esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Design of a Transient-State Resonant Converter Used as a Frequency Multiplier
## 3 Design Methodology and Experimental Testing
### Implications on the Efficiency of the Resonant Converter Acting as a Frequency Multiplier
Figure 17 shows the increase in inductance L\({}_{\rm s}\) as the value of the multiplier increases, where this occurs due to the increase in the load resistance at the output R\({}_{\rm L}\) and the increase in the equivalent series resistance R\({}_{\rm s}\) as the operating frequency \(f_{o}\) in the resonant tank increases. Figure 16: Increase in series inductance L\({}_{\rm s}\) by increasing the decay factor \(d_{f}\). Figure 14: Measurements on the input power P\({}_{\rm in(inver)}\) and output power P\({}_{\rm o(inver)}\) to determine the efficiency in the inverter stage. Figure 15: Measurements on the input power P\({}_{\rm in(inver)}\) and output power P\({}_{\rm o(inver)}\) to determine the efficiency in the inverter stage. Figure 18 shows the measurements performed to determine the efficiency of the resonant tank, where it was necessary to measure the input power \(\rm P_{int(tank)}\) and output power \(\rm P_{o(tank)}\). The measurements were performed with the switching frequency \(f_{sw}=300\) kHz and with the multiplier \(n=3\) to check the performance of the converter operating with different decay factors. Figure 19 shows the tank efficiency using values at \(d_{f}=0.50\), \(0.675\), \(0.785\), \(0.90\). As shown in Figure 19, the size of the series inductance \(\rm L_{s}\) affects the efficiency of the resonant tank because the higher the inductive value, the higher the losses related to the high operating frequency, which greatly reduces the efficiency of the tank. Using high values in the multiplier \"\(n\)\" and high decay factors such as \(d_{f}=0.9\) would cause the inductance value \(\rm L_{s}\) to increase greatly, being necessary to use high permeability and special magnetic cores for high frequency, to achieve the required inductance with the lowest number of turns. In addition, the use of special wiring such as Litz wire is necessary to reduce losses due to skin effect, eddy currents or proximity effect.
## 2 Background of the Proposed Method
### Speeded-Up Robust Features (SURF)
#### 2.1.1 Interest Point Detection Based on a Hessian Detector  
SURF uses a Hessian matrix for detecting interest points, which represent the characteristics of the image and include useful information for identification, including corner points. An integral image is used in an approximated Hessian matrix to improve the calculation speed of box-type convolution filters. The integral image \(J(x,y)\) at coordinate \(x=(x,y)\)\(x=(x,y)\) is the sum of all pixels within a rectangular area formed by the origin and coordinate \(x\) in input image \(I\), the equation of which is as follows:  
\[J(x,y)=\sum_{i=0}^{i\leq x}\sum_{j=0}^{j\leq y}I(i,j). \tag{1}\]  
Given point \(x=(x,y)\) in input image \(I\), a Hessian matrix \(H(x,\sigma)\) at a scale of \(\sigma\) is defined through Equation (2).  
\[H(x,\sigma)=\left[\begin{array}{cc}L_{xx}(x,\sigma)&L_{xy}(x,\sigma)\\ L_{xy}(x,\sigma)&L_{yy}(x,\sigma)\end{array}\right], \tag{2}\]where \(L_{xx}(x,\sigma)\), \(L_{xy}(x,\sigma)\), and \(L_{yy}(x,\sigma)\) represent the convolution of the Gaussian second-order derivative space with the image at coordinate \(x=(x,y)\).  
[PERSON] et al. proposed the use of \(9\times 9\) box filters to approximate the second-order Gaussian partial derivatives and rapidly compute the image convolutions using integral images [17]. An approximation of the second-order derivatives is denoted by \(D_{xx}(x,\sigma)\), \(D_{xy}(x,\sigma)\), and \(D_{yy}(x,\sigma\). [PERSON] et al. suggested using a \(\sigma\) of 1.2, which is the lowest scale [17]. Therefore, the determinant of an approximated Hessian matrix is calculated using Equation (3).  
\[\det(H_{approx}(x,\sigma))=D_{xx}(x,\sigma)D_{yy}(x,\sigma)-\big{(}0.9D_{xy}( x,\sigma)\big{)}^{2}. \tag{3}\]  
To obtain the robust features to scale, SURF forms a pyramid scale space with various scales on the original image. The size of the box filter expands along with the pyramid scale space while the size of the original image remains fixed. Consequently, we can have an effect of enlarging or reducing the size of the image without scaling the original. Moreover, we can apply box filters of any size at the same speed directly to the original image, which can improve the computational speed.  
After the determinant of the approximated Hessian matrix is calculated at each scale, non-maximum suppression in a \(3\times 3\times 3\) neighborhood is applied to find the maxima, which describe the edge of the features best. The maxima are then interpolated in terms of both the scale and image space [18]. Finally, we can detect the stable location of the feature through one of the maxima.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 4 System Architecture
### Flow Diagrams of the Processes
#### 4.3.2 Data Forwarding Process  
When a relay node receives data packets from a monitoring node, it needs to forward them towards the control room. Since the relay nodes are static, the forwarding table for the relay nodes is the same over time, unless one of the relay nodes runs out of battery. The relay node might receive the same data packet from more than one neighbor relay nodes. In this case, the relay node, transmit the packet only once and drop any future instances of the packet. The diagram of the data forwarding process is shown in Figure 4.
# Light Propagation in Confined Nematic Liquid Crystals and Device Applications
## 4 Device Applications  
### On-Off Optical Switch and Variable Optical Attenuator  
Light propagation can be controlled in a SiO\({}_{2}\)/Si LCW by either electrooptic or nonlinear optical effect or both. Since LC molecules can be reoriented along an external electric field, a square wave voltage is applied between the ITO electrode and the n-type Si substrate acting as a second electrode. An ac field is required to avoid electrolysis of the LC. Figure 11 shows the working principle of LCW acting as an on-off switch. A vertically polarized light signal runs into an increasing refractive index of the LC approaching the value of \(n_{e}\) of the LC along the molecular director \(\mathbf{n}\) as the voltage increases.  
If voltage is not applied, the polarized light beam comes across the ordinary refractive index \(n_{o}\) of the LC, which is lower than the glass refractive index, as shown in Figure 11a. When a voltage is applied, the LC molecules are mostly reoriented perpendicularly to the propagation direction and to the glass plates, and confined light propagates in the LC core, as shown in Figure 11b. In fact, as shown in Figure 12, when a voltage is higher than just 2 V, light starts to propagate, and transmission increases with an on-off extinction ratio (\(ER\)) above 40 dB at a voltage of about 8 V, with \(ER\) defined as:  
\[ER=10\log_{10}\frac{P(V)-P_{dark}}{P(0)-P_{dark}} \tag{6}\]  
where \(P(V)\) is the waveguide output power at voltage \(V\) and \(P_{dark}\) is the optical power at the photodetector without any input signal. The LCW acts both as an on-off switch and as a variable attenuator for voltages from 2 to 5 V as shown in Figure 12. The switching time is related to reorientation dynamics of the LC in the range of a few ms depending on the viscosity and elastic properties of the LC mixture. The applied voltage controls the number of supported optical modes, which increase as the voltage amplitude increases.  
Figure 10: Computed contour map of TM-like propagation of 1550 nm wavelength in a 10 μm wide LCW with applied lines of electric field force (red arrows) for an applied voltage of 10 V: (**a**) fundamental mode; (**b**) higher order mode. The color maps show the field intensity distribution.
## 4 Empirical Study
### Performance Analysis in Different Settings for the Compared CNNs with Transfer Learning
#### 4.3.1 Impact of Data Augmentation  
The first result to assess, in terms of the problem of insufficient data, is the impact of data augmentation. Figure 9 shows the effectiveness of different models with different data augmentations. In these results, different data augmentation types had different impacts for each model. On average, the performance rank for three augmentation types were rotation \(15^{\circ}\), rotation \(-15^{\circ}\), and flipping. Overall, for any of the individual augmentations, the performance was better than that without augmentation (denoted as \"original\"). Besides the individual augmentations, further comparison between those with and without augmentations was made. Figure 10 reveals the results in a manner similar to that of Figure 9. Three viewpoints for the results are shown here. First, the accuracies of VGG Nets and EfficientNets did not improve significantly. Second, the best improvement was  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ModelMetric** & **Sensitivity** & **Specificity** & **Precision** & **F1-Score** & **AUC** & **Kappa** & **Accuracy** \\ \hline VGG16 & 0.083 & * 1.000 & * 1.000 & 0.154 & 0.610 & 0.083 & 0.542 \\ VGG19 & * 0.972 & 0.222 & 0.556 & 0.707 & 0.610 & 0.194 & 0.597 \\ RN50 & 0.194 & 0.861 & 0.583 & 0.292 & 0.580 & 0.056 & 0.528 \\ RN101 & 0.361 & 0.833 & 0.684 & 0.473 & 0.660 & 0.194 & 0.597 \\ RN152 & 0.444 & 0.889 & 0.800 & 0.571 & 0.770 & 0.333 & 0.667 \\ DN121 & 0.722 & 0.722 & 0.722 & 0.722 & * 0.810 & * 0.444 & * 0.722 \\ DN169 & 0.750 & 0.694 & 0.711 & 0.730 & 0.790 & * 0.444 & * 0.722 \\ DN201 & 0.833 & 0.611 & 0.682 & * 0.750 & 0.790 & * 0.444 & * 0.722 \\ INv3 & 0.694 & 0.500 & 0.581 & 0.633 & 0.610 & 0.194 & 0.597 \\ ENB0 & 0.694 & 0.556 & 0.610 & 0.633 & 0.630 & 0.250 & 0.625 \\ \hline \hline \end{tabular} Note that * indicates the best performance in each metric (attribute).  
\end{table}
Table 5: Effectiveness of compared CNNs without transfer learning for different metrics.  
for RN101, which reached an improvement rate of approximately 73%. Third, the potential interpretation for the difference in effectiveness of augmentation on different models is that the model architectures were sensitive to data augmentation, considering the pre-trained model. In detail, the residual idea works well for ResNets and DenseNets. This is also the main contribution to discerning the difference among AI Nets. Fourth, on the whole, most models were enhanced through data augmentation. Note that this evaluation was carried out based on the settings presented in Table 3.
## 6 Discussion  
Compared with the traditional precision level, the inclination measurement principle based on the light refraction proposed in this report has some significant advantages.  
1. The proposed inclination measurement principle does not require any moving parts, so the systematic error caused by the improper design and manufacturing error of the pendulum or hinge can be avoided.
2. Compared with the commercial precision level of other inclination angle measurement principle based on the light reflection or refraction of the liquid level surface, the measurement principle proposed in this report if simpler, uses fewer optical components, and effectively avoids the influence of stray light on the measurement accuracy.
3. The cost of the sensor and light source used is lower, and there are fewer components that require a high manufacturing accuracy; therefore, while the measurement accuracy is ensured, the cost of the proposed precision level can be kept very low, which provides convenience for the application.  
On the basis of this research, the final goal is to design a precision level as a sensor to monitor the inclination angle of a precision instrument or machine. To achieve this goal, the following issues still need to be further studied.  
1. A mathematical model of the surface adhesion at the liquid-solid interface should be built and verified. Thus, the design of the sensor can be optimized through the mathematical model, improving the dynamic performance and reducing the size of the inclination sensor.
2. The range of the inclination angle measurement should be increased by compensating the systematic error of QPD in a large-range measurement or using PSD as the detector in the precision level, under the premise of ensuring the accuracy and resolution of it.
3. In order to reduce the size and improve the reliability of the proposed precision level, a further integrated optimization design is still necessary.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 4 The Filter Design of KF Based on AR Model for Heading Estimation  
In this paper, KF based on AR model is used to estimate the heading. In Section 2, the AR model is introduced to build KF for heading estimation, and the heading measurement is introduced in Section 3. In this section, we sum up the process of KF for heading estimation. In this paper, the current heading is predicted by the previous four samples in AR model constructed in Section 2, so \(S\) is set as four. The fused heading presented in Section 3.3 is used for the measurement of the KF update. The state and measurement equations of KF based on AR model for the proposed heading estimation can be expressed as follows,  
The state model can be obtained by  
\[x_{k}^{AR}(-)=\Phi_{k,k-1}^{AR}x_{k-1}^{AR}(+)+w_{k}^{AR} \tag{38}\]  
where, \(x_{k}^{AR}(-)=\left[\begin{array}{cccc}att_{k}^{-}&att_{k-1}^{-}&att_{k-2}^{ -}&att_{k-3}^{-}\end{array}\right]^{T}\), \(x_{k-1}^{AR}(+)=\left[\begin{array}{cccc}att_{k-1}^{+}&att_{k-2}^{+}&att_{k-3 }^{+}&att_{k-4}^{+}\end{array}\right]^{T}\) and \(\Phi_{k,k-1}^{AR}\) is the state transition matrix that can be obtained by Equation (10). \(att_{k-1}^{+}\) is the estimated heading at time \(k-1\), \(att_{k}^{-}\) is the predicted heading at time \(k\).  
The measurement model can be calculated by  
\[Z_{k}^{AR}=H_{k}^{AR}x_{k}^{AR}(-)+V_{k}^{AR} \tag{39}\]  
where, \(Z_{k}^{AR}=\psi_{k}\) that can be calculated by Equation (32), \(H_{k}^{AR}=\left[\begin{array}{cccc}1&0&0&0\end{array}\right]_{1\times 4}\).  
In this paper, Equations (38) and (39) were constructed to predict and update the KF for heading estimation.
# Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment
## Methods
### Cluster Analysis as a Random Process and the Markov Moment of Its Stopping
The random process \(\xi=\{\xi_{t},t\in T\}\) is called consistent with the filtration \(\mathfrak{F}\), if for \(\forall\ t\in T:\,\sigma(\xi_{t})=\mathcal{F}_{\xi_{t}}\subset\mathcal{F}_{t}.\) Obviously, any random process is consistent with its natural filtration. The mapping \(\tau:\,\Omega\longrightarrow T\) is called Markov moment with respect to the filtering \(\mathfrak{F}\), if for \(\forall\ t\in T\) the preimage of the set is \(\{\tau\leq t\}\in\mathcal{F}_{t}\). If, in addition, the probability \(P(\tau<+\infty)=1\), then \(\tau\) is called the Markov stopping time [22]. In other words, let \(\tau\) be the moment of the occurrence of some event in the random process \(\xi=\{\xi_{t},t\in T\}\). If for \(\forall\ t_{0}\in T\) we can definitely say whether the event \(\tau\) occurred or not, provided that the values of \(\xi_{t}\) are only known in the past (to the left of \(t_{0}\)), then \(\tau\) is the Markov moment relative to the natural filtering \(\mathfrak{F}\) of the random process \(\xi=\{\xi_{t},t\in T\}\). If the moment of the occurrence of \(\tau\) has a probability equal to one, then \(\tau\) is the Markov stopping time. For a random sequence of minimum distances \(F_{t}\), when we cluster the sample \(X\subset\mathbb{R}^{n}\), the natural filtration consistent with the process is the \"sample \(\sigma\)-algebra\" \(\mathcal{S}\left(\mathbb{R}^{n}\right)\). Subsequently, by definition, the Markov moment of stopping the agglomerative process of clustering will be statistics  
\[\tau=\min\{t\in T\ \mid\ \delta_{t}^{2}>0\}. \tag{18}\]  
Thus, the statistical criterion for the completion of the agglomerative process of clustering can be formulated, as follows. Let \(\{F_{1},\,F_{2},\,\ldots,\,F_{k}\}\) be a linearly ordered set of minimum distances, and the set \(\{y_{1},\,y_{2},\,\ldots,\,y_{k}\}\) be the \"trend set\" obtained using the transformation \(y_{i}=F_{i}+q\cdot i\), where \(q\) is the \"trend coefficient\", and \(i\) is the iteration number of the agglomerative clustering algorithm \(\mathcal{A}\). The clustering process is considered to be completed at the \(k\)-th iteration, if for the nodes \(y_{k-4},y_{k-3},y_{k-2},y_{k-1}\) the inequality \(\delta^{2}\leq 0,\) and for the set of points \(y_{k-3},y_{k-2},y_{k-1},y_{k}\), the inequality \(\delta^{2}>0\). In other words, the Markov moment of stopping the agglomerative clustering process is the minimum value \(t\) at which the null hypothesis \(H_{0}\) is rejected (\"the values of the elements of a linearly ordered trend set increase linearly\") and the alternative hypothesis is accepted \(H_{1}\) (\"the values of the elements of a linearly ordered trend set increase parabolically\") [15].
## 4 Influence Parameter Analysis
### Bearing Eccentricity Ratio  
The eccentricity ratio of the bearing refers to the deviation between the rotor center and the bearing center to the gas film clearance, that is \(e/c_{0}\). The difference in the size of the gas film clearance on both sides is caused by the eccentricity of the bearing, which has a great impact on the performance of the bearing. The influences of different eccentricity ratios on bearing performance are calculated while other parameters remain invariant, as shown in Table 1. The load capacity and attitude angle of hydrogen gas bearings with eccentricity ratio are shown in Figure 15. The bearing load capacity increases with the eccentricity ratio. In addition, the differences in load capacity under different eccentricity ratios increase with increasing rotational speed. Simultaneously, the attitude angle gradually decreases with increasing eccentricity ratio.  
The friction torque of the hydrogen gas bearing with eccentricity ratio is shown in Figure 16. The friction torque of hydrogen gas bearings increases with increasing eccentricity ratio. In addition, the difference in friction torque under different eccentricity ratios increases with rotational speed.  
The critical mass of hydrogen gas bearings with eccentricity ratio is shown in Figure 17. The critical mass of the hydrogen gas bearing increases with eccentricity ratio, and thus exhibits improved dynamic performance and stability.  
Figure 16: Friction torque of hydrogen gas bearings with eccentricity ratio.  
Figure 15: (**a**) Bearing load capacity and (**b**) attitude angle of hydrogen gas bearings with eccentricity ratio.
# Tire Defect Detection via 3D Laser Scanning Technology
## 2 Materials and Methods
### Data Augmentation  
Obtaining a robust generalized deep detector using very little training data is impossible. However, because of various restrictions in industrial data collection, the size of the training dataset is small. Previous methods augmented datasets in trivial ways, such as rotation and flipping (see Figure 5). However, these previous methods brought limited benefits because of the following: (1) the number of defects remains unchanged because rotation or flipping cannot change the forms of defects; (2) the geometric transformation of images changes the tire texture and then leads to distortion; and (3) most existing methods are devised for classification tasks specifically and thus are not suitable for detection tasks.  
To this end, we propose a novel approach of data augmentation, which can generate synthetic images with natural and diverse tire defects. The proposed data augmentation is devised for tiny defect detection tasks but can be extended to other detection tasks.  
Figure 5: Diagram of the proposed data augmentation. Red frames indicate the defects. \(R_{i}\) and \(D_{i}\) are the corresponding rendered and depth images, sharing the same annotation. The data augmentation mainly consists of six steps: (1) extracting defect from the annotated depth images; (2) applying a random transformation to generate a novel defect; (3) integrating the generated defect into another depth image; (4) recovering a point cloud based on the synthetic depth map; (5) reestimating the normal; and (6) obtaining a new rendered image with 3D to 2D projection.  
As aforementioned, one depth image has a corresponding rendered image, and both of them share a unified annotation. Generating synthetic depth images with defects is the first step of our data augmentation. According to the annotations, we first extract the tiny defects in depth images. Then, for each defect, we employ random deformation to obtain several defects with various forms. Lastly, by randomly choosing a template depth image (with or without defects), we synthesize a new depth image with automatically generated annotation.  
The second step of data augmentation is to obtain rendered images from synthetic depth images. As shown in Figure 3, the projection from depth images to point clouds is easily accomplished without a loss of information, and normal vectors of point clouds are recalculated and projected to rendered images.  
By repeating the above two steps of our data augmentation, a sufficient and nearly natural dataset is generated.
## 3 Novel Taxonomy for SSNs
### SSNs Using Hybrid Level Virtualization  
SSNs using hybrid level virtualization presents a combination of LLV and HLV to support different areas of networking. This category has three branches, which are middleware/cluster based, middleware/virtual networks (or overlay) based, and virtual machine/dynamic grouping based solutions, as in Figure 36. A brief discussion regarding the well-known software in this area addition to a comparative analysis between them will be presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Solution** & **Type** & **LLV** & **HLV** & **Programming Model** & **Programming Language** & **Task Execution** & **OS \& **& **On the Hy Reprogramming** \\ \hline Sensomax & & MW & & & & & & & & \\
2013 & MW/Cluster & Agent-Based & Cluster-Based & Thread-Based & Java & Sequential & Loosely coupled & \(\surd\) \\ \hline Multi-set Architecture & & MW & & & & & & & \\
2010 & Agent-Based & Cluster-Based & Event-Driven & negC & Sequential & Loosely coupled & - \\ \hline SerShare & & Multi-tasking OS + & Overlay based MW (HAL) & Event-Driven & negC & Simultaneous & Loosely coupled & x \\ \hline A framework for service provisioning in VSNs & MW & & Overlay based on DVNS & - & - & - & - & - \\
2012 & MW & Overlay based on DVNS & & & & & \\ \hline Melete & VMDynamic & & & & & & & \\
2006 & Grouping & VM & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics Summary of SSNs’s Hybrid Solutions.