esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fuzzing System Design
### Fuzzing Based on the PSO Algorithm  
The key to automatic vulnerability detection is to generate high-quality samples. In target-oriented fuzzing, high-quality samples are those that can be executed to the target point. We use the PSO algorithm to generate many high-quality samples through continuous iterative learning to reduce access to the invalid path space and improve the efficiency of vulnerability detection. The ability to achieve efficient learning among multiple samples by means of the PSO algorithm can improve the convergence speed of the target orientation to focus limited computing resources on the path space that is considered to be important.  
Sample generation in fuzzing is performed continuously, which is consistent with the process of continuous iteration of the PSO algorithm. The core elements of the PSO algorithm include the particles, the positions of the particles and the values of the particles. Other elements, such as the particle speed, global optimal position and local optimal position, are calculated based on the three core elements. The samples in fuzzing are mapped to the particles in the PSO algorithm. The distance between the sample and the target point is mapped to the distance between the swarm and the target point. The degree of approximation to the vulnerable point is mapped to the weight of the particles. By the mapping operation, we design a sample generation mechanism, which is described in Algorithm 1:  
The PSO algorithm describes an iterative process that includes three steps in each iteration.  
* Update the local optimal particle: compare the fitness value of the current sample \(S[i]\) with the optimal fitness value of this sample in history \(L[i]\). If \(f(S[i])<f(L[i])\), then update \(L[i]\) to \(S[i]\). The fitness function \(f\) is used to obtain the distance between the current sample and the target point.
* Update the global optimal particle: compare each evaluation value of the local optimal samples \(L[i]\) with the evaluation value of the global optimal sample \(g\). If \(f(L[i])<f(g)\), update \(g\) with \(L[i]\).
* Generate samples: map the current sample into the template, establish the relationship between sample and vector, and then generate new sample based on Formulas (4) and (5). At this time, the sample has good guidance but lacks enough destructiveness to trigger vulnerabilities. PSOFuzzer uses mutation algorithms applied to different types of data fields under a certain probability to improve the effect of mutation. The mutation algorithms the PSOFuzzer used are shown in Table 1.  
Figure 4: Principle of sample normalization.  
\(S\): current samples, \(n\): number of samples, \(L\): local optimal samples, \(g\): global optimal sample, \(M\): Data Model, \(f\): function of calculating the fitness value of the sample.
## 3 Results
Differences across car brands are depicted in Table 6. The highest values for the front axle were recorded for Peugeots (9.27%), while the lowest were for Toyota (6.4%) (Figures 7 and 8). For the rear axle wheels, the highest values were recorded for Fiat (26.02%), and the lowest were for Ford (9.80%). Statistically significant differences were recorded for all brands except the front axle. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Brand**} & \multirow{2}{*}{**Breaking Force**} & \multirow{2}{*}{**Breaking Force**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**Breaking Force**} & \multirow{2}{*}{**Breaking Force**} & \multirow{2}{*}{**BRking Force**} & \multirow{2}{*}{**\(p\)-Value**} & **Breaking Force** \\  & & & & & **LR (kN)** & & **RR (kN)** \\ \hline \multirow{4}{*}{VW (\(n=158\))} & 6.2 (1.4) & 6.2 (1.4) & & 2.0 (0.6) & 1.9 (0.6) & & 5.1 (1.3) \\  & 2.5–11.4 & 2.5–10.0 & & 0.5–4.0 & 0.4–4.0 & 0.7681  & 2.0–10.0 \\  & 6.0 & 6.1 & & 2.0 & 1.9 & 5.0 \\  & [6.0; 6.4] & [6.0; 6.4] & & [1.9; 2.1] & [1.8; 2.0] & & [4.9; 5.4] \\ \hline \multirow{4}{*}{Opel (\(n=115\))} & 6.3 (1.6) & 6.3 (1.5) & & 1.9 (0.6) & 1.9 (0.5) & & 4.4 (1.3) \\  & 3.1–11.4 & 3.3–11.4 & 3.3–11.4 & 0.6–4.0 & 0.6–3.3 & 0.0–7.9 \\  & 6.1 & 6.2 & 1.9 & 1.8 & 1.8 & 4.5 \\  & [6.0; 6.6] & [6.0; 6.6] & & [1.8; 2.0] & [1.8; 2.0] & & [4.1; 4.6] \\ \hline \multirow{4}{*}{Ford (\(n=88\))} & 6.8 (1.8) & 6.8 (1.8) & & 2.2 (0.7) & 2.1 (0.7) & & 5.5 (1.9) \\  & 2.7–11.4 & 2.6–11.4 & 0.6–5.6 & 0.5–5.6 & 0.5–5.6 & 1.4–11.0 \\  & 6.7 & 6.8 & 2.3 & 2.2 & 2.2 & 5.3 \\  & [6.4; 7.1] & [6.4; 7.2] & & [2.1; 2.4] & [2.0; 2.3] & & [5.1; 5.9] \\ \hline \multirow{4}{*}{Toyota (\(n=72\))} & 5.7 (1.6) & 5.7 (1.6) & & 1.7 (0.5) & 1.6 (0.5) & & 4.7 (1.4) \\  & 3.0–10.0 & 3.0–10.0 & 0.5–3.3 & 0.5–3.3 & 0.5–3.3 & 0.9331  & 2.7–8.7 \\  & 5.0 & 5.4 & 1.7 & 1.6 & 6.0 \\  & [5.3; 6.0] & [5.4; 6.1] & & [1.6; 1.8] & [1.5; 1.8] & & [4.4; 5.1] \\ \hline \multirow{4}{*}{Renault (\(n=68\))} & 5.7 (1.5) & 5.7 (1.4) & & 1.7 (0.4) & 1.7 (0.4) & & 4.5 (1.3) \\  & 3.4–9.8 & 3.3–9.4 & 0.9–2.8 & 0.8–2.9 & 0.8–2.9 & 1.2–8.7 \\  & 5.4 & 5.4 & 0.9601  & 1.7 & 1.6 & 0.9445  & 4.5 \\  & [5.3; 6.0] & [5.3; 6.0] & & [1.6; 1.8] & [1.6; 1.8] & & [4.2; 4.8] \\ \hline \multirow{4}{*}{Peugeot (\(n=59\))} & 6.3 (1.5) & 6.2 (1.5) & & 1.8 (0.5) & 1.7 (0.4) & & 4.6 (1.3) \\  & 3.7–10.0 & 3.3–10.0 & 0.4–3.0 & 0.7–3.2 & 1.2–8.8 \\  & 6.0 & 5.9 & 0.4658  & 0.4–6.4 & 0.7–3.2 & 1.2–8.8 \\  & [5.9; 6.7] & [5.8; 6.6] & & [1.7; 2.0] & [1.6; 1.9] & & [4.2; 4.9] \\ \hline \multirow{4}{*}{Fiat (\(n=53\))} & 5.2 (1.9) & 5.1 (2.0) & & 1.3 (0.8) & 1.3 (0.9) & & 3.6 (1.7) \\  & 1.9–10.7 & 1.5–10.3 & 0.3–3.2 & 0.1–3.8 & 0.1–3.8 & 0.9195 [FOOTNOTE:5]Footnote 5: 8: 8:8 8:8 8:8 8:8 8:8 8:8 8:8 8:8 8:88 8:8 8:8 8:8 8:8 8:88 8:8 8:88 8:8 8:8 8 8:8 8 8:8 8:8 8 8:8 8:8 8:8 8:8 8 8:8 8:8 8 8:8 8 8:8 8 8 :8  
Figure 4: Breaking force of rear left, rear right, and parking brake (mean (SD)). Figure 5: Breaking force difference in % for front axle of 1067 cars.
# The Effect of Antagonist Abiotic Stress on Bioactive Compounds from Basil (_Ocimum basilicum_)
## 3 Results
### The Effects of Antagonistic Stress on Total Flavonoid Content  
As has been observed in Figure 5, the total flavonoid content in basil leaves decreases for all stresses compared with control from 90 \(\pm\) 10 mg rutin equivalents/mL to 39 \(\pm\) 6 mg rutin equivalents/mL (\(p\) < 0.001). There are no statistical differences between different stress treatments (\(p\) = 0.103).
# Dual-Band UWB Monopole Antenna for IoT Applications +
## 5 Results  
All of the simulations of the proposed antenna were performed on the Ansys Electronics desktop student 2022 version. We gathered simulated results from both dual-band and UWB, in which the first band is a 3.65-7.49 GHz band with a frequency of 11.5-13.4 GHz, which lies in the Ku-band with a bandwidth of 3.9 and 1.7 GHz, respectively, which lies in the C-band. The return loss of the proposed antenna is \(-\)31 dB at the resonant frequency of 4.94 GHz, which can be seen from Figure 3b. To simulate and plot the radiation pattern results in the x-z and y-z planes presented in Figure 4, we can see that the radiation pattern is both bidirectional and omnidirectional in both E and H planes, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** \\ \hline
**W** & 24 & h & 8 & l\({}_{\rm f}\) & 2.2 \\ \hline
**L** & 25 & g & 0.2 & d & 3.25 \\ \hline
**H** & 1 & w\({}_{1}\) & 0.5 & l\({}_{1}\) & 5.7 \\ \hline
**W\({}_{\rm f}\)** & 2 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of a dual-band CPW antenna.  
Figure 2: Scattering parameters of the proposed antenna (**a**) with and without slit; (**b**) variation in slit width; (**c**) variation in slit length.  
Because the radiation pattern of this antenna is both omni- and bidirectional, it can be used in any direction for multiple purposes and is highly directive. The minimum gain of the proposed antenna is 2.81 dB, and the maximum gain is 4.68 dB. Due to its radiation pattern in all directions, we can use this antenna in many applications, such as mobile towers, radar systems, and satellite communication system. The surface current distribution of a proposed antenna is shown in Figure 5 for 4.9 GHz and 12.5 GHz. The proposed antenna is compared with the literature work in Table 2.  
Figure 4: Radiation Pattern of a proposed antenna. (**a**) E and H plane at 4.9 GHz; (**b**) E and H plane at 12.5 GHz.  
Figure 5: The current distribution of a proposed antenna. (**a**) distribution = 4.9 GHz; (**b**) distribution = 12.5 GHz.  
Figure 3: Simulated results of a proposed antenna. (**a**) VSWR; (**b**) S-Parameters.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 2 Materials and Methods
### Generate Prior Boxes with K-Means++
Therefore, this study uses the k-means++ as a replacement for the k-means to optimize the prior box generation process and improve the quality of the prior boxes. The basic idea  
Figure 2: The process of generating prediction boxes. of k-means++ for selecting the initial clustering center is that samples further away from the existing clustering center are preferentially selected as the next clustering center. The detailed process is as follows:  
1. Randomly select an initial clustering center from the size set C, composed of the crop pest sizes contained in the dataset labels. 2. Calculate the minimum distance \(d_{i}\) of each sample in the size set C from the existing clustering centers. 3. Calculate the probability \(p_{i}\) of each sample being selected as the next initial clustering center, as shown in Equation (5), and select the next initial clustering center with the roulette wheel method. \[p_{i}=\frac{d_{i}}{\sum_{i=1}^{N}d_{i}}\] (5)
4. Repeat step 2 and step 3 until k initial clustering centers are selected. To perform k-means++ clustering for the pest sizes contained in the dataset labels, we first coded a script that reads the xml label files in VOC format. The script used the xml python library to read the coordinates of the pests contained in the xml label files. Then, the pest coordinates are converted to pest sizes by another python script. Finally, the initial clustering center selection process and the clustering process of the k-means++ method is completed with the numpy library in the third python script. It must be noted that the k-means++ method uses the same clustering process as the k-means method. The above three scripts were designed as a continuous workflow to cluster the pest sizes contained in the dataset labels. The nine prior boxes were generated by this workflow. Since the more distant samples from the existing clustering centers occupy a larger cumulative probability interval, the initial clustering centers selected with the roulette wheel method are more widely distributed in the size space of the samples. Hence, the prior boxes generated by k-means++ clustering are better matched with the dataset, which is beneficial for improving the crop pest detection precision in multiple sizes.
# Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter
## 2 System Composition  
### System Structure  
The system structure of the battery surface and edge defect inspection based on sub-regional Gaussian and moving average filtering mainly include linear array charge-coupled device (CCD) camera, light source, and industrial personal computer (IPC) with screen and test samples (Figure 2a). The uncertain geometrical defects on the battery's surface (especially the concave and convex defects) are the drawbacks of the battery surface defect inspection. The battery's test samples (Figure 2b) are made of metal, and uneven illumination may occur during the test. The linear array light-emitting diode (LED) [23] and linear array CCD camera [24] have been used to avoid these effects during image acquisition and defect inspection. The linear array LED light source is used to provide suitable illumination. The linear array CCD camera continuously scans the battery's surface to form a uniform 2D image that can complete image acquisition on the entire battery's surface. The image of the battery's surface collected through this approach is rectangular in shape. This shape easily and accurately segments the defects. Feature extraction is convenient, and the accuracy of detecting the battery's microdefects is improved.  
The linear array CCD camera has higher inspection precision and lower cost than the face array CCD camera when inspecting images with the same size and resolution. The linear array CCD camera has high scanning speed, high frequency response, and can realize dynamic measurement and work under low illumination intensity.  
The images are acquired using one \(2048\times 1\) pixel linear array CCD camera produced by Teledyne DalSA. The pixel size of the camera is \(7.04~{}\upmu\mathrm{m}~{}\times 7.04~{}\upmu\mathrm{m}\), the maximum line frequency can reach up to 80 KHz, and sensitivity is \(320~{}\frac{\mathrm{DN}}{\left(\frac{n\mathrm{I}}{cm^{2}}\right)}\), \(12~{}\mathrm{bit}\), \(1\times gain\), and supports multiple regions of interest (ROI) functions.
## 2 Design of the Generator  
### Structure of the Electric Pulse Generator  
The block diagram of the Marx generator is shown in Figure 1. In the developed topology of this device, diodes are used for charging capacitors and active semiconductor switches (thyristors) for discharging. Compared to Marx generators that are charged with resistors, diode charging proves to be more efficient as it reduces energy loss by up to 50% compared to charging via resistors [29].  
Figure 1: Block diagram of the two-range, 12-stage Marx generator.  
The Marx generator, designed to generate electrical pulses with different durations, consists of two modules (A2 and A3) equipped with different capacitors (\(C=0.25\)\(\upmu\)F and \(C=1\)\(\upmu\)F). Each module consists of 12 stages, including capacitors, discharge switches (thyristors with drivers), charging diodes, and measuring units for the output voltage (A10 and A12) and current (A11 and A13). Both modules are housed in a common casing, supplied by the same power supply (A1), and controlled by a single control unit.
## 3 Nonlinear Motions of the Brush Seal Rotor System
### Parameter Maps of SN and PD  
Since the saddle node and period-doubling bifurcation have the most influence in the nonlinear motions of the brush seal rotor system, Figure 11 depicts parameter maps for stable and unstable domains of saddle node and period-doubling bifurcations. Figure 11a displays the parameter maps of the period-doubling bifurcations of nonlinear motions. The maps for unstable nonlinear motions are depicted by the blue and black, while the stable motion domain is depicted by green. The boundaries are period-doubling bifurcations for producing period-2 motions. On the boundaries, the period-1 motions are turned to period-2 motions. Under such parameters, the left unstable domain disappears at \(\Omega=2.0520\) with \(e=0.0024\). The lower left period-doubling bifurcation domain disappears at \(\Omega=1.9809\) with \(e=0.00494\). For the right side, the unstable domain increases as the rotational speed increases.  
The parameter map for the saddle node bifurcations is shown in Figure 11b. The purple represents the unstable map while the green represents the stable map. The saddle node bifurcations are located on the boundaries between the stable and unstable maps. On such boundaries, the nonlinear motions of the brush seal rotor are characterized with a chattering phenomenon. Safe operation should be considered to avoid damage to the rotor system.  
Figure 10: Semi-analytical Poincare map of the nonlinear period-2 motions in the rotor system within \(\Omega\in(10.1,12.6)\): **(a)** Poincare displacement, \(x_{1,k}\); **(b)** Poincare velocity, \(x_{2,k}\); **(c)** Poincare displacement, \(y_{1,k}\); **(d)** Poincare velocity, \(y_{2,k}\); \((k=0,1,2,\cdots,)\).
## 6 Parametric Study  
### The Effect of Hydraulic Conductivity Anisotropy  
The permeability of an aquifer is a significant hydrogeological parameter that describes its hydraulic conductivity, which greatly influences the effectiveness of excavation pit dewatering: a higher permeability coefficient implies easier groundwater migration within the aquifer. Due to the natural sedimentation processes, aquifer permeability often exhibits anisotropic characteristics. In order to investigate the impact of this anisotropy on dewatering effectiveness, Figure 7 plots the horizontal distribution of the dimensionless drawdown at the top of the anisotropic confined aquifer with different anisotropic degrees (\(k=k_{\rm h}/k_{\rm v}\)). As can be seen, the anisotropy of the confined aquifer has a significant impact on the dimensionless drawdown near the cut-off wall, and this effect diminishes with increasing horizontal distance. With the increase in the anisotropic degree (\(k=k_{\rm h}/k_{\rm v}\)), the dimensionless drawdown gradually decreases and tends to become more uniform. It is noteworthy that, with the increase in the degree of anisotropy, which enhances the permeability in certain directions, the pressure transmission within the aquifer becomes more efficient. This, in turn, strengthens the hydraulic connection between the aquifer and the lateral boundaries. Consequently, under constant pumping rates, the variation in water level within the aquifer due to the dewatering of the excavation becomes less pronounced. In natural aquifers, sedimentation leads to particle alignment, resulting in aquifers often exhibiting anisotropy. However, conventional dewatering designs for excavations typically use permeability coefficients derived from pumping tests that do not differentiate between horizontal and vertical directions. Therefore, when dewatering confined aquifers, it is essential to consider the impact of this anisotropy. In specific scenarios, such as the one in this analysis, the dewatering effectiveness under designed pumping rates may deviate from expectations due to this anisotropic behavior.  
Figure 6: Flow lines and isopotential lines resulting from dewatering in the pit with the presence of a cut-off wall in the confined aquifer.
## 5 Discussion  
In this work, we demonstrated the effectiveness of applying deep learning models to segment arteries in PC MRI images and compute hemodynamic parameters. Specifically, we evaluated the effectiveness and accuracy of using both magnitude and phase parts of the PC MRI data as input for the deep learning model. The inclusion of phase contrast images gave the model the ability to measure flow velocity and volume within arteries of interest. Our data revealed that the model can efficiently segment the arteries of interests with low false positive rates and high DSC. Furthermore, all three models accurately estimated volume flow for healthy controls with Nested U-Net and Attention U-NET showing marginally higher accurate estimations than the Standard U-NET. The novelty and main contributions of this work include that (1) we utilized both magnitude and phase components of the MRI data for deep learning based segmentation tasks; and (2) our models were capable of detecting relatively small anatomical features in MRI data.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 3 Materials and Methods
### Discussion
Our filter bank gating network algorithm, though, does not require significant computations beyond the filtering algorithms because it is largely based on parameters already computed in the filters (e.g., predicted measurement residuals). For ideal operation, it would require some history of how the weights evolve over time, but this can be tunable based on the allocated computational resources. Our simulations in Section 4 were not produced with a flight-like code architecture, so we do not provide computational load metrics as this would not be representative of an actual on-board implementation. This computational performance is likely to be application-specific too, so it is left as future work to quantify this for specific applications of interest. Our algorithm, as designed, is meant to identify the best-performing (in a statistical sense) filters within a filter bank. These filters will be given the largest weights, while the poorer performing filters will be assigned lower weightings. If all filters are properly performing, then we expect that the weights for each filter will be essentially equal. When this is not the case, we take this as a signal that some anomaly may be occurring within the system. By investigating which filters are performing best and which are performing worst, we can generate an informed hypothesis about what caused the anomaly. Examples of how this gating network may be applied to the problem anomaly detection and characterization in measurement-fused spacecraft navigation are provided in Section 4.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 2 Experimental Setups  
Two listening experiments were designed to evaluate the influence of BRIRs with different room-related auditory information on externalization in real (desktop-based experiment) and visual-only virtual (VR-based experiment) listening environments, respectively. The experimental setups were chosen to simulate common use cases of binaural listening. In this study, Oculus Rift was used as the VR headset for the VR-based experiment.