esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Antenna Pattern Errors
### Antenna Pattern Differences
Within the range of the antenna main beam (\(\xi\in[\xi_{1},\ \ldots,\ \xi_{m}]\), \(\eta\in[\eta_{1},\ \ldots,\ \eta_{n}]\)), the amplitude difference \(AM^{k}\) and phase difference \(PH^{k}\) of the antenna k are defined as follows:  
\[AM^{k}=\sqrt{\frac{\sum\limits_{\xi=\xi_{1}}^{\xi_{m}}\frac{\eta_{n}}{\eta- \eta_{1}}\left(\frac{\left|F_{nk}(\xi,\eta)\right|-\left|\overline{F_{n}}(\xi, \eta)\right|}{\left|\overline{F_{n}}(\xi,\eta)\right|}\right)^{2}}{\text{m}*n}} \tag{7}\]  
\[PH^{k}=\sqrt{\frac{\sum\limits_{\xi=\xi_{1}}^{\xi_{m}}\frac{\eta_{n}}{\eta- \eta_{1}}\left(\varphi_{k}(\xi,\eta)-\overline{\varphi}(\xi,\eta)\right)^{2}} {m*n}} \tag{8}\]  
where m and n represent the number of measurement points in direction \(\xi\) and \(\eta\) of the antenna pattern within the range of the main beam. The amplitude difference and phase difference of the array are defined as the average of the differences of all elements in the array:  
\[C_{am}=\frac{AM^{1}+AM^{2}+\ldots+AM^{M}}{M} \tag{9}\]  
\[C_{ph}=\frac{PH^{1}+PH^{2}+\ldots+PH^{M}}{M} \tag{10}\]  
\(C_{am}\) and \(C_{ph}\) can be used to quantitatively evaluate the degree of antenna pattern differences for the aperture synthesis radiometer.
## 6 Performance Analysis
### Simulation Setup and Environmental Parameters
#### 6.1.1 Neural Network Configuration Parameters  
As shown in Table 1, the neural network was configured with specific parameters for the study. The neural network was trained on an ASUS Gaming Computer equipped with a 3.60 GHz CPU and a GeForce T1088i GPU with 32 G of RAM. Our program was written in Python 3.6, and the neural network was built using TensorFlow 1.18. The neural network comprised three stacks of LSTM, with 64 hidden layers each. The batch size was set to 64. We used an Adam optimizer to train our models [54]. A learning rate of 10 \({}^{-3}\) was initially used during training.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Description** & **Value** \\ \hline Neural Network Configuration & LSTM-based RL model \\ LSTM Layers & 3 stacks with 64 hidden layers each \\ Batch Size & 64 \\ Epoch & 50 \\ Optimizer & Adam \\ Activation Function & ReLu \\ Learning Rate & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neural network parameters.  
The parameters in Table 1 were optimized using the Optuna framework. The tuning process incorporated a comprehensive range of potential configurations, focusing primarily on optimizing learning rates, the number of LSTM layers, and the units in each layer to ensure robust learning capabilities. For pre-training, the network utilized policy-gradient methods on a large dataset to effectively initialize LSTM weights. This technique optimizes cumulative rewards, aligning the network's outputs with long-term goals and expediting convergence during subsequent training phases.
## 4 Discussion  
In order to utilize detailed phase information, the 4 QAM-BPS is applied in the proposed MFI scheme to roughly recover the phase of the CMA-equalized signals before the MFI operation. The capability of the phase pre-compensation algorithm is one of the important factors to determine MFI performance. In our future work, there is still more room for improvement in phase pre-compensation.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods  
### Study Areas and Drought Events  
Three Brazilian biomes with great importance for the country's water, energy, and food security were chosen as study areas (Figure 1): the Amazon, Cerrado, and Pampa. Major drought events occurred over these areas in the last two decades, such as the 2010 and 2015 events in Amazon, the 2014 drought over southeastern Brazil (where the Cerrado biome predominates in combination with the Atlantic Forest biome), and the 2012 event in the southern region (mostly covered by the Pampa biome). It is worth mentioning that other severe drought events occurred in Brazil in the last years, although they were not addressed in this study, such as the 2019-2020 drought in the Pantanal biome [46] and the 2020 drought in the southern region [12].  
The analysis of droughts in Brazil covered three study areas delimitated by regions of 500 \(\times\) 500 km\({}^{2}\) in each of the three biomes. The location of these regions was defined to coincide with large areas affected by major drought events in the last decades.
## 4 Results
### Flight Experiment
Figure 12 presents an optical image of a coastline scene, with the black line indicating the flight path of the aircraft. Its corresponding reconstructed BT image is shown in Figure 13. There is a significant contrast in BT between land and water. However, the BT image exhibits distortion. The boundary of the blue low-temperature zone in the experimental results delineates the coastline. As illustrated in Figure 11a,b, the X polarization and Y polarization reconstructed images present significantly different coastal features, and these features are all different from those depicted in the optical images. The two-step correction method described above was used to correct the measured visibility function without considering the contribution of the backlobe. After the antenna pattern correction method is applied, the corrected image results are shown in Figure 14. The image quality of the corrected image is improved and the geometry of the coastline is well preserved. In Figure 14, the distribution areas and shapes of the ocean and land are highly consistent with those of the optical image. Through imaging analysis of this area, the validity of the correction method was qualitatively verified.
## 4 Performance Analysis  
The entire performance is studied by applying the optimal method, namely robust FDL, ODL, and NVL techniques. In this research paper, the LCAA, LCPAA, LRAA, and LCCAA beamformers are considered. The steering angle is \(50^{\circ}\), the actual signal direction is \(52^{\circ}\), the interference angle is \(30^{\circ}\), and the signal frequency is taken as \(300\) MHz for this performance analysis.
## 4 Results and Discussion
### Steady-State Regulation  
Figure 14 shows system and heat exchange parameter variation curves with the heat source temperature. The evaporation pressure increased from 814.46 kPa to 1136.46 kPa, the condensation temperature from 294.46 kPa to 412.01 kPa, and the system efficiency from 7.83% to 7.95%. This is because as the heat source temperature rises, the output power generation will fluctuate. To balance the fluctuations, it is necessary to adjust the changes in two parameters, which are the mass flow rate and the specific enthalpy difference between the inlet and outlet of the expander. The mass flow rate increases with a heat source temperature increase. The increase in evaporation pressure causes the heat transfer temperature difference in the evaporator to decrease, which can weaken the increase in the mass flow rate. This can simulate a basic change in the mass flow caused by the constant pumping power in the actual process.  
After balancing out the fluctuations in mass flow, the increase in evaporation pressure increases the specific enthalpy difference between the inlet and outlet of the expander, resulting in output power fluctuations. Then, the condensation pressure decreases, which decreases the specific enthalpy difference to reduce the fluctuation in output power. The increase in the system temperature difference leads to an increase in the system efficiency. Among them, for every 1 \({}^{\circ}\)C increase in the heat source water temperature, the evaporation pressure increased by 26.83 kPa, an increase of 3.29%; the condensation pressure increased by 9.79 kPa, an increase of 3.32%; and the system efficiency increased by 1.2%.  
Increases in the evaporation pressure and heat source temperature increase the overall temperature of the evaporator, which cause the working fluid to shift to a more superheated state. Since the increase in condensation temperature is smaller than the increase in the evaporation pressure, the supercooled zone proportion increases. The two-phase zone proportion decreased from 69.65% to 67.82%, the supercooled zone proportion increased from 28.70% to 30.12%, and the superheated zone proportion increased from 1.86% to 2.06%. These changing rules are shown in Figure 15.  
Figure 14: System and heat exchanger parameter variation curves with \(T_{\text{Inp}}\).  
Figure 16 presents the linear relationship between the heat transfer coefficient, mass flow rate, and heat flux in the evaporator. The mass flow rate was kept constant, so the heat transfer coefficient was mainly affected by the heat flux. As the heat flux increased from 59.40 kW/m\({}^{2}\) to 65.80 kW/m\({}^{2}\), the heat transfer coefficient increased from 2776.07 W/(m\({}^{2}\cdot\)K) to 2992.74 W/(m\({}^{2}\cdot\)K). This part of the results was mentioned before, so we will not go into much more detail.
## 2 Antenna Design Methodology
The geometric details of the dual-band notch UWB antenna are shown in Figure 3 and Table 2.  
The surface current distributions of the UWB and UWB band notches are illustrated in Figures 4 and 5, correspondingly. Regarding the UWB antenna without notch features, it can be noticed in Figure 4a-c that the maximum current concentration is at the feedline at resonance frequencies of 4.5 GHz, 8.5 GHz, and 12.7 GHz. Figure 5a,b shows the current distribution of the UWB antenna with notch characteristics. At the notch frequencies 5.4 GHz and 7.6 GHz, it can be observed that the slots are not allowing the field to propagate into the radiator from the feedline; instead, the maximum current concentration is around the slots. The current distribution plot signifies that the antenna is nonresponsive at the notch frequencies. To calculate the length of the parasitic elements, the following Equations (4) and (5) can be used [11]:  
\[L_{parasitic\ element}=\frac{\lambda_{g}}{2}\quad, \tag{4}\]  
where \(\lambda_{g}\) is the guided wavelength and is equal to \(\lambda_{g}=\frac{\lambda_{0}}{\sqrt{\varepsilon_{eff}}}\) ; and  
\[\therefore\ L_{parasitic\ element}=\frac{\lambda_{0}}{2\sqrt{\varepsilon_{ eff}}}=\frac{c}{2f_{r}\sqrt{\varepsilon_{eff}}}, \tag{5}\]  
where \(\varepsilon_{eff}\) is the effective dielectric constant, \(f_{r}\) is the resonant frequency in GHz, and \(c\) is the speed of light. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameters/Antenna Design** & **t1** & **t2** & **t3** & **t4** & **t5** & **t6** & **t7** & **t8** \\ \hline UWB dual-band notch & 0.2 & 0.2 & 8 & 0.5 & 0.4 & 2 & 8.2 & 0.6 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimized physical parameters of the band notch elements (dimensions in mm). Figure 3: Geometry details of the proposed UWB dual-band notch antenna.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 5 HD Maps  
One of the goals of Smart Mobility is to have fully autonomous vehicles. In order to do that, vehicles need to have an overview of the traffic situation in the vicinity and they also need to have road infrastructure information. At the same time, they need information even outside of the shared data between the different participants in the V2X communication in order to cover all of the above areas. One possible solution is to use High-Definition Maps (HD Maps), as a part of the way the vehicle is oriented on the road. In general, we can mark HD maps as high-resolution maps that have information and accuracy at centimeter level. They contain information whether static or dynamic, such as:  
* map documents;
* traffic situations;
* traffic signs and intersections;
* weather reports;
* urban facilities on city streets [28].  
According to the Automotive Edge Computing Consortium (AECC), the use of HD maps is a key issue in Mobility-as-a-Service, ADAS (Advanced Driver Assistance System) and autonomous driving [28]. Together with all of the sensors available in vehicles, the information obtained will contribute to updating HD maps, which will help to move automated and autonomous transport from level 2 automation. Vehicles from this level of automatization require precise localization capabilities to adapt to the environment and make real-time decisions for driving.  
As an example, we will provide a simple repair of the pothole on the road, where the repair takes several days. This is not a problem at lower levels as drivers automatically avoid the repair site. The problem arises with autonomous driving when the vehicle needs to be informed that there is an obstacle on the road and instructions to avoid it. For this purpose, it is necessary to build a real-time data processing, storage architecture and the ability to provide the information collected to other vehicles on the road.
## Nomenclature  
\begin{tabular}{l l l l} \(u\), \(v\) & Horizontal and vertical component of velocity & \(\mathrm{T_{w}}\) & Wall temperature \\ \(v=\frac{\mu+S}{\rho}\) & Kinematic viscosity & \(T_{\infty}\) & Ambient temperature \\ \(F=\frac{c_{b}}{xx^{\frac{1}{2}}}\) & Nonuniform inertia coefficient & \(c_{b}\) & Drag coefficient \\ \(c_{p}\) & Specific heat capacity & \(K^{*}\) & Permeability of porous space \\ \(\rho\) & Density of the fluid & \(\alpha\) & Thermal diffusivity \\ \(Q_{0}\) & Coefficient of heat source & \(k_{c}\), \(k_{s}\) & Rate constants \\ \(\tilde{h}\) & Convective heat transfer coefficient & \(\tilde{k}\) & Thermal conductivity \\ \(\sigma\) & Microrotation component & \(K_{1}=\frac{S}{\rho}\) & Coupling constant \\ \(K_{b}\) & Coupling constant parameter & \(S\) & Constant characteristic of the fluid \\ \(\lambda\) & Porosity parameter & \(F_{r}\) & Inertia coefficient \\ \(G\) & Microrotation parameter & \(R_{d}\) & Radiation parameter \\ \(P_{r}\) & Prandtl number & \(S_{c}\) & Schmidt number \\ \(K\) & Homogenous reaction parameter & \(K_{2}\) & Heterogeneous reaction parameter \\ \(\delta\) & Ratio of diffusion coefficients & \(Bi\) & Biot number \\ \end{tabular}
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 5 Experimental Evaluation
### Accuracy Evaluation
However, the difference is very small. Table 4 also shows that the best results are obtained with 5 and 10 neurons in the hidden layer. With more than five neurons, results show no improvement and with more than 10 neurons both approaches tend to lose accuracy. In Table 5, we show results obtained with (1) the real execution of the query processing strategies for different datasets, (2) the proposed query running time prediction algorithm, and (3) the baseline query running time prediction algorithm, both operating with five neurons in the hidden layer. In the columns, QRT is the average query running time and \(max_{t}\) is the maximum query running time observed in the set of processed queries. AAE is the average absolute error and \(max_{e}\) is the maximum error observed in the predicted running times for the set of processed queries. In general, the results in Table 5 show that both approaches are able to make excellent prediction of the QRT values. The proposed algorithm presents smaller error values than the baseline algorithm (numbers in bold font in the table). For the AAE metric, the reduction is in the range between 5% and 15%, whereas for \(max_{e}\) the reduction is in the range between 49% and 74% for WAND and between 1.6% and 52% for BM-WAND. The \(max_{t}\) value indicates the single query that demands the maximum running time. For this case, both approaches outperform each other depending on the dataset and query processing strategy. For WAND, the baseline algorithm achieves better overall predictions where it underestimates the exact value by 4% on average whereas the proposed algorithm underestimates the exact value by 14% on average. In this case the baseline algorithm is able to predict the exact values for two maximum queries. Furthermore, the computations associated with the TF-IDF method are much lighter than the BM25 ones which increases the linear effect. For BM-WAND, this underestimation is similar in both approaches with 19% for the baseline algorithm and 18% for the proposed algorithm. To evaluate the effect of the index size on our DFT-based method, Table 5 shows that the \(max_{e}\) reported with the ClueWeb09 tends to be 10% higher when executing the WAND and 15% higher when executing the BM-WAND. This is mainly because as we increase the number of documents some posting lists tend to be significantly larger, which tends to increase the maximum error in of the prediction algorithm. However, the AAE increases only 0.0035 s for the WAND and 0.0055 s for the MB-WAND. Notice that when using the BM-WAND and the TF-IDF, there is no difference between the AAE reported with both datasets. In other words, with a larger dataset, the maximum errors increase by 15% at most, but the average of the absolute error reported by our DFT-based algorithm are very similar.
# Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
## 4 Numerical Result
### The Permittivity Is Between 2 and 2.5 with 5% Noise  
In this case, the dielectric coefficient is assigned values ranging from 2 to 2.5. The simulation setup incorporates 5% of Gaussian noise to account for measurement uncertainty. For input data, a subset of handwritten digits (0-9) is randomly selected from the Modified National Institute of Standards and Technology (MNIST) database--a widely used benchmark dataset in the field of handwriting recognition. Each MNIST image consists of a \(28\times 28\) grayscale pixel array, and the dataset includes approximately 70,000 samples, with 10,000 examples available for each digit. To introduce variability, every group of 50 images corresponds to a distinct handwriting style, and each style is further augmented by applying 50 different rotation angles. Owing to its simplicity and representativeness, MNIST has become a standard resource for training and evaluating image-based neural network models.  
For each digit class, 50 samples are selected, resulting in a dataset comprising 500 images in total (\(10\times 50\)). The dataset is partitioned with 80% allocated for training and the remaining 20% reserved for testing. BPS is first applied to preprocess the data, which is subsequently used to train both the baseline GAN and the GAN with SA models. The reconstruction performance of both architectures is then quantitatively evaluated and compared.  
Figure 13a,b show the original \(\epsilon_{1z}\) and \(\epsilon_{1x}\) dielectric coefficient distributions of the object with 5% noise. Figure 14a,b present the \(\epsilon_{1z}\) and \(\epsilon_{1x}\) distributions reconstructed by the GAN with 5% noise. The results in Figure 14 indicate that the general shape of the dielectric distribution is recognizable. Figure 15a,b display the TE and TM distributions reconstructed by GAN with SA under 5% noise. The reconstructions in Figure 15 demonstrate that the image is clearly identifiable based on the permittivity distribution. More importantly, it closely resembles the original permittivity distribution. The corresponding RMSE and SSIM values are summarized in Table 4, which clearly shows that the GAN with SA provides better reconstruction performance than the GAN.  
Based on the simulation results from the three scenarios discussed above, we discover that lower dielectric coefficient values result in better reconstruction outcomes. Moreover, a more uniform distribution of the dielectric coefficient leads to improved reconstruction performance.  
The experiments are conducted on a desktop equipped with an Intel Core i7-14700K CPU and 64 GB RAM. With the SA module enabled, training takes approximately 78 min with peak RAM usage of 1.3 GB. Without the SA module, training time is 76 min with 1.2 GB RAM usage. For both configurations, inference on a single sample takes less than 0.3 s, demonstrating that the trained model is suitable for near real-time image reconstruction.
## 3 Proposed Method
### Multi-Head Attention Gating Unit  
One defect of the traditional MLP model is that it ignores the interdependencies between different time steps in the sequence data when processing sequence data. The presented model aims to solve this defect by adding MAGU to the MLP model. MAGU mainly consists of three parts, namely MHA, gating operation, and linear transformation. The MHA can extract features from different subspaces in parallel and better capture the dependencies between time series data. The gating operation can selectively control the information flow by ignoring irrelevant information or amplifying important information, enabling the improved MLP model to better process sequence data. A linear transformation can map input data into a new representation space, which usually includes matrix multiplication operations and offset addition operations.  
The MAGU network structure in Figure 4 mainly includes an input layer, a multi-head attention layer, a gating subunit, a linear transformation operation, and an output layer. The gating subunit contains a linear layer and a sigmoid activation function, which can generate gating weights through the sigmoid function to achieve gating operations. The linear transformation layer includes a linear transformation layer and a linear activation function. Linear transformation refers to linear transformation of the input data and the hidden state of the previous time step, and then mapping them to a new feature space.  
Figure 4: Network structure diagram of the multi-head attention gating unit.  
During the forward propagation, the model works as follows: First, an input is received. Then, it is passed to the multi-head attention layer. Subsequently, it is passed to the gating subunit. In the gating subunit, the gating signal is calculated by the sigmoid activation function, and after the calculation is completed, the input is passed to the linear transformation layer. Afterward, the input is linearly transformed in the linear transformation layer. The final output is the result of multiplying the gating signal by the transformation result and adding the input residual.  
MAGU is a module that combines MHA and gating operation to enhance the model's ability to process time series data. MHA can capture complex dependencies in the input sequence. Gating operation can improve the nonlinear expression ability of the model by dynamically adjusting the flow of information. Next, the two important components of the MAGU module, the MHA and the gating operation, will be introduced in detail.
###### Abstract  
To understand and analyze crater damage of rocks under hypervelocity impact, the hypervelocity impact cratering of 15 shots of hemispherical-nosed cylindrical projectiles into granite targets was studied within the impact velocity range of 1.91-3.99 km/s. The mass of each projectile was 40 g, and the length-diameter ratio was 2. Three types of metal material were adopted for the projectiles, including titanium alloy with a density of 4.44 g/cm\({}^{3}\), steel alloy with a density of 7.81 g/cm\({}^{3}\), and tungsten alloy with a density of 17.78 g/cm\({}^{3}\). The projectile-target density ratio (\(\rho_{\rm p}/\rho_{\rm t}\)) ranged from 1.71 to 6.86. The depth-diameter ratios (\(H\)/\(D\)) of the craters yielded from the experiments were between 0.14 and 0.24. The effects of \(\rho_{\rm p}/\rho_{\rm t}\) and the impact velocity on the morphologies of the crater were evaluated. According to the experimental results, \(H\)/\(D\) of craters is negatively correlated with the impact velocity, whereas the correlation between \(H\)/\(D\) and \(\rho_{\rm p}/\rho_{\rm t}\) is weak positive. The crater parameters were expressed as power law relations of impact parameters by using scaling law analysis. The multiple regression analysis was utilized to obtain the coefficients and the exponents of the relation equations. The predicted values of the regression equations were close to the experimental results.  
Keywords:projectile; hypervelocity impact; crater; granite target; scaling law +
Footnote †: journal: Applied