esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Large-Scale Fast Reprogramable SOA-Based Photonic Integrated Switch Circuits
## 4 Monolithically Integrated InP 8 \(\times\) 8 Space and Wavelength Selecting Switch
The first investigation on the impact of varied switch loads from unicast to multicast to broadcast on device performance was also performed for this chip [35]. Electrical power dissipation may be estimated from the mean bias current (50 mA) and the mean voltage across the SOAs (1.2 V). The preamplifiers and wavelength-select stage SOAs are operated continuously and the space-select stages are scheduled. This leads to an electrical input power of 0.78 W for unicast and 2.88 W for broadcast operation. If dynamic scheduling was implemented for the wavelength-select stages as well, a more modest 1.44 W electrical power would be required with only 1 dB gain reduction. Further energy savings are also feasible through optical circuit level optimizations [34].
## 3 Data Collection Setup and Analysis
The peak wavelengths of the three selected lobe interferences are 1546.88 nm (interference 1), 1547.11 nm (interference 2), and 1547.39 nm (interference 3). Figure 4 clearly shows the fixed sensing wavelengths of the interferometric spectrum for different interference states at each strain step, the theoretical values of the two FBG wavelengths, and the overlapping areas of the two FBG wavelengths under different strain steps. Figure 2: (a) The unstrained FBG wavelength falls to the left of the peak of the lobe of the interference pattern. (b) The unstrained FBG wavelength falls to the peak of the lobe of the interference pattern. (c) The unstrained FBG wavelength falls to the right of the peak of the lobe of the interference pattern.
# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 3 Earthquake Detection Methodologies
### Current Approaches  
Actual developments of detection algorithms take advantage of current technological advances that allow the capture, processing, and storage of data with high resolution. A large amount of available data today is used for advanced and still rarely used techniques: local similarity (quantifies consistency of data between neighboring stations) [29], probability (parameters such as distance to the seismogenic zone or signal phases are treated as random variables with an associated probability) [30], data mining (establishes a fingerprint of seismic waves for later comparison) [31], neural networks (neural network-based algorithms are trained to identify several characteristics of seismic waveforms) [19], and social sensing (based on trending hashtags or key words on social media, algorithms can trigger responses on alert systems) [32; 33; 34]. These approaches are all computational powerful and help identify waveforms on large historic seismic arrays that were not processed so far or that could have been processed by techniques with less accuracy. The cost of these techniques is the computational time. In terms of computational efficiency, the STA/LTA concept [26; 35] arises as a traditional and yet highly efficient parameter for earthquake detection.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
###### Abstract  
Fifth generation (5G) is a recent wireless communication technology in mobile networks. The key parameters of 5G are enhanced coverage, ultra reliable low latency, high data rates, massive connectivity and better support to mobility. Enhanced coverage is one of the major issues in the 5G and beyond 5G networks, which will be affecting the overall system performance and end user experience. The increasing number of base stations may increase the coverage but it leads to interference between the cell edge users, which in turn impacts the coverage. Therefore, enhanced coverage is one of the future challenging issues in cellular networks. In this survey, coverage enhancement techniques are explored to improve the overall system performance, throughput, coverage capacity, spectral efficiency, outage probability, data rates, and latency. The main aim of this article is to highlight the recent developments and deployments made towards the enhanced network coverage and to discuss its future research challenges.  
cellular network; network coverage; small cells; device to device communication; non-orthogonal multiple access; optimization; multiple input and multiple output +
Footnote †: journal: _Sensors_**2023**, 23, 2356. [[https://doi.org/10.3390/s2042356](https://doi.org/10.3390/s2042356)]([https://doi.org/10.3390/s2042356](https://doi.org/10.3390/s2042356))
## 4 Model Verification
### Theoretical Method  
Firstly, the theoretical equation based on the Rayleigh Method [28] is utilized to verify the following numerical simulation. Each petal can be equivalent to a cantilever beam. On account of energy conservation principle, the approximate solution of frequency \(\omega\) can be expressed as  
\[\omega^{2}\ =\ \frac{\int_{r}^{R}EI[Y^{\prime\prime}(x)]^{2}\mathrm{d}s+\left.k_ {t}[Y^{\prime}(x)]^{2}\right|_{x=r}}{\int_{r}^{R}m[Y(x)]^{2}\mathrm{d}s}, \tag{1}\]  
where the \(\mathrm{d}s\) is integrated along the parabolic curve as \(\mathrm{d}s=(1+z^{\prime 2})^{1/2}\ \mathrm{d}x\). \(E\) is the elastic modulus shown in Table 1, while \(k_{t}\) is the stiffness of torsion spring shown in Table 2. The petal section moment of inertia is approximately defined as \(I=(\pi/10)\cdot xt^{3}/12\), where \(t\) is the petal thickness listed in Table 2. The mass of each \(\mathrm{d}x\) segment is defined as \(m=\rho\ \pi xt\ /10\), where \(\rho\) is the petal density listed in Table 1. The shape function \(Y(x)\) is the undetermined function to approach the actual mode shape.  
When the shape function is defined as \(Y(x)=a\ x^{2}\), the equation can be rewritten as  
\[\omega^{2}\ =\ \frac{\frac{\pi t^{2}E}{120}4a^{2}\int_{r}^{R}x\sqrt{1+\left( \frac{x}{960}\right)^{2}}\mathrm{d}x+\left.4a^{2}k_{t}x\right|_{x=r}}{\frac{ \rho\pi t}{10}a^{2}\int_{r}^{R}x\sqrt{1+\left(\frac{x}{960}\right)^{2}}x^{4} \mathrm{d}x}. \tag{2}\]  
The first natural frequency is calculated as \(\omega=0.6863\) Hz. This result is basically assumed that the vibration firstly occurs in the petal because of its flexibility. Although it may have the error because of this evaluation method and equivalent assumptions for this complex structure, this result can generally assess the dynamic property of this kind of deployable petal-type reflector structure.
## 2 Mathematical Models of an S-Shaped Tooth Profile Gear
### Tooth Surface Equation of the Pinion  
For an S-shaped tooth profile, the equations of the gear and pinion tooth profiles are in the same form. In Figure 7, the pinion tooth profile can be given by the following equation in the Cartesian coordinate system.  
\[\begin{array}{l}x_{1}(u_{1})=u_{1}\cos\ (\alpha+\varphi_{1})+\lambda\sin\ \omega u_{1}\sin\ (\alpha+\varphi_{1})+r_{1}(\cos\ \varphi_{1}+\varphi_{1}\sin\ \varphi_{1})\\ y_{1}(u_{1})=u_{1}\sin\ (\alpha+\varphi_{1})-\lambda\sin\ \omega u_{1}\cos\ (\alpha+\varphi_{1})+r_{1}(\sin\ \varphi_{1}-\varphi_{1}\cos\ \varphi_{1})\end{array} \tag{9}\]  
And \(u_{1}\), \(\varphi_{1}\) satisfy the following equation  
\[f(u_{1},\varphi_{1})=u_{1}+\lambda\omega r_{1}\varphi_{1}\cos\ \alpha\cos\ \omega u_{1}-r_{1}\varphi_{1}\sin\ \alpha-\lambda^{2}\omega\sin\ 2 \omega u_{1}/2=0 \tag{10}\]  
where \(-\pi/\omega\leq u_{1}\leq\pi/\omega\), \(r_{1}=mz_{1}/2\) represents the radius of the reference circle, and \(z_{1}\) represents the number of pinion teeth.  
A cylindrical gear can be divided into a spur gear and a helical gear; to facilitate subsequent analysis and calculations, the unified mathematical equations of spur gear and helical gear tooth profiles are established. According to the forming theory of a helicoid, the tooth surface equations are given by  
\[\left\{\begin{array}{l}r_{1}^{(p)}(u_{1},v_{1})=[x_{1}\cos\ \kappa v_{1}-y_{1} \sin\ \kappa v_{1}]i+[x_{1}\sin\ \kappa v_{1}+y_{1}\cos\ \kappa v_{1}]j+p_{1}v_{1}k\\ f(u_{1},\varphi_{1})=0\end{array}\right. \tag{11}\]  
where the helix parameter \(P_{1}=r_{1}\tan(\pi/2-\beta)\), \(0\leq v_{1}\leq B/p_{1}\), and \(B\) represents tooth width. Introducing the parameter \(K\) as a helix angle coefficient, its value is  
\[K=\begin{cases}0,\beta=0\\ 1,\beta\
eq 0\end{cases} \tag{12}\]  
The normal vector of a pinion tooth is given by  
\[n_{1}^{(p)}=\frac{r_{u1}\times r_{v1}}{|r_{u1}\times r_{v1}|} \tag{13}\]  
Figure 7: One tooth surface of the pinion.  
\[r_{ul1}^{(\rm p)}=\left[\frac{\partial x_{1}(u_{1},v_{1})}{\partial u_{1}},\frac{ \partial y_{1}(u_{1},v_{1})}{\partial u_{1}},\frac{\partial z_{1}(u_{1},v_{1})} {\partial u_{1}}\right] \tag{14}\]  
\[r_{v1}^{(\rm p)}=\left[\frac{\partial x_{1}(u_{1},v_{1})}{\partial v_{1}},\frac {\partial y_{1}(u_{1},v_{1})}{\partial v_{1}},\frac{\partial z_{1}(u_{1},v_{1}) }{\partial v_{1}}\right] \tag{15}\]  
And the derivation formulas of function \(f(u_{1},\varphi_{1})\) are  
\[\frac{\partial f(u_{1},\varphi_{2})}{\partial u_{1}}=1-\lambda\omega^{2}r_{2} \varphi_{2}\cos\,\alpha\sin\,\omega u_{1}-\lambda^{2}\omega^{2}\cos\,2\omega u _{1} \tag{16}\]  
\[\frac{\partial f(u_{1},\varphi_{2})}{\partial\varphi_{2}}=-r_{2}\sin\,\alpha +\lambda\omega r_{2}\cos\,\alpha\cos\,\omega u_{1} \tag{17}\]  
\[\frac{\mathrm{d}\varphi_{1}}{\mathrm{d}u_{1}}=-\frac{f_{ul}}{f_{\varphi 1}} \tag{18}\]
## 1 Introduction
[31] investigated that parabolic relief is less sensitive to load changes and modification parameters compared to linear relief. Later, [PERSON] and [PERSON] [32] proposed a circular arc crowing, which is simple and easy to produce. [PERSON] et al. [33] and [PERSON] et al. [34] described the benefits of double circular-arc helical gears, which decrease contact stress level and have the ability to increase the resistance to tooth bending. As a result, this type of gear is commonly used to shift greater torque. Furthermore, [PERSON] and [PERSON] [35] suggested logarithmic crowning to shift the peak load from the edges to the middle of the tooth surface. The logarithmic modification was first designed for roller bearings to improve stress distribution, but there is not much work that would show that it could be used to modify the lead tooth surface of gears. The logarithmic crowning was created using [PERSON]'s theory [36], and it was found to be superior to traditional modification. The findings revealed that as opposed to typical lead changes, the logarithmical curve has significantly lower contact pressures for minor misalignments. Later, [PERSON] et al. [37] described the long-term development history of the logarithmic equation, in which many researchers made improvements and corrections to the theoretical logarithmic convex equation [38; 39; 40; 41]. As stated by [PERSON] and [PERSON] [42], lead modifications (longitudinal crowning) are used to compensate for misalignments and selected with respect to company-based requirements, manufacturing accuracy, or by a load distribution analysis with established peak misalignments. This study presents a new surface engagement model for non-conjugate tooth contact analysis, which allows determining the contact of gear tooth surfaces at every instant in space without convergence issues that appear in [PERSON]'s model and with robust and fast numerical solutions. Building on our prior work on two-dimensional contact modelling [5; 6], we propose an extended three-dimensional system of surface contact equations with two unknowns and one independent parameter rotating about two fixed axes in space, which, compared to [PERSON]'s method, gives more explicit and numerically stable results without convergence issues. To demonstrate the developed model and method, the sensitivity to in-plane and out-of-plane misalignments on the contact paths, TE, and contact stresses is analyzed by means of parametric implementation of the effect of the longitudinal crown on a spur and helical gears. The three different types of longitudinal modification are expressed mathematically and compared based on appropriately introduced metrics. Loaded tooth contact analysis (LTCA) is also conducted, and contact ellipses are predicted. The presented method is shown to be suitable for accurate, numerically convergent, and unconditionally stable mapping of the parametric design space, allowing the determination of optimal designs.
## 2 Test Evaluation Method  
### Theory of Distance Measurement Using a Monocular Camera  
In a previous study, a distance calculation method using geometric information of lanes, vanishing points, and preceding vehicles detected in the camera image mounted on the vehicle was proposed. The method is as follows [10]: First, the lane width in the image may be calculated by using the lane and vanishing point detected in the camera image. The detected lanes and vanishing points are shown in Figure 1.  
Where L1 is the width of the lane for the contact portion of the rear tire of the preceding vehicle with the ground, L2 is the width of the lane from the top of the car hood, and H1 is the vertical distance between L1 and L2. H2 is the vertical distance from the vanishing point to L1.  
Figure 2 shows the principle of distance calculation using geometric information with the preceding vehicle.  
Equation (1) can be obtained using a proportional expression in Figure 2:  
\[a=b*(h_{1}/h_{2})-c \tag{1}\]  
where h1 is the height of the camera from the ground, h2 is the height from the hood to the camera, b is the distance from the camera to the top of the hood, and c is the distance from the camera to the front bumper.  
The term a is the distance from the camera, which is the length of the hood of the car to the front bumper. It can be calculated from the camera's installation height, installation angle, and vehicle specifications. \(\theta\) can be calculated as the difference of a from the angle from the camera to the rear wheel of the vehicle, and l is the height visible from the image to the rear wheel of the preceding vehicle in the ground covered by the hood of the vehicle. I' can be calculated as the difference of l from the camera installation height. Therefore, it can be expressed by Equations (2)-(4) using the width of the lane calculated in the image:  
\[1=h_{1}*(L_{2}-L_{1})/L_{2} \tag{2}\]  
\[\Theta=\tan^{-1}((a+c)/h_{-}1) \tag{3}\]  
\[\alpha=-\Theta+\tan^{-1}((a+c)/h_{1}-1) \tag{4}\]  
The method to obtain the distance from the image by using Equations (1)-(4) is defined by Equation (5):  
\[d_{image}=l*\tan(\Theta+\alpha) \tag{5}\]  
\(d_{image}\) is a distance calculated from the lane height in the image. However, it is assumed that the specifications of the preceding vehicle (near overhang (d\({}_{OH}\))) are known from the image.  
Figure 1: Lane and loss points detected from the image.  
Figure 2: Principle of distance calculation.  
The relative distance from the preceding vehicle can be defined based on geometrical information. It is shown in Equation (6):  
\[\mathrm{d_{real}}=\mathrm{d_{image}}+\mathrm{a}-\mathrm{d_{O.H}} \tag{6}\]  
where \(\mathrm{d_{real}}\) is the distance from the preceding vehicle and can be expressed by \(\mathrm{a}\), Equations (1)-(5).  
Equation (6) is in the curve, the distance to the leading vehicle or pedestrian is expressed in a straight line. Also, in the case without \(\mathrm{d_{O.H}}\), it is expressed as the distance to the rear tire of the leading vehicle, not the value for the end of the bumper, but for pedestrians, \(\mathrm{d_{O.H}}\) is 0.
# Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity
## 2 Preliminaries
### Generative Adversarial Network  
The generative adversarial network (GAN) was proposed in 2014 by [PERSON] [6]. GAN is used for image generation task and is learned by a competition between two neural network models. Two neural network models are called the generator, G, and the discriminator, D. The goal of G is to generate images that D cannot distinguish from real images, and the goal of D is to differentiate real images and generated images. Therefore, the goal of GAN can be expressed as:  
\[\min_{G}\max_{D}E_{x\sim P_{r}}[\log(D(x))]+E_{z\sim P_{z}}[\log(1-D(G(z)))], \tag{1}\]  
where \(x\) is real data, \(z\) is random Gaussian noise. \(P_{r}\) and \(P_{z}\) are the distributions of real images and generated images, respectively, and the base architecture of GAN is shown as Figure 3.  
The objective function of GAN is a type of min-max problem, and it is hard to achieve Nash equilibrium. For this reason, GAN has an insecure training process and some problems, such as the gradient vanishing or exploding problem and mode collapse.  
Figure 4 shows the optimal solution for GAN. The distribution of \(D\) should be a flat form, and the distribution of \(G\) should be the same as the real data, resulting in generating the best data. When \(D\) is perfect, GAN is guaranteed with \(D(x)=1\) and \(D(G(z))=0\). Then, the objective function falls to zero and the loss updating gradient becomes zero, presenting the gradient vanishing problem. When \(D\) does not operates properly, inaccurate feedback is fed into G, and objective function cannot represent the reality. Also, if \(G\) does not learn the distribution of the entire training data and only a fraction of the training data is learned, the mode collapse problem occurs. In this case, G generates a limited diversity of samples or even the same sample, regardless of the input.  
Figure 3: Architecture of GAN.  
To avoid the above problems and make stable training process, Wasserstein GAN was proposed [8]. It has a new objective function derived from the Wasserstein distance, which is a measure of the distance between two joint probability distributions. A detailed description of WGAN will be mentioned in 2.2.
## 5 Deep Learning Model Combining VGGNet and SVM for Design Pattern Identification
### Model Configuration  
In VGG, the back propagation (BP) network and Adam optimizer are used to optimize the parameters of neural network bias and weights, etc., with a degenerate learning rate being added, and the initial value of the learning rate is set to 0.0005. The loss function is set as cross-entropy loss. The activation function relu and L2 regularization are added to the convolutional layer to enhance the model's nonlinear expression ability and prevent the model from overfitting. Batch Normalization (BN) is added to alleviate the network gradient dispersion and accelerate the convergence of the model. The VGG for each pattern is trained for 70 cycles. Figure 7 shows the loss rate and accuracy rate of the training and validation sets for the State pattern.  
The loss curve in Figure 8a shows that the loss rate reached the maximum convergence after 27 cycles of training. Meanwhile, the loss rate of the training set was 0.1180 and that of the validation set was 1.0236. When the training was continued, the loss rate of the validation  
Figure 7: Model structure.  
set increased slightly and overfitting occurred in the training. Figure 8b shows that, after 27 cycles of training, the accuracy rate of the training set was basically unchanged, while the accuracy of the validation set decreased with fluctuation. At the 27 th cycle, the accuracy rate of the training set was 99.31% and that of the validation set was 92.73%.  
After the training of extracted features in the convolutional neural network, the trained one-dimensional features are further trained for binary classification by SVM with a regularization model learned by stochastic gradient descent (SGD). While using hinge_loss as the loss function and adding the L2 penalty parameter, the training enters the phase of batch learning with batch_size = 100, 200, 300, 400, and 500. With the highest accuracy rate of 92.79% for the validation set at batch_size = 400, the final SVM results for the State pattern classifier are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Batch_Size** & **Accuracy Rate of Validation Set** \\ \hline
100 & 90.56\% \\
200 & 91.23\% \\
300 & 91.78\% \\
400 & 92.79\% \\
500 & 91.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: SVM training results of different batches for the State pattern classifier.  
Figure 8: VGG training results of different batches for the State pattern classifier. (**a**) Loss Curve. (**b**) Acc Curve.
# Optimal Energy Management within a Microgrid: A Comparative Study
## 2 Model for Microgrid Components
### Batteries  
The batteries are elements that can operate in charge or discharge mode to provide or consume a net amount of active power in their connection node. Then, the \(j\)th battery is represented by two sources of active power, as shown in Figure 3[22]. One of them represents the charge power \(P_{Bc\ j}^{t_{z}}\leq 0\) and the other the discharge power \(P_{Bd\ j}^{t_{z}}\geq 0\); the sum of both powers represents the net power \(P_{Bn\ j}^{t_{z}}\) provided or consumed by the battery in their connection node.  
\[P_{Bn\ j}^{t_{z}}=P_{Bc\ j}^{t_{z}}+P_{Bd\ j}^{t_{z}};0\leq P_{Bd\ j}^{t_{z}} ;P_{Bc\ j}^{t_{z}}\leq 0;\forall t_{z}\in T \tag{9}\]  
In addition, the voltage phasor in the connection node is represented by its magnitude \(V_{j}^{t_{z}}\) and its angle \(\theta_{j}^{t_{z}}\). Thus, the decision variables of the \(j\)th battery are \(\left[P_{Bd\ j}^{t_{z}},P_{Bc\ j}^{t_{z}}\right]\in y_{B}\) (\(\forall t_{z}\in T\)) where \(y_{B}\) are the battery variables, while \(\left[V_{j}^{t_{z}},\theta_{j}^{t_{z}}\right]\in y_{RD}\) (\(\forall t_{z}\in T\)). Moreover, the State of Charge (SOC) of the \(j\)th battery in the time \(t_{z}\) (SOC\({}_{B\ j}^{t_{z}}\)) can be approximated by means of Equation (10). In Equation (10), SOC\({}_{B\ j}^{t_{0}}\) represents the SOC at a given initial time \(t_{0}\), whereas \(E_{nom}\) is the energy capacity of the battery, whose efficiencies for charge and discharge operations are given by \(\varepsilon_{cj}\) and \(\varepsilon_{dj}\), respectively. Lastly, \(\Delta t\) is a fixed time step length.  
Figure 3: Battery model.  
\[\text{SOC}_{\bar{B}\,j}^{t_{z}}=\text{SOC}_{\bar{B}\,j}^{t_{0}}-\frac{\varepsilon_ {\bar{\sigma}}\Delta t}{E_{Bnomt}}\sum_{t=1}^{t_{z}}P_{Bc\,j}^{t}-\frac{\Delta t }{E_{Bnom\,j}\varepsilon_{dj}}\sum_{t=1}^{t_{z}}P_{Bd\,j}^{t} \tag{10}\]  
It is assumed that the energy provided by the batteries has no cost since it is absorbed and provided in the same node of connection. However, since the optimization algorithm will manage the power, batteries will be charged in periods of low-cost energy and will be discharged in periods of high cost. This fact involves an economic benefit in the operation of the microgrid.