esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Properties of Electro-Insulating Liquids Modified by Fullerene C\({}_{60}\)
On the other hand, when analyzing the results of specific heat measurements, it can be noticed that in the case of mineral oil and synthetic ester, the modification of the surfactant caused the increase of the specific heat. On the other hand, in the case of a natural ester modified with a surfactant, the specific heat decreased. When analyzing the effect of modifying electro-insulating liquids with titanium dioxide TiO\({}_{2}\) nanoparticles, an increase in the thermal conductivity of the generated nanofluids relative to the base liquids can be observed. Other researchers also observed such results [53]. The increase in the thermal conductivity of the resulting nanofluids should be related to the high thermal conductivity of titanium dioxide TiO\({}_{2}\) nanoparticles (approximately 22 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)) [64]. Taking into account the results of measurements of specific heat of electro-insulating liquids modified with TiO\({}_{2}\) nanoparticles, it can be noticed that in the case of mineral oil and natural ester, the value of specific heat in relation to the base liquids was reduced. On the other hand, in the case of a synthetic ester, an increase in the value of specific heat relative to the base liquid is noticeable. The obtained results may be related to the size of the nanoparticles. According to literature [90], the smaller the particle size is, the specific heat is higher. The obtained results suggest that in the case of a synthetic ester, a dispersion containing the smallest-sized particles was obtained. This effect is attributed to the surface energy, distortion, and lattice energy of the nanoparticles [91]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kind of Liquid**} & \multicolumn{4}{c}{**Liquid Temperature**} \\ \cline{2-5}  & **25**\({}^{\circ}\)C & **40**\({}^{\circ}\)C & **60**\({}^{\circ}\)C & **80**\({}^{\circ}\)C \\ \hline MO & 33.9 & 29.4 & 25.2 & 22.4 \\ MO + C\({}_{60}\) & 33.9 & 29.3 & 25.2 & 22.4 \\ \hline SE & 40.4 & 34.1 & 28.6 & 24.9 \\ SE + C\({}_{60}\) & 40.1 & 33.9 & 28.7 & 24.9 \\ \hline NE & 37.8 & 33.0 & 28.4 & 25.1 \\ NE + C\({}_{60}\) & 37.9 & 32.8 & 28.2 & 24.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Calculation results of the temperature decrease in pure electro-insulating liquids and nanofluids based on fullerene C\({}_{60}\).
# Improving the Solving of Optimization Problems:
## 3 Quantum-Compliant Problem Formulations
### Frameworks and Tools  
All the steps discussed above require substantial expertise in QUBO formulation, which many users may lack. This knowledge gap limits access to the potential benefits of quantum computation. Moreover, tasks such as variable encoding, solution decoding, and function rewriting can be time-consuming and lead to errors when performed manually. Managing penalty functions is equally important, especially the operation of translatinga constraint into a suitable penalty function. This process can be even more challenging when auxiliary variables are needed for inequality constraints. Additionally, selecting an appropriate penalty weight is crucial for obtaining satisfactory results. For these reasons, libraries and frameworks that automate the translation of optimization problems into solver-compatible formats are essential in assisting end-users.  
The main libraries include pyqubo [24, 25], qubovert [26], dimod [27], Fixstarts Amplify [28], Qiskit-optimization [29], and openQAOA Entropica [30]. Additionally, several key frameworks have emerged in the last two years to address user demands for tools that automate the entire process, including AutoQUBO [31, 32, 33], QUBO.jl [34], and mqt-qao [35, 36, 37]. Refs. [34, 35] offer a detailed overview of these tools, along with a comparison of their features. In addition to these tools, a framework for formulating graph-based optimization problems into QUBO format has been proposed in [38], as part of the Munich Quantum Toolkit [37]. Furthermore, in [39], a similar tool is introduced for formulating discrete optimization problems such as QUBO.  
Finally, D-Wave has released a QUBO preprocessing toolchain [40] optimized for their Quantum Annealer platform to reduce problem complexity. This toolchain includes methods for reducing the number of QUBO variables and estimating the lower bound of the objective function, based on techniques from [41]. Alternatively, the tool Qoolchain, proposed in [42], offers further methods to reduce QUBO size, decompose it, and estimate function bounds especially focusing on GAS.
## 3 Proposed Method
### MSR Generation Using T-Spline Local Refinement
#### 3.2.2 Geometrical Stage of MSR Generation  
The geometrical stage of MSR generation aims to calculate the Cartesian coordinate value of inserted control points using blending function refinement [8]. We denote T-spline surface before and after refinement as \(S\) and \(\overline{S}\), which should satisfy the condition as follows:  
\[S\equiv\overline{S} \tag{13}\]  
Substituting Equation (1) into Equation (13), we have:  
\[\sum_{i=1}^{n}P_{i}B_{i}(s,t)=\sum_{i=1}^{\overline{n}}\overline{P_{i}} \overline{B_{i}}(s,t) \tag{14}\]  
wherein, symbols without tilde represent the T-spline surface before refinement, and symbols with tilde represent the T-spline surface after refinement. \(B_{i}\) can be written as a linear combination of \(B_{j}\) based on blending function refinement as below:  
\[B_{i}(s,t)=\sum_{j=1}^{\overline{n}}c_{i}^{j}\overline{B_{j}}(s,t) \tag{15}\]  
Substituting Equation (15) into Equation (14), Equation (13) can be simplified:  
\[\overline{P}_{j}=\sum_{i=1}^{n}P_{i}c_{i}^{j} \tag{16}\]  
Figure 5: An example of PolyBoundingBox construction for watershed result. From (**a–d**) show the construction of PolyBoundingBox.  
\(c_{i}^{j}\) in Equations (15) and (16) is calculated by means of blending function refinement that can be decomposed into the product of two B-spline basis function refinement. Cubic T-spline is considered in our research, and for each blending function in Equation (2), the B-spline basis function \(N_{i}(s)\) and \(N_{i}(t)\) are defined using five knots separately. Hence, there are four cases for B-spline basis function refinement as below.  
We denote \(s=[s_{0},s_{1},s_{2},s_{3},s_{4}]\) as knot vector for \(N(s)=N[s_{0},s_{1},s_{2},s_{3},s_{4}](s)\) generally, and \(k\) is the knot that will be inserted.  
- Insert \(k\) between \(s_{0}\) and \(s_{1}\).  
\[N[s_{0},s_{1},s_{2},s_{3},s_{4}](s)=\frac{k-s_{0}}{s_{3}-s_{0}}N[s_{0},k,s_{1},s_{2},s_{3}](s)+N[k,s_{1},s_{2},s_{3},s_{4}](s) \tag{17}\]  
- Insert \(k\) between \(s_{1}\) and \(s_{2}\).  
\[N[s_{0},s_{1},s_{2},s_{3},s_{4}](s)=\frac{k-s_{0}}{s_{3}-s_{0}}N[s_{0},s_{1},k,s_{2},s_{3}](s)+\frac{s_{4}-k}{s_{4}-s_{1}}N[s_{1},k,s_{2},s_{3},s_{4}](s) \tag{18}\]  
- Insert \(k\) between \(s_{2}\) and \(s_{3}\).  
\[N[s_{0},s_{1},s_{2},s_{3},s_{4}](s)=\frac{k-s_{0}}{s_{3}-s_{0}}N[s_{0},s_{1},s _{2},k,s_{3}](s)+\frac{s_{4}-k}{s_{4}-s_{1}}N[s_{1},s_{2},k,s_{3},s_{4}](s) \tag{19}\]  
- Insert \(k\) between \(s_{3}\) and \(s_{4}\).  
\[N[s_{0},s_{1},s_{2},s_{3},s_{4}](s)=N[s_{0},s_{1},s_{2},s_{3},k](s)+\frac{s_{4 }-k}{s_{4}-s_{1}}N[s_{1},s_{2},s_{3},k,s_{4}](s) \tag{20}\]  
If \(k<s_{0}\) or \(k>s_{4}\), \(N(s)\) does not change. The calculation of \(c_{i}^{j}\) for blending function refinement can be derived by the refinement of \(N_{i}(s)\) and \(N_{i}(t)\). And the insertion of multiple knots can be performed by repeating the insertion of a single knot. Finally, the geometric information for control points that are used for MSRs construction can be obtained by solving Equation (16).
###### Abstract  
Correntropy has been proved to be effective in eliminating the adverse effects of impulsive noises in adaptive filtering. However, correntropy is not desirable when the error between the two random variables is asymmetrically distributed around zero. To address this problem, asymmetric correntropy using an asymmetric Gaussian function as the kernel function was proposed. However, an asymmetric Gaussian function is not always the best choice and can be further expanded. In this paper, we propose a robust adaptive filtering based on a more flexible definition of asymmetric correntropy, which is called generalized asymmetric correntropy that adopts a generalized asymmetric Gaussian density (GAGD) function as the kernel. With the shape parameter properly selected, the generalized asymmetric correntropy may get better performance than the original asymmetric correntropy. The steady-state performance of the adaptive filter based on the generalized maximum asymmetric correntropy criterion (GMACC) is theoretically studied and verified by simulation experiments. The asymmetric characteristics of queue delay in satellite networks is analyzed and described, and the proposed algorithm is used to predict network delay, which is essential in space telemetry. Simulation results demonstrate the desirable performance of the new algorithm.  
1
Footnote 1: School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China; [EMAIL_ADDRESS] (H.Q.); [EMAIL_ADDRESS] (M.W)  
2
Footnote 2: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; [EMAIL_ADDRESS]  
3
Footnote 3: School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710061, China  
4
Footnote 4: Zhejiang Lab, Hangzhou 310000, China; [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (T.L.)  
5
Footnote 5: Corresponding: [EMAIL_ADDRESS]  
6
Footnote 7: These authors contributed equally to this work.  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Generalized Asymmetric Correntropy for Robust Adaptive Filtering: A Theoretical and Simulation Study. _Remote Sens._**2022,** _14_, 3677. [[https://doi.org/10.3390/rs14153677](https://doi.org/10.3390/rs14153677)]([https://doi.org/10.3390/rs14153677](https://doi.org/10.3390/rs14153677))  
Received: 26 June 2022  
Accepted: 28 July 2022  
Published: 1 August 2022  
**Publisher:** **Notic:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 5 Performance Evaluation
### System Parameters
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Slot time (\(\sigma\)) & 9 us \\ SIFS period (\(T_{SIFS}\)) & 10 us \\ PIFS period (\(T_{PIFS}\)) & 19 us \\ DIFS period (\(T_{DIFS}\)) & 28 us \\ EIFS period (\(T_{EIFS}\)) & 88 us \\ Beacon interval & 100 ms \\ Minimum CW size (\(CW_{min}\)) & 15 \\ Maximum CW size (\(CW_{max}\)) & 1023 \\ Preamble time (\(T_{pre}\)) & 16 us \\ Signal time (\(T_{sig}\)) & 4 us \\ OFDM symbol period (\(T_{sym}\)) & 4 us \\ Signal extension period (\(T_{sigEx}\)) & 6 us \\ Service bits (\(I_{seqr}\)) & 16 b \\ Tail bits (\(L_{tail}\)) & 6 b \\ Length of a beacon (\(L_{B}\)) & 20 B \\ Length of RTS (\(L_{RTS}\)) & 20 B \\ Length of PS-Poll (\(LPS_{PS-Poll}\)) & 20 B \\ Length of CTS (\(L_{CTS}\)) & 14 B \\ Length of ACK (\(L_{ACK}\)) & 14 B \\ Length of the MAC header (\(L_{MAChdr}\)) & 30 B \\ Length of FCS (\(L_{FCS}\)) & 4 B \\ Transition time from idle to sleep (\(T_{i\to sl}\)) & 250 us \\ Transition time from sleep to idle (\(T_{sl\to i}\)) & 250 us \\ Transmission power consumption (\(\rho_{1}\)) & 1.65 W \\ Reception power consumption (\(\rho_{r}\)) & 1.4 W \\ Idle power consumption (\(\rho_{i}\)) & 1.15 W \\ Sleep power consumption (\(\rho_{sl}\)) & 0.045 W \\ Idle to sleep transition power consumption (\(\rho_{i\to sl}\)) & 0.045 W \\ Sleep to idle transition power consumption (\(\rho_{sl\to i}\)) & 1.725 W \\ Holding time & 100 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System Parameters. Unless otherwise specified, an infrastructure BSS consisting of an AP and 20 STAs, a 1500-byte MSDU length, and PHY control and data transmission rates of 24 and 54 Mbps were considered to derive the analytical and simulation results.
## 3 A 3 Phase 6/4 SRM Mathematical Model and Operation  
The layout of a three-phase machine with six poles on the stator and four poles on the rotor is shown in Figure 1. The stator is equipped with concentrated winding, while the rotor has no winding or magnets. The SRG properties are determined by the machine structure and magnetization characteristics of the laminations, as well as the converter architecture and controller methods.  
The voltage equation of each motor phase is given by  
\[V=Ri+\frac{d\lambda(i,\theta)}{dt} \tag{1}\]  
\(i\) is the phase stator current in A, \(R\) is the phase resistance in \(\Omega\), and \(\lambda\) is the stator flux per phase in Wb, where  
\[\lambda(i,\theta)=L(i,\theta)\cdot i \tag{2}\]  
Figure 1: Three-phase 6/4 SRM layout’s construction.  
The phase inductance \(L(i,\theta)\) in H depends on the rotor position, maximum at the aligned position, and minimum at the unaligned position. Using (1) and (2) leads to the general excitation voltage equation for SRM (3).  
\[V=Ri+L\ \frac{di}{dt}+i\frac{dL(i,\theta)}{d\theta}\frac{d\theta}{dt} \tag{3}\]  
where the last term is the induced emf in \(V\). In SRG, the excitation is applied at the position of maximum inductance, which is the turn-on angle \(\theta_{\text{on}}\), and the turn-off angle \(\theta_{\text{off}}\) at the negative slope of the inductance profile  
\[T_{e}=\ \frac{1}{2}i^{2}\ \frac{dL(\theta,i)}{d\theta} \tag{4}\]  
The electromagnetic torque \(T_{e}\) is seen to be dependent on the rate of change of phase inductance due to rotor position and the square of phase current. As a result, the drive system applies the current pulses when \((\frac{dI}{d\theta}>0)\) under motoring operation, and when \((\frac{dI}{d\theta}<0)\) under generating mode. The asymmetric half-bridge (AHB) converter is the most commonly used converter in such drive systems. SRG operates during the excitation period and generation period, where the excitation period is defined during the turn-on and turn-off angle of the AHB [46]. The inductance profile and the current waveform for one phase of SRG are illustrated in Figure 2.
## 4 Results and discussion  
### Model Validation  
In order to verify the rationality of the constructed evolutionary model, the evolutionary model curves were compared with the fitted curves of the concrete dissolution degradation test performed at Zhejiang University [34]. The fitted curve in reference [34] for porosity is plotted against the evolution equation curve, as shown in Figure 7, and the fitted curve in reference [34] for the elastic modulus is plotted against the evolution equation curve, as shown in Figure 8.  
Figure 5: Culvert damage simulation distribution map.  
Figure 6: Characteristic curves of calculated parameters of culvert damage analysis with different calendar time.  
The reasonableness of the numerical model is reflected qualitatively by comparing the consistency of the porosity and elastic modulus variation laws with time and evolution equation curves fitted to the data from reference [34]. As can be seen from Figure 7, with the deepening of dissolution, both curves reflect a gradual increase in concrete porosity, and with the change in time, it gradually tends to become smoother. As can be seen from Figure 8, with the deepening of dissolution, both curves reflect a gradual decrease in the modulus of elasticity of concrete, and the consistency is good. Therefore, the constructed model for the evolution of concrete dissolution loss can reasonably reflect the change law of each physical parameter of concrete as the degree of dissolution deepens.  
In order to verify the reasonableness of the numerical model, it is proposed to compare the simulated maximum surface settlement results with the actual field measurement results. Since the field test area is not completely consistent with the simulated area, there are a few missing data, and here the results and laws of the field test of underground culverts by [PERSON] et al. [35] are used to complete the measured data in the hydrogeological report of the project. The reasonableness of the numerical model is reflected qualitatively by comparing the consistency of the maximum surface settlement curve calculated by the model with the variation pattern of the measured surface settlement values. As seen in Figures 9 and 10, the maximum settlement curve of the ground surface obtained from the ABAQUS-based simulation is essentially consistent with the measured vertical displacement variation law of the ground surface, with a maximum relative error of 1.22%, and the simulation calculation results are more consistent. Therefore, the constructed numerical model of concrete dissolution degradation can reasonably reflect the changes in the displacement field.  
Figure 8: Comparison of changes in modulus of elasticity [34].  
Figure 7: Comparison of porosity variation [34].
## 3 Analytical Modeling of the 2-DOF CPM
### Natural Frequency  
In order to ensure the excellent dynamic property of the CPM, its mechanical natural frequency should be large. The first two natural frequencies of the 2-DOF CPM are identical, and the corresponding vibration motions should be the translational modes along the two axes. Owing to the adopted decoupler, the coupling effects between the actuators of the CPM can be ignored. Therefore, the translational mode along the \(x\)-axis can be depicted as shown in Figure 13, where \(m_{a}\) and \(m_{o}\) are the masses of the actuation platform and terminal platform, respectively, and \(m_{1}\) and \(m_{2}\) are the masses of the single leaf spring in Parts I and II, respectively.  
Figure 12: Stiffness model of CPM along the \(y\)-axis.  
Due to the continuum structure of the CPM, its dynamic response analysis should utilize the partial differential equation models which are difficult to solve. Considering that the axial stiffness of Part II of each chain is large, the displacements of the actuation platform, terminal platform and Part II of the first and third chains can be considered to be almost identical in the translational mode along the \(x\)-axis. Therefore, the CPM is equivalent to a generalized single degree-of-freedom (SDOF) system in this vibration mode, as shown in Figure 14.  
The Rayleigh method can be utilized to calculate the natural frequency of the equivalent SDOF system as  
\[f=\frac{1}{2\pi}\sqrt{\frac{K_{\text{eq}}}{M_{\text{eq}}}} \tag{31}\]  
where \(K_{\text{eq}}\) and \(M_{\text{eq}}\) are the equivalent stiffness and mass of the system.  
The equivalent stiffness of the SDOF can be obtained as  
\[K_{\text{eq}}=8\int_{0}^{L}EI_{1}(x)[\psi^{\prime\prime}(x)]^{2}dx+6\int_{0}^{L }EI_{2}(x)[\psi^{\prime\prime}(x)]^{2}dx \tag{32}\]  
Figure 14: Translational mode of an equivalent generalized SDOF system.  
Figure 13: Translational mode along the \(x\)-axis of 2-DOF CPM.  
where \(I_{1}(x)\) and \(I_{2}(x)\) are the moment of inertia of the single leaf spring in Parts I and II, respectively, and \(\psi(x)\) is the shape function.  
The equivalent mass of the SDOF system can be obtained as  
\[M_{\rm eq}=M_{T}+M_{f} \tag{33}\]  
where \(M_{T}=2m_{a}+m_{o}+6m_{2}\) is the mass of the rigid moving part, and \(M_{f}\) is the equivalent mass of the flexure members, which  
\[M_{f}=8\int_{0}^{L}m_{1}(x)[\psi(x)]^{2}dx+6\int_{0}^{L}m_{2}(x)[\psi(x)]^{2}dx \tag{34}\]  
According to Figure 15, the shape function of the flexure members can be determined as  
\[\psi(x)=\frac{v(x)}{Z_{0}}=\frac{3x^{2}}{L^{2}}-\frac{2x^{3}}{L^{3}} \tag{35}\]  
Substituting Equation (34) in Equations (31) and (32) can obtain the equivalent stiffness and mass of the SDOF system as  
\[K_{\rm eq}=\frac{8 EDI_{1}^{3}}{L^{3}}+\frac{6 EDt_{2}^{3}}{L^{3}} \tag{36}\]  
\[M_{\rm eq}=2m_{a}+m_{o}+\frac{104}{35}m_{1}+\frac{288}{35}m_{2} \tag{37}\]
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 2 Methods
### KLT Optical-Flow Method  
#### 2.1.1 Assumptions of Optical-Flow Method  
The optical-flow method is based on two assumptions: brightness constancy and small motion; that is, the pixel value of the same points between frames are unchanged and the motion of the points is small. Figure 2 shows three target points in two adjacent images. The position of the target points in the second image can be determined by finding the point whose pixel value is consistent with the target points in the first image.
## 2 Ground Motion Intensity Measures
### Vector Valued IMs  
To account for lack of information in scalar IMs discussed above, some authors propose vector-valued IMs. [PERSON] et al. [47] proposed a combination of \(PGA\) and moment magnitude \(M_{w}\) as a more efficient predictor than \(PGA\) alone. A combination of \(Sa(T_{f})\) and \(\varepsilon(T_{f})\) (Equation (20)) was proposed by [PERSON] and [PERSON] [48]. For a particular magnitude \(M_{w}\), distance \(R\) and other site-source properties denoted by \(\varphi\), \(\varepsilon(T_{f})\), a spectral shape measure is defined as the number of standard deviations that separate a given \(lnSa(T_{f})\) value from the mean predicted \(\mu_{\ln Sa(T_{f})}\) from a ground motion model (Equation (20)). [PERSON] and [PERSON] [1] also proposed a combination of \(Sa(T_{f})\) and \(R\) = \(Sa(T^{*})\)/\(Sa(T_{f})\). Here, \(T^{*}\) is any other period that is chosen to represent the spectrum's shape, and \(T^{*}\) = \(2T_{f}\) and \(T^{*}\) = \(T_{2}\) for accounting for period elongation or higher mode effects was suggested by [PERSON] and [PERSON] (2008a) [1]. Other vector-valued measures that use \(Sa(T_{f})\) and a few unitless quantities are discussed in the literature. [PERSON] et al. [49] proposed \(I_{A}\), normalized by the product of \(PGA\) and \(PGV\), whereas [PERSON] et al. [50] proposed \(S_{dN}(T_{f}T_{2})\), determined by integrating the displacement response spectrum and normalizing with \(Sd(T_{f})\).  
\[\varepsilon\Big{(}T_{f}\Big{)}=\frac{\mathit{In}\ \mathit{Sa}\Big{(}T_{f} \Big{)}-\mu_{\ln Sa(T_{f})}(M_{w},R,\varphi)}{\sigma_{\ln Sa(T_{f})}(M_{w},R, \varphi)} \tag{20}\]  
The above summary of proposed IMs illustrates that with the ever-increasing number of IMs available in the literature, it is important to understand their correlation with building response. The number of buildings instrumented to record the shaking response during earthquakes is small. However, due to the efforts of the California Strong Motion Instrumentation Program (CSIMP) and the Japanese Building Research Institute (BRI), this database is also growing. [PERSON] and [PERSON] [51] used these data to calibrate existing EDP prediction models; they found that the variability in total drift ratio can be reduced by classifying the buildings based on construction material and height. [PERSON] et al. [52] developed a flat file of some of the EDPs and IMs calculated from the recorded building responses and earthquake ground motions from CSIMP and BRI databases.  
Recent research has focused on ensuring that hazard consistency is enforced during the selection of records, rather than covering response estimates for ground motions generated by all possible scenarios. Therefore, modern ground motion selection techniques account for a variety of IMs in the process of record selection. Let us therefore review different methods of selecting ground motions in the following section.