esa-sceva/satcom-chunk-collection huggingface dataset:
# A CFD Study of the Hydrodynamic Characteristics of an Autonomous Underwater Helicopter
## 1 Introduction
[18] based their research on the proportional-integral-derivative (PID) control system, enabling the transition from hover style to flight style. [PERSON] et al. [19] focused on the hydrodynamic performance of a head AUH. It is described through skin friction distribution, wake area, and flow characteristics. Computational fluid dynamics (CFD) is a crucial tool for analyzing the hydro-aerodynamic properties of fluid motion and has been utilized in applications such as microchannels, sterilizing chambers, torpedo-shaped underwater gliders, and heat transfer inside the gearbox [20; 21; 22; 23; 24]. The numerical results solved that heat, mass transport, and multiphase flow [25] issues significantly influence the motion behavior of fluids. Currently, to monitor the necessary seabed, there is very little equipment that can be surveyed here. [PERSON] et al. [26] applied the Reynolds Stress Model (RMS) to hydrodynamic characteristics with many different changes. CFD examines drag force performance through differences in the length-over-depth ratio (L/D) [27]. [PERSON] et al. [28] studied the role of the leeward vortical flow structure in the hydrodynamic behavior of underwater vehicles with the Froude number constant, submergence depths, and drift angles. With the increasing demand and more challenging tasks that humans set for underwater exploration, underwater vehicles are continuously being rapidly developed to serve that purpose. [PERSON] et al. [29] analyzed hydrodynamic characteristics that are caused by appendages such as rudders, hydroplanes, and sails. CFD is used to estimate the hydrodynamic coefficients of the circulating water channel (CWC) side wall for AUVs [30]. In recent years, much hydrodynamic research on AUHs has been conducted, but there is less research on hydrodynamic disturbance to AUHs' motion with various submarine speeds. The CFD technique is intriguing for analyzing the hydrodynamic characteristics of an AUH, validating experimental results, enhancing control algorithms, and proposing an optimal design for further investigation. In this study, numerical methods are utilized to investigate the hydrodynamics of the AUH. Governing equations and the Lagrangian-Eulerian (ALE) technique are used to obtain the transient velocity and pressure pattern around the AUH. It is known that the drag and lift force is changed through changes in velocity and pressure fields. The effects of velocity, the angle of attack (AOA), and the length/height ratio are considered. The numerical simulation results are also discussed and compared with previous experimental results.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 3 Construction of the Environment Model
### Simulation Cases of Different Barrier Rates  
The vehicle travel time was selected as the major indicator for the algorithm's overall performance. The impact of two speed influence coefficients on the vehicle travelling speed is shown in Equation (12), where _Tra(g\({}_{i,j}\))_ is the speed influence coefficient corresponding to the grid in row \(I\) and column \(j\).  
\[v_{i,j}\ =\ v_{0}\cdot Tra\big{(}g_{i,j}\big{)} \tag{12}\]  
Then, the time that the vehicle travels through the route \(i-j\) can be expressed as:  
\[t_{i,j}\ =\ l_{i,j}/v_{i,j} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Surface Feature** & **Speed Influence Coefficient of Surface Feature** \\ \hline
1 & Highway & 1 \\
2 & Bare land & 0.6 \\
3 & Grassland & 0.5 \\
4 & Sandy ground & 0.3 \\
6 & Residential area & 0.2 \\
5 & Forest & 0.1 \\
7 & Mire \& river & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between surface features and speed influence coefficient of surface features.  
The improved ACA should adapt to different barriers since the real-world off-road environment is complex. Without a loss of generality, three barrier modes were set respectively on 20 \(\times\) 20 grid maps. The proportions of the barrier grids, fuzzy grids, and free grids were (15%, 45%, 40%), (25%, 40%, 35%), and (35%, 35%, 30%), respectively. The proportions of these three types of grids were input into Matlab R2019a, and values were assigned randomly to each grid to obtain three generalized barrier grid maps, as shown in Figure 3.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Dataset Selection
#### 3.1.2 Feature Extraction  
Multiple experiments were performed to simulate both stationary and mobile autonomous vehicles at different sites and altitudes. During each scenario, the researchers extracted thirteen features of GPS signals collected from each channel of the designed GPS receiver; see Table 2. The features were obtained at different levels of signal processing blocks; this includes pre-correlation, during correlation, and post-correlation stages.  
Figure 1: Workflow of our methodology.
## 3 Numerical Results and Discussion
### Sensitivity Analysis of a Jeffcott Rotor
Figure 13: The x-direction response curve of the Jeffcott rotor with different nominal values of the investigated parameters: (**a**) Response curves with different initial gaps at \(k_{y}\) = 8 \(\times\) 10\({}^{4}\) N/m and \(\mu\) = 0.7; (**b**) Response curves with different normal stiffness at \(g\) = 1 mm and \(\mu\) = 0.7; (**c**) Response curves with different normal friction coefficients at \(k_{y}\) = 8 \(\times\) 10\({}^{4}\) N/m and \(g\) = 1 mm. Figure 14 shows the sensitivity coefficients of the response, which is defined as the variation of maximum displacements when the parameters of rub interfaces change by one percent. In Figure 14a, first-order sensitivities with respect to all parameters of rub interfaces are equal to zero when the rotation speed range is outside of 138-195 rad/s. A similar phenomenon can be found in Figure 13a, where the response characteristics of the rotor can be regarded as a linear system outside the speed range 138-195 rad/s. The abrupt jump of the sensitivity curve with respect to initial gap \(g\) represents the change of contact/separation status. Among them the initial gap \(g\) plays a dominant role since it determines whether rub-impact exists and the results is consistent with that obtained by the OAT method. Similar phenomena also exist in the second-order sensitivity curves with the initial gap being the most significant impact factor, as illustrated in Figure 14b. It should be noted that first-order sensitivities are much larger than second-order sensitivities, so our discussions are mainly based on the former case. In Figures 15-17 results are shown for cases when normal values of parameters are varied. As illustrated in Figure 15a,b, the first-order sensitivities decrease while friction coefficients are rising. Moreover, an obvious offset of the sensitivities is also observed, which reflects the emergence of a multi-solution region. Figure 16 shows the results of sensitivities with different normal stiffness coefficients. The variation of sensitivities is relatively much smaller in this case, which implies that the response is relatively less sensitive to \(k_{y}\). As is shown in Figure 17, the response becomes more sensitive with the increase of the initial gap and after it reaches a threshold the rotor becomes a linear system, as seen in the separated condition illustrated in Figure 13a.
# Stimulation Montage Achieves Balanced Focality and Intensity
## 5 Conclusions  
In this paper, we proposed a novel optimization algorithm: Stimulation with Balanced Focality and Intensity (SBFI) to support multiple electrode tDCS. SBFI can provide a balancebetween stimulation intensity and focality by adjusting the optimization parameter \(\lambda\) with a reasonable number of electrodes. Compared to the conventional montage and other popular optimization methods, SBFI can not only obtain sufficient stimulation intensity but also minimize target error and improve stimulation focality simultaneously. A series of simulation experiments present its potential for use in different clinical applications, especially stimulation for multiple targets. One limitation of the method is that the optimization parameter \(\lambda\) is problem-dependent. In the discussions above, we outline possible solutions that will be explored in the near future. Furthermore, in the robustness studies, the proposed method SBFI shows a good robustness with different tissue conductivity variations. Among the tested algorithms, SBFI has relatively lower deviations from the overall electric field distribution and less intensity changes at the target area. The robustness experiments further suggest the high impact of skull conductivity variations, which requires more consideration in modeling studies and clinical implementation.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a15050169/s1](https://www.mdpi.com/article/10.3390/a15050169/s1)]([https://www.mdpi.com/article/10.3390/a15050169/s1](https://www.mdpi.com/article/10.3390/a15050169/s1)), Figure S1: Robustness test results-MSE; Figure S2: Robustness test results-Intensity; Figure S3: Robustness test results-TE; Figure S4: Robustness test results-Focality; Table S1: Synthetic Multiple targets dissimilarity results of the robustness test; Table S2: Synthetic Multiple targets intensity results of the robustness test; Table S3: Synthetic Multiple targets TE results of the robustness test; Table S4: Synthetic Multiple targets focality results of the robustness test.  
Conceptualization, W.L.; Data curation, Y.W.; Formal analysis, Y.W. and J.B.; Funding acquisition, W.L.; Investigation, Y.W.; Methodology, Y.W., and W.L.; Resources, W.L.; Supervision, W.L.; Validation, J.B.; Writing-original draft, Y.W.; Writing-review & editing, Y.W., J.B., and W.L. All authors have read and agreed to the published version of the manuscript.  
The research is partially supported by the Bioengineering Department fellowships, Distinguished Professorship, Patrick Soon-Shiong Endowment Funds, and Chan Soon-Shiong Bionic Engineering Center at UCLA (Project Number: CNSI-2012-1184), all at UCLA.  
Not applicable.  
The data used to support this study are included within the article and its Supplementary material.  
WL holds shareholder interest in Niche Biomedical Inc. The others declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Model of Energy Optimization
### Maximum Revenue Target  
For users with distributed generation units that are connected to the power grid, their economic benefit comes from two sources: (i) the subsidies for the local consumption of the distributed generation; and (ii) the money earned by selling redundant DG power to the power grid. In order to recover the cost of distributed generation equipment in the shortest time, the maximum revenue objective function ought to be adopted.  
The maximum revenue target is shown as Equation (5):  
\[\begin{array}{ll}\max F=&\sum\limits_{k=1}^{T}C_{PV}P_{PV}(k)x_{PV}(k) \Delta t+\sum\limits_{k=1}^{T}C_{Wind}P_{Wind}(k)x_{Wind}(k)\Delta t\\ &-\sum\limits_{k=1}^{T}C_{B}P_{B}(k)\Delta t-\sum\limits_{k=1}^{T}C_{G}(k)P_{G }(k)\Delta t\end{array}. \tag{5}\]  
In Equation (5), the production planning cycle (usually be one day) is divided into \(T\) time slots, and each time slot lasts for \(\Delta t\) (usually one hour). In addition, the optimal strategy is allowed to execute at the initial moment of the \(k\)th time slot. The first part of users' revenue comes from the distributed generation (including the PV system and wind generation). Among the first part of the revenue, \(x_{PV}(k)\) and \(x_{Wind}(k)\) are the states of PV generation and wind generation, respectively, the controlled variables of the PV system and wind generation, where \"1\" refers to \"on\" and \"0\" refers to \"off.\" \(P_{PV}(k)\) and \(P_{Wind}(k)\) represent the active power of PV generation and wind generation. At the same time, \(C_{PV}\) and \(C_{Wind}\) are the PV system's and wind generation's subsidized price of self-occupation, and usually are the same value. The second component of the objective is the revenue from the storage battery. Comparing with the distributed generation, the battery is allowed to charge or discharge. Therefore, the active power of the storage battery \(P_{B}(k)\) can be negative. When \(P_{B}(k)>0\), the storage battery discharges, and, when \(P_{B}(k)<0\), the storage battery charges. Moreover, \(C_{B}\) is the generating cost of the storage battery. At last, the third part of the revenue comes from buying or selling the residuary power to the grid. Correspondingly, \(P_{G}(k)\) represents the active power of the electric supply. When \(P_{G}(k)<0\), the distributed generation offers the electric energy back, and \(C_{G}(k)\) is the difference between the acquisition price of DG and DG subsidized price of self-occupied at the \(k\)th time slot. However, when \(P_{G}(k)>0\), \(C_{G}(k)\) is the electricity price of the initial moment of the \(k\)th time slot.
## 3 Energy Dissipation Analysis
### Optimal Critical Velocity Under Harmonic Motion  
In order to determine the maximum energy dissipation capacity of ECDs, here we use FVDs as the reference, and take the dimensionless energy ratio \(E_{\text{ECD}}/E_{\text{FVD}}\) as the objective function. It should be noted that the maximum velocity of the harmonic motion \(u=u_{0}\sin\left(\omega t\right)\) is \(u_{0}\omega\). Here, we take the dimensionless velocity ratio \(X=v_{cr}/u_{0}\omega\) as the dependent variable. From the governing Equation (1) of FVDs, the maximum damping force is  
\[F_{max}=c_{d}(u_{0}\omega)^{\alpha} \tag{5}\]  
From Equations (3)-(5), the dimensionless energy ratio can be obtained as  
\[\frac{E_{\text{ECD}}}{E_{\text{FVD}}}=\frac{2\sqrt{\pi}\Gamma(\frac{\alpha+3} {2})}{\Gamma(\frac{\alpha}{2}+1)}X(1-\frac{1}{\sqrt{1+\frac{1}{X^{2}}}}) \tag{6}\]  
Through observing the mathematical structure of Equation (6), it can be seen that the term \(\frac{2\sqrt{\pi}\Gamma(\frac{\alpha+3}{2})}{\Gamma(\frac{\alpha}{2}+1)}\) is determined by the velocity exponent \(\alpha\) of the FVDs, and the other term \(X(1-\frac{1}{\sqrt{1+\frac{1}{X^{2}}}})\) is determined by the dimensionless velocity ratio \(X\) of ECDs.  
For a certain design of FVD, the velocity exponent \(\alpha\) is specified. Here, we use the dimensionless velocity ratio \(X\) as the dependent variable to find an optimal design of ECD such that the energy dissipation of ECD is maximized as compared to that of the FVD. Let's denote \(Y=X(1-\frac{1}{\sqrt{1+\frac{1}{X^{2}}}})\), and take its derivatives, we can know that when \(X=\sqrt{\frac{1}{2}(-1+\sqrt{5})}\approx 0.786\), the energy dissipation ratio \(\frac{E_{\text{ECD}}}{E_{\text{FVD}}}\) reaches the maximum (See Appendix B). It can be seen that when the ratio of the critical velocity to the maximum velocity is \(0.786\), the energy dissipation capacity of ECD reaches its maximum. It should be noted that the energy dissipation ratio is also dependent on the velocity exponent \(\alpha\) of FVDs. The energy dissipation ratio versus the velocity ratio is plotted in Figure 3.  
Figure 3: The energy dissipation ratio versus the velocity ratio.  
From Figure 3, it can be seen that it always can find a better design of ECD when the velocity exponent \(\alpha\) of FVD is larger than 0.2 such that the energy dissipation capacity of ECD is larger than that of FVD under the same harmonic motion. As we know, the velocity exponent \(\alpha\) of FVDs ranges from 0.35 to one for seismic applications, and therefore the ECDs can be used as good alternative devices for seismic response mitigation. It is particularly worth pointing out that the critical velocity corresponding to the maximum damping force is not the designed maximum velocity of the damper and the damping force will fall down when the velocity is larger than the critical velocity. This unique characteristic guarantees that the failure risk of dampers and mounting supports can be reduced in case of an over-limit earthquake.
## 3 Case Study
### TEP Dataset
#### 3.1.5 Effectiveness in Various Scenario Settings  
To accurately identify faults within few-shot practical industrial contexts, it is not merely a question of overcoming the issue of insufficient samples, but also necessitates the capability for scene migration adaptability. When the training and testing sets comprised distinct categories, which were further delineated based on operational conditions, GOPML achieved a notably high precision. This demonstrates GOPML's adaptability to various fault scenarios under differing operational conditions. However, given that the categories of both the training and testing sets were allocated randomly, it became impractical to ascertain whether the precision was a consequence of the faults in the testing set being readily identifiable. Therefore, to mitigate the influence of randomness in fault category allocation, we repeatedly conducted random selections of fault categories within the training and testing sets. Subsequent experiments were carried out across these varied data divisions (Datasets 1 to 4). The results, depicted in Table 6 and Figure 6, indicate that GOPML could secure favorable outcomes across different divisions, substantiating the stability of GOPML's learning capabilities. The generalized knowledge acquired from certain faults could be employed to articulate the impacts on other faults.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & \multicolumn{2}{c}{**3-Way Accuracy**} & \multicolumn{2}{c}{**8-Way Accuracy**} \\  & **5-Shot** & **6-Shot** & **5-Shot** & **6-Shot** \\ \hline Dataset 1 & 93.53 & 97.48 & 84.01 & 86.59 \\ Dataset 2 & 92.41 & 96.21 & 83.58 & 85.38 \\ Dataset 3 & 92.52 & 96.87 & 83.84 & 86.49 \\ Dataset 4 & 93.35 & 96.37 & 83.91 & 85.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault classification results in different scenarios on the TEP Dataset.  
Figure 5: The 8-way 6-shot accuracy in the outer loop.
## 2 Materials and Methods  
### Preparation of the Samples and Graphene Nano-Coatings  
The material for the tribological samples was a technically pure copper Cu-ETP (EN 13599:2014) widely used for electrical purposes. Table 1 reports the standardized chemical composition of the Cu-ETP copper grade and its mechanical properties. Five rectangular samples of size 12 \(\times\) 10 mm\({}^{2}\) and about 5 mm thick were cut from a square conductor for electrical cabinets (ItalWeber, Assago, Italy) [50]. The choice of this copper grade is in line with previous experimental investigations [23]. The surface of the samples was prepared by grinding with sandpaper and polishing with diamond slurries to achieve high planarity and a high surface finish. The average roughness was Ra 0.182 \(\upmu\)m. After cleaning in an ultrasonic bath with acetone for 10 minutes and drying with a lint-free tissue, samples were coated with graphene. Preparation of samples was performed at the MIT Laboratories (Cambridge, MA, USA).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Chemical Composition** & & \\ \hline Cu & Bi & Pb & O \\
99.90\% & Max 0.0005\% & Max 0.005\% & Max 0.040\% \\ \hline \hline \multicolumn{5}{c}{Mechanical properties} \\ \hline Tensile strength & Yield strength & Elongation at break & Specific weight & Electrical resistivity & Hardness HV \\
220 MPa & 160 MPa & 33\% & 8.89 g/cm\({}^{3}\) & 0.017 \(\upmu\Omega\cdot\)m & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the copper substrate.  
Four different types of graphene nano-coating were synthesized and deposited on one sample. Five samples were tested overall, including one bare copper sample to identify the reference tribological behaviour of the substrate:  
* 1 sample was coated with graphene directly grown onto the bulk copper substrate by APCVD;
* 1 sample was coated with a high-purity graphene sheet synthesized by LPCVD on a Cu foil and then transferred to the end copper sample;
* 1 sample was coated by self-assembled graphene flakes after 3 h of sonication in the DI (see Section 2.1.3), referred to as 3 h-percolative;
* 1 sample was coated by self-assembled graphene flakes after 6 hours of sonication, referred to as 6 h-sonicated;
* 1 bare copper sample.
## 4 Experiments and Results
### Part A--Cold Gas Propulsion  
The testing and validation of the principle of solar-thermal cold gas propulsion were carried out in logical stages, in order to ultimately obtain a conclusive result. It was noticed from the first tests that the opening time of the solenoid valve was a crucial factor for the obtained value of the mean force obtained in a given \(\Delta t\) and for the total impulse obtained, utilizing a well determined N\({}_{2}\) quantity. In this regard, multiple experiments were carried out at low temperature for which the impulses for more opening times of the valve were measured. A fixed quantity of N\({}_{2}\) was used (which corresponds to the fill up of the buffer tank at 20 bar). The measured impulses were later summed up in order to make a quantitative comparison. The results for such a test are presented in Table 1.  
For the solenoid valve opening times of 0.1 and 0.3 s, the values were analyzed below.  
The initial data revealed the fact that there was an error for the force measurement, therefore the calibration of the force transducer was set to start at the value of 10 N and not 0 N.  
In this regard, it was noticed that there was no major difference between the total impulse obtained with the different opening times. Therefore, the tests were oriented at small valve opening times such that the average force obtained would be in the value vicinity of 1 N, common force values for satellite attitude correction.  
As it can be noticed in Figure 11, an exponential drop of force was obtained when using cold gas propulsion.  
from the graphical representation that the mass of discharged fluid from the system can be determined from the difference of weight (at the beginning and at the end of the experiments).