esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section
### Distance and LED-OFC Recognition Result Analysis  
As mentioned before, the change of distance will lead to the change of fringe number. What is more, the light intensity also decreases as the distance increases, which will have an impact on the final recognition accuracy. It is worth mentioning that, for each LED-OFC, the absolute width of the stripes at different distances does not change, since the focal length does not change at different distances. An experiment is designed to explore the impact of the distance and other parameters was conducted as follows: The phase difference was 0\({}^{\circ}\), the encoding sequence was \"01\", the duty ratio was 50%, and different frequencies were used to generate different LED-OFC ID at each distance (500 Hz-6500 Hz with interval of 400 Hz). We acquired about 3000 LED-OFC images for training and 300 images for testing at each distance. The experiment results are shown in Figure 8.  
The recognition accuracy fluctuated little at the initial stage until the distance exceeded 3.5 m, and then began to decline rapidly, as seen above. This indicates that the proposed method can achieve much greater distance than the traditional modulation and demodulation method, which can only achieve 10 cm to 50 cm transmission distances. As mentioned before, as the distance increases, the number of stripes decreases, which causes the transmitted data frame structure to be lost, so the transmission distance is strictly limited. For example, the number of stripes cannot less than 8 if the transmission encoding sequence has 8 bits, otherwise a complete packet cannot be received, which will make the transmitted data incorrectly demodulated. The above experiment result proves that the proposed method does not have this limitation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The focal length/mm & 4.25 \\ The resolution of the camera & 4032 \(\times\) 2448 \\ The exposure time of the camera/ms & 0.1 \\ The ISO of the camera & 100 \\ The aperture of the camera & F1.7 \\ The diameter of the LED downlight/cm & 6 \\ The power of each LED/W & 9 \\ Current of each LED/mA & 85 \\ Voltage of each LED/V & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware parameters of the experiments.  
Figure 8: Recognition accuracy rates at different distances.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
###### Abstract
Decompiled code is only an approximation of pseudo-code, and most symbols, such as variables and function names, may be compressed and optimized. Furthermore, due to compiler optimization, some decompiled pseudo-code can be incomplete. Decompiled pseudo-code only reflects the program's behavior and is unsuitable for directly recompiling binary files, making it challenging to conduct BOF vulnerability mining using static code scanning tools. Secondly, the code base for ADASs is huge, and, hence, a manual analysis is time-consuming. The scale of these code bases can encompass millions of lines of code, which makes it arduous for humans to fully comprehend the intricate interdependencies and linkages between different components of the code. Additionally, uncovering and evaluating potential vulnerabilities in code necessitate an in-depth understanding of the programming language, architecture, and intended system behavior. Moreover, a manual analysis requires an exhaustive inspection of every line of code, which is time-intensive, especially considering the several iterations required to ensure the complete coverage of the code base. Moreover, a manual analysis is susceptible to human error, and it can be time-consuming to identify subtle or concealed vulnerabilities that may escape human observation.
## 5 Simulation Results
### Simulations of Wave Propagation  
One-dimensional simulations of wave propagation are considered in a homogeneous FO medium, with the values of \(\alpha\) slightly below the unit value. The source signal, in the form of a Gaussian-modulated sinusoidal pulse (presented in Figure 2), is injected \(0.1\,\mathrm{\SIUnitSymbolMicro m}\) from the left boundary of the computational domain and propagates rightwards.  
Figure 1: Stability limit for varying parameter \(\alpha\) (\(\Delta z=0.02\,\mathrm{\SIUnitSymbolMicro m}\)).  
Figure 2: Source-signal waveform.  
Results of the FDTD simulations in the form of the electric-field waveforms measured at the distance \(10\,\mathrm{\SIUnitSymbolMicro m}\) from the source are presented in Figure 3.  
In the simulation with \(\alpha=1\), the pulse propagates without deformation. It is expected, because this case is equivalent to wave propagation in a vacuum. In other cases, wave attenuation and distortion are observed, which is consistent with the results in [22]. Furthermore, the pulse maximum is also observed earlier than in a vacuum. All the described effects (i.e., wave attenuation, dispersion, and increased group velocity) increase with the decrease in the parameter \(\alpha\).  
The obtained results are compared with the results of the frequency-domain method presented in [22]. The electric-field waveforms are shown for different measurement positions in Figure 4. There are visible differences in the field values between the results of both methods, which diminish when the spatial-step size \(\Delta z\) is decreased for FO FDTD.  
The differences between the waveforms are visible when the spatial resolution is insufficient in FO FDTD. It is clear, however, that the results of the FO FDTD simulations converge to those obtained using the frequency-domain method when the spatial-step size \(\Delta z\) is decreased. This confirms that the FO FDTD simulation method is developed correctly.
## Materials and Methods  
### Geographical Area of Interest  
The geographical area of interest on which the present study was focused is the upper reaches of the Teles Pires River within the Amazon basin, located in the state of Brazil (refer to Figure 1). The upper Teles Pires River basin encompasses a drainage region of 14,030.98 km\({}^{2}\), with average elevation of 455.6 m, average slope of 0.03 mm\({}^{-1}\), maximum altitude of 895 m, and minimum altitude of 272 m (refer both to Figures 1 and 2) [30, 51].  
The confluence of the Teles Pires and the Juruena Rivers gives rise to the Tapajos River, a significant tributary of the Amazon River. Moreover, the upper Teles Pires River traverses the Amazon biomes, regions where the primary economic activities are represented by  
Figure 1: Map representation of the upper Teles Pires River basin. On the left side, a map of the state of Brazil delineates the boundaries of all its 27 federal states. In particular, the Teles Pires River basin extends across the states of Mato Grosso and Pará. On the right side, both the entire basin and the upper basin of the Teles Pires River are represented, where the latter one is reported with latitude and longitude coordinates. The figure reported by [PERSON] et al. [30] under the terms of the Creative Commons Attribution License—CC BY 4.0 ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) accessed on 1 September 2024).  
agriculture and livestock farming, producing commodities such as soybeans, corn, and cotton [52]. The considered basin is even a significant source of electrical power due to the presence of three sequentially built HPPs [31, 36]: moving downstream, the first HPP is Sinop, followed by the Colder Pesqueiro do Gil HPP, and finally, the Teles Pires HPP [36].  
The upper Teles Pires River basin region primarily experiences a tropical hot and humid climate. The mean annual precipitations in the latter region range between 1706.7 mm and 2036.2 mm, and the peak rainfall occurs from October to April (rainy season), while the lowest precipitation period occurs from May to September (dry season). The monthly rainfall in June, July, and August is typically less than 20 mm, with 0 mm being the most common registered value. Furthermore, the average monthly air temperature in the considered region varies from 23.0 \({}^{\circ}\)C to 25.8 \({}^{\circ}\)C, with an annual mean of 24.7 \({}^{\circ}\)C [19, 53].
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 2 Preliminaries
### Deep Neural Networks
#### 2.1.1 Recurrent Neural Networks (RNNs)  
An RNN is a type of DNN that has an architecture adapted to the processing of sequential data. It works on the principle of recording the previous outputs in memory to generate the subsequent output [29]. A simple RNN faces problems known as gradient vanishing problems when it processes data that have long-term dependencies [30; 31]. Thus, RNNs were developed with a more complex architecture type called the long short-term memory network (LSTM) to manage these problems.  
The LSTM has some advantages and disadvantages, which are listed as follows:  
**Advantages:**  
1. Sequential Processing: LSTMs are well-suited for tasks involving sequential data, making them effective for sentiment analysis, where the order of words in a sentence can be crucial [30].
2. Capturing Temporal Dependencies: LSTMs can capture long-term dependencies in sequences, which can be beneficial for understanding the context and sentiment in a sentence [30].
3. Interpretability: LSTMs process input sequentially, which can make it easier to interpret the model's decision-making process, as you can trace the flow of information through the time steps [29].
4. Smaller Datasets: LSTMs can perform reasonably well with smaller datasets, which is advantageous when labeled sentiment analysis datasets are limited [31].  
**Disadvantages**:  
1. Limited Parallelization: LSTMs process sequences sequentially, limiting parallelization during training, which can result in longer training times [29].
2. Difficulty with Long-Range Dependencies: While LSTMs are designed to capture long-term dependencies, they may still struggle with very long-range dependencies in sequences [30].
## 2 Methods and Background  
### Flow and Mixing Characteristics at a Confluence  
#### 2.1.1 Separation and Recirculation Zone Downstream of a Confluence  
The momentum flux in a tributary channel may cause the flow to detach as it joins the main channel, creating a recirculation zone as a result of low pressure. When two water bodies with the same density are combined into one, the momentum flux ratio is calculated using the following equation:  
\[M_{r}=\frac{Q_{l}U_{l}}{Q_{m}U_{m}} \tag{1}\]  
where \(M_{r}\) is the momentum flux ratio, \(Q_{m}\) and \(Q_{l}\) are the flow rates of the main and tributary channels, respectively, and \(U_{m}\) and \(U_{l}\) are the cross-sectional averaged velocities of the main and tributary channels, respectively. As the momentum ratio and discharge ratio increase, the width and length of the recirculation zone also increase, and the recirculation zone range is affected by junction angle changes.  
The length and width of the recirculation zone can be predicted based on the discharge ratio between the main and tributary channels, as well as the junction angle. [PERSON] et al. [38] presented an empirical formula to calculate the width and length of a recirculation zone based on experimental results. The recirculation zone characteristics for subcritical flow can be estimated according to the junction angle as  
\[\frac{L_{s}}{W}=3.8\sin^{3}\!\alpha\bigg{(}1-\frac{1}{2}\mathrm{Fr}_{d}\bigg{)} Q_{r}^{1/2} \tag{2}\]  
\[\frac{b_{s}}{W}=\frac{1}{2}\bigg{(}\mathrm{Fr}_{d}-\frac{2}{3}\bigg{)}^{2}+0. 45Q_{r}^{1/2}\bigg{(}\frac{\alpha}{90^{\circ}}\bigg{)} \tag{3}\]  
where \(L_{s}\) and \(b_{s}\) are the length and width of the recirculation zone, respectively, as depicted in Figure 1, \(\alpha\) is the junction angle, \(Q_{r}\) is the discharge ratio defined as \(Q_{m}/(Q_{m}+Q_{l})\), and \(\mathrm{Fr}_{d}\) is the Froude number downstream of the confluence defined as \(U/\sqrt{gh_{0}}\) in which \(U\) is the cross-sectional averaged velocity and \(h_{0}\) is the downstream water depth. The recirculation zone becomes a storage zone for solutes and affects the mixing characteristics downstream of the junction [26]. Therefore, the accurate prediction of the size of the recirculation zone is important.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Traditional Methods
#### 4.1.1 Methods Based on Statistical Models  
The core of the method based on statistical models lies in the in-depth analysis of the intrinsic distribution law of data by applying statistical theories and methods to accurately identify behaviors that deviate significantly from the normal behavior pattern. It builds statistical probability models to describe the statistical characteristics of normal behavior features and then uses these models to detect anomalies in the data.  
The Gaussian Mixture Model (GMM) [29] is based on the Gaussian probability density function. By calculating parameters such as the mean and variance of pixel intensity, a model can be established, which can effectively distinguish the foreground and background and then identify the crowd behavior pattern. In high-density crowd scenes, GMM can be used to capture and describe different dynamic characteristics of the crowd, such as density, movement speed, trajectory, etc., thereby identifying abnormal behaviors that do not conform to the normal behavior pattern. The parameter estimation of GMM usually adopts the Expectation Maximization (EM) algorithm, which is an iterative algorithm including the E step (calculating the expected value) and the M step (maximizing the likelihood function) and continuously optimizes to approximate the true distribution of the data. [PERSON] et al. [30] discussed and summarized four enhanced methods based on GMM, including the basic GMM, the combination of GMM and Markov Random Field (MRF) (GMM-MRF) [31], the Gaussian-Poisson Mixture Model (GPMM) [32], and the combination of GMM and a Support Vector Machine (SVM) (GMM-SVM) [33], pointing out that when dealing with complex crowd scenes, a combination of multiple methods can be used to improve the ability to recognize abnormal behaviors.
## 3 Results
Among them, the milk stage of fresh WCR had the highest phenolic acid profile (ferulic acid, p-coumaric acid, chlorogenic acid, prophyl gallate, vitexin, myricetin, and quercetin were 2.49, 1.93, 1.97, 1.57, 0.98, 1.14, and 1.55, ug/g, respectively). However, similar metabolites were noted at lower values at the booting and heading stages. The luteolin range decreased with maturity. On the other hand, the contents of caffeic acid, rutin, and kaempferol were significantly higher in fresh freeze-dried WCR at the milk stage than that at the booting and heading stages (\(p<0.05\)). Overall, the whole crop rice fresh freeze-dried extract had a higher phenolic acid profile at the milky stage compared to the booting and heading stages. In addition, there were no significant changes in the flavonoid content between the milky and heading stages (\(p<0.05\); Table 3). The heading stage of WCR hay had a slightly higher amount of phenolic acids and flavonoids among WCR silage and fresh samples at the heading stage; however, ferulic acid and p-coumaric acid were detected at higher levels at the milk stage of WCR silage, hay, and fresh freeze-dried extract. The stage of maturity may also have a significant impact on fermentation efficiency in terms of the organic acid profile, nutrient composition, and microbial diversity (Table 4) of the WCR silage/haylage samples. Hence, the optimum level of phenolic acids and flavonoids may depend on the stage of crop maturity, and it could be one of the important factors influencing feed quality for silage-making and/or feed development. In addition, the pH, organic acid profiles, and microbial data of LAB-inoculated and noninoculated WCR silage samples are summarized in Table 4. The LAB-inoculated WCRs at the milk stage (Stage 3) showed a higher concentration of lactic acid and a lower amount of butyric acid than the noninoculated WCRs; it clearly indicates that the inoculated LAB utilize the WSC in WCRs efficiently and improve the WCRs fermentation quality. Lower (\(p<0.05\)) concentration of organic acid profiles at the booting stage (Stage 1) is due to lower LAB survival and nutritional availability in the WCRs silage, which may not be suitable for feeding ruminants. Therefore, WCRs preparation should be required for an optimum harvesting period. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{8}{c}{**Phenomic Acids (ug/g)**} & \multicolumn{8}{c}{**Flavonoids (ug/g)**} \\ \hline
**Testments/Sample** & **Harvest** & **Caffeic** & **Ferulic** & **P-Counamic** & **Chlorogenic** & **Di-hydroxy** & **Prophyl** & **Rutin** & **Lucolin** & **Kaempfer** & **Vilecin**
## 3 Test Results and Discussion
### Void Ratio  
The test results of the mixtures of CEN sand and kaolinite clay demonstrate that the characteristics of the mixtures are ascribable to the presence of the kaolinite in the samples tested in an oedometer. From the one-dimensional compression results, it was proved that the presence of kaolinite had a significant effect on the compressibility of the soil under various loading conditions. The variation of void ratio with vertical effective stress in each fines content are presented in Figures 6 and 7. As can be seen from Figure 6, the values of the initial void ratio for the mixtures are scattered in a relatively wider band (\(\Delta\)e = 0.38) and the differences became smaller at the end of the test (\(\Delta\)e = 0.33). Compared to the variation of the initial void ratio in each mixture, the change of void ratio by increasing vertical effective stress from 24.52 kPa to 784.53 kPa was very small (about 0.84 on average). This is because the initial condition was determined by the compaction with 90% of the maximum dry density. Under the higher compaction energy, the specimen showed a higher strength and lower settlement leading to lower void ratio change [24]. Regarding the percentage of fines content, the change of void ratio in low fines content was smaller than that in high fines content. This is because the fines do not participate in the resistance of shear in the case of low fines content; on the contrary, the sand grains contribute to the shearing resistance under the high fines content [5].  
In Figure 7, the 3D graph clearly illustrates the overall behavior of the mixtures: as the fines content increased, the initial void ratio also increased. The vertical effective stress led to an overall decrease in the void ratio. However, exceptional change was observed; that is, even though fines content increased from 15% to 20%, the void ratio decreased regardless of vertical effective stress. This phenomenon is similar to the observation in Figure 6, where maximum dry density increased until fines content became 20%. Beyond 20% of fines content, maximum dry density decreased. Therefore, it can be concluded that a specific range of fines content would improve a mixtures' behavior. In the case of the sand-kaolinite mixture, the soil that contained 15% and 20% of fines is appropriate in embankment construction when considering minimum residual settlement.  
Figure 5: Maximum dry unit weight and optimal water content in various fines contents.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 2 Fault Feature Extraction of Rolling Bearings
### Feature Extraction Method for Rolling Bearings Based on SSA Optimization VMD  
#### 2.2.1 Variational Mode Decomposition  
Variational mode decomposition (VMD) [17] can effectively deal with nonlinear, non-smooth signals, which can be decomposed into Intrinsic Mode Functions (IMF) with finite bandwidth and different center frequencies. The constrained variational decomposition model is as follows:  
\[\begin{cases}\underset{\{u_{k}\},\{\omega_{k}\}}{\text{min}}\left\{\sum_{k} \left\|\partial_{t}\left[\left(\sigma(t)+\frac{j}{\pi t}\right)\times u_{k}(t )\right]\!e^{-j\omega_{k}t}\right\|_{2}^{2}\right\}\\ \sum_{k=1}^{K}u_{k}(t)=f(t)\end{cases} \tag{2}\]  
where \(u_{k}=\{u_{1},u_{2},\cdots,u_{k}\}\) represents each IMF component of the decomposition, \(\omega_{k}=\{\omega_{1},\omega_{2},\cdots,\omega_{k}\}\) is the center frequency of each IMF, \(f(t)\) is the input signal, and \(\sigma(t)\) is the unit pulse signal.  
In order to transform the constrained problem of Equation (2) into an unconstrained problem, the augmented Lagrange function is introduced by taking advantage of the quadratic penalty term and the Lagrange multiplier method as follows:  
\[L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k}\left\|\partial_{t}\left[ \left(\sigma(t)+\frac{j}{\pi t}\right)\times u_{k}(t)\right]\!e^{-j\omega_{k}t }\right\|_{2}^{2}+\left\|f\!\sum_{k}-u_{k}(t)\right\|_{2}^{2}+\left\langle \lambda(t),f(t)-\sum_{k}u_{k}(t)\right\rangle \tag{3}\]  
where \(\alpha\) is the penalty factor and \(\lambda\) is the Lagrange sub.  
However, in the VMD decomposition process, the penalty factor \(\alpha\) and the number of modal decompositions K need to be determined artificially, which relies heavily on the a priori knowledge of professionals [18]. It is found that the larger the value of \(\alpha\) taken, the smaller the bandwidth of each modal component, and vice versa. When the value of K is smaller, the signal is not completely decomposed and the phenomenon of under-decomposition occurs, while in the reverse case, some unknown components are decomposed, i.e., over-decomposition occurs. Therefore, the advantages of VMD can only be realized if decomposition parameters are precisely selected. In the following, an intelligent optimization algorithm is used to optimize the parameters of the VMD.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 5 Experimental Results
### RQ3: How Much Time Does It Take for CIB to Run?  
To answer this question, we measure the training and testing time of CIB and the baselines. The training time includes the time taken for data preprocessing and model training. The testing time is the time that is taken to process the testing dataset until the value of performance measures is obtained.  
Tables 13 and 14 separately show the training and testing time of our CIB and the baselines. From the tables, we can see that (1) CIB needs the most training time and, on average, the training of CIB takes about 223.25 s; (2) the testing time of all methods is very small, on average, CIB takes 0.168 s in the testing. Therefore, we suggest that train [PERSON] offline.  
Figure 3: Boxplot of \(F_{1}\), AGF, AUC, and MCC across all PROMISE datasets for our CIB and the baselines. The horizontal line in the box denotes median and the asterisk indicates mean value.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security
### Security Standards  
In the domain of vehicular communications, different standards compete to establish the core guidelines for security-oriented services. In particular, the 3 GPP consortium, ETSI and IEEE, define different set of standards. In the context of the intra-vehicle network, the standard documents of interests are:  
* ETSI Technical specifications 102 731 [39];
* 3 GPP Technical specifications 33.401 and TS 33.501 [40];
* The IEEE 1609.2 standard [41].  
However, despite the relevant efforts spent, there is no standard worldwide that is accepted and the different standards (and the newer being developed) still compete.