esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 2 Axisymmetric AD/NS Method  
### Governing Equations  
The governing equations of the present AD/NS simulation are the incompressible axisymmetric NS equations. In a cylindrical coordinate system as shown in Figure 1, the axial direction is defined along the \(z\)-axis, the radial direction is represented by \(r\), and the tangential direction is represented by \(\theta\), the continuity equation is written as  
\[\
abla\cdot\overset{\rightarrow}{u}=\frac{\partial u_{z}}{\partial z}+\frac {\partial u_{r}}{\partial r}+\frac{u_{r}}{r}=0, \tag{1}\]  
the axial and radial momentum equations read  
\[\frac{\partial u_{z}}{\partial t}+\frac{1}{r}\frac{\partial}{\partial z}(ru_{ z}^{2})+\frac{1}{r}\frac{\partial}{\partial r}(ru_{t}u_{t})=-\frac{1}{r}\frac{ \partial}{\partial z}+\frac{1}{r}\frac{\partial}{\partial z}\left[ru\left(2 \frac{\partial u_{z}}{\partial z}-\frac{2}{3}(\
abla\cdot\overset{\rightarrow }{u})\right)\right]+\frac{1}{r}\frac{\partial}{\partial r}\left[ru\left(\frac{ \partial u_{z}}{\partial r}+\frac{\partial u_{r}}{\partial z}\right)\right]+f _{z}, \tag{2}\]  
\[\begin{array}{l}\frac{\partial u_{r}}{\partial t}+\frac{1}{r}\frac{\partial} {\partial z}(ru_{z}u_{r})+\frac{1}{r}\frac{\partial}{\partial r}(ru_{r}^{2})=\\ -\frac{1}{\rho}\frac{\partial r}{\partial r}+\frac{1}{r}\frac{\partial}{ \partial z}\left[ru\left(\frac{\partial u_{r}}{\partial z}+\frac{\partial u_{ r}}{\partial r}\right)\right]+\frac{1}{r}\frac{\partial}{\partial r}\left[ru\left(2 \frac{\partial u_{r}}{\partial r}-\frac{2}{3}(\
abla\cdot\overset{\rightarrow }{u})\right)\right]-2v\frac{u_{r}}{r^{2}}+\frac{2}{3}\frac{v}{r}\left(\
abla \cdot\overset{\rightarrow}{u}\right)+\frac{u_{0}^{2}}{r}+f_{r}\end{array} \tag{3}\]  
and the tangential momentum equation is  
\[\frac{\partial u_{\theta}}{\partial t}+\frac{1}{r}\frac{\partial}{\partial z }(ru_{z}u_{\theta})+\frac{1}{r}\frac{\partial}{\partial r}(ru_{t}u_{\theta})= \frac{1}{r}\frac{\partial}{\partial z}\left(ru\frac{\partial u_{\theta}}{ \partial z}\right)+\frac{1}{r^{2}}\frac{\partial}{\partial r}\left[{}^{3}v_{r} \frac{\partial}{\partial r}\left(\frac{u_{\theta}}{r}\right)\right]-\frac{u_{ r}u_{\theta}}{r}+f_{\theta}. \tag{4}\]  
Figure 1: Definition of coordinates and the stream tube through an actuator disc.  
In the above equations, \(u_{z}/u_{r}/u_{\theta}\) is the axial/radial/tangential velocity, \(p\) is the static pressure, \(\rho\) is the air density, \(\
u\) is the kinematic viscosity coefficient of air, \(f_{z}\), \(f_{r}\), \(f_{\theta}\) are the source term of the axial, radial, tangential external body forces, respectively.
## 3 Results
### Afterberg Limits of DKM
#### 3.2.2 Effect of Diatom Crushing  
Figure 9 shows how diatom crushing affects the Atterberg limits of the DKM samples. The crushed DKM samples have lower \(w_{\rm L}\) and \(w_{\rm P}\) values than uncrushed ones. In particular, this difference in \(w_{\rm L}\) and \(w_{\rm P}\) between crushed and uncrushed DKMs is more remarkable for samples 80D-20K and 100D-0K. The \(w_{\rm L}\) values of crushed 80D-20K (58.4%) and 100D-0K (62.4%) are much lower than those of uncrushed 80D-20K (128%) and 100D-0K (175%). This pattern of \(w_{\rm L}\) is similar to \(w_{\rm P}\). The results mean that the high \(w_{\rm L}\) and \(w_{\rm P}\) values for samples 80D-20K and 100D-0K are due mainly to the intact microstructure of the diatoms. As shown in Figure 3h,i, crushed diatom fragments form a chain-like structure that cannot store water, because the water in the crushed diatom can only be stored in the skeletal pores of the fragments and not in the intra-skeletal pores.  
Figure 8: Relationship between \(I_{\rm P}\) and clay fraction for different DKM samples. Data sources: DKMs [6; 12; 31; 32; 34], Natural diatomaceous soils [2; 3; 4; 5].  
Figure 7: Casagrande plasticity chart for diatomaceous soil (DS). Data sources: DKMs [6; 12; 31; 32; 34], Natural diatomaceous soils [2; 3; 4; 5].  
Naturally, milling for longer leads to further fragmentation of diatom frustules and lower \(w_{\text{L}}\) and \(w_{\text{p}}\) (Figure 9) because crushing intact diatoms causes collapsed intra-skeletal pores and reduced water retention capacity. Nevertheless, the \(I_{\text{P}}\) values for the DKM samples were scarcely affected by diatom crushing, meaning that the water stored in the intra-skeletal pores of intact diatom frustules contributes little to the plasticity of the DS.
## 3 Results and Discussions
### DLS (Dynamic Light Scattering) and Zeta-Potential Measurements  
According to the size distribution diagram (Figure S1), 77.39% of the blank liposomes presented a diameter below 500 nm, while AALipo15%, AALipo20%, and AALipo25% showed 85.39%, 85.73%, and 77.3% size distribution, respectively, below 500 nm, as tabulated in Table 1.  
The electric surface charge of the liposomes with or without AA was also determined, and it was noticed that, regardless of the empty or loaded liposomes, the zeta potential value was negative, ranging between \(-16.6\) mV and \(-20\) mV, which denotes a good stability of the formulations.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Formulation**} & **Size Percentage Up** & **Zeta Potential (mV)** & **EE\%** \\  & **to 500 nm** & **AA** & **AA** \\ \hline Lipo & 77.39\% & \(-16.6\) & 0 a \\ AALipo15\% & 85.39\% & \(-17.02\) & 77.3 \\ AALipo20\% & 85.73\% & \(-19.85\) & 85.73 \\ AALipo25\% & 77.3\% & \(-20.00\) & 79.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DLS results (size distribution and zeta potential) and EE% of AA in liposomal formulas (AALipo15%, AALipo20%, AALipo25%).  
Figure 3: Inverted light microscopy in phase-contrast mode of: (**a**) blank liposomes; (**b**) loaded liposomes with 15% AA; (**c**) loaded liposomes with 20% AA; (**d**) loaded liposomes with 25% AA. White arrows indicate homogeneous dispersion of spherical, stable liposomes with and without AA included.  
The stability of liposomal emulsions is a very important feature because a lower stability is associated with a faster release (or even spontaneous release) of the incorporated molecules, and therefore a controlled and targeted release cannot be achieved [27].  
Also, the instability of liposomal particles is followed by the phenomenon of aggregation and fusion of the particles, increasing their size, which leads to a shorter lifetime of liposomal transport systems [27; 39]. The zeta potential or the electric surface charge of the liposomes is required to be highly negative so that the particles repel each other. According to literature [38], the electrical surface charge of the particles is also influenced by the hydrophilic phase used for the lipid film hydration; in this case, the hydration was carried out using a phosphate buffer (which provides the negative electrical charge).
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 3 Two-Tier Collaboration Based High-Reliable and Lightweight Forwarding (TTCF) Mechanism for WBAN
### Forwarding Model Based on Multi Factor Dynamic Fusion in TTCF
```
Input: the starting node \(i\) with transmitted data, the target node \(\xi\); Output: forwarding path, namely, the node set containing all relay nodes \(j\); \(1\)for each node \(j\) in \(G\) at time \(t\)do  initialize the position coordinates of \(j\);  if\(t_{j}>T_{j}\)then \(\theta_{j}=1\); else  calculate \(\theta_{j}\) based on Equation (10);  endif  calculate \(\delta_{j}\) and \(\eta_{j}\) of node \(j\) based on Equations (9) and (11);  endfor  for \(a=0\) to \(l-1\)do // suppose it exists \(l\) nodes in the network at this time. for \(b=0\) to \(l-1\)do  randomly generate communication relation matrix \(RE\) between nodes; //when \(a\)==\(b\), its value is set to \(0\) to avoid nodes forming a closed //loop, else 1.  calculate the forwarding decision value between nodes based on the Euclidean distance between nodes and Equations (12)-(15), and form a decision matrix \(D\);  endfor  endfor  use Dijkstra algorithm to find the shortest path \(Road(i,\zeta)\) between node \(i\) and \(\zeta\) based on matrices \(RE\) and \(D\); //\(Road(i,\zeta)\) is the transmission path with //the lowest energy consumption and the highest efficiency. return \(Road(i,\zeta)\); // return in node traversal order between \(i\) and \(\zeta\). ```  
**Algorithm 1** Improved Dijkstra algorithm  
```
Input: the starting node \(i\) with transmitted data, the target node \(\xi\); Output: the flag flag indicating whether the packet is successfully forwarded;  flag = 0; while\(i\)!=\(\xi\)do  for any packet \(Pk\) to be transmitted on node \(i\) at time \(t\)do  compress \(Pk\) based on STPCSE model;  use Algorithm 1 to gain \(Road(i,\xi)\); if\(Road(i,\zeta)\)!=Null then  node \(i\) transmits \(Pk\) to node \(\xi\);  flag = 1;  delete \(Pk\) from the queue of node \(i\);  else  node \(i\) continues to hold \(Pk\), waiting for the calculation,  judgment and transmission of the next time, or if \(Pk\) expires,  node \(i\) directly deletes \(Pk\) and terminates this cycle;  endif  return flag; endfor  endwhile
```  
**Algorithm 2** TTCF algorithm  
It can be seen from the above, the nodes carrying the forwarded data in WBAN can complete the lightweight data transmission from the source node to the sink node by calling Algorithm 2.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Model Evaluation  
In this study, three statistical indicators, including coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE), were chosen to analyze and compare the accuracy and stability of different models for estimating T\({}_{\text{s}}\)[50; 58]. The corresponding formulas are:  
\[\text{R}^{2}=\frac{\left[\sum_{i=1}^{n}\left(Y_{i,m}-\overline{Y}_{i,m} \right)\left(Y_{i,e}-\overline{Y}_{i,e}\right)\right]^{2}}{\sum_{i=1}^{n} \left(Y_{i,m}-\overline{Y}_{i,m}\right)^{2}\sum_{i=1}^{n}\left(Y_{i,e}- \overline{Y}_{i,e}\right)^{2}} \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i,m}-Y_{i,e}\right)^{2}} \tag{5}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|Y_{i,m}-Y_{i,e}\right| \tag{6}\]  
where \(Y_{i,m}\), \(Y_{i,e}\), \(\overline{Y}_{i,m}\), \(\overline{Y}_{i,e}\), and \(n\) are the measured daily T\({}_{\text{s}}\), the estimated daily T\({}_{\text{s}}\), the measured average value of daily T\({}_{\text{s}}\), the estimated average value of daily T\({}_{\text{s}}\), and the number of observations, respectively. Higher R\({}^{2}\) values (closer to 1) and lower RMSE and MAE values indicate better estimation performance of the model.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 2 Methodology  
The CCSM methodological framework was proposed for global continuous multi-scale contour simplification. The main idea of this framework is to derive 2D contours from a 3D surface by using the level set method. The 3D surface was constructed by the non-uniform rational B-spline (NURBS), a common mathematical form for representing standard analytical shapes and free-form curves/surfaces [28; 29]. The built 3D surface, namely, the continuous changing surface (CCS), was used to guide the entire simplification process. The framework consists of the following main steps as shown in Figure 1:  
(1) Extracting characteristics. The extraction of contour characteristics aims to preserve the global morphology features of simplified contour lines. These characteristics include characteristic points, characteristic sub-polylines, and characteristic links. To define CCSM constraints, the most significant morphology feature points in the shape were extracted from the initial contour to form a target contour by using the DP algorithm. The initial and target contours were converted into NURBS curves, and the two contour lines were assigned two height values. Accordingly, the NURBS curves were segmented into characteristic sub-polylines based on the characteristic points. Characteristic links were then constructed by linking the characteristic points of these two curves in order.  
(2) Building the CCS. The extracted characteristics were used to weave a wireframe (WF) that constrains the construction of the CCS. Faced with this constraint, the CCS was built by using the NURBS function.  
(3) Generating global continuous multi-scale simplified contours. First, the CCS was converted into a triangle mesh (CCS-mesh), which was then intersected with a set of horizontal planes with continuous height values. The generated intersection lines served as global continuous multi-scale simplified contours.
## 4 Experimental Results
### Correlation Analysis of Indicators
#### 4.2.2 Analysis of Test Results of Poisson Regression Model  
The Poisson regression model is tested using the correlation method in Section 2.2.3, and the results of the model are shown in Table 7.  
The test result of the Poisson regression model in Table 7 is \(p(\chi^{2})<0.001\), which indicates that the Poisson regression model passed the significance test; secondly, since the value of the log-likelihood function of the Poisson regression model is greater than the value of the likelihood function that retains only the constant term, it indicates a better fit of the model. The analysis of the correlation between urban road emergencies and these indicators using the Poisson regression model is reliable. The results of this regression model analysis can be used in this research for urban road traffic situation assessment under emergencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & **Standard Error** & **Coefficient** & \(\mathbf{Z}\) & \(\mathbf{p}\) \\ \hline Constant & 0.297 & 3.195 & 10.741 & 0.000 *** \\ Road length (km) & 0.034 & 0.136 & 4.048 & 0.009 *** \\ Road width (m) & 0.012 & 0.008 & 0.658 & 0.511 \\ Traffic lights & 0.016 & \(-\)0.026 & \(-\)1.608 & 0.108 \\ Intersections & 0.017 & 0.045 & 2.663 & 0.008 *** \\ Road enforcement officers & 0.014 & \(-\)0.005 & \(-\)0.353 & 0.724 \\ Traffic flow (pcu/15 min) & 0.001 & \(-\)0.001 & \(-\)2.739 & 0.023 ** \\ Road construction and maintenance & 0.024 & \(-\)0.023 & \(-\)4.961 & 0.006 *** \\ Rainfall (mm/min) & 0.17 & 0.429 & 2.53 & 0.011 ** \\ Temperature (\({}^{\circ}\)C) & 0.009 & \(-\)0.012 & \(-\)1.345 & 0.179 \\ Visibility (m) & 0.0 & \(-\)0.001 & \(-\)2.443 & 0.015 ** \\ \hline \hline \end{tabular} Note.: \({}^{\star}\), *** represent significance with significance levels of 0.1, 0.05 and 0.01, respectively.  
\end{table}
Table 6: Results of parameter estimation of the Poisson regression model.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 4 Discussion
### Hybrid Networks  
In recent years, hybrid neural network architectures have also been used, which combine different types of models to better exploit their individual properties. The most common combination is the integration of Convolutional Neural Networks (CNNs) with recurrent neural networks (RNNs) such as LSTM or GRU. CNNs are used to extract spatial features from images or video frames, while RNNs allow modelling temporal or sequential dependencies. Combining both types of networks allows for the analysis of image sequences, for example in recognising actions on recorded video, tracking objects, or analysing a series of medical images. CNNs are first used to extract features, then RNNs/LSTMs are used to model their changes over time [3; 123].  
Although the literature review did not take this into account due to the adopted criteria, it is possible to find integrations of CNNs with self-attention mechanisms known from transformer networks, as exemplified by Vision Transformer (ViT) architectures and their various modifications. The aim of such solutions is to combine local feature analysis (typical of CNNs) with global modelling of dependencies enabled by transformers. This approach results in improved results in classification, segmentation, and object detection tasks, especially when global relations between objects placed in the image are important [26].  
The combination of generative architectures such as GANs with other models is also important. For example, U-Net can act as a generator in supervised GANs for tasks such as segmentation or image translation between domains (e.g., CycleGAN, pix2 pix). The advantage of such solutions is the ability to generate realistic images in new styles or improve the quality of image data for reconstruction or super-resolution [19; 124]. This is especially relevant in recent times due to the development of detection systems using imaging techniques, in particular combining multispectral imaging using deep neural networks technology to identify fire sources in different environments [125; 126; 127; 128; 129; 130; 131; 132; 133].
## 4 Fractional Nonlinear Damage Creep Model
### Sensitivity Analysis  
Equation (27) can fully describe the instantaneous creep, decelerating creep, steady-state creep, and accelerating creep stages of rocks. Investigating the effects of fractional-order and time-dependent parameters on the creep curve is crucial for understanding the creep characteristics of rocks.  
When \(\sigma<\sigma_{s}\), with \(\sigma=20\) MPa, setting \(\sigma/E_{1}=0.1\) represents the instantaneous strain, \(\sigma/\eta_{1}=1.0\), \(\sigma/E_{2}=1.0\), and \(E_{2}/\eta_{2}=0.1\), and the resulting creep curves for varying fractional orders \(n\) are shown in Figure 15a. With other parameters unchanged and the fractional order fixed at \(n=0.2\), changing \(\eta_{1}\) and \(\eta_{2}\) yields the creep curves shown in Figure 15b,c, respectively. In addition, the creep curves with the increasing \(E_{2}\) are depicted in Figure 15d.  
Figure 15: Effect of different parameters on the creep curve.  
The fractional order, \(n\), mainly affects the \(t_{d}\) and the steady-state rate. In Figure 15a, as \(n\) increases, \(t_{d}\) and the rate of steady-state creep increases. The closer \(n\) is to 1, the more the curve resembles a linear function. \(\eta_{2}\) and \(E_{2}\) primarily affect the decelerating creep stage, while \(\eta_{1}\) mainly impacts the rate of steady-state creep. In Figure 15b, the steady-state rate exhibits a rapid increase as the rise of \(\eta_{1}\). The parallel curves of the steady-state creep stage indicate that \(\eta_{2}\) and \(E_{2}\) have little effect on the steady-state creep rate. Figure 15e illustrates the creep curves with varying \(\eta_{n1}\). The \(\eta_{n1}\) determines the accelerating creep rate. The curve becomes steeper with the decrease in \(\eta_{n1}\). This shows that the model is capable of effectively depicting the characteristics of rocks in different creep stages.
## 2 RISs and Their Integration with NTNs
### Comparison with Other Technologies  
The research in [49] explores an array of different solutions, uncovering significant SNR improvements in massive MIMO and RIS systems. Under far-field conditions, SNR grows linearly with the number of antennas (\(N\)) in massive MIMO but quadratically (\(N^{2}\)) with RISs. However, near-field complexities limit RIS's asymptotic SNR despite its faster growth. It emphasizes RIS's potential for enhanced SNR, scalability, and cost-effectiveness compared to massive MIMO. Table 1 gives a comparative overview of these technologies. RIS, massive MIMO, relays, and backscatter operate across several parameters such as duplex capability, the number of transmit RF chains, hardware cost, and energy consumption. RISs, operating through passive or active reflection, stand out with their FD capabilities and zero RF chain requirement, leading to low hardware costs and minimal energy consumption. In contrast, although offering full or half duplex options, massive MIMO and relay systems necessitate numerous RF chains, resulting in significantly higher hardware costs and energy usage. Backscatter, similar to the RIS in terms of low cost and energy needs, operates passively but generally underperforms compared to RIS.
## 3 Results and Analysis
### Test 5: Luminous Flux Regulation  
Taking into account that the combined operation of the LED current controller and the ZAD controller imposes the desired current to the LED groups, such a closed-loop system behaves as a current source ensuring \(i_{LED}=i_{LEDref}\). Then, the temperature compensator of Figure 9 adjust the reference of the LED current controller \(i_{LEDref}\) to ensure the desired luminous flux for any operation temperature. Figure 14 shows the performance of the system under the start-up condition (up to 1 s), where the current of the LED group \(i_{LED}\) is adjusted to produce the desired luminous flux \(EFx_{LEDref}=6000\) lm. Moreover, the figure also confirms that the \(PI_{T}\) controller imposes the desired settling time (0.5 s).  
A longer simulation is performed to test the system behavior during the thermal stabilization of the LED groups. Figure 15 shows the simulation of the system with an initial reference of the luminous flux equal to \(EFx_{LEDref}=6000\) lm, where the temperature of the LED group has an increase of 20 \({}^{\circ}\)C during 117 min (1 h and 57 min) over the ambient temperature \(T_{a}=30\)\({}^{\circ}\)C, reaching a steady state temperature equal to 50 \({}^{\circ}\)C. Since such a temperature increase reduces the effective luminous flux \(EFx_{LED}\) produced by the LED groups, the thermal regulator controller \(PI_{T}\) adjusts the current of the LED groups \(i_{LED}\) to provide the desired effective luminous flux \(EFx_{LEDref}\) for any thermal condition. It is satisfactory that the proposed thermal compensator ensures the production of the desired effective luminous flux \(EFx_{LED}=EFx_{LEDref}\) even with a temperature change equal to  
Figure 13: Results for the LED current control with a discharged 12-volt battery.  
66.6% of the initial (ambient) temperature. Then, after 250 min (4 h and 10 min of operation) the reference of the luminous flux is changed to \(EFx_{LEDInf}=5000\) lm, which forces the \(PI_{T}\) controller to change the current of the LED groups in order to produce the desired effective luminous flux (5000 lm), also compensating the change in the temperature caused by the reduction in the LEDs power. Figure 15 shows a zoom in this transient between minutes 250 and 251, where the correct current and luminous flux regulations are observed.  
Finally, the simulations reported in this subsection confirm the correct regulation of the luminous flux provided by the proposed solution. This is evident from the system performance without the temperature compensator (Figure 8) and with the temperature compensation activated (Figure 15), where the designed temperature compensator success  
Figure 14: Regulation of the luminous flux adjusting the LED group current.  
Figure 15: System operation during the LED temperature stabilization.  
fully regulates the luminous flux for a wide range of temperature conditions. Moreover, the luminous flux can be increased or decreased by changing the reference \(EFx_{LEDref}\) of the \(PI_{T}\) controller, thus providing higher flexibility to the LED system.