esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Driving Video Traffic Generation  
Having obtained all the characterization parameters for traffic generation in previous sections, we are ready to emulate our wireless video traffic. Figure 12 shows the schematics of our wireless driving video traffic generation model from the superposition of \(n\) mini-sources [56]. Each mini-source represents a video traffic stream regulated by a switch to give a long-range dependency. We shall model the on-time switch by a Pareto distribution given in Equation (1) and the off-time by a Poisson process [an exponential distribution as given in Equation (4)].  
Figure 11: The pdf f(x) of video trace frames.  
Figure 12: Model description for driving video traffic generation.  
Table 4 shows an implementation of using nine mini-sources, each representing one of the 3 \(\times\) 3 sectors of a camera screen. Here, frame duration is the transmission time of a frame (_i.e._, frame size divided by bit rate). Assume the camera is facing the front when driving on a road. The middle is usually relatively still when compared to the sides that see more activities. The left turns, right turns, and uphills or downhills are all different scenes that can lead to special packet size distributions. Big packet size is produced by a fast side view of the camera while the small size is produced by the almost still middle view. In the characterization of each mini-Pareto source in the table, we use the same shape parameter obtained from the Hurst parameter, but the location parameter is different for different packet size bin.  
We have also tried other numbers such as \(n=3\times 4=12\) mini-sources to match the horizontal and vertical ratio of the camera. As verified by our experiments, the more mini-sources created, the higher the accuracy. However, the computational complexity also increases due to the more parameters needed to match the trace. This is translated into longer simulation time. So we have chosen \(n=9\) for our model for the best trade-off we found between parsimony and accuracy.  
Figure 13 shows the video traffic generated mini-pareto video traffic,
## 3 Theory of Proposed Method
### Filtering Outliers by Weight Estimation
#### 3.1.1 Weight Estimation
Figure 2: Change of weights with the increasing absolute residuals and the probability of the absolute residuals less than \(k\sigma\) (\(1\bigtriangleup\)\(1<k\sigma\)). To determinate the parameter \(k_{L}\), half of the residuals are considered to be situated in the third region (i.e., outside of the probability band \(\mid\triangle\mid\leq k_{L}\sigma\)). Therefore, the probability band (\(\mid\triangle\mid\leq k_{L}\sigma\)) should only account for \(n-\frac{1}{2}\) residuals [53]. The probability \(P_{in}\) equal to \(n-\frac{1}{2}\) out of \(n\) residuals is looked for, which can be expressed as:  
\[P_{in}=1-\frac{1}{2n} \tag{11}\]  
where \(P_{in}\) is the probability band, and \(n\) is the number of residuals. The possibility \(P_{in}\) for the residual \(\triangle\) falling in the interval of \(-k_{L}\sigma\) to \(k_{L}\sigma\) can be obtained by the integral of the probability density function:  
\[ \begin{array}{ll}P_{in}&=\int_{-k_{L}\sigma}^{k_{L}\sigma}\frac{1}{\sqrt{2 \pi}\sigma}e^{-\frac{(\Delta-\mu)^{2}}{2\sigma^{2}}}d\Delta\\ &=2\int_{0}^{k_{L}\sigma}\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(\Delta-\mu)^{ 2}}{2\sigma^{2}}}d\Delta\\ &=2\Big{[}\frac{1}{\sqrt{2\pi}\sigma}\sqrt{\frac{\pi}{2}}\sigma\cdot erf \Big{(}\frac{\Delta-\mu}{\sqrt{2\sigma}}\Big{)}\Big{]}_{0}^{k_{L}\sigma}\\ &=\text{erf}\Big{(}\frac{k}{\sqrt{2}}\Big{)}\end{array}  \tag{12}\]  
where \(erf\) is the error function with expression \(\text{erf}(t)=\frac{2}{\sqrt{\pi}}\int_{0}^{k_{L}\sigma}e^{-t^{2}}dt\). Therefore, the threshold parameter \(k_{L}\) determined by \(n\) can be obtained from Equations (11) and (12). \[k_{L}=\sqrt{2}\cdot\text{erfinv}\bigg{(}1-\frac{1}{2n}\bigg{)} \tag{13}\]  
where \(erfinv\) is the inverse error function corresponding to the \(erf\) function.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Proposed Scheme for Producer  
In a critical situation, the push-based data packet is generated by the producer and forwarded to the CH by using Algorithm 1. Every vehicle advertises its location, speed, and direction to all its neighboring vehicles. During the fuzzy logic process, every node/vehicle in the cluster identifies the CH and cluster GWs. Therefore, we assume that the producer also belongs to a cluster and has established a connection with the CH. When the producer receives a regular interest packet for non-critical data content, it generates an ordinary data packet and forwards it to the requested vehicle. However, if there is a critical situation, the producer generates a critical data packet and forwards it to the CH of that cluster. The CH is solely responsible for broadcasting it to all the member vehicles in that cluster. The pseudo-code for the CH and GW vehicles is presented in Algorithm 2.  
```
1:Possible events: (Startup, interest \(i\), consumer \(c\), critical content \(b\))
2:Case Event:
3:if Producer == New then
4: advertise each non-critical content Object \(O\)
5:if non-critical content \(i\)then
6: send \(i\) to consumer \(c\)
7:if critical content \(r\)then
8: construct a critical data packet \(b\)
9: send critical content \(b\) towards CH
10:endif
11:endif
12:endif

\begin{table}
\begin{tabular}{c} \hline \hline
**Field Name** \\ \hline Content \\ \hline Data \\ \hline Alert Type (Critical or Normal) \\ \hline Location \\ \hline Timer \\ \hline Signature \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proposed data packet format.
```  
**Algorithm 1** The proposed push-based critical data forwarding algorithm
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 5 Experiments and Analysis of Results  
### Simulation Architecture  
NS-3 is a network simulator with the advantage of providing full-stack analog simulation from the physical layer to the application layer, but it is unable to implement AI algorithms. Therefore, this study relies on the NS3-AI interface provided by NS-3 to assist the communication between the NS-3 environment and AI framework. The NS3-AI is the NS-3 AI algorithm interface for network research, which is an interaction module between NS-3 and several Python-based AI frameworks, and it can be realized through shared memory for efficient and fast data exchange between the AI algorithms and NS-3 [36]. This module consists of two parts: the NS-3 interface developed in C++ and the AI interface developed in Python. It is the core module for transferring data from one C++ program to another Python program. The NS-3 simulator and the AI framework run in different processes, and data transfer is mainly carried out in two cases: sending NS-3 simulation data to the AI model in Python and testing Python output values in NS-3. The NS-3 simulator is used to establish networks and topological structures and generate simulation data required by artificial intelligence algorithms. The AI framework uses the data in NS-3 to train the model and returns the output of the model to NS-3 for testing. This paper uses the NS3-AI interface and implements DDQN through the AI framework to ensure the continuous interaction between environmental state information and routing decision results during the simulation process.  
In NS-3, this study modifies the original GPSR module, extracts the environment state information \(s_{t}\) and inputs it into the shared memory of the NS3-AI interface during the simulation process. Based on the NS3-AI to realize the adaptation of the data structure atboth ends, the AI framework PyTorch can immediately obtain the state information from NS-3 through the shared memory, input it into the DDQN, and obtain the routing decision result. The routing decision result is returned to NS-3 through shared memory, and the node forwards the next hop.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 4 Simulation and Analysis
The position errors before and after filtering are shown in Figure 4.  
The detection range of the pursuer is a circular area with a radius of 20 m. When the escaping target enters the pursuer's perceptual range around t = 23 s, the pursuer adjusts the direction of movement and performs the pursuit. However, due to the limitation of the minimum turning radius of the pursuit USV and the influence of the evader's rapid adjustment of the direction of motion, there will be a short deviation in the route when the pursuer tracks the evader in real time. Figure 5 shows the angle changes in the pursuer and evader. When the evader enters the perceptual range of the pursuer around t = 23 s, the pursuer adjusts the motion angle to intercept the target. From Figure 6, it can be seen that in the process of performing the pursuit, although the evader adopts a variety of motion modes, such as linear motion and curve motion, the  
Figure 4: Filter performance. Figure 5: Heading angles of pursuer and evader. Figure 3: The trajectories of pursuer and evader. pursuer always maintains a good tracking effect. It can be seen that in about 150 s, the distance between the pursuer and the escaping target is less than 1 m. This is enough to ensure successful interception of the escaping target by the pursuit USV. In order to test the estimation effect of the disturbance observer on the uncertainty of the system, the actual value of the total disturbance momentum of the system in this simulation is compared with the estimated value. Because the USV belongs to the underactuated control system, only the designed thrust disturbance and rudder force disturbance are compared. The observation results of system disturbance are shown in Figures 7 and 8. From the disturbance observation results, it can be observed that the estimated values of system disturbances \(\tau_{wu}\) and \(\tau_{uw}\) fit the expected values more accurately. The designed disturbance observation algorithm has a good estimation effect on unknown system parameter perturbation and the time-varying external disturbance force.
# Spatial Modes of Laser-Induced Mass Transfer in Micro-Gaps
## 2 Experimental Setup Details and General Procedure
### LIFT Surfaces  
The upper surface (donor slide) consisted of a microscope glass slide (Paul Marienfeld GmbH & Co. KG, Lauda-Konigshofen, Germany) as the substrate, covered with a light-absorbing self-adhesive polyimide film (70110, CMC Klebetechnik GmbH, Frankenthal, Germany; comprising 25 \(\upmu\)m Kapton(r) HN, DuPont, St Joseph, MO, USA) and spin-coated (SCC-200, Schaefer Technologie GmbH, Langen, Germany) with a styrene-acrylic copolymer (SLEC PLT 7552, Sekisui Chemical GmbH, Germany; 135 mg/slide in 1 mL dichloromethane; rotation speed 80 rps for 40 s, resulting in a thickness of typically 1 \(\upmu\)m) embedding fluorenylmethoxyscarbonyl-glycine-pentafluorophenyl esters (Fmoc-Gly-OPfp; Sigma-Aldrich, St. LouisCity, MO, USA and Merck Schuchardt OHG, Hohenbrunn, Germany; 15 mg/slide). Glycine is an amino acid and Fmoc is the protection group for the terminal amino group.  
The lower surface (acceptor slide) was a glass slide functionalized with a polymer layer (10:90 amino-terminated poly(ethylene glycol)methacrylate and methyl methacrylate) bearing free amino groups (PEPEperPRINT GmbH, Heidelberg, Germany). Acceptor slides with dry-etched micro-cavities were also used in Reference [8].  
The spherical particles used as obstacles in Figure 2c-e were commercially available cross-linked poly(methyl methacrylate) spheres (Spheromers CA-10, Microbeads, Skedsmokorset, Norway).  
Figure 1: Experimental scheme: (**a**) A focused laser beam (1) heated an absorption layer (2) and a coated polymer thin film containing the compounds to be transferred (3). This generated a micro-jet (4) confined between the two plates, probably by evaporating solvent residues and creating an aerosol. Concentric deposition patterns (5) were observed thereafter on the acceptor slide. (**b**) Image of self-assembled concentric amino acid patterns (fluorescently labeled), as transferred onto the acceptor slide by the laser process. Increasing irradiation time (1, 1.5, 2, 3, 5, 7.5, 10, and 20 ms) resulted in the occurrence of additional concentric rings of the deposited material. The scale bar represents 1 mm. The laser beam diameter was 15 \(\upmu\)m.
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 3 Results
### Permanent Deformation Resistance  
Figure 12 presents the deformation curves for the cyclic compression tests. Although the curves are not direct performance indicators, they allow us to observe the general resistance of the asphalt mixtures. Apart from the mixture with 4% ABS, all the others with the same percentage of plastic waste revealed more deformation than the reference mixture. For 8% of plastic waste, only the mixture with HDPE had higher deformation than the reference. Apart from the exceptions mentioned, generally speaking, the more the plastic waste quantity, the more resistance to deformation is expected to occur.  
Figure 11: Comparison of tensile strain at 10,000, 100,000, and 1 million loading cycles used as a fatigue resistance indicator at 20 \({}^{\circ}\)C.  
Figure 12: Variation of the accumulated permanent deformation measured in cyclic compression tests at 60 \({}^{\circ}\)C for both the reference and the asphalt concrete with different plastic wastes.  
Figure 13 highlights the influence of plastic content in the resistance of the asphalt mixtures to permanent deformation measured in the cyclic compression test. Although there is no clear conclusion, the results allow us to present a number of findings. Increasing from 4% to 6% of ABS or HDPE was beneficial but from 6% to 8% was not. In the case of HDPE500 and LDPE(uw), only the 8% content showed an improvement of resistance. The lower quantities of HDPE500 seem to have a detrimental contribution. Therefore, the percentage of plastic should be conveniently studied to ensure that the improvement sought is achieved.  
A more detailed analysis of the deformation curves provided the values of _fc_ (creep rate in the quasi-linear zone) and \(\varepsilon\)3600 represented in Figure 14.  
Considering the results measured for _fc_ and \(\varepsilon\)3600, the resistance to permanent deformation of the studied mixtures is ranked as shown in Table 2. The combined ranking is obtained by adding the ranking positions of both parameters.  
The results show that the permanent deformation resistance depends on plastic type and content. The deformation curves are used to derive two parameters. The cyclic compression test shows that ABS increases rutting resistance whatever the considered content and evaluation parameter. Regarding creep rate (_fc_), the different types of plastics improve the resistance to permanent deformation as compared to the mixture without plastic. Overall, the of 6% and 8% plastic contents were the most effective. The results for \(\varepsilon\)3600 were not entirely aligned with those of _fc_. In this case, and for 4% of plastic, only ABS delivered better permanent deformation than the reference mixture. Increasing the plastic content to 8% increased the rutting resistance, except for HDPE500.  
Figure 13: Influence of the plastic waste content on the accumulated permanent deformation measured in cyclic compression tests at 60 \({}^{\circ}\)C for the four types of plastic waste used.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 2 Framework Design
### Code Context Analysis  
To capture the vulnerable programming patterns that lead to vulnerabilities, it is crucial to analyze the code context. A typical vulnerable pattern can be that when a variable flows from an appropriate source to a corresponding sink, which does not go through proper validation (e.g., buffer size check). Many different types of vulnerabilities share such a pattern [33]. As it is shown by the code examples in Figure 2, it depicts a vulnerable context: the source, which is a local variable testStr (at line 4) is assigned with a string whose length exceeds the size of buffer which is defined at line 3. When testStr is passed to the sink sprint at line 6, there is no size check of variable testStr, which leads to a buffer overflow vulnerability.  
Figure 1: The proposed framework is designed for the scenario where there are some labeled data for a software project. This scenario consists of three stages: the first stage is to train the neural network with the labeled data; the second stage is to feed both the labeled and the unlabeled data to the trained network to obtain one of the hidden layers’ output as the learned function-level embeddings; and in stage three, the resulting embeddings can be used as feature sets for subsequent process, e.g., to train a conventional ML classifier for further classification.  
To identify such a vulnerable code context that spans across multiple statements from a source variable to a potentially vulnerable sink, a detection method should be able to (1) identify the potentially vulnerable context, which consists of at least a source and a sink, and (2) recognize that there has been no proper validation prior to passing the source variable to the sink. To achieve these goals, a method should be able to learn the semantics of code tokens and capture the contextual dependencies that range from source variable declaration/assignment to a sink function invocation. Our embedding framework adopts a neural network consisting of multiple layers to learn both code semantics and long-term contextual dependencies.
## 5 Performance Comparison
Figure 16: Precision–recall curve of **(a)** YOLOv3 model without the pretrained weights; **(b)** YOLOv3 model with the pretrained weights. Figure 15: F1-Score curve of **(a)** YOLOv5 model without the pretrained weights; **(b)** YOLOv5 model with the pretrained weights. From the above Table 1, it is evident that, in general, YOLOv3 models have performed better than YOLOv5 in terms of F1 Score and mAP. The performance difference between the YOLOv3 model with and without pretrained weights is negligible. YOLOv3 with pretrained weights performed slightly better than the YOLOv3 model without pretrained weights in terms of F1 Score (0.76326); whereas the latter performed better in terms of mAP (0.7626). In the case of YOLOv5, the model without pretrained weights performed poorly in terms of both F1 score and mAP compared to the model with pretrained weights [F1-Score (0.7378) and mAP (0.26588)]. The performance overview of all the models is presented in Figures 18-21. Even with an NMS (nonmaximum suppression) ensemble of four models trained using YOLOv5 of the DhakaAI dataset, the YOLOv5 managed to achieve an mAP (@ 0.5) of only 0.458 as mentioned in Section 1. The performance of a single-fold trained YOLOv5 model on the DhakaAI dataset was low compared to our model. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Sno Citation** & **F1-Score** & **mAP (\textless{}0.5)** \\ \hline
1 [PERSON] et al., 2019 & 99.94\% & N/A \\
2 [PERSON] et al., 2021 & N/A & 88\% \\
3 Nepal and Eslamiat 2022 & 65.5\% & 63.3\% \\
4 [PERSON] et al., 2019 & N/A & 88.3\% \\
5 [PERSON] and [PERSON] 2017 & 24.24\% & N/A \\
6 [PERSON] et al., 2022 & N/A & 45.8\% \\
**7 Proposed Work** & 76.32\% & 75.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed work with the existing works. Figure 17: Precision–recall curve of (**a**) YOLOv5 model without the pretrained weights; (**b**) YOLOv5 model with the pretrained weights. Figure 19: Comprehensive results of YOLOv3 model with the pretrained weights. Figure 20: Comprehensive results of YOLOv5 model without the pretrained weights.
## 3 Results
### Platelet Deposition and Adhesion  
Qualitative analysis of platelet deposition on the scaffold was visualized using SEM imaging, as shown in Figure 6a. The platelets deposition and aggregation on Hep G2-covered scaffold from the SEM images revealed large number of depositions attached to the surface when compared to the co-culture-coated scaffold. Platelets deposited on the surface could be identified as shown by yellow-marked arrows in Figure 6a.  
The quantitative analysis of platelet adhesion was estimated by the measurement of percentage of platelet adhesion ratio (% of PAR). The platelet adhesion ratio of the Hep G2-covered scaffold was 1.38-times higher than the control (no cell). Upon coming into contact with plasma, Hep G2 showed a greater adherence to platelets and aggregation. However, significant difference (\(p<0.05\)) in % of PAR was observed in the case of the co-culture (Hep G2+ HUVEC) model of endothelial Hep G2 scaffold when compared to the control, as shown in Figure 6b. The result demonstrated that the antiplatelet adhesion of the HUVECs co-cultured with Hep G2 was higher than only Hep G2 cultured on the scaffold. The antiplatelet adhesion behavior of the co-culture model showed that the HUVECs layer served as barrier between the liver cells and platelets. If the platelets could interact with exposed Hep G2 on the scaffold, the interaction between them would become stronger. The co-culture of Hep G2 with HUVECs indicated better antiplatelet adhesion with the channel system with the current design, mimicking the flow system. The improved antiplatelet adhesion property of the endothelial Hep G2-cultured scaffold  
Figure 5: (**a**) Glucose consumption and lactate production of pure culture Hep G2 and co-culture Hep G2/ HUVEC under perfusion culture system at day 10; (**b**) albumin production between Hep G2 and co-culture Hep G2/ HUVECs showed significant different with \(p<0.05\) indicated by * at day 10. Each column represents the mean \(\pm\) SD (\(n=3\) independent experiments), * \(p<0.05\).  
could be related to the endothelial cell glycocalyx layer reported by [PERSON] et al. (2016), thereby rendering hemocompatibility.