esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Partial Least Squares Analysis Further Verifies the Relationship between Aroma Compounds and Notes
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Mean Score**} \\ \cline{2-9}  & **Fresh** & **Green** & **Fruity** & **Fatty** & **Citrus** & **Floral** & **Peely** & **Woody** \\ \hline O1 & 3.5 c & 3 c & 5.4 bc & 0.3 d & 9.8 a & 6.5 b & 6.1 b & 2 cd \\ O2 & 0 c & 2.6 bc & 3.6 b & 0 c & 9.2 a & 4.8 b & 5 b & 1 c \\ O3 & 0 c & 4.7 b & 4 b & 0 c & 9 a & 3.9 b & 0.3 c & 4.2 b \\ O4 & 0 d & 3.3 b & 1 cd & 0.2 d & 8.8 a & 4.2 b & 3.6 b & 2.4 c \\ O5 & 2.3 b & 2.5 b & 8 a & 0.8 c & 8 a & 2 b & 0.2 c & 3.3 b \\ \hline \hline \end{tabular} Values with different roman letters (a–d) in the same row are significantly different according to the Duncan test (\(p<0.05\)).  
\end{table} 
Table 4: The mean intensity values of the eight attributes for the five sweet oranges in descriptive sensory evaluation. Figure 2: Analysis of correlation between aroma active substances and sensory properties in sweet orange juices (the compound name of A1–A49 is listed in Table 2). From Figure 2, the blue dots are clustered near the red dots, indicating that these volatile compounds contributed to the note. Points within the circle indicate that the contribution of aroma was less than 50%, and points on the circle indicate that the contribution of the aroma was between 50% and 100%. The first quadrant basically distributes the fruity note and representative substances A7-A20 (including ethyl butyrate, ethyl 2-methylbutyrate, ethyl isovalerate), fresh note A1 (acetaldehyde) and fatty note A21,A28 (nonanal and octanal); the second quadrant distributes the peely note A31 (decanal), floral note A33, A38, A42, A47(linalool, helon, neral, citral) and citrus note A11-A25 (\(\alpha\)-pinene, 3-carene, myrcene, a-terpinene, limonene, 4-terpineol, o-Cymene,\(\alpha\)-Terpinolene, 4-Isopropyltoluene), the scattered distribution is more intensive, and their aroma contribution is larger; the third quadrant is the woody note A35 (trans-caryophyllene); the fourth quadrant distributes the green note A10, A26, A27 (hexanol, hexanal, cis-3-hexen-1-ol). PLSR data analysis revalidates the high correlation between aroma substances detected by GC-O and notes of sensory evaluation.
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Path Loss  
The loss brought on by the spread of radio waves through space is referred to as path loss. The channel's propagation characteristics and the radiation diffusion of the transmission power are to blame for this. This indicates the shift in the signals' average power at the macroscale. The strength of the received signal is greatly impacted by obstructions in the signal route between the transmitter and receiver in a wireless propagation scenario. Path loss describes the attenuation of this signal strength. Planning, optimization, and interference analysis for networks all depend on precise path loss characterization and modeling. Several path loss prediction algorithms have been put forth recently to enhance network performance. Implementing a single-path loss prediction model that is appropriate for all wireless propagation conditions, however, is a basic issue that the majority of these models fail to address [8]. That is to say, ML can give a flexible network topology and can use a lot of data to increase the accuracy and performance of signal prediction. People start looking for new ways to overcome these difficulties.  
[PERSON] et al. [74] suggested and assessed a model for an artificial neural network to predict macrocell path loss. We examined neural networks of various sizes from multilayer FNN neural models. To improve prediction accuracy and generalization properties while shortening training time, a quicker training algorithm was incorporated into the training process. [PERSON] et al. [75] developed an optimization model for path loss using a feedforward neural network technique. With the help of normalized terrain profile data and normalized distance, a single-layer fuzzy neural network was trained to calculate the corresponding path loss value using the Lvenberg Marquardt algorithm. By varying the number of hidden layer neurons, the ANN with the best prediction accuracy was found. [PERSON] et al. [76] predicted path loss in a research environment using radial basis function and support vector regression. The SVR and RBF ML algorithms' precise system architecture was optimized by adjusting the hyperparameters of the RBF model. Five other empirical models' performance was compared to that of SVR and RBF ML, and it was found that the ML models' performance was superior to those of the empirical models. In order to provide a resilient network structure, robust adaptability, and wide-ranging data availability, the ML algorithm was applied to signal propagation modeling. Many components, from the transmitter to the receiver, can be accurately modeled using ML for signal propagation. However, researchers have discovered that more sophisticated neural networks might not be more effective, and larger FNN might provide erroneous predictions as a result of overtraining. In addition, rather than overcorrecting and overpredicting, more focus should be placed on carefully choosing training data and broader datasets.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Evaluation Method  
We evaluated our tracker on the OTB-100 dataset [17] and TColor-128 dataset [49]. In these datasets, precision rate (PR) and success rate (SR) were adopted to evaluate the quantitative performances of trackers. The precision shows the ratio of frames that the distance between the estimated bounding box and the given ground-truth is smaller than a threshold. OTB-100 uses the score when the threshold is set to 20 pixels as the representative precision rate score for each tracker. By measuring the overlap ratio between the estimated bounding box and ground-truth, success rate counts the number of successful frames whose overlap ratio is larger than the given threshold 0.5. The area under curve (AUC) of each success plot is adopted to rank the tracking algorithms.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Experiment Settings
#### 5.1.3 Baselines  
To better evaluate the performance of VEPO-S2S, we compared it with several baseline models using the same dataset. In contrast to Seq2 Seq, recurrent neural networks are limited to predicting multiple consecutive track points. In our experiments, we continuously predicted five trajectory points using an RNN, which was achieved through the sliding window method. The baselines were as follows:  
1. Kalman: A linear optimal estimation model;
2. VAR: A statistical model for multivariate time-series prediction;
3. ARIMA: A statistical time-series forecasting model;
4. LSTM: A type of recurrent neural network, which consists of two layers;
5. BiLSTM: Similarly to LSTM, the BiLSTM is composed of two bidirectional LSTM layers;
6. GRU: Similar to an LSTM;
7. BiGRU: Similar to a GRU but with two bidirectional GRU layers;
8. LSTM-LSTM: A Seq2 Seq model with a two-layer LSTM as the encoder and decoder;
9. BiLSTM-LSTM: A Seq2 Seq model with a two-layer BiLSTM as the encoder and a four-layer LSTM as the decoder;
10. GRU-GRU: Similar to an LSTM-LSTM;
11. BiGRU-GRU: Similar to a BiLSTM-LSTM;  
Figure 11: The influence of different hidden sizes on the prediction of the VEPO-S2S model, according to the RMSE. The X-axis represents the number of hidden sizes, and the Y-axis represents the RMSE loss value.  
Figure 10: The influence of different GRU layers on the prediction of the VEPO-S2S model, according to the RMSE. The X-axis represents the number of layers, and the Y-axis represents the RMSE loss value.  
12. Transformer: A Seq2 Seq model based on attention mechanism;
13. METO-S2S: An S2S-based vessel trajectory prediction method with a multiple-semantic encoder and a type-oriented decoder.
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances
#### 2.3.4 HYSATA Technology  
The Korean Institute of Science and Technology recently conducted a test with a team of researchers on a novel membrane and electrode module. This module showcases a remarkable 10-fold performance advantage compared to current proton exchange membrane (PEM) technology. The company Hysata has developed this groundbreaking technology, which operates differently from traditional electrolysis cells (Figure 12). Instead of having the anode and cathode electrodes in direct contact with the liquid, this technology utilizes a porous hydrophilic separator placed between the cathode and anode. Moreover, it leverages capillary action to draw up the electrolyte on only one side of the electrode, thereby facilitating the direct production of hydrogen and oxygen without the presence of air bubbles. This innovative approach achieves an impressive efficiency rate of up to 98%, while requiring an energy expenditure of 40.4 kWh/kg. These results surpass those of current commercial electrolytes [53].  
One of the primary challenges associated with electrolyzers is their high consumption of pure water during hydrogen production. However, in many regions of the world, pure water is a scarce resource with limited availability, posing difficulties for electrolyzers that rely on it. To address this issue, the development of a technique that enables the use of abundant saltwater as a viable alternative is crucial. In addition to enhancing the efficiency of the electrolysis process, effective water desalination plays a vital role in prolonging the life span of electrolyzers. By removing impurities, water desalination ensures clean electrodes and facilitates smooth operation of the electrolysis process. This, in turn, optimizes the performance and efficiency of the electrolyzers, leading to increased hydrogen gas production and reduced energy consumption [55].
# A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
## 4 Experimental Results
### Quantitative Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**PSNR (dB)**} & \multicolumn{3}{c}{**SSIM**} \\  & **15** & **25** & **50** & **Average** & **15** & **25** & **50** & **Average** \\ \hline BM3D & 32.36 & 29.96 & 26.7 & 29.67 & 0.9499 & 0.9228 & 0.8664 & 0.9130 \\ DnCNN & 32.67 & 30.35 & 27.18 & 30.07 & 0.9526 & 0.9286 & 0.8769 & 0.9194 \\ FFDNet & 32.77 & 30.46 & 27.35 & 30.19 & 0.9542 & 0.9308 & 0.8821 & 0.9224 \\ DRUNet & 33.25 & 30.94 & 27.90 & 30.70 & 0.9577 & 0.9363 & 0.8939 & 0.9293 \\ SwinIR & 33.36 & 31.01 & 27.91 & 30.76 & 0.9584 & 0.9368 & 0.8940 & 0.9297 \\ Restormer & 33.35 & 31.04 & 28.01 & 30.80 & 0.9609 & 0.9376 & 0.8958 & 0.9314 \\ MT & 29.24 & 27.06 & 19.00 & 25.10 & 0.8496 & 0.7504 & 0.3389 & 0.6463 \\ DIP & 31.35 & 28.92 & 25.56 & 28.61 & 0.9354 & 0.9007 & 0.8279 & 0.8880 \\ MPFDIP & **33.68** & **31.19** & **28.11** & **30.99** & **0.9625** & **0.9388** & **0.8969** & **0.9327** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of denoising effects on the SET12 dataset for synthesized Gaussian noise images. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**PSNR (dB)**} & \multicolumn{3}{c}{**SSIM**} \\  & **1** & **2** & **3** & **Average** & **1** & **2** & **3** & **Average** \\ \hline BM3D & 29.45 & 25.89 & 23.43 & 26.26 & 0.9133 & 0.8307 & 0.7738 & 0.8393 \\ DnCNN & 29.02 & 24.61 & 23.00 & 25.54 & 0.8115 & 0.6343 & 0.6111 & 0.6856 \\ FFDNet & 28.86 & 24.30 & 22.47 & 25.21 & 0.7878 & 0.5771 & 0.4986 & 0.6212 \\ DRUNet & 28.83 & 24.26 & 22.72 & 25.27 & 0.7978 & 0.6111 & 0.5781 & 0.6623 \\ SwinIR & 28.82 & 24.27 & 22.63 & 25.24 & 0.7966 & 0.6052 & 0.5726 & 0.6581 \\ Restormer & 28.71 & 24.05 & 21.73 & 24.83 & 0.8856 & 0.7320 & 0.6685 & 0.7620 \\ MT & 27.97 & 23.90 & 17.83 & 23.23 & 0.8125 & 0.6083 & 0.3206 & 0.5805 \\ DIP & 27.58 & 25.80 & 23.05 & 25.48 & 0.8655 & 0.8157 & 0.7236 & 0.8016 \\ MPFDIP & **30.65** & **27.39** & **23.85** & **27.30** & **0.9310** & **0.8748** & **0.7840** & **0.8633** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of denoising effects on the BSD68 dataset for mixed Gaussian–Poisson noisy images.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 3 Materials and Methods
### Document Representation Models and Dimensionality Reduction
#### 3.2.2 Continuous-Space Models  
For the continuous space document representation models we utilized the word2 vec and doc2 vec methods as implemented in the gensim library [68]. The implementation of word2 vec at input took a list of documents, each of them represented as a list of words, to train a neural network model, which can be used to calculate a vector representation for each word. We used the following parameter settings. The parameter min_count set a lower bound of a word frequency; since we preprocessed the data set, we set this threshold to 1. The parameter size denotes the dimensionality of the feature vectors, to this end, we selected four values of 25, 50, 75 and 100 (hence, four variants of the model). To obtain the representation of the whole document, we calculated the average of feature vectors for the words occurring in the document [42]. For the other parameters, we retained the default settings.  
The implementation of doc2 vec at input took a list of documents, their unique identifiers, and a list of words in each document. The trained neural network can be used to calculate a vector representation for a given document. Since the doc2 vec implementation extended the word2 vec class, we applied the same settings of the shared parameters. Following the recommendation of the gensim library manual [68], we set the number of iterations over the training documents to 20, where in each iteration a random sequence of training documents was fed into the neural network. Again, we varied the dimensionality of the resulting embedding vectors in the interval between 25 to 1000, leading to seven variants of the doc2 vec model.
## 2 Acoustic Metasurfaces
### Amplitude-Modulating Metasurfaces  
Amplitude-modulating metasurfaces are designed to attenuate the sound pressure level by inducing energy dissipation or scattering. This class includes porous layers, micro-slit arrays, and viscous-damping-based units and is commonly used for indoor acoustic management and echo reduction. Although typically simpler than phase-modulating types, they may offer lower frequency selectivity [47]. Figure 8 presents an example of a hybrid design that incorporates amplitude modulation as part of a more complex Phase-Amplitude-Phase (PAP) structure.  
[PERSON] et al. proposed a low-frequency absorption method for the soft wall space coil parameter (SSM) using a soft wall [44]. The method is absorbed through friction-like splitting along the inner space coil channel and features informative splitting of the soft wall. And the half-power bandwidth is 1.6 times wider than when using the rigid wall space coil parameter (RSM) of the same geometric dimension. The method consists of a soft wall made from Dragon Skin(tm) 10 NV and a frame made from the PLA used to support it. The total thickness of the SSM sample used in the experiment is less than 1/25 wavelength, and all structural dimensions, including the fine channel and soft wall thickness, are designed to be very thin relative to the wavelength.  
[PERSON] et al. proposed a unit cell of an acoustic metasurface capable of phase-amplitude-phase (PAP) control, and through this, an arbitrary acoustic impedance matrix was implemented [45]. This structure allows wave control of a higher degree of freedom than the existing phase control central metasurface, and can precisely control both amplitude and phase. The unit cell transverse size (width) of the metasurface is designed to be 1.12 cm  
Figure 7: Diagram of Thermally Induced Phase Transition and Focal Shift in a PVA-PNIPAm Hydrogel Lens. Reproduced with permission from ACS Omega [43], © 2019 American Chemical Society. [[https://pubs.acs.org/doi/10.1021/acsomega.9b02374](https://pubs.acs.org/doi/10.1021/acsomega.9b02374)]([https://pubs.acs.org/doi/10.1021/acsomega.9b02374](https://pubs.acs.org/doi/10.1021/acsomega.9b02374)) (accessed on 11 July 2025).  
(\(\lambda\)/9), and the unit cell thickness is 3.5 cm (main body 3.3 cm + cover 0.2 cm) and the interior includes two phase modulators and a central amplitude modulator. Through numerical simulations and acoustic experiments, the proposed metasurface has been demonstrated to be effective in the design.
## 3 Methods
### Spiking-Softmax Method
#### 3.3.1 Spiking Exponential Neuron  
Inspired by FS-neuron [52], the Spiking Exponential Neuron (SI-exp) has been improved, allowing it to simulate exponential operations by emitting a few spikes within K time steps. In SI-exp, it is necessary to optimize the internal parameters T(t), h(t), and d(t), where \(t=1,\dots,K\). T(t) and d(t) respectively represent the threshold voltage and spike output of the t-th time step, while h(t) is used to control the membrane potential changes at each time step. Through optimization, the exponential operation of a given ANN is simulated by a weighted sum of spikes \(\sum_{t=1}^{K}d(t)z(t)\), where z(t) denotes the spike train produced by the neuron. Figure 9 illustrates the inference process of the SI-exp neuron.  
\[v(t+1)=\begin{cases}x,t=0\\ v(t)-h(t)z(t),0<t<K\end{cases} \tag{9}\]  
where x denotes the floating-point input, and v(t) denotes the membrane potential of the neuron. If the neuron triggers a spike at time step t, then v(t) must exceed the current discharge threshold T(t), at which point the neuron generates a spike signal \(z(t)=1\); otherwise, \(z(t)=0\). After spiking at time step t, the membrane potential v(t) is reset to  
Figure 8: Principle of the Spiking–Softmax method implementation. The \(\exp_{i}\) denotes the i-th SI-exp neuron, where \(i=1,2,...,n\). The \(V_{th}\) (the sum of all SI-exp neuron outputs) denotes the threshold voltage of the SI-norm neuron, and the regular spike sequence sr denotes the input to the SI-norm neuron. Simultaneously, the output of the SI-exp neurons serves as the weight w for the SI-norm neuron.  
\(\mathrm{v(t)-h(t)}\). The change in membrane potential \(\mathrm{v(t)}\) over K time steps is represented by equation (2). The spike signal \(\mathrm{z(t)}\) of the SI-exp neuron is defined by Equation (10):  
\[\mathrm{z(t)}=\Theta(\mathrm{v(t)-T(t)}) \tag{10}\]  
where \(\Theta\) represents the Heaviside step function. After K time steps of inference, the final output of the SI-exp neuron is as shown in Equation (11). Since the Spiking-Softmax method includes multiple SI-exp neurons, here \(\mathrm{exp_{i}}\) is used to represent the output of the i-th SI-exp neuron.  
\[\mathrm{exp_{i}}=\sum_{t=1}^{K}\mathrm{d(t)z(t)} \tag{11}\]  
When optimizing the internal parameters h(t), d(t), and T(t) of the SI-exp neuron, a selection of 10,000 numbers as independent variables, evenly spaced in the interval \([-1,1]\), was chosen as training data, with their corresponding exponential values used as training labels. The distribution of the optimized internal parameters T(t), h(t), and d(t) is shown in Figure 10. Using the optimized parameters, the output of the SI-exp neuron after the K time steps of inference is compared with the exponential function \(\mathrm{exp(x)}\) of ANNs. The root mean square error is 0.0015413, indicating that the average deviation between the inference of SI-exp and the true values can be negligible.
# Novel Hybrid PETG Composites for 3D Printing
## 3 Results
### Properties of Virgin PETG Filled with Carbon Fibers
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(E\) **(MPa)** & \(\sigma_{Y}\) **(MPa)** & \(\sigma_{B}\) **(MPa)** & \(\epsilon_{B}\) **(\%)** & \(E_{r}\) **(GPa)** & \(H\) **(GPa)** \\ \hline
**A** & 1600 \(\pm\) 90 & 58.3 \(\pm\) 2.1 & 59.7 \(\pm\) 5.5 & 362.5 \(\pm\) 10.9 & 2.59 \(\pm\) 0.50 & 0.13 \(\pm\) 0.03 \\
**AF-1** & 1850 \(\pm\) 100 & 60.1 \(\pm\) 1.8 & 66.4 \(\pm\) 1.8 & 6.2 \(\pm\) 2.7 & 2.72 \(\pm\) 0.12 & 0.14 \(\pm\) 0.0 \\
**AF-2** & 1990 \(\pm\) 135 & 61.2 \(\pm\) 4.3 & 64.9 \(\pm\) 5.6 & 5.7 \(\pm\) 1.2 & 2.69 \(\pm\) 0.22 & 0.14 \(\pm\) 0.02 \\
**AF-3** & 2110 \(\pm\) 110 & 65.6 \(\pm\) 3.0 & 65.0 \(\pm\) 3.2 & 5.0 \(\pm\) 0.1 & 2.60 \(\pm\) 0.18 & 0.15 \(\pm\) 0.01 \\
**AF-4** & 2370 \(\pm\) 285 & 66.3 \(\pm\) 2.2 & 66.4 \(\pm\) 2.1 & 4.8 \(\pm\) 0.3 & 2.60 \(\pm\) 0.37 & 0.13 \(\pm\) 0.02 \\
**AF-5** & 2860 \(\pm\) 190 & 75.3 \(\pm\) 4.5 & 72.1 \(\pm\) 1.0 & 5.1 \(\pm\) 0.5 & 2.72 \(\pm\) 0.31 & 0.13 \(\pm\) 0.02 \\
**AF-10** & 3430 \(\pm\) 120 & 75.4 \(\pm\) 3.4 & 73.5 \(\pm\) 6.6 & 5.7 \(\pm\) 1.3 & 2.75 \(\pm\) 0.53 & 0.13 \(\pm\) 0.03 \\
**AF-20** & 3580 \(\pm\) 275 & 75.8 \(\pm\) 1.7 & 74.4 \(\pm\) 2.1 & 3.9 \(\pm\) 1.3 & 3.09 \(\pm\) 0.67 & 0.13 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mechanical properties of virgin PETG filled with carbon fibers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(T_{10}\) **(C)** & \(T_{50}\) **(C)** & \(m_{550}\) **(\%)** & \(\alpha\) **(10–6 K–1)** \\ \hline
**A** & 402.1 & 431.8 & 9.0 & 60.12 \\
**AF-1** & 397.2 & 425.9 & 7.1 & 55.12 \\
**AF-2** & 399.6 & 427.4 & 7.8 & 51.46 \\
**AF-3** & 399.3 & 427.3 & 9.5 & 48.16 \\
**AF-4** & 399.7 & 427.5 & 9.2 & 45.35 \\
**AF-5** & 400.3 & 428.4 & 11.3 & 39.74 \\
**AF-10** & 401.0 & 429.6 & 14.8 & 34.35 \\
**AF-20** & 401.1 & 432.7 & 23.7 & 28.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Thermal properties of the prepared composite materials–virgin PETG and CFs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Density (g-cm–3)** & **Contact Angle (°)** \\ \hline
**A** & 1.27 & 86.9 \(\pm\) 2.6 \\
**AF-1** & 1.27 & 77.5 \(\pm\) 4.9 \\
**AF-2** & 1.24 & 76.5 \(\pm\) 3.5 \\
**AF-3** & 1.16 & 77.9 \(\pm\) 4.4 \\
**AF-4** & 1.12 & 75.4 \(\pm\) 3.3 \\
**AF-5** & 1.13 & 74.7 \(\pm\) 4.3 \\
**AF-10** & 1.13 & 70.3 \(\pm\) 3.0 \\
**AF-20** & 1.12 & 64.8 \(\pm\) 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The densities and contact angles of the prepared virgin PETG composite materials with the CFs. area of filament was observed and no CF clusters occurred. Figure 2B captures the fracture area at 1228\(\times\) magnification under the electron beam. CFs that were coated with a polymer matrix were captured thereon. Different holes were present. It was probably caused by low interfacial forces between the fibers and polymer matrix.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 2 Materials and Methods
### Signal Processing
#### 2.3.4 Polarimetric Imaging  
After the SAR processing, we obtain a reconstructed image in each polarization channel. The full polarimetric information can be represented in a 2 \(\times\) 2 scattering matrix [5][25], which is given by  
\[[S]=\left[\begin{array}{cc}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{array}\right] \tag{5}\]  
To view the combined contribution from different polarization channels, a RGB image can be generated presenting the full polarimetric information with the intensities of the Pauli decomposed components: \(|S_{HH}-S_{VV}|\), \(|S_{HH}+S_{VV}|\) and \(2|S_{HV}|\), which are color-coded in the red, blue, and green channels, respectively. From the resulting polarimetric image, we can obtain more information on the targets by analyzing the scattering mechanism.