esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Vision Transformer Models
### Hybrid Vision Transformer Architectures  
Vision transformer differs from conventional convolutional neural networks (CNNs) in several ways, including its capacity to learn long-range dependencies in the image and its training on very large datasets. Although ViTs have certain drawbacks, such as high computing costs and a propensity to disregard geographical data, researchers have proposed several hybrid vision transformer (HVT) architectures that combine CNNs and transformers to address these issues.  
To grasp long-distance relationships, the LeViT [30] design utilizes a CNN to transfer the local features extracted from the image to a ViT encoder. This approach allows the model to promptly capture spatial data from the picture, improving its ability to execute tasks such as object detection and segmentation. As well, several HVT models, including EarlyConViT [31], Mobile ViT [32], Region ViT [33], and PiT [34] are integrated with CNN.  
We investigated the use of facial emotion recognition with a variety of vision transformer (ViT) models, including those mentioned above.
# The Applications of Plasma Techniques
### Chosen Aspects of the Electromagnetic Compatibility of Plasma Reactors with Gliding Arc Discharges  
[PERSON] in [4] presented an analysis of electromagnetic disturbance interactions inside the three-phase gliding arc plasma generation installation. This constitutes the main part of the electromagnetic compatibility analysis of the reactor installation. All elements of the nonthermal plasma installation are described from the perspective of disturbance generation and its influence on the power supply system. The analysis is based on the results of tests carried out in accordance with the guidelines of the electromagnetic compatibility (EMC) directive and harmonized standards. The disturbances measured are large--over 20 dB above the limits--and allow for valid conclusions to be reached in relation to this type of installation. This implies a need for plasma reactors to be designed with elements that reduce radiated and conducted interference.
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 3 Results and Discussion
### Theoretical Uncertainty of \(d\Delta\varphi\)  
According to Equations (11) and (12), some simulations were performed to explore the relationship between the baseline and \(\gamma_{geom}\) and \(\gamma_{vol}\) using S1 nominal values of Table 1 (Figure 4a,b). Figure 4a shows that expected forest height (i.e., the thickness of forest volume) does not significantly affect \(\gamma_{vol}\) if \(B\) is maintained lower than 50 m. Differently, for \(B\) values \(>\) 50 m, \(h_{v}\) participates to reduce \(\gamma_{vol}\). Figure 4b shows a perfect negative linear correlation between \(B\) and \(\gamma_{geom}\) having a steeper decreasing rate for lower look angles. Because \(\gamma_{baseline}=\gamma_{geom}\cdot\gamma_{vol}\), according to Equation (9), one can admit that the higher the baseline, the lower \(\gamma_{baseline}\) and the higher \(\sigma_{\Delta\varphi}\).
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis
### Diversity-Multiplexing Trade-off  
For the DMT derivation, we first present its definition and two related lemmas.  
**Definition 1** (of DMT [12; 13]).: _Consider a family of codes \(\chi\left(\rho\right)\) operating at SNR \(\rho\) with rate \(R\left(\rho\right)\) bits/s/Hz. If \(\mathrm{P}_{out}\left(\rho\right)\) is the outage probability of the protocol for rate \(R\left(\rho\right)\), the multiplexing gain \(r\) and the diversity gain \(d\) are defined as \(r=\lim_{\rho\rightarrow+\infty}\frac{R\left(\rho\right)}{\log\rho}\) and \(d=-\lim_{\rho\rightarrow+\infty}\frac{\log\left(\mathrm{P}_{out}\left(\rho \right)\right)}{\log\rho}\), respectively._  
The DMT metric takes into account both transmission reliability and throughput performance simultaneously and provides a more comprehensive view on the performance of communications systems. Note that when calculating DMTs of different protocols, the target data rate \(C\) scales with \(r\log\rho\) in different forms (see in the following subsections).  
**Lemma 1** ([13]).: _If \(h\) is a complex Gaussian random variable with zero mean and given variance, let \(\left|h\right|^{2}\doteq\rho^{-v}\), then the probability density function of \(v\) is given by \(p_{v}\doteq\rho^{-v}(v>0)\)._  
**Lemma 2** ([13]).: _For independent random variables \(\left\{v_{i}\right\}_{i=1}^{N}\) distributed identically with \(v\), the probability \(\mathrm{P}_{\Theta}\) that \(\left(v_{1},v_{2},\ldots,v_{N}\right)\) belongs to the set \(\Theta\) is characterized by \(\mathrm{P}_{\Theta}\doteq\rho^{-d_{0}}\) with \(d_{0}=\inf_{\left(v_{1},v_{2},\ldots,v_{N}\right)\in\Theta^{+}}\sum_{i=1}^{N}v_ {i}\), where \(\Theta^{+}\) is the set consisting of only non-negative elements of the set \(\Theta\)._
## 4 Methodology
### K-Nearest Neighbors (KNN)  
For a given test dataset, the K-nearest neighbors (KNN) algorithm predicts the location by identifying the k closest position data points in the training set based on a chosen distance metric. Using the information from these k \"neighbors\", the predicted location is determined. For a dataset \(D=\left\{{{(X_{1},y_{1}),(X_{2},y_{2}),\ldots,(X_{p},y_{p})}}\right\}\) of length \(p\), where \(X_{i}\) represents the \(i\)-th data point with \(m\) features, and \(y_{i}\) denotes the label of the \(i\)-th data point, the computational process of the KNN algorithm is as follows:(1) Compute the distance between the test point \(x_{j}\) and each point in the training set.  
\[D(x_{j},x_{i})=\sqrt{\sum_{k=1}^{m}|x_{i}{}^{(k)}-x^{(k)}|^{2}} \tag{4}\]  
(2) Sort the computed distances in ascending order and select the k closest data points from the dataset. In this study, \(k\) is set to 5.  
(3) Based on the labels of the selected k data points, use the label with the highest frequency as the predicted result.
## 2 Band Structure
### Graphene Nanoribbons  
Let us now discuss the so-called graphene nanoribbons (GNRs), _i.e._, ribbons obtained by cutting a graphene layer, characterized by a high aspect-ratio, namely a transverse width, \(w\), much smaller than the longitudinal ribbon length. Figure 3 shows the two basic shapes for GNRs, namely nanoribbons with armchair edges and nanoribbons with zigzag edges (e.g., [20]). These edges have a \(30^{\circ}\) difference in their orientation within the graphene sheet. We assume that all dangling bonds at graphene edges are terminated by hydrogen atoms and, thus, do not contribute to the electronic states near the Fermi level. In the following, we will assume the Fermi energy to be zero. However, this level may move to values such as 0.2-0.4 eV, considering the interactions at the GNR/substrate interface.  
Figure 2: Graphene electronic band structure. Inset: the neighborhood of a Fermi point.  
As shown in Figure 3, the width, \(w\), of a graphene nanoribbon is directly related to the integer, \(N\), indicating the number of dimers (two carbon sites) for the armchair nanoribbons and the number of zigzag lines for the zigzag nanoribbons. For armchair GNRs, the unit cell has a length \(T=\sqrt{3}a\) and a width \(w=Na/2\), whereas for zigzag ones, it is \(T=a\) and \(w=\sqrt{3}Na/2\).  
Given the number \(N\) and, thus, the GNR width, \(w\), the energy spectrum of the \(\pi\)-electrons can be obtained by slicing the band structure of graphene in Figure 2 (e.g., [20,22,23]). Figure 4 shows the band structure for an armchair GNR, assuming \(N=5\) and \(N=6\). For \(N=\)3\(q-1\) (\(q=1\),\(2\),...), there exists a sub-band, \(\mu\), for which the direct bandgap is zero; hence, the GNR behaves as a metal. For other values, the armchair GNRs is semiconducting. However, the direct bandgap decreases with \(w\) increasing and approaches zero in the limit of very large w, consistent with the behavior of the graphene layer reported in Section 2.1. Therefore, the value of the width (hence, the number, \(N\)) determines whether the nanoribbon is metallic or semiconducting.  
Figure 4: Energy band structure of an armchair nanoribbon _versus_ the normalized wavenumber \(k^{\prime}=kl/\pi\). **(a)**\(N=5\); **(b)**\(N=6\).  
Figure 3: The structure of graphene nanoribbons. **(a)** Armchair edges; **(b)** zigzag edges.  
Figure 5 refers to a zigzag GNR, assuming \(N=5\) and \(N=20\): such nanoribbons have partially flat bands owing to the edge states; hence, their bandgap is always zero, and their behavior is always metallic.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites
### Synthesis of GO Membranes Composites  
[PERSON] et al. [54] firstly prepared hydroxylated graphene (GOH), by using ferrous chloride and hydrogen peroxide at certain volume ratios, while adjusting the pH with diluted HCl, and GO with a modified Hummers method. Then, 1 mg of GO and 1 mg of GOH were mixed into a beaker, under ultrasonication for 30 min to form a uniform dispersion and the solution was put onto the polyethersulfone membranes of pore sizes 0.22 um and 1 um under vacuum.  
The morphology and validation of the deposition of the material on the membrane was done via scanning electron microscopy (SEM) and X-ray diffraction (XRD). The performance of the adsorption properties was evaluated with dye compounds, such as rhodamine B and methyl orange, and with the protein albumin from bovine serum.
## 7 Performance Evaluation  
Many simulations were carried out in order to evaluate the performance of the proposed idea in terms of average prediction error, number of involved cells and system utilization.
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 4 Analysis of Additional Longitudinal Forces
### Additional Bending Force  
In the calculation of additional bending force, the load case of a moving train can be simplified to apply a uniform load on the whole length of the bridge [29, 30]. The ZK live load (equivalent to the 0.8 UIC live load), which is the standard live load for HSR bridges in the Chinese Code TB-10621-2014 [31], was adopted in the calculation of the additional bending force, as shown in Figure 10. Usually, the static live load is adopted, without considering the impact effect of passing trains.  
To get close to the actual operation conditions of the two-line high-speed railway, the additional bending forces under both single-line trains and double-line trains were calculated and analyzed. In the calculation, the ZK live load (equivalent to the 0.8 UIC live load) was loaded on the two integral rigid-frame bridges (from pier 4 to pier 10).  
Similarly to the common simply supported box-girder and continuous box-girder HSR bridges, the additional bending stress of the rails located in the integral rigid-frame bridge was very small, with an absolute value less than 4.3 MPa under the action of a single-line train and less than 8.5 MPa under double-line trains (as shown in Figure 11). The reason is that the vertical stiffness of the integral rigid-frame bridge is relatively large and the bending deformation of the beam caused by the vertical load is very small, which consequently gives rise to the very small longitudinal displacement along the neutral axis of the beam body. Furthermore, the additional bending stress of the rail showed a decreasing trend after adopting the small resistance fasteners, with a maximum stress (absolute value) of 2.83 MPa under a single-line train and 5.6 MPa under double-line trains.  
Figure 10: Graphical representation of the ZK live load.  
Figure 9: Additional expansion force of rails under bridge temperature variation: (**a**) temperature rising; (**b**) temperature dropping.
## 3 Proposed Feed-Forward NN-Based DR Estimation Method
### Back-Propagation Learning Algorithm  
Among the learning algorithms reported in the literature for performing NN estimation, the back-propagation algorithm is commonly used for training a network owing to its simplicity and applicability [54]. This algorithm comprises two sequential steps: first, the weights of the NN are initialized randomly, and then, the output of the NN is computed and compared with the actual output. The error at the output layer, that is, the network error, is calculated by comparing the actual output \(\hat{y}_{out}\) to the desired value \(y_{out}\) to iteratively adjust the weights of the hidden and output layers in accordance with the following function:  
\[E=\frac{1}{2}\sum(y_{out}-\hat{y}_{out})^{2}. \tag{6}\]  
Epoch is a hyperparameter that defines the number of times the entire training vectors are used to update the weights. During the training process, the error is optimally minimized. The iterative step of the gradient descent algorithm changes weights \(w_{j}\) based on the following relationship:  
\[w_{j}\to w_{j}+\Delta w_{j},\ \text{where}\ \Delta w_{j}=-\eta\frac{ \partial E(w)}{\partial w_{j}}. \tag{7}\]  
The parameter \(\eta\) (>0) is called the learning rate. There are numerous variations in the basic algorithm based on other optimization techniques (namely, conjugate gradient and Newton methods).  
The Marquardt-Levenberg algorithm (trainlm) approximates [PERSON]'s method, which is designed to approach the second-order training speed without applying the Hessian matrix.  
The resilient back-propagation training algorithm (trainrp) is a local adaptive learning technique, which performs supervised batch learning in a feed-forward NN. Trainrp removes the negative impact of the dimension of the partial derivative. Therefore, only the sign of the derivative implies the direction of a weight update process.
# Differential Signalling in Free-Space Optical Communication Systems
## 4 Accounting Atmospheric Turbulence
### Moderate to Strong Turbulence  
For moderate (\(\sigma_{R}^{2}\approx 1\)) to strong (\(\sigma_{R}^{2}>1\)) turbulence regimes, we assume \(h=XY\), where \(X\) and \(Y\) refer to large-scale and small-scale atmospheric turbulence effects, respectively [33]. Knowing that both \(X\) and \(Y\) are expressed by the gamma distribution, we model \(h\) by the PDF of gamma-gamma (GG) distribution as [38]  
\[f_{h}(h)=\frac{2(ab)^{\frac{a+b}{2}}h^{\frac{a+b}{2}-1}}{\Gamma(a)\Gamma(b){h_{ 0}}^{\frac{a+b}{2}}}K_{a-b}\left(2\sqrt{ab\frac{h}{h_{0}}}\right), \tag{20}\]  
where \(a\geq 0\) and \(b\geq 0\) are also known as the effective numbers of large- and small-scale turbulence cells, respectively [38; 39]. \(K_{l}(\cdot)\), and \(\Gamma(\cdot)\) denote the modified Bessel function of 2 nd kind and order \(l\), and the gamma function, respectively. The two parameters of \(a\) and \(b\) characterize the irradiance fluctuation PDF and are related to the atmospheric conditions, which are given by [33]  
\[a =\frac{1}{exp\big{(}\sigma_{lnX}^{2}\big{)}-1}, \tag{21a}\] \[b =\frac{1}{exp\big{(}\sigma_{lnY}^{2}\big{)}-1}. \tag{21b}\]  
For the plane wave propagation model and considering the Rx aperture diameter \(D_{\text{Rx}}\), the closed form expressions for \(\sigma_{lnX}^{2}\) and \(\sigma_{lnY}^{2}\) are given by [33]  
\[\sigma_{lnX}^{2}=\frac{0.49\sigma_{R}^{2}}{\left(1+0.65d^{2}+1.11\sigma_{R}^{ 12/5}\right)^{7/6}}, \tag{22a}\] \[\sigma_{lnY}^{2}=\frac{0.51\sigma_{R}^{2}\Big{(}1+0.69\sigma_{R}^{12/5}\Big{)} ^{-5/6}}{1+0.90d^{2}+0.62d^{2}\sigma_{R}^{12/5}}, \tag{22b}\]  
where \(d=\left(kD_{\text{Rx}}^{2}/4L\right)^{0.5}\)[40]. Knowing that for GG distribution, we have [41; 42]  
\[E[h^{n}]=\left(\frac{ab}{h_{0}}\right)^{-n}\frac{\Gamma(a+n)\Gamma(b+n)}{ \Gamma(a)\Gamma(b)}, \tag{23}\]  
then it is easy to show that  
\[Mean(h_{i})=h_{0}, \tag{24a}\] \[Var(h_{i})={h_{0}}^{2}\bigg{(}\frac{1}{a}+\frac{1}{b}+\frac{1}{ab}\bigg{)}. \tag{24b}\]  
Thus, it is possible to formulate DTL average and variance of the DSS link with the closed-form expression as in Table 1. To derive the BER expression, the Bessel function in Label (20) is replacedwith Meijer-G function, which results in simplified BER expression in Table 1. See Appendix A for more details.
## Materials and Methods
### Wind Tunnel Simulation Test Section
#### 2.2.2 Spray Test Design of Wind Tunnel  
The spray control system used in the test was designed by the NPACC independently. The system included water tank, relief valve, boost pump, pressure gauge, pressure reducing valve, flow meter, and nozzles. The spray pressure was regulated in two stages with a safety valve and a pressure reducing valve for precise adjustment of the spray pressure in the range of 100-500 kPa (14-73 psi).  
The nozzles tested were randomly selected from the helicopter spray rod and three nozzles were selected for each type. The three CP-02 nozzles were named CP02-1, CP02-2, and CP02-3; the CP-03 nozzles were named CP03-1, CP03-2, and CP03-3; the CP-04 nozzles were named CP04-1, CP04-2, and CP04-3. All test nozzles had been used for more than three years (Figure 2).  
The droplet size distribution was measured by DP-02 laser diffraction device (LDD) (Zhuhai OMEC Instrument Co., Ltd, Zhuhai, China). The emitter and receiver of LDD were horizontally placed on the special working platform at the observation windows on both sides of the wind tunnel test section. The laser had 48 independent detection units with the sampling time less than or equal to 2 min, the measurement range was 1 to 1500 \(\upmu\)m, and the median diameter repeatability accuracy was \(\pm\) 3%. After each measurement, the droplets size test report can be generated. The nozzles were placed horizontally, 55 cm from the upper and lower layers of the wind tunnel, and 95 cm from the LLD. The spray plume was in the same horizontal plane with the laser line and spaced 40 cm apart. The spray plume was perpendicular to the laser line and paralleled to the wind tunnel observation window (Figure 3).  
According to the medium and low flight velocities of the helicopter's daily operation, six constant airflow velocities were set in the wind tunnel test, 0 m/s (0 km/h), 16.7 m/s (60 km/h), 19.4 m/s (70 km/h), 22.2 m/s (80 km/h), 25.0 m/s (90 km/h), and 27.8 m/s (100 km/h), among which the 0 m/s was used as a static comparison reference. At the same time, according to the actual spray pressure of each nozzle during helicopter spraying operation, the spray pressure of CP02, CP03 and CP04 were set as 345 kPa (50 psi), 330 kPa (48 psi), and 310 kPa (45 psi), respectively.  
Figure 3: Schematic of the spray test layout.  
Figure 2: Nozzles tested.  
Before the wind tunnel test, each nozzle was installed on the spray track and connected with the spray system for a flow test. Each nozzle was measured five times by SC-1 flow meter (Rittenhouse Company Inc., Philadelphia, PA, USA), and the average value was recorded as the spray flow rate. After the flow measurement, the droplet size was measured by LDD. The room temperature and air humidity were kept constant during the test, and the test reagent was water. Each test was repeated at least three times with a test time of 30 seconds to ensure that the standard deviation of the droplet size data of the same group was not greater than 10% [5].
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 3 Results and Discussions
### Chromatic Dispersion Parameter Prediction  
The proposed model's prediction of the CD parameter is visually depicted in Figure 3e-h. Figure 3e shows the model's ability to predict the CD parameter alone without any other impairments, with very narrow boxes indicating high accuracy. The obtained R-squared and RMSE values were 1 and 0 ps/nm, respectively. When the signal was corrupted with severe turbulence, defined by \(10^{-13}\) m\({}^{-2/3}\) turbulence, the R-squared value slightly dropped to 0.98, and the RMSE increased slightly to 0.2 ps/nm. Nonetheless, the model was still capable of  
Figure 3: Prediction accuracy of the GPR model represented visually using boxplot. (**a–d**) Prediction accuracy of the OSNR parameter, (**e–h**) prediction accuracy of the CD parameter, and (**i–l**) prediction accuracy of the \(C_{n}^{2}\) parameter. Severe impairments values are jointly included in predicting the impairments defined by 12 dB OSNR, 500 ps/nm CD, and \(10^{-13}\) m\({}^{-2/3}C_{n}^{2}\). The channel conditions are depicted on each subfigure.  
predicting the CD parameter with high accuracy, as shown in Figure 3f. Similarly, when the system was corrupted with ASE noise defined by 12 dB OSNR, the R-squared value dropped slightly to 0.96, while the RMSE increased slightly to 0.27 ps/nm. With this slight degradation in the model performance, the model still accurately predicted the CD parameter, as depicted in Figure 3g. Finally, Figure 3 shows the model's performance in predicting the CD parameter when the system was corrupted with both ASE noise defined by 12 dB OSNR and turbulence defined by \(10^{-13}\) m\({}^{-2/3}\). The joint impairments slightly degraded the model's accuracy, with the R-squared value dropping to 0.93, and the RMSE value increasing to 0.37 ps/nm. In general, the model was able to predict the different CD parameter values with high accuracy.
## 3 Results and Discussion
### Deformation Mechanism Analysis  
From April 2019 to the end of 2019, the system recorded the development of Shangtan landslide deformation process. Figure 7 shows the landslide's cumulative deformation time series curves and monthly rainfall-time curves (as space is limited, the analysis is conducted for the Y-directional displacement, which has the largest deformation). Significant displacements from the GD03 and GD04 monitoring stations were observed, and the deformation of the Shangtan landslide can be roughly divided into three stages. Slow deformation stage: The accumulated landslide deformation increased slowly in April and May, and the accumulated deformation of the seven GNSS monitoring stations was about 10-20 mm. Evenly accelerated deformation stage: The results from most of the GNSS monitoring stations showed that the landslide deformation displayed an increasing trend in June, and the deformation rate remained stable, with a maximum average rate of 19.2 mm/d and a minimum average rate of 4.8 mm/d. Variable acceleration deformation stage: The GNSS monitoring results show that the landslide entered a stage of accelerated deformation in  
Figure 6: Threshold calculation process.  
early July, and the accumulated deformation increased sharply, with the maximum deformation rate of the GD03 and GD04 monitoring stations reaching 119.6 mm/d (15.3 mm/h) and 148.4 mm/d (17.9 mm/h); the accumulated deformation was more than 1 m by the end of September. It is worth stating that the accumulated displacement of GD08 did not exceed 10 mm, indicating that the road foundation is mostly stable and it is an effective relative stability reference station.  
By counting the rainfall data, the cumulative rainfall in 2019 was 2806.6 mm, and the cumulative rainfall from July to October was 2400.0 mm, accounting for 86%, with the concentrated rainfall showing obvious time-domain distribution characteristics. According to the relationship between the accumulated landslide deformation and rainfall in Figure 7, it can also be seen that there is an obvious positive correlation between landslide deformation and the concentrated rainfall, and the influence of the concentrated rainfall on the rate of landslide deformation is significant, indicating that rainfall is the main factor inducing the accelerated deformation of the Shangtan landslide.  
However, when the displacement data were analyzed using the empirical warning model [43], it was found that the tangent angle calculated at the GD03 and GD04 monitoring stations in July exceeded 85\({}^{\circ}\) many times, which indicates that the landslide is in the pros-slide stage.