esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
For the last decade, vehicular ad hoc networks (VANETs) have attracted a great deal of attentions due to the development of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. It is a trend of modern vehicles to equip on-board unit (OBU) devices which allow vehicles to communicate with each other as well as roadside units (RSUs) [1]. As a result, up to date, various VANET applications using V2V and V2I communications have been presented to realize not only comfortable driving conditions but also valuable infotainment services on the road. Furthermore, recently, the concept of vehicular networking is extended to vehicular cloud computing by integrating vehicular communications with cloud computing to provide vehicles with pervasive services on the road [2; 3; 4].  
One of the promising applications is a location-aware service [5] which provides vehicles with useful information for a certain geographic area of interest by taking advantage of vehicular cloud computing. Based on vehicular cloud computing technologies, RSUs deployed on the certain areas can collect and provide local-interesting information to vehicles such as traffic conditions and available facilities. However, it is challenging on the VANET how we can effectively deliver such service messages to vehicles which are continuously moving on the road. Due to the dynamic mobility and opportunistic connectivity in VANETs, the probability of successful hop-by-hop (among neighboring vehicles) message forwarding to a destination vehicle at a long distance is low and it would result in high message loss. As a solution to this challenge, trajectory-based message delivery with the help of RSUs in VANETs have been researched [6; 7].  
For example, let us consider a scenario as shown in Figure 1 in which the area around sRSU1 is an interested spot (named socialspot) of a vehicle Vd and sRSU1 provides location-aware service around its local area to Vd. By using trajectory-based message delivery, it would be possible that sRSU1 can efficiently disseminate the service messages to Vd through RSU1 and RSU2 acting as relay nodes along [PERSON]'s trajectory assuming that sRSU1 knows the driving path of Vd.  
On the other hand, driving route or location information of a vehicle is regarded as personal data of the driver, so location privacy is one of the critical security requirements for VANETs as well as identity privacy preservation. In the above trajectory-based message delivery environment, if a vehicle needs to share its driving route with message senders (sRSUs) deployed on some socialspots in which the vehicle is interested for location-aware service, how to share vehicle's trajectory securely with the sRSUs in the system for privacy preservation is required. In other words, the system must be carefully designed not to expose the driving locations of a vehicle to other entities except the sRSUs designated by the vehicle.
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 5 Experimental Results and Discussion
### Results and Discussion
#### 5.2.2 Performance of Robustness
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Common Attacks** & **NC** & **Geometric Attacks** & **NC** \\ \hline No attacks & 0.9980 & Scaling 0.25 & 0.9904 \\ JPEG (50) & 0.9966 & Scaling 0.5 & 0.9969 \\ JPEG (70) & 0.9971 & Scaling 0.9 & 0.9961 \\ Median filter (3 \(\times\) 3) & 0.9939 & Scaling 1.2 & 0.9911 \\ Median filter (4 \(\times\) 4) & 0.9868 & Rotation 2\({}^{\circ}\) & 0.9704 \\ Median filter (5 \(\times\) 5) & 0.9858 & Rotation 5\({}^{\circ}\) & 0.9542 \\ Gaussian noise (0, 0.05) & 0.9293 & Rotation 10\({}^{\circ}\) & 0.9784 \\ Gaussian noise (0, 0.1) & 0.8315 & Rotation 30\({}^{\circ}\) & 0.9685 \\ Speckle noise (0.01) & 0.9873 & Rotation 45\({}^{\circ}\) & 0.9608 \\ Speckle noise (0.1) & 0.9254 & Horizontally translation (256 pixels) & 0.9974 \\ Salt and pepper noise (0.01) & 0.9856 & Vertically translation (256 pixels) & 0.9974 \\ Salt and pepper noise (0.05) & 0.9452 & Horizontally translation (128 pixels) & 0.9978 \\ Center cropping (25\%) & 0.8461 & Vertically translation (128 pixels) & 0.9978 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Robustness of the proposed algorithm on image Elaine. Figure 11: Attacked images and extracted watermarks under geometric attacks: (**a**) 5\({}^{\circ}\) rotation; (**b**) extracted watermark from (**a**); (**c**) 10\({}^{\circ}\) rotation; (**d**) extracted watermark from (**c**); (**e**) 30\({}^{\circ}\) rotation; (**f**) extracted watermark from (**e**); (**g**) 45\({}^{\circ}\) rotation; (**h**) extracted watermark from (**g**); (**i**) scaling (0.25); (**j**) extracted watermark from (**i**); (**k**) scaling (0.5); (**l**) extracted watermark from (**k**); (**m**) scaling (0.9); (**n**) extracted watermark from (**m**); (**o**) scaling (1.2); (**p**) extracted watermark from (**o**); (**q**) vertical translation (256 pixels); (**r**) extracted watermark from (**q**); (**s**) horizontal translation (256 pixels); (**t**) extracted watermark from (**s**).
## 3 Results and Discussion
### Laboratory Demonstration
We continue to increase the frequency up to 0.9 Hz but do not see any re-occurrence of such phenomena. The same event occurs when tuning the frequency backwards. Therefore, it is a certain physical effect that the wave amplitude is enhanced, although not as high as expected. Two factors may contribute to the discrepancy between the experimental observation and the theoretical prediction. Froude scaling was used to coordinate the results from our reduced-scale experiments and the prototype observations under real-scale sea conditions. With a Froude scaling factor \(s\) (\(\sim\)100 in our experiment), the spatial dimension (water depth, wave height, etc.) scales as \(1/s\) and temporal dimension (wave period) scales as \(1/\surd s\). Therefore, velocity scales as \(1/\surd s\) while gravity is in the same scale of the prototype observation. However, surface tension and viscosity (dissipation) are much too large in the reduced-scale experiment, thus diminishing the observed wave amplitude enhancement. Therefore, it is well expected that the theoretical prediction would overestimate the wave amplitude enhancement. Before concluding our work, we would like to emphasize that it is crucial to perform a reduced-scale physical test like the one presented here because the investment on a real-scale test without careful evaluation can be very risky. A lab test can help validate the theoretical predictions and minimize the unexpected physical factors that would complicate the on-site deployment. Our reduced-scale test demonstrates the feasibility of the proposed solution to energy enhancement, which provides a faithful basis toward the real-scale test.
## 4 Experimental Data
### Summary Statistics
Each gas type was utilized within particular plasma intensity ranges based on its role and efficiency in semiconductor processes. In the case of distinct operational ranges for each gas, some gases like NF3 and SF6 are employed at lower plasma intensities (!100) due to their specific reactivity profiles and application requirements, such as surface treatment or cleaning processes. Other gases, such as CH2F2 and CHF3, are typically used at medium plasma intensity levels (500-700) for processes requiring moderate energy inputs, while high-intensity plasma levels (?1000) are reserved for gases like C4F6, C4F8, and CF4, which are better suited for etching applications that demand higher energy levels to achieve desired material removal. In the case of process-specific limitations, certain gases are incompatible with plasma conditions outside their designated ranges due to stability issues or inefficiencies in reaction kinetics. For example, using NF3 and SF6 at higher plasma intensities could lead to undesirable side reactions or inefficiencies whereas gases like C4F6, C4F8, and CF4 are optimized for higher intensities and may not exhibit effective performance at lower intensities. These constraints ensure that each gas operates within its optimal range, maintaining both process stability and efficiency during semiconductor manufacturing. In the case of experimental constraints, the distribution of plasma intensity levels across gases reflects practical limitations in conducting measurements and ensuring reliable data acquisition. Some intensity levels were excluded for specific gases to avoid redundancy or irrelevance to the research objectives. This selective approach was necessary to focus on collecting data that would provide meaningful insights while adhering to the operational requirements of each gas. By tailoring the intensity ranges to the most relevant conditions, \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline  & **40** & **50** & **60** & **70** & **80** & **90** & **500** & **600** & **700** & **1000** & **2000** & **3000** & **Total** \\ \hline C4F6 & - & - & - & - & - & - & - & - & - & 302 & 302 & 302 & 906 \\ C4F8 & - & - & - & - & - & - & - & - & - & 1033 & 855 & 609 & 2577 \\ CF4 & - & - & - & - & - & - & - & - & - & 305 & 304 & 305 & 914 \\ CH2F2 & - & - & - & - & - & - & 441 & 442 & 429 & - & - & - & 1312 \\ CHF3 & - & - & - & - & - & - & 409 & 410 & 411 & - & - & - & 1230 \\ NF3 & 96 & - & 98 & - & 96 & - & - & - & - & - & - & - & 290 \\ SF6 & - & 363 & - & 366 & - & 366 & - & - & - & - & - & - & 1095 \\ \hline \end{tabular}
\end{table} 
Table 6: Frequency of plasma level by gas. the study ensured a robust and accurate analysis of the relationship between plasma intensity and 1-Ui.
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 4 Flow Physics at the Optimal Attitude  
In this section, we aim to analyze the hydrodynamic effects of different postures based on the downforce generated in each posture. The mechanism by which downforce is generated due to the fluid flow around the robot can be explained by two principles. The first principle is that the robot's body acts as a lifting body, generating downward lift. Through this, we identified the pitch angle that produces the maximum downforce, i.e., the downward lift. This optimal pitch angle is found to correlate with the angle of attack that generates the maximum lift before a stall in a typical foil. The second principle is the Venturi effect, which occurs between the robot's lower body and the seabed. In the space between the robot's underside and the seafloor, the flow accelerates, causing a pressure drop that pulls the robot's body downward. This phenomenon, often referred to as the Venturi effect, is similar to the principles utilized in the design of many motorsport vehicle bodies. We aim to analyze these two downforce generation mechanisms independently through a parametric study. Each of the two parameters controlling the posture will be examined individually.
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 2 Method  
Our method is modularity-based, and multiresolution. It includes three components: (1) a modified community fitness function with a tunable resolution parameter and a scaling factor, (2) a heuristic Louvain algorithm to maximize this community fitness function, and (3) a strategy to filter the output and retrieve the most stable and significant results. Next, we introduce these three components separately; at the end of this section we display the whole framework of our approach.
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 4 Substrate Integrated Waveguide (SIW) Antennas
Figure 17: Basic 4 \(\times\) 2 antenna block in SIW technology. Figure 18: SIW array prototype: (**a**) Sketch of the prototype; (**b**) Photograph of the prototype. The measured radiation pattern is shown in Figure 19, while the variation of the main lobe with frequency is shown in Figure 20. The results show that the main lobe was almost fixed with the frequency. The gain of the antenna was 25 dB and it was almost constant from 10.7 GHz to 12.7 GHz. The value was slightly lower than expected from simulations (27 dB), probably because of higher losses than expected in the substrate.
## 3 Influence Factors Discussion  
### Structure Damping  
Structure damping is a parameter reflecting the energy dissipation in the vibration process, the effect of which on TACR energy transmission is studied in this section. Aluminum alloy wheel structure damping generally varies from 1% to 2%; thus, the structure damping was set to be 0.01, 0.015 or 0.02 in the simulation, while other parameters remained constant. The input and output power flow calculation results in the two sizes of wheels are shown in Figure 17.  
These results indicate that with the increase in structure damping, the input and output power flows both decreased, as expected. The power flow change and rate of decrease are shown in Table 4. Damping represents the energy dissipation ability, so TACR energy loss in the transmission process was greater under the same conditions with the  
Figure 16: Locations of the structure intensity max values in the two wheels: (**a**) 14-inch wheel and (**b**) 15-inch wheel.  
Figure 17: Input and output power flow in the wheels versus structure damping: (**a**) input power flow and (**b**) output power flow.  
increase in structure damping. There are several effective ways to increase the wheel structure damping, such as additional components or using fiber-reinforced composite materials [28; 29].
## 5 Analyses and Results
### Results of Structural Model Assessment  
The results of the structural model assessment revealed that the coefficient of determination (\(R^{2}\)) value was 0.37 of the endogenous latent variables in the structural model, which is more than the minimum acceptable level of \(R^{2}\) (See Figure 3). Thus, it can be concluded that 37% of the variation in the SaaS quality was explained by the variations in the configuration, integration, extension, modification, and composition variables at a 95% confidence level.  
Testing hypotheses using maximum likelihood estimation (MLE) technique revealed that four of the five hypotheses, H1, H2, H3, and H5 are statistically supported. The results  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **SaaS Quality** & **Configuration** & **Integration** & **Extension** & **Modification** & **Composition** \\ \hline SaaS Quality & \(0.745\) & & & & \\ Configuration & \(0.39\) & \(0.740\) & & & \\ Integration & \(0.11\) & \(0.02\) & \(0.760\) & & \\ Extension & \(-0.15\) & \(0.06\) & \(0.11\) & \(0.76\) & \\ Modification & \(-0.03\) & \(0.57\) & \(-0.02\) & \(0.000\) & \(0.851\) & \\ Composition & \(0.31\) & \(0.03\) & \(0.11\) & \(0.15\) & \(0.03\) & \(0.716\) \\ \hline \hline \end{tabular} Note: Diagonal elements (bold figures) are the square roots of AVE values, and off-diagonal elements are the correlations among the constructs.  
\end{table}
Table 4: Discriminant validity and correlations.  
Figure 3: Results of structural model assessment.  
show that standardized estimates for these hypotheses 0.589, 0.33, \(-\)0.144, and \(-\)0.386 respectively) are statistically significant, whereas one hypothesis, H4, with standardized estimate (0.052) was found to be statistically insignificant. This hypothesis, statistically, should be rejected. The overall results of hypothesis testing are presented in Table 5.  
Subsequently, we test the structural model once more, by omitting the integration construct. It revealed the same results of \(R^{2}\) and P values for H1, H2, H3, and H5 as that of the initial structural model. Furthermore, the Beta values implied that configuration, modification, composition, and extension, respectively, have the most impact on SaaS quality. Figures present detailed structural model with/out integration construct are available in the Supplementary Materials.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 6 Performance Evaluation and Analysis  
In order to evaluate the performance of the key recovery algorithms, 14-nodes national science fund network (NSFNET) topology is used in simulation, as shown in Figure 9. The costs of the links are related to the values of L\({}_{\text{r}}\).  
The classification and characteristics of the services in the simulation are shown in Table 2. Among the different service types, only low security level services can be encrypted using the recycled key in order to ensure the security quality. And when both the original key and the recycled key satisfy the encryption requirements of the service, the recycled key is used preferentially.  
Figure 9: NSFNET topology with 14 nodes, which is the network topologies used in simulation.  
Table 3 shows the relative security factors for different types of keys. From the perspective of the source of the key, the original key is generated directly by QKD, so the original key is the safest and should be set to 1 compared to the recycled key. Part of the mixed-recycling key comes from the all-recycling key, and the other part comes from the partial-recycling key, so its relative security factor should be between the all-recycling-key security factor and the-partial-recycling key security factor. The specific value is related to the decision ratio of the mixed-recycling algorithm.  
In order to reflect the value and cost of the QKR algorithm, the simulation results are divided into two categories. The first category expresses how the QKR algorithm optimizes the QKD network by showing the impact of different service failure rates (SFRs) and demonstrates the cost of deploying the QKR algorithm by showing the relative security level. The second category shows how the different algorithms are affected by the ratio of different security requirements and the rate of key generation. It also shows the balance of the algorithms and provides ideas for further optimization.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution
### Factors Affecting Minimum Cable Tension
#### 4.5.1 Cable Preload  
The pretension force of the cable is the key parameter to ensure that the cable always stays in a tensioned state. The choice of preload force directly affects the range of tension variation in the cable in different motion states. If the preload is too low, the cable may relax, causing the system to lose control. If the preload is too high, it will increase the burden on the cable and drive system and may even cause the cable to break.
## 4 Performance Analysis
### Effective Capacity
#### 4.4.2 MRC System  
By inserting (20) into (51) and performing some similar manipulations as (52), the integral term \(I_{5}\) in (51) can be derived as  
\[I_{5}=(\Theta_{1}/\Gamma(A))H_{1,0,[1,2]_{2}]_{-1:\pm}}^{0,1:[2,1]_{-1:\pm}} [1/\lambda_{1},\cdots,1/\lambda_{L}|_{-}^{(1-A;(0.5\alpha_{l})_{l=\pm L})}|_{( (n,0.5\alpha_{l})_{l})_{l=\pm L}}^{((1-\mu_{l},1))_{l=\pm L}}]. \tag{55}\]  
For the two-branch correlated case, by inserting (33) into (51), the integral term \(I_{5}\) in (51) can be expressed as  
\[I_{5}=\sum_{k=0}^{\infty}\sum_{l=0}^{\infty} \frac{0.25\Phi_{p}^{k}\Phi_{p}^{k}[\Gamma(A)]^{-1}}{k!\Gamma(k+ \mu)\Gamma(l+n)} \tag{56}\] \[\times H_{1,01,21,21,2}^{0,1:2,2:1}[1/\theta_{1},1/\theta_{2}|_{-}^{(1 -A,0.5\alpha_{1},0.5\alpha_{2})}|_{(0,0.5\alpha_{l}),(n+l,0.5\alpha_{1})}^{(1 -\mu-k,1)}(0.5\alpha_{2}),(n+l,0.5\alpha_{2})].\]  
Hence, the effective capacity for the MRC system over independent and correlated \(\alpha\)-\(\mu\)/IGA fading channels can be obtained by plugging (55) and (56) into (51), respectively. Similarly, by inserting (24) into (51), a unified expression of the effective capacity by using the mixed \(\alpha\)-\(\mu\) model can also be obtained. As an alternative approach to finding the analytical expression of the effective capacity, an MGF-based approach has been adopted [51]. Here, we employ the Gauss-Legendre quadrature method again, an alternative approximated form of (51) can be expressed as  
\[\Re=-\frac{1}{A}\log_{2}\left(\frac{1}{\Gamma(A)}\int_{0}^{\infty}x^{A-1}\exp (-x)MGF_{\gamma}(x)dx\right)=-\frac{1}{A}\log_{2}\left(\frac{1}{\Gamma(A)} \sum_{i=1}^{N}\omega_{i}t_{i}^{A-1}MGF_{\gamma}(t_{i})\right), \tag{57}\]  
where \(MGF_{\gamma}(*)\) can be obtained by using (9), (22), (32), and (A1).
## 3 Efficient Superframe Structures with Adaptive Duty Cycle \((Ess_{ADC})\)  
In this work, an efficient superframe structure with adaptive duty cycle \((Ess_{ADC})\) is proposed that optimally adapts the duty cycle of superframe structure of \(Ess\) to meet the adaptive GTS requirements of GTS requesting nodes. The proposed algorithm in \(Ess_{ADC}\) offers reduced delay and increased GTS utilization as compared to \(Ess\) and the standard by effectively using some existing and unused parameters of the standard.  
Initially all the GTS requesting nodes \((Node_{GTS})\) are required to determine the number of GTS required \((GTS_{req})\) to send their data. \(GTS_{req}\) in the standard is calculated by knowing the requested data (DR) and data slot capacity \((DS_{cap})\) of each slot of the superframe as:  
\[GTS_{req}=\left\lceil\frac{DR}{DS_{Cap}}\right\rceil \tag{9}\]
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.1 Effectiveness of Knowledge Transfer  
In the following two experiments, the network structure of VGG-16-7 is used. The image blocks with a size of 23 \(\times\) 23 were extracted from the DVIRE dataset, and the image blocks with a size of 31 \(\times\) 31 were extracted from the CHASE dataset. On the DRIVE dataset, three experiments were used to verify the effectiveness of transfer learning. Firstly, the random initial model is used for training and testing, and the experimental results are obtained. Secondly, using the transfer parameters to initialize the model, we remain the transfer parameters of the convolution layer and only the finetuning parameter of the classifier. Finally, the transferring parameters are used to initialize the model and finetune the whole model. The same operation is used for CHASE_DB1. The experimental results in Dataset 1 and Dataset 2 are shown in Tables 2 and 3.  
In Table 2, when knowledge transfer was used and the whole model was fine-tuned, Acc and SE reached the highest values, 96.58% and 79.35% respectively, which were increased by 0.06% and 2.92% compared with the random initial model. According to the data on the DRIVE dataset, Acc and SE of the whole model using knowledge transfer and finetuning are higher than those of the random initial model and using knowledge transfer only in the finetuning classifier.  
In Table 3, Acc and SE improved by 0.07% and 2.39%, respectively, when using knowledge transfer and finetuning of the whole model compared with the random initial model. Because the parameters of knowledge transfer are trained, the model after knowledge transfer has the ability to extract features. After finetuning with a dataset of retinal images, the model gains the ability to extract features of retinal blood vessels. Consequently, on the DRIVE and CHASE_DB1 datasets, better results can be achieved when using knowledge transfer and finetuning the whole model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline Random initialization & 96.52 & 76.43 & 98.47 \\ Finetuning classifier & 96.56 & 73.69 & **98.80** \\ Finetuning whole & **96.58** & **79.35** & 98.27 \\ network & \multicolumn{1}{c}{} & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters for finetuning the whole network are compared with the parameters of the random initialization and finetuning classifier in the DRIVE dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline Random initialization & 97.03 & 73.30 & **98.76** \\ Finetuning classifier & 97.07 & 75.17 & 98.66 \\ Finetuning whole & \multicolumn{1}{c}{} & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters for finetuning the whole network are compared with the parameters of the random initialization and finetuning classifier in the CHASE_DB1 dataset.