esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Description of Ocean Front Characteristic Model
Then the values of the corresponding melt parameters can be determined according to Figure 3 to quickly reconstruct the two-dimensional model of the sound speed field of the KEF. In order to reduce the dependence of ocean front modeling on the data of the whole sea area and the whole sea depth, only the surface sound speed of the study area was clustered with the fuzzy C-means (FCM) algorithm. FCM algorithm was proposed by [PERSON] et al. in the 1980s [30; 31]. FCM obtains the membership degree of each sample point to all cluster centers by optimizing the objective function, thereby determining the category of sample points to automatically classify the sample data [32]. In this paper, we use the surface sound speed as data points and cluster the surface sound speed through the FCM algorithm, which has achieved the effect of detecting the frontal zone. There are 26 * 26 = 676 data points in the study area surface, FCM divides 676 sound speed data \(x_{i}\) (i = 1, 2,..., 676) into \(c\) groups, and identifies the cluster center of each group by minimizing the objective function of the dissimilarity index. The expression of the objective function is as follows:  
\[J(Q,V)=\sum_{k=1}^{n}\sum_{i=1}^{c}{(r_{k})^{q}\|u_{k}-V_{i}\|}^{2} \tag{5}\]  
where \(Q\) is the membership matrix, \(V\) is the cluster center, \(r_{ik}\) is the membership degree of the \(k\) sample point for \(i\) category, \(n\) is the number of the sample point (in this study, \(n=676\)), \(c\) is the number of categories (considering the existence of ocean front, the study area is divided into three water masses, namely, high temperature and salinity water mass located in the south of the frontal zone, low temperature and salinity water mass located in the  
Figure 3: The curve of melt function \(m\) with melt parameter \(a\) in the range of \(-1.5\)-\(1.5\) (the interval of \(a\) is 0.1); The black thick line in the figure corresponds to the melt function when \(a=0\). The red dotted line is the transverse tangent when the melt function value \(m=0.5\). The blue dotted line is the transverse tangent when \(m=0.1\) and 0.9 respectively, and the corresponding abscissa distance is defined as the starting position and ending position of the frontal zone respectively. According to the starting position, ending position of the frontal zone (it can be obtained through FCM) and the function curve of melt function \(m\) with melt parameter \(a\), we can identify the selection of the corresponding melt parameters.
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 2 Types of 2D Nanofluids
### Hexagonal Boron Nitride
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Authors/Year** & **Concentration** & **Base Fluid** & **Main Findings** & **Remarks** \\ \hline
[25] & Zhi et al., 2011 & 6\% vol. & Water & \
\begin{tabular}{c} 2.6-fold enhancement of the TC. \\ \end{tabular} &
\begin{tabular}{c} Relatively low viscosity increase. \\ \end{tabular} \\ \hline
[26] & Fang et al., 2014 & 1, 2, 5, and 10\% wt. & \begin{tabular}{c} Paraffin phase \\ change materials \\ \end{tabular} & \
\begin{tabular}{c} 60\% increase in the TC. \\ \end{tabular} &
\begin{tabular}{c} Minor decrease in the latent heat capacity. \\ \end{tabular} \\ \hline
[27] & Ilhan et al., 2016 & From 0.03 to 3\% vol. & Water, water ethylene glycol mixture, and ethylene glycol &
\begin{tabular}{c} TC increases of 26\%, \\ 22\%, and 16\% at 3\% \\ \end{tabular} \\ \hline
[27] & Ilhan et al., 2016 & From 0.03 to 3\% vol. & Water, water ethylene glycol &
\begin{tabular}{c} TC increases of 26\%, \\ 22\%, and 16\% at 3\% \\ \end{tabular} \\ \hline
[28] & Ilhan and Erturk, 2017 & 0.1, 0.5 and 1.0\% vol. & Water & \
\begin{tabular}{c} Heat transfer enhancements of 7, \\ 10, and 15\%, \\ \end{tabular} &
\begin{tabular}{c} Formation of percolating structures \\ and branching \\ network. \\ \end{tabular} \\ \hline
[29] & Hou et al., 2018 & 5, 10, 15, 20, and 25\% vol. & Water & \
\begin{tabular}{c} 298\% increase in the \\ TC at 25\% vol. \\ \end{tabular} &
\begin{tabular}{c} Considerable increase in \\ the viscosity. \\ \end{tabular} \\ \hline
[30] & Hussein et al., 2020 & \begin{tabular}{c} Hybrid Gr-h-BN at 0.05, \\ 0.08 and 1\% wt. \\ \end{tabular} & Water & \
\begin{tabular}{c} 85\% increase in the \\ solar collector \\ efficiency. \\ \end{tabular} &
\begin{tabular}{c} Use of Tween \\ 80 surfactant. \\ \end{tabular} \\ \hline
[31] & Zhang et al., 2021 & 0.8\% wt. & Solar Salt & \
\begin{tabular}{c} 16\% increase in the \\ specific heat capacity. \\ \end{tabular} &
\begin{tabular}{c} Excellent thermal and \\ long-term stabilities \\ after 7200 min. \\ \end{tabular} \\ \hline
[31] & Zhang et al., 2021 & 0.8\% wt. & Solar Salt & \
\begin{tabular}{c} 16\% increase in the \\ specific heat capacity. \\ \end{tabular} &
\begin{tabular}{c} The viscosity of the \\ solar salt increased \\ by 2 to 3 times with \\ the addition of \\ the h-BN. \\ \end{tabular} \\ \hline
[32] & Farbod and Rafati, 2022 & 1\% vol. & Ethylene glycol & \
\begin{tabular}{c} 24\% increase in the \\ TC and \\ 30\% increase in the \\ heat transfer rate. \\ \end{tabular} &
\begin{tabular}{c} No increase in the \\ friction factor in \\ turbulent regime. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies on h-BN and hybrid nanofluids.
# Dynamic Response of Slope Inertia-Based Timoshenko Beam under a Moving Load
## 6 Conclusions  
A relatively newly developed beam theory, namely the slope inertia-based Timoshenko (SIBT) beam theory is applied to analyze the dynamic response of a simply supported beam subjected to a moving load. An analytical solution to this problem is presented by using the coupled Fourier and Laplace-Carson integral transformation method.  
The finite element solution is also presented and compared with the analytical approach. An excellent agreement is found, thereby implying that the proposed solutions are effective and accurate.  
A parametric study is carried out to investigate the difference between the SIBT, Euler-Bernoulli and Timoshenko beam theories for the moving load problem. With the increase of the moving speed of the load, the dynamic magnification factors of the beam displacement and bending moment obtained by the SIBT change nonlinearly, which are related to the dynamic characteristics of the beams. For slender beams, the influence of shear deformation and shear inertia is very small. Therefore, little difference between the dynamic response of the SIBT and that of the Euler-Bernoulli and Timoshenko beams is observed. However, with the increase in the ratio of the cross-sectional size (height or diameter) to beam length, the shear deformation and shear inertia effects are significant, which leads to the dynamic magnification factors for mid-span displacement obtained by the SIBT and Timoshenko beams becoming larger and larger than that obtained by the  
Figure 12: Dynamic magnification factors of the beam with different slenderness ratios.  
Euler-Bernoulli beams. Furthermore, until the ratio is greater than \(1/3\), the difference between the calculated results of the SIBT and Timoshenko beams is apparent.  
Conceptualization, T.L.; Methodology, Y.Z. and Y.Y.; Software, B.X. and R.Y.; Validation, Y.Y. and B.X.; Writing--original draft, T.L. and Y.Z.; Writing--review and editing, T.L.; Supervision, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51868073) and Natural Science Foundation of Shaanxi Province (Grant No. 2021 JM-178).  
Not applicable.  
Data Availability Statement: Data are presented in the article.  
The authors declare no conflict of interest.
###### Abstract  
The circulating cooling water of underwater vehicle power systems is discharged through the discharge port and is mixed with the environmental water to form a thermal jet, which diffuses, floats up in the environmental water and displays infrared characteristics on the surface of the water body. In order to explore the influence of the discharge port structure on the infrared characteristics of underwater vehicle thermal jets, this paper adopts the methods of simulation analysis and experimental verification, and it establishes an underwater vehicle motion model based on the CFD computing software platform to design elliptic discharge port structures with different radius ratios and to compare their different thermal jet infrared characteristics. The influence of the oval discharge port radius ratio on the infrared characteristics of thermal jets is verified by the reduced-scale water tank experiment, and the authenticity of the simulation calculation method and design parameters is verified. On the basis of oval discharge ports, the number and distribution position of discharge ports are further designed to suppress the infrared characteristics of thermal jets and to improve the thermal stealth performance of underwater vehicles. According to the simulation calculation and experimental results, under the condition of the same discharge flow, as the radius ratio becomes smaller, the mixing heat transfer effect of the thermal jet becomes better, and the infrared characteristics become less obvious. Moreover, increasing the number of discharge ports and adopting the symmetrical arrangement of discharge ports can further strengthen the temperature attenuation of thermal jets and reduce the surface maximum temperature.  
underwater vehicle; thermal jet; discharge port; structure design; infrared characteristics +
Footnote †: journal: Applied sciences  
Academy of Power Engineering, Naval University of Engineering, Wuhan 430033, China;  
[EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (L.Y.)  
* Correspondence: [EMAIL_ADDRESS]
## 3 Methodology
### Generalized Expansion Method (GEM)  
The GEM has emerged as a powerful approach for solving finite queueing networks and has demonstrated several advantages compared to other methodologies. GEM offers accurate performance analysis by considering finite capacities, blocking phenomena, and service time distributions [78]. It provides a robust framework for analysing complex systems with multiple interconnected queues, enabling researchers to derive analytical expressions for performance measures. Regarding implementation cost, GEM offers an advantageous position as it relies on existing mathematical techniques and equations without requiring extensive computational resources or specialised software [79]. Additionally, GEM is relatively easy to use, providing researchers with clear analytical insights into system behaviour and facilitating sensitivity analysis. While other methodologies, such as simulation-based approaches, may offer more flexibility in capturing system complexities and stochastic behaviour, they often require significant computational resources and expertise in simulation software [80]. Overall, GEM is a valuable methodology for accurate performance analysis of finite queueing networks, offering a cost-effective and user-friendly alternative to other methodologies. The GEM, as proposed by [PERSON] and [PERSON] [73], comprises three fundamental steps: network reconfiguration, parameter estimation, and feedback elimination (Figure 3). This study employs GEM as an approximation technique to analyse the finite queueing network and the blocking phenomenon. The first step, network reconfiguration, involves restructuring the original queueing network by introducing auxiliary nodes (holding nodes) and links to account for the blocking effects. This reconfiguration enables the representation of blocking occurrences within the network model. The second step, parameter estimation, aims to estimate the unknown parameters of the modified queueing network. This estimation step involves determining the routing probabilities, blocking probabilities, squared coefficient values, service times, and other relevant parameters crucial for accurately modelling the system behaviour. The final step, feedback elimination, focuses on eliminating the feedback loops that may arise due to the reconfiguration. Feedback loops can lead to complexity in the analysis and may hinder the accurate estimation of performance measures. Removing these feedback loops can simplify and analyse the modified queueing network more efficiently. The following mathematical notations were employed to derive equations for the performance measures of the finite queueing network using GEM.
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 3 Results and Discussion
### Experimental Results
The dark core and the annular bright ring have a direct relation with the order of the OAM mode, where an increase in the order of the vortices makes a corresponding increase in the areas.
# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 3 Methodologies
### Network Framework Structure  
This paper adopts a cycle generative adversarial network-based framework structure. The generator in this paper adopts the ResNet network with a stronger feature extraction capability and introduces the channel and spatial attention mechanisms to the ResNet network, improving the quality of generated images and solving the severe problem of lacking texture information in the generated images.In the discriminator, we introduce a gradient normalization module to stabilize the training process of the model and prevent the model from collapsing due to the unstable state during the training process. The overall network framework structure is shown in Figure 3 below. The general flow of network implementation is as follows: the real visible image is input into Generator_A to generate a fake infrared image, and then the fake infrared image is input into Discriminator_B together with the real infrared image for discrimination. The result obtained from the discrimination is fed back to Generator_A. Similarly, the real infrared image is input into Generator_B to generate a fake visible image, the fake visible image is then input into Discriminator_B together with the real visible image to discriminate between the real and fake images, and the results are fed back to Generator_B.  
In this paper, the specific training process of the network model is represented in a pseudocode form, as shown in Table 1. The prerequisite to ensure the output of infrared images is whether the discriminator can accurately distinguish between true and false infrared images. For this, a batched cycle training form of training the dual discriminator K times in line and then training the generator once, and continuing to iterate to the maximum training number M, is adopted.  
Figure 3: Flowchart of the overall framework of the cycle generative adversarial network.
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation
#### 4.2.5 Czech Republic: Significant Fluctuations and Repeated Extremes--Figure 6  
The results (Figure 6) for the Czech Republic show a clear increase in the incidence of negative electricity prices in recent years, especially in cases where the cumulative generation from renewable sources (solar and wind) exceeds 5500 MW. This phenomenon has been most pronounced since 2021 and corresponds to the overall trend of increasing price volatility in Central Europe.  
Power interval analysis shows that once a certain threshold level of combined solar and wind generation is exceeded, the probability of negative prices increases significantly. For example, ranges from 12,000 MW onwards already show a consistent occurrence of extremely low prices, including negative values.  
The Czech electricity system has long relied on generation from nuclear and coal-fired power plants, which provide stable baseload power, but with low flexibility. However, the rapid growth of generation from solar plants, especially in the summer months, combined with limited storage infrastructure, leads to surplus energy during peak daily generation. Unlike some neighbouring countries, the Czech Republic has not yet implemented demand-side management or dynamic end-user pricing on a large scale. This means that the market has a limited ability to respond flexibly to supply surpluses, which increases the likelihood of negative prices.  
The methodology used, based on a cross analysis of production bands and the frequency of negative prices, confirmed that the occurrence of extreme prices in the Czech Republic is not random, but systematically linked to the combined high values of solar and wind production. The results show that while in the lower power bands (up to 4000 MW), the occurrence of negative prices was negligible, in the bands above 5000 MW, a more systematic occurrence of negative prices is starting to appear. This trend is most pronounced in 2022 and 2023, which were also marked by a high share of renewable generation and lower demand during the spring and summer months.  
The findings for the Czech Republic suggest that the market has reached a level where, without the introduction of additional measures (e.g., flexible appliances, storage, real-time cross-border trading), the incidence of negative prices will continue or even increase. This trend may jeopardise the economic return of investment in renewables and reduce price stability for consumers.  
Figure 5: Share of negative price in % in Slovakia.  
It is crucial for market stabilisation to  
1. Introduce flexible price signals to incentivise consumption in times of surplus;
2. Strengthen storage capacities (e.g., battery storage);
3. Deepen cross-border cooperation with Germany and Slovakia, which face similar challenges.
## 1 Introduction  
Structural control systems have been extensively applied to suppress vibration responses of structures subjected to dynamic loads, particularly earthquake excitation. Modern structural control systems can be divided into passive control, active control, semi-active control, and hybrid control systems according to the characteristics of control devices and approaches [1]. Among the four strategies, active control is able to adapt structural response to take control actions to the structure during dynamic loads. Therefore, structures with active control application are mostly referred to as smart structures which can regulate structural response within the linear range and reduce or eliminate structural damage after strikes of earthquakes. For the realization of active control, two critical issues must be considered. The first issue is a structural control algorithm that is robust and clear enough to compute the control force to be imposed on the structure in real time. The other issue is an actuator controller that is able to apply the desired control force to the structure with acceptable tracking error in real time.
# Leading Edge Blowing to Mimic and Enhance the Serration Effects for Aerofoil +
###### Abstract
The brief literature review above concerns exclusively the aerofoil with serrated leading edge acting as a simple and effective passive control device for the reduction of interaction broadband noise and mitigation of the boundary layer separation. A research question worth exploring is whether both the aeroacoustics and flow control mechanisms can be replicated by a completely different flow control approach. \"Active\" flow control has some advantages of being versatile when the flow condition can change considerably over a short period of time, and producing little parasite drag when the control device can be well integrated to the aerofoil system. The analytical solution of Amiet [1] described in Equation 1 provides an explicit relationship between the level of leading edge noise radiation and the turbulence intensity and integral length scale of the incoming flow. Therefore, one avenue to reduce the leading edge noise is to seek method to weaken the fluid-leading edge interaction. Leading edge blowing is an attractive method to achieve this objective because the injection of mass flow from the leading edge against the turbulent inflow could potentially decrease the turbulence intensity, hence the level of interaction noise radiation. Another hypothesis is that the interaction between the leading edge jet and the freestream inflow can create a Kelvin-Helmholtz type of shear mechanism to generate vortical structures propagating in the downstream direction along the aerofoil surface. It is expected that these vortices can draw the high momentum flow from the outer wall towards the low speed fluid at the near wall, in a similar manner as the one produced by a serrated leading edge for the mitigation of the boundary layer separation. The main research questions here first concern whether, for a straight leading edge aerofoil, the level of mass flow from the leading edge blowing, and the spanwise distance between each successive orifices, can be correlated with the serration amplitude and serration wavelength, respectively, for a serrated leading edge aerofoil. The second research question concerns whether combining the leading edge blowing to a serrated leading edge can enhance and further elevate both the aeroacoustics and aerodynamic performances. This paper will present experimental results for an asymmetrical aerofoil subjected to (1) serrated leading edge, and (2) straight leading edge with blowing, both of which will be subjected to exactly the same flow conditions, and finally, (3) a hybrid device containing both the serration and blowing features. Comparisons of the aeroacoustics and aerodynamics results achieved by these approaches will be made quantitatively to demonstrate whether the serration effects on an aerofoil can be mimicked and enhanced by a leading edge blowing.
## 5 Experiments and Results
### Case Studies and Scenarios  
We propose five case studies comprising swarms of three, five, ten, fifteen, and thirty UAVs. We have calculated 100 scenarios per case study where the UAVs begin the simulation at different positions, away from the rogue drone at the centre, in order to address different initial conditions which also require different trajectories to achieve the desired formation. The UAVs' initial positions for each case study are shown in Figure 6, where each scenario is represented by a different colour. In addition, a 10 metre radius sphere has been left empty at the centre to simulate the UAVs approaching the rogue drone from different distant points.  
Figure 6: UAVs’ initial positions for the five case studies (100 scenarios per case study). The UAVs in each scenario are represented by a different colour.  
These scenarios were modelled in ARGoS using the Spiri UAV model. We worked in an area of 30 \(\times\) 30 \(\times\) 30 m. However, the system can be adapted to other area dimensions by scaling the UAVs' parameters appropriately [24]. The formation radius (\(D_{CENTRE}\)) used was three metres for swarms of three, five, and ten UAVs, whereas four and five metre radii were used for swarms of fifteen and thirty UAVs, respectively. This was necessary since more UAVs require more space to form a successful formation in order to avoid collisions. The characteristics of the proposed case studies are detailed in Table 1.  
The UAVs move in the direction provided by the DFA\({}^{3}\) running onboard, keeping the parametrised speed (\(SPEED\)). Since the same repelling forces between UAVs prevent them from being too close to each other, no extra collision avoidance algorithm was needed, providing the swarm parameters are optimal, allowing it to work as intended.