esa-sceva/satcom-chunk-collection huggingface dataset:
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 2 LCoS Working Principles  
### Birefringence Effect of Liquid Crystals  
Figure 2 illustrates the working mechanism of an LC phase retarder. Here, we assume the LC molecules are uniformly aligned along the x-axis with a tilt angle \(\theta\) (Figure 2a). The uniaxial LC exhibits an extraordinary refractive index \(n_{e}\) along its optical axis and an ordinary refractive index \(n_{o}\) along the two orthogonal axes. Thus, the corresponding birefringence is defined as \(\Delta n=n_{e}-n_{o}\). In Figure 2b, when an incident light passes through the LC layer along the z-axis, the x-polarized light experiences a tilt-angle-dependent refractive index \(n_{x}\) (\(\theta\)), while the y-polarized light experiences a constant index \(n_{y}=n_{o}\). The accumulated phase retardation between the two linear polarizations is  
\[\Gamma=[n_{x}-n_{y}]\cdot\frac{2\pi}{\lambda}\cdot d. \tag{1}\]  
Here \(d\) is the cell gap (LC layer thickness), and \(n_{x}\) (\(\theta\)) follows  
\[\frac{1}{n_{x}(\theta)^{2}}=\frac{\cos^{2}\theta}{n_{e}^{2}}+\frac{\sin^{2} \theta}{n_{o}{}^{2}}. \tag{2}\]  
In a reflective display, the incident light passes through the LC layer twice. As a result, the effective cell gap is \(2d\) and \(\Gamma\) is doubled.  
Figure 1: The schematic of an LCoS structure.  
Figure 2: (**a**) The schematic structure of an LC phase retarder. (**b**) Refractive index ellipse of a uniaxial liquid crystal.  
When an external voltage is applied, the LC molecules (with \(\Delta\varepsilon>0\)) would reorient toward the electric field direction which, in turn, modifies the phase and polarization of the outgoing light. Depending on the surface alignment and the LC material properties, three LC modes are widely adopted: vertical alignment (VA) and mixed-mode twist nematic (MTN) mainly for intensity modulation, as well as the homogeneous alignment for phase modulation.
## 4 Results and Discussion
### CBR Tests  
The CBR is a test usually performed to assess the strength of subgrade soils and base course materials in pavement work. As summarized in Table 6 and Figure 6, the CBR values of the crude oil contaminated soils under un-soaked conditions significantly decreased compared with the natural ones. This reduction is probably due to the combination of excessive oil presence and the low maximum density of the contaminated soil. These results are consistent with those of [PERSON] et al. (2019) and [PERSON] (2019). In contrast, the values of CBR for natural and contaminated soils under soaked conditions were similar.  
Figure 6 also shows that, for both soaked and un-soaked treated (CEM II and OPC) soils, the values of CBR were significantly improved in comparison to those for the natural soil. Based on the review of [PERSON] (2018), cement contains hydration products that increase therapy strength and performance. The enhancement in un-soaked and soaked CBR values is due to the production of cementitious components, such as calcium silicate hydrates and calcium aluminate hydrates in the contaminated soil stabilized/solidified by cement ([PERSON] et al., 2018).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Identification**} & **Maximum Dry Unit Weight** & **Optimum Moisture** & \multicolumn{2}{c}{**CBR \%**} \\ \cline{3-5}  & **(kN/m\({}^{3}\))** & **Content \%** & **Unsoaked** & **Soaked** \\ \hline Natural Soil & 17.60 & 12.6 & 41.88 & 25.25 \\ Contaminated Soil & 15.89 & 12.2 & 26.24 & 23.72 \\ Treatment Soil with CEM II & 16.55 & 18.7 & 75.16 & 38.26 \\ Treatment Soil with OPC & 16.01 & 14.5 & 60.35 & 34.68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CBR test results for natural, contaminated, and treated (CEM II and OPC) soil samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Properties**} & **Natural** & **Contaminated** & **Treatment Soil** & **Treatment Soil** \\  & **Soil** & **Soil** & **with CEM II** & **with OPC** \\ \hline Coefficient of permeability & \(k\) (cm/s) & \(3.47\times 10^{-5}\) & \(7.34\times 10^{-6}\) & \(4.55\times 10^{-8}\) & \(4.87\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Permeability test results.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Methodology
#### 2.2.3 Artificial Neural Network Hybrid Approach  
The multilayer perceptron (MLP), which is a multilayer feed-forward ANN, is one of the most widely used ANNs, mainly in the field of water resources [79; 80]. A multilayer perceptron has one or more hidden layers between its input and output layers. The neurons are organized in layers such that neurons of the same layer are not interconnected and that the connections are directed from lower to upper layers. Each neuron returns an output based on a weighted sum of all inputs and according to a nonlinear function called the transfer or activation function. The input layer, made up of different metrics from Sentinel-1 and Sentinel-2 data, is connected to the hidden layer(s), which is made up of hidden neurons. The final estimates of the ANN are given by an activation function associated with the final layer called the output layer, using a sum of the weighted outputs of the hidden neurons.  
The ANN model architecture consists of three hidden layers of 20 neurons with a rectified linear function (ReLu) as activation functions and an output layer with a single neuron with a linear activation function. The mean square error was used as the loss function and the gradient backpropagation was carried out using a first order stochastic gradient-based optimizer (Adam).  
Different predictors based on Sentinel-1 and Sentinel-2 were tested to estimate soil moisture: VV, VH, incidence angle, VH/VV, \(NDVI\), and \(I_{SSM}\).  
1. The VV and VH signals are identified for their high sensitivity to soil moisture.
2. The classical change detection \(SSM\) index \(I_{SSM}\) is calculated as a function of radar backscattering coefficients in VV polarization to use it for soil moisture estimation.
3. The incidence angle has an effect on the contribution of soil and vegetation components on the radar signal.
4. The \(NDVI\) index is identified to take into account the effect of vegetation cover on the backscattering signal.
5. The VH/VV ratio is identified to take into account the effect of vegetation cover on the backscattering signal [81].
6. \(SSM_{t}\) estimated from the classic change detection approach described in Section 3.1, Equation (2) is also considered as input.  
The ANN models were trained using in situ soil moisture measurements retrieved from the ISMN as target. The training of the ANN models was conducted using 70% of the data samples. Thirty percent were kept for validation.
## 1 Introduction
[25], a good HF laser beam quality with a far-field divergence angle of 1.7 times the diffraction limit was attained using the method of calibrated apertures on an unstable telescope cavity with a magnification of \(M=2\). In Ref. [26], a radiation divergence of \(\theta_{0.5}=0.29\) mrad (containing 50% encircled energy), which corresponds to four diffraction limits, was achieved using an unstable telescopic resonator with \(M=3\) on a self-sustained volume-discharged non-chain DF laser. The radiation divergence measurements were carried out by the method of a focal spot with application of a reflecting wedge. In Ref. [27], a stable HF laser beam was obtained by adopting a confocal positive branch resonator with a magnification of \(M=3.4\). However, due to the lack of relevant beam quality diagnostic equipment, they did not give achieve a far-field divergence angle of the unstable laser system. The unstable resonators used in the abovementioned studies applied external cavity structures, with discharge chambers sealed by CaF\({}_{2}\)/BaF\({}_{2}\) windows and cavity mirrors placed outside the chambers. This type of resonator has the disadvantages of long structure, being difficult to adjust, suffering additional energy loss, and incurring extra costs caused by CaF\({}_{2}\)/BaF\({}_{2}\) windows. In this paper, a compact intra cavity resonator was used. The goal governing the practical long-distance applications of this laser is increasing the energy (average power) of laser radiation and beam quality. The current paper performed a detailed investigation on the beam quality of a compact close-cycle electric-discharged non-chain pulsed DF laser with a confocal positive branch resonator, and the aim was the compression of the divergence angle in a limited energy range.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 2 Materials and Methods
### Problem Formulation  
Let \(\left\{\mathbf{I}_{i},\boldsymbol{y}_{i}\right\}_{i=1}^{n}\) be a training set where \(\mathbf{I}_{i}\in\mathcal{R}^{w\times d\times 3}\) is an image of size \(w\times h\times 3\), and \(\boldsymbol{y}_{i}\in\mathcal{R}^{1\times c}\) is its corresponding \"object indicator\" vector of size \(c\), which is equal to number of objects of concern. Vector \(\boldsymbol{y}_{i}\) is composed of a binary sequence where one indicated the presence of an object in the scene, while zero indicated otherwise. Let also \(f_{l}^{\text{CNN}},\;l=1,\ldots,L\) be a CNN model with \(L\) layers pre-trained on a large amount of labeled auxiliary images from a different domain. Here, \(f_{l}^{\text{CNN}}\) refers the output of the \(l^{th}\) layer. Our aim is to develop a deep learning system that can map the test images \(\left\{\mathbf{I}_{i}\right\}_{i=1}^{m}\) to binary vectors \(\boldsymbol{y}_{i}\) (which indicates the presence or no presence of objects) based on the available training set as well the pre-trained CNN model. Figure 1 clarifies the image multi-labeling problem further.
## 5 Empirical Evaluation: Setup and Findings
### Selected Ensemble Model  
The ensemble of machine learning models X [54; 55] is a recognized technique for improving performance via several classifiers. The collection of the outcome from various ensemble models could truly enhance prediction precision [56]. Equation (10) was used to perform weighted aggregation as follows:  
\[Conv_{i}^{en}=\frac{\sum_{j=1}^{n=4}\left(W_{j}\times Conv_{ij}\right)}{\sum_{i =1}^{C=2}\sum_{j=1}^{n=4}\left(W_{j}\times Conv_{ij}\right)} \tag{11}\]  
where \(Wj\) is the corresponding AUC, j is the classifier number, \(Conv_{i}^{en}\) represents confidence values \(\in[0,1]\), and \(i\) is the class number. Each model's output, \(\forall j\) (\(j=1,3,\ldots,n=4\)) \(\in\) R  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Machine Learning Methods**} & \multicolumn{2}{c}{**A Key-Value Pair for Hyperparameters**} \\ \hline \multirow{8}{*}{KNN} & \(\bullet\) & No. of queries’ neighbors \(\Rightarrow\) 27 \\  & \(\bullet\) & Computing the NN such that \\  & \(\checkmark\) & Each node represents a D-dimensional hyper-sphere (ball-tree); \\  & \(\checkmark\) & Every leaf node represents a D-dimensional point given that the leaf size is 30 KD (KD-tree); \\  & \(\checkmark\) & A search based on brute-force (BF). \\  & \(\bullet\) & The size of the leaf based on the nature of the problem for BT or KDT \\  & \(\bullet\) & Determination of the Euclidean distance \\ \hline NB & \(\bullet\) & A part of the feature’s largest variance \\  & \(\bullet\) & Var smoothing \(\Rightarrow\) 0.01 \\ \hline DT & \(\bullet\) & Criterion: GINI \\  & \(\bullet\) & Number of estimators \(\Rightarrow\) 100 \\ \hline \multirow{8}{*}{Proposed Model (TFKNN)} & \(\bullet\) & No. of s of the query \(\Rightarrow\) 23 \\  & \(\bullet\) & Nearest neighbors search Method \\ \cline{1-1}  & \(\checkmark\) & Ball tree (BT) \\ \cline{1-1}  & \(\checkmark\) & Brute force (BF) \\ \cline{1-1}  & \(\bullet\) & Distance \(\Rightarrow\) Euclidean distance \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key-value pair for hyperparameters of different ML approaches used by the grid-search.  
Figure 7: The training, testing, validation, and 10-cross-fold validation of the applied PIDD dataset.  
assigns C (diabetes or not) confidence values \(Conv_{i}\in\mathbb{R}\). Therefore, the final class label C\({}_{i}\) of the unobserved data is determined such that for the ensemble X \(\in\mathbb{R}_{C}\), the \(Conv_{i}^{cn}\) = max (Y (X)).
## 3 Satellite Precipitation Uncertainty Distribution Approximation
### The GMM Model Distributions and Performances
To better illustrate that, we expanded the AIC performance evaluation to multiple NRT satellite-only precipitation products. In addition to the GIRAFE and the GSMaP NRT precipitation products, the Climate Prediction Center (CPC) morphing technique Version 1.0 RAW (CMORPH-RAW, [53]), the Integrated Multi-satellite Retrievals for GPM V06--the Early/precipitationCal version (IMERG V06E, [54]), the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis Version 7 Real Time (TMPA-RT, [55]), the TAPEER Version 1.5 (TAPEER 1.5, [20; 23]) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, [56; 57]), were also assessed. Although the main distributions of uncertainty as a function of precipitation accumulation were retained between the products, more apparent AIC goodness-of-fit differences (second-order uncertainty) were detected for low-to-moderate precipitation accumulations. This can be attributed to various factors, including the algorithm itself as well as the source of observations (e.g., PMW rain rates, IR images and so on), which can be the subject of further investigations.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter
### Routing Evaluation Parameter
#### 2.2.5 Residual Energy Level Factor  
In general, node energy is always a vital index for designing and evaluating routing algorithm in wireless sensor networks. For those parameters of this paper defined above, to take care of the energy consumption of the current node \(i\), we have considered the residual energy of node \(i\), single-hop data transmission distance \(d_{i,j}\), and forward distance \(d_{i,j^{\prime}}\). However, it also should not happen that the routing ignores the residual energy state of the forward neighbor \(j\), and it is necessary to introduce the residual energy level as a reference factor. Here, the residual energy level factor \(RE^{ij}\) of the forward neighbor \(j\) is defined as follows:  
\[RE^{ij}=\frac{E_{j}}{E_{o}} \tag{20}\]  
where \(E_{j}\) is the residual energy of forwarding neighbor \(j\). \(E_{o}\) is the initial node energy.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 5 Numerical Simulation of VVSDSI Bearing
### Contact Unit Definition  
During the operation of disc springs, friction forces are generated between both the contact surfaces of disc springs and connecting plates, as well as between the end faces of disc springs and connecting plates. Based on the relevant literature [32,33] and finite element trial calculation results, the friction coefficients in the finite element simulations are determined as follows: the friction coefficient between disc springs and the connecting plate contact surfaces is set to 0.6, while that between the disc spring end faces is set to 0.25. The contact type employs a surface-to-surface interaction, with normal contact behavior simulated using hard contact elements and tangential contact behavior modeled through the penalty function method.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Experimental Analysis
#### 4.5.2 Confidence Interval Analysis  
Confidence intervals are used to estimate the range of a parameter with a high probability that the true parameter value falls within that range. A 95% confidence interval indicates a 95% probability that the true mean value lies within that interval. For a sample mean and standard error (SE), the confidence interval (CI) is calculated as follows:  
\[\text{CI}=\overline{X}\pm\frac{\mathfrak{t}}{\uparrow}\text{-SE} \tag{17}\]  
where \(\mathfrak{t}\) is the z-value corresponding to the confidence level, and, for the 95% confidence level, \(\mathfrak{t}\approx 1.96\).  
* On the R8 dataset, the 95% confidence interval for the accuracy of the BGF model is [98.10, 98.80], and, for the BertGCN model, it is [97.85, 98.49].
* On the R52 dataset, the 95% confidence intervals for the accuracy of the BGF model are [93.45, 94.09], while, for the BertGCN model, they are [92.50, 93.24].
* On the MR dataset, the 95% confidence intervals for accuracy are [85.89, 86.65] for the BGF model and [85.93, 86.55] for the BertGCN model.  
These confidence interval results illustrate the reliability and stability of the model performance. The BGF model shows wider confidence intervals on the R8 and R52 datasets, indicating better performance on these datasets. In contrast, on the MR dataset, the confidence intervals almost overlap, suggesting little difference in performance between the two.
## 1 Introduction
* Quantum walks on circles are used to transfer the random sequence to verify the validity of the proxy warranty and the corresponding quantum teleportation protocol is performed to transmit the message copy of ciphertext from the proxy signatory to the verifier, which assists the verifier to complete the verification of the validity of the proxy signature, in which it is unnecessary to generate entangled states in advance as quantum channels and the essential entangled states can be created by quantum walks. We note that this model differs from the formalisms of quantum walks employed in [12; 14] and that it is firstly employed in quantum proxy signature. * The proposed scheme may be easy to implement owing to the experimental realizations of quantum walks [7; 8] and the designed QOTP-CNOT encryption. Furthermore, it may be applied into electronic payment or electronic commerce. The paper is organized as follows. In Section 2, we present the methods involving the employed models, i.e., quantum walks on circles and the corresponding quantum teleportation, and the designed quantum proxy signature scheme consisting of initializing phase, authorizing phase, signing phase and verifying phase. In Section 3, we elaborate on the results referring to the security of the scheme. In Section 4, we discuss the complexity and the applications of the presented scheme. In Section 5, a conclusion is shown.
## 3 Numerical Validation and Parametric Analysis
### The Timoshenko Model  
Table 2 displays the parameters of the beam and foundation utilized for the numerical simulation of the Timoshenko beam model. The vehicle parameters are taken from the values listed in Table 1. Similar to the previous subsection, the correctness of the analytical results is first verified. By substituting the parameter values from Tables 1 and 2 into Equation (25), one can calculate  
\[ \begin{split}\|w_{0}\|=6.763\times 10^{-4},\quad\|w_{1}\|=9.134 \times 10^{-6},\quad\|w_{2}\|=4.249\times 10^{-7},\\ \phi_{0}=\frac{\|w_{1}\|}{\|w_{0}\|}=\frac{9.134\times 10^{-6}}{6.7 63\times 10^{-4}}\approx 0.0135<1,\\ \phi_{1}=\frac{\|w_{2}\|}{\|w_{1}\|}=\frac{4.249\times 10^{-7}}{9. 134\times 10^{-6}}\approx 0.0465<1.\end{split}  \tag{27}\]  
Therefore, the approximate analytical solution obtained from Equation (25) will rapidly converge to the actual solution of Equation (20). Figure 9 presents the first three terms of \(w_{j}\), along with a comparison between the approximate analytical solution and the numerical solution, demonstrating that Equation (25) is sufficiently accurate for discussing the impact of parameters on pavement deformation. Figure 10 demonstrates how changes in the amplitude of pavement roughness affect pavement deformation across a range of vehicle velocities, using parameters as specified in Tables 1 and 2. At lower vehicle velocities, pavement deformation increases with the growth of \(A_{0}\); at higher vehicle velocities, pavement deformation is virtually unaffected by changes in \(A_{0}\). Comparing Figure 10 with Figure 4, the patterns of influence by \(A_{0}\) in the two different models are broadly similar, with the difference being that an increase in \(A_{0}\) imparts a more pronounced fluctuation to the pavement deformation simulated by the Timoshenko beam model. Figures 11 and 12 show the influence of different vehicle velocities and the nonlinear stiffness of the foundation on the pavement response, with \(A_{0}\) set to \(0.01\,m\) and \(0.05\,m\), respectively, and all other parameters maintained at the values listed in Tables 1 and 2. The two figures reveal that for the nonlinear stiffness coefficient \(k_{3}\), a threshold exists. If \(k_{3}\) is less than the threshold, pavement deformation stays constant with an increase in \(k_{3}\). Beyond this threshold, the pavement deformation first experiences a minor reduction and then escalates notably as \(k_{3}\) rises. The critical value for \(k_{3}\) is not static; it increases in conjunction with the vehicle velocity's acceleration. Upon examining Figures 11 and 12 in contrast to Figures 5 and 6, it becomes clear that the wheel's location does not coincide with the area of the pavement that undergoes the most significant deformation. Instead, there is a noticeable deviation between the two. When simulating the pavement with an E-B beam, this offset is quite pronounced and increases with the rise in nonlinear stiffness.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults
#### 3.1.1 Overcurrent Algorithm  
For the OC algorithm, the threshold of 200 A was chosen based on the maximum expected current during normal operation and the fault conditions anticipated within the MVDC grid. This value is determined by considering the typical fault current magnitudes for pole-to-pole and pole-to-ground faults, as well as the electrical characteristics of the system components, such as cables, converters, and protection devices. A lower threshold could lead to unnecessary tripping during transient conditions, while a higher threshold could delay fault detection, potentially compromising the system protection.  
Figure 3 shows how the fault detection time varies depending on the type of fault and on the current flow in each adjacent link. The overcurrent algorithm does not meet the requirements to protect the VSC during high-resistance faults, since the fault detection speed is not fast enough.  
Observing Table 1, it can be noted that relay R24 exhibits higher fault detection time values. Despite the fact that the current flowing through link L24 is higher in a steady state, it changes polarity when a fault occurs. As a result, it takes more time for the  
Figure 3: Fault at bus 2, DC current at R21 and R24: (**a**) pole-to-pole fault, and (**b**) pole-to-ground fault.  
current to cross the selected threshold in absolute values. The most challenging cases in this scenario are ground faults, particularly for relay R24. In both cases, as shown in Figure 2(b), the current does not cross the threshold during the fault condition with a 20 \(\Omega\) resistance. Fault detection occurs when the measured current crosses the selected threshold in absolute values, without considering the direction of the current. This represents one of the limitations of the algorithm, along with its inability to effectively handle faults with higher resistance. For high fault resistances, the algorithm does not meet the DC protection requirements.