esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Internal Vibration Signal Transmission Mechanism of Converter Transformer
### Vibration Mechanism of Converter-Transformer Winding  
When affected by the inrush current, the converter-transformer winding is based on the principle of electromagnetic induction and it vibrates violently due to the force generated by the induction. The structure of the disc-type coil is generally used in the converter transformer. For its axial vibration, the mass-spring-damping model can be used for analysis [22; 23]. In detail, the winding wire cake can be regarded as a rigid body with the same mass, and the pad between the winding and the connecting line can be equivalent to an ideal spring.  
Figure 4 shows the equivalent model of the axial vibration of the winding. \(K\) is the stiffness coefficient of the metal wire and the pad, \(c\) is the damping coefficient between the pad and the insulating oil, and \(m\) is the mass of each wire cake. It is assumed that the displacement of each wire cake is the same under the action of electric force. In this case, the axial vibration of the winding conforms to the following equation:  
\[M\frac{\mathrm{d}^{2}x}{\mathrm{d}t^{2}}+C_{1}\frac{\mathrm{d}x}{\mathrm{d}t }+Kx=F+Mg \tag{13}\]  
Among the terms, \(C\) is the damping coefficient matrix between the pad and the insulating oil, \(F\) is the Lorentz force matrix of the winding pancake, \(M\) is the mass matrix of the converter-transformer winding pancake, \(g\) is the gravity acceleration, \(K\) is the stiffness coefficient matrix of the winding pancake and \(x\) is the displacement matrix generated by the force of the winding pancake.  
Figure 4: Equivalent model of winding axial vibration.  
Figure 3: Transmission process of vibration signal.  
Solving x, we can get  
\[x=e^{-\frac{C}{2M}t}\bigg{(}C_{1}\cos\frac{\sqrt{4 MK-C^{2}}\times t}{2M}+C_{2} \sin\frac{\sqrt{4 MK-C^{2}}\times t}{2M}\bigg{)}+\frac{F}{M}+g \tag{14}\]  
The magnetic induction intensity \(B_{1}\) of the leakage magnetic field is  
\[B_{1}=k_{1}i \tag{15}\]  
Among the terms, \(k\) is the proportional coefficient of the leakage flux density and winding current.  
The electrodynamic force \(F\) of the converter-transformer winding under the action of magnetizing irnush current is  
\[F=2\pi RiB_{1}=\frac{2\pi Rk_{1}N^{2}}{{L_{\rm m}}^{2}}(-\phi_{\rm m}\cos(\omega t +\theta)+\left(\phi_{\rm m}\cos\theta+\phi_{\rm r}\right)e^{-\frac{c}{2}t}- \phi_{\rm s})^{2} \tag{16}\]  
Among the terms, R is the winding radius. Substituting F into Equation (14), the second derivative of x is obtained, and the winding acceleration is  
\[a=-\omega_{0}^{2}(C_{1}cos\omega_{0}t+C_{2}sin\omega_{0}t)+A\Big{(}\phi_{\rm m }\omega^{2}cos(\omega t+\theta)\Big{)}(\phi_{\rm m}cos\theta+\phi_{\rm r}- \phi_{\rm s}) \tag{17}\]  
In the formula, \(\omega 0\) and A are constants related to the winding parameters: \(\omega_{0}=\sqrt{\frac{k_{1}}{M}-\left(\frac{C}{2M}\right)^{2}}\approx\sqrt{ \frac{k_{1}}{M}}\), \(A=\frac{4\pi Rk_{1}N^{2}}{{L_{\rm m}}^{2}M}\).
## 2 Methods for Drag Reduction in Drafting Formations in Race Walking
### Numerical Simulation Method Based on Computational Fluid Dynamics
#### 2.2.2 Governing Equation  
Computational fluid dynamics (CFDs) is utilized with the finite volume method to compute the flow field variables and resultant aerodynamic forces acting on the race walkers. The Navier-Stokes equations, governing momentum conservation, are discretized  
Figure 2: The model of (**a**) core race walker and (**b**) an example of double drafting formation. The core race walker is in blue.  
using the Reynolds-averaged (RANS) approach. The simulations are performed with the commercial CFD code Ansys Fluent v19 [23]. Under the assumption of an unsteady state, the governing equation for the mainstream region can be expressed as follows [24]:  
(1) Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i}u_{j} )=0, \tag{1}\]  
(2) Momentum equation:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{i}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial\tau_ {ij}}{\partial x_{i}}+F_{i}, \tag{2}\]  
where \(\rho\) is the air density, \(u_{i}\) is the velocity of the air in each direction, \(p\) is the hydrostatic pressure, \(\tau_{ij}\) is the stress tensor in each direction, \(F_{i}\) is the external forces in each direction.  
(3) Energy equation:  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{i}}[u_{i}( \rho E+p)]=\frac{\partial}{\partial x_{i}}\Bigg{[}k_{eff}\frac{\partial T}{ \partial x_{i}}-\sum_{j}h_{j}J_{j}+u_{j}\big{(}\tau_{ij}\big{)}_{eff}\Bigg{]} +S_{h}, \tag{3}\]  
where \(k_{eff}\) is the effective conduction coefficient, \(h\) is the enthalpy, \(J\) is the diffusive flow, \(S_{h}\) is the heat source term.  
The turbulence model plays a crucial role in aerodynamic drag calculations, particularly in capturing momentum transfer near surfaces and within boundary layers. This in turn influences the occurrence of flow separation, which is pivotal for precise drag predictions. The \(k-\omega\) SST turbulence model has undergone extensive validation in the field of external aerodynamics, exhibiting superior performance in flow situations involving adverse pressure gradients and free shear layers when compared to other models such as the Spalart-Allmaras and \(k-\varepsilon\) models [25]. In this study, a recently developed generalized \(k-\omega\) turbulence model is utilized, delivering comparable accuracy to the \(k-\omega\) SST model while demanding fewer mesh requirements. Moreover, the discretization in this study employs a second-order scheme for the spatial derivatives. Pressure, velocity, and enthalpy are calculated using a coupled approach. Convergence is achieved in this study when the total residual value in all the above-mentioned equations falls below \(10^{-6}\).
# Cubic \(q\)-Bezier Triangular Patch for Scattered Data Interpolation and Its Algorithm
## 3 Scattered Data Interpolation Using \(q\)-Bezier Triangular Patch
### Inner Bezier Ordinates  
The remaining \(b_{1,1,1}^{i},i=1,2,3\) is obtained using the cubic precision method of [PERSON] [17], as shown in Figure 5. For complete derivation, the reader can refer to [17].  
Let \((r,s,t)\) be the barycentric coordinates of \(\bar{P}_{1}\) with respect to the triangle \(P_{1}P_{2}P_{3}\). In order to achieve \(C^{1}\) continuity along all edges, the following equations must be satisfied:  
\[d_{0,1,2}=r^{2}b_{0,3,0}+2 rsb_{1,2,0}+2 rtb_{0,2,1}+s^{2}b_{2,1,0}+2 stb_{1,1,1}^{ 1}+t^{2}b_{0,1,2} \tag{9}\]  
\[d_{1,0,2}=r^{2}b_{1,2,0}+2 rsb_{2,1,0}+2 stb_{2,0,1}+s^{2}b_{3,0,0}+2 rtb_{1,1,1}^{ 1}+t^{2}b_{1,0,2} \tag{10}\]  
In reversing the triangles where \((u,v,w)\) are the barycentric coordinates of \(P_{1}\) with respect to the triangle \(\bar{P}_{1}\bar{P}_{2}\bar{P}_{3}\), the following equation is obtained:  
\[b_{0,1,2}=u^{2}d_{0,3,0}+2 uvd_{1,2,0}+2 uwd_{0,2,1}+v^{2}d_{2,1,0}+2 vwd_{1,1,1} ^{1}+w^{2}d_{0,1,2} \tag{11}\]  
\[b_{1,0,2}=u^{2}d_{1,2,0}+2 uvd_{2,1,0}+2 vwd_{2,0,1}+v^{2}d_{3,0,0}+2 uwd_{1,1,1} ^{1}+w^{2}d_{1,0,2} \tag{12}\]  
The value of \(d_{111}^{1}\) is obtained by adding Equations (10) and (11) together. Thus, we obtain  
\[\begin{split} d_{1,1,1}^{1}&=\frac{1}{2w(u+v)}\Big{(} -u^{2}d_{0,3,0}-u^{2}d_{1,2,0}-2 uvd_{1,2,0}-2 uvd_{2,1,0}-2 uwd_{0,2,1}\\ &\quad-v^{2}d_{2,1,0}-v^{2}d_{3,0,0}-2 vwd_{2,0,1}-w^{2}d_{0,1,2 }-w^{2}d_{1,0,2})\end{split} \tag{13}\]  
Similarly, by adding Equations (8) and (9), we obtain  
\[\begin{split} b_{1,1,1}^{1}&=\frac{1}{2t(r+s)}(-r^ {2}b_{0,3,0}-r^{2}b_{1,2,0}-2 rsb_{1,2,0}-2 rtb_{0,2,1}\\ &\quad-s^{2}b_{2,1,0}-s^{2}b_{3,0,0}-2 stb_{2,0,1}-t^{2}b_{0,1,2 }-t^{2}b_{1,0,2})\end{split} \tag{14}\]  
The final scattered data interpolation scheme using the cubic \(q\)-Bezier triangular patch can be expressed as follows:  
\[R(u,v,w)=\sum_{i+j+k=3}B_{i,j,k}^{3}(u,v,w)b_{i,j,k}+6 uvw(\tau_{1}b_{1,1,1}^{1 }+\tau_{2}b_{1,1,1}^{2}+\tau_{3}b_{1,1,1}^{3}) \tag{15}\]  
where  
\[\tau_{1}=\frac{vw}{uv+vw+wu}\quad,\quad\tau_{2}=\frac{wu}{uv+vw+wu}\quad, \quad\tau_{3}=\frac{uv}{uv+vw+wu}\]  
Figure 5: Two adjacent \(q\)-Bezier triangular patches.  
The computational complexity of our proposed scattered data interpolation scheme for a single triangular patch involves 38 multiplications, 13 additions, and 1 division. This complexity is comparable to that of classical methods, such as those in [16], which require a total of 52 operations.  
The overall algorithm used in this study with descriptions is below.
## 4 Discussion and Conclusions  
The conclusions set out here refer to a stationary situation which the spatial distribution and cavity geometry are assumed to be invariable over time. This is a condition that is not present in reality but can be useful when fulfilled approximately for short times within the life of engineering works, except for the most soluble evaporate rocks (halite, thenadite, and, to a lesser amount, gypsum). For geological scales, the conceptual model can be very different, as it can evolve significantly over time. For example, erosion can bring cavities closer to the surface and even exhume them.  
Even contemplating \"engineering times\", and in reference to the three rock scenarios contemplated, the evolution over time of the geometry and size of the cavities can affect the mechanical results differently. In limestone and volcanic rocks, dissolution and alteration can be very slow within the life of an engineering work, and in some evaporitic rocks, the high dissolution rate has to be considered. That is, the results will remain valid longer in limestone rocks than they will in highly soluble salt rocks. This evolution and instability of cavities in evaporitic rocks is closely related to water variations, whether natural or anthropic.  
Climate plays a key role in the long-term evolution of major exokarstic and endokarstic forms. Not only do the dimensions of the gypsum cavities vary with the climate, but appreciable geomorphological differences are also found in them. This great capacity for dissolution and instability makes them very evolutionary over time, so that most of the gypsum cavities are of recent ages (Holocene, above all), while those of limestones have remained stable even since the Tertiary. However, these long-term considerations go beyond the subject matter of this article.
# Antioxidant and Antibacterial Activity of Extracts from Selected Plant Material
###### Abstract  
Plants are a valuable source of biologically active molecules, mainly phenolic compounds. In the present study, the total phenolic content (TPC), DPPH - and ABTS - scavenging activity as well as ferric reducing ability (FRAP) of aqueous ethanolic (70%) extracts of _Cistus inanus_ L. and _Asarum europneum_ L. herb, _Geum urhumum_ L. rhizome, _Angelica_ \(n\). root, white mulberry (_Morus alba_ L.), lemon balm (_Melisa officinalis_ L.), red raspberry (_Rubus ideas_ L.) and _Betula pendula_ Roth. leaves were determined. In addition, the phenolic profiles of the studied plant extracts and antibacterial activity have been investigated. The extracts from _C. incanus_ and _G. urhumum_ demonstrated the highest TPC and antioxidant capacity, while the extracts from _A. archangelica_ and white mulberry were characterized by the lowest values. A remarkable correlation was also found between the TPC and antioxidant activity of the examined extracts. HPLC analysis showed that the studied extracts were sources of both phenolic acids and flavonoids. More flavonoids than phenolic acids were identified in the extracts of _C. incanus_, _M. alba_, _R. ideaus_ and _B. pendula_ compared to the other extracts tested. Not all extracts showed a significant impact on the growth of the tested bacterial strains. _Escherichia coli_ was the most sensitive strain to lemon balm extract (MIC, 0.125 mg/mL), whereas the strains of _Acinetobacter baumannii_ and _Bordetella bronchiseptica_ were sensitive to the _G. urhumum_ extract (MIC, 0.125 mg/mL). Among Gram-positive bacteria, _Enterococcus faecalis_ was the most sensitive to _G. urhumum_ extract. In turn, _Staphylococcus aureus_ and _Staphylococcus epidermidis_ were sensitive to the extracts from _C. incanus_ herb (MIC, 0.125 mg/mL), red raspberry (MIC, 0.125 mg/mL) and lemon balm leaves (MIC. 0.25 mg/mL). Based on the obtained results, the applicability of the studied plant extracts as additives to food and cosmetic products may be considered in the future.  
aqueous ethanolic extracts; total phenolics; HPLC analysis; DPPH; ABTS; FRAP
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Printing
#### 3.4.1 Screen Printing  
The first evolution of screen printing dates back to the beginning of the twentieth century. At the time of printing, there was no actual loss of coating solution. The method is shown in Figure 7 and includes a screen of woven material (i.e., synthetic fiber or steel mesh) that has been attached to a tensioned frame. The pattern is generated by filling the screen with an emulsion that is impermeable to the coating solution in regions where no printing can occur. The region of the printed pattern remains open (without emulsion). The screen is then packed with a coating solution and brought close to the substrate. The squeegee is forced to the screen, bringing it into touch with the textile substrate and then linearly drawn across the screen, pushing the conductive coating solution through the open regions onto the textile substrate and thus reproducing the pattern [255].  
Screen printing is cheap and is highly efficient in providing thick conductive film [256]. Conductive silver-based inks can be applied to various woven and non-woven textile substances using screen printing to track various vital signs [257]. [PERSON] et al. have developed a multifunctional werf-knitted cotton fabric through screen printing of CNT ink for wearable electronic devices, smart displays, and cold weather conditioners [251]. There are some difficulties and disadvantages of screen-printed conductive textiles, such as low abrasion resistance and drying out of the ink on the screen, which destroys the design of the screen [249]. The screen printing method is not ideal for single output compared to the direct write printing method [258].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Process** & **Materials** & **Printing Parameters** & & **Advantages** & **Disadvantages** & **Ref.** \\ \hline \multirow{4}{*}{Screen printing} & & & &  • cheap & & \\  & & & Mesh Density: 300 & • good efficient process of & & \\  & & & Drying temperature: & & forming thick & & \\  & & 60 \({}^{\circ}\)C & • Good electrical & & & \\  & & & 60 \({}^{\circ}\)C & & conductivity with a surface & & \\  & & & & resistance of 50.75 \(\Omega\)/sq & & & \\ \hline \multirow{4}{*}{Inljet printing} & & & & Bitmap:100 dpi & • no masking required & & \\  & Reactive silver & • & Resolution: 100 steps per inch in the x and y axes & • based on the drop & & \\  & ink/cotton & & & Volume of released & & & \\  & fabric/PET fabric/ & & drops: ca. 30 nil. & • high resolution printing & & \\  & wool fabric & • & Sintering temperature: & • required low viscosity & & \\  & & & 90 \({}^{\circ}\)C & & & achieved surface resistance & \\  & & & & range 0.155–0.235 \(\Omega\)/sq & & & \\ \hline \multirow{4}{*}{3D printing} & & & & & \\  & Ninja flex filament/ & & & & & \\  & PEDOT:PSS/ & & & & \\  & Polyester fabric & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of different kinds of conductive printing processes.
## 2 System Model
### Target Sensing Network  
The target sensing network model is shown in Figure 2. There are three kinds of nodes in the network--anchors, radars, and targets--where the targets' positions need to be estimated. Anchors and radars can obtain TSOA measurements by receiving non-line-of-sight (NLOS) signals reflected by the targets.
## 4 Functionality Benchmark
### People Perception  
A functionality benchmark for evaluating the ability of robots in correctly detecting, identifying and locating humans was designed and developed. The benchmark requires an empty space of predefined dimensions (\(3\) m \(\times\) 3 m) that will host a set of human targets. The coordinates of this area and the world's reference frame are provided to the the benchmark users. This area is captured by a MoCap system that measures the ground-truth position of the humans inside the target area. Human targets are asked to enter the target area, upon request of the RSBB system, while holding a MoCap marker set, shown in Figure 11, or to wear a hat with the markers.  
A set of human targets consisting of both males and females are chosen for the benchmark. The procedure for this FBM is described as follows:  
1. The robot is placed on a predefined location outside the specified target and is connected to the RSBB network.
2. The test starts by the RSBB upon receiving the readiness signal from the robot.
3. The RSBB then randomly selects a subject from the set of human targets and requests the person to move inside the area.
4. After the person is in place, the operator uses the RSBB's GUI to press the \"Manual Operation Complete\" button.
5. The RSBB sends a request signal for the robot to start with the the functionality attempt, i.e., to detect, identify and locate the target.
6. The robot then communicates the perception results to the RSBB.
7. The RSBB asks the subject to move out of the area and randomly selects the next person to enter.  
Steps 3 to 5 are repeated for all subjects, and the RSBB automatically computes and communicates the trial score.  
For each perception attempt, the robot is expected to communicate the following:  
* The 2D position of the person with respect to the world reference frame.
* The identity of the person who is inside the target area.  
At the end of a trial, the RSBB evaluates the perception performance of a robot by computing:  
1. The percentage of correctly recognized peoples;  
Figure 11: MoCap marker set used on the human targets and the robots to measure their true position in the world.  
2. The localization error for all the detected peoples;
3. The trial's execution time.  
Before the benchmark is executed, the robot can collect information about the subjects in order to collect training data.
## 4 Parameters for the Optimal Election of CH
### Security  
Security mode: This selects the CH that satisfies the security needs. In Equation (8), \(q_{r}\&q_{s}\) mentions security needs linked with CHS and security rank, in that order. The nodes are signified by CH, if \(q_{s}\leq q_{r}\).  
Risky mode: Here, an existing CH is elected to achieve optimal CH for capturing each risk. As a result, this mode is called insistent mode during the CHS procedure.  
\(\gamma\)-risky mode: The CH enduring the severe risk are chosen in \(\gamma\)-risky mode. Additionally, \(\gamma\)-risk is called \(u_{risk}\). Subsequently, \(\gamma\) point out the probability metric with \(\gamma=0\) and \(\gamma=1\), much like the other two modes.  
The likelihood of security variables is shown in Equation (14). In addition, if the chosen CH achieves the state \(q_{s}>q_{r}\), the risk should be less than 50%. If the condition is \(0<q_{s}-q_{r}\leq 1\), the selection process would be implemented, and if the state is \(1<q_{s}-q_{r}\leq 2\), there will be a delay in the selection process. However, the CHS process would not be completed, and the corresponding function should continue for the state \(2<q_{s}-q_{r}\leq 5\). For the security analysis, the risk factor is considered. Therefore, the security must be low.  
\[Se=\begin{cases}0&if\quad\ q_{s}-q_{r}\leq 0\\ 1-e^{\frac{(q_{s}-q_{r})}{2}}&if\quad 0<q_{s}-q_{r}\leq 1\\ 1-e^{\frac{3(q_{s}-q_{r})}{2}}&if\quad 1<q_{s}-q_{r}\leq 2\\ 1&if\quad 2<q_{s}-q_{r}\leq 5\end{cases} \tag{14}\]
## 1 Introduction
Aiming at the above problems, this paper constructs a SysML model dataset, combines natural language processing (NLP) techniques and proposes a recommendation method for the MBSE modeling process based on domain knowledge and SysML historical model, which recommends the domain expertise for system modelers and assists in modeling to improve the modeling efficiency, accuracy and reliability. The sections of this paper are organized as follows. Section 1 introduces the concept of MBSE and the background of its development, illustrating the inevitable trend of the development of the combination of systems engineering and artificial intelligence. Section 2 describes the current state of research on assisted modeling in the context of AI4 SE and introduces the methodology of this paper. In order to better train the data, Section 3 proposes a dataset construction technique, taking the aerospace domain as an example, constructing an aerospace SysML model, studying the validation rules of data validity, validating the SysML model data and forming a SysML model dataset. Section 4 formally proposes a recommendation method for the MBSE modeling process based on the domain knowledge and SysML historical model, describes the various steps of the method, based on which experiments are carried out in Section 5 to construct the evaluation indexes for the new recommendation, and briefly analyzes the experimental results. Section 6 summarizes the paper's methodology and proposes future directions for the methodology.