esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation-Based Example and Feature Down Selection
Moreover, as seen in Figure 20, the band-pass filter effects can only provide moderate differentiation between the spoofer and GNSS. These results, at a large degree, imply that the phase noise and DAC non-linearity are 'weak' features in the GNSS RFF context, while PA and I/Q imbalance, as well as BPF to some extent, are'strong' features. This is also qualitatively illustrated in the next section. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \hline Observation interval (ms) & 2 \\ \hline Galileo band & E1 \\ \hline Intermediate frequency (MHz) & 61.38 \\ \hline Maximum Doppler shift (kHz) & 5 \\ \hline TX filter bandwidth (MHz) & 100 \\ \hline \hline \multicolumn{2}{c}{**Parameters Used in Genuine GNSS Simulator**} \\ \hline DAC phase noise & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & \(1\) & \(-90\) \\ \hline DAC non-linearity & \(y=x-0.0038x|x|^{2}\) \\ \hline Clock unit phase noise & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & \(1\) & \(-95\) \\ \cline{2-3}  & \(10\) & \(-125\) \\ \cline{2-3}  & \(100\) & \(-135\) \\ \hline Clock unit non-linearity & Ignored \\ \hline \multirow{4}{*}{Up-conversion unit phase noise} & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & \(1\) & \(-50\) \\ \cline{2-3}  & \(10\) & \(-70\) \\ \cline{2-3}  & \(100\) & \(-95\) \\ \cline{2-3}  & Amplitude (dB) & Degree \\ \cline{2-3}  & \(1\) & \(3\) \\ \hline Band-pass filter & See Figure 5(a) \\ \hline \multicolumn{2}{c}{**Parameters Used in Spoofer Simulator**} \\ \hline DAC phase noise & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & \(10\) & \(-50\) \\ \cline{2-3}  & \(100\) & \(-70\) \\ \cline{2-3}  & \(500\) & \(-85\) \\ \hline DAC non-linearity & \(y=x-0.05x|x|^{2}\) \\ \hline \multirow{4}{*}{LO phase noise} & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & Frequency offset (Hz) & Level (\(\text{dBc}/\text{Hz}\)) \\ \cline{2-3}  & \(1\) & \(-80\) \\ \cline{2-3}  & \(10\) & \(-110\) \\ \cline{2-3}  & \(100\) & \(-135\) \\ \hline \multirow{2}{*}{Mixer I/Q imbalance} & Amplitude (dB) & Degree \\ \cline{2-3}  & \(3\) & \(5\) \\ \hline Band-pass filter & See Figure 5(b) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters in simulation. Figure 19: The illustration for normalized confusion matrix. FN is short for false negative rate, FP is short for false positive rate, TN is short for a true negative rate, TP is short for a true positive rate. Figure 20: _Count._
## 6 Conclusions  
This study presents the methodical formulation of an innovative RLCA framework for robust optimal longitudinal motion control of UAVs, enhancing its trajectory tracking precision and disturbance rejection capacity. The baseline elevator position control procedure is realized by implementing a fixed-gain LQI tracking control law. However, the baseline LQI controller is limited by its inability to adapt to disturbances and changing flight conditions, resulting in either excessive control effort or poor tracking performance. Therefore, the baseline control procedure is supplemented by augmenting it with a learning-based adaptative control law that employs self-adjusting dissipative, anti-dissipative, and model-reference tracking control terms. The resulting RLCA scheme dynamically modulates its learning gains, ensuring robust disturbance rejection, smoother transitions, reduced control energy consumption, and enhanced stability during the vertical (longitudinal) flight phase of the UAV. By dynamically balancing aggressiveness and conservatism in control actions, the RLCA effectively reduces overshoot, enhances convergence speed, and ensures robust trajectory tracking, making it a more reliable choice for UAV longitudinal motion control. The proposed control algorithm is benchmarked against the baseline LQI controller by performing credible MATLAB simulations. The simulation results verify that the structured adaptability allows the RLCA to outperform the traditional LQI control in managing the UAV's longitudinal motion under bounded exogenous disturbances.  
Several potential research directions can be explored in the future to further enhance the robustness of the proposed RLCA for UAV longitudinal motion control. The effectiveness of the RLCA framework can be further validated through hardware-in-the-loop flight experiments to assess its practical feasibility under real aerodynamic conditions and actuator constraints. The hyperbolic gain scaling mechanism of the learning gains can be replaced by fuzzy inference systems, neural networks, or other computational intelligence algorithms. To validate the RLCA's adaptability to payload variations, tailored simulations can be conducted that involve variations in the system's stability derivatives to alter the UAV's mass distribution. Finally, the proposed controller design can be extended by employing data-driven and reinforcement-learning-based schemes to analyze the system's adaptability, practicality, and computational complexity in real-time applications.  
Conceptualization, O.S.; methodology, O.S.; software, O.S. and A.T.; validation, J.I.; formal analysis, O.S. and A.T.; investigation, A.T.; resources, J.I.; data curation, A.T.; writing--original draft preparation, O.S.; writing--review and editing, J.I.; visualization, A.T.; supervision, J.I.; project administration, J.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data supporting the conclusions of this article will be made available by the corresponding author on a reasonable request.  
The authors declare no conflicts of interest.
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2.4 Risk Acceptability Criteria  
Risk acceptability criteria will help to identify whether the threats and vulnerabilities are acceptable or unacceptable based on a set of criteria defined by the security and privacy evaluation team. There are no standard guidelines available to define the set of criteria. However, the team can consider various factors while defining the criteria such as:  
* The organization's goals and objectives.
* Business operations.
* Application use case and technology stack used for developing the application.
* Legal and regulatory aspects.
* Budget and time for developing the application.  
Table 10 outlines the risk acceptability criteria based on the risk score calculated using the qualitative approach. The proposed criteria treat the risks with a low or very low score as acceptable risks, and rest as unacceptable risks. If required, the evaluation team can make adjustments to the selection criteria. Finally, all unacceptable and acceptable risks with the rationale need to be documented in the security and privacy risk assessment report.
## 3 Results and Discussion
### Comparison of Animation Rendering Results for 3D Models Generated Using Three Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Project** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Contrast the** \\ **Object** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Single-Frame** \\ **Time (ms)** \\ **Mean \(\pm\) SD** \\ **[95\% CI]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average FPS** \\ **Mean \(\pm\) SD** \\ **[95\% CI]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GPU Peak (\%)** \\ **Mean \(\pm\) SD** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CPU (\%)** \\ **Mean \(\pm\) SD** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Significance vs.** \\ **Traditional** \\ \end{tabular} } \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Blender \\ Platform \\ \end{tabular} } & TPOT-based fur simulation algorithm & 43.48 \(\pm\) 4.2 [39.8–47.2] & 23 \(\pm\) 1.8 [21.2–24.8] & 34 \(\pm\) 3.1 & 30 \(\pm\) 2.5 & \(p\) = 0.024 \\ \cline{2-6}  & Particle hair algorithm & 166.67 \(\pm\) 12.5 [15.2–182.1] & 6 \(\pm\) 0.9 [5.1–6.9] & 44 \(\pm\) 4.3 & 95 \(\pm\) 4.8 & \(p\) < 0.001 \\ \cline{2-6}  & Traditional texture mapping algorithm & 47.46 \(\pm\) 3.9 [43.6–51.3] & 24 \(\pm\) 1.7 [22.3–25.7] & 30 \(\pm\) 2.8 & 24 \(\pm\) 2.1 & Baseline \\ \hline \multirow{4}{*}{
\begin{tabular}{c} UnityAR \\ Platform \\ \end{tabular} } & TPOT-based fur simulation algorithm & 7.69 \(\pm\) 0.8 [6.8–8.5] & 130 \(\pm\) 5.2 [124.8–135.2] & 31 \(\pm\) 2.6 & 25 \(\pm\) 1.9 & \(p\) = 0.63 \\ \cline{2-6}  & Particle hair algorithm & 15.15 \(\pm\) 1.5 [13.6–16.7] & 66 \(\pm\) 3.7 [62.3–69.7] & 40 \(\pm\) 3.4 & 88 \(\pm\) 5.2 & \(p\) < 0.001 \\ \cline{2-6}  & Traditional texture mapping algorithm & 7.35 \(\pm\) 0.7 [6.6–8.1] & 136 \(\pm\) 4.8 [131.2–140.8] & 27 \(\pm\) 2.3 & 21 \(\pm\) 1.7 & Baseline \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of experimental results providing empirical support and a theoretical foundation for subsequent research. Figure 6: Performance of the white-lipped deer model rendered on a mobile device using the proposed method. head, making it particularly well-suited for mobile deployment and the broader promotion of realistic digital wildlife representations of the Sanjiangyuan region to the public.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Gesture Recognition (HGR)
#### 3.3.3 Posture-Based Gesture Classification Module  
We design a network model using a multilayer perceptron (MLP) [33] to classify posture-based gestures from keypoint sequences. The MLP is a feedforward neural network consisting of an input layer, hidden layers, and an output layer. Generally, the input layer size corresponds to the number of input features, while the output layer size equals the number of classes. The number and width of the hidden layers are crucial factors that directly impact the performance of the MLP. By adjusting the number of hidden layers and their sizes, the MLP can be scaled to handle a larger set of hand gestures, enhancing its generalization capabilities. These hyperparameters are determined based on the complexity and variability of the training data. The general MLP model with \(N\) hidden layers is expressed as follows:  
\[h_{1} =\sigma(\text{Dropout}(W_{1}p+b_{1})) \tag{9}\] \[h_{2} =\sigma(\text{Dropout}(W_{2}h_{1}+b_{2}))\] (10) \[\vdots\] (11) \[h_{N} =\sigma(\text{Dropout}(W_{N}h_{N-1}+b_{N}))\] (12) \[h_{\text{FC}} =W_{\text{FC}}h_{N}+b_{\text{FC}}\] (13) \[\text{output} =\text{softmax}(W_{\text{out}}h_{\text{FC}}+b_{\text{out}}), \tag{14}\]  
where \(p\in\mathbb{R}^{D}\) denotes the input tensor of dimension \(D\); \(W_{k}\) and \(b_{k}\) are the weight matrix and bias of the \(k\)-th layer, respectively; and \(h_{k}\) represents the features at this layer. Each layer's transformation, involving matrix multiplication and summation, is performed by a fully connected (FC) layer that connects all inputs to all outputs. Dropout [34] is applied during training to randomly set a fraction of the activations to zero, helping to prevent  
Figure 4: An illustration demonstrating the manipulation of the yaw axis of the drone. When the hand rotates counterclockwise, the positive rotation angle causes the drone to turn left along the yaw axis. Conversely, the drone turns right if the rotation angle is negative.  
overfitting. \(\sigma\) is the activation function, set to ReLU [35]. After \(N\) hidden layers, an FC layer aggregates and transforms the features into a final representation before classification. Finally, softmax [36] is applied to the output layer for classification.  
An overview of our model is shown in Figure 5. The input layer consists of 42 nodes, representing the \((x,y)\) coordinates of the 21 keypoints. The output layer has ten nodes, corresponding to the ten posture-based gestures defined in Section 3.2. Generally, increasing the number of hidden layers and nodes can improve the model accuracy but also increases the training time and computational requirements. To achieve high classification accuracy for the ten classes while maintaining efficiency for edge device deployment, we apply hyperparameter optimization using a grid search [37] to determine the optimal number of hidden layers and the width of each layer. The implementation details are provided in Section 4.2.2.
## 5 Analyses and Simulation Results
### Analyses on the Fair Camera Node Selection (FCNS) Algorithm  
In this subsection, performance and fairness of the presented node selection algorithm in subsection 4.2.2 is simulated and analyzed. Coverage Data Structure related to the high-resolution image in Figure 8 which is produced by stitching low-resolution images in Figure 7 are used in these simulations. At first, using a random selection for uncovered grid cells instead of selecting them sequentially is analyzed. It is assumed that Sink sends some queries to the specific cluster head and the standard deviation of camera sensor activation times are computed for each group of the queries. The computational results are shown in Figure 9. Using the random selection for uncovered grid cells or selecting them sequentially (pseudo-random) has no difference in results. Whereas computational overhead in pseudo-random selection is less than random selection, it is suitable for presented camera selection algorithm. Figure 8: High-resolution and wide stitched image in sink. Figure 9: Computing standard deviation of camera activation times for each group of the queries. Simulation results in Figure 9 show that the presented algorithm has low standard deviation in camera sensor activation times (less than 1.0). Consequently, this algorithm is fair enough and selects camera sensor nodes in each group of queries almost equal times. However, to show that equalization of camera sensor activation times is useful in achieving a high-resolution image with complete coverage for a longer time, a greedy set cover algorithm [48] is compared with presented algorithm. The aim of greedy set cover algorithm is to cover entire desired area with minimum number of cameras which is named Minimum Camera Node Selection (MCNS). In this comparison, it is assumed that Sink sends multiple groups of queries to the specific cluster head and numbers of selected camera sensors are computed for each group of queries. The results of these computations are shown in Table 2.  
Analyzing the results in Table 2 shows that for each group of the queries the average number of selected nodes by MCNS algorithm is lower than presented algorithm. But, often, MCNS algorithm selects sensor nodes that cover more grid cells than the other nodes. Therefore, some nodes are used more than others. Unbalanced usage of the camera nodes causes energy to diminish faster for nodes
## 4 Materials and Methods for Designing Tether-Deployed Constellations  
### Conceptual Design  
#### Conceptual Example \"SwingSat\"  
Current research is exploring the potential of METs to enhance satellite constellation deployment. The scope of this work primarily revolves around three major task areas given below.  
1. What are some best practices for performing satellite constellation deployments with tethers?
2. How should constellation spacecraft made for deployment by tethers be designed?
3. How does the performance of tethered constellation deployment compare against traditional deployment methods?  
Of these, task areas 1 and 2 have received the most effort to date, and work is ongoing relating to task area 3. A summary of results from investigating task areas 1 and 2 make up the majority of the rest of this article, focusing on orbital maneuvers and spacecraft design. Future work will be published upon further progress in all three task areas.  
For this article, a conceptual satellite constellation deployed by tethers will be discussed, and referred to by the name \"SwingSat\". This constellation is not a system currently being produced, but is solely for referring to a MET-based system when discussed in parallel with existing systems. It is primarily to help give a name and a visual representation to the reader for a system utilizing the concepts discussed throughout this article. An example of this system has been produced in the form factor of a 3U CubeSat, which is composed of two 1.5U halves, one being a \"driver\" hosting a motor drive system that rotates the \"deployer\" half, which hosts one or more sets of symmetrical tether deployment and release mechanisms for a set of small payloads. In operation, this system could spin up both halves in opposing directions (conserving angular momentum), extend the tethers (in this case,tape measure booms), then upon proper alignment, speed, and timing, release the tethers and payloads. A representation of this system is shown in Figure 1. Note that this design is purely conceptual and as a visual aid to assist in explaining the concept.
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.3 Higuchi  
The Higuchi fractal dimension (HFD) [53] quantifies the intricacy or roughness of a signal by observing how its characteristics evolve through progressive downsampling, as outlined in Equations (8)-(10):  
\[HFD=-\text{slope}(\log(x),\log(L)) \tag{8}\]  
\[L_{k}=\frac{1}{k}\sum_{m=0}^{k-1}\left(\frac{N-1}{k(N-m-1)}\sum_{i=1}^{\lfloor \frac{N-m}{k}\rfloor}\lvert\text{data}[m+ik]-\text{data}[m+(i-1)k]\rvert\right) \tag{9}\]  
\[x=[1,2,\dots,k_{\text{max}}] \tag{10}\]  
In this context, \(HFD\) indicates the computed Higuchi fractal dimension. The parameter \(L_{k}\) denotes the average curve length computed over \(k\) subsets, where \(k\) is an integer specifying the interval used in the calculation of \(L_{k}\). The maximum value of \(k\), referred to as \(k_{\text{max}}\)establishes the various scales for analyzing the time series. Additionally, \(N\) represents the total number of data points, and \(m\) ranges from 0 to \(k-1\). The curve length corresponding to a specific scale \(k\) and starting point \(m\) is represented by \(L_{m,\ k}\), with \(i\) indexing the summation over \(N-m\) data points. Here, the term \"data\" refers specifically to the EEG time series under examination, whereas \(x\) is an integer sequence from \(1\) to \(k_{\text{max}}\), used alongside \(L\) to determine the slope.  
In a previous study [36], the theoretical formulation of HFD remained valid, but an issue in the implementation of the algorithmic function caused negative values due to an incorrect sign convention in the log-log regression step. To address this, a refined function is developed in this study. The new implementation ensures mathematically valid fractal dimension estimation by correctly applying log-log scaling and regression without introducing sign errors. This refined method is used in all fractal feature combinations that include Higuchi FD, ensuring consistency across different experiments.
## 4 Simulation Results  
In this section, we present the simulation results for the temporal response and BER performance of a LoS UWOC system in the presence of multipath attenuation. Table 3 lists the coefficients, symbols, and associated values for all the important parameters used for temporal dispersion characterization and MC-based channel simulation. The system consists of a single transmitter  
Figure 5: Comparison of the magnitude response of harbor water under various aperture sizes and FoVs for a 3 m LoS link.  
and a receiver established in three different water types, namely harbor, coastal, and ocean. In our simulations, we have considered a 3 m device-to-device distance, keeping in view the highly scattered nature of underwater channels and LoS conditions. The optical beam is modulated and detected symbol-by-symbol using Intensity Modulation/Direct Detection (IM/DD). All photons have been transmitted initially using the same transmit power, i.e., unity, and the same beam divergence of 1.5 mrad. On the receiver end, the apertures and FoVs are varied for depicting various device sizes. Although, accurate pointing and tracking is not fully achievable underwater, but with the use of a collimated light beam, the LoS geometry is not affected by severe temporal dispersion to the point where multiple scattering dominates. This is why we see a lower degree of temporal dispersion in less turbid waters.
## 2 Simultaneous Localization and Mapping
### Data Association
\]  
where \(H_{a,i}\) are the Jacobians of the observation model with respect to the landmark pose and \(H_{p,i}\) are the Jacobians of the observation model with respect to the robot pose. Therefore, the multiplication of \(HPH^{T}\) results in the following:  
\[HPH^{T}= \begin{bmatrix}H_{a_{0}}^{T}C_{l_{0}}H_{a_{0}}&0&0&\cdots&0\\ 0&H_{a_{1}}^{T}C_{l_{1}}H_{a_{1}}&0&\cdots&0\\ 0&0&H_{a_{2}}^{T}C_{l_{2}}H_{a_{2}}&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&H_{a_{n-1}}^{T}C_{l_{n-1}}H_{a_{n-1}}\end{bmatrix} \]  
which is a block diagonal matrix, where on the principal diagonal we have \(2\times 2\) symmetric matrices. Therefore, the inversion of the matrix is, in turn, a block diagonal matrix whose principal diagonal has the inverse of each covariance matrix. Consequently, the calculation of the joint compatibility as in Equation (3) can be computed in the form of a sum, as in the following:  
\[D^{2}=\sum_{l=1}^{N_{a}}h_{l}^{T}(H_{a_{l}}^{T}C_{l}H_{a_{1}})^{-1}h_{l}\]