esa-sceva/satcom-chunk-collection huggingface dataset:
# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 4 Driving Environment Representation  
The representation of the driving environment serves as the foundation for building the entire decision-making model. In the proposed method, we discretize the driving environment spatially into numerous lane cells and subsequently convert it into a directed graph to solve optimal lane-level driving tasks. Before this work, such methods have been successfully applied to navigation route planning [37] and driving trajectory planning [38], but the granularity of environmental discretization differs depending on the level of driving tasks.  
Here, we define the smallest discretized unit as a \"lane cell\", which is a short lane segment where internal traffic rules and traffic flow states are considered to be consistent or similar. Each lane cell corresponds to a node in the directed graph, as shown in Figure 4, and is connected via edges to other lane cells. These edges represent possible actions, i.e., lane-following or lane-change maneuvers. By assigning appropriate costs to these edges, the driver's preferences for different driving styles, such as travel efficiency, driving risk, regulatory compliance, etc., can be represented. The optimal lane-level driving tasks can be further determined. In subsequent section, we will establish cost functions for edges representing lane-following and lane-change maneuvers based on traffic flow states and traffic regulations at each lane cell.  
Regarding the size of lane cells, given that the proposed method aims to plan lane-level driving tasks, the lateral dimension can be directly equal to the lane width at that location. The choice of longitudinal dimension, on the other hand, is a crucial issue. If the longitudinal dimension is small, it can more accurately represent the lane-change position in the path, but it will also increase the number of nodes in the directed graph, thereby increasing computational overhead. Conversely, if the longitudinal dimension is large, it may fail to promptly reflect changes in traffic situations and traffic rules, thus making the vehicle's response sluggish.  
Here, all lane cells in the driving environment are denoted as \(N=\{n_{i}\}\). The longitudinal dimension of cell \(n_{i}\) is denoted as \(\Delta l\), and the value of this variable represents the spatial granularity of the discretized driving environment. The traffic flow attributes at cell \(n_{i}\) are denoted as \(\{v_{i},\;\rho_{i}\}\), which represent the traffic flow speed and density, respectively, at the location of the cell.
## 4 Outage Probability Analysis
### Stationary Probabilities  
We denote the state transition matrix \(P\) as an \((L+1)^{K}\times(L+1)^{K}\) matrix.  
\[p_{SI}^{S\to R}=\frac{1}{K_{SI}^{S\to R}}\left(1-p_{out}^{S\to R} \right), \tag{27}\]  
\[P_{Sl}=\left\{\begin{array}{ll}p_{out}^{Sl}&if\ \ S_{n}=S_{i},\\ p_{Sl}^{R\to D}&if\ \ S_{n}\in U_{Sl}^{R\to D}\\ p_{Sl}^{S\to R}&if\ \ S_{n}\in U_{Sl}^{S\to R}\\ p_{Sl}^{S\to R+R\to D}&if\ \ S_{n}\in U_{Sl}^{S\to R+R\to D}\\ 0&elsewhere\end{array}\right. \tag{33}\]  
where \(p_{out}^{Sl}\), \(p_{Sl}^{R\to D}\), \(p_{Sl}^{S\to R}\), and \(p_{Sl}^{S\to R+R\to D}\) represent the probability of outage, the probability of a packet being transmitted from the relay to the destination, from the source to the relay, and simultaneously from the source to the relay and the relay to the destination, respectively.  
State the transition diagram in Figure 10 shows transitions from one state to another depending on the link state, these transitions are governed by Equation (33). The state transition matrix for a two-relay network (\(K=2\)), each with a buffer size of two (\(L=2\)), developed through this model, is depicted in Figure 11.
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## 3 Results and Discussion
### Characterisation of Bentonite and Modified Bentonite
#### 3.1.2 XRD Analysis  
The swelling of bentonite is a consequence of its hydrated structure, in which cations are located in the interlayer space and solvated by water molecules adsorbed from the atmosphere [31]. XRD patterns of bentonite (34.8% montmorillonite) (Figure 1) show characteristic diffraction peaks at 20 = 6.3, 19.8 and 34.9. Quartz (Q) and cristobalite (C) impurities are observed in the patterns. The d\({}_{001}\) reflection at 20 = 6.30 corresponds to d-spacing = 14.02 A, which is an interlayer comprising the mono- and bi-layers of water molecules [32; 33]. A decrease in d-spacing from calcination was caused by the removal of interlayer water and the migration of exchangeable cation [31]. Moreover, d-spacing decreases from the acid treatment result from the removal of some cations. BC\({}_{500}\) shows d-spacing = 9.65 A, while BA\({}_{0.5}\) shows d\({}_{001}\) = 13.00 A, as shown in Table 2, indicating that heat treatment significantly affects basal spacing. All adsorbents, except BC\({}_{500}\), show small changes in d-spacing after the pesticide adsorptions, excluding paraquat, suggesting that the pesticides probably bind to the active sites of the face and edge, whereas the pesticides adsorptions may also involve the interlayer space for BC\({}_{500}\). In the case of d-spacing for cationic pesticide (paraquat), it shows a similar value (12.05-12.38 A), except BA\({}_{0.5}\)C\({}_{500}\), suggesting that hydrated paraquat possibly exchanges in the interlayer space with a smaller size compared to interlayer water.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{5}{c}{**d-spacing (Å)**} \\ \cline{2-6}  & **No Adsorption** & **Atrazine** & **Diuron** & **24-D** & **Paraquat** \\ \hline Bentonite & 14.02 & 14.59 & 14.01 & 14.21 & 12.27 \\ BC\({}_{500}\) & 9.65 & 12.38 & 12.29 & 12.90 & 12.05 \\ BA\({}_{0.5}\) & 13.00 & 13.71 & 12.47 & 13.36 & 12.16 \\ BC\({}_{500}\) A\({}_{0.5}\) & 12.08 & 12.76 & 12.17 & 12.54 & 12.38 \\ BA\({}_{0.5}\)C\({}_{500}\) & 9.59 & 9.51 & 9.32 & 9.21 & 9.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The d-spacing (Å) of bentonite and modified bentonite changes upon atrazine, diuron, 2,4-D, and paraquat adsorption.  
Figure 1: XRD patterns for bentonite (B), BC\({}_{500}\), BC\({}_{500}\)A\({}_{0.5}\), BA\({}_{0.5}\), and BA\({}_{0.5}\)C\({}_{500}\) adsorbents. (Q = quartz; M = montmorillonite; C = cristobalite.).
## 3 Research Methodology
### Performing Snowballing and Data Synthesis  
The snowballing phase is a systematic literature search method where references or citations in a document are used to locate more articles. This approach enabled the identification of articles relevant to the research and their inclusion as an information source to aid in the understanding and explanation of the study's subject [87]. The data synthesis process, which is the most critical stage in an SLR, aims to address the research questions, synthesize the retrieved data, and report the findings [88]. The publications were classified into designated groups based on the retrieved data to address the associated research topics. Subsequently, the findings were summarized and shown appropriately. The extensive explanations of the reported findings were included to elicit and emphasize the most critical elements of each research issue. Additionally, key results, such as current study directions, accomplishments regarding the use of attention mechanisms, unresolved issues, and recommendations for future studies, are included.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Research papers in the computer science field. & Research papers that are out of the field of computer science and duplicate papers with fewer than three pages. \\ Research papers published in journals & Research papers published in book chapters and workshops. \\ or conferences. & Research papers published in languages other than English, such as Chinese, Turkish, and Spanish. \\ An English-language research article. & Research papers published before 2012. \\ Research article published from 2012–2023. & Research papers published before 2012. \\ Research papers related to and answering the research questions. & Research papers that are not related and do not answer the research questions. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Inclusion and Exclusion Criteria.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID** & **Questions** \\ \hline Q1 & Are the study’s objectives presented in a comprehensible manner? \\ Q2 & It likely that all related studies were included in the literature review? \\ Q3 & Does the study use primary data to support its arguments? \\ Q4 & Does the study provide an adequate explanation of the research method? \\ Q5 & Does the study specifically concentrate on APTs on mobile devices? \\ \hline \hline \end{tabular}
\end{table}
Table 8: Assessment for Quality of Papers.
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## 4 Methodology
### Data Analysis and Feature Extraction  
First, we shall analyze the distribution of the time lag between lifting the accelerator and pressing the brake pedal completely (emergency cases) among all testers. The importance of the distribution is that it allows us to estimate the average time that the proposed brake assisting system could save. As illustrated in Figure 7, in most (55%) of the emergency braking events, the time lag covers the interval (0.2 s, 0.4 s). For the car moving at a speed of 50 km/h, this time lag matches to a traveled distance of between 2.8 m and 5.6 m. Furthermore, a time lag of 0.4 s or higher is registered in about 27% of the emergency braking cases, suggesting that, in more than half of cases at the speed of 50 km/h, the corresponding traveled distance can be even longer than 5.6 m. Consequently, in more than half of the cases of emergency braking, an eventual automated application of brakes would be able to reduce the overall stopping distance, remarkably, by at least 5.6 m (when the driving speed is 50 km/h). As well as measuring the time lag for all drivers, we also extracted several key features of both pedals. Among them are the highest position of the accelerator pedal before starting the deceleration (mP), the maximum and average rate of lifting the accelerator (mR and aR respectively), the maximum and the average rate of pressing the brake pedal, and the maximum position of the pressed brake pedal. The average rate is the pedal speed on the interval where the pedal position is decreasing or increasing (in the case of applying brakes). The maximum rate value is the maximum pedal speed among 0.04 s periods of the mentioned interval.  
According to our assumption, it could be possible to classify the driver's intention to apply emergency braking if the value of mK or aR is higher than a certain threshold. As Figures 8 and 9 illustrate, higher rates of pressing the brake pedal correspond to emergency braking situations and often come before the swift release of the accelerator pedal. Consequently, some of the samples belonging to the same class are very close to each other.
## 2 2D FIR Image Filtering and Its Acceleration
### General Acceleration of FIR Image Filtering  
Several approaches have been taken with regard to the acceleration of general FIR filters. These include separable filtering [13], image subsampling [15], and kernel subsampling [16]. In the separable filtering, the filtering kernel is separated into vertical and horizontal kernels as a 1D filter chain using the separability of the filtering kernel. The general 2D FIR filter has the computational order of \(O(r^{2})\) for each pixel, where \(r\) denotes the kernel radius of the filter. The computational order of a separable filter is \(O(r)\). If the filtering kernel is not separable, either singular value decomposition (SVD) or truncated SVD can be used to create separable kernels. When truncated SVD is used, the image is forcefully smoothed with a few sets of separable kernels for acceleration. However, when kernel weight changes for each pixel, we need SVD computation for every pixel. Here, the separable approach is inefficient.  
Image subsampling resizes an input image and then filters it. Finally, the filtered image is upsampled. This subsampling greatly accelerates filtering, but the accuracy of approximation is not high. Further, the method has the significant drawback of losing high-frequency signals.  
Kernel subsampling reduces the number of reference pixels in the filtering kernel as a similar approach to image subsampling. Figure 1 represents kernel subsampling. The reduction of computational time in kernel subsampling is not as extensive as that of image subsampling, while kernel subsampling could keep a higher approximation accuracy than image subsampling. Thus, we focus on kernel subsampling. In the Appendix A, we examine the processing time and accuracy of image subsampling and kernel subsampling more closely. The approximation accuracy of these types of subsampling depends on the ratio and pattern of subsampling. Image and kernel subsampling generate aliasing, but a randomized algorithm [34; 35] moderates this negative effect. Random sampling reduces defective results from aliasing for human vision [36]. Random-sampling algorithms were first introduced in accelerating ray tracing and were utilized for FIR filtering in [15; 16].  
The main subject of this paper is the general acceleration of FIR filtering by using SIMD vectorization. We adopt kernel subsampling for acceleration because kernel subsampling has a high accuracy of approximation and is not limited by the type of kernel.
# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 5 Performance Evaluation
### Privacy Analysis  
In this part, we analyze the privacy protection performance with privacy degree to make sure that the adversary can not infer the real user's location among the users of an anonymous set. We quantify the privacy protection degree with anonymity parameters and distribution parameter \(\lambda\). The anonymous parameters contain the number of users and edges in the anonymous set, which are frequently used to evaluate the degree of privacy protection. Obviously, it would be easy to track the user when there are few elements in the anonymous set. Usually, the user will set a threshold to realize location privacy protection \(\{k_{0},l_{0},\lambda_{0}\}\).  
With the background information, the adversary can get the locations of users and utilize the non-uniform distribution in the road network to determine the correct road section where a user lies in. We consider the distribution degree \(\lambda\), which describes the biggest difference in the distribution of users in different sections. Our method is based on road truncation. The road sections with more users are truncated. As a result, the distribution degree \(\lambda\) decreases. The divergence between different road sections decreases, which is harder  
Figure 5: The workflow of our algorithm based on road truncation.  
for the adversary to track the user. Our scheme performs well and resists the side-weight inference attack.
# An Evaluation of the Ramus Mandibular Asymmetry on the Panoramic Radiography
## 5 Conclusions  
The conclusions of the study showed that:  
* The results of the study showed the presence of significant asymmetry at the condylar level, obtaining values of over 3% for CAI and non-significant asymmetry (values of less than 3% for RAI and MAI) in the case of vertical ramus.
* Regarding sexual dimorphism, there are significant differences in condylar asymmetry index (CAI) between females and males, which are in favor of males. These differences are not statistically significant for the ramus asymmetry index (RAI) and the mandibular asymmetry index (MAI).
* We would have expected the greatest asymmetry in the condyle to appear in the UPC, and despite this, we found higher values of asymmetry in Angle Class I malocclusions in all groups (15.3% males and 10.5% females) and Class II/1 in the group of females (10.7%).
* It was observed that in unilateral posterior crossbite, UPC, asymmetry was recorded at the condylar level in the case of both genders, also recording notable differences between females and males, but the values obtained are lower than the asymmetry observed in Angle Class I malocclusions and in Class II/1 in the female group.
* In Angle Class II/2 malocclusion, the smallest asymmetry was recorded at the posterior part of the mandibular vertical ramus.
* The study carried out was performed on growing patients aged between 7 and 15 years. The presence of an increased condylar asymmetry index is a sign of altered skeletal growth and should be considered in the diagnostic process and treatment plan. Because of the high prevalence of condylar asymmetry in unilateral posterior crossbite, the orthodontic treatment should be accomplished early, and the advantage of Habets method is that it uses routinely acquired panoramic radiographs and does not require additional exposure to ionizing radiation, which makes it very suitable for younger patients.  
Conceptualization, A.R.H. and E.G.; methodology, E.I. and E.T.; software, A.R.H. and E.G.; validation, E.I., E.T. and S.M.J.; formal analysis, A.R.H. and E.G.; investigation, A.R.H. and E.G.; resources, E.G. and E.T.; data curation, A.R.H., E.G. and E.I.; writing--original draft preparation, A.R.H., writing, review and editing, E.G. and E.I.; visualization, A.R.H. and E.G., supervision, E.I.; project administration A.R.H., S.M.J. and E.I. All authors have read and agreed to the published version of the manuscript.  
No external funds were received for this research.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the respective institution (\"Carol Davila\" University of Medicine and Pharmacy [protocol code PO-35-F-03, 28 March 2022]).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Image of Debonding Defect with Air-Coupled Ultrasonic Waves  
According to the above simulation results and signal processing experimental results, ACUT technique can reasonably evaluate debonding defects of honeycomb sandwich composite. In order to verify the actual detection results, C-scan of honeycomb sandwich composite was carried out. The scanning speed is 15 mm/s, the excitation voltage is 400 V, the gain compensation is 105.9 dB, the operating frequency of air-coupled transducer is 400 kHz, the scanning area is 60 mm \(\times\) 173 mm, and the scanning step is 0.5 mm. The C-scan result of honeycomb sandwich composite is shown in Figure 12. It can be seen that the result is consistent with the layout of debonding defects shown in Figure 5, and the natural debonding defects in the honeycomb sandwich composite sample are also detected, which further illustrates the correctness of theoretical simulation and actual detection.
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Validation of the Designed Channel Estimation Model Using Diverse SNR Rate  
Computation of the performance offered by the proposed channel estimation in MIMO systems regarding diverse SNR rates is shown in Table 8. The experimentation on the designed RP-WHEVO-HSCN-based model shows that the proposed scheme attains an enriched performance compared to several other baseline approaches.
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 3 The Computational Findings
### The Support Vector Machines Model  
We provide results after applying our methodology with a support vector machine model. The resample procedure works with parameters \"replace = True\", and \"n_samples = 1600\" on the minority class. It is obtained as a subset with the following number of observations in each class: class 0: 2814 and class 1: 1600. Applying the command train_test_split with parameters test_size = 0.5 and random_state = 31, we are ready to define a support vector model:  
SVC (C = 10, kernel = 'rbf', gamma = 'auto', class_weight = 'balanced', probability = True).  
The parameter \"probability\" takes the value \"True\" because we need to plot an ROC curve as a result.  
We compute the confusion matrix and the classification report on the test set to estimate the model fitted on the train subset. The confusion matrix is shown in Table 7. Our model used a smaller number of observations from the second class, and it obtained bigger values of estimated parameters as can be seen in the classification report given in Table 8. The high values of the recall parameter for both classes (0.97912 and 0.9902) show that both classes are well predicted.  
The SVM model achieved an AUC value of 98.46%, which indicates the model is 98.46% accurate at predicting whether the patient suffered from a stroke or not.  
There are several advantages achieved by the support vector machine model. We compare with the results obtained by the random forest model (Table 4).  
1. The Recall values in Table 7 are bigger than random forest model (see values in Table 4), i.e., 0.97266 and 0.98776 from Table 8.
2. The values obtained in Table 8 are based on the test set with 1389 observations for the negative class and 818 observations for the positive class. The smaller number of observations of the second class is an advantage because the resample procedure is used to increase the observations of the second class. The execution of the resample procedure needs CPU time for computational work. For this reason, a smaller number of observations is an advantage.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 2 Algorithm Description
### Loss Function  
Unlike traditional end-to-end speech enhancement models that only estimate the real part information in the time domain, our proposed method trains CNABCFCN to recover both the RI parts of clean speech. The loss function used to train the proposed model is  
\[\text{SI-SDR} =(1-\lambda)\times 10\log_{10}\frac{\|\beta_{r}\times Re(s^{a}[t]) \|^{2}}{\|\beta_{r}\times Re(s^{a}[t])-\hat{R}[t]\|^{2}} \tag{7}\] \[+\lambda\times 10\log_{10}\frac{\|\beta_{i}\times Im(s^{a}[t]) \|^{2}}{\|\beta_{i}\times Im(s^{a}[t])-\hat{I}[t]\|^{2}},\]  
\[\beta_{r}=\frac{\hat{R}_{l}^{T}Re(s^{a}(t))}{\|s^{a}[t]\|^{2}}=\underset{\beta _{r}}{\arg\min}\|\beta_{r}\times Re(s^{a}(t))-\hat{R}[t]\|^{2}, \tag{8}\]  
\[\beta_{i}=\frac{\hat{I}[t]^{T}Im(s^{a}(t))}{\|s^{a}[t]\|^{2}}=\underset{\beta _{i}}{\arg\min}\|\beta_{i}\times Im(s^{a}(t))-\hat{I}[t]\|^{2}, \tag{9}\]  
where \(Re(\cdot)\) and \(Im(\cdot)\) denote the RI part operations, respectively. Because the features we consider are essentially time-domain information, we adopt the scale-invariant signal-to-distortion ratio (SI-SDR) loss function to train the proposed model, which results in better speech enhancement performance [20]. The RI information of each frame estimated by the CNABCFCN model is denoted by \(\hat{R}[t]\in\mathbb{R}^{N\times 1}\) and \(\hat{I}[t]\in\mathbb{R}^{N\times 1}\), respectively. And \(s^{a}[t]\) is the analytic function of the reference channel that received clean speech \(s_{0}[t]\). In (7), \(\lambda\) is a constant within \([0,1]\) that weighs RI information. When \(\lambda=0\), the network prefers to use real information to train the network parameters, whereas \(\lambda=1\) indicates a preference for using imaginary information to update the network. From the experiments, a good empirical hyperparameter \(\lambda=0.5\) was chosen.