esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Converting a Pyramidal Horn Antenna into a Circularly Polarized Antenna  
### Analysis of the Original Pyramidal Horn Antenna  
We departed from an existing, general-purpose horn antenna (Figure 6a) designed for the X band (8 \(\div\) 12 GHz). The transverse dimensions of the waveguide were \(a=22.86\) mm and \(b=10.16\) mm, and the length was 90 mm. The critical frequency of the fundamental mode was 6.55 GHz, and the critical frequency of the next higher mode is 13.11 GHz. The horn aperture length was \(A=81\) mm, the width \(B=61\) mm, and the height of the horn was 86 mm. The length of the monopole exciting the waveguide was 7.5 mm, with a radius of 0.76 mm, and its position relative to the short circuit wall was 11.5 mm.  
In simulations, a Perfect Electric Conductor (PEC) was considered as the material for the waveguide and the pyramidal horn, and copper for the monopole (Figure 6b). Figure 7 shows the radiation patterns in the E and H planes at 12 GHz, and Figure 8 displays the gain variation over the X-band, in the main direction of radiation. At 12 GHz, the simulated gain was 10.6 dBi, whereas the measured figure was 9.22 dBi; higher discrepancies can be noted at frequencies below 9 GHz, possibly due to errors occurring when measuring the physical size of the inner components of the coaxial-to-waveguide adapter. As further development including FSS polarization transformers was focused on frequencies around 12 GHz, no other optimization was performed for simulation at the lower frequencies in the X-band.  
Figure 6: Pyramidal horn antenna: radiator and coaxial-to-waveguide adapter (**a**), and simulation model (**b**).  
Figure 7: Simulation of the E-plane and H-plane radiation patterns of the horn antenna at 12 GHz.  
As shown in Figure 9, the magnitude of the reflection coefficient is below \(-11\) dB over the entire frequency band, both for measured and simulated figures.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 3 Neural Network Based Estimators  
Having access to certain variables that are typically not measured could improve the control and condition monitoring of wind turbines; however, this would require potentially expensive sensors for each turbine. To solve this problem, we propose that only a single turbine in a wind farm of multiple turbines (five in this paper) be equipped with a potentially expensive sensor. Each remaining turbine could be equipped with a nonlinear estimator based on an NN, which would essentially replace the expensive sensor as illustrated in Figure 4. This scheme could be used to estimate various variables in a cost-effective manner; however, this paper only considers two variables, that is, wind speed and aerodynamic torque.  
The NN structure used in this paper, and the full procedure for designing and training the NN-based estimators for wind speed and torque estimation, are given in Section 3.2. The estimation results of the NN-based estimators are compared to those of conventional mathematical torque and wind speed estimators in Section 3.3.
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 3 The Proposed Watermarking Scheme
### Invisibility Analysis  
Invisibility means that the similarity between the watermarked image and the original cover image is difficult to identify. This slight change is almost invisible to the human eye. In this study, two key indicators, PSNR and SSIM, were selected to evaluate the watermarking methods applied on six different cover images. Among them, PSNR is used to measure the transparency of the watermark image and the original cover image in visual perception, while SSIM is responsible for evaluating the contrast, brightness and structural similarity of the image. When the PSNR value exceeds 30 dB, it means that the watermark image has an acceptable visual quality and maintains a high degree of similarity with the original image. When the SSIM value is close to 1, it indicates that the watermark image has reached a high level of perceptual quality [26]. Figure 7 shows these cover images with watermarks and the successfully extracted watermarks.  
The PSNR, MSE and SSIM of different cover images are shown in Table 2. The average PSNR and SSIM of the scheme are 45.9382 dB and 0.9986, respectively. It can be seen that the scheme has good imperceptibility.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cover Image** & **PSNR** & **NSE** & **SSIM** \\ \hline Airplane & 45.3999 & 1.8754 & 0.9980 \\ Baboon & 46.1830 & 1.5660 & 0.9994 \\ Goldhill & 46.3581 & 1.5041 & 0.9998 \\ House & 45.7942 & 1.7126 & 0.9985 \\ Lake & 46.0233 & 1.6246 & 0.9986 \\ Peppers & 45.8709 & 1.6826 & 0.9985 \\ \hline \end{tabular}
\end{table}
Table 2: The imperceptibility results of different watermark images.  
Figure 6: Cover image and watermark image.  
Image histogram is a tool similar to PSNR, which is widely used to measure the difference between the cover image and the watermarked image to evaluate the influence of the watermark on the original cover image. The image histogram intuitively shows the distribution of different pixel values in the image, thus revealing the core features of the image. By comparing the histograms of the two, we can more reliably judge the similarity between images, which also shows that the watermark embedding method does not significantly change the original characteristics of the cover image. Figure 8 intuitively shows the histogram comparison between the original cover image and the watermarked image. It can be observed from the figure that there is almost no significant change in the histogram before and after watermark embedding, which fully shows the excellent performance of our scheme in imperceptibility.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 4 Artificial Scenario Validation
The resulting total coherent field is shown in Figure 7. We should note that the corresponding computation time (excluding the surface sampling) was, in this case, about 70 s longer compared to the time required by the synthesizer, which may be caused by the denser surface sampling in FEKO. Now we can clearly see that the proposed simulator correctly provides the diffraction pattern around the tree canopies and also correctly identifies the LoS areas where the signal is close to its free space value of 0 dB, as shown by the red curve in Figure 6 representing the coherent scattered field. The excess attenuation in the shadowed areas about is 4 dB, which nicely corresponds to the path through a canopy (about 4 m for the plane wave incident under the elevation of 45\({}^{\circ}\)) multiplied by the specific attenuation (about 1 dB/m at 2 GHz as in [7]). We should note that, for the case of the totally absorbing tree canopies, the resulting attenuation is much stronger in Figure 7; over 15 dB. Concentrating on the black curve in Figure 6 representing the total field (coherent and incoherent), we notice that under LoS conditions, the resulting signal variations are weaker (about 1 dB) than within the shadowed areas (about 2 dB) which is to be expected. Within LoS, the strong direct signal dominates and the resulting signal characteristics can be approximated by the Rice distribution, whereas in the shadowed areas, the direct component gets attenuated and the characteristics are closer to the Rayleigh distribution where the variations are more pronounced. Figure 6: The coherent scattered field (red) and total resulting field (black) for the scenario from Figure 5.  
Figure 7: The coherent scattered field as obtained by PO in FEKO for the scenario from Figure 5 for the case of totally absorbing tree canopies. Based on the above provided analysis, the synthesizer may be claimed to be validated. However, a further validation against actual experimental data set is of interest as well and is, thus, presented in Section 5.
## 2 Numerical Model
### Model Setup
The horizontal (\(D_{h}\)) and vertical (\(D_{v}\)) diffusion coefficients can be related to the eddy viscosity:  
\[D_{h}=\frac{A}{\sigma_{T}},\ D_{v}=\frac{\upsilon_{t}}{\sigma_{T}}\]  
where \(A\) is the horizontal eddy viscosity; \(\upsilon_{t}\) is the vertical eddy viscosity. According to the previous research in the YRE, a dispersion coefficient in the range of 10-1000 m\({}^{2}\)/s could make the salinity distribution stable [52]. A sensitivity study revealed that the horizontal and vertical dispersion coefficients of 300 and 200 m\({}^{2}\)/s, respectively, could be chosen as the optimal dispersion coefficients in this study, which were successfully used in our previous study [46]. The bathymetry is derived from the Navigation Guarantee Department of the Chinese Navy Headquarters (China Navigation Publications Press). The scale of bathymetry map is 1:120,000 with varied survey times in different areas. The bathymetry was surveyed in 2009 and 2012 for the SB and Hangzhou Bay, in 2008 for the NB, in 2010 for the NC, in 2011 and 2012 for the SC, in 2011 for the NP and the SP and in 1999 for the open seas. The hydrodynamic and salinity transport model apply a hot start, which is the result of running for 2 months from a cold start (the initial tidal level, velocity and salinity are defined as 1 m and 0 m/s, respectively) to get relatively realistic salinity distribution. The time step is controlled by the Courant-Friedrich-Levy (\(CFL\)) number with the requirement that \(CFL<1\), in order to keep the computational stability, where \(CFL=\left(\sqrt{gh}+|u|\right)\frac{\Delta t}{\Delta x}+\left(\sqrt{gh}+|v| \right)\frac{\Delta t}{\Delta y}\). The time step interval can self-regulate, ranging from 0.01 to 30 s to meet the \(CFL\) restriction as the velocity changes with time at all computational nodes and time steps.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 5 Results
### Cross-Talk and Lighting Characterization  
Our Python model simulated an indoor environment for computing the received power, normalized cross-talk interference, and lighting parameters. Figure 5a shows the configuration for the indoor CSK-based VLC system. These parameters were simulated for six different positions of the photodetector on the receiver plane. In accordance with the specifications outlined in the IEEE standard for CSK modulation, the system employed a symbol constellation design based on a sixteen-point configuration [2]. The luminous flux per symbol was set at 5000 lm. The simulation considered the spectral power distribution of the LEDs, the spectral response of the photodetectors, the reflectance of the walls at the LEDs' central wavelengths, and up to ten reflection orders to calculate the received power. Figure 5b shows the spectral response of the three LEDs, the spectral responsivity of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Received Power [\(\mu\)W]**} \\ \hline
**Configuration** & & **A** & **B** & **C** & **D** \\ \hline \multirow{4}{*}{Model [17]} & LoS & 1.23 & 0.239 & – & – \\  & k = 1 & 0.505 & 0.0185 & – & – \\  & k = 2 & 0.43 & 0.0399 & – & – \\  & k = 3 & 0.269 & 0.0098 & – & – \\  & Total & 2.434 & 0.307 & 0.284 & 0.69 \\ \hline \multirow{4}{*}{Python-based} & LoS & 1.232 & 0.239 & 0.205 & 0 \\  & k = 1 & 0.506 & 0.0183 & 0.014 & 0.507 \\ \cline{1-1}  & k = 2 & 0.433 & 0.039 & 0.052 & 0.093 \\ \cline{1-1}  & k = 3 & 0.263 & 0.0097 & 0.015 & 0.046 \\ \cline{1-1}  & Total & 2.434 & 0.307 & 0.286 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results of the received power of the four indoor configurations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{5}{c}{**Relative Error [\%]**} \\ \hline
**Configuration** & **A** & **B** & **C** & **D** \\ \hline LoS & 0.16 & 0.00 & – & – \\ k = 1 & 0.20 & \(-1.08\) & – & – \\ k = 2 & 0.70 & \(-2.26\) & – & – \\ k = 3 & \(-2.23\) & \(-1.02\) & – & – \\ Total & 0.00 & 0.00 & 0.70 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative power error between the model from [17] and our Python-based model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Received Power [\(\mu\)W]**} \\ \hline
**Configuration** & **A** & **B** \\ \hline LoS & 1.232 & \(2.39\times 10^{-1}\) \\ k = 1 & 0.505 & \(1.83\times 10^{-2}\) \\ k = 2 & 0.433 & \(3.9\times 10^{-2}\) \\ k = 3 & 0.263 & \(9.7\times 10^{-3}\) \\ k = 4 & 0.185 & \(3.9\times 10^{-3}\) \\ k = 5 & 0.124 & \(
# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 3 Proposed Affordable Fast Early Warning System  
### System Design  
This section describes the overall design for the proposed affordable fast early warning system (AFEWS) system. Figure 1 shows that the proposed AFEWS system consists of an edge device and visualization tools. The edge device consists of single board computer (SBC), sensors, hybrid fault model, and data storage, and gathers and processes environmental conditions from the workstation. The hybrid fault model embedded in the edge device triggers alert/warning messages in fast response when a fault is detected during process. The generated sensor data and prediction results are stored, as well as pushed, to the data visualization layer. Management can monitor the status of every workstation through the web dashboard and receive warning messages via email and/or instant messaging (IM) when a fault is detected.  
Edge devices were installed at every workstation to allow conditions to be monitored for each workstation during process. Figure 2 shows an example assembly line process for the automobile parts factory, with edge devices in four workstations with different assembly processes. The edge devices act independently to collect, store, process, and predict fault states in fast response without requiring data communication to the cloud.
## 2 Materials and Methods
### Model Configuration and Simulation Setup
#### 2.2.4 Model Verification and Validation  
For the verification of NPP, we simulated and extracted the monthly NPP values from 2005 to 2021, plotted them as an NPP inter-monthly change figure, and compared it with the measured monthly growth changes of _C. lanccolata_ or _P. massoniana_ forests. For the validation, we compared the differences between the yearly MODIS-NPP product (MOD17A3 HGF) [77] ([[https://lpdaac.usgs.gov/product_search/](https://lpdaac.usgs.gov/product_search/)]([https://lpdaac.usgs.gov/product_search/](https://lpdaac.usgs.gov/product_search/))) (accessed on 16 March 2022) and the model simulated NPP, and based on previous studies [25], we assumed that the mean relative error for MODIS-NPP was 23.58%. The validation data are divided into two distinct periods, the first period is from 2005 to 2013, and the second period is from 2014 to 2021; we calculated the root mean squared error (RMSE, Equation (2)) and mean absolute error (MAE, Equation (3)) based on the ED-2.2 simulation and the MODIS-NPP in the two periods, respectively:  
\[\text{RMSE}=\sqrt{\frac{\sum(\text{Sim}_{\text{i}}-\text{Obs}_{\text{i}})}{ \text{N}}} \tag{2}\]  
\[\text{MAE}=\frac{\sum|\text{Sim}_{\text{i}}-\text{Obs}_{\text{i}}|}{\text{N}} \tag{3}\]  
where \(\text{Sim}_{\text{i}}\) was NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)] simulated by ED-2.2; \(\text{Obs}_{\text{i}}\) was NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)] of the MODIS product; N was the number of years.  
For the verification of monthly NPP, we found that the monthly variation of NPP over the years (2005-2020) decreased slowly after reaching the peak in May (Figure 1), slowed down from July to September, and accelerated after September. For the validation of annual NPP (Figure 2), in the first period (2005-2013), MAE and RMSE were 0.157 and 0.178, respectively. The NPP simulations basically fall in the error range of MODIS-NPP. In the second period (2014-2021), MAE and RMSE are 0.149 and 0.150, respectively. NPP simulation is all falling within the error range of MODIS-NPP. Compared with the first period, the MAE and RMSE in the second period decreased by 5.09% and 15.7%, respectively. The mismatch between ED-2.2 simulated NPP and MODIS-NPP gradually decreases over time although the model generally overestimates NPP compared with MODIS products. The ED2 simulated NPP vs. MODIS-NPP for a separate plot, as shown in Figure S1.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### Signal Reconstruction  
According to the detailed coefficients of the location point layer and the following layer of the wavelet decomposition, and the detail coefficients from the first layer to the front layer of the locating point after threshold de-noising, and the approximate coefficients, the signal is reconstructed by the wavelet transform, which is the inverse discrete wavelet transform.
# Tensor-Based Algorithms for Image Classification
## 1 Introduction
Combining an efficient computational scheme for the corresponding subproblems and truncated singular value decompositions [17], we call the resulting algorithm alternating ridge regression (ARR) and discuss connections to MANDy and other regularized regression techniques. Although we describe the classification problems using the example of the iconic MNIST data set [18] and the fashion MNIST data set [19], the derived algorithms can be easily applied to other classification problems. There is a plethora of kernel and deep learning methods for image classification; a list of the most successful methods for the MNIST and fashion MNIST data sets including nearest-neighbor heuristics, support vector machines, and convolutional neural networks can be found on the respective website ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)), [[http://github.com/zalandoresearch/fashion-mnist](http://github.com/zalandoresearch/fashion-mnist)]([http://github.com/zalandoresearch/fashion-mnist](http://github.com/zalandoresearch/fashion-mnist))). We will not review these methods in detail, but instead focus on relationships with data-driven methods for analyzing dynamical systems. The main contributions of this paper are as follows. * Extension of MANDy: We show that the efficacy of the pseudoinverse computation in the tensor train format can be improved by eliminating the need to left- and right-orthonormalize the tensor. Although this is a straightforward modification of the original algorithm, it enables us to consider large data sets. The resulting method is closely related to kernel ridge regression. * Alternating ridge regression: We introduce a modified TT representation of transformed data tensors for the development of a tensor-based regression technique which computes low-rank representations of coefficient tensors. We show that it is possible to obtain results which are competitive with those computed by MANDy and, at the same time, reduce the computational costs and the memory consumption significantly. * Classification of image data: Although originally designed for system identification, we apply these methods to classification problems and visualize the learned classifier, which allows us to interpret features detected in the images. The remainder is structured as follows. In Section 2, we describe methods to learn governing equations of dynamical systems from data as well as a tensor-based iterative scheme for image classification and highlight their relationships. In Section 3, we describe how to apply MANDy to classification problemsand introduce the ARR approach based on the alternating optimization of TT cores. Numerical results are presented in Section 4, followed by a brief summary and conclusion in Section 5.
## 5 Results and Discussion
### Manoeuvring Tests
#### 5.2.2 Horizontal Zigzag Test
Figure 16a-f display the time series of surge (\(u\)), sway (\(v\)), heave (\(w\)), roll (\(p\)), pitch (\(q\)), and yaw rates (\(r\)) at different sailing speeds and corresponding amplitude changes. In the horizontal zigzag test, the relationship between rudder angle and course angle was analysed. Accordingly, the velocities and angular velocities in the six degrees of freedom exhibited more significant changes when \(U=1.5\) m/s than when \(U=1\) and \(0.5\) m/s. In Figure 16c,e, when the sailing speed reaches close to \(1.5\) m/s, some vertical vibrations will begin to appear in the heave and pitch rates. Figure 15: Time series of rudder and yaw angles at (**a**) \(U=0.5\) m/s, (**b**) \(U=1\) m/s, and (**c**) \(U=1.5\) m/s. The maximum overshooting angle is highlighted by the gray area. Figure 16: 6-DOF velocity components in the horizontal zigzag test at different sailing speeds. (**a**) Surge rate: \(u\) [m/s], (**b**) sway rate: \(v\) [m/s], (**c**) have rate: \(w\) [m/s], (**d**) roll rate: \(p\) [rad/s], (**e**) pitch rate: \(q\) [rad/s], and (**f**) yaw rate: \(r\) [rad/s].
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
###### Abstract  
This manuscript presents the Microwave Temperature and Humidity Profiler (MTHP), a dual-band spectroradiometer designed for measuring multi-incidence angle temperature and humidity atmospheric profiles from an aircraft platform. The MTHP bands are at 60 GHz for measuring the oxygen complex lines, therefore at this band, MTHP has a hyperspectral radiometer able to provide 2048 channels over an 8 GHz bandwidth, and 183 GHz for measuring water vapor, which only uses four channels since this absorption band's spectral richness is simpler. The MTHP builds upon the Microwave Temperature Profiler (MTP) with the inclusion of the hyperspectral radiometer. The instrument's design, components, and calibration methods are discussed in detail, with a focus on the three-point calibration scheme involving internal calibration loads and static air temperature readings. Preliminary results from the Technological Innovation into Iodine and GV aircraft Environmental Research (TIGER) campaign are presented, showcasing the instrument's performance during flights across diverse geographical regions. The manuscript presents successful antenna temperature measurements at 60 GHz and 183 GHz. The hyperspectral measurements are compared with a simulated antenna temperature using the Atmospheric Radiative Transfer Simulator (ARTS) showing an agreement better than R\({}^{2}\) > 0.88 for three of the flights analyzed. Additionally, the manuscript draws attention to potential Radio Frequency Interference (RFI) effects observed during a specific flight, underscoring the instrument's sensitivity to external interference. This is the first-ever airborne demonstration of a broadband and hyperspectral multi-incidence angle 60 GHz measurement. Future work on the MTHP could result in an improved spatial resolution of the atmospheric temperature vertical profile and, hence, help in estimating the Planetary Boundary Layer (PBL) with better accuracy. The MTHP and its hyperspectral multi-incidence angle at 60 GHz have the potential to be a valuable tool for investigating the PBL's role in atmospheric dynamics, offering insights into its impact on Earth's energy, water, and carbon cycles.  
atmospheric, sounding; mmWave; radiometry; calibration 2023  
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