esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of the Combination of Axial Loading, Concentrated Mass, and Elastic Supports
As illustrated in Figure 17, when the spring values increase, the bending displacement at the support point converges to zero. Compared with the tapered beams, the effect of the elastic supports on the mode shapes was more significant for the uniform beams. The mode shapes of the single- and double-tapered beams changed with a similar trend; however, the effect of the elastic support was slightly greater for \(n=1\) than for \(n=2\). The values of the elastic springs and natural frequencies used to plot the mode shapes are presented in Table 13; the values of the rotational and translational springs increased with the same value. Figure 16: Variation in the first three mode shapes of tapered beams for Type A. The numerical method presented in this study can be adopted to analyze the variation in the dynamic characteristics of elastically restrained tapered beams with axial loading and concentrated mass. The critical buckling load for the first mode of the beams, which is calculated by considering the taper ratios and elastic boundary conditions, can serve as a useful criterion for the dynamic analysis of structures such as columns and beams. In addition, the analyzed results can be applied to the engineering design of beam-like structures with elastic supports.
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
###### Abstract  
The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in power outages and disruptions. This paper presents a semi-supervised hybrid deep learning model that combines a Gated Recurrent Unit (GRU)-based Stacked Autoencoder (AE-GRU) with anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptical Envelope. Using GRU units in both the encoder and decoder sides of the stacked autoencoder enables the effective capture of temporal patterns and dependencies, facilitating dimensionality reduction, feature extraction, and accurate reconstruction for enhanced anomaly detection in smart grids. The proposed approach utilizes unlabeled data to monitor network traffic and identify suspicious data flow. Specifically, the AE-GRU is performed for data reduction and extracting relevant features, and then the anomaly algorithms are applied to reveal potential cyberattacks. The proposed framework is evaluated using the widely adopted IEC 60870-5-104 traffic dataset. The experimental results demonstrate that the proposed approach outperforms standalone algorithms, with the AE-GRU-based LOF method achieving the highest detection rate. Thus, the proposed approach can potentially enhance the cybersecurity in smart grids by accurately detecting and preventing cyberattacks.  
c 2024 16 184 10.3390/61/060184
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 5 Performance Evaluation
### Performance Evaluation of the Proposed Protocol at T-function  
In this section, the protocol is tested in two T-junction scenarios. Figure 5 graphically illustrates the performance of the proposed protocol compared to its absence at T-junction stop-sign-controlled scenarios. The first scenario represents the case where stop signs control all input traffic flows. The second scenario represents the stop sign only being installed at the input flow of the short leg at the road intersection. We can see from Figure 5a,b that the proposed protocol increases the braking distance of vehicles by 30% on average. This enhances the safety conditions on the investigated road scenario. The reduction rate in the breaking distance is decreased by increasing the traffic density in both scenarios. The same breaking distance is obtained in highly congested scenarios. The driving assistance protocol aims to recommend a suitable time to start reducing the speed (i.e., safe braking distance). This mainly depends on the traveling speed of each vehicle. Vehicles are forced to drive at low speeds when the density is high. Then, the safe braking distance is decreased to the minimum value when no assistance protocol is used (i.e., assist-absent). At the same time, the proposed protocol aims to find the safe value and compute it by Equation (1) in the scenario where the proposed protocol is used. Both scenarios have agreed on the same safe braking distance. Fewer vehicles are stopping in the scenario where a secondary road crosses the main road. Thus, the braking distance is less in that scenario.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time (s) & 1500 \\ \hline Simulation area (m\({}^{2}\)) & 2000 m \(\times\) 2000 m \\ \hline Number of vehicles & 200–1000 \\ \hline Simulation map & 3-leg and 4-leg signalized intersection \\ \hline Number of stop signs & 1, 2, 3, 4 \\ \hline Traffic speed & 1–19 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: _Cont._On the other hand, the accident rate is 10% more on the main road scenario compared to the all-ways stop signs, as appears in Figure 5c,d, respectively. Figure 5c represents the accident percentage for the all-ways stop signs intersection, and Figure 5d represents the accident percentage for the main road intersection scenario. Comparing these two figures, we can see that for all traffic densities, the accident percentage on the all-ways stop sign intersection is 10% more than the acquired percentage on the main road intersection scenario. However, the assistant driving protocol completely eliminated accidents in both investigated scenarios.  
Finally, the throughput of the investigated road intersection was studied in this set of experiments. As we can infer from Figure 5e,f, the proposed protocol (i.e., assist-used scenario) increases the throughput on both tested scenarios. The throughput of the main road scenario is higher than the all-ways stop sign scenario because many vehicles can proceed through the intersection without stopping.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Experiment Results and Analysis
#### 4.2.4 Robustness to Different Parameter \(\lambda\)  
Let the other parameters be fixed apart from the linear parameter of the node capacity. We examine the influence of parameter \(\lambda\) on the robustness of the DGCN of CSOS. Let the parameter \(\lambda_{\text{G}}\) of the functional net change in [0.5, 3] at an interval of 0.5. Similarly, the parameter \(\lambda_{\text{W}}\) of the communication net also varies according to this rule. Simulation experiments are carried out with different parameters for two different hierarchical networks, and the results are shown in Figure 11. It can be seen from the figure that for the functional net, the robustness of the system improves with the increase of parameter \(\lambda_{\text{G}}\), indicating that increasing the node capacity of the functional net can strengthen the robustness of the system. The magnitude of improvement for different model networks is NW-net \(>\) Goh-Net \(>\) ER-Net. When the initial failure proportion is small, the robustness-decreasing curves are almost the same, and the proportion of overlap increases with the accumulation of \(\lambda_{\text{G}}\), indicating that there is an upper limit on the robustness-decreasing curve. As the failure proportion increases, the difference under different conditions is gradually obvious, and then gradually approaches 0 due to the reduction in robustness. For the communication net, the robustness curves of the system almost completely coincide with the increase of \(\lambda_{\text{W}}\), and the reason is the same as that in Section 4.2.3.  
Figure 10: Robustness with different \(\kappa\). Panels (**a–c**) represent the robustness under different \(\kappa_{\text{G}}\) when the subnet is ER-net, Goh-net, and NW-net, respectively; (**d–f**) represent the robustness under different \(\kappa_{\text{W}}\) when the subnet is ER-net, Goh-net, and NW-net, respectively.
## 1 Introduction
Previous studies on DBD plasma actuators can be classified into four topics: (1) research on low-temperature plasma dynamics and analysis of the induced flows by the phenomenon of dielectric barrier discharge; (2) analysis of the induced velocity and efforts to maximize it; (3) determining optimum parameters for good flow control; and (4) industrial applications. Several studies have focused on Topics (1) and (2), since strong induced velocities essentially lead to better flow control. We have focused on Topic (3), because we believe that understanding of the reason that burst mode works better than continuous mode for the same applied voltage would lead to finding the key features of the DBD plasma actuator. Computational Fluid Dynamics (CFD) has become an important tool for scientific and engineering studies not only in the academic world but also in the industry [17; 18]. Until recently, there was a clear boundary between: (1) practical CFD studies for engineering problems with turbulence modeling; and (2) basic CFD studies on turbulence in structures with simple geometries until recently. Progress in CFD methods and supercomputer performance in the last 20-30 years is now removing this boundary. Analysis of detailed flow structures using high-fidelity numerical simulations is now contributing strongly to clarifying the fluid physics of practical problems. This is also true for studies on the flow control mechanism for DBD plasma actuators [14; 19; 20]. In this study, flow structures induced by the DBD plasma actuator and the control strategies are discussed. The results involving the simulations and wind tunnel experiments of low-speed flows over airfoils with a DBD plasma actuator attached near the leading edge are examined. It is concluded that the effectiveness of the DBD plasma actuator for flow control comes from the three features that exist in the induced flows. By a proper setting of the actuator parameter, some of the flow features can be enhanced and become dominant in the flows. Therefore, the parameters should be optimized for specific applications to achieve effective flow control. Based on the analysis, guidelines for the effective use of the DBD plasma actuator are proposed. The DBD plasma actuator is also applied to the flows at cruise condition and the aerodynamic performance of a simple airfoil with a DBD plasma actuator is compared with that of a well-designed airfoil. This manuscript reviews the research activities of the author's group with regard to DBD plasma actuators for low-speed flows over airfoils and draws some conclusions. Based on these extensive studies, previously unreported insights are also presented.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
###### Abstract
In summary, the main contributions of this paper are as follows:  
* We propose an improved lightweight Siamese network (ILSN) with an optimized feature-extraction network design and similarity measurement. The ILSN had obvious advantages in tracking accuracy and the tracking speed, demonstrated through simulations and a system test. * We optimize the feature network extraction by designing a position-wise attention module and localization information fusion, which in turn improves the accuracy and real-time performance of ISLN target tracking. * Finally, we establish a simulation environment and a UAV-based experimental platform. By comparing with comparison algorithms such as SiamRPN, DaSiamRPN, and SlamRPN++, we verify the fallibility and feasibility of the ILSN. Figure 1: The issues for unmanned aerial vehicles (UAVs).
## 1 Introduction
In addition, [PERSON] et al., [26] reported that C-band SAR data were particularly well suited for monitoring and mapping rice because of their abilities to acquire information on rice growing areas with frequent cloud cover and a remarkable increase in backscattering coefficients throughout the rice growth cycle. [PERSON] et al., [28] also showed that the sensitivity of C-band sensors to plant biomass depends on the type of crop. In Camargue, the rice crop plays a crucial role for the hydrological balance [29]. This region is one of the main rice producers and suppliers in France [30]. Therefore, understanding and analyzing rice growth of Camargue is significant for agriculture in France. The aim of the paper is to analyze the capability of SAR Sentinel-1 data to assess the rice parameters in Camargue. Rice crop height is an important agronomic feature linked to plant type and yield potential. The rice plant height estimation,particularly, is considered a simple method for determining rice growth because this parameter greatly influences the yield potential. Rice biomass is regarded as an important indicator of ecological and management processes in the vegetation. In the literature of remote sensing, various machine learning algorithms are available for regression [31; 32; 33; 34]. In practice, the algorithms are selected based on the trade-off between the performances in terms of estimation of a given biophysical parameter, computation time and interpretability of the results [35]. Most of remote sensing works are based on classical algorithms, such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF) [36; 37]. Although they have been introduced since the early 2000s, they still compete with other approaches in many applications. In this paper, classical machine learning methods (Multiple Linear Regression, Support Vector Regression and Random Forest) are applied in order to estimate rice height and dry biomass in the Camargue region. This paper includes six sections. In Section 2, characteristics ofthe study site, weather, cultivation of Camargue area and the information of ground truth measurement are described. Section 3 presents the principle of three methods used to estimate rice parameters (Multiple Linear Regression, Support Vector Regression and Random Forest). The results are shown in Section 4. The discussions are presented in Section 5 and finally, the Section 6 involves the main conclusions of this paper.
## 2 Review of Kernel-Based TF Methods  
TFDs are in general classified into linear and quadratic methods [22]. The quadratic class of TFDs is frequently applied because of the signal energy interpretation in the TF domain. The Wigner distribution (WD) is the prototype TFD, as all distributions can be derived from the convolution of the WD with a, possibly signal dependent, kernel [42]. The WD can be obtained by [26]  
\[W_{x}(t,f)=\int R_{x}(t,\tau)e^{-2\pi f\tau}d\tau, \tag{1}\]  
where \(R_{x}(t,\tau)\) is the instantaneous auto-correlation function of the real-valued signal \(x(t)\), defined by  
\[R_{x}(t,\tau)=x(t+\tau/2)x(t-\tau/2). \tag{2}\]  
The WD offers a high energy concentration for linearly mono-component frequency modulated signals, but suffers from cross-terms regarding multi-component or non-linear frequency modulated signals [43]. The aforementioned limitation of the WD can be suppressed by smoothing with a two-dimensional kernel,  
\[\rho(t,f)=\gamma(t,f)\underset{(t,f)}{**}W_{x}(t,f), \tag{3}\]  
where \(\rho(t,f)\) represents any distribution in the quadratic class of distributions and \(\gamma(t,f)\) represents a TF smoothing kernel. The relation can also be expressed in the dual domain as,  
\[\rho(t,f)=\int\int A_{x}(\
u,\tau)g(\
u,\tau)e^{i2\pi\
u t}e^{-2\pi f\tau}d\tau \;d\
u, \tag{4}\]  
where \(g(\
u,\tau)\) is the Doppler-lag kernel and \(A_{x}(\
u,\tau)\) is the Doppler-lag domain representation given as  
\[A_{x}(\
u,\tau)=\int x(t+\tau/2)x(t-\tau/2)e^{-j2\pi t\
u}dt. \tag{5}\]  
The Doppler-lag kernel can be related to the TF smoothing kernel as,  
\[g(\
u,\tau)=\int\int\gamma(t,f)e^{-j2\pi\
u t}e^{j2\pi f\tau}dt\;df. \tag{6}\]A number of TF kernels have been developed to suppress cross-terms while retaining the resolution of auto-terms. In the following, we discuss the most widely employed TF kernel based methods--namely, the spectrogram, the S-method, and the adaptive optimal kernel TFD, in addition to one recently developed method, the adaptive directional TFD.
## 6 Issues and Future Directions  
### Precise Image Alignment  
Spatiotemporal data fusion is implemented on images from two different satellite sensors. These images have many differences although they have similar spectral configuration. These differences include spatial resolution, band width, spectral response function, atmospheric conditions, solar geometry and viewing angle. In addition, with the fast development of UAVs witnessed in recent years, fusing UAV images with satellite images is needed in the future. However, the difference between UAV and satellite images is even larger than that between two satellite sensors [101]. Theoretically, spatiotemporal data fusion requires that the coarse pixel and its internal fine pixels observe the same piece of land surface with same observing geometry. A precise image alignment is then required to mitigate the differences between the two types of images. Specifically, differences in band width, spectral response function, and even atmospheric condition (when the image size is small) more than likely cause systematic bias between the two types of images. This systematic bias can be reduced through relative calibration, such as using a linear regression or histogram matching method. However, most spatiotemporal data fusion methods, such as STARFM and ESTARFM, already take the systematic bias into account, so systematic bias is not a factor affecting the fusion accuracy very much. Different solar geometry and viewing angles of two sensors lead to pixel-wise difference because of the BRDF effect. For example, MODIS has a larger swath with larger viewing angles than Landsat, and its sun elevation is also a little bit different from Landsat. BRDF effect causes both MODIS and Landsat to not observe the exactly same part of Landsat surface. Pixel-wise difference is hard to remove during the data fusion process. As a result, BRDF adjustment may be a necessary preprocessing step to improve the accuracy of data fusion, although it is often ignored in most studies. Lastly, different spatial resolution is the reason for spatiotemporal data fusion, but it also brings the most difficult part in geo-registration between two types of images. Geo-registration error is the major error source in data fusion. Existing studies use both manual method and automatic method for geo-registration, but the accuracy cannot reach fine-pixel level. Future studies can consider developing spatiotemporal fusion methods which are less sensitive to the above differences between two sensors, especially the geo-registration error.
## 5 Physical Layer: Five Decades of Evolution (from 45 Mb/s to 100+ Tb/s)
### Physical Layer Evolution in Access
#### 5.3.1 Candidate Modulation Formats for Access
Next, when advancing from 25G to 50G, a large increase in cost should be considered, mainly due to the electro-absorption modulated laser-semiconductor optical amplifier that is used in the transmitter part, along with EDB detection and possibly even using an optical pre-amplified receiver when PAM4 is employed. Finally, a full coherent 100G system seems too costly for access, whilst a simplified coherent system can be a good candidate since it balances the cost with the significantly increased power budget, as is shown in Figure 34.
## 2 Materials and Methods
### Data collection and Processing
#### 2.2.3 UAV Lidar Data  
The UAV lidar data were acquired in July 2021 with an average point cloud density of 60 points/m\({}^{2}\). The process of lidar data mainly includes filtering, denoising, normalization, and generating CHM data [40]. The Lidar360 software was used to remove noise in the point cloud data, such as bird points, low points, and utility poles. The discrete point cloud echo points were divided into ground and non-ground points. The ground points were used to generate a digital elevation model (DEM) by inverse distance weighted interpolation method. All non-ground points were interpolated to a digital surface model (DSM) with a spatial resolution of 1 m. Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. The lidar data obtained in July 2021 and sample plot data surveyed at the same time were used to establish ACS prediction model, that is, 17 observations were used in the model. Stepwise regression method was used to establish a carbon stock estimation model with the variables from the CHM acquired from lidar with a resolution of 20m \(\times\) 20m (the same as plot size) [41]. Two variables (mean CHM and skewness) for ACS prediction were identified using stepwise regression method. Then, leave-one-out cross-validation was used in the evaluation processes, with 16 samples used to train the model, and the established model was used to predict the ACS value of the one observation left out of the model. The validation shows that the model works quite well, with R\({}^{2}\) (coefficient of determination), RMSE (root mean square error), and RRMSE (relative root mean square error) values of 0.87, 8.73 Mg/ha, and 18.72%, respectively. The average H and average ACS of each FSC were calculated based on the modelled data, and used to assess the 3-PG model simulation results (Figure 2a,b).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age (Year)** & **Number (n)** & **Mean DBH (cm)** & **Mean H (m)** & **Total Area (ha)** \\ \hline \(\leq\)4 & 55 & \(<\)9.5 & 2.5–10.8 & 105.38 \\
5–8 & 26 & 10.9–17.6 & 12.3–22 & 115.75 \\
9–12 & 53 & 11.5–24.4 & 14.3–29.2 & 143.25 \\
13–17 & 6 & 20.8–24.6 & 21.7–28.3 & 14.82 \\ Total & 140 & 0–27.6 & 2.5-29.2 & 379.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of the 140 eucalyptus FSC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organ** & **Fitting Equation** & **R\({}^{2}\)** \\ \hline Stem & W = 0.0259 \(\times\) DBH\({}^{2.8762}\) & 0.978 \\ Branch & W = 0.0263 \(\times\) DBH\({}^{2.2471}\) & 0.887 \\ Bark & W = 0.0539 \(\times\) DBH\({}^{1.7802}\) & 0.949 \\ Foliage & W = 0.1785 \(\times\) DBH\({}^{1.1753}\) & 0.871 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model for estimating aboveground biomass (stem, branch, bark, and foliage) of eucalyptus.
## 2 Material and Methods
### QA and QC
#### 2.5.2 Trace Metals  
The method detection limits (MDLs) were within the range 0.500-5.00 ng g\({}^{-1}\) for ICP-MS determined elements and 25.0-50.0 ug g\({}^{-1}\) for FAAS determined elements. For statistical calculations, values below the MDLs were assigned the method detection limits divided by \(\surd 2\). The quality assurance was provided by analyzing the certified reference material (CRM) NIST 1649a (urban dust), which includes reference values for selected trace elements.The recoveries for As, Ba, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn in the CRM were in the range \(\pm\)20%. Further, recovery efficiency for spiking sample analysis was \(\pm\)25% for all elements.