esa-sceva/satcom-chunk-collection huggingface dataset:
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 2 Wireless Power Transfer System Model
### Waveforms Structure  
The waveform signals that have been utilized in this paper incorporate multiple sub-carriers evenly-spaced in the frequency domain. These signals are pulsed waves of a periodic form with a pulse period of \(T_{o}=\frac{1}{\Delta f}\), considering that the frequency space between two adjacent tones is \(\Delta f\). Each transmitted signal can be expressed in the time domain by:  
\[s(t)=\Re\bigg{\{}\sum_{n=1}^{N}a_{n}e^{i\theta_{n}}e^{j2\pi f_{n}t}\bigg{\}}= \sum_{n=1}^{N}a_{n}\cos(2\pi f_{n}t+\theta_{n}) \tag{1}\]  
where \(n\) is a positive integer, \(N\geq 1\) is the total number of sub-carriers, and \(a_{n},\theta_{n},f_{n}\) are the amplitude in Volts, phase in radians, and frequency in Hz, of the \(n\)th sub-carrier respectively. We organize the \(a_{n},\theta_{n},f_{n}\) values in vectors of the following form:  
\[\begin{split}\mathbf{a}&=[a_{1},a_{2},\cdots,a_{N}]\\ \theta&=[\theta_{1},\theta_{2},\cdots,\theta_{N}]\\ \mathbf{f}&=[f_{1},f_{2},\cdots,f_{N}]\end{split} \tag{2}\]  
According to [PERSON]'s theorem, if \(S(f)\) is the Fourier transform of \(s(t)\), then the average power of \(s(t)\) in Watts can be calculated as:  
\[P_{s}=\frac{1}{T_{o}}\int_{0}^{T_{o}}s^{2}(t)dt=\int_{-\infty}^{+\infty}|S(f) |^{2}df=\sum_{n=1}^{N}\frac{a_{n}^{2}}{2} \tag{3}\]  
The definition of the waveform's central frequency (\(f_{c}\)), bandwidth (\(B\)), and pulse period (\(T_{o}\)) determines the total number of sub-carriers and the associated \(f_{n}\) frequencies of the signal. The  
Figure 1: The applied Wireless Power Transfer system model.  
minimum and maximum frequency tones in the spectrum are limited by \(f_{min}=f_{c}-\frac{B}{2}\) and \(f_{max}=f_{c}+\frac{B}{2}\), accordingly. The resulted frequencies \(f_{n}\) of the sub-carriers correspond to the multiples of \(1/T_{o}\) in the interval \([f_{min},f_{max}]\).  
To compute the received signal at the input of the rectenna, the frequency response function of the propagation across the medium is required. For a selected frequency \(f_{n}\), this can be expressed by:  
\[TF_{n}=b_{n}e^{i\psi_{n}} \tag{4}\]  
where \(b_{n}\) is the amplitude and \(\psi_{n}\) is the phase of the propagation channel's frequency response, respectively. These values can be also organized in vectors \(\mathbf{b}\) and \(\mathbf{\psi}\) accordingly. If we presume that the propagation channel is linear time-invariant, the incident wave at the input of the rectenna \(r(t)\) is given by:  
\[r(t)=\Re\bigg{\{}\sum_{n=1}^{N}a_{n}b_{n}e^{i\theta_{n}}e^{i\psi_{n}}e^{i2\pi f _{n}t}\bigg{\}}=\sum_{n=1}^{N}a_{n}b_{n}\cos(2\pi f_{n}t+\theta_{n}+\psi_{n}) \tag{5}\]
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.1 Definition of Cropland Abandonment  
At present, the definition of abandoned cropland remains controversial around the world. The Food and Agriculture Organization of the United Nations (FAO) defines abandoned cropland as that which has not been cultivated for at least 5 years [45]. In Asia, abandoned cropland has a shorter time boundary due to more frequent land-cover changes. In this study, abandoned cropland referred to areas that were cultivated in the first year but no longer cultivated in the subsequent 2 years [46], which included abandoned irrigated cropland and abandoned rainfed cropland. Correspondingly, active cropland was designated as the areas cultivated for three consecutive years. Intermittent cropland was designated as the areas cultivated for only 2 years among the three consecutive years, which included intermittent irrigated cropland and intermittent rainfed cropland. Herbaceous vegetation was defined as any unplowed area that was not dominated by shrubs or trees. Wood vegetation was defined as areas where shrubs and trees were dominant. The annual land-cover maps enabled the identification of abandoned rainfed cropland and irrigated cropland by assessing land-cover trajectories along with time.
## 2 Experimental Procedures
### Raman Scattering  
Raman scattering spectroscopy measurements are performed on several GS-MBE-grown InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs samples by exploiting a Reinshaw InVia spectrometer with a 532 nm DP-SSL as an excitation source. On each sample, the incident power is set at 60 mW after allowing the optical losses. A holographic notch filter is employed to block unwanted reflections. The results of composition-dependent optical phonons are obtained for InP\({}_{1-x}\)Sb\({}_{x}\) alloys in the frequency range of \(\sim\)40 cm\({}^{-1}\) to 800 cm\({}^{-1}\). Several scans are considered on each sample at different locations with a run of 16 accumulations for reducing the effects of noise. Despite setting the exposure time of 10 s to minimize heating effects on each sample, it remained inevitable that the local heating would cause broadening in the observed phonon bands (Section 4.1).  
Polarization-dependent Raman spectra have also been recorded (Section 4.1.1), keeping the incident beam fixed. We analyzed the scattered beam by a Polaroid sheet combined with a half-wave plate placed in front of the entrance slit for recording the spectra with two different polarization geometries, z(Y,X)\(\overline{z}\) and z(X,X)\(\overline{z}\), where X, Y, and Z denote the coordinates of (100), (010), and (001) directions with respect to the n-InAs substrate. On InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs samples, the results with spectral linewidth \(\sim\)2.0 cm\({}^{-1}\) are attributed to the scattering from optical phonons activated by disorders and/or impurities. Clearly, the observed Raman intensity line shapes are represented (Sections 4.1 and 4.1.1) as the phonon density of states, corroborating the two-phonon mode behavior in InP\({}_{1-x}\)Sb\({}_{x}\) alloys.
## 4 Results and Discussion
### Navigation Using Aruco Markers
During navigation, the user held the smartphone in his hands roughly at chest level with the screen facing towards him. The smartphone was held in portrait orientation while slightly tilted at an angle nearly perpendicular to the horizontal plane. As shown in Figure 13, using this angle is enough for covering the walking area in front of the PVI and identifying markers. For a hands-free option, the smartphone may also be mounted on the user's chest. Audio feedback is provided to the user via headphones connected to the smartphone or by a smartphone's speaker. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Command Type** & **Name** & **Description** \\ \hline \multirow{4}{*}{“Turn left”, “Turn right”} & The prototype directs the user to turn left or right. \\  & “You have detected your next & The prototype informs the user that the next \\  & point, so, you should go straight to & point is detected, and the user should \\  & reach it” & approach it. \\  & “You have passed this point & The prototype informs the user that they \\  & successfully” & passed this point successfully and have \\  & “You have reached your & started navigating to the next point. \\  & destination so, go straight to it” & Once the user reaches the desired destination, \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 12: Path to destination example.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO
### Impedance Sensitivity Analysis
#### 3.1.2 Impedance Sensitivity of Current Drops  
Voltage remaining no changed at 1.0 pu, the impedance characteristics dynamics when the current reference \(l_{drf}\) drops from 1.0 pu (1847 A) to 0.6 pu is shown in Figure 11. It is worth noting that the output fundamental current equals to \(l_{drf}\) under the control strategy. \(X_{PMSG}\) gets lager and \(R_{PMSG}\) decreases while the output current drops. It can be inferred that this system will be more unstable.
## 4 Brief Description of the Learning Models
### Performance Evaluation of the ML Models
It is shown in Figure 14a that there is a small deviation (close to 0) between the actual and predicted frequencies for DTR. Among the 18 test samples, 3 test samples were accurately predicted, with zero percentage of error. In addition, the percentage of error in most cases is less than one. As a result, the predicted result almost perfectly matches the simulated result, as shown in Figure 14a. Somehow, there are deviations for CNN with magnitudes ranging from 0.95446% to 20.65657% as depicted in Figure 14b. It is found that the percentage of predicted errors in CNN is slightly higher  
Figure 12: Pie chart for splitting the data set. Figure 13: Correction matrix. than in DTR. Hence, DTR is selected for better prediction performance as compared to CNN. The performance metrics of ML for CNN, linear regression, random forest regression, decision tree regression (DTR), lasso regression, ridge regression, and XGB regression algorithms are tabulated in Table 5. The magnitude values of MAE, MSE, RMSE, and the variant score are evaluated to determine the error performance of each algorithm. It is shown that the DTR model generates a small percentage error for MAE, MSE, RMSE, and the variant score magnitude with values of 5.63%, 0.71%, 8.42%, and 99.68%, respectively. The DTR method has performed better than the rest of the regression models and yields the highest quality results in all four scenarios. It is also shown that the XGB model is considered as a secondary selection, where it generates values of 7.03%, 1.06%, 10.27%, and 99.54%, respectively. It depicts the XGB model, performing close to DTR. Somehow, the performance of the CNN and Lasso models produced lower percentage magnitudes of 76.41%, 92.49%, 96.17%, and 57.62% and 55.01%, 63.25%, 79.53%, and 70.97%, respectively. Hence, they are not good for method selection for proposed research work. The performance comparison for all algorithms viewed in the bar chart is illustrated in Figure 15. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **MAE** & **MSE** & **RMSE** & **Var Score** \\ \hline CNN & 0.7641 & 0.9249 & 0.9617 & 0.5762 \\ \hline Linear Regression & 0.5312 & 0.5226 & 0.7229 & 0.7627 \\ \hline Random Forest Regression & 0.1261 & 0.0352 & 0.1875 & 0.9848 \\ \hline Decision Tree Regression & 0.0563 & 0.0071 & 0.0842 & 0.9968 \\ \hline Lasso Regression & 0.5501 & 0.6325 & 0.7953 & 0.7097 \\ \hline Ridge Regression & 0.5061 & 0.5159 & 0.7183 & 0.7668 \\ \hline XGB Regression & 0.0703 & 0.0106 & 0.1027 & 0.9954 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The resonant frequency prediction performance of ML algorithms. Figure 14: Simulated vs. predicted frequency using (**a**) DTR and (**b**) CNN.
# Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements
## 1 Introduction  
Heterodyne Interferometry has some merits, such as high accuracy, high stability, and real-time measurement for many optical measurement applications. It is the most popular non-contact measurement technology for interferometry [1; 2; 3; 4; 5; 6; 7; 8]. However, most structures of heterodyne interferometers are not common-path configuration. Therefore, if high measurement stability needs to be maintained, the interferometer needs to be set up in a special laboratory with environmental controls to relieve all environmental interferences. If the interferometer is absolute in common-path configuration, it cannot measure anything except the extra phase shift between two polarizations in the optical path due to the birefringence or complex refractive index of the sample. To achieve the best resolution, we proposed Heterodyne Angle Deviation Interferometry (HADI) to measure the deviation angle in order to obtain some physical parameters in general fields. We used two AOM (acousto-optic modulation) devices and a laser to become the heterodyne light source and used the technique of the common-path heterodyne interferometry (CPHI) to measure the phase shift of an angular sensor for displacement measurement [9]. The angular sensor can be a pair of parallel metallic plane mirrors or a surface plasmon resonance (SPR) prism. When the test beam is reflected from the sample and then incident into the angular sensor, the phase shift of the angular sensor due to angle deviation can be represented as some parameters that we want to achieve. For example, the frequency of small vibrations can be led into the optical interference signal in the frequency domain. We could utilize a lock-in amplifier to compare the test signal with the reference signal of the same frequency to achieve the phase signal during the vibration measurement. The frequency and amplitude of the phase signal represent the frequency and amplitude of the vibration. The vibration level can be assessed from the Fourier frequency transformation. In this paper, we performed two experiments to demonstrate the proposed method; one is the vibration measurement, and the other is the water flow test. The phase resolution is 0.01\({}^{\circ}\). For the vibration measurement, the vibration frequency range is within 500 Hz for the beat frequency of 10 kHz. For the water flow measurement, the minimum bubble size is 0.2 mmand the maximum speed of water flow is about 0.6 m/s. The method has some merits, such as accuracy, anti-influence, simple structure, easy operation, and real-time measurement.
## 2 Materials and Methods
### Nozzle Monte Carlo Modelling of SAPT  
FSCF, CSCF and CRH were derived from the Monte Carlo simulation. Firstly, the SAPT nozzle was modeled in TOPAS (version 3.1) [17]. The FSCF was obtained by simulating a 10 cm \(\times\) 10 cm field and 20 cm \(\times\) 20 cm scanning field with spot space 2.5 mm. The Sensitive volume of the Markus chamber is modeled to obtain the central dose of the two large fields. FSCF denotes the ratio between the dose of the two fields. CSCF is acquired by scoring the dose in a cylindrical region with radius 4.08 cm and 10 cm. It denotes the ratio between the simulation results of two different scoring radii. In order to obtain CRH, the actual response curve of the BPC is simulated. The spot is scanned along the radius of the BPC from the chamber center to the boundary. Detailed calculation can be found in [4]. The simulated particles for each spot were 10\({}^{6}\). The virtual source to axis distance (VSAD) from the scanning magnet U and V to the ISO-center is 2.87 m and 2.49 m respectively [16]. Beam monitors are composed of the main and secondary dose monitors and a position monitor. The water equivalent thickness (WET) of the three dose monitors is 1.9 mm. Protons pass through the vacuum chamber in the nozzle. A simplified SAPT nozzle model used for the TOPAS simulation is shown in Figure 3. The Markus chamber was positioned in a 40 \(\times\) 40 \(\times\) 40 cm\({}^{3}\) water phantom at depth 2 cm.  
A single spot was simulated and the dose was scored in a cylindrical volume with a radius of 4.08 cm. The actual delivered particles are given by:  
\[N_{dclivered}^{p}=\frac{DAP}{\frac{\sum_{i}\int(S_{el,i}/\rho)_{w}\Phi_{el}^{ A}(z_{ret})dE}{N_{MC}^{p}}A} \tag{4}\]  
where \(DAP\) is the calculated dose-area product derived from the Markus chamber measurement. \(S_{el,i}/\rho\) is the mass stopping power for species \(i\). TOPAS uses revised ICRU  
Figure 3: A simplified SAPT nozzle model for TOPAS simulation (not to scale).  
73 [18] stopping power tables. \(\Phi^{A}_{\Sigma,i}(z_{ref})\) is the fluence differential within the area \(A\). \(A\) denotes the area of the cylindrical volume. The denominator can be derived by the Monte Carlo simulation [14; 19].
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology  
### Overview  
In this paper, a Vertical Handover Decision Algorithm based on Fuzzy is proposed along with mobility prediction. MFNN (Multi-layer Feed Forward Network) is used for user mobility prediction. Once the mobility of the user is predicted and the next cell is determined; mobile terminal conditions, network conditions are checked against the user preferences and application requirements. If the predicted cell does not fulfill the preferences and requirements, then the best access network has to be selected. To do so, a multi-attribute Access Network Selection Function (ANSF) [33] is used to select the best access network. It is efficient for utilizing radio resources and improves quality of service. The parameters included are Data rate, reliability, signal strength, battery power and mobility. The parameters are input for the Fuzzy logic-based decision making and as an output, the best network is selected.  
Figure 1 represents the proposed block diagram. First, mobility prediction is done using Multi-layer Feed Forward Network (MFNN) to obtain the next cell along with network condition. In case the predicted cell does not fulfill the requirement, then the best network selection is selected by applying the Multiple-attribute Access Network Selection Function. The attribute data rate, reliability, signal strength, battery power and mobility are taken as input for the fuzzy logic decision making engine and the output is taken as best network.
## 3 Methods
### NPP Trend Analysis  
The combination of the Theil-Sen median trend analysis with the Mann-Kendall trend test approach is a valuable method to identify trends in long-term sequence data [32]. This method has become increasingly popular for analyzing long-term vegetation data. For this research, we utilized the Theil-Sen median to examine the trends in vegetation NPP over the past two decades in Chinese terrestrial ecosystems.  
Theil-Sen median trend analysis is a reliable non-parametric statistical approach to trend analysis that can reduce the impact of outliers [33]. Its formula is  
\[\beta=median\left(\frac{\mathrm{NPP}_{j}-\mathrm{NPP}_{i}}{j-i}\right),2001 \leq i<j\leq 2020 \tag{4}\]  
where median is the median function and NPP\({}_{j}\) and NPP\({}_{i}\) are the NPP in year \(j\) and year \(I\), respectively. When \(\beta>0\), it means that NPP shows an increasing trend, while the shows a decreasing trend.  
The Mann-Kendall test is a non-parametric test that neither demands a sample to obey a specific distribution nor is influenced by a few outliers [34]. During the test, the vegetation NPP results for the period from 2001-2020 were constructed as a set of time series with per-pixel metavalues to determine significance differences:  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{\mathrm{var}(S)}},S>0\\ 0,S=0\\ \frac{S+1}{\sqrt{\mathrm{var}(S)}},S<0\end{array}\right. \tag{5}\]  
\[S=\sum_{j=1}^{n-1}\sum_{i=j+1}^{n}\mathrm{sgn}\big{(}\mathrm{NPP}_{j}-\mathrm{ NPP}_{i}\big{)} \tag{6}\]  
\[\mathrm{sgn}\big{(}\mathrm{NPP}_{j}-\mathrm{NPP}_{i}\big{)}=\left\{\begin{array} []{c}1,\mathrm{NPP}_{j}-\mathrm{NPP}_{i}>0\\ 0,\mathrm{NPP}_{j}-\mathrm{NPP}_{i}=0\\ -1,\mathrm{NPP}_{j}-\mathrm{NPP}_{i}<0\end{array}\right. \tag{7}\]  
\[\mathrm{var}(S)=\frac{n(n-1)(2n+5)}{18} \tag{8}\]  
where \(Z\) is the statistic of the NPP series, \(S\) refers to the test statistic, \(\mathrm{var}(S)\) is the variance of the \(S\) statistic, \(n\) denotes the time series length, \(\mathrm{sgn}\) denotes a symbolic function, and \(\mathrm{NPP}_{i}\) and \(\mathrm{NPP}_{j}\) are the time series datasets. In the double-lateral trend test, \(Z>0\) shows an uptrend, \(Z<0\) shows a downtrend, and \(Z=0\) shows unchanged. Thus, provided the level of importance \(\alpha\), if \(|Z|>u_{1-\alpha/2}\) this implies the fact that the NPP time series shows significant changes at the \(\alpha\) level. In this paper, we determined the importance of change trends in vegetation NPP time series at the 0.05 confidence level.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Node-Location Patterns
#### 4.2.3 Three-Dimensional Scenario  
We developed the 3D case for the sake of completeness. However, these networks seem more suitable for a fleet of drones than for VANETs. The model is a 3D space of random nodes with density of nodes \(\lambda\) expressed in nodes per cubic meter. The exact model would require computing number \(N_{i,j}\) of nodes at \(j\) hops from node \(i\), and evaluating the mean value of \(N_{i,j}\) averaged on many spatial configurations. As for the 2D case, the largest area in which the \(N_{i,j}\) nodes could be located was assumed to be between the sphere of radius \((j-1)R\) and the sphere of radius \(jR\). This was obviously true when there was very high density of nodes \(\lambda\), and this property was also assumed to hold with smaller densities. This was also verified by simulations.  
The mean number of nodes at \(j\) hops from node \(i\) was  
\[E(N_{i,j})\leq\frac{4\lambda\pi R^{3}}{3}(j^{3}-(j-1)^{3})=\frac{4\lambda\pi R^ {3}}{3}(3j^{2}-3j-1)\quad\forall j>0.\]  
Figure 5: Node locations in a 2D urban scenario.
## 4 Numerical Modelling and Calculation Methodology
### Establishment of Numerical Model of the Surrounding Rock-Initial Support
#### 4.1.2 Calculation Parameters  
(1) In Situ Stress  
The in situ stress is determined based on measured results and a simplified approach, considering only the three-dimensional normal stresses while temporarily neglecting shear stresses, which has been widely accepted and is used in numerous studies (e.g., [37; 38; 39]). The in situ stress values are graded according to different burial depth (\(H\)) ranges, as shown in Table 2.  
(2) Surrounding Rock Parameters  
Empirical studies and experimental data on rock behavior under static loading conditions have demonstrated that the Mohr-Coulomb model provides an accurate representation of the material's shear strength and failure criteria [40; 41; 42]. For numerical simulation calculations, the widely accepted Mohr-Coulomb model is applied to the surrounding rock parameters, with assumptions that simplify the complex geological conditions, such as uniformity in material properties and isotropic stress conditions. This approach allows for efficient analysis while maintaining a reasonable level of accuracy in predicting rock behavior under various loading conditions [43; 44; 45; 46]. The physical and mechanical parameters for the surrounding rock and support used in tunnel construction with the drill-and-blast method are provided in Table 3.