esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of Three-Stage Turbomachine
## 2 Transfer Matrix Theory
### Element Matrices
#### 2.2.3 Beam Element Matrix  
The matrix defining the shaft properties is known as the field element matrix. Coefficients in this matrix are derived using the Krylov equation, in accordance with the Timoshenko beam theory [31], as shown in Equation (8).  
\[\frac{\delta^{4}Y(z)}{\delta z^{4}}+\frac{\overline{m}\omega^{2}}{EI_{d}} \bigg{(}\frac{EI_{d}}{GA_{s}}+\rho_{z}^{2}\bigg{)}-\frac{\overline{m}\omega^{ 2}}{EI_{d}}\bigg{(}1-\frac{\overline{m}\rho_{z}^{2}\omega^{2}}{GA_{s}}\bigg{)} Y(z)=0 \tag{8}\]  
In this equation, \(\overline{m}\) represents the mass per unit length, \(E\) represents the elastic modulus, \(G\) represents the shear modulus, \(\rho_{z}\) represents the radius of gyration, and \(A_{s}\) represents the cross-sectional area. Equation (8) can be derived and expressed in terms of the parameters of the state vector in Equation (1), as shown in the following equations.  
\[Y^{i}(z)=A^{i}\text{cosh}(\lambda_{1}z)+B^{i}\text{sinh}(\lambda_{1}z)+C^{i} \text{cos}(\lambda_{2}z)+D^{i}\text{sin}(\lambda_{2}z) \tag{9}\]  
\[\theta^{i}(z)=A^{i}F_{1}\text{sinh}(\lambda_{1}z)+B^{i}F_{1}\text{cosh}( \lambda_{1}z)+C^{i}F_{2}\text{sin}(\lambda_{2}z)-D^{i}F_{2}\text{cos}(\lambda _{2}z) \tag{10}\]  
\[M^{i}(z)=A^{i}E_{1}\text{cosh}(\lambda_{1}z)+B^{i}E_{1}\text{sinh}(\lambda_{1} z)+C^{i}E_{2}\text{cos}(\lambda_{2}z)+D^{i}E_{2}\text{sin}(\lambda_{2}z) \tag{11}\]  
\[V^{i}(z)=A^{i}\frac{\overline{m}\omega^{2}}{\lambda_{1}}\text{sinh}(\lambda_{ 1}z)+B^{i}\frac{\overline{m}\omega^{2}}{\lambda_{1}}\text{cosh}(\lambda_{1}z) +C^{i}\frac{\overline{m}\omega^{2}}{\lambda_{2}}\text{sin}(\lambda_{2}z)-D^{i }\frac{\overline{m}\omega^{2}}{\lambda_{2}}\text{cos}(\lambda_{2}z) \tag{12}\]  
The expansions of the abbreviations \(E_{1}\), \(E_{2}\), \(F_{1}\), \(F_{2}\), \(\lambda_{1}\), and \(\lambda_{2}\), used in the equations are provided in the following equations:  
\[E_{1}=EI_{d}\bigg{(}\frac{\overline{m}\omega^{2}}{GA_{s}}+{\lambda_{1}}^{2} \bigg{)},\quad E_{2}=EI_{d}\bigg{(}\frac{\overline{m}\omega^{2}}{GA_{s}}-{ \lambda_{2}}^{2}\bigg{)} \tag{13}\]\[F_{1}=\frac{\overline{m}\omega^{2}}{GA_{s}\lambda_{1}}+\lambda_{1},\quad F_{2}= \frac{\overline{m}\omega^{2}}{GA_{s}\lambda_{2}}-\lambda_{2} \tag{14}\]  
\[\lambda_{1,2}=\sqrt{\sqrt{\sqrt{\left(\frac{\overline{m}\omega^{2}}{EI_{d}} \right)^{4}+\frac{1}{4}\left(\frac{\overline{m}\omega^{2}}{GA_{s}}-\frac{ \overline{m}\rho_{z}^{2}\omega^{2}}{EI_{d}}\right)^{2}\pm\frac{1}{2}\frac{ \overline{m}\omega^{2}}{GA_{s}}+\frac{\overline{m}\rho_{z}^{2}\omega^{2}}{EI_{ d}}}}} \tag{15}\]  
Since the matrix coefficients obtained involve complex differential expressions that vary with shaft parameters, \([\delta_{X}]\) and \([\delta_{Y}]\) are used here to represent the generalized forms along the X- and Y-axes. The combined expression of the matrix is provided in Equation (16).  
\[\left[U_{FIELD}\right]_{8\times 8}=\left[\begin{array}{cc}\left[\delta_{X} \right]_{4\times 4}&O_{4\times 4}\\ \mathcal{O}_{4\times 4}&\left[\delta_{Y}\right]_{4\times 4}\end{array} \right]_{8\times 8} \tag{16}\]
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.11. Endonuclease 3c and Endonuclease-NS Domain  
The endonuclease 3c domain is widely distributed among the family of DNA repair proteins such as endonuclease III and DNA glycosylase (MutY or MBD4). The members of this family possess a conserved helix-hairpin-helix (HhH), a Gly/Pro-rich loop, as well as a conserved aspartate residue [124; 125]. On the other hand, the endonuclease-NS domain has been explicitly reported in the DNA/RNA non-specific endonucleases and found both in prokaryotes and eukaryotes. The endonuclease-NS domain, containing endonucleases, showed an Mg\({}^{2+}\) dependent cleavage of double-stranded as well as single-stranded nucleic acids. The extracellular _Serratia marcescens_ nuclease is a well-characterized example of an endonuclease with an endonuclease-NS domain having a conserved histidine residue. The _Serratia marcescens_ nuclease requires magnesium ion, three acidic (Asp107, Glu148 and Glu232) amino acid residues, as well as a few basic amino acid residues (Arg108, Arg152) for the endonuclease activity [126; 127]. Proteins with the endonuclease-NS domain are broadly involved in hydrolase activity, nucleic acid binding and metal ion binding [126].
## 6 Influence of the Electronic Dephasing  
In all simulations above, the electronic dephasing (\(T_{\rm 2,elec}\equiv\Gamma_{ij}^{-1}\)) was kept constant at 100 fs. The role of the electronic dephasing, however, has been already experimentally observed and theoretically discussed in several coherent control experiments of small and large molecular systems. In general, the magnitude of electronic dephasing of a given electronic transition determines how fast the induced dipole coherently oscillates after interacting with the laser electric field. In this regard, molecular systems with very fast electronic dephasing rates will be potentially less affected by, e.g., long tailored excitations than by shorter laser pulses, since the electronic coherence already dephases faster in the former case. We have considered the influence of electronic dephasing by studying the specific values \(T_{\rm 2,elec}=5\) fs, 25 fs, 50 fs, 100 fs and 200 fs with a TL laser pulse of 30 fs centered at 19,700 cm\({}^{-1}\). The case of \(T_{\rm 2,elec}=100\) fs corresponds to the one considered in Figure 5 (left panels) and it is shown with thicker blue lines, both in Figures 5 and 7, for a clearer comparison.  
The results in Figure 7 are basically expected. They show how a faster electronic dephasing destroys the enhancement/suppression effect of the chirped pulses both in the populations of the ground and excited states (Figure 7a,c), so that for \(T_{\rm 2,elec}=5\) fs no enhancement/suppression is obtained. This is clearly due to the lack of population transfer at this fast dephas  
Figure 6: Enhancement of in \(\mid\rho_{34}\mid\) with \(\widetilde{\upsilon}\simeq\) 19,365 cm\({}^{-1}\) and for pulse durations of 30 fs and 17 fs, as indicated.  
Figure 7: Influence of electronic dephasing (\(T_{\rm 2,elec}=5\) fs, 25 fs, 50 fs, 100 fs and 200 fs, as indicated) on the enhancement of (**a**) population in the ground state; (**b**) vibrational coherence in the ground state; (**c**) population in the upper state; and (**d**) vibrational coherence in the upper state, considering an excitation frequency of 19,700 cm\({}^{-1}\). The bold blue lines show the electronic dephasing time \(T_{\rm 2,elec}=100\) fs for a clearer comparison with Figure 5.  
coherences of the ground and upper states show a decrease in the enhancement in all cases as the dephasing times are reduced, and they also show a clear symmetric reduction for increasing positive and negative values of chirp as compared with the TL interaction (Figure 7b,d).
## 1 Introduction
In contrast, the nonlinear vibration response of a rotor system with a massless flexible shaft in [3] was determined using the multiple harmonic balance method (HBM) and fourth-order Runge-Kutta method. The study investigates the dynamics of a simplified aircraft engine rotor that is supported by SFD and subjected to two harmonic forces. The contribution [24] shows that undesirable subharmonic or quasiperiodic vibrations can occur in the vibrational response of a rigid rotor with asymmetric stiffness of the centralizing spring. The results are provided for a fixed value of the nondimensional damper design parameter. [PERSON] and [PERSON] [25] proposed an analytical approach and a simple iterative method for evaluating the stability threshold of rotors mounted in hydrodynamic bearings with SFDs. The vibration stability of the rigid and flexible rotor models was assessed with a linearized motion equation. The dependency of the viscosity on temperature was considered and the hydraulic force was expressed with the short bearing assumption. In the paper [26], the same authors present an analytical prediction for the optimum passing through the bending critical speeds of the flexible rotor with the SFD. Optimum damping parameters for the retainer spring, shaft stiffness, rotor and damper mass has been found. The new SFD design has been introduced [27; 28; 29; 30; 31; 32] to improve vibration response and mitigate undesirable nonlinear behaviour, however, it may still occur. In this article, the rigid unbalanced rotor coupled with SFDs is studied by means of numerical simulations. It addresses the issues of multiple responses and the presence of an isolated branch in the frequency response curve. Moreover, in the region of the isolated solution, an undesirable vibration response can be induced due to the effect of hydraulic forces. Particularly at high rotational speed, it can lead to large orbits and cause impacts in a narrow damper clearance. As a result, additional stress is induced, which can significantly reduce the lifespan of the rotor system. A preliminary investigation of these topics was conducted by authors [PERSON] and [PERSON] in [33]. The achieved numerical results provide new insight into: (i) the basins of attraction of the studied system, (ii) the circumstances of the arise of nonsynchronous vibration, and (iii) the location specification of the isolated solution, as a function of design and load parameter.
# A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel
## 3 Utility Optimal Algorithm
In summary, the algorithm of crowd cooperative spectrum sensing is as follows (Algorithm 1):  
```
1:for all SUs do
2: Solve the Equations (17) and get the optimal sensing time \(t_{i}\) and emission power \(s_{i}\)
3: Calculate the \(utility_{i}\) according to (10);
4:if\(utility_{i}>0\)
5: SU \(i\) take part in the spectrum sensing and send the result to the base station;
6: The base station receives the sensing result \(R_{i}\) from SU \(i\);
7:endif
8:endfor
9: The base station fuses the results from SUs and gets the fusion result \(R\);
10:for all SUs participating in the sensing do
11:if\(R=R_{i}\)
12: SU \(i\) gets correct reward \(k\);
13:else
14: SU \(i\) gets incorrect reward \(k^{\prime}\);
16:endif
17:endfor
```  
**Algorithm 1** Crowd Cooperative Spectrum Sensing Algorithm under Non-Ideal Channel.
# Analysis of Score-Level Fusion Rules for Deepfake Detection
## 1 Introduction  
A deepfake is a video or image containing one or more individuals whose identity or action has been manipulated through deep-learning techniques. Deepfakes can be difficult to detect to the naked eye, especially those of the new generation, as they can often look and sound realistic.  
There are many risks associated with deepfake technology since it can be applied for malicious uses [1, 2]. Deepfakes can be used to create fake news and spread misinformation [3, 4]. Thus, they can have a devastating impact on society, especially concerning the ones that are convincing enough. Moreover, deepfakes can be used to bully or harass by creating false images or videos of people in compromising or embarrassing situations, such as those related to child pornography or revenge porn [2, 5].  
For this reason, in recent years, multiple technologies, especially those based on deep-learning, have been exploited to detect deepfake videos and images [1]. Although many deepfake detectors have reached high accuracy values, generalization is still an open problem. In particular, deepfake detectors are capable of detecting only the manipulations on which they have been trained; however, often even genuine data has different characteristics from dataset to dataset [6, 7]. A possible solution for this issue is the joint use of classifiers that are capable of analyzing videos in different ways [8]. This approach refers to the theory of multiple classifier systems [9] and, in the case of this paper, to the score-level fusion.  
In other words, understanding if and how it is possible to exploit the complementarity of different models to make a deepfake detection system more capable of generalizing is the goal of this work. This paper is the follow-up of ref. [10]: in particular, we expanded the experimental investigation to more complex fusion approaches, explored the relationshipsbetween outcome and generalization ability and built a set of insights to guide the designer to the most appropriate ensemble-based approach.  
This paper is organized into the following sections: Section 2 provides a review of the existing literature on deepfake detection; Section 3 describes the purpose of the experimentation and the proposed fusion methods; Section 4.1 presents the experimental protocol; the experimentation results are reported in Section 4.2; and our conclusions are drawn in Section 5.
## 2 Materials and Methods
### EOA Computation
#### 2.4.2 AST Computation and EOAAST Measurement  
When translated to MRI on a native valve [16], EOA was computed from the flow computed on a plane placed using the cine MRI anatomical images. When computed from 2D Phase-Contrast MRI images, the location of the plane through the cardiac cycle is fixed. In echocardiography, the probe itself moves with the thorax, while the targeting measured area is fixed in the motion of the heart itself. In continuous wave Doppler, it is fine as the maximum velocity is taken from the beam. It will not miss the maximum velocity while not defining the exact plane where the maximum velocity occurs. In MRI and in accord with [PERSON]'s EOA hypothesis [17], the location of this maximum velocity downstream of the BMHV should be targeted for the EOA computation. The acoustic source term (AST) method was previously used for aortic stenosis EOA computation by MRI [16; 18]. Such computation from echocardiographic measurements is not possible as echography does not provide the three components of the velocity (at least not for basic echocardiography in the clinical context), which are necessary for such a computation.  
From the vortex sound theory described by [PERSON] [19] and [PERSON] [20], the AST method has been used for the computation of the EOA of a valve in the context of aortic stenosis [16; 18]. The assumptions for the computation are listed by [PERSON] et al. [16] as incompressible and homentropic fluid, high Reynolds number, neglect of viscous effects, and absence of a source of matter. Thus the computation of AST is performed as follows [20]:  
\[\text{AST}=\
abla\cdot(\omega\,\text{{}^{\circ}}\,V), \tag{2}\]  
with the vorticity  
\[\mathbf{\omega}=\begin{pmatrix}\frac{\partial V_{x}}{\partial y}-\frac{\partial V_ {
## 2 Results and Discussion
### Expression Analysis of Main Transcription Regulators
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Transcription** & **Role of the Transcription** & **Protein Name** & **Gene\_ID** & **Average** & **Protein\_ID** \\
**Factor Family** & **Factor Family** & **Protein Name** & **Gene\_ID** & **Value\({}^{*}\)** & **Protein\_ID** \\ \hline
**LuxR** & Involved in quorum sensing, regulates virulence gene & LuxR-02440 & JUK32\_RS02440 & 445 & WP\_204137261.1 \\
**C-terminal-related** & **Production, motility, plasmid transfer, and biofilm formation [55; 56] & LuxR-04290 & JUK32\_RS04290 & 935 & WP\_204137618.1 \\ \cline{2-6}  & & LuxR-05665 & JUK32\_RS05665 & 653 & WP\_239112365.1 \\ \hline
**LysR family** & **Regulates virulence, metabolism, quorum sensing, and motility [57] & Ly8R-07280 & JUK32\_RS07280 & 645 & WP\_204138189.1 \\ \cline{2-6}  & & Ly8R-08635 & JUK32\_RS08635 & 1034 & WP\_204138452.1 \\ \cline{2-6}  & & Ly8R-09925 & JUK32\_RS09925 & 266 & WP\_204138702.1 \\ \cline{2-6}  & & Ly8R-11580 & JUK32\_RS11580 & 654 & WP\_204139027.1 \\ \cline{2-6}  & & Ly8R-12705 & JUK32\_RS12705 & 613 & WP\_204139229.1 \\ \cline{2-6}  & & Ly8R-20905 & JUK32\_RS20905 & 1630 & WP\_204140807.1 \\ \hline
**GntR family** & **Regulates motility, glucose metabolism, and virulence [58] & GntR-03040 & JUK32\_RS03040 & 2277 & WP\_239112277.1 \\ \cline{2-6}  & & GrtR-24680 & JUK32\_RS24680 & 1551 & WP\_239113044.1 \\ \hline
**Crp/Fnr family** & **Regulates virulence, the enzymes that degrade** & Crp-04910 & JUK32\_RS04910 & 4875 & WP\_204137735.1 \\ \cline{2-6}
**transcriptional regulator** & **Acmatic rings, genes involved in nitrogen fixation and photosynthesis [59; 60] & Crp-18565 & JUK32\_RS18565 & 8743 & WP\_204140370.1 \\ \hline
**XRE family** & **Unknown function, probably** & **Cry-24180** & JUK32\_RS20005 & 592 & WP\_204140632.1 \\ \cline{2-6}
**transcriptional regulator** & **Involution function, probably** & **Cry-24180** & JUK32\_RS24180 & 3763 & WP\_204141438.1 \\ \hline
**BolaA family** & **Involution function, probably** & **XRE-06870** & JUK32\_RS06870 & 3544 & WP\_204138108.1 \\ \cline{2-6}  & & & & & \\ transcriptional regulator & **Involution function, probably** & **Cry-24180** & JUK32\_RS25875 & 2291 & WP\_204141766.1 \\ \hline \end{tabular} \({}^{*}\) Average value of the reads measured in the RNA-seq experiment under BA+ growing conditions.  
\end{table} 
Table 2: _Cont._of the gene _lptc_ was clearly identified (average reads 2830). The other EPS-related genes were assigned general categories, such as the \"ABC-related transporter\", and no clear correspondence was identified between this general category and the genes described within Section 2.3.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 3 Observer Design for Switching Systems
### Observer Design  
Having established the system's properties, we can now proceed to design a switching observer tailored to our specific system dynamics. Equation (8) provides the foundation for constructing this observer.  
The proposed switching observer takes the following form:  
\[\left\{\begin{array}{l}\text{SOC}=A_{\sigma}\hat{\text{SOC}}+B_{\sigma}i+L_{ \sigma}(v-\vartheta)\\ \vartheta=C_{\sigma}\hat{\text{SOC}}+D_{\sigma}i\end{array}\right. \tag{18}\]  
In this observer structure, L\({}_{\sigma}\) represents the observer gain, a crucial design parameter that needs careful selection to ensure the estimated state, and SOC converges asymptotically to the true state, SOC.  
To analyze the observer's convergence, we can combine Equation (8) with Equation (18) to obtain the error dynamics:  
\[\dot{e}=\bar{A}_{\sigma}e \tag{19}\]  
Here, \(e=\hat{\text{SOC}}-\text{SOC}\) represents the estimation error. Our goal is to ensure the asymptotic stability of this error system. The matrix \(\bar{A}_{\sigma}\) governing the error dynamics is given by  
\[\bar{A}_{\sigma}=A_{\sigma}-L_{\sigma}C_{\sigma} \tag{20}\]  
By carefully choosing the observer gain L\({}_{\sigma}\), we can manipulate the eigenvalues of \(\bar{A}_{\sigma}\) to ensure that the error dynamics converge to zero, implying that our estimated SOC will approach the true SOC over time.  
Next, let us discuss the errors introduced by parameter variations. When the supercapacitor is not charging, both \(A_{\sigma}\) and \(B_{\sigma}\) in the equation are 0, so the observed SOC remains constant. However, when the switch is switched and the supercapacitor starts charging, parameter variations will have an impact. Take ESR as an example, which is a parameter often subject to errors.  
When the ESR changes, that is, when \(D_{\sigma}\) changes, and assuming the change value is \(\Delta D_{\sigma}\), a new error term will be introduced:  
\[e_{y}=y-\hat{y}=(D_{\sigma}+\Delta D_{\sigma}-D_{\sigma})u=\Delta D_{\sigma}u \tag{21}\]  
Thus, the new error equation becomes  
\[\dot{e}=(A_{\sigma}-L_{\sigma}C_{\sigma})e+L_{\sigma}e_{y}=(A_{\sigma}-L_{ \sigma}C_{\sigma})e+L_{\sigma}\Delta D_{\sigma}u \tag{22}\]  
At this point, the error term \(\Delta D_{\sigma}u\) is introduced as a new disturbance term into the observer system. This disturbance term will affect the convergence of the error dynamics and may lead to observer increased error.  
However, the ESR of a supercapacitor is generally very small. Even though it may increase after prolonged use, the absolute change remains minimal, with the value of \(\Delta D_{\sigma}\) typically in the order of \(10^{-4}\) or lower. Therefore, the value of the error term \(L_{\sigma}\Delta D_{\sigma}u\) is relatively small, making it still reliable to use this observer for supercapacitors.
## 1 Introduction
### Novelties and Motivations  
Reviewing the published articles in this field, it can be concluded that the structuring of the power system based on the microgrid model has not been completed simultaneously for the economic base and the qualities of reliability. Additionally, in probabilistic model research, wind and solar sources as a source with uncertainty have not been determined. In addition, solving the above problem by considering cases related to these principles will be very complex. Using the methods used in the articles cannot guarantee an optimal answer. Therefore, there is a current need to present an optimization algorithm that will be more efficient. After reviewing the mentioned works, the use of solar radiation causes the forecast of production of these sources to be accompanied by uncertainty. For example, in planning for the next day, it is impossible to precisely determine the amount of renewable energy production. Forecasting the future is filled with mistakes, which challenges energy planning. Since most distribution systems are now traditional networks fed by the global network, their transformation into smaller networks in the presence of distributed generation sources with probabilistic, non-probabilistic production and reactive power supply requires new design methods. Given that scattered products are an integral part of microgrids, planning and exploiting these resources in new networks requires a new design. In addition, published articles use methods that are not guaranteed to find the optimal answer. It is possible to be in local points by increasing the number of constraints and decision variables. Here, the graph theory model is used to create microgrids to cover these cases. The zoning step to create microgrids in the distribution network is associated with a planning problem. To more accurately model the problem of distributed generation with probabilistic nature and loads on an hourly basis, all costs and complete indicators to consider power for the reliability of microgrids are also considered. Accordingly, the main essential novelties and highlights of this paper are summarized as follows:  
* Employed the reliability indices in optimal and secure reactive power planning.
* Proposed a modified version of the Seagull optimization algorithm.
* Used the graph method to determine the boundaries of microgrids and evaluate its capacity in the power system.
* Investigated microgrid performance in various operating conditions.  
* Considered the probabilistic and uncertainty items of renewable resources.