esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Red tides caused by _Margalefdinium polykrikoides_ occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widely used for detecting red tide blooms, but their low spatial resolution restricts coastal observations. Contrarily, terrestrial sensors with a high spatial resolution are good candidate sensors, despite the lack of spectral resolution and bands for red tide detection. In this study, we developed a U-Net deep learning model for detecting _M. polykrikoides_ blooms along the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The U-Net model was trained with four different datasets that were constructed with randomly or non-randomly chosen patches consisting of different ratios of red tide and non-red tide pixels. The qualitative and quantitative assessments of the conventional red tide index (RTI) and four U-Net models suggest that the U-Net model, which was trained with a dataset of non-randomly chosen patches including non-red tide patches, outperformed RTI in terms of sensitivity, precision, and F-measure level, accounting for an increase of 19.84%, 44.84%, and 28.52%, respectively. The _M. polykrikoides_ map derived from U-Net provides the most reasonable red tide patterns in all water areas. Combining high spatial resolution images and deep learning approaches represents a good solution for the monitoring of red tides over coastal regions.  
Margalefdinium polykrikoides; PlanetScope; southern coast of Korea; convolutional neural network; U-Net +
Footnote †: journal: Sensors  
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# Acoustic Equivalent Lasing and Coherent Perfect Absorption Based on a Conjugate Metamaterial Sphere
## 3 AEL Based on a Small Monolayer Sphere
Similarly, when \(l=1\), \(a_{1}^{\rm L}=-0.6818\pi\) and \(\left|n_{1}^{\rm L}\right|=2.3674\) for Equation (15); \(a_{1}^{\rm L}=0.3182\pi\) and \(\left|n_{1}^{\rm L}\right|=1.5894\) for Equation (16). When \(l=2\), \(a_{1}^{\rm L}=-0.8170\pi\) and \(\left|n_{1}^{\rm L}\right|=2.9715\) for Equation (15); \(a_{1}^{\rm L}=0.1830\pi\) and \(\left|n_{1}^{\rm L}\right|=2.1567\) for Equation (16). It is worth noting that Equations (15) and (16) have more solutions with larger \(\left|n_{1}^{\rm L}\right|\) due to oscillatory properties of the function \(A_{l}\) for a fixed order \(l\). The AEL modes are further numerically simulated by using the finite element method based on the above analytical derivations. Figure 3 shows the total acoustic pressure fields in the \(yz\)-plane for spherical wave with an order \(l\) impinging on an ACM sphere with particular acoustic parameters, in which Figure 3a-c correspond to \(l=0\), \(1\) and \(2\), respectively. Without loss of generality, the background medium is set as air with the mass density \(\rho_{0}=1.21\) kg/m\({}^{3}\) and the bulk compressibility \(\beta_{0}=1/\rho_{0}c_{0}^{2}\) in all simulations. Here, the sound speed of air \(c_{0}=343\) m/s. The incidence wavelength is set as \(\lambda_{0}=1\) m, and the distribution of the incident pressure field at the outer simulation boundary (\(r=\lambda_{0}\)) is set to \(P_{l}(\cos\theta)\). The inner black circles in the figures denote the outer radius of the ACM sphere (\(R_{1}=0.35\lambda_{0}\)). For \(l=0\) (Figure 3a), \(a_{1}^{\rm L}=-0.6358\pi\) and \(\left|n_{1}^{\rm L}\right|=1.7163\) are taken to realize the AEL mode. Similarly, for \(l=1\) (Figure 3b), \(a_{1}^{\rm L}=-0.6818\pi\) and \(\left|n_{1}^{\rm L}\right|=2.3674\) are taken, while for \(l=2\) (Figure 3c), \(a_{1}^{\rm L}=-0.8170\pi\) and \(\left|n_{1}^{\rm L}\right|=2.9715\) are taken. For \(l=0\), the acoustic field distribution shows the shape of concentric circles and is completely determined by the coordinate \(r\); when \(l=1\), the acoustic intensity along the \(z\)-axis is the strongest, and the field is symmetric about the \(z\)-axis; when \(l=2\), the maximum of the acoustic intensity is distributed along the \(y\)-axis and \(z\)-axis, and the acoustic field is symmetrical about the \(y\)-axis and \(z\)-axis. In all cases, the total acoustic pressure fields are very intense (see color bars) relative to the incidence. Therefore, the AEL mode is successfully realized based on an ACM sphere with appropriate parameters, where a strong spherical wave with the desired order \(l\) is scattered. Figure 2: (a) Phase \(\phi_{l}\) and (b) amplitude \(\xi_{l}\) of \(B_{l}\) versus radius \(R_{1}/\lambda_{0}\). Black solid, red dashed, and blue dotted curves correspond to \(l=0\), \(1\), and \(2\), respectively. (c) The function \(A_{l}\) versus the refractive index \(|n_{1}|\) for \(l=0\).
## 3 Numerical Examples
### Boundary Randomness
#### 3.2.2 Effect of Porosity of Sedimentary Domain on Saturated Sedimentary Valley with Random Boundary
As can be seen in Figure 9, the porosity of the sedimentary domain has a great influence on the interior of the sedimentary valley, and the coefficient of variation of the sedimentary part is larger than that of the half-space. Under vertical incidence conditions, the interior of the sedimentary valley is magnified 3.60 times (\(n_{\rm c}=0.1\)), 3.97 times (\(n_{\rm c}=0.2\)), and 4.27 times (\(n_{\rm c}=0.3\)). With the increase in porosity \(n_{\rm c}\), the amplification effect is more obvious, and the influence of boundary randomness on the outer half-space increases gradually. Compared with the case of vertical incidence, the spatial variation of the coefficient of variation of surface displacement amplitude is significantly different at a wave incidence of \(30^{\circ}\), especially at \(\eta=2\), and the distribution of coefficient of variation of vertical displacement is more discrete. The coefficient of variation of the incident proximal end (the left side of the surface) is generally larger than that of the incident distal end (the right side of the surface), which is consistent with the above conclusion in the valley with certain boundary. The mean values of variation coefficients of horizontal and vertical displacement amplitudes with sediment porosity are listed in Tables 4 and 5, respectively. It can be seen that the average value of the coefficient of variation of displacement increases for both directions with the increase in dimensionless frequency, which is 1.3-4 times larger than that of \(\eta=0.25\), and almost reaches the maximum at \(\eta=1.5\). The average value of the coefficient of variation is small when dimensionless frequency \(\eta=2\), because at some frequencies (such as \(\eta=2\)), the amplification is close to 0, resulting in a standing wave phenomenon. The change of incident angle also has an influence on the mean value of  
Figure 9: Coefficient of variation in horizontal direction. (**a**) \(n_{\rm c}=0.1\); (**b**) \(n_{\rm c}=0.2\); (**c**) \(n_{\rm c}=0.3\). the coefficient of variation. On the whole, the variation of the coefficient of variation is more complex due to the influence of the scattering effect, waveform conversion, and other factors when the seismic wave is obliquely incident.
## 3 Winding Simulation at Strands Level  
### Simplified Flat Winding Model  
In order to evaluate the electromagnetic performance of the windings, the winding domain must be divided into smaller regions in each slot during finite element analysis (FEA). Doing that for a complete machine is time-consuming and almost not possible. That is why the benchmarked machine is simplified to a motorette of two slots with an E-shape core. Using a two-slot model, a coil is simulated at the stranded level, with each conductor represented individually, as shown in Figure 3.  
Furthermore, the current density is evaluated in the optimal 8-turn coil under sinusoidal wave current at two different frequencies (100 Hz and 400 Hz), as shown in Figure 3b. It is evident from the data that the current density exhibits a gradual rise along the slot height, with the highest point of current density positioned in the upper conductor. This concentration can be attributed to the elevated slot leakage flux in the proximity of the slot opening. Moreover, as the frequency escalates from 100 Hz to 400 Hz, the maximum current density experiences a substantial increase of more than 130%.  
We explore the previously mentioned two-slot model under two distinct current waveforms, as depicted in Figure 4. The first waveform is a pure sinusoidal one devoid of any harmonic content. The second waveform is a pulse-width modulation (PWM) current characterized by a substantial presence of harmonics. To ensure a fair comparison, it is important to note that both the sinusoidal and PWM currents have identical root mean square (RMS) values. Also, the PWM waveform has the same base frequency of 100 Hz and a switching frequency of 6600 Hz. The power losses in each turn are calculated, as shown in Figure 5. Notably, the PWM current has a high impact on the losses, especially in the upper conductors. It is also clear that over 75% of the total losses are generated within the top three conductors near the slot opening.  
A transient FE analysis was conducted using the aforementioned current waveforms. We calculated and compared the instantaneous copper losses for both waveforms at 100 Hz, as illustrated in Figure 6. Notably, the presence of harmonic content results in an  
Figure 3: The flat coils and the 2-slot simulation. (**a**) Current density under pure sinusoidal current of 40A. (**b**) Local current density along Y-line at 100 Hz and 400 Hz.  
Figure 4: The investigated current waveforms at 100 Hz.  
Figure 5: Power loss in each turn at 100 Hz under pure sinusoidal and PWM currents.
## 3 Stable Source Identification Algorithm
### Forward Problem  
In this section, we consider the following functional spaces:  
\[L_{2}(S_{i})=\bigg{\{}h:S_{1}\rightarrow\mathbb{R}:\langle h,h\rangle_{L_{2}( S_{i})}=\int_{S_{i}}h^{2}(x)dx<\infty\bigg{\}},i=1,2,\]  
\[L_{2,\perp}(S_{i})=\Big{\{}h\in L_{2}(S_{i}):\langle h,1\rangle_{L_{2}(S_{i})} =0\Big{\}},i=1,2,\]  
\[L_{2}(\Omega)=\bigg{\{}u:\Omega\rightarrow\mathbb{R}:\langle u,u\rangle_{L_{2 }(\Omega)}=\int_{\Omega}u^{2}(x)dx<\infty\bigg{\}},\]  
\[H^{1}(\Omega)=\big{\{}u\in L_{2}(\Omega):\text{its derivative is in }L_{2}(\Omega)\big{\}},\]  
\[H^{1,\perp}(\Omega)=\Big{\{}u\in H^{1}(\Omega):\langle u,1\rangle_{L_{2}( \Omega)}=0\Big{\}},\]  
where \(\langle\cdot,\cdot\rangle\) is the inner product in the space indicated by the subscript.  
To find the solution to problem (1)-(5), we consider that  
\[g(\theta)=\sum_{k=1}^{\infty}g_{k}^{1}\cos k\theta+g_{k}^{2}\sin k\theta, \tag{7}\]  
where \(g_{k}^{1}\) and \(g_{k}^{2}\) are the Fourier coefficients of \(g\). The solution to the forward problem is given by  
\[\begin{split} V(\theta)&=A(g)(\theta)=u(R_{2}, \theta)\\ &=\sum_{k=1}^{\infty}a_{k}g_{k}^{1}\cos k\theta+a_{k}g_{k}^{2} \sin k\theta,\end{split} \tag{8}\]  
where  
\[a_{k}=\frac{2R_{1}^{k+1}R_{2}^{k}}{k[(\sigma_{1}-\sigma_{2})R_{1}^{2k}+( \sigma_{1}+\sigma_{2})R_{2}^{2k}]}. \tag{9}\]  
The linear operator \(A:L_{2,\perp}(S_{1})\longrightarrow L_{2,\perp}(S_{2})\) is defined as \(A(g):=u|_{S_{2}}\), where \(u\in H^{1,\perp}(\Omega)\) is the solution to problem (1)-(5).
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 3 SDN-Driven Traffic Migration Engineering (TME) Model
### Complexity Analysis of the Algorithm  
**Theorem 1**.: _The complexity of the TME algorithm is \(O(2N+|E|+N^{2})\), where \(|E|\) denotes the number of links in the network._  
Proof of Theorem 1.: Firstly, the algorithm traverses all OVSs once according to the minimum delay, and the complexity is \(O(N)\). Then, we have a traffic prediction for all the virtual links and the process traverses all the links once and all OVSs twice. The complexity of this process is thus \(O(|E|+N^{2})\). Finally, in the process of traffic migration, all OVSs are traversed once and the complexity is \(O(N)\). Therefore, the run-time complexity of the TME algorithm is \(O(2N+|E|+N^{2})\) totally.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### Conceptual Basis of the U-CLX Model
#### 4.1.3 Dimensions  
* The dimensions of the U-CLX Model are Time, Place, Medium (Device), and Context. According to the U-CLX Model, people are the center of the model, and their learning processes are where U-Learning is measured. Therefore, the left side of the model represents the Pedagogical component (A), and the right side the Technological component (B). Each component contains the elements for evaluating U-Learning with the U-CLX Model. Table 1 presents dimensions of the model.
* Any Time: this dimension refers to the possibility of carrying out learning processes and experiences simultaneously, at any time, or at different times. It is possible to carry out these processes online or offline, and to carry out learning processes continuously at any time without affecting learning.
* Anywhere: this dimension refers to the possibility of carrying out learning processes and experiences in any place or physical space in the natural or virtual world, or a mixture of both. It also refers to the possibility of moving in any direction and continuously developing these learning processes, without being affected by physical or virtual changes of location.
* Any Medium (Device): the medium or device refers to the possibility of using any technological element (computers, servers, smart devices, sensors, networks, cloud technologies, standards, interactions with different technologies, etc.) that allow ubiquitous learning processes and experiences using computing and ubiquitous technologies.
* Any Context: this dimension refers to the possibility of developing learning processes and experiences in any context in the real, virtual, or augmented world, for formal or non-formal education, and by a variety of people. It refers to the learning process defined by each person's unique context.  
The dimensions are the model variables with which the level of U-Learning will be measured. Each component has its elements and attributes. The evaluation is performed on these elements. Their respective components are evaluated in the four dimensions model: any place, time, moment, and context to measure ubiquitous learning.  
In each dimension, the institution's compliance is measured according to the elements, i.e., the evaluation indicates whether it has a low, medium, or high compliance. This evaluation makes it possible to quantify how an element of the model exists in the four dimensions. By evaluating all elements of the two components in the four dimensions, it is possible to indicate the level of U-Learning of a virtual education institution.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Dimensions of the U-CLX Model** \\ \hline A & Time \\ N & Where \\ Y & Device \\ \hline \hline \end{tabular}
\end{table}
Table 1: U-CLX Model Dimensions.
## 3 Target Tracking
### Target Localization  
Consider a monocular camera model as shown in Figure 9.  
When an arbitrary point \(p(x_{w},y_{w},z_{w})\) in world frame \(W\) is detected by the camera, its 2D position \(p_{i}(x_{i},y_{i})\) on the image plane can be given as:  
\[x_{i}=\frac{fx_{c}}{z_{c}}=(u-u_{0})dx \tag{51}\]  
\[y_{i}=\frac{fy_{c}}{z_{c}}=(v-v_{0})dy \tag{52}\]  
where \((x_{c},y_{c},z_{c})\) is the position of \(p\) in the camera frame, \(f\) is the focal length, \((u,v)\) is the target position in pixel values, \((u_{0},v_{0})\) is the intersection of the optical axis, and the image plane, \(dx\) and \(dy\) are the physical length per pixel in the \(x_{i}\) and \(y_{i}\) axis directions. Equations (51) and (52) can be integrated as:  
\[\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=M_{in}\left[\begin{array}{c}x_{c}/z_{c}\\ y_{c}/z_{c}\\ 1\end{array}\right]=M_{in}\left[\begin{array}{c}x_{1c}\\ y_{1c}\\ 1\end{array}\right] \tag{53}\]  
where \([x_{1c},y_{1c},1]^{T}\) is the point on the normalized plane and \(M_{in}\) is the intrinsic parameter matrix of the camera, which is given as:  
\[M_{in}=\left[\begin{array}{ccc}f/dx&0&u_{0}\\ 0&f/dy&v_{0}\\ 0&0&1\end{array}\right] \tag{54}\]  
Figure 9: Coordinate system of a monocular camera.  
With a calibrated camera, the pointing error angle \(\varepsilon\) in elevation and azimuth can be computed by using the above relations, as shown below:  
\[\varepsilon_{\chi}=\arctan(x_{c}/z_{c}) \tag{55}\]  
\[\varepsilon_{\gamma}=\arctan(y_{c}/z_{c}) \tag{56}\]  
where \(\varepsilon_{\chi}\) and \(\varepsilon_{\gamma}\) are the pointing error or boresight error in azimuth and elevation, respectively, which can be put into the target aiming system as input information to control the gimbal.  
A scalable KCF tracker is used in this paper, with the scale changes of the target taken into consideration. The tracker not only updates the centroid position of the target in the image frame, but also outputs the target size in pixel values. This can be used to control the distance between the interceptor and the target while tracking, though the physical size of the target is unknown. The running results of tracking a pedestrian with the proposed tracker are shown in Figure 10. The bounding box changes with adaption to the target variations in the video streams.
## 3 Results
### Equilibrium and Kinetics Studies of The Adsorption of BF on RPNS
#### 3.4.1 Equilibrium Isotherms  
Four isotherm models were used to investigate the adsorption process of BF on RPNS. These models include Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich (DR) isotherm. Langmuir isotherm explains the single-layer homogeneous adsorption on a surface, Figure 8A and Table 6. Langmuir equation is shown in Equation (5),  
\[q_{e}=\frac{q_{m}\ K_{L}\ C_{e}}{1-K_{L}\ C_{e}} \tag{5}\]  
where \(q_{m}\) is monolayer adsorption capacity and \(K_{L}\) is the Langmuir equilibrium coefficient. In addition, the Langmuir equation can be stated using the dimensionless formula, Equation (6):  
\[R_{L}=\frac{1}{1-K_{L}\ C_{0}} \tag{6}\]  
where \(R_{L}\) is the separation factor, and \(C_{0}\) (mg/L) is the initial concentration. The adsorption favorability as reported previously is dependent on the value of \(R_{L}\). If \(R_{L}\) is \(>\)1, then the adsorption process is considered as unfavorable and if \(R_{L}\) = 1 in this case the adsorption is linear. On the other hand, if the \(R_{L}\) value is between zero and one, then the adsorption is favorable (occurs spontaneously) while if it = 0, the adsorption is irreversible [31]. The calculated \(R_{L}\) value in the current investigation was found to be \(<\)1 and higher than 0, implying that the adsorption process was spontaneous, with maximum adsorption (\(q_{max}\)) = 58.82 mg/g and a good fit as revealed by the coefficient of determination, R\({}^{2}\) = 0.967. The Freundlich isotherm used to describe heterogeneous surface energies and given by the equation:  
\[q_{e}=\ K_{F}C_{e}^{\frac{1}{n}} \tag{7}\]  
Here the \(C_{e}\) is the equilibrium concentration of BF (mg L\({}^{-1}\)); \(q_{e}\) is the amount of BF adsorbed/unit mass (mg g\({}^{-1}\)), K\({}_{F}\) (mole.g\({}^{-1}\))(L.mole\({}^{-1}\))\({}^{1/n}\), and 1/n, are Freundlich coefficients. The data are shown in Figure 8B and Table 8 illustrates lower R\({}^{2}\) value, R\({}^{2}\) = 0.932 implying that Freundlich isotherm is not applicable to study the adsorption process of BF on RPNS. On the other hand, the Freundlich coefficient \(1/n\) equals 0.637 and \(n\) = 1.569 meaning that the adsorption process is homogeneous, and adsorption is favorable hence \(1/n\) \(<\) 1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **RPNS** & **PNS250** & **PNS350** & **PNS500** \\ \hline Langmuir SA (m\({}^{2}\)/g) \(\pm\) \%Er & 4.24 \(\pm\) 0.09\% & 4.62 \(\pm\) 0.11\% & 4.72 \(\pm\) 0.07\% & 76.94 \(\pm\) 0.15\% \\ Total pore volume (cm\({}^{3}\)/g) & 0.008174 & 0.008711 & 0.01360 & 0.07234 \\ Average pore radius (\({}^{\circ}\)A) & 61.7 & 50.6 & 89.8 & 22.7 \\ \hline \%Er: \% Error & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Brunauer Emmett-Teller (BET) analysis of RPNS and thermally treated PNS.
## 3 Analysis of Safety Thickness
### Critical Safety Thickness  
To align with the principles of the cusp catastrophe model, we can reformulate Equation (10) as follows:  
\[\left\{\begin{array}{l}x=\left(\frac{EI\pi^{6}}{2L^{5}}\right)^{\frac{1}{2}}w\\ u=\frac{\pi^{2}}{2L}\left(\frac{2L^{5}}{EI\pi^{6}}\right)^{\frac{1}{2}}\left( \frac{4 EI\pi^{2}}{L^{2}}-(qD\sin\beta+p)L-N\right)\\ v=-\frac{qD\sin\beta}{2}\left(\frac{2L^{5}}{EI\pi^{6}}\right)^{\frac{1}{4}} \end{array}\right. \tag{11}\]  
The canonical representation of the potential function in the cusp catastrophe model, along with the bifurcation set equation for the rock system cusp catastrophe model, are discussed  
\[\left\{\begin{array}{l}V(x)=\frac{1}{4}x^{4}+\frac{1}{2}ux^{4}+vx\\ \Delta=4u^{3}+27v^{2}\end{array}\right. \tag{12}\]  
Upon analysis, system failure is determined to transpire exclusively when the control variables \(u\) and \(v\) fulfill the bifurcation set equation. Consequently, the requisite and sufficient criteria for rock system failure are derived:  
\[\left\{\begin{array}{l}\left(\frac{4 EI\pi^{2}}{L^{2}}-(qD\cos\alpha+p)L-N \right)^{3}+\frac{27 EIq^{2}D^{2}\cos^{2}\alpha}{2}=0\\ \frac{4 EI\pi^{2}}{L^{2}}-(qD\cos\alpha+p)L-N\leq 0\end{array}\right. \tag{13}\]  
In practical engineering applications, when the tunnel is not over-buried, the value of \(N\) is usually not large, so it can be considered that the tectonic force \(N\) has little influence on the potential function, so the model can be further simplified, and the critical condition for the sudden instability of the karst tunnel system can be obtained as follows:  
\[\left\{\begin{array}{l}\left(\frac{4 EI\pi^{2}}{L^{2}}-(qD\sin\beta+p)L\right) ^{3}+\frac{27}{2}EIq^{2}D^{2}\sin^{2}\beta=0\\ \frac{4 EI\pi^{2}}{L^{2}}-(qD\sin\beta+p)L\leq 0\end{array}\right. \tag{14}\]  
where the inertia moment of rock beam \(I\) is obtained as \(I=D^{3}/12\).  
When the structural force \(N\) is ignored, further calculation finds that the results of Equations (13) and (14) show no significant difference [26]. Therefore, the critical safe thickness of the karst tunnel roof can be further simplified as:  
\[D^{3}-\frac{3 qL^{3}\sin\beta}{E\pi^{2}}D-\frac{3 pL^{3}}{E\pi^{2}}\leq 0 \tag{15}\]  
Let \(P=\frac{3 qL^{3}\sin\beta}{E\pi^{2}}\), \(Q=-\frac{3 pL^{3}}{E\pi^{2}}\), and Equation (15) is explained as follows:  
\[H^{3}+PH+Q\leq 0 \tag{16}\]  
The equation represented by Equation (16) is used to derive the critical safety thickness expression.  
\[D=\sqrt[3]{-\frac{Q}{2}+\sqrt{\frac{Q^{2}}{4}+\frac{P^{3}}{27}}}+\sqrt[3]{- \frac{Q}{2}-\sqrt{\frac{Q^{2}}{4}+\frac{P^{3}}{27}}} \tag{17}\]