esa-sceva/satcom-chunk-collection huggingface dataset:
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 5 Evaluation
### Environment  
**Experimental Setup:** The experimental topology is shown in Figure 3, simulated by Mininet, using the OpenFlow1.3 protocol as the southbound interface, running on an Ubuntu virtual machine with an Intel Core i5-8400 2.80 GHz CPU and 8 GB of memory. We select \(h1\) and \(h6\) as attackers. The \(h1\) is responsible for launching the packet injection attack, and \(h6\) implements DoS attacks based on injected hosts. Based on this, we evaluate the performance and overhead of the PIEDefender.  
**Parameters Setting:** To evaluate the injection detection module, we capture 1 h of normal traffic and count the changes in the number of data packets in each flow. After comprehensively considering the verification efficiency and effect, we set the threshold _counterof the _OFPT_PORT_STATUS_ message verification process to 8. We can change the value by evaluating the actual network state.  
**Dataset:** To evaluate the simplified DoS detection module, we generate a data set in the environment mentioned above. Through the training sample generation, we collect 30,000 traffic, including 16,925 normal traffic and 13,075 attacks traffic.
## 2 Results and Discussion
### VOCs from Honey
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Component 1** & **LRI 2** & **LRI 3** & **Wildflower Honey** &
\begin{tabular}{c} **Wildflower Honey** \\ **+ Spirulina** \\ \end{tabular} \\ \hline
1 & 3-hexen-1-yne & 623 & 625 & 8.0 \(\pm\) 0.05 & - \\
2 & 3-butyn-1-ol & 662 & 660 & 0.5 \(\pm\) 0.02 & - \\
3 & glutaraldehydehyde & 890 & 895 & 0.7 \(\pm\) 0.03 & - \\
4 & santolina alcohol & 1038 & 1041 & 2.4 \(\pm\) 0.08 & - \\
5 & linalool oxide & 1060 & 1056 & - & 0.3 \(\pm\) 0.02 \\
6 & isomyrenol & 1075 & 1072 & 2.7 \(\pm\) 0.02 & - \\
7 & \(\alpha\)-linalool & 1091 & 1085 & 5.9 \(\pm\) 0.03 & - \\
8 & nonanal & 1107 & 1104 & - & 1.7 \(\pm\) 0.03 \\
9 & hotrienol & 1110 & 1114 & 36.5 \(\pm\) 0.15 & 2.1 \(\pm\) 0.04 \\
10 & nerol oxide & 1140 & 1137 & 2.7 \(\pm\) 0.02 & - \\
11 & nonanoic acid & 1265 & 1260 & 7.2 \(\pm\) 0.04 & - \\
12 & borneol acetate & 1273 & 1270 & 4.2 \(\pm\) 0.02 & - \\
13 & dehydro-ar-ionene & 1338 & 1336 & - & 1.1 \(\pm\) 0.03 \\
14 & (Z)-\(\beta\)- & & 1390 & 1382 & - & 0.2 \(\pm\) 0.02 \\
15 & trans-\(\beta\)-ionone & 1465 & 1460 & - & 2.3 \(\pm\) 0.03 \\
16 & pentadecane & 1510 & 1512 & - & 0.6 \(\pm\) 0.02 \\
17 & lauric acid & 1555 & 1561 & 2.3 \(\pm\) 0.02 & tr \\
18 & hexadecane & 1611 & 1612 & - & 0.8 \(\pm\) 0.04 \\
19 & heptadecane & 1708 & 1711 & - & 9.8 \(\pm\) 0.05 \\
20 & palmit acid & 1945 & 1951 & 17.8 \(\pm\) 0.11 & 24.3 \(\pm\) 0.12 \\
21 & 14-octadecancal & 2000 & 2007 & 2.5 \(\pm\) 0.02 & - \\
22 & oleic acid & 2138 & 2141 & 5.0 \(\pm\) 0.03 & 15.9 \(\pm\) 0.09 \\
23 & elaidic acid & 2145 & 2144 & tr & 40.7 \(\pm\) 0.22 \\
24 & stearic acid & 2168 & 2172 & 1.6 \(\pm\) 0.04 & tr \\ \hline  & SUM & & & 100.0 & 99.8 \\  & Monoterpenoids & & & 5.1 & 2.8 \\  & Monoterpenes & & & 12.8 & - \\  & Fatty acids & & & 33.9 & 80.9 \\  & Others & & & 48.2 & 16.1 \\ \hline \hline \end{tabular}  
* the components are reported according to their elution order on apolar column; 2 Linear Retention Indices measured on apolar column; 3 Linear Retention indices from the literature; tr: percentage mean values \(<\) 0.1.  
\end{table} 
Table 1: Chemical volatile composition (percentage mean value \(\pm\) standard deviation) of honey samples.
## 2 VDES Data Link Credibility Monitoring Approach
Any deviation beyond this range indicates a potential spoofing source, prompting further scrutiny through the spoofing source identification algorithm to pinpoint the position or range of the spoofing source. Conversely, compliance with the tolerance range affirms the absence of a spoofing source, thereby confirming the credibility of the VDES data link. Figure 3: Main structural diagram of VDES data link credibility monitoring approach based on a combined detection method.
## 2 Continuous Model
### Equations of Motion  
Kinematics of the beam are assumed to be linear; nonlinearities are accounted for in the description of the aerodynamic forces only. The current configuration, both of the beam and of the cable, is described by the common transverse displacement field \(v\left(s,t\right)\), with \(s\in\left(-l/2,l/2\right)\) an abscissa  
Figure 1: Single-span suspension bridge model.  
along the beam axis and \(t\) the time. The equations ruling the in-plane motion, obtained eliminating the reactive forces \(r(s,t)\) between the cable and the beam equations, are:  
\[\begin{split}& E_{b}I_{b}v^{\prime\prime\prime\prime}-T_{0}v^{ \prime\prime}+E_{c}A_{c}\frac{k^{2}}{l}\int\limits_{-l/2}^{l/2}vds+\left(m_{b} +m_{c}\right)\vec{v}-\left(p_{b}+p_{c}\right)=0\\ & v\left(-l/2,t\right)=0,\quad\ E_{b}I_{b}v^{\prime\prime}\left(- l/2,t\right)=0\\ & v\left(l/2,t\right)=0,\quad\ E_{b}I_{b}v^{\prime\prime}\left(l/2,t \right)=0\end{split} \tag{1}\]  
where \(E_{b}I_{b}\) is the flexural stiffness of the beam, \(E_{c}A_{c}\) is the axial stiffness of the cable, and \(m_{b}\) and \(m_{c}\) are the beam and cable linear mass density, respectively (and therefore the total bridge linear mass density is \(m:=m_{b}+m_{c}\)). Here \(T_{0}\) is the prestress of the cable, assumed constant on \(s\), and \(k=mg/T_{0}\), with \(g\) the acceleration gravity, is the prestress curvature, also assumed to be constant. The integral term accounts for the increment of tension, which depends on the time only.  
In Equation (1), the non-inertial contribution of the external loads are decomposed in the damping and aerodynamic parts, as:  
\[p_{\alpha}:=p_{\alpha}^{d}+p_{\alpha}^{a} \tag{2}\]  
where the subscripts \(\alpha=b,c\) refer respectively the beam and cable.  
The Equation (1) is a generalization of the motion equations provided for the first time by [PERSON] et al. in [13], and recently re-examined in [18]. They include damping and aerodynamic loads, not present in the original formulation.
# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 3 Materials and Methods
### Data Collection
lected based on accessibility, ability to perform rides without interruption, distinguishable differences in elevation throughout the route, and good reception of GPS and internet connectivity. Furthermore, the same route was used to obtain data for different battery levels to ensure the proper distribution of data. In Table 3, we have listed down the data that will be collected using the application from the experimental test rides, among them, which data are being collected every second of the ride and which of them are being input by the user. All the data are collected for every ride separately. We collected data from approximately 100 trips conducted by a rider where the average distance and time for the trips is around 7.46 km and 24.67 min. This rider was male, around 28 years of age, and weighed around 68 kg. The data were gathered using the application, which was provided to the rider on a device for his use during various trips along the designated test routes. All the routes were covered during summer days. The application only asks the rider to give input regarding battery levels at the start and end of each trip. The rest of the data are collected and calculated from different sensors and APIs from within the application in the background throughout the ride. We have used \"Flutter Weather\" plugin to collect weather type and temperature of rider's current location when they navigate. The application was integrated with google maps API as a map server to collect route information. Furthermore, we have used direction API to obtain the list of routes. The GPS sensor along with the geocoding plugin from flutter gives us relevant information regarding location, distance, and speed every second. Subsequently, we have stored all these raw data in cloud storage for further analysis and calculation because of the restricted storage and processing capability of mobile devices [27]. The collection of personal data, such as user locations, riding habits, or other identifiable information, raises privacy concerns. To address this issue, all information related to the users was securely stored in a central database. The collected data were strictly used for the intended research purposes, and no unauthorized usage or sharing of the data was allowed. Proper access controls were implemented to ensure that only authorized personnel had access to the data for research purposes.
## 4 Use Case
### Design
If the operator is in the collaborative area, the robot controller gets the alert signal to reduce the speed, and if he/she is too close to the robot, the robot controller gets the alert signal to stop. Figure 4: Flowchart of the use case process with the tasks of each key player. * HMI. A touch screen is connected to the control system to permit the human interaction so that the operator can know what operations and actions must perform, and get feedback of his/her performance from the system. * Digital twin. The PC which hosts the virtual mirror of the real cell is connected to the cell network to receive the real-time information. * Sensors. The real-time information about movements, events, etc. is provided by the safety system, the presence sensors, the robots, the inspection systems, etc. * Virtual reality interface. Among the different available commercial hardware, _HTC Vive_ glasses were selected. _Unity3D_ is used as the VR engine. Figure 6 shows the layout that has been designed according to the previous steps. Figure 5: System architecture particularized to the use case: subsystems and components. Figure 6: Layout for the physical location of the use case components.
# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 7 Conclusions  
Detecting objects with high accuracy is crucial for autonomous vehicles. Vehicles are required to be aware of every object in their surrounding environment and assure riders' safety. Enhancing object detection accuracy in sandy weather is particularly challenging due to several factors, such as poor object visibility and varying lighting conditions during storms. In this paper, we scrutinized three distinct activation functions (SiLU, ReLU, and LeakyReLU) with two YOLO models (v5 and v7). The highest achieved mAP score in our research was 94% with the SiLU activation function and the augmented DAWN dataset. Notably, our mAP was higher than the other documented outcomes in sandy weather contexts.  
Furthermore, our experiments underscore the fact that no single activation function optimally suits all object detection scenarios. In some cases, YOLOv5 with LeakyReLU  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Activation Function** & **mAP** \\ \hline SiLU & 82\% \\ \hline ReLU & 76\% \\ \hline LeakyReLU & 77\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: SiLU achieved the highest mean average precision in all scenarios using YOLOv5 and YOLOv7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP** & **Function** & \begin{tabular}{c} **Car** \\ **mAP** \\ \end{tabular} & \
\begin{tabular}{c} **Person** \\ **mAP** \\ \end{tabular} &
\begin{tabular}{c} **Bicycle** \\ **mAP** \\ \end{tabular} \\ \hline YOLOv5s & 77\% & SiLU & 82\% & 84\% & 64\% \\ \hline YOLOv5s & 73\% & ReLU & 83\% & 83\% & 54\% \\ \hline YOLOv5s & 75\% & LeakyReLU & 85\% & 84\% & 55\% \\ \hline YOLOv5m & 77\% & SiLU & 85\% & 87\% & 58\% \\ \hline YOLOv5m & 77\% & ReLU & 87\% & 86\% & 58\% \\ \hline YOLOv5m & 79\% & LeakyReLU & 86\% & 87\% & 65\% \\ \hline YOLOv5l & 82\% & SiLU & 86\% & 87\% & 73\% \\ \hline YOLOv5l & 78\% & ReLU & 88\% & 86\% & 60\% \\ \hline YOLOv5l & 79\% & LeakyReLU & 86\% & 85\% & 66\% \\ \hline YOLOv7 & 76\% & LeakyReLU & 95\% & 85\% & 49\% \\ \hline YOLOv7 & 94\% & SiLU & 96\% & 89\% & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of augmented DAWN using YOLOv5 and YOLOv7.  
produced the highest mAP, while in others, YOLOv7 with SiLU proved to be the top performer. For those using YOLOv5 in sandy weather, we recommend adopting the LeakyReLU function for greater accuracy. Conversely, if YOLOv7 is used, employing the SiLU function is highly recommended for optimal results.
## 4 D2D Group Communication Scheme with Bidirectional and InCrentental A-Star (BICA*) Routing
### Control Flow of D2D Group Communication  
When a UE requests a particular content, the proposed D2D group communication scheme will be invoked to find the best path to the content with maximal utility. The control flow for the proposed routing scheme for D2D group communication is given in Figure 4.  
A member UE results its group header for the availability of certain content. The header will execute a decision procedure, which will be discussed in the next subsection, to decide whether the content should be downloaded from the possessing UE or the core network. If it is preferred to download the content from the possessing UE, the header will inform the requesting UE, relay UEs, and possess UE to conduct the content delivery. After  
Figure 4: The control flow for D2D group communication.  
the completion of the delivery, the requesting UE may retain the content in its storage and report to the group header. A signaling diagram for such a D2D intra-group communication is given in Figure 5. On the other hand, if no one in the group possesses the content or downloading the content via D2D intra-group communication fails to meet the delay constraint, the group header will instruct the requesting UE to download the content from the eNB if the eNB holds onto the requested content or the core network if the eNB cannot provide the content. The signaling of downloading data from the eNB is straightforward and omitted here. The signaling diagram is given in Figure 6[33] for downloading content from the core network.  
Upon receiving a request for content \(k\) from UE \(i\), the group header has to decide whether the content should be downloaded from the base station or from possessing UE \(j\). The group header answers this question by the evaluation and comparison of \(\mathrm{U}_{total}^{i,k,eNB}\) and \(\mathrm{U}_{total}^{i,k,j}\), and it follows the decision procedure in Figure 7. The evaluation of \(\mathrm{U}_{total}^{i,k,eNB}\) is straightforward, while the evaluation of \(\mathrm{U}_{total}^{i,k,j}\) is much more involved. The path selection algorithm, discussed in the next subsection, will be invoked for the evaluation of \(\mathrm{U}_{total}^{i,k,j}\). The path selection algorithm is designed to determine the best path connecting UE \(i\) and UE \(j\) with maximal utility. The group header will instruct involved parties to download content \(k\) from the base station if \(\mathrm{U}_{total}^{i,k,eNB}\geq\mathrm{U}_{total}^{i,k,j}\), and download content \(k\) from UE \(j\) otherwise.  
Figure 5: Signaling diagram for downloading content from another group member.  
Figure 6: Signaling diagram for downloading content from the core network.  
Figure 7: Decision procedure of group header.
## 3 Parameter Selection for the MSFGWO Algorithm
### Population Size and Iterations
#### 3.1.1 Effects of Population Size  
The experiment was conducted with 20 iterations, with the population size ranging from 1 to 10 with a step of 1. Four satellites were selected, and the Monte Carlo experiment was repeated 50 times to obtain the average value. The impact of population size on the algorithm's performance was analyzed, as detailed in Figures 5 and 6.  
Combining Figures 5 and 6, as the population size increases, the average DGDOP value gradually approaches the optimal selection result of the exhaustive method. The  
Figure 5: Effect of population size on the performance of the MSFGWO algorithm.  
Figure 6: Effect of population size on DGDOP error.  
average DGDOP value decreased from 444.3042 to 384.1425, with an average error reduction from 42.03% to 22.80%, while the time consumption increased from 0.0251 s to 0.0777 s. From Figures 5 and 6, it is evident that when the population size was set to five, both the average DGDOP and DGDOP average error significantly decreased while time consumption showed linear growth. Therefore, selecting a population size of five will result in less time consumption and a closer approximation of DGDOP values when compared to those obtained using the exhaustive method.
## 1 Introduction
The R, G and B channel greyscales are quantized to enhance image contrast, the colour constancy of Retinex is used to eliminate the adverse effects of scene illumination, and colour distortion, background light, backscatter and direct component transmittance are estimated. The defogged image is obtained by inverse resolution. Finally, the three input images and the corresponding weight maps are fused in a multi-scale framework to provide high-quality, sharpened results. [PERSON] [16] proposed a new RetinexDIP image enhancement method in which noise is considered a factor in image decomposition using deep learning generation strategies. The noise intervention makes the image more realistic, weakens the coupling between the three components, avoids overfitting and improves generalisation. However, these methods still need to be addressed and optimised for the problems of halos and overexposure during image enhancement. In response to the above research, an improved algorithm based on Retinex is proposed, based on homomorphic filtering, fusing bilateral filtering and Retinex, with the addition of luminance processing to achieve image enhancement, which is expected to essentially reduce the effects of haloing in images and can result in more precise details and better processing results. The improved algorithm (BF-MSRCR algorithm) consists mainly of the enhancement of the luminance component V and the adaptive stretching of the saturation component S so that the details of the image are better preserved during the enhancement process,without blurring the edges and without problems, such as overexposure and haloing. Compared with the previously proposed algorithm, this uses multiple scale operators for synthesis in the processing, and does not perform simple mean processing, which takes less time and is not very demanding for the enhancement equipment, and is not prone to the halo phenomenon in the strong shadow transition area, which can solve the problem of uneven illumination of the image under the mine. The common algorithm makes the image overexposed to halos.
# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 4 System Model and Problem Formulation
### Time Cost Model
#### 4.2.2 User Interaction Model  
In training simulation, a user can subscribe to messages of any other user on demand. If user \(U_{i}\) subscribes to the messages of \(U_{j}\), indicating that the results of service requests submitted by \(U_{j}\) will be sent to \(R(U_{j})\) from \(R(U_{i})\), we define \(Ita_{ij}=1\). Thus, the time cost of the subscription of \(U_{i}\) to \(U_{j}\)'s messages due to the service request \(S_{l}\) from \(U_{j}\) is  
\[t^{M}_{ijl}=\lceil\frac{a_{R(U_{j}),R(U_{i})}}{a}\rceil\frac{ms^{out}_{tms_{l \bar{l}}}}{BW} \tag{7}\]  
Therefore, the time cost of the subscription of \(U_{i}\) to \(U_{j}\) can be calculated as  
\[t^{M}_{ij}=\sum_{l=1}^{m}t^{M}_{ijl}d_{jl} \tag{8}\]  
Furthermore, considering the subscription relationship between users, the interaction time cost is formulated as  
\[t^{M}=\sum_{i=1}^{u}\sum_{j\
eq i}(t^{M}_{ij}Ita_{ij}+t^{M}_{ji}Ita_{ji}) \tag{9}\]  
In summary, the first objective function, total time cost due to data transmission between tasks among each service request from all users and interactions between them, can be represented as  
\[f_{1}(DS)=t^{S}+t^{M} \tag{10}\]
## 4 Simulation and Results
### Protocols Compared in the Simulation  
In this simulation, we evaluate and compare the performance of several clustering protocols for WSNs, including classical LEACH, LEACH-B [8], Improved-LEACH [10], LEACH-K (K-means integrated with LEACH) [15], LEACH-GK (grid-based K-means LEACH) [17], PSO-based clustering [22], and the proposed IK-MACHES algorithm. Table 2 summarizes the key features, strengths, and weaknesses of each protocol based on their clustering mechanisms. Classical LEACH employs a probabilistic CH selection mechanism, while LEACH-B improves energy efficiency by incorporating residual energy into CH selection. Improved-LEACH introduces additional randomness to reduce repeated CH assignments. LEACH-K refines cluster formations by leveraging K-means clustering based on the node distance and residual energy, and LEACH-GK further enhances scalability and clustering accuracy by integrating grid-based K-means for WSNs. PSO-based clustering utilizes an evolutionary optimization approach, balancing residual energy and distance metrics for higher adaptability and efficiency. The proposed IK-MACHES extends K-means clustering by integrating mobility-aware CH scoring based on residual energy, mobility stability, and distance metrics, achieving a better stability and network lifetime in dynamic WSN environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multirow{2}{*}{**Clustering**} & \multirow{2}{*}{**Key Metrics**} & \multicolumn{2}{c}{**Feature**} \\ \cline{3-5}  & & & **Strength** & **Weakness** \\ \hline LEACH & Random CH Selection & None & Simplicity & Uneven energy consumption, instability \\ LEACH-B [8] & Residual Energy-Based & \multirow{2}{*}{Residual Energy} & Improved energy efficiency & No mobility, limited scalability \\ \cline{3-5}  & CH Selection & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key features of the clustering protocols for WSNs for performance comparison in this simulation.  
Figure 2: An illustration of the 200 m\({}^{2}\) network area simulation setup with distributed sensor nodes, the CH, and the BS and the autonomous vehicle with a sensor node for each scenario; the BS location at (100 m, 300 m) for Scenario 1 and at (100 m, 100 m) for Scenario 2.