esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 2 Problem Formulation and Preliminaries
### UMVs System Modeling
These noise components are mutually independent, with \(W\) and \(V\) serving as their respective covariance matrices. In a UMVs system, optimal state estimation \(\hat{x}(k|k)\) can be achieved by implementing a Kalman filter. Sensor data are forwarded to an estimator that applies the Kalman filter for estimating the state. The system is presumed to initiate operations at \(-\infty\), allowing for the convergence of the Kalman filter into a constant-gain linear estimator. The update process is outlined in Algorithm 1. ```
1:Step 1: Input \(A_{dp},B_{dp},C,W,V\)
2:Step 2: \(\hat{x}(k+1|k)=\sum_{p=1}^{3}\hbar_{p}(\varrho(k))(A_{dp}\hat{x}(k|k)+B_{dp}u (k))\)
3:Step 3: \(\hat{x}(k|k)=\hat{x}(k|k-1)+Kz(k)\)
4:Step 4: \(z(k)=y(k)-\hat{x}(k|k-1)\)
5:Step 5: \(L=PC^{\top}(CPC^{\top}+R)^{-1}\)
6:Step 6: \(P=\sum_{p=1}^{3}(A_{dp}PA_{dp}^{T}+W)-A_{dp}PC^{\top}(CPC^{T}+R)^{-1}CPA_{dp}^ {T}\)
7:Step 7: Output \(\hat{x}(k|k)\)
```  
**Algorithm 1** Kalman Filter Utilized in T-S UMVs Model
## 2 Methodology
### Fatigue Damage Evolution  
#### 2.2.1 Fatigue Damage Evolution Equation  
The fatigue damage variable can be constructed based on the maximum fatigue strains of each cycle. Because of initial defects and the preloading effect, the strain obtained from the first loading cycle usually includes some initial strain of discrete nature besides that caused by cyclic loading. Therefore, to estimate the fatigue damage accumulated by cyclic loading more accurately, a fatigue damage variable in a normalized linear form is proposed, as shown in Equation (19):  
\[D_{\rm f}=\frac{\epsilon_{\rm max}^{\rm n}-\epsilon_{\rm max}^{1}}{\tilde{ \epsilon}_{\rm max}^{1}-\epsilon_{\rm max}^{1}}. \tag{19}\]  
\(\epsilon_{\rm max}^{1}\) is the maximum fatigue strain during the first loading cycle, corresponding to the strain at maximum fatigue load; \(\epsilon_{\rm max}^{\rm n}\) is the maximum fatigue strain during the \(n\)th loading cycle; \(\epsilon_{\rm max}^{\rm f}\) is the maximum fatigue strain during the last loading cycle when fatigue failure occurs. These fatigue strains are shown schematically in Figure 2.  
Since maximum fatigue strain typically follows three-stage development, the damage variable from Equation (19) also presents the three-stage development characteristic. In contrast to the piece-wise description widely used in the literature [26; 27; 28], a continuous  
Figure 2: Sketch of the definition of damage variable based on the evolution of maximum fatigue strain from initial loading toward fatigue failure.  
nonlinear equation is proposed to describe the three-stage evolution of the fatigue damage variable, as expressed in Equation (20):  
\[D_{\text{f}}(n/N_{\text{f}})=a(\frac{b}{b-n/N_{\text{f}}}-1)^{1/c}. \tag{20}\]  
\(D_{\text{f}}(n/N_{\text{f}})\) is the damage variable corresponding to the \(n\)th fatigue loading; \(a\), \(b\), and \(c\) are unitless equation parameters obtained by fitting the experimental data whose physical meaning and value range will be identified in the following sections. For normalizing loading cycles, the cycle ratio \(n/N_{\text{f}}\) is defined as the number of applied cycles \(n\) divided by fatigue life \(N_{\text{f}}\), the total number of cycles a specimen can sustain before fatigue failure. Although any division of cycle ratio is acceptable, only values at given cycle ratios (i.e., \(n/N_{\text{f}}\) = 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 or \(n/N_{\text{f}}\) = 0, 0.2, 0.4, 0.6, 0.8, 1.0) are presented in this study for illustration purpose.
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 1 Introduction
### VR in Education  
Academic research has documented numerous contributions and benefits of VR-based instruction. The recent proliferation of consumer VR hardware has made a wide range of experiences, locations, and environments more accessible than ever before [30]. This technology has proven effective in enhancing learning by enabling students to connect theoretical knowledge with practical experiences within a virtual environment. According to [PERSON] et al. (2022) [20], learning in a virtual world not only facilitates the comprehension of complex concepts but also enriches the educational experience by making it more interactive and engaging. Similarly, [PERSON] et al. (1999) [31] explained how this technology promotes complex learning, both in teaching methodologies and academic outcomes. Their study highlights how immersive experiences improve the understanding of challenging subjects by providing dynamic and interactive learning opportunities, thereby optimizing educational processes.  
The teaching of history is particularly well-positioned to benefit from this technology [32] as it allows students to explore historical sites and environments that would otherwise be inaccessible in person [26]. In this context, VR technology offers unrestricted access to both real and digitally reconstructed spaces, eliminating the need for physical travel and providing continuous availability. This facilitates greater interaction, enabling engagement from any location and at any time [33]. The publication of [PERSON] and [PERSON] (2016) compare VR to a time machine, highlighting its ability to virtually recreate any space and place it within any historical period [34].
## 4 Constant Power Speed Range Performance and Anti-Demagnetization Performance
### Constant Power Speed Range Performance Comparison  
For PMA-SynRMs, wide constant power speed range performance (CPSR) is as important as high torque, high power density, and high efficiency. Comparison of torque-speed and power-speed curves for the three models is shown in Figure 15, and comparison of speed-efficiency plots is shown in Figure 16. In the calculation, the peak current is set to 7.07A. All three models use maximum torque per ampere (MTPA) control and flux-weakening (FW) control. As shown in Figure 15a, the proposed and optimized models exhibit higher torque in the constant torque region compared to the conventional model. From Figure 15b, it can be seen that the power of the conventional model starts decreasing after 2000 rpm, while the power of the proposed and optimized models starts decreasing after 3500 rpm. This demonstrates that the proposed and optimized models have higher power density compared to the conventional model. From Figure 16, it can be seen that the efficiency of the proposed and optimized models is slightly higher than that of the conventional model in the high speed range and slightly lower than that of the conventional model in the high speed range. In conclusion, the proposed and optimized models have better CPSR performance compared to the conventional model.  
Figure 14: Comparison of the torque of the three models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **12** & **14** & **30** & **31** \\ \hline Error & 2.71\% & 0.52\% & 4\% & 4.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Output torque errors between finite element simulation results and experimental results.  
Figure 16: Cont.  
Figure 15: Comparison of the torque–speed and power–speed curves for the three models. (**a**) Torque–speed. (**b**) Power–speed.
## 3 Aeronautical Application
### Engineering Case Study  
The aircraft model was taken from the EU-funded project LOSITA (low subsonic investigation of a large complete turboprop aircraft), where the design and testing of an advanced turboprop regional aircraft was tackled. The experimental campaign, with the various WT conditions simulating the cruise, take-off and landing configurations, allowed for an accurate evaluation of the low-speed performance of the associate scaled-down (1:6.5) aircraft model [26].  
In the present work, three-dimensional CFD computations were conducted to predict the mean turbulent flow around the aircraft model that was provided by the industrial researchers. The simplified aircraft geometry, which is depicted in Figure 1, contained the horizontal and vertical tails as well as the engine nacelle. The model has a length of 4.8 m, a height of 1.5 m, and a span of 4.7 m. The clean configuration of the aircraft was considered, with no high-lift surfaces deployed, while not modelling the effect of the propellers. The calculations were performed for a freestream velocity of 68 m/s, with the Mach and Reynolds numbers corresponding to 0.17 and 1.6 \(\times\) 10\({}^{6}\), respectively, where the mean aerodynamic chord (MAC) was used as a reference length [26].  
The \"free-air\" simulations (as neither the WT walls nor the model support system were included in the computational geometry) were conducted for zero sideslip angle (\(\beta=0^{\circ}\)) while varying the incidence angle in a typical range of interest (that is, \(-6^{\circ}<\alpha<22^{\circ}\)) [26]. Owing to the mean flow symmetry, the computational domain was constructed around the semi-aircraft geometry model with \(\mathrm{Y}>0\) while imposing a symmetry boundary condition at \(\mathrm{Y}=0\). Specifically, the governing equations were solved in a hemisphere with a radius of 100 MAC. Figure 2 shows a schematic illustration of the computational domain with artificially reduced size. As to the boundary conditions, the aircraft surfaces were considered as adiabatic no-slip walls, while characteristic-based relations were employed for the farfield conditions. Note that some further details can not be provided for industrial property reasons.  
Figure 1: Computational geometry of the mid-range aircraft model.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 4 Algorithm-Based Energy Sustainability in WSNs
### Algorithm-Based Methods for Energy Sustainability in WSNs
#### 4.3.1 Data Driven Methods
In this category, each node locally computes a model-based on the data trend and creates the information that will be sent to the BS, instead of transmitting a number of raw samples to the BS. When there is no remarkable deviation between the sensed data and the model prediction, nodes do not have to communicate with the sink. When the sensed data differ from the model, nodes must update their model and accordingly report to the BS. Such models can be statistical, machine-learning, probabilistic etc. [37; 58].
## 2 Materials and Methods
### Methodology
#### 2.2.2 Elliptic Fourier Analysis  
After normalization procedures, cross-sections characterized using the semilandmark models were also analyzed using a Fourier Analysis (FA) approach computed in R [47; 48]. Fourier series are used to describe shapes by decomposing a periodic function into a sum of simpler trigonometric functions, weighted with harmonic coefficients. Among FA approaches, Elliptic Fourier Analysis (EFA) is based on a series of linearly transformed circular coordinates [49; 50; 51]. EFA approaches, in comparison with other Fourier descriptors of morphology, are the most robust to irregularities along the outline, as they do not require equally spaced points.  
Figure 1: Landmark and semilandmark models created for the present study on an example of a leopard score cross-sectional profile, where fixed landmarks are red and semilandmarks are white. (**a**) corresponds to the 7-landmark model; (**b**) to the 10 semilandmark-model slid on a single curve; (**c**) to the 25 semilandmark-model slid on a single curve; (**d**) to the 50 semilandmark-model slid on a single curve; (**e**) to the 10 semilandmark-model slid on two curves defined by three fixed points; (**f**) to the 20 semilandmark-model slid on two curves defined by three fixed points; and (**g**) to the 30 semilandmark-model slid on two curves defined by three fixed points.  
Elliptic Fourier coefficients are divided into four; coefficients \(a\) and \(b\)--equivalent to the trigonometric moments around \(x\) coordinate values -, and coefficients \(c\) and \(d\), which define the \(y\) coordinate projection from circular to linear space [49; 50; 51]. Depending on the number of harmonics (\(n\)) used to describe the Fourier series, the Fourier coefficients--\(an\), \(bn\), \(cn\), and \(dn\)--can then be subjected to multivariate statistical analyses to empirically define each outline. The optimal number of harmonics was calculated by estimating the cumulative power for each harmonic, and selecting those that represent over 95% of the morphological variation. Coefficients can then be directly subject to PCA for the analysis of form, while the analysis of shape requires cancelling out coefficients \(a1\), \(b1\), and \(c1\) so as to normalize the data [52], eliminating any remaining influence that size or rotation may have on subsequent analyses. Then, PCAs were conducted on the normalized data for dimensionality reduction purposes and to visualize sample variance with the aid of TPS transformation grids.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Semantic Segmentation Algorithm  
PSP-net has achieved good performance in semantic segmentation tasks and has been widely used in image segmentation, especially in tasks that require fine segmentation of complex scenes.The PSP model proposes a pyramid pooling module that is able to aggregate the contextual information of different regions to improve the ability to obtain global information. The model structure is shown in Figure 6. A deep convolutional neural network, such as ResNet or VGG, is first used to extract features from the input image. The acquired feature layer is divided into grids of different sizes, i.e., subregions of \(6\times 6\), \(3\times 3\), \(2\times 2\) and \(1\times 1\) sizes and average pooling is performed inside each subregion. The module aggregates contextual information from different regions to improve the ability to acquire global information. The output of the pyramid pooling module is combined with the feature maps extracted from the backbone network through operations such as up-sampling and finally generates pixel-level semantic segmentation predictions.
## 3 Available Photovoltaic Software
### PV System Simulation Tools  
Simulation tools are software programmed to simulate or predict power system performances as designed by users, using the best meteorological algorithm and/or collected meteorological database. The software were developed to simulate, analyze, monitor and visualize power system performances, but unable to optimize the system.  
Figure 2: Classification of simulation software based on its application.  
Integrated Simulation Environment Language (INSEL) is simulation software developed by the University of Oldenburg, Germany. INSEL is able to create system model and configurations for planning and monitoring electrical and thermal system. This software is suitable to simulate time series solar irradiance, PV power plant, solar cooling and heating system. The user will construct the proposed power system configuration by connecting blocks provided in the library. The advantage of this software is that it provides PV components database, such as PV modules, inverters, and thermal collectors. Besides, the software is also able to detect faults in the system. It is said that the software able to solve any computer simulation problem. The software has meteorological database from 2000 locations worldwide, and it can generate hourly irradiance, temperature, humidity and wind speed. The output consist graphical and numerical output. Price for full version software is 1700 euro and 85 euro for student. Trial version for this software is free and available in the website [13; 47; 48].  
Another simulation tool available is Transient System Simulation (TRNSYS), which was developed by the University of Wisconsin, Madison, USA. Compared to INSEL, TRNSYS able to simulate cogeneration power system other than PV system and thermal system, including wind turbine, fuel cells, and batteries. The objectives of the software are to simulate low energy and HVAC system sizing, system analysis, multizone airflow analysis, electrical simulation, solar energy and thermal system design, and control scheme. This software is flexible, where the users able to modify mathematical model in its library. Besides, meteorological data and component designed by users are required as the input in simulation. A demo version for the software is available in its website, and the price for educational use is $2100 [13; 14; 49].
## 3 Results
### Main Study Outcome
MRT values measured at 0 and 8 weeks showed no significant difference between SLA and machined surfaces. [PERSON] et al. [36] aimed to investigate the effects of surface-treated miniscrews in diabetic rabbits. In this study, SLA and machined surfaces were compared. MIT values showed no difference between the diabetic and control groups. In surface-treated miniscrews, MRT was higher in both the diabetic and control groups, but there was no significant difference between the diabetic and control groups in MIT, regardless of surface treatment. [PERSON] et al. [39] aimed to evaluate the stability and resistance to rotational moments of early-loaded SLA and machined surface miniscrews. MIT values of SLA miniscrews were found to be lower than the machined surface screws. However, when MRT values were compared, there was no statistical difference between the two surfaces. [PERSON] et al. [27] compared anodized surface and machined surface miniscrews. While there was no difference between the two groups in terms of MIT values, MRT values were found to be significantly higher on the anodized surface. [PERSON] et al. [34] evaluated the stability of miniscrews subjected to anodization, cyclic pre-calcification, and heat treatment. Compared to the control group, MRT values measured at 3 and 6 weeks were higher in the surface treatment group. [PERSON] et al. [35] evaluated the contribution of cyclic precalcification treatment to osseointegration and the bioactivity of miniscrews. The experimental groups of this study were as follows: anodized and heat-treated group; anodized, heat-treated and cyclic pre-calcification treatment group; and control group. When the MRT values were examined, the highest value was in the anodized, heat-treated and cyclic pre-calcification treatment group. [PERSON] et al. [28] compared the effects of SLAO and SLA surface treatments on the mechanical stability of miniscrews in vitro and in vivo. In the in vivo study, MRT values were significantly higher in the SLAO group than in the SLA and control group. In contrast, MIT values were higher in the control group compared to the SLA and SLAO groups. [PERSON] et al. [42] analyzed miniscrews with machined, SLA, and SL/NaOH (alkaline-etched) surfaces implanted in rabbit tibia and reported MRT in the SLA and SL/NaOH groups compared to the machined surface. In a study by [PERSON] et al. [29] evaluating the early stability of miniscrews with machined and RBM surfaces, MIT values were significantly higher with the machined surface compared to the RBM surface. In contrast, MRT values at week 2 were significantly lower in the machined surface group than in the RBM group, which was attributed to greater bone resorption in the early period. At week 4, there was no significant difference in MRT values between the groups. [PERSON] et al. [26] aimed to increase the osseointegration capabilities of miniscrews with UV-treated surfaces and evaluated their stability under loading. Whether force was applied or not, MRT values were significantly higher in UV-treated groups.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods
### Microbubble Localization  
#### 2.2.1 PSF Models  
The impulse response of an ultrasound scanner to a point-like source is described by the PSF. A common approximation of the real PSF is a symmetric two-dimensional Gaussian function given by the formula [13]:  
\[PSF_{G}(x,y|\mathbf{\theta})=\frac{\theta_{N}}{2\pi\theta_{\sigma}^{2}}exp\left(- \frac{\left(x-\theta_{x}\right)^{2}+\left(y-\theta_{y}\right)^{2}}{2\theta_{ \sigma}^{2}}\right)+\theta_{b}, \tag{1}\]  
where \(PSF_{G}(x,y|\mathbf{\theta})\) gives the expected microbubble count at the integer pixel position (\(x\),\(y\)) for the parameters \(\mathbf{\theta}=[\theta_{x};\theta_{y};\theta_{\sigma};\theta_{N};\theta_{b}]\). The entries of the vector \(\mathbf{\theta}\) are as follows: \(\theta_{x}\) and \(\theta_{y}\) are the sub-pixel microbubble (group) coordinates, \(\theta_{\sigma}\) is the imaged size of the microbubble (group), \(\theta_{N}\) corresponds to the total number of the microbubbles at this position, and \(\theta_{b}\) corresponds to the background noise level.  
Figure 1: Super-resolution imaging process: Step 1. Scanning and comparing, Step 2. Microbubble localization, Step 3. Repeating 1 & 2 for each frame, Step 4. Trajectory tracking.  
Though being simple, the original two-dimensional Gaussian function omits the prior knowledge about the imaging priori knowledge. The integrated form of a symmetric two-dimensional Gaussian function [21] is used in our method to help take into account the discrete nature of pixels presented in ultrasound images. Assuming a uniform distribution of pixels with the unit size, a single microbubble intensity profile can be expressed as:  
\[PSF_{\mathrm{G}}\left(x,y|\boldsymbol{\theta}\right)=\theta_{N}E_{x}E_{y}+ \theta_{b}, \tag{2}\]  
where \(PSF_{\mathrm{G}}\left(x,y|\boldsymbol{\theta}\right)\) gives the expected microbubble count at the integer pixel position (\(x\),\(y\)) for the parameters \(\boldsymbol{\theta}=\left[\theta_{x};\theta_{y};\theta_{c};\theta_{N}; \theta_{b}\right]\) and  
\[E_{x}=\frac{1}{2}\mathrm{erf}\left(\frac{x-\theta_{x}+\frac{1}{2}}{\sqrt{2} \theta_{\sigma}}\right)-\frac{1}{2}\mathrm{erf}\left(\frac{x-\theta_{x}- \frac{1}{2}}{\sqrt{2}\theta_{\sigma}}\right), \tag{3}\]  
\[E_{y}=\frac{1}{2}\mathrm{erf}\left(\frac{y-\theta_{y}+\frac{1}{2}}{\sqrt{2} \theta_{\sigma}}\right)-\frac{1}{2}\mathrm{erf}\left(\frac{y-\theta_{y}+ \frac{1}{2}}{\sqrt{2}\theta_{\sigma}}\right). \tag{4}\]
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 1 Introduction
The studies mentioned above mainly focused on cluster division considering voltage control and power balance but did not take into account the impact of strong volatility and uncertainty in the PV output on the system operation state, especially the frequency regulation ability. Motivated by this issue, in this paper we start with the characteristics of renewable energy power generation and propose new functional indexes considering frequency regulation response speed to realize the cluster division of distributed generation and lay a foundation for subsequent frequency regulation control. This paper proposes a cluster division method based on a comprehensive performance index system and an improved GA, taking into account intra-cluster climbing demand flexibility and supply-demand balance flexibility and the subsequent operation and scheduling requirements, in order to give full play to the cluster autonomy capability and improve the frequency regulation capability. The method comprehensively considers the complementarity and correlation between nodes within clusters and pays attention to the dynamic balance of the active power of the system based on ensuring a reasonable match between the source and the load within the clusters. Finally, these indexes were combined with the modularity indexes, and an adaptive GA based on the weighted neighbor matrix was used to improve the IEEE 33-node system as an example. The comprehensive indexes proposed in this article were applied to DGC division.