esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 4 Current Issues in Electric Vehicles
### Security and Privacy  
The security issues seriously influence the CAV sector due to the comprehensive connectivity over the internet and cloud. The essential threat in CAV derives from exchanging information with other vehicles and servers. The CAV can share the ID details, battery information, or vehicle location. The data exchange between CAVs and the server might be subject to cyber attacks, such as (DoS, spoofing, privacy attacks, modification, etc.). Several studies in the literature surveyed the issues of security and privacy in electromobility. Many approaches and technologies have been innovated to increase the protection level of the EV. [PERSON] et al. [64] proposed an anomaly detection system to enhance the cyber security of the steering stability control system in the CAV. The proposed approach can identify the threats to control inputs and sensors by combining two approaches, physics-based and learning-based approaches. The results have shown an improvement in the cyber-physical security of EVs. [PERSON] et al. [65] introduced a robust authentication protocol for charging electric cars while driving. Secure and lightweight primitives, such as elliptic curves and hash functions, are used in the proposed protocol.  
The protocol's security is being investigated to show that it can handle different attacks. [PERSON] et al. [66] developed a cyber-resistive model for detecting vehicular cyber attacks. A hybrid smart structure built of wavelet decomposition technology and a modified support vector machine can detect any malicious behavior in the controller area network bus. The proposed approach performs excellently in detecting cyber attack communications while also detecting regular data. Thus, it is essential to find a way to validate the data exchange and transformation between the vehicles and infrastructure before the system implementation. The DT will give the ability to validate the security and privacy of the IoV by simulating the network scheme and using AI and ML technologies; it will allow for predicting threats that can be harmful to the vehicular network [67]. Safety and security functions are of basic importance in AV. Reference [68] aimed to identify a standard vehicular DT framework that facilitates the data collection, processing, and analytics phases. The DT was explored to automate the decision-making process inside an AV using radar sensor data collected from the initial, analytic, and reporting phases, and generate reports to be sent to AVs. The recommended model aims to identify, analyze, and assess the threats, and provide the user with an opportunity to take appropriate countermeasures in ensuringsafety and security using DTs in driverless vehicles. Some of the advantages presented using the car-follower model can reduce the risks of cyber attacks and accidents.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 4 Decision-Making Algorithm Based on Anti-Jamming Knowledge Graph
### Anti-Jamming Knowledge Graph Search Statement Design
#### 4.2.1 Question Answering Template Design  
The question answering (Q&A) template is essentially a sub-graph of the anti-jamming knowledge graph. For example, the template 'entity + attribute' represents nodes and an edge in the graph. Because the large-scale data set for the anti-jamming knowledge questions is not sufficient, this paper constructs the question template manually based on the constructed anti-jamming knowledge graph, and designs the type template of the anti-jamming knowledge question, as listed in Table 1.
## Materials and Methods
### Patient-Specific Multi-Objective DIR  
DIR is typically formulated as the minimization of a single cost function that consists of several terms describing objectives of interest, such as the degree of dissimilarity between the two images and the amount of deformation. The typical formulation is to find, for a given weight vector \(\mathbf{\lambda}\in[0,1]^{m}\), the transformation \(\mathbf{T}\) that minimizes:  
\[C(\mathbf{T},\mathbf{\lambda})=\lambda_{0}\mathcal{O}_{0}(\mathbf{T})+\lambda_{1}\mathcal{ O}_{1}(\mathbf{T})+\cdots+\lambda_{m-1}\mathcal{O}_{m-1}(\mathbf{T}). \tag{1}\]  
Here, \(\mathcal{O}_{j}\), \(j\in\{0,\ldots,m-1\}\) are the \(m\) objectives considered in the DIR method. The weight vector \(\mathbf{\lambda}=(\lambda_{0},\ldots,\lambda_{m-1})\) determines the trade-off between the objectives and needs to be set before the start of optimization. Each different weight vector \(\mathbf{\lambda}\) corresponds to a different cost function and therefore to a different (local) minimum. The objectives can be highly non-convex, as is often the case, e.g., with the objective describing the (dis)similarity between the images to be registered [15].  
Now, let \(\mathbf{T}(i,\mathbf{\lambda})\) be the transformation resulting from performing DIR with a typical DIR method on instance \(i\). The more complex the registration problem (e.g., content mismatch, large deformations), the more likely \(\mathbf{T}(i,\mathbf{\lambda})\) does not correspond to the global optimum of \(C(\mathbf{T},\mathbf{\lambda})\), given the local nature of the gradient descent optimizers typically used in DIR.  
We can use this definition to formulate the patient-specific multi-objective weight-tuning problem:  
Figure 3: From (**left**) to (**right**): Cases B6, B9 and B10 (from prone-supine Group B). (**Upper**) row: source image acquired in prone orientation. (**Lower**) row: target image acquired in supine orientation.  
Figure 2: From (**left**) to (**right**): Cases A1, A2, A3 and A4 (from prone-supine Group A). (**Upper**) row: source image acquired in prone orientation. (**Lower**) row: target image acquired in supine orientation.  
\[\min_{\mathbf{\lambda}}\left\{(O_{0}(\mathbf{T}(i,\mathbf{\lambda})),O_{1}(\mathbf{T}(i,\mathbf{ \lambda})),\ldots,O_{m-1}(\mathbf{T}(i,\mathbf{\lambda})))\right\}.\]  
Solving this multi-objective parameter-tuning problem to optimality results in a set of solutions that can be considered equally good, i.e., no solution can be improved in one objective without deteriorating one or more other objectives. This set of solutions in parameter space is called the Pareto set, and the set of corresponding objective values in objective space is called the Pareto front [16]. When solving this problem in practice, however, the solutions obtained may or may not lie on the Pareto front; therefore, these solutions form a trade-off, or non-dominated, front.
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 2 Materials and Methods
### Long-Term Climate and Runoff Variations
\begin{table}
\begin{tabular}{c c|c c c|c c c|c c c} \hline \hline \multicolumn{2}{c|}{**Indicator**} & \multicolumn{2}{c|}{**Station**} & \multicolumn{2}{c|}{**Annual**} & \multicolumn{2}{c|}{**Warm**} & \multicolumn{2}{c}{**Gold**} \\ \multicolumn{2}{c|}{**Avg**} & \multicolumn{2}{c|}{**LinT**} & \multicolumn{2}{c|}{**Avg**} & \multicolumn{2}{c|}{**LinT**} & \multicolumn{2}{c|}{**Avg**} & \multicolumn{2}{c}{**LinT**} \\ \hline TG & SO & \(-5.2\) & 0.31 & ** & \(-0.3\) & 0.31 & *** & \(-10.7\) & 0.34 & *** \\ (\({}^{\text{C}}\)10y\({}^{-1}\)) & ZU & \(-4.4\) & 0.20 & ** & 0.4 & 0.22 & *** & \(-9.6\) & 0.19 & * \\  & HO & 7.0 & 0.30 & ** & 12.4 & 0.30 & *** & 1.5 & 0.31 & *** \\  & IN & 9.8 & 0.29 & ** & 15.9 & 0.25 & *** & 4.1 & 0.33 & *** \\ \hline \(P\) & SO & 1664 & 91 & ** & 847 & 48 & *** & 540 & 25 & ** \\ (mm 10y\({}^{-1}\)) & ZU & 2006 & 26 & & 960 & 20 & * & 714 & \(-1\) & \\  & HO & 1162 & 2 & & 772 & 8 & & 261 & \(-7\) & \\  & IN & 886 & 3 & & 587 & 10 & + & 193 & \(-6\) & \\ \hline \(SD\) & SO & 272 & \(-11\) & + & 256 & \(-13\) & * & 348 & \(-15\) & \\ (cm 10y\({}^{-1}\)) & ZU & 214 & \(-5\) & 169 & 0 & & 317 & \(-9\) & + \\  & HO & 19 & \(-1\) & + & 6 & 0 & & 21 & \(-2\) & * \\ \hline \(Q\) & b1,INN & 24.7 & 0.34 & + & 37.8 & 0.23 & & 10.5 & 0.31 & * \\ (m\({}^{3}\) s\({}^{-1}\)0y\({}^{-1}\)) & b2,RAB & 9.0 & \(-0.22\) & + & 12.5 & \(-0.42\) & * & 4.9 & \(-0.08\) & ** \\  & b3,LIE & 39.3 & 0.33 & & 66.0 & 0.11 & & 10.3 & 0.39 & * \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Seasonal means, decadal linear trends (_LinT_) and trend significance of the observed climate (\(TG\), \(P\), \(SD\)) and runoff (\(Q\)) indices for the annual, warm and cold seasons in period 1951–2018. Notation of trend significance: *** for \(p\) < 0.001, ** for \(p\) < 0.01, * for \(p\) < 0.05, + for \(p\) < 0.1. Figure 2: Annual means, 11-year moving averages and linear trends of the observed climate indices (\(TG\), \(SD\)) at stations Sonnblick (SO), Zugspüze (ZU) and Hohenpeissenberg (HO), and runoff index (\(Q\)) at stations Innsbruck (INN), Rabland (RAB) and Lienz (LIE) in period 1951–2018. Notation of trend significance: *** for \(p\) < 0.001, ** for \(p\) < 0.01, * for \(p\) < 0.05, + for \(p\) < 0.1.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 5 Information Sharing Mechanism Based on Content
Besides, \(TRto\) increases with the increase of \(k\), i.e., \(TRto\) will be larger when the number of entity classes in \(T_{LEC}(V_{EC})\) is larger, which means that the less structural the categories, attributes, and states describing entities, the larger the \(TRto\)Generally, since the concept-logic tree can be designed based on different topics and information systems usually only care about the information of targets relevant to their missions, the number of entities and entity classes can be controllable when describing a specific battlefield space, especially the number of first level entity classes. For example, when describing an entity on geographic coordinates, the concerned information mainly includes what it is, where it is, what it is doing, what it needs, and what state it is in. On the contrary, since entities are rarely completely isolated from the outside world, i.e., there exist semantic relationships between almost any two entities and the semantic relationships between every two entities may be more than one, which means that d is usually large, if every two entities possesses a kind of relationship, then the value of d can approximately be \((n-2)/(1+l/k)+1\), which is much larger than two. The corresponding TRto will usually be smaller than one, and the larger the d, the smaller the TRto, which indicates that the time complexity of this content and semantic based method in information query is usually smaller than that of graph matching. To sum up, this content and semantic based algorithm adopts the idea of trading space for time, which will be useful in realizing sharing of battlefield situation information where the number of combat units is controllable and the requirement of real time is more important than space.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 7 Comparison to State-of-the-Art Algorithms  
Most of the state-of-the-art articles focus on simulated images [33], a deep analysis of complex data [34], or approaches using two or more images of the same area [28]. These approaches require excellent knowledge of the sensor parameters and the platform, including its kinematics/dynamics.  
This assumption cannot be taken for most commercial applications, such as Search and Rescue or surveillance missions. This is because airplanes' or UAVs' dynamics and paths are heavily weather-dependent. Thus, it is difficult to gather datasets for interferometry-based algorithms [35]. At the same time, simulation-based training requires precise data on the radar build, which is part of the radar system suppliers' intellectual property and is hardly ever shared.  
For these reasons, the paper focused purely on training with real data. A single UAV mission (4-8 h) can generate hundreds of scans. The same amount of data takes days of work for the professional analyst to label. Thanks to a more universal approach focused on analyzing a single final scan, virtually any scans gathered by the system can be used for pre-training. There is no need to prepare special trajectories or missions in favorable atmospheric conditions for dataset gathering.  
The goal of the article was to present a lightweight algorithm (both in terms of processor load and implementation requirements) for boosting the effectiveness of autoencoder pretraining. As presented in Section 6, autoencoding capabilities noticeably improved thanks to label image filtering. As such, any quantitative comparison to the cited articles is inappropriate. This is the reason why the performances of the networks with the modified training algorithm are juxtaposed with classic training for comparison purposes. The networks are not supposed to be better at autoencoding/filtering than those of the cited articles but provide a Bag of Words for transfer learning or statistical analysis (e.g., clustering [36]).
## 3 Analysis and Experimental Verification  
### Analysis of Displacement and Offset  
According to Equations (1)-(9), the displacement \(\Delta\) and the offset amount \(d\) of the two waves at any incident direction can be calculated, and their spatial distribution is shown in Figure 4. The row and column direction coordinates correspond to the angles in the \(y\) and \(z\) directions. The calcite prism used in the simulation calculation has a structure angle \(\beta=27.15^{\circ}\), a thickness \(t=10.2\) mm  
special incident modes of light. When \(\omega\) is a constant, taking \(\omega\) = 0\({}^{\circ}\) as an example, the law of \(\Delta\) and \(d\) changing with \(i\) is shown in Figure 5:  
In the main section, \(\Delta_{y}\) = 0, which proves that the two waves do not shift at all in the \(y\) direction, which is consistent with the physical properties of birefringent crystals because the wedge plane is perpendicular to the main section. \(\Delta_{z}\) decreases linearly with the increase in \(i\), from 0.60225 to \(-\)1.62831 for eooe waves and from 1.80883 to \(-\)0.55791 for eoeo waves. The range and rate of change in the eoeo wave are slightly larger, which is determined by the birefringence of calcite. While \(d_{y}\) = 0, \(d_{z}\) decreases linearly from 1.20658 to 1.0704. As the change in \(d_{z}\) is small, and the rate is slow, it can be approximated as a constant.  
When \(i\) is a constant, taking \(i\) = \(\pm\)5\({}^{\circ}\) as an example, the law of \(\Delta\) and \(d\) with \(\omega\) is shown in Figure 6:  
When \(i\) is fixed, the incident ray is in a conical surface at this time. According to the \(y\)-axis direction of the incident light direction vector, the incident angle is positive if the y component is positive. If \(i>0^{\circ}\) as \(\omega\) increases, \(\Delta_{y}\) increases approximately sinusoidally, from \(-\)1.14089 to 1.14089 for eooe light and from \(-\)1.14138 to 1.14138 for oeeo light.  
Figure 5: Offset and displacement as function of \(i\) when \(\omega\) = 0\({}^{\circ}\): (**a**) Along \(y\) direction; (**b**) Along \(z\) direction.  
Figure 6: Offset and displacement as function of \(\omega\) when \(i\) = \(\pm\)5\({}^{\circ}\): (**a**) Along \(y\) direction; (**b**) Along \(z\) direction.  
approximates a cosine convex, increases from 0.61682 to 1.80883, and then decreases to 0.61682 for the eooe wave, and then it increases from \(-\)0.5107 to 0.60225 and decreases to \(-\)0.5107 for the oeeo wave. If \(i\) < 0\({}^{\circ}\) as \(\omega\) increases, \(\Delta_{y}\) decreases approximately sinusoidally, from 1.14089 to \(-\)1.14089 for the eooe light and from 1.14138 to \(-\)1.14138 for the oeeo light. \(\Delta_{z}\) approximates a cosine concave, decreases from \(-\)0.5107 to \(-\)1.62832, and then increases to \(-\)0.5107 for eooe wave, and decreases from 0.61682 to \(-\)0.55792 and then increases to 0.61682 for the oeeo wave. \(d_{y}\approx 0\), which proves that the two waves hardly produce relative displacements in the \(y\) direction under any circumstances. The variation in \(d_{z}\) is small and can be approximated as a constant.
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailless Flapping Wing Micro Air Vehicles
#### 3.2.4 Parameter Estimation for Linear Damping Model of DelFly Nimble  
The tailless version of DelFly, known as DelFly Nimble, was used in closed-loop modeling of the longitudinal dynamics of a tailless flapping wing robot [158]. The idea of the modeling is to investigate the effect of changing the robot physical parameters and the controller gains to the robot's flight dynamics. Due to the lack of tail that provides passive stabilization, the robot relies on the wing actuation system and active control system to provide its stability and control. The linear damping model was selected as it can approximate the aerodynamics of complex flapping wings with a minimal number of parameters in hovering and forward cruise flight (with quasi-steady assumption) that were measured in a wind tunnel, as shown in [139; 159]. It has also proven its ability in predicting the horizontal drag forces of flapping wings. In [158], vertical dynamics and pitch control mechanisms were included. The overall model structure consists of controller dynamics and open loop dynamics, which are comprised of the actuator dynamics and the longitudinal flight dynamics.  
System identification was used to determine the dihedral actuator dynamics and force coefficients, \(b_{x}\) and \(b_{z}\), which define the longitudinal and vertical components of the aerodynamic drag forces as described by the following equations.  
\[\begin{array}{l}D_{x}=-b_{x}fu,\\ D_{z}=-b_{z}fw,\end{array}\]  
where \(f\) is the flapping frequency; \(u\) and \(w\) are the longitudinal and vertical body velocities, respectively. Flight data were collected as the robot was flown in an open jet wind tunnel at various wind speeds ranging from 1 ms\({}^{-1}\) to 2.4 ms\({}^{-1}\). Both \(u\) and \(w\) were computed based on the averaged wind speed and body pitch. Linear least squares method was used to determine \(b_{x}\) and \(b_{z}\). Validation was performed by comparing the estimated state derivatives from the model with those derived directly from the flight data. The overall results show that the developed model can capture the robot's dominant dynamic effects and can be useful for initial tuning of the control parameters. The performance of this approach, however, will depend on the controller used.
## 5 Experimental Implementation and Results
### Experimental PRM, RRT and RRT\({}^{*}\) Results for Map2  
PRM, RRT and RRT* algorithms are also applied on this map, and the generated paths are shown in Figure 14. The measured experimental values like traveled distance, average velocity, travel time, and absolute error (X, Y) are shown in Figure 15.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS 28.0 statistical software (IBM; Chicago, IL, USA). To select the kinematic variables provided by RunScribe, bivariate Spearman correlations were performed beforehand, and only variables with significant relationships with the maximum speed of the test were included. Correlations between the dependent variables and maximum speed were carried out using [PERSON]'s R for continuous and normally distributed variables or [PERSON]'s Rho. Mean (M) and standard deviation (SD) or median (Mn) and interquartile range (IQ) were used as descriptive statistics. K-S or Shapiro-Wilks tests for normality and Mauchly's test for sphericity were performed beforehand. To analyze the effect of menstrual cycle phases and test intensity on variables with a normal distribution, a general linear model of repeated measures was applied, with two intrasubject independent variables, menstrual cycle phases (with three levels: Menstruation, Follicular, and Luteal) and running intensity (with four levels relative to the maximum speed reached in the test: 100%, 80%, 60%, and 50%). The tests used the F statistic of assumed sphericity or Greenhouse-Geisser with post-hoc tests with Bonferroni adjustment. For variables with non-normal distributions, Friedman tests were performed with Wilcoxon post-tests adjusted for significance according to Bonferroni. Statistical significance was adjusted for \(p\)-values lower than 0.05. As effect size statistics, eta squared was used, with values of 0.01 small, 0.06 medium, and 0.14 large, while for the Wilcoxon tests, the r statistic was used, with values of 0.5 large, 0.3 medium, and 0.3 small.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 3 System Models
### Problem Definition  
Based on the eavesdropping condition, we then define the _eavesdropping probability_ denoted by \(P(E)\) as follows.  
**Definition 2**.: _Eavesdropping Probability is the probability that at least one transmission has been wiretapped by an eavesdropper._  
From the definition we know \(P(E)\) is the probability to show how likely is any transmission eavesdropped. In order to derive \(P(E)\), we need to calculate the probability that one transmission has been eavesdropped, which is denoted by \(P_{e}\). Considering the situation that no transmission being eavesdropped will be easier than considering all the situations that a certain number of transmissions being eavesdropped. Then, we find that \(P(E)\) can be expressed by \(P_{e}\) as follows,  
\[P(E)=1-(1-P_{e})^{N}, \tag{2}\]  
where \(N\) is the expected number of legitimate users in the network.  
Another concern of this paper is to investigate the impacts of our Fri-Jam schemes on the legitimate communications. Thus, we define the transmission probability denoted by \(P(C)\) as follows.  
**Definition 3**.: _Transmission Probability is the probability that a legitimate user (transmitter) can successfully transmit with another legitimate user (receiver)._  
To ensure the legitimate transmission, we require \(\mathrm{SINR}>\beta\) at the legitimate receiver, where \(\beta\) is the threshold value of the receiving power for a successful reception. Thus, we have \(P(C)\stackrel{{\Delta}}{{=}}P(\mathrm{SINR}>\beta)\). Following a similar approach to [31], we can obtain \(P(C)\).