esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Hyperparameter Tuning of the ANN Model  
Similar to the other ML models, the key hyperparameters of the ANN model are optimized by minimizing the average MAPE values obtained from repeated five-fold cross validation tests (number of repeats = 3) carried out on the initial training dataset. The average values of testing MAPE of many different ANN models resulting from the cross validation tests are presented in Figure 12 (each data point represents the average of 15 different MAPE values). Multiple ANN models (with different architecture) are developed to find the optimum number of hidden layers (L) and number of neurons (N) in each  
Figure 11: Comparisons of ANN model predictions with experimental results for AAR during initial evaluation of the model: (**a**) training phase and (**b**) testing phase.  
hidden layer (Figure 12a,b), while a fixed network architecture is used to tune number of epochs and the learning rate (LR) of the SGD algorithm (Figure 12c,d).  
Results presented in Figure 12a show that as L increases, the error (average MAPE) in predictions decreases up to when L = 4. When L increases further, the ANN model seems to overfit the training data slightly (increase in testing error). A similar trend is observed for the number of neurons (N) used in each hidden layer (Figure 12b). Based on these observations, the combination of L = 4 and N = 40 is chosen as the optimum combination for the architecture of the ANN model for the problem considered. These observations are confirmed and verified independently by using grid search and random search algorithms going through multiple ANN model architectures with several possible combinations of L and N. As the number of iterations (epochs) increases, as expected, the average MAPE in predictions decreases (Figure 12c); however, once the number of iterations reaches around 200 to 300, no further significant improvement in MAPE is observed with the number of iterations. As for the learning rate (LR) of the SGD algorithm, the optimum learning rate is found to be between 0.01 and 0.1 (Figure 12d). The optimum values chosen for the number of iterations and the learning rate are 300 and 0.01, respectively. Table 2 summarizes the optimum values chosen for the key hyperparameters of the ANN model developed in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Hyperparameter of the ANN Model**} & **Value** \\ \hline Number of hidden layers (L) & 4 \\ Number of neurons in each hidden layer (N) & 40 \\ Activation function & ReLU 1 \\ Optimizer & SGD 2 \\ Learning rate & 0.01 \\ Batch size for training & 2 \\ Number of epochs & 300 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Rectified linear unit function. 2 Stochastic gradient descent.  
\end{table}
Table 2: Optimum values chosen for major hyperparameters of the ANN model.  
Figure 12: Results of hyperparameter tuning of the ANN model: Variation of average MAPE with (**a**) number of hidden layers, (**b**) number of neurons in each hidden layer, (**c**) number of iterations for each batch of training data, and (**d**) learning rate of SGD algorithm.
## 3 Solution of the Problem
### Determination of the Temperature Field  
Applying the Laplace transform to Equation (18), and bearing in mind the corresponding initial and boundary conditions, we find that:  
\[a_{3}q^{\alpha}\overline{\theta}(y,q)=\frac{\partial^{2}\overline{\theta}(y,q )}{\partial y^{2}}\,;\,y>\,0, \tag{22}\]where \(q\) is the transform parameter and the Laplace transform \(\overline{\theta}(y,q)\) of \(\theta(y,t)\) has to satisfy the following conditions:  
\[\overline{\theta}(0,q)=\frac{1}{q};\ \overline{\theta}(y,q)\ \to\ 0\ \text{as}\ y\to\infty. \tag{23}\]  
The solution to the problems (22) and (23) is:  
\[\overline{\theta}(y,q)=\frac{1}{q}\exp\bigl{(}-y\sqrt{a_{3}q^{\alpha}}\bigr{)} ;\,y\,>\,0. \tag{24}\]  
Applying the inverse Laplace transform to Equation (24), and using Equation (A1) from the Appendix A, we find that:  
\[\theta(y,t)=\Psi\biggl{(}1,\frac{-\alpha}{2};-y\sqrt{a_{3}}t^{-\frac{\alpha}{ 2}}\biggr{)}\ \text{for}\ 0<\alpha\leq 1, \tag{25}\]  
where:  
\[\Psi(a,-b;z)=\sum_{n=1}^{\infty}\frac{z^{n}}{n!\Gamma(a-nb)};\,b\in(0,1), \tag{26}\]  
is the Wright function [32]. For \(\alpha=1\), Equation (25) becomes:  
\[\theta(y,t)=erfc\biggl{(}\frac{y\sqrt{a_{3}}}{2\sqrt{t}}\biggr{)};\,y,t\,>\,0. \tag{27}\]  
Of course, a simple analysis clearly shows that this result is in accordance with that obtained by [PERSON] and [PERSON] [13], Equation (3.19).
## 4 Discussion
### The Influence of Lateral Environmental Wind  
Firstly, it is evident that at lower wind speeds (less than 3 m/s), the yaw force, yaw moment, and roll moment increase slowly. However, at higher wind speeds (greater than 4.5 m/s), these forces and moments experience a rapid increase, which is highly detrimental to the flight stability and control. Data analysis reveals an approximate linear relationship between the yaw force, yaw moment, roll moment, and square of the wind speed. It should be noted that the athlete-generated yaw force, yaw moment, and roll moment play a dominant role, while the influence of the skis is relatively small. Therefore, it is crucial to focus on the impact of the athlete's posture under lateral wind conditions on the aerodynamic characteristics and stability during the flight phase.  
Secondly, at lower wind speeds (less than 3 m/s), the lift, drag, and pitch moment increase gradually. However, at higher wind speeds (greater than 4.5 m/s), the lift, drag, and pitch moment start to increase rapidly, imposing higher demands on the athlete's flight control. An interesting observation is that the increase in total lift, total drag, and pitch moment is almost identical to the increase in the athlete's lift, drag, and pitch moment. This suggests that, due to the similarity between the athlete's body shape and an aircraft wing,  
Figure 9: Flow field form under different roll rotation angles. The equal-vorticity contours are shown.  
the lateral wind not only generates drag effects (as mentioned above in the yaw force) but also produces lift effects. Consequently, the athlete's lift, drag, and pitch moment change, leading to variations in the total lift, total drag, and pitch moment of the multi-body system. However, the mechanical characteristics of the skis show minimal changes.  
Furthermore, from the flow field results depicted in Figure 7, it is observed that as the lateral wind speed increases, the scale of vortex structures behind the skis undergoes relatively minor changes. However, the vortex patterns behind the athlete exhibit significant variations, shifting increasingly towards the direction of the lateral wind. This indicates that the athlete is more affected by the lateral wind, resulting in more pronounced changes in forces and moments generated by the athlete. This observation aligns with the previously obtained numerical statistics of the mechanical characteristics.
# Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
## 2 Proposed CERMB-SVM Technique
### Edge Selection  
Input image edge selection process is as follows: the proposed algorithm first performs Canny edge detection to each resulting intensity band \(f\in F\). A Gaussian filter with sigma 0.5 is applied on each resulting intensity band to smoothing the band. Then, the intensity gradient of the created band is computed in four directions (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\)) utilizing a first-order derivative function. The horizontal and vertical edge gradients are first determined and employed to calculate the gradients of the diagonals. The non-maximum suppression algorithm is then performed on the resultant gradients to maintain the edges with the greatest gradients. The leftover edge pixels are then exposed to two empirical thresholds, low and high. Pixels lower than the low threshold value are discarded, pixels with value greater than the high threshold are categorized as strong edges, and pixels in the middle of the two thresholds are believed to be weak edges. Ultimately, the resultant edges are exposed to edge tracing by hysteresis, in which a weak-edge pixel is discarded if none of its 8-connected neighborhood pixels are strong-edge pixels. The resulting edge maps are then binarized, generating a binary edge map \(g\in G\) for each band.  
Each resultant binary edge map \(g\in G\) is then exposed to two morphological operations. First, single edges are set to zero. Second, a 'bridge' morphological operator is performed on the resultant edge map. If a zero-value pixel has two or more non-zero neighbors, its value is set to one, by this means decreasing the quantity of distinct contours in the binary image map. The resultant contours in the maps more precisely demonstrate the ear features of the input image.  
One side-impact of applying edge detection on each band is that multi-band image generation may generate extra edges, as pixels with values just above and under the given boundary values are often contiguous; therefore, those pixels are inaccurately categorized as edges. To solve this problem, these pixels are assessed with their 8-connected neighborhood. If any of the neighbors has a value of zero, the edge is believed to be generated by multi-band image creation and is then removed. After all redundant edges have been eliminated, the binary image bands \(g\in G\) are mixed, creating the ultimate binary edge image.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### EV Charging Methods  
_What is the challenge?_  
EVs can be charged via different charging methods and technologies based on the type of EVs, i.e., 2W, 3W, and 4W. It also depends on the charging type, i.e., conductive or inductive charging in which different charging technologies are involved. We can consider the example of numerous wireless charging technologies such as inductive wireless charging, capacitive wireless charging, and resonant wireless charging. Additionally, charging standards use distinct communication protocols, which can cause incompatibility in the charging.  
_Possible solution: Universal charging methodology or communication protocol_  
The possible solution is to adapt a universal charging technology or communication protocol for efficient and scalable EV charging. It can also reduce the infrastructure cost and complexity of the CS due to the use of a universal charging methodology or communication protocol that makes the charging of EVs easier.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Areas to Explore
#### 9.2.2 D2D Communications in IoT Devices  
The communication between passive IoT devices using D2D communications needs continuous power supply for its operation. Various solutions are proposed to address energy efficiency in BackCom IoT devices [5; 26; 138; 27]. It is estimated that, by 2020, there will be 26 billion IoT devices, apart from PCs, smartphones, and tablets [5]. All these devices need to be operated by battery. The limited battery life problem would be among the challenges faced by IoT technology. To combat the limited battery life challenge in IoT devices, BackCom could be one of the viable solutions, as it does not require a battery. Furthermore, cluster-based D2D communications between BackCom IoT devices can also assist each other in establishing an energy-efficient network.  
The University of Washington created a backscatter-based IoT ecosystem in a suite, where web-supported tools and applications were designed to empower its users. This suite also helped individuals to understand and manage the RFID data [226]. [PERSON] et al. showed the feasibility of WiFi-powered sensors in IoT [227]. This work further highlights that detecting objects in homes (such as smoke detectors, air conditioners sensors, motion detectors, etc.) is more feasible with BackCom than with traditional battery-powered counterparts.
## 3 Experimental Setup  
The experimental setup for the KK receiver-based optical transmission is built for the investigation of the SBS induced nonlinear distortion for SSB signals, as shown in Figure 2.  
At the transmitter, a pseudo-random bit stream (\(2^{17}-1\)) is generated by a 54 GSa/s arbitrary waveform modulator (AWG), which is mapped into a QAM16 signal after encoding. The baud rate \(R_{s}\) is set to 10 Gbaud and 28 Gbaud, respectively. Then, the signal is pulse-shaped with a root-raised cosine (RRC), and the roll-off coefficient \(\alpha=0.01\). The signal spectrum bandwidth is \(B_{s}=(1+\alpha)R_{s}\). A continuous wave light from an external cavity laser (ECL1) at 1550 nm is fed into the IQ modulator. Additionally, the other ECL (ECL2, with a linewidth of 15 KHz) generates a continuous wave (CW) as an optical carrier, whose power can be adjusted to obtain a different output total power and CSPR. The optical SSB QAM16 signal is then launched into the 80 km standard single mode-fiber (SSMF) at 1550 nm.  
At the receiver, the transmitted optical field is first detected by the PD with a 3 dB bandwidth of \(\sim\)22 GHz, and the electrical signal is captured by an 80 GSa/s digital storage oscilloscope (DSO, Keysight DSOZ592A). As shown by the inset of Figure 2, an optical bandpass filter (OBPF) before PD can be used for the filtering of the SBS influence, so as to reduce the nonlinear distortion.  
Since the nonlinear square root operation and logarithmic operation of the KK algorithm will broaden the frequency spectrum of the signal, the signal is up-sampled before the KK algorithm processing, and the sampling rate is generally set to 4 [7]. Then, the KK receiver algorithm is used to recover the amplitude and phase of the signal, and the detail is shown in Figure 3. Due to the frequency interval between the carrier and the signal, the signal is an intermediate frequency signal at this time, and it needs to be down-converted to demodulate the baseband signal. To mitigate the inter-symbol interference caused by the limited bandwidth, a feedforward equalizer (FFE) is applied. However, the FFE boosts the in-band noise, which reduces the in-band signal-to-noise ratio (SNR). Therefore, a maximum likelihood sequence decision (MLSD) equalizer consisting of a post-filter and a conversational MLSD is employed. The post-filter is a two-tap finite impulse response filter. In addition, post-dispersion compensation is used in DSP for the 80 km SSMF transmission. After recovering the optical field with a KK receiver, the time domain information of the signal is transformed to the frequency domain by the fast Fourier transform (FFT) technique, and then the phase shift due to dispersion is converted back. Finally, the signal is converted to the time domain by the inverse fast Fourier transform (IFFT) technique.  
Figure 2: Experiment setup and DSP. Here, ECL: external cavity laser; PC: polarization controller; IQM: in-phase and quadrature modulator; AWG: arbitrary waveform generator; VOA: variable optical attenuator; EDFA: erbium-doped fiber amplifier; OBPF: optical bandpass filter; DSO: digital storage oscilloscope.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 2 Related Works
This paper proposes a novel method for routing campus opportunity networks based on improved Markov which predicts node paths by collecting historical movement trajectories of nodes and models message utility values reasonably based on message diffusion and consumption of node energy to improve cache utilization by deleting delivered messages in time. The efficiency of the suggested method is confirmed in the experimental section of this study by comparison with the RDR [19] and FCIM [20] algorithms, as well as the classical methods Epidemic and Prophet.
## 1 Introduction
ZCP detection circuits are complex and require more accurate detection; due to this complexity few works are carried out by avoiding ZCP, such as soft-switching control without zero-crossing detection for the cascaded buck-boost converters [16]. Detection of ZCP using Proteus ISIS software with a microcontroller is proposed in [17] for power factor correction and a trigger angle on the SCR trigger for DC motor speed control is for the rocket launch angle adjuster. A simple detection method for the ZCP of converter current using a saturable transformer for use in high-current and high-frequency pulse-width modulated power electronic converter applications is proposed in [18]. All the methodologies mentioned above provide valuable contributions to the ZCP problem. However, to improve the accuracy in predicting the true ZCPs, a new deep neural network (DNN) based machine learning model is developed in this paper. In order to make this DNN model more generalized, a variety of distorted signals are generated and extensive features such as slope, y-intercept, correlation and root mean square are extracted and used as dataset samples. The main motivation for this work is to identify the most accurate zero-crossing points of distorted voltage signals for proper protection of a power system using switch-gear equipment and efficient power conversion by generating proper triggering pulses in conversion circuits. The main contributions of this paper are as follows:  
* A properly tuned DNN model is developed for accurate ZCP detection. * Four new datasets are created with a variety of distorted sinusoidal signals by considering noise levels from 5% to 15% and THD levels from 10% to 50%. The remaining part of this paper is structured as follows: Section 2 presents a methodology that includes DNN model configuration and dataset preparation, Section 3 demonstrates results analysis and Section 4 describes conclusions.
## 2 Data and Pre-Processing  
### Data  
Landsat-8 data from the website ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/)) (accessed on 20 October 2021)) of the United States Geological Survey are used. Landsat-8, launched as a collaboration between the United States Geologic Survey (USGS) and National Aeronautics and Space Administration (NASA) on 11 February 2013, carries onboard the OLI push broom multispectral radiometer [53]. As shown in Table 1, the Landsat-8 OLI/TIRS imagery has 11 spectral bands in total, including eight spectral bands (i.e., three visible bands, two bands for describing aerosol, water vapor, and cirrus clouds, two short-wave infrared bands (SWIR) and near infrared (NIR)) with spatial resolution of 30 m, one panchromatic spectral band with a spatial resolution of 15 m, and two thermal spectral bands with a spatial resolution of 100 m [54]. Landsat-8 remote sensing images (path 123; raw 039) of the same area acquired on 4 October 2019 and 20 October 2019 are used in our experiment. Specifically, the data on 20 October 2019 are used to establish the model and compare the effect of each algorithm, and the data on 4 October 2019 are used to examine the performance of model transfer. Three different areas with different surface features are selected from remote sensing images. As shown in Figure 1, Area1 has a large area of water with relatively simple surface object types, while Area2 has a small water area and complex surface environment, and its water extraction is affected by numerous vegetation and mountain shadow. Area3 is located in the urban built-up area and has multiple contiguous water bodies; thus, the water extraction is affected by nearby buildings and roads.  
To avoid the effects of too many clouds and aerosol, images with fewer clouds are selected here. All original data are processed by converting the original digital number (DN) value into spectral radiance, through Equation (1) [55]. The formula is given as follows:  
\[L_{\lambda}=M_{L}\cdot Q_{cal}+A_{L} \tag{1}\]  
where:  
\(L_{\lambda}=\text{spectral radiance}\big{(}\text{W}/\text{m}^{2}\cdot\text{sr} \cdot\text{um}\big{)}\);  
\(M_{L}=\text{radiance multiplicative scaling factor for the spectral band(radiance\_mult\_band\_n from the metadata);}\)  
\(A_{L}=\text{radiance additive scaling factor for the spectral band(radiance\_add\_band\_n from the metadata);}\)  
\(Q_{cal}=\text{raw digital numbers }(\text{DN}).\)  
\begin{table}
\begin{tabular}{c c c} \hline
**Landsat-8 OLI and TIRS Bands** & **Wavelength (um)** & **Spatial Resolution (m)** \\ \hline Coastal/Aerosol & 0.435–0.451 & 30 \\ Blue & 0.452–0.512 & 30 \\ Green & 0.533–0.590 & 30 \\ Red & 0.636–0.673 & 30 \\ NIR & 0.851–0.879 & 30 \\ SWIR-1 & 1.566–1.651 & 30 \\ TIR-1 & 10.60–11.19 & 100 \\ TIR-2 & 11.50–12.51 & 100 \\ SWIR-2 & 2.107–2.294 & 30 \\ Pan & 0.503–0.676 & 15 \\ Cirrus & 1.363–1.384 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Spectral band spatial resolution and wavelength of the Landsat-8 image.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 2 XPCS Concepts
### Double-Pulse XSVS  
While XSVS at storage rings as quasi-continuous X-ray sources is performed by changing the exposure time on the sample or the detector, this concept cannot be applied at FEL sources for studying fast time scales. Here, the pulse length can be varied, however, this is limited to the femtosecond range [53; 54; 55]. This time scale can be extended using a double-pulse XSVS scheme. Therein, either two pulses are generated by the FEL, or a split-and-delay device is used to generate two X-ray pulses (see Section 4.2.4). The detector measures the sum of both speckle patterns, allowing to use detectors with a slower read out than the actual time scale that should be probed. Similar to XSVS, the speckle contrast is analyzed to obtain the sample dynamics, but as a function of time delay \(\Delta t_{\text{DP}}\) between both patterns. It can be shown [56] that the measured contrast is given as  
\[\beta_{\text{DP}}(q,\Delta t_{\text{DP}})=\frac{\beta_{\text{SP}}(q)}{2}(1+|g_ {1}(q,\Delta t_{\text{DP}})|), \tag{11}\]  
with the speckle contrast of a single pulse \(\beta_{\text{SP}}\). The intermediate scattering function \(g_{1}(q,t)\) can be measured in a similar way than in standard XPCS. Note that the maximum of the double-pulse contrast equals the single-pulse contrast, while the minimum, i.e., full decorrelation, results in an experimental contrast of \(\beta_{\text{SP}}/2\).  
This calculation holds if both pulses are equal in size, coherence, intensity and pointing. However, this is hardly achieved in experiments because of pulse-to-pulse fluctuations or imperfect diffractive optics. Such fluctuations may result in different intensities as well as varying overlap of both pulses. In these cases, a decoherence factor has to be measured, e.g., from scattering from a static sample [57]. This typically affects the maximum and minimum contrast in Equation (11). In addition, the intensity ratio of both pulses will affect the measured contrast significantly. Therefore, the intensity of each pulse has to be measured separately, and the intermediate scattering function can be obtained via [56; 57]  
\[\beta_{\text{DP}}(q,\Delta t_{\text{DP}})=\beta_{\text{SP}}\left(\frac{r_{ \text{split}}^{2}+1+2r_{\text{split}}|g_{1}(q,\Delta t_{\text{DP}})|^{2}}{r _{\text{split}}^{2}+1+2r_{\text{split}}}\right), \tag{12}\]  
where \(r_{\text{split}}=I_{1}/I_{2}\) denotes the splitting ratio of the intensities \(I_{1}\) and \(I_{2}\) of both pulses. This double-pulse XSVS has been demonstrated user laser scattering [58] and is currently applied at FEL sources covering delay times up to nanoseconds (see Section 4.2.4 for more details) [57].  
Figure 5: (a) Speckle patterns calculated from diffusing disks with different exposure times ranging from from 1 to 100. The contrast calculated for the \(q\)-value marked by the dashed black circle drops from 1 to 0.48. (b) \(g_{2}(\Delta t)\) compared to the XSVS function using the same intermediate scattering function.