefficients) in an unshrouded turbine using the three-dimensional computational fluid dynamic (CFD) numerical method. The blade geometrical parameters were based on a SNECMA transonic experimental rig. The simulations were performed by solving the compressible Reynolds-averaged Navier-Stokes (RANS) equations. The multi-frequency elliptical whirling orbit model and an improved mesh deformation method based on the transient analysis were utilized. The effects of operating conditions on the rotordynamic coefficients and the unsteady flow were also found. The results show that the positive direct stiffness, which confirmed the direct force contribution in the tip excitation forces and the cross-coupling stiffness, were dependent on the whirling frequencies. Damping effects were shown to be negligible. The rotational speed, inlet flow angle, eccentric ratio (\(ER\)), and mean tip clearance had impacts on the stiffness, and some effects of these variables on the rotordynamic coefficients were found to be frequency dependent. Additionally, increasing the rotor eccentricity and the mean tip clearance led to the nonuniformity of the circumferential pressure distributions.  
multi-frequency whirling model; rotordynamic coefficients; tip clearance excitation forces; unshrouded turbine; improved mesh deformation +
Footnote †: journal: Applied Physics Letters
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 5 Validation of Developed Application for Common Emergency Services
### Analysis of Data Packet in Case 2  
In this scenario, Car1 directly sends emergency data to every available car and train at the location where the emergency situation occurs. This communication is facilitated through a second wireless link specifically dedicated to emergency data transmission. For the purpose of demonstration, Car1 sends 500 UDP data packets to Train1, and the measured average end-to-end latency is 7.5 ms, as shown in Figure 10.  
In comparison to case 2, case 1 exhibits higher end-to-end latency. This disparity arises due to the process followed in case 1, as explained in Section 4.2. Initially, all emergency packets are transmitted to the SDN controller. Upon receiving the emergency data packets, the controller duplicates them and sends packets to the designated emergency servers, namely, CarEmer and RailEmer. Subsequently, the emergency servers disseminate the emergency packets to all the nodes present at the specified location.  
\begin{table}
\begin{tabular}{c} **Train Servers** \\ **(Emergency \& Service)** \\ \end{tabular} & **Car Servers** \\ \hline
**trains** & \(\checkmark\) & x & \(\checkmark\) & x & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline
**cars** & x & \(\checkmark\) & x & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data differentiation and distribution of emergency data packet.  
Figure 10: Latency test between Car1 and Train1 using dedicated wireless interface.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 3 Types of Road Intersection Controlled by Stop Signs
### Three-Leg Road Intersections
#### 3.1.1 T-Junction  
In this junction, three roads meet at the shared point, and a small road joins a larger road at a right angle (i.e., 90\({}^{\circ}\)). The small road or the larger ones can be one- or two-direction roads. The stop signs can be installed in all or some input traffic flows (i.e., the flows moving towards the junction) according to the priority of passing through the intersection.  
Figure 1 illustrates six possible cases of stop sign installation at T-junction road design. In Figure 1a-c, both roads are two-direction roads. The stop signs are installed on all  
input flows in Figure 1a. In Figure 1b, the stop signs are installed on the larger road of the intersection. Finally, Figure 1c illustrates a scenario where only one stop sign is installed on the short road. Although the last scenario is very common, vehicles on the long road cannot turn left (i.e., 2-4) directly without stop waiting for the direct flow (i.e., 1-1) to pass.  
On the other hand, Figure 1d-f represent the scenarios when one of the two roads is designed in one direction. In Figure 1d, the short road is only an input flow direction. A stop sign may install on this road to safely control the traffic there. On the other hand, in Figure 1e, the short road is only an output flow direction. Stop signs are not required in this scenario. Finally, Figure 1f illustrates the scenario where the long road is designed in a one-direction design.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 3 Offline Resource Allocation Scheme
### Temporal and Frequency Resource Allocation  
In this subsection, we propose a heuristic scheme to allocate the temporal and frequency resource. First, we construct a graph that reflects major interference occurringamong beams from a single satellite or different satellites. According to the graph theory proposed in [28], the corresponding interference graph is denoted by \(\mathbf{E}(p,q,k)=\{0,1\}\), where \(p\) and \(q\) represent any two cells in the network during the \(k\)th time-slot. The concrete principles of the interference graph construction are as follows:  
1. If both beams directed to cell \(p\) and \(q\) are from the same satellite, let \(\mathbf{E}(p,q,k)=1\).
2. If both beams directed to cell \(p\) and \(q\) are from different satellites, and interfere seriously with each other when using the same subcarrier, let \(\mathbf{E}(p,q,k)=1\).
3. If (1) and (2) are not met, let \(\mathbf{E}(p,q,k)=0\).  
Note that if \(\mathbf{E}(p,q,k)=1\), both beams directed to cell \(p\) and \(q\) cannot use the same subcarrier in the same time slot. Hence, principle (1) ensures no interference among beams of the same satellite, and principle (2) prevents major interference among beams of different satellites.  
Second, we propose the temporal and frequency resource allocation scheme shown in Algorithm 1. Note that \(V\) represents all the cells in the network, and \(\overline{p}=p_{max}/NK\) represents average power distribution in advance. Afterward, we select the cells with the highest demand for resource allocation in the current system while avoiding the allocation of connected beams to the same time-frequency resource lattice.  
```
Input:\(H_{jm,k}^{j}\), \(T_{m}^{j}\), \(\mathbf{E}(p,q,k)\), \(\forall j\), \(\forall m\), \(\forall n\), \(\forall k\) Output:\(\mathbf{A}_{m}^{j}\), \(\forall j\), \(\forall m\) Initialize:\(\mathbf{A}_{m}^{j}=\mathbf{0}\), \(\forall j\), \(\forall m\); for\(k=1\) to \(K\)do for\(n=1\) to \(N\)do \(p=1\); \(\Delta_{nk}^{p}=V\); while\(p\leq j\) and \(\Delta_{nk}^{p}\
eq\emptyset\)do \(m^{\star}=argmax\limits_{m\in\Delta_{nk}^{p}}T_{in}^{j^{\star}}\); cell \(m^{\star}\) served by the satellite \(j^{\star}\) \(d_{m^{\star},nk}^{j^{\star}}=1\); \(T_{m^{\star}}^{j^{\star}}=max\left(T_{m^{\star}}^{j^{\star}}-B_{sc}\log_{2} \left(1+\frac{\overline{p}H_{j^{\star}m^{\star},k}^{j^{\star}}}{N_{0}}\right),0\right)\) \(\Lambda_{m^{\star}}^{j^{\star}}=\emptyset\); for\(j=1\) to \(J\)do for\(m=1\) to \(M\)do if\(m\
eq m^{\star}\) and \(j\
eq j^{\star}\) and \(\mathbf{E}(m,m^{\star},k)=0\)then \(\Lambda_{m^{\star}}^{j^{\star}}=\Lambda_{m^{\star}}^{j^{\star}}\cup m\); endif endfor  endfor \(\Delta_{nk}^{p+1}=\Delta_{nk}^{p}\cap\Lambda_{m^{\star}}^{j^{\star}}\); \(p=p+1\); endwhile endfor endfor
```  
**Algorithm 1** Temporal and Frequency Resource Allocation
## 2 Experimental Measurement of Truck Characteristics
### Determination of Truck Mass and Center of Gravity Coordinates
#### 2.1.2 Determination of Vertical Coordinate of the Center of Gravity (\(z_{G}\))  
This experiment aimed to determine the position of the center of gravity in the vertical direction \(z_{G}\) or \(h_{G}\). This consisted of lifting the truck from its front as represented in Figure 2. The lifting point was horizontally located at a distance \(x_{l}\) from the front axle along the \(x\)-axis and vertically at a distance \(h_{l}\) from the point of contact of the wheels with the ground along the \(z\)-axis.  
Scales were placed under the rear wheels to measure the mass distribution at different angles \(\beta\). The results of these measurements for each angle \(\beta\) are detailed in Table 2.  
The angles of \(\beta=16^{\circ}30^{\prime}\) and \(\beta=18^{\circ}30^{\prime}\) were used for calculating the vertical coordinates of the center of gravity. The problem was the accuracy of the scales, which had a measurement error of \(+5\) kg. This error considerably influenced the results. For example, when \(5\) kg was added to the mass measured at an angle of \(16^{\circ}30^{\prime}\) the resulting change in height was \(20\) mm. Conversely, at an angle of \(9^{\circ}\), the same addition of mass resulted in a change in height of more than \(40\) mm, i.e., larger angle values gave more accurate results. The coordinate of the center of gravity in the vertical direction, as provided by Equation (3), was calculated by applying [PERSON]'s second law.  
\[z_{G}=(L-x_{G})\text{ctg}(\beta)-\bigg{(}1-\frac{m_{\text{rear}}}{M_{\text{f}} }\bigg{)}(L+x_{l})\text{ctg}(\beta)+\bigg{(}1-\frac{m_{\text{rear}}}{M_{\text{ f}}}\bigg{)}h_{l}+r_{\text{s}}+\Delta z_{\beta} \tag{3}\]  
Here, \(r_{\text{s}}\)--static radius of the wheels; \(\Delta z_{\beta}\)--the difference between the sum of the rear leaf spring's vertical displacement and the rear tire's vertical displacement when the truck is lifted at an angle \(\beta\) and those when in a static state \(\beta=0\). This vertical displacement value can be obtained from the force-displacement curves of the rear leaf spring and the tire, which are explained in the following sections. The characteristics of the truck are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\beta\) & \(6^{\circ}10^{\prime}\) & \(7^{\circ}20^{\prime}\) & \(9^{\circ}\) & \(11^{\circ}30^{\prime}\) & \(14^{\circ}10^{\prime}\) & \(16^{\circ}30^{\prime}\) & \(18^{\circ}30^{\prime}\) \\ \hline Mass Value & Left & 1970 & 1980 & 1980 & 1990 & 2010 & 2010 & 2020 \\ (kg) & Right & 2130 & 2130 & 2140 & 2150 & 2160 & 2160 & 2170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mass measurements at various angles of inclination.  
Figure 2: Truck measurements mass at angles (\(\beta\)): (**a**) experiment; (**b**) schematic representation.
# Characteristic Analysis of Compact Spectrometer Based on Off-Axis Meta-Lens
## 3 Results and Discussion  
### Simulation Results of a Typical Meta-Lens-Based Spectrometer  
It is pivotal to analyze the dispersive characteristics of the spectrometer based on the off-axis meta-lens first. According to Equations (1)-(4), here we give the simulation results of the configuration with parameters \(f\) = 20 mm, aperture diameter \(D\) = 2 mm, \(\alpha\) = 45\({}^{\circ}\) and \(\beta\) = 45\({}^{\circ}\) (the OP is perpendicular to the optical axis) at \(\lambda_{\mathrm{d}}\) = 1550 nm, as shown in Figure 2. It corresponds to a small numerical aperture NA = 0.035 (see Equation (5) below). The focal line profile along the \(u\)-axis as \(\lambda\) varies in 1520-1580 nm is shown in Figure 2a. The dispersion-caused spatial focal spot distribution ensures the technical practicability to develop a meta-lens based spectrometer. One can see the focal line gets widened obviously as wavelength significantly deviates from the design wavelength due to the chromatic aberration and the mismatch of the actual focal plane and the OP. In this spectral range, the displacement \(\Delta u\) is nearly linearly dependent on wavelength, suggesting a linear dispersion \(\mathrm{d}u/\mathrm{d}\lambda\) = 9.95 \(\upmu\)m/nm. A diffraction-limited spatial full width at half-maximum (FWHM) of the focal spot of 19.6 \(\upmu\)m (12.6 \(\lambda_{\mathrm{d}}\)) at \(\lambda_{\mathrm{d}}\) = 1550 nm is observed from Figure 2b. It raises slowly as wavelength deviates from \(\lambda_{\mathrm{d}}\). We introduce the effective spectral range \(\Delta\lambda\) to evaluate the performance of the spectrometer. Within this spectral range, the FWHM of the spot line is less than 1.2 times \(\left.\mathrm{FWHM}\right|_{\lambda=\lambda_{\mathrm{d}}}\). In fact, this criterion is much stringent compared with previous studies about optical spectrometers and the commercial products [21]. For the configuration proposed here, \(\Delta\lambda=1574\) nm \(-\) 1526 nm = 48 nm. The spectral resolution \(\delta\lambda\) as a function of wavelength define by the FWHM value of the \(U_{\mathrm{o}}(\lambda)\) curve is also shown in Figure 2b. The inset of Figure 2b plots the \(U_{\mathrm{o}}(\lambda)\) intensity along the dashed line in Figure 2a at \(u=0\). Over the spectral range 1524-1574 nm, the spectral resolution is within 1.8 nm. Figure 2c plots the field intensity (normalized by the maximum of the field intensity at the design wavelength \(\lambda_{\mathrm{d}}\)) profile along the \(u\)-axis at different wavelengths at an interval of 1.5 nm. Different spectral lines are distinguishable in this interval. These results are acquired without considering the actual pixel size of the detector.
## 4 Results
### System Dynamics Modelling Results
It can be noted that the effect of the variations on the input variable deploy only after an initial period in which the input has no influence on the reliability outcome. This can be explained by the dynamic character of the high-level loop. For the same input variable 'perception' the ERL and accident rate exhibit large variations in behaviour after year 7. For the detection node 'fire yes, detection yes' (FYDY), the sensitivity to the 'perception' factor is extremely low, with variations in the range from 40.05% to 40.50% as shown in the curve of Figure 6c. The detection node (right side) shows that variations occur after about 7 years, meaning that the influence of the human and organization factors on technical systems develops after a certain period of time. The results indicate that a very small variation in the input variable 'risk perception' generates very large variations in the output ERL value. It can thus be concluded from the sensitivity analysis that a very small behavioural change in risk perception of a building management team can lead to a very large risk to life variations over time through the self-reinforcing feedback loop. Figure 7a,b indicate the SD plots for ERL variations over time for each Trial Design (TD01-16) for case #1 to #7. The magnitude of the ERLs is different for different test cases and for different trial designs. It can be noted that the main trends in the ERL curves are maintained throughout all cases and determined by the HOE loop which the dynamic behaviour of the model is based on. Changes in ignition frequencies do not impact the evolution of the risk curve, although variations in absolute values are given. Dynamic risk variations are noticeable in the first 8 trial designs (TD01-08) with various active systems ON: in Year 6, risk levels improve due to improving safety measures from building management team peaking in year 7. Thereafter, lax safety behaviour is to be expected following the good outcome in the first six years, leading to risk levels increasing again. This behaviour is not as pronounced in Trial Designs TD09 to TD12 and is not present in TD13 to TD16 due to an absence of various active systems sensitive to HOEs. In summary, the high-level cycle, with its HOEs variables, is responsible for the dynamic risk curve behaviour while fire ignition frequency, building population, and consequences are parameters that impact only the absolute value of ERL at each time step. Figure 7: _Cont_. Figure 7: (a): SD plots—ERL variations over time for Trial Designs 01–08, Case #1 to #7. (b): SD plots—ERL variations over time for Trial Designs 09–16, Case #1 to #7.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Aluminum-Doped Zinc Oxide Films  
Aluminum (Al)-doped zinc oxide films were produced under various dipping cycles [178]. The XRD analysis showed the hexagonal phase, and the intensities of the samples increased when the dipping cycle increased. Small grain sizes and smoother morphology could be observed in all films based on the SEM images. The photoluminescence (PL) spectra indicated that good films (lesser defect density) could be seen when the deposition cycle was 30 cycles. The optical transmittance was 87% in the visible region, while the band gap was 3.23 eV.