esa-sceva/satcom-chunk-collection huggingface dataset:
# The Symmetry of GPS Orbit Ascending Nodes
###### Abstract  
Theoretical nominal GPS orbits are parallel and share six ascending nodes of orbital planes. However, due to the perturbations and continuous modernization of the system, this state does not occur. The configuration of satellite orbits is continuously monitored by the control segment and presented regularly in the form of a GPS almanac. Almanac, however, do not contain a parameter defining the convergence of orbits. This work presents a novel method of assessment of the configuration of orbit ascending nodes compared with the nominal constellation state. The method is a tool for space segment monitoring and detection of anomalies. The source data were 7035 System Effectiveness Model almanacs published from the 847 th to 2123 rd GPS weeks (March 1996-September 2020). The algorithm uses the procedure of assigning satellites to orbital planes and both the robust estimation and the least-squares methods to determine the estimates of the angular separation of orbit ascending nodes. A long-term analysis of the symmetry and trend of changes in the position of the ascending nodes was conducted. The study showed the occurrence of significant anomalies. The research provides information on the trend of satellite orbit separations and deviations of orbital planes from the initial hexagonal GPS symmetry.  
as 2021, 13, 387. [[https://doi.org/10.3390/rs13030387](https://doi.org/10.3390/rs13030387)]([https://doi.org/10.3390/rs13030387](https://doi.org/10.3390/rs13030387))
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 3 The PDL-Involved CVQKD System
### The PDL-Involved CVQKD System  
The quantum state received at [PERSON]'s side can be denoted by the quadratures (X,P) which satisfy  
\[\begin{split} X&=\sqrt{\eta T}(X_{A}+\delta X_{c})+ \delta X_{A},\\ P&=\sqrt{\eta T}(P_{A}+\delta P_{c})+\delta P_{A}, \end{split} \tag{3}\]  
where \(X_{A}\) and \(P_{A}\) are the modulated values with variance \(\langle X_{A}^{2}\rangle=\langle P_{A}^{2}\rangle=V_{A}\), and \(\delta X_{A}(\delta P_{A})\) and \(\delta X_{c}(\delta P_{c})\) are originated from the shot noise and channel excess noise, which satisfy \(\langle\delta X_{A}^{2}\rangle=\langle\delta P_{A}^{2}\rangle=1\) and \(\langle\delta X_{c}^{2}\rangle=\langle\delta P_{c}^{2}\rangle=\varepsilon_{c}\) in shot noise units, respectively.  
Here, we show the derivation of the excess noise due to the effect of PDL. Under the perfect detection, we denote the quadratures of the output mode of quantum channel as \((X^{t},P^{t})\). The quadratures without phase compensation can be expressed as  
\[\begin{split} X_{B}&=X_{t}+X^{pll},\\ P_{B}&=P_{t}+P^{pl},\end{split} \tag{4}\]  
where \(X^{pll},P^{pll}\) are the added quadratures arising from the phase error \(\phi\) due to lack of orthogonality, which satisfy \(X^{pll}=|\alpha|sin\phi\) and \(P^{pll}=|\alpha|cos\phi\), and \(\alpha\) is the parameter of coherent states. It can be regarded as a phase shift operation \(U(\delta\phi)=exp(i\delta\phi a^{\dagger}a)\) on [PERSON]'s measurement results, and \(\delta\phi\) follows a probability of \(p(\delta\phi)\).
## 5 Conclusions  
The eight-day GPP at eight sites with different land cover types in China during 2003-2005 was estimated from five models, namely the VPM, TG, AVM, GR, and MODIS GPP algorithm (MOD_ALG), using climate variables, flux observations, and MODIS images. The above five models were calibrated and validated in different ecosystems. The first four models generally provided better estimates than MOD17/MOD_ALG, which indicates that they have potential to capture the physical process of GPP variations in China. Different models resulted in different GPP estimations. As far as the TG model is concerned, R\({}^{2}\) ranged from 0.34 in EBF to 0.93 in MF; RMSE varied from 30.37% in MF to 55.72% in grasslands. The model performances have been compared at five land cover types and VPM may be the most suitable model for GPP estimation in China, with R\({}^{2}\) ranging from 0.34 in EBF to 0.90 in grasslands.  
Different statistic indices for the five land cover types show that there are shifts of importance among various factors that determine the GPP variations. The impacts of EVI, PAR, LST, air temperature, and LSWI on photosynthesis have been further investigated for improving the model accuracies; for example, there is a good correlation between EVI and GPP for croplands, grasslands, and MF (r = 0.82, 0.91, and 0.95, respectively), while r values were only 0.41 and 0.55 (P \(<\) 0.01), respectively, for EBF and ENF. Thus, environmental factors (PAR, temperature, and water stress) should be considered carefully for GPP estimations in different ecosystems. Further research is needed to improve the model accuracy by studying the interactions between ecosystem and climatic variables. Spatial and temporal variations of GPP at the national scale will also be investigated in future.
## 5 Conclusions  
An aircraft maintenance virtual reality (AMVR) system was developed for educational and training purposes in the aviation industry. CATIA V5 software was used for developing the 3D model of the Dornier 228 aircraft. Unity 3D software is also used for designing the virtual environment in which the aircraft's 3D model was imported. Walk-around inspection, fuel system introduction, and horizontal elevator assembling/disassembling were included in the AMVR system.  
The developed AMVR system was applied to training students and evaluated by them. Questionnaire surveys were conducted to analyze the effectiveness of the developed  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Questionnaire Groups** & **Pre-Test** & **Post-Test** \\ \hline Problem Organization & 3.62 & 4.14 \\ Problem Analysis Ability & 3.44 & 4.14 \\ Problem Comment Ability & 3.75 & 4.11 \\ Problem Solving Ability & 3.70 & 4.13 \\ Teamwork & 3.95 & 4.20 \\
**Average** & **3.69** & **4.14** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the questionnaire groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Pretest**} & \multicolumn{2}{c}{**Posttest**} & \multirow{2}{*}{**t-Value**} & \multirow{2}{*}{_p-Value_} \\ \cline{2-2} \cline{5-6}  & **Mean** & **Stand-Devi** & & & **Mean** & **Stand-Devi** \\ \hline
1 & 3.52 & 0.831 & 4.15 & 0.675 & \(-\)2.961 ** & 0.007 \\ \hline
2 & 3.76 & 0.827 & 4.00 & 0.748 & \(-\)1.272 & 0.215 \\ \hline
3 & 3.59 & 0.905 & 4.27 & 0.667 & \(-\  
system for 26 students before and after training them with the AMVR system. The survey results show that the application of the AMVR helped help students improve their learning capability. After using the AMVR system, each student's ability to organize, analyze, comment, solve, and work in groups improved significantly when compared to the pre-survey result. Thus, there is a great potential of the AMVR after using AMVR to perform the Do 228 walk-around inspection in the aviation industry.  
However, there is still room for improving the user experience, such as the resolution of the application and reflecting the user's movement in the VR environment to enhance the interaction between users and 3D objects. It is also required to upgrade the virtual environment to make it more realistic. In addition, the hand-tracking technology on VR devices is still incomplete, leading to a subpar user experience.  
Conceptualization, W.-C.W.; validation, W.-C.W. and V.-H.V.; writing--review and editing, W.-C.W. and V.-H.V.; resources, supervision, project administration, W.-C.W.; methodology, W.-C.W. All authors have read and agreed to the published version of the manuscript.  
This work was support by the Ministry of Science and Technology of the Republic of China under Grand Contract MOST 109-2637-E-150-008 and Ministry of Education the Republic of China under Grand Contract PEE1101037.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 5 Simulations with Measured Data
### Simulation Results  
Figure 13 shows BER performance of the MRC scheme using two different datasets: independent Rayleigh channel coefficients and measurement data. Dashed-dot and solid lines indicate QPSK and 16-QAM results, respectively. In addition, lines without any marker indicate results for SISO (i.e., without MRC). Similarly, lines with square and inverted-triangle markers indicate \(1\times 2\) and \(1\times 4\) MRC results, respectively. When we considered Rayleigh channel, which reflects rich scattering environment based on multipath, the BER performance (Figure 12(a)) was clearly improved by using MRC schemes. However, when we considered the simulation results based on measurement data (Figure 12(b)), only power gain could be achieved by multiple receive antennas rather than diversity gain represented by the slope of the graph. As discussed in Remark 1, this was mainly due to the dominant effect of LOS in our measurement data.  
Figure 14 shows BER performance of the STBC scheme using two different datasets: independent Rayleigh channel coefficients and measurement data. Dashed-dot and solid lines indicate QPSK and 16-QAM results, respectively. In addition, lines without any marker indicate results for SISO (i.e., without STBC). Similarly, lines with square and inverted-triangle markers indicate \(2\times 1\) and \(4\times 1\) STBC results, respectively. Similar to the simulation presented in Figure 13, diversity gain was only achieved when we assumed Rayleigh fading channel. However, the results based on measurement data show better BER performance compared to the ones with \(M=1\) and \(M=2\) based on Rayleigh channel coefficients. In addition, the BER performance gap between modulations (QPSK and 16-QAM) increased when we considered the results based on measurement data, compared to the ones with Rayleigh fading channel.  
Figure 13: BER performance of MRC scheme in uplink transmission: Rayleigh fading vs. measurement data.  
**Remark 3**.: _Note that our observations in Figures 12(b) and 13(b) such as no diversity gain, better BER performance, and bigger BER performance gap between modulations can be utilized to design wireless technologies applicable inside the aircraft._
## 3 Imperfections of Scalar Approximation  
### Threshold Value  
In DOCs, diffraction efficiencies are the most important reference data for fitting the parametric model to obtain local geometric parameters. Deviations in the intensity distribution directly affect the accuracy of the reconstructed surface relief. To compare the diffraction efficiencies, calculations were conducted for different order transmission gratings with duty cycles \(D\) of 0.5. The transmitted light becomes evanescent when the  
Figure 2: Process of rigorous simulations based on the finite-difference time-domain (FDTD) method. \(E\) and \(H\) denote the electric and magnetic fields, respectively.  
grating period is less than the incident wavelength, thus the interval from \(\lambda\) to \(10\lambda\) was discussed.  
As shown in Figure 3a, the scalar results were similar to the rigorous values when the period was larger than \(4\lambda\). However, the scalar approximation was inaccurate when the period of the CGH was less than \(4\lambda\), and the deviation increased as the period decreased. As shown in Figure 3b, the diffraction efficiency was more sensitive to smaller period gratings.  
In addition to the diffraction efficiency, the deviation in the transverse electric (TE) and transverse magnetic (TM) polarization calculation results is also different. Polarization must be considered in rigorous calculations, but it is completely ignored in scalar approximations. The calculated results for the first-order diffraction efficiency and phase variance with the grating period subject to TE and TM polarizations are shown in Figure 4. There is a sudden and significant change when the period is approximately equal to \(2\lambda\). This is a typical phenomenon caused by the redistribution of energy induced by the abrupt change in the electromagnetic field.  
Figure 4: Comparison of illumination in transverse electric (TE) and transverse magnetic (TM) polarized light for (**a**) diffraction efficiency and (**b**) phase generated by transmission grating with a variable period.  
Figure 3: Comparison of the diffraction efficiency of scalar approximation and rigorous simulation for a binary grating with a duty cycle of 0.5. (**a**) Efficiencies of different orders (\(m=0\), \(m=1\), \(m=2\)) and (**b**) sensitivity of the efficiencies of these orders with the respect to the period. The sensitivity function is defined as \(\partial\eta_{m}/\partial S\).  
In Figure 4b, the reconstructed phases subject to TE and TM polarizations are consistent. When the grating period approaches 5\(\lambda\), the phase deviation between the approximation and rigorous results reaches 1/100\(\lambda\). In terms of the application of CGHs, the phase accuracy requirement is stricter than the diffraction efficiency. Therefore, the validity of the scalar approximation must be reconsidered when the grating period is less than 3 \(\upmu\)m (-5\(\lambda\)).
###### Abstract  
This work proposes a new analytical model to extract the 1-Diode/2-Resistor solar cell/panel equivalent circuit parameters. The methodology is based on a reduced amount of experimentally measured information: short-circuit current, the slope of the \(I\)-\(V\) curve at that point, the open-circuit voltage, and the current and voltage levels, together with the slope of the \(I\)-\(V\) curve at the instantaneous operation point. This procedure is specially designed to analyze the performance of autonomous photovoltaic systems, which are most of the primary sources for spacecraft power. Results show good agreement with experimental data. Furthermore, this methodology allows for fast and accurate \(I\)-\(V\) curve maximum power point (MPP) identification.  
solar cell; solar panel; photovoltaic array modeling; parameter extraction; MPP Article
## 2 Materials and Methods
### Network Implementation and Training  
All networks were created using Keras with TensorFlow 2.2. Training and testing were performed on the Sabine clusters at the University of Houston Hewlett Packard Enterprise Data Science Institute (HPE DSI) on Nvidia V100 tensor core GPUs, in all experiments, we used the Adam optimizer [55], with a mean squared error loss function, a learning rate of 0.001, and a batch size of 32. Each of the three UNets and each of the two acquisition scenarios (6 cases of training sessions) were trained for 100 and 1000 epochs. The best results were obtained when a constant learning rate was used to run all the epochs. Experiments with dynamic learning rates produced inferior results and are not part of this paper.  
The learning rate parameter was chosen based on the common trait that the network uses dense and convolution layers and a learning rate of 0.001 has shown the best results when dense layers were chosen. A similar learning rate can be seen in the literature, as well [21; 22; 23]. We used mean squared error as our loss function for training all the neural networks. We recognize that the loss function will also have a great impact on the upscaling of the images; however, a detailed comparison of different loss functions,  
Figure 4: (a) Anisotropic UNet (AUNet) Architecture, (b) Bottleneck Block, and (c) Residual Core.  
which may include mean squared errors, mean intensity errors, structural similarity, edge enhancement loss, and perceptual loss, to name a few, is beyond the scope of this paper, and will be explored in one of our future works. As can also be seen in the literature, the mean squared error is able to produce a pixel-by-pixel loss and can work satisfactorily for our problem [21; 22; 23] as it is commonly used to upscale images.  
Since neural architectures typically use input images with the same digital matrix sizes, our datasets were pre-processed before being used as inputs to the networks. Specifically, our datasets included 176 \(\times\) 208 augmMRI images and one of synLQ that was 22 \(\times\) 26, 44 \(\times\) 52, or 88 \(\times\) 104. Preprocessing entailed interpolating the synLQ images to 176 \(\times\) 208 using the nearest-neighbor interpolation to preserve the image distribution at high resolution, and the pixel values are normalized in the range of 0-1 to avoid gradient explosion [56; 57]; subsequently, identically sized synLQ and augmMRI images were stacked together and supplied to the networks.  
Training and validation loss was monitored to avoid overfitting of the networks. The difference between the training loss and validation loss indicates that there is no overfitting. To further ensure that there is no overfitting, only the best weight from the training process is taken, which corresponds to the lowest validation loss. To prevent bias from data leakage, training, and testing were performed on images collected from different subjects.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 9 Planetary Gear Train Design Optimization Problem
### Fitness Function  
The final fitness function \(Fit\) integrates the fitness function \(f\) and a penalty term, as expressed in Equation (56):  
\[Fit=f+Penalty \tag{56}\]  
This formulation ensures that any design violating the constraints incurs a significantly higher fitness value due to the large penalty factor (\(\lambda\)), effectively discouraging such solutions. The objective of the optimization process is to minimize the \(Fit\) value, thereby identifying a planetary gear train design that aligns with the desired gear ratios while satisfying all imposed constraints.  
As presented in Tables 9 and 10, the results for solving the planetary gear train design optimization problem demonstrate the performance of various algorithms, measured by the best fitness value (\(BestF\)) achieved, alongside the corresponding design variables (\(x_{1}\) to \(x_{7}\)).  
The APO-JADE algorithm attained the best fitness value (\(BestF\)) of 0.525769, with the design variables \(x_{1}=35.4056\), \(x_{2}=26.0947\), \(x_{3}=24.5062\), \(x_{4}=24.4957\), \(x_{5}=22.1659\), \(x_{6}=87.2760\), and \(x_{7}=0.5529\). This outcome indicates that APO-JADE identified the most optimal solution by minimizing the deviation in gear ratios while adhering to all constraints.  
The Grey Wolf Optimizer (GWO) closely followed APO-JADE, achieving a fitness value of 0.526281, indicating competitive performance but with a slightly higher deviation. The Slime Mould Algorithm (SMA), with a fitness value of 0.537059, performed marginally worse than APO-JADE and GWO, indicating a larger deviation from the target gear ratios. The Whale Swarm Optimization (WSO) and Whale Optimization Algorithm (WOA) both achieved fitness values around 0.53, demonstrating reasonable performance, though less optimal compared to APO-JADE.  
Other algorithms, such as COOT, ChOA, and SMA, produced fitness values around 0.537059, while the Owl Optimization Algorithm (OOA) and Binary Wolf Optimization (BWO) reported significantly higher values of 0.774379 and 0.868333, respectively, indicating suboptimal solutions in comparison to APO-JADE.
## 3 Result and Discussion
### Comparison of the Proposed Method with Other Researchers  
After investigating the proposed algorithm's performance using different criteria, the results were compared with other researchers' results. Table 5 gives a comparison of the different studies. As can be seen, the proposed method based on spectral data has a high regression coefficient at the best state of training and using the most effective wavelengths, indicating the high performance of the proposed method.  
Most methods published on a non-destructive estimation of fruit properties are based on samples collected and assessed under controlled laboratory conditions after fruit temperature equilibration [37]. Predictions of non-destructive methods based only on internal validations tend to be overestimated in the literature. A comparison between internal and external validations is needed to demonstrate NIR's real potentialities in field conditions [38].  
Previous research from [PERSON] et al. [39] using linear (partial least squares regression, PSLR) and nonlinear (artificial neural network, ANN) regression estimation of firmness, acidity (pH), and starch content of 160 Fuji apple fruit showed less robust estimation of starch content with R\({}^{2}\) 0.865 for ANN and 0.91 for PLSR.
## 3 Proposed Antenna for On- and Off-Body Conditions
After investigating and evaluating the proposed antenna's performance under various conditions, it can be concluded that the proposed antenna is a reliable candidate as a wearable device for controlling breast cancer at its early stage. Table 4 illustrates the close comparison of the proposed work with some recent similar works. It shows that the proposed antenna achieved wider BW, higher gain and radiation efficiency, and lower SAR values. The proposed antenna obtained these outcomes with smaller dimensions (it should be mentioned that the width of an antenna plays a direct impact on its efficiency and BW: when an antenna has larger dimensions, both gain and efficiency increase [42; 48]). Among these works, only one, presented in [49], used a full ground. When a full ground is used, obtaining a wide BW is very difficult and most UWB, wideband, and broadband antennas use truncated grounds in order to increase BW. Figure 4:3 Reconstructed images using received signals from four arrays and a robust time reversal algorithm [47]: (**a**) central tumour with skin, (**b**) central tumour without skin, (**c**) two tumours and (**d**) three tumours.
## 4 Comparison with Volterra Method
### Memory Polynomial Model (MP)  
The Memory polynomial (MP) model is capable of resolving both memory effects and nonlinearities simultaneously. It is also referred to as diagonal Volterra model as all its diagonal terms are non-zero. It is the middle ground between a memoryless model and a full Volterra model due to existence of diagonal memory (non-diagonal terms are zero). The MP model is generally used as it is less complex compared to Volterra series. It can also emulate the nonlinear behavior of Power Amplifiers to an extent which is why it has been used to model Power Amplifiers previously. The equation is as follows [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]:  
\[y(n)=\sum\limits_{k=0}^{k-1}\sum\limits_{q=0}^{Q-1}C_{kq}x(n-l)|x(n-l)|^{k} \tag{10}\]  
where \(K\) is non-linearity order, \(Q\) is the memory depth, \(y(n)\) is input sequence of the predistorter, \(x(n)\) represents baseband input and \(c_{kq}\) is the model coefficient.