esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimization Algorithm for Planning
First, a single BCCH must be allocated to each unplanned cell:  
\[\forall c\in\mathbf{c}:\sum_{f\in\mathbf{f}}\mathbf{X}_{cf}=1 \tag{4}\]  
Secondly, cells of the same site \(\mathbf{s}\) (which may contain both planned and unplanned cells), cannot reuse the same BCCHs:  
\[given\ \ \mathbf{s},\ \ \ \forall f\in\mathbf{f}:\sum_{c\in\mathbf{s}}\mathbf{X}_{cf}\leq 1 \tag{5}\]  
Thirdly, the cells of \(\mathbf{s}\) cannot have adjacent frequencies. The sorted set of frequencies \(\mathbf{f}\) is indexed, starting in index 0 and going until index \(dim(\mathbf{f})\). This restriction can be expressed mathematically as follows:  
\[\forall f_{i},i\in[1,dim(\mathbf{f})]:\sum_{c\in\mathbf{s}}\left(\mathbf{X}_{cf_{i-1}}- \mathbf{X}_{cf_{i}}\right)\leq 1 \tag{6}\]  
Regarding the planning of TCH, each cell has several TCH frequencies allocated. The same set of rules is applied to the optimization of TCH frequencies, with the exception that there needs to be at least a difference of two frequencies between TCHs of the cells of the same site:  
\[\forall f_{i},i\in[1,dim(\mathbf{f})]:\sum_{c\in\mathbf{s}}\sum_{t\in\mathbf{c}_{i}}\left( \mathbf{X}_{tf_{i-2}}-\mathbf{X}_{tf_{i-1}}-\mathbf{X}_{tf_{i}}\right)\leq 1 \tag{7}\]  
where \(\mathbf{t}_{c}\) identifies the set of available TCH channels of cell \(c\).
## 7 Summary of Research Results
It was found that the simplifications used in the shell-beam models from the initial phase of FEM analysis led to an underestimation of the maximum values of the reduced stresses H-M-H. Stress concentrations in these models occur especially in the areas of modeled connections and amount to a maximum of \(\sigma\)max= 7596 MPa. The reduced stresses H-M-H occurring in the more accurate 2D-3D models amount to a maximum of \(\sigma\)max = 1234 MPa. The results obtained from the multi-variant finite element method (FEM) simulations indicate that the modular design of the platform not only meets but also has the potential to exceed established industry standards under a variety of loading conditions. This demonstrates the platform's high adaptability and robustness, making it particularly suitable for intermodal transport systems. Compared to conventional intermodal containers, the proposed platform exhibits markedly improved stress distribution and reduced displacement, which collectively highlight its superior structural efficiency and reliability. These attributes suggest that the platform is especially well suited for applications involving heavy or unevenly distributed loads.
# A Simulator for Investigation of Breakdown Characteristics of SiC MOSFETs
## 2 Simulation Method and Validation
### Simulation Method
In each step of the Newton iteration, manual differentiation is used to compute the elements of the Jacobian matrix due to its fast speed. Then, the solving process is performed using the Portable, Extensible Toolkit for Scientific Computation (PETSc) library [20]. Various models, including mobility models, such as bulk mobility, doping and temperature-dependent mobility (the Masetti mobility model), surface degradation mobility and high-field saturation mobility model [21; 22; 23; 24; 25; 26; 27], Auger recombination [28], Shockley-Read-Hall recombination [29], impact ionization [30], incomplete ionization [31] and bandgap narrowing [32], are incorporated into the simulator to provide a comprehensive understanding of the device's behavior and to ensure that the relevant factors have been taken into account. This has enabled us to simulate the electrical and breakdown characteristics more accuracy. The incorporation of these diverse models into the simulation framework is particularly instrumental in accurately characterizing the breakdown voltage. If a high electric field is present alongside a long space charge region, the carrier acceleration length will be larger than the mean free path between two impact ionizations,resulting in carrier multiplication. The ionization coefficient, \(\alpha\), is defined to represent the reciprocal of the mean free path. Therefore, the generation rate can be expressed as  
\[G_{ii}=\frac{1}{q}(\alpha_{n}|\overrightarrow{J_{n}}|+\alpha_{p}|\overrightarrow {J_{p}}|) \tag{4}\]  
where \(J_{n}\) and \(J_{p}\) are the electron and hole current density as in the carrier transport model and based on the van Overstraeten-de Man model, the ionization coefficient \(\alpha\) is  
\[\alpha(E)=\gamma ae^{(-\frac{15}{E})} \tag{5}\]  
with  
\[\gamma=\frac{\tanh(\frac{\hbar\omega_{\rm op}}{\rm{Ze}_{B}T_{0}})}{\tanh(\frac {\hbar\omega_{\rm op}}{\rm{Ze}_{B}T})} \tag{6}\]  
where \(T\) is the lattice temperature, \(T_{0}\) the fixed temperature of 300 K, \(E\) the parallel electric field and the factor \(\gamma\) is expressed by the optical phonon energy \(\hbar\omega_{\rm op}\).
## 4 Numerical Experiment
### Impact of Wavelet Basis Functions on Separation Performance
(**b**,**f**,**j**) represent the reconstructed profiles of the first to third level high-frequency vertical detail coefficients. (**d**,**h**,**l**) represent the reconstructed profiles of the first to third level high-frequency diagonal detail coefficients (the location indicated by the arrow shows a weak-energy reflected wave). By examining Figures 8-10, it can be concluded that different wavelet basis functions used in the MWT could cause variations in the rate of energy decomposition for reflection and diffraction into high-frequency detailed coefficients in synthetic data. In general, diffraction decompose into high-frequency coefficients at a faster rate than reflection. Figure 11 presents the diffraction and reflection profiles obtained after MWT-MSSA separation using the haar, rbio4.4, and db4 wavelet basis functions. The diffraction profile separated by haar wavelet basis function exhibits some reflective energy remained, as shown by the black arrow in Figure 11a. Despite having less reflective energy, there is no significant difference between the diffraction profiles separated by both rbio4.4 and db4 functions and the bior4.4 wavelet basis function. Figure 11: Diffraction separation results with different wavelet basis functions. (**a–c**) show the diffraction separated using haar, rbio4.4, and db4 wavelet basis functions, respectively. (**d–f**) show the reflection separated using haar, rbio4.4, and db4 wavelet basis functions, respectively. Figure 10: Third level wavelet transform coefficient profiles of the GPR data shown in Figure 3b using the db4 wavelet. (**a,e,i**) represent the reconstructed profiles of the first to third level low-frequency approximation coefficients. (**b,f,j**) represent the reconstructed profiles of the first to third level high-frequency horizontal detail coefficients. (**c,g,k**) represent the reconstructed profiles of the first to third level high-frequency vertical detail coefficients. (**d,h,l**) represent the reconstructed profiles of the first to third level high-frequency diagonal detail coefficients. The A-scan waveform comparison of the diffraction vertex and wings, illustrated in Figure 12, demonstrates that diffraction separated using MWT-MSSA method with different wavelet basis functions exhibit similar energy loss at the vertex. However, all cases show stronger vertex energy than the PWD method, as shown in Figure 12a. For the diffraction wings, the waveform comparison in Figure 12b indicates that the haar and the db4 wavelet basis functions result in the most energy loss, whereas the bior4.4 wavelet basis function achieves the least energy loss, which is more closely matched to the synthetic data. In summary, the MWT-MSSA approach utilizing the bior4.4 wavelet basis function demonstrates a minimal energy loss and improved separation performance for our proposal.
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 4 The PM-QKD with Discrete Phase Modulation of Coherent State Sources
### The Decoy-State Method  
In discrete phase randomized PM-QKD, we estimate the yield \(Y_{1|\mu}^{D}\) of the single-photon signal state. We use the vacuum and one decoy state, which is similar to the BB84 decoy state analysis [24].  
We know that, in the security proof of the decoy state method with continuous phase randomization, there is an important assumption  
\[Y_{j|signal}=Y_{j|decoy} \tag{16}\]  
However, it is not strict in the condition of discrete phase randomization, \(Y_{j|signal}^{D}\
eq Y_{j|decoy}^{D}\); the reason lies in  
\[\left|\lambda_{j|\mu}^{D}\right>\
eq\left|\lambda_{j|v}^{D}\right> \tag{17}\]  
Consider the properties of trace distance; we need to estimate the difference of yields for different intensities as [33]  
\[\left|Y_{j|\mu}^{D}-Y_{j|v}^{D}\right|=\sqrt{1-\left(F_{j|\mu v}^{D}\right)^{2}} \tag{18}\]  
where \(F_{j|\mu v}^{D}=\sum\limits_{l=0}^{\infty}\frac{(\mu v)^{(D+l)/2}}{(lD+j)!} \Bigg{/}\sqrt{\sum\limits_{l=0}^{\infty}\frac{\mu^{(D+l)/2}}{(lD+j)!}\sum \limits_{l=0}^{\infty}\frac{\mu^{(D+l)}}{(lD+j)!}}\), that is the fidelity of \(\left|\lambda_{j|\mu}^{D}\right>\) and \(\left|\lambda_{j|v}^{D}\right>\).  
The estimation of the yield \(Y_{1|\mu}^{D}\) is similar to continuous phase randomization. The equation can be written as  
\[\begin{split} Q_{\mu}&=\sum\limits_{j=0}^{D-1}P_{j| \mu}^{D}Y_{j|\mu}^{D}\\ Q_{v}&=\sum\limits_{j=0}^{D-1}P_{j|v}^{D}Y_{j|v}^{D }=\sum\limits_{j=0}^{N-1}P_{j|v}^{D}Y_{j|\mu}^{D}+\sum\limits_{j=0}^{D-1}P_{j| 0}^{D}(Y_{j|v}^{D}-Y_{j|\mu}^{D})\end{split} \tag{19}\]  
We have  
\[\begin{split} Y_{1|\mu}^{D}=&[P_{2|\mu}^{D}Q_{v}-P_{2| v}^{D}Q_{\mu}-(P_{2|\mu}^{D}P_{0|v}^{D}-P_{0|\mu}^{D}P_{2|v}^{D})Y_{0|\mu}^{D} \\ &-P_{2|\mu}^{D}\sum\limits_{j=0}^{D-1}P_{j|v}^{D}(Y_{j|v}^{D}-Y_{ j|\mu}^{D})-\sum\limits_{j\geq 3}^{\infty}(P_{2|\mu}^{D}P_{j|v}^{D}-P_{j|\mu}^{D}P_{2|v }^{D})Y_{j|\mu}^{D}]\\ &/(P_{2|\mu}^{D}P_{1|v}^{D}-P_{1|\mu}^{D}P_{2|v}^{D})\end{split} \tag{20}\]  
with \(\sum\limits_{j\geq 3}^{\infty}(P_{2|\mu}^{D}P_{j|v}^{D}-P_{j|\mu}^{D}P_{2|v }^{D})Y_{j|\mu}^{D}\leq 0\), \(Y_{0|\mu}^{D}\leq Q_{\omega}/P_{0|\omega}^{D}+\sqrt{1-\left(F_{0|\mu\omega}^{ D}\right)^{2}}\) and \(\sum\limits_{j=0}^{D-1}P_{j|v}^{D}(Y_{j|v}^{D}-Y_{j|\mu}^{D})=\sum\limits_{j=0}^{D -1}P_{j|v}^{D}\sqrt{1-F_{j|\mu v}^{D}}\).  
Then  
\[\begin{split} Y_{1|\mu}^{D}\geq\frac{P_{2|\mu}^{D}Q_{\rm{p}}-P_{2|\
u} ^{D}Q_{\rm{r}}-(P_{2|\mu}^{D}P_{0|\
u}^{D}-P_{0|\mu}^{D}P_{2|\
u}^{D})Y_{0|\mu}^ {D}-P_{2|\mu}^{D}\sum_{j=0}^{D-1}P_{j|\mu}^{D}\sqrt{1-F_{j|\mu\
u}^{D}}^{2}}{P_ {2|\mu}^{D}P_{1|\
u}^{D}-P_{1|\mu}^{D}P_{2|\
u}^{D}}\end{split} \tag{21}\]
# Controlled-Atmosphere Sintering of KNbO\({}_{3}\)
## 2 Materials and Methods  
KNbO\({}_{3}\) powder was prepared via solid state reaction. K\({}_{2}\)CO\({}_{3}\) (\(>\)99.5%) and Nb\({}_{2}\)O\({}_{5}\) (\(>\)99.9%) (Daejung Chemicals) were dried (250 \({}^{\circ}\)C, 5 h) to eliminate adsorbed moisture. Stoichiometric amounts were weighed and ball milled for 24 h using high-purity ethanol (99.9%) and ZrO\({}_{2}\) balls in a polypropylene jar. After milling, the slurry was heated on a hotplate/magnetic stirrer to evaporate the ethanol, followed by drying in an oven at 80 \({}^{\circ}\)C. After drying, the slurry was ground and passed through a 180 \(\upmu\)m sieve to remove agglomerates, followed by calcination at 850 \({}^{\circ}\)C for 5 h in flowing O\({}_{2}\) (flow rate 100 cc.min\({}^{-1}\)) in a covered alumina crucible. The calcined powder was examined by X-Ray Diffraction (XRD, X'Pert PRO, PANalytical, Almelo, the Netherlands) using CuK\({}_{\alpha}\) radiation with 20 = 20-90\({}^{\circ}\), a scan speed of 3\({}^{\circ}\).min\({}^{-1}\), and a step size of 0.02\({}^{\circ}\). The calcined powder was ball-milled again to reduce particle size.  
Powder was hand pressed into pellets using a 6 mm die, then cold isostatically pressed at 147 MPa. Pressed pellets were buried in KNbO\({}_{3}\) packing powder in a covered alumina crucible and sintered in atmospheres of O\({}_{2}\), air, N\({}_{2}\), N\({}_{2}\)-5 vol % H\({}_{2}\), and H\({}_{2}\) (flow rate 50 cc.min\({}^{-1}\)) at 1040 \({}^{\circ}\)C for 1-10 h. Heating and cooling rates were 5 \({}^{\circ}\)C.min\({}^{-1}\). The sintered sample density was measured by the Archimedes method. Samples were vertically sectioned, polished with diamond suspension (1 \(\upmu\)m finish), and thermally etched at 990 \({}^{\circ}\)C for 1 h in the same atmosphere in which they were sintered. The microstructure of Pt-coated samples was examined by Scanning Electron Microscopy (SEM, Hitachi S-4700 FE-SEM, Hitachi High-Tech, Tokyo, Japan) with an attached Energy Dispersive Spectrometer (EDS, EMAX Energy EX-200, Horiba, Kyoto, Japan) using standardised quantification. Mean matrix grain size and grain size distributions were analyzed using ImageJ y1.46 (National Institute of Mental Health, Bethesda, MD). Samples sintered at 1040 \({}^{\circ}\)C for 5 h were examined using XRD as before. Differential Thermal Analysis (DTA, DTG-60, Shimadzu, Kyoto, Japan) was carried out on samples sintered at 1040 \({}^{\circ}\)C for 3 h. Analysis was carried out in the temperature range 50-450 \({}^{\circ}\)C in N\({}_{2}\) with heating and cooling rates of 10 \({}^{\circ}\)C.min\({}^{-1}\).
## 4 Experiment Verification
### Security Aspect
#### 4.3.3 Security against Encryption Key Hijacking Attack
In addition, only theoretical verification of the randomness and unpredictability of the security aspects of the measures proposed in this study was carried out. Further studies would like to verify through the implementation of statistical randomness verification provided by NIST (National Institute of Standards and Technology) [37]. Formal analysis, S.C.; Investigation, S.C. and D.K. ; Methodology, S.C.; Software, S.C.; Validation, S.C.; Writing--original draft, S.C.; Writing--review & editing, D.K. and S.P. All authors have read and agreed to the published version of the manuscript. This research was funded by Korea east-west power company. This paper was supported in 2020 with the support of Korea east-west power company. This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program( IITP-2020-2017-0-01628 ) supervised by the IITP(Institute for Information & communications Technology Promotion). The authors declare no conflict of interest.
## 4 Case Study
### Influence of Parameter Uncertainty
Therefore, considering the uncertainty of the failure threshold in the evaluation process is expected to improve the calculation accuracy. Since the uncertainty of the failure threshold has been considered in the modeling above, in this part, \(\omega\) is taken as a fixed value, and then, the reliability is calculated. The results are shown in Figure 10b, from which it can be seen that the uncertainty of the parameter \(\omega\) has not been considered, resulting in a large deviation of the reliability evaluation result, and its MAE is 0.091, a 207% increase compared to the MAE between M3 and M1. It can be seen that considering the influence of the uncertainty of the failure threshold, in the process of a storage reliability assessment of batch products, can reflect the influence of the heterogeneity of batch products, so more accurate assessment results can be obtained.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 3 Results  
### Simulation Results  
#### 3.1.1 Mises Stress  
When the spring is fully tightened, its height is 216 mm. At this time, the inner rings and the outer rings' conical surfaces are completely in contact with each other. The entire spring is a solid structure whose stiffness is approaching infinity. At the same time, the meaning of the spring is lost. To avoid such a situation, the spring should keep the minimum spacing within a normal use limit, such as 1-3 mm. After calculation, the amount of spring deformation in the normal state can be 36 mm. The amount of deformation in the limit state of the spring can be 48 mm.  
Figure 4: Structure size of ring spring (unit: mm).  
Figure 3: Finite element model of ring spring.  
When compressing the ring spring to 36 mm and 48 mm, respectively, then unloading it completely, the stress contours (Figure 5) are obtained. Lastly, the simulation results are compared with the calculation results from the formula.  
When the ring spring is at the normal service limit (36 mm), it can be seen from the stress cloud diagram that the stress changes uniformly with the diameter in the diameter direction. The stress of the inner rings and outer rings is gradually reduced as the rings' diameter increases outward. The surface stress at the outer ring's cone angle is the maximum stress of the ring spring, reaching 1340 MPa. The maximum stress of the ring spring is less than the ring spring's yield stress, which is 1375 MPa, indicating that the spring is in an elastic deformation state under normal use limit. The stress change of the outer ring is 959.7 MPa to 1337 MPa, and the maximum stress appears at the cone angle. The stress change of the inner ring ranges from 933.5 MPa to 1284 MPa, and the maximum stress happens in the innermost section.  
When the ring spring is at the limit of load capacity (48 mm), nearly half of the cross-sectional stress of the inner rings and the outer rings reaches the yield stress (Figure 5). The maximum stress reaches 1383 MPa, which is slightly greater than the yield stress of the ring spring. At the stress level of 1383 MPa, the spring undergoes slight plastic deformation. The stress change of the outer ring is 1195-1381 MPa. The stress change of the inner ring is 1170-1381 MPa.
###### Abstract  
Magnetic gear mechanisms have advanced to have a promising future in transmission technology. Previous research indicates that magnetic gear mechanisms might replace mechanical gear mechanisms in some applications. Small-scale wind turbines (SWT) with counter-rotating rotors that were initially fitted by bevel gears are proposed to be replaced by a coaxial magnetic gear train (CMGT). The CMGT is intended for use as a speed multiplier in order to obtain maximum power at low wind speeds, due to its beneficial transmission of power without physical contact. The primary objective of this study is to build a dual-input CMGT that will be employed in the transmission system of small-scale counter-rotating wind turbines. A dual-input CMGT is built through the analytical modeling of an equivalent magnetic circuit (EMC), which aims to predict the magnetic flux density in the air-gaps of CMGT. Several models within design constraints were compared to obtain the optimum design parameters of the preliminary CMGT design resulting from an EMC analysis. The optimized critical design parameters were then selected and analyzed using finite-element analysis (FEA) to depict the performance of the proposed SWT design. According to the findings, the developed design can generate an inner air-gap flux density of 0.8314 T and an outer air-gap flux density of 1.0200 T. The model likewise produces promising simulation results with an output transmitted torque in the inner rotor (output link) of 8.7 Nm, 56.9 Nm in the outer rotor, and 48.0 Nm in the carrier with pole-pieces. Thus, this design can generate higher torque than a bevel-geared wind turbine. The speed characteristics are also compromised in order to raise the generator's rotating speed to generate more power. Finally, this study demonstrates the performance and embodiment design of the proposed SWT using CMGT.  
magnetic gear train; small-scale wind turbine; equivalent magnetic circuit; finite-element analysis +
Footnote †: journal: Applied Sciences
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### Network Model Architecture  
Figure 1 illustrates the general proposed SDN network model and design components. Three layers make up the model: the application plane, control plane, and data plane; we will discuss each layer briefly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Queue reservation with \\ differentiating service \\ based on DSCP. \\ \end{tabular} \\ \hline \hline \end{tabular}  
* SR: Shortest Path Routing. QR: QoS based Routing based Solution. RE: Reservation based Solution. DRE: QoS Differentiation and Reservation based Solution. N/D: Not Defined.  
\end{table}
Table 1: A comparison of some SDN QoS ARCHITECTURES related research with the proposed VQoSRR.
## 3 Design Process of the Single Element  
Initially, a compact single-element monopole antenna achieving the FCC frequency band requirements was designed to form the MIMO antenna. The strategy adopted, illustrated in Figure 2, was as follows: a pentagonal patch monopole antenna was designed on a low-cost FR4 substrate with an L-shaped defective ground plane (Ant-a).  
As shown in Figure 3, illustrating the reflection coefficient S11, this antenna operates in an ultra-wide frequency band under \(-\)10 dB, extending from 3.4 GHz to 11.39 GHz. To expand the lower frequency band and to improve the impedance matching, a rectangular parasitic element is added (Ant-b). According to the S11 plot in Figure 3, the lower bandwidth limit is shifted to 3.3 GHz. This slight improvement in the lower frequency band is supplemented by a more significant improvement in the impedance matching. In the last configuration, Ant-c, the ground plane is loaded by three CSRR cells. A more significant bandwidth enhancement is achieved in this case, ranging from 3.01 GHz to 12.34 GHz, covering the total FCC bandwidth allocated to UWB applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline \(L\) & 20 & \(y_{2}\) & 9.7 \\ \(W\) & 30 & \(w_{f}\) & 1.5 \\ \(l_{g}\) & 4.78 & \(l_{p}\) & 2 \\ \(L_{1}\) & 6.9 & \(w_{p}\) & 10 \\ \(L_{2}\) & 6.41 & \(l_{1}\) & 5.5 \\ \(P_{1}\) & 2.49 & \(l_{2}\) & 4.5 \\ \(P_{2}\) & 1.7 & \(w_{1}\) & 5.5 \\ \(P_{3}\) & 5.76 & \(w_{2}\) & 4.5 \\ \(x_{1}\) & 5.75 & \(g_{1}\) & 2.25 \\ \(x_{2}\) & 15.5 & \(g_{2}\) & 0.1 \\ \(y_{1}\) & 10.3 & \(d_{x}\) & 12.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimized parameters of the proposed UWB-MIMO antenna.  
Figure 1: Proposed UWB-MIMO antenna: (**a**) top layout, and (**b**) bottom layout.  
A parametric analysis was conducted to highlight the effects of the CSRR structure lengths (Figure 4a,b) and the parasitic-element length and width (Figure 4c,d) on the antenna bandwidth and impedance matching.  
As illustrated in Figure 4a,b, the effect of the CSRR structure lengths is weak on low frequencies but is more important on high frequencies, where a wide spreading of the spectrum is observed with the increase in these lengths. It can also be observed that different resonances are created for these different lengths, which proves the effectiveness of these structures in bandwidth enhancement.  
The effect of varying the dimensions of the parasitic element, as illustrated in Figure 4c,d, is manifested by a slight shifting of the frequency band for either low or high frequencies. Its effect on the impedance-mismatching improvement is also observed.  
Figure 3: Simulated reflection coefficient S11 of the three antenna structures.  
Figure 2: Design steps of single-element antenna.