esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 6 Simulation and Real-Time Results
### Case 1:  
In this case, it is supposed that the load demand of the isolated MG is unvarying, i.e., \(\Delta P_{L}=0\). On the other hand, the power randomness of the WTG (\(\Delta P_{w}\)) and PVG (\(\Delta P_{pv}\)) are involved in the LFC-based MG. The profile of the wind power fluctuation, which is extracted from an offshore wind farm in Sweden [37], is depicted in Figure 6a, while Figure 6b illustrates the solar radiation data in Aberdeen [38], which was used in this test MG. By employing the real data, the frequency outcomes of the MG system in the HIL environment are depicted in Figure 7.  
Figure 5: The real-time experimental setup; (**a**) the real-time simulation of MG and controller in the real-time simulator laboratory (RTS-LB) (**b**) the compilation process.  
According to Figure 7, the desired level of the MG outcome (\(\Delta f\)), regarding settling time and transient fluctuations, is provided by the FOSMC controller. In comparison, the suggested technique can achieve much smaller fluctuations of \(\Delta f\) with quicker outcome specifications than the MPC and SMC approaches. In other words, the real-time outcomes confirm that the suggested technique can handle the randomness of the WTG and PV more effectively as compared to the considered model-based approaches.  
**Case 2:**  
In this case, to confirm the robustness of the FOSMC controller, some parameters of the MG (i.e., \(R\), \(D\), \(H\), \(T_{inv}\), and \(T_{g}\)) are changed. The percentage of the variations for the test system is furnished in Table 2.  
As observed in Table 2, a sever scenario of changing the MG parameters is considered to assure the robustness of the suggested FOSMC scheme. The real-time results obtained by the suggested controller, MPC, and conventional SMC are depicted in Figure 8.  
Figure 6: Power fluctuation, (**a**) wind power generator (WPG), (**b**) photovoltaic (PV).  
Figure 7: Curves of the MG response with the application of WPG and PV.  
The results of Figure 8 reveal that when the system parameters are changed, both the MPC and conventional SMC can still provide robust LFC performance, but they are not adequately optimal. Moreover, the results indicate that the proposed controller provides a higher degree of robustness in comparison with the other two control strategies.
## 4 Simulation Results and Discussions
### Results and Discussions
#### 4.3.1 Evaluation Result of the SSA and the ASA Systems
ASA applies the ASP equation to adaptively modify the priority of each slice to protect slices from each other, preventing the URLLC slice from starving the other slices (i.e., eMBB and mMTC slices) and degr  
Figure 12: TBP of system vs. total offered traffic loads (Experiment 1). Figure 13: TBP of network slices vs. total offered traffic loads (Experiment 1). ASA outperformed SSA, especially for eMBB and mMTC slices, with slight degradation in the QoS of the URLLC service. ASA improves system utilization by allowing unutilized resources from one slice to be utilized by other slices that require additional resources. Finally, ASA achieves a superior balance of efficiency and fairness compared to SSA.
## 3 Results and Discussion
### Event 4 (30 April 2022)  
The event of 30 April 2022 occurred in the solar spot region AR12994, located on the solar disk at N19 W90. Despite being in this location, it is possible to analyze the impact of this flare on the Earth's ionosphere, which was generated at 13:37 UTC, reached its maximum at 13:47 UTC, and ended at 13:52 UTC. The results of this event show a considerable increase in the wavelets after the event (Figure 9). This is a visible pattern where the flare did not have much impact. In this case, it is mainly noticeable in satellite 4. Additionally, it is possible to appreciate that there is not as much impact for the SPIG station as in the others, because this station is further north. The same happened in the other case where the location of the flare on the solar disk was very close to the edge.  
During this event, we observed more distinct patterns of the diurnal cycle during the calm day. As the ionosphere began to charge, frequencies that impact the wavelet results  
Figure 8: Visualization of Total Electron Content Wavelet Analysis from GNSS Signals for the Event on 30 March 2022.  
were detected. Once the increase subsided, the wavelets also decreased. However, concerning the event detection, the day of the event was clearly and concisely distinguishable (Figure 9).
# Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer
###### Abstract  
Silicon nitride is a material compatible with CMOS processes and offers several advantages, such as a wide transparent window, a large forbidden band gap, negligible two-photon absorption, excellent nonlinear properties, and a smaller thermo-optic coefficient than silicon. Therefore, it has received significant attention in the field of silicon photonics in recent years. The preparation of silicon nitride waveguides using low-pressure chemical vapor deposition methods results in lower loss and better process repeatability. However, due to the higher temperature of the process, when the thickness of the silicon nitride film exceeds 300 nm on an 8-inch wafer, it is prone to cracking due to the high stress generated by the film. Limited by this high stress, silicon nitride waveguide devices are typically developed on wafers with a thickness of 4 inches or less. In this work, we successfully fabricated a 400 nm-thick silicon nitride waveguide on an 8-inch wafer using a Damascene method similar to the CMOS process for copper interconnects and demonstrated propagation losses of only 0.157 dB/cm at 1550 nm and 0.06 dB/cm at 1580 nm.  
LPCVD; silicon nitride; waveguide; low loss +
Footnote †: journal: Applied  
**Circulation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer. _Appl. Sci._**2023**, _13_, 3660. [[https://doi.org/10.3390/appl306360](https://doi.org/10.3390/appl306360)]([https://doi.org/10.3390/appl306360](https://doi.org/10.3390/appl306360))
## 3 Results
### Composition of Detected Outliers  
The outliers detected by our approach showed lower compositional rates than those of EdgeR and DESeq2. The average composition percentages of outliers detected by BRaVO, EdgeR, and DESeq2 were 0.017%, 0.250%, and 0.727%, respectively (Table 1). This table revealed that our approach showed higher sensitivity for minor compositional outliers than those of EdgeR and DESeq2. Furthermore, the distributions of the compositional percentages were apparently different among these three approaches. The composition percentages of outliers detected by BRaVO, EdgeR, and DESeq2 were in the ranges (min-max) of 0.00014-0.27178%, 0-4.34274%, and 0.00014-7.53432%, respectively (Figure 2).  
In the animal-based diet group, EdgeR detected 8 zero-inflated features (_Prevotella stercorea, Sphingobacteriaceae bacterium SOC A20(36), Erwinia pyrifoliaiae, primary endosymbiont of Sitophilus oryzae, Fusobacterium equinum, Enterococcus eccorum, Shigella sontei, Escherichia althetii_) that were not observed in all samples, but BRaVO and DESeq2 could not detect them as outliers. In the plant-based diet group, EdgeR could not detect any outliers and DESeq2 detected one outlier. In contrast, our method detected 19 features as outliers. In the plant-based diet, the outliers detected by BRaVO also showed a lower composition percentage than that of DESeq2, as in the animal-based diet group (Table 1).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Animal-Based**} & \multicolumn{3}{c}{**Plant-Based**} \\ \cline{2-7}  & **BRAVO** & **EdgeR** & **DESeq2** & **BRAVO** & **EdgeR** & **DESeq2** \\ \hline Outliers & 28 & 42 & 27 & 19 & 0 & 1 \\ Composition (avg.) & 0.017 & 0.250 & 0.727 & 0.019 & N.A. & 0.098 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of outliers detected by three methods. This table shows a summary of outliers detected by BRaVO, EdgeR, and DESeq2. Composition means the compositional percentages of outliers detected by each method. Outliers indicate the number of features with compositional changes in their taxonomic abundance.  
Figure 2: Composition and distribution of outliers. This figure shows the compositional percentages of taxonomic abundance for outliers detected by BRaVO, EdgeR, and DESeq2. Horizontal lines inside these box plots are their median values. Lower and upper box limits are the 25 th and 75 th percentiles in their distribution. Less than 25 th percentile and more than 75 th percentile are shown as unfilled circles. The compositional percentages of outliers detected by BRaVO, EdgeR, and DESeq2 were in the range (min-max) of 0.00014–0.27178%, 0-4.34274%, and 0.00014–7.53432%, respectively.
## 3 Experimental Evidence for Trouton's Rule
### Salt Ammoniates  
Figure 7 shows a collection of pT-lines of ammoniate transitions of chloride salts [11]. We extrapolated [PERSON]'s plot to \(1/T=0\), so that an apparent intersection of pT-lines around \(1/T=0\) emerges, which is predicted by [PERSON]'s rule (9) with Equation (4). Figure 7 also contains the pT-line of the ammonia vapor-liquid transition (red). This transition does not belong to the class of vapor-sorbed ammonia transitions, and indeed the pT-line does not intersect with the others around \(1/T=0\). With the fitted transition entropies and enthalpies \(\Delta s\) and \(\Delta h\), we constructed a plot of [PERSON]'s rule for chloride ammoniates, shown in Figure 8. We considered a pool evaporator-condenser at 20 \({}^{\circ}\)C, giving an NH\({}_{3}\) vapor pressure of 8.6 bar. Hence, the temperature \(T_{d}\) in Figure 8 is the deanmoniation temperature at 8.6 bar.  
The fit provides \(\Delta h/T_{d}=116.7\) J/molK = \(14.0R\) = \(\Delta s\) at 8.6 bar. The regression coefficient is even higher than for the common gases shown earlier. This may be due to the fact that for all salts, the anion Cl\({}^{-}\) was the same, which might provide a well-defined binding environment for the NH\({}_{3}\) molecules within the crystal lattices and coordination to the respective cations. Adding data for other ammoniate salts than chlorides [27] seems to result in a less accurate correlation. It would be interesting to further study the influence of anions and cations on the validity and accuracy of [PERSON]'s rule for salt ammoniates. Figure 9 shows \(\Delta s_{j}\) for all chloride ammoniate transitions \(j\), with an average of about \(135\) J/molK at \(p_{0}=1\) atm. In orange, we have added the transition entropy of \(98.2\) J/molK of ammonia vapor and liquid at 1 atm, which considerably differs from the average, in agreement with Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fluid** & **T\({}_{b}\) (K)** & **T\({}_{c}\) (K)** & **\(\Delta h\) (J/molK)** & **\(\Delta h\)/RT\({}_{b}\)** & **\(\Delta h\)/RT\({}_{c}\)** \\ \hline H\({}_{2}\)O & 373.15 & 647.6 & 44.0 & 14.2 & 8.2 \\ NH\({}_{3}\) & 239.75 & 405.5 & 23.35 & 11.7 & 6.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trouton’s rule for H\({}_{2}\)O and NH\({}_{3}\) at 1 atm.  
Figure 6: Correlations of latent heat \(\Delta h\) versus the normal boiling temperature \(T_{b}\) (Trouton’s Rule, blue line) and of \(\Delta h\) versus the critical temperature \(T_{c}\) (principle of corresponding states, orange line) for some common fluids [17]. For this data, the regression coefficient is not better for the principle of corresponding states than for [PERSON]’s rule.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 1 Introduction
However, few studies have worked on enhancing the comprehensive efficiency with stable solar thermal resources and utilizing compression heat to supply heat to residents. Unstable thermal resources cause low system electrical storage, round-trip and exergy efficiencies. In this paper, a novel solar-thermal-assisted A-CAES (ST-CAES) is proposed to attain high efficiency. During the charging process, low-price electricity such as curtailed wind, solar, and off-peak electricity, is used to compress ambient air to high-pressure air and store it in a steel pipeline tank (SPT), recovering compression heat that could be used for heating. Solar-thermal energy is collected and stored by STC, which can supply stable thermal energy. During the discharging process, the stored high-pressure air is released and preheated with the exhaust air of a turbine, and further heated with the stored stable solar-thermal energy to generate electricity. The use of solar thermal energy improves the intake air temperature of the turbine generator, thereby enhancing the efficiency of ST-CAES. In consequence, ST-CAES can provide benefits to the smart grid and integrated RES. The major contributions of this paper are: (1) A novel hybrid A-CAES based on the utilization of solar energy, which can be used for RES integration and peak-shaving and, furthermore, could significantly improve the round-trip efficiency, is proposed and analyzed; (2) Compared to the current HA-CAES, ST-CAES eliminates the combustion chamber that has already been proposed in [22]. Consequently, it is a zero-carbon-emission system. Moreover, ST-CAES can provide stable high-grade thermal energy in the discharging process, which is compared with HA-CAES in [23], and can greatly simplify the regenerative system. The rest of this paper is organized as follows. Section 2 elaborates the overall design scheme and mathematic model of ST-CAES. Section 3 presents an energy and exergy analyses of ST-CAES. This is associated with the influence of primary parameters on system performance in Section 4, followed by the conclusions in Section 5.
## 5 Dynamic Analysis
### Simulation Results
#### 5.3.3 Test Case 3--3-Steps Burr Profile \"High-High-High\"  
A 3-steps burr profile \"high-high-high\" (see Figure 12 (top)) is used as an input for the dynamic simulation. Similarly to the previous test case, 3 steps with increasing height are investigated. Differently with respect to the previous test case, in this case, for all the 3  
Figure 11: Three-steps burr profile (“low–low–high” (**top**)); position (**middle**) and velocity (**bottom**) of the grinding wheel support.  
steps the cutting force is sufficiently high to make the mechanism work in the compliant mode of operation. The aim of this test case is to verify that, when it passes from one step to another, the system moves from an equilibrium condition to a new equilibrium condition. The simulation results are presented in Figure 12 (middle and bottom). It can be noticed that, at each step, the grinding wheel moves backwards thanks to the compliance of the mechanism. Three different equilibrium conditions are achieved (after a transient of about 0.25 s each time) with increasing height of the residual burr and, inside each step, a cut profile with constant height is obtained (after the transient), as it can be noticed in Figure 12. The simulation results of this test case confirm the expected dynamic behavior of the system: each time that the system encounters a higher step, it moves to a new equilibrium condition with a higher height of residual burr (in particular, Condition 2 of Section 5.3 is satisfied for all the 3 steps). The maximum (normal) deburring force reduces to 147.3 N from the nominal value of 360 N (corresponding to the nominal height of 6 mm), i.e., roughly a 60% (automatic) force reduction is obtained, which is related to the 60% reduction of the cutting height (2.5 instead of 6 mm).
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## Appendix B  
In the proposed RANSAC approach described in Section 3.2.3, it is essential to find three correct pairs from candidates, because three points are enough to solve Equation (5) and if these three points do not include an outlier, the estimated attitude will be correct. Then, to calculate the expected number of repetitions required to find three correct pairs, the fundamental quantity is the probability of finding three correct pairs from all of matched pairs (including both correct and incorrect pairs) in one repetition. This probability is equal to the probability of drawing three red balls from an urn with \(L\) red balls and \(N-L\) blue balls, which is \(\left(\begin{array}{c}L\\ 3\end{array}\right)/\left(\begin{array}{c}N\\ 3\end{array}\right)\) (\(L<N\)). Since each repetition is an independent trial, sampling is with replacement. Then, the probability of finding three correct pairs for the first time at the \(k\)th trial is \((1-r)^{k-1}r\), where \(r\) represents \(\left(\begin{array}{c}L\\ 3\end{array}\right)/\left(\begin{array}{c}N\\ 3\end{array}\right)\). The expected number of repetitions required to find three correct pairs is  
\[n=\sum_{k=1}^{\infty}k(1-r)^{k-1}r=\frac{1}{r}\;.\] (B1)  
In addition, the probability of having three correct pairs by \(k\)/th repetition times is  
\[r^{\prime}=\sum_{k=1}^{k^{\prime}}(1-r)^{k-1}r\;.\] (B2)  
Equation (B2) assumes that the iterations terminate once three correct pairs have been found. The expected number of repetitions and the required number of repetitions when \(r^{\prime}\) exceeds 99.9% are plotted as functions of \(r\) in Figure 11.  
Figure 11: Number of repetitions as a function of \(r\) for \(r^{\prime}\) exceeding 99.9% (solid line) and the expected number of repetitions for finding three correct pairs for the first time (dashed line). Both reduce as \(r\) approaches to 1.
## 3 Materials and Methods
### LC-UV/DAD Instrumental Set-Up and Analysis Conditions  
LC-UV/DAD analyses were carried out on a Spectra System (SCM1000, P4000, AS3000) provided with a Spectra System UV6000 LP Diode Array Detector (Thermo Fisher Scientific, Waltham, MA, USA). The LC column was a Supelco Asentis(r) Express RP-C18 (150 mm \(\times\) 4.6 mm; spherical particles with fused core(r) technology, 90 A, 2.7 \(\upmu\)m) from Merck; a pre-column (5 mm \(\times\) 4.6 mm; spherical particles with fused core(r) technology, 90 A, 2.7 \(\upmu\)m) was installed to preserve the analytical column for pollution.  
Operative conditions were as follows: injection volume 8 \(\upmu\)L; detection wavelengths: 280 nm for flavan-3-ols and caffeine, 350 nm for flavonol-3-O-glycos, 380 nm for theaflavins; mobile phases: (A) H\({}_{2}\)O + 0.1% formic acid, (B) ACN + 0.1% formic acid; flow rate: 1.10 mL/min; mobile phase program: from 95% H\({}_{2}\)O (4.7 min) to 85% H\({}_{2}\)O (15.8 min) to 75% H\({}_{2}\)O (9.2 min), hold for 12 min, then to 65% H\({}_{2}\)O, and then to 100% ACN, hold for 3 min. Before re-injection, the LC system was stabilized for at least 5 min.
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 4 Bridge Damping Ratio Extraction Method
### Numerical Case Analysis  
This case analysis used the bridge model in Section 3, where the bridge damping factor was set to \(c_{2}=3.42\ \times 10^{4}\)Ns/m, which corresponded to a first-order damping ratio of 1%. The authors of this study assumed that the average vehicle mass was \(m_{v}=1000\) kg, the average vehicle stiffness was \(k_{v}=5.070\ \times 10^{5}\)N/m, and the average damping factor was \(c_{v}=3.82\ \times 10^{3}\)Ns/m. The VBI system frequencies and damping ratios for different number of vehicles were then calculated. The known conditions were the number of vehicles \(n\), and the vehicle mass \(m_{v}\), as well as the frequency \(\omega\) and damping ratios \(\xi\) of the VBI system.  
The average vehicle stiffness \(k_{v}\) was estimated using a PSO algorithm with the parameters set as follows: the number of populations was 30, the number of iterations was 30, and the range of particles was \(1\ \times 10^{5}\ \sim 1\ \times 10^{6}\)N/m, which corresponded to the vehicle self-oscillation frequency range of 1.6-5 Hz.  
The optimization value of the objective function is shown in Figure 9. By the 30 th iteration, the objective function values of all particles were less than 0.5 \(\times 10^{-3}\), so the global optimum had converged. The final estimate of the average vehicle stiffness was 5.072 \(\times 10^{5}\)N/m, which indicated that this algorithm could accurately estimate the equivalent vehicle stiffness. The estimated equivalent vehicle stiffness was used to calculate the bridge frequency, and the results are shown in Figure 10. The calculated bridge frequency was very close to the actual value and did not change with the number of vehicles.  
After obtaining the equivalent vehicle stiffness estimate of \(k^{\prime}\), the ratio of bridge vibration to vehicle vibration amplitude \(t\) could be calculated according to Equation (10). The curve of the identified values of the bridge damping coefficient \(c_{2}\) for different number  
Figure 10: Estimation of bridge frequency under different number of vehicles.  
Figure 9: The objective function value of each particle.  
of vehicles was plotted by substituting a series of trial values of \(c\) in Equation (24). Here, the range of the average vehicle damping coefficient was 0\(\sim\)4.5 \(\times\) 10\({}^{4}\), corresponding to the vehicle damping ratio of 0\(\sim\)1. The vertical coordinate corresponding to the intersection point of the curve was the identification value of the bridge damping coefficient.  
Figure 11 shows the bridge damping coefficient identification curve in relation to different numbers of vehicles. The curves corresponding to different numbers of vehicles intersect at one point. The intersection point corresponds to the horizontal coordinate of 3.82 \(\times\) 10\({}^{3}\), and the vertical coordinate of the intersection point is 3.42 \(\times\) 10\({}^{4}\), indicating that the average vehicle damping coefficient and bridge damping coefficient could be accurately estimated by this method.