esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Experimental Results and Analysis
#### 3.3.4 Performance Comparison  
To verify the superiority of the leading autonomous vehicle experiment (the proposed experiment), we compared it with other experiments, including an all human-driven vehicle experiment and a leading human-driven vehicle experiment. The comparison between the proposed experiment and the all human-driven vehicle experiment is shown in Figure 10. Regarding mobility, the proposed experiment achieved a higher average speed and a lower delay time compared with the all human-driven vehicle experiment. As seen in Figure 10a,b, the 10% AV penetration rate achieved an improvement in average speed of 1.16 times and an improvement in delay time of 1.44 times compared with all human-driven vehicle experiment. Furthermore, full-autonomy traffic achieved an improvement in average speed of  
Figure 9: The results of the measures of effectiveness (MOE) evaluation based on the AV penetration rate: (**a**) average speed vs. AV penetration rate; (**b**) delay time vs. AV penetration rate; (**c**) fuel consumption vs. AV penetration rate; and (**d**) emissions vs. AV penetration rate.  
1.38 times and an improvement in delay time of 2.55 times compared with the all human-driven vehicle experiment. In terms of energy efficiency, the proposed experiment achieved lower fuel consumption and emissions compared with the all human-driven vehicle experiment. As shown in Figure 10c, the 10% AV penetration rate achieved an improvement in average reward of 3.63 times compared with the all human-driven vehicle experiment. In addition, full-autonomy traffic achieved an improvement in average reward of 24.77 times compared with the all human-driven vehicle experiment. Hence, the proposed experiment outperformed the all human-driven vehicle experiment in terms of both mobility and energy efficiency.  
The comparison between the proposed experiment and the leading human-driven vehicle experiment is shown in Figure 11. Regarding mobility, the proposed experiment achieved a higher average speed and a lower delay time compared with the leading human-driven vehicle experiment. As seen in Figure 11a,b, the proposed experiment achieved an improvement in average speed of 1.02 times and an improvement in delay time of 1.06 times compared with the leading human-driven vehicle experiment. In terms of energy efficiency, the proposed experiment achieved lower fuel consumption and emissions compared with the leading human-driven vehicle experiment. As shown in Figure 11c, the proposed experiment achieved an improvement in average reward of 1.22 times compared with the leading human-driven vehicle experiment. Hence, the proposed experiment outperformed the leading human-driven vehicle experiment in terms of both mobility and energy efficiency.  
Figure 10: Comparison between the leading autonomous vehicle experiment and the all human-driven vehicle experiment: (**a**) average reward vs. AV penetration rate; (**b**) average speed vs. AV penetration rate; and (**c**) delay time vs. AV penetration rate.
## 2 Related Work and Background
### Network Layer  
Due to the limitations of IP networks, most existing solutions for utilizing user bandwidth resources through network layer technologies rely on ICN.  
In one study [23], the authors combined the concept of crowdsourcing with ICN and proposed Crowdsourced CCN Mobile Networks (CCCMNs). CCCMN considers a group of mobile users, where all mobile devices are equipped with name-based routing and content caching (CCN services). Nearby users can form a collaboration group through Wi-Fi, crowdsourcing their wireless bandwidth and caching resources for collaborative content distribution. Contribution-aware ICN [24] utilizes user-side resources to assist in content delivery. CA-ICN extends the ICN architecture by introducing a content distribution manager to manage and generate user contribution levels (CLs). It also proposes contribution-aware name routing to incentivize users to contribute in ICN. In [25], the authors proposed a method to construct a Named Data Networking (NDN) resource leasing market using blockchain technology. The market is designed as a reverse auction operated by content providers, and incentive mechanisms for user access points are studied. All of these methods are based on name-based routing in the ICN paradigm and are mainly applicable for innovative ICN architecture research. They are difficult to integrate with existing network infrastructure.  
Table 1 compares and summarizes the above methods for aggregating and utilizing idle bandwidth. Although there are differences in the hierarchy and application scenarios of these methods, they all indicate that using idle bandwidth can bring improvements and benefits to the corresponding systems, and these methods can be further divided into centralized and distributed methods. The centralized method uses a central index server to establish resource indexes and be responsible for task scheduling, while the distributed method adopts the DHT approach. Although it avoids the drawbacks of centralized architecture, it will affect the efficiency of routing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme Category** & **Typical Representative** & **Application Scenarios** & **Characteristics of the Scheme** \\ \hline \multirow{4}{*}{Application layer} & \multirow{2}{*}{HCDN [16]} & \multirow{2}{*}{Content Distribution} & Uses P2P technology at the edge of CDN networks to leverage users’ idle bandwidth and storage resources to assist content distribution \\ \cline{3-4}  & & & Uses chord DHT to maintain the entire network topology and routing, introduced blockchain technology \\ \cline{2-4}  & Bittorrent [21] & File Sharing & Multipoint file transfer and P2P technology \\ \hline \multirow{4}{*}{Network layer} & \multirow{2}{*}{CCCMN [23]} & \multirow{2}{*}{Content Distribution} & Name-based routing and content caching, edge wireless base station scenario \\ \cline{2-4}  & & & Introduces a content distribution manager to manage and generate user contribution levels \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of methods for utilizing idle bandwidth in different scenarios.
## 6 Discussion and Limitations
### Watermark Generation and Encoding  
Watermarks in DICATION can be either randomly generated or derived from meaningful messages (e.g., model owner and receiver identifiers). When the identifier's encoding space does not match the watermark space, cryptographic hashing, such as HMAC [10], serves as an effective solution. An interesting enhancement involves including a frozen part of the original model in the hash calculation, creating a watermark that attests to both the user's identifier and the link between the original and watermarked models, and providing additional authenticity verification.
# A TAM-Based Study of the Attitude towards Use
## 3 Research Method
### Research Tools  
In this study, a questionnaire survey was employed to investigate the acceptance of integrated multimedia teaching material of school teachers in Chiayi County.  
The survey instrument is based on constructs validated in [PERSON] (2016), and adapted to the context of this study. The survey items include perceived usefulness, perceived ease of use, attitude, and intention to use. All questionnaire items were measured using a 5-point Likert scale ranging from \"strongly agree\" to \"strongly disagree\". The survey instrument consists of 4 items for perceived ease of use, 4 items for perceived usefulness, 4 items for attitude toward using, and 5 items for intention to use. The survey questions were written as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Classes** & **No. of Schools** & **No. of Teachers** \\ \hline Less than 6 & 69 & 738 \\ Between 7 and 12 & 21 & 364 \\ Between 12 and 24 & 20 & 519 \\ Greater than 25 & 12 & 696 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of school size in Chiayi County, 2016.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & **Age** & **Education** \\ \hline
**Item** & **No.** & **Item** & **No.** & **Item** & **No.** \\ \hline Female & 295 & Under 30 & 30 & Bachelor’s degree 174 \\ Male & 146 & Between 31 and 40 & 135 & Master’s degree 267 \\  & & Between 41 and 50 & 228 & \\  & & Above 51 & 48 & \\ \hline Total & 441 & & 441 & 441 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographic information of the participants.  
Perceived Usefulness  
Q1. Using the multimedia material in my class helps me to control the pedagogy.  
Q2. Using the multimedia material in my class enhances the teaching performance.  
Q3. I find the multimedia material useful in my class.  
Q4. Using multimedia materials makes it easier to catch individual students' needs.  
Perceived Ease of Use  
Q5. It is easy to become skillful at using multimedia materials.  
Q6. I find it easy to apply the multimedia material in my class.  
Q7. Using multimedia materials is easy and understandable.  
Q8. Using multimedia materials is more flexible to teach than traditional one.  
Attitude Toward Using  
Q9. Using multimedia material in class is good.  
Q10. My using multimedia material in class is favorable.  
Q11. It is a positive influence for me to use multimedia material in class.  
Q12. I think it is valuable to use multimedia material in class.  
Q13. I think it is a trend to use multimedia material in class.  
Intention to Use  
Q14. I tend to use using multimedia materials in my class.  
Q15. I increase the occurrences of using multimedia materials in class.  
Q16. Using multimedia materials in my class to enhance students' learning interest.  
Q17. I'd love to use multimedia materials in my class.  
Q18. I use multimedia materials to provide multi-approaches on teaching.
###### Abstract  
Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue. In this paper, we propose an end-to-end system that can detect Bengali sign characters from input images or video frames and generate meaningful sentences. The proposed system consists of two phases. In the first phase, a quantization technique for the YoloV4-Tiny detection model is proposed for detecting 49 different sign characters, including 36 Bengali alphabet characters, 10 numeric characters, and 3 special characters. Here, the detection model localizes hand signs and predicts the corresponding character. The second phase generates text from the predicted characters by a detection model. The Long Short-Term Memory (LSTM) model is utilized to generate meaningful text from the character signs detected in the previous phase. To train the proposed system, the BdSL 49 dataset is used, which has approximately 14,745 images of 49 different classes. The proposed quantized YoloV4-Tiny model achieves a mAP of 99.7%, and the proposed language model achieves an overall accuracy of 99.12%. In addition, performance analysis among YoloV4, YoloV4 Tiny, and YoloV7 models is provided in this research.  
deep learning; natural language processing; sign character detection; sign language; computer vision; Yolov4-Tiny; long short-term memory +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Performance Evaluation
### The Optimization Results of the Control Parameters for MPC Control  
Figure 10 displays the optimization results for MPC control parameters. In the case of MPC, the selection of weighting coefficients \(\alpha_{1}\) and \(\alpha_{2}\) influences the controller's performance. A larger \(\alpha_{1}\) leads to quicker system response in bringing the output voltage close to the reference value, while a larger \(\alpha_{2}\) enhances the stability of maintaining the output voltage around the reference value. After multiple manual adjustments, the parameters \(\alpha_{1}=100\) and \(\alpha_{2}=1\) are chosen for comparison with subsequent optimization  
Figure 9: The optimization results for SMC control parameters: (**a**) The output voltage of the DAB; (**b**) the evolution of control parameters; (**c**) Bode diagram of the closed-loop system with pre- and post-optimized control parameters.  
results. The optimized MPC control parameters obtained through GA are \(\alpha_{1}=655.5732\) and \(\alpha_{2}=78.1755\).  
Figure 10a illustrates the output voltage of the DAB, demonstrating a reduced voltage drop from the maximum drop value of 990 V to 993 V when the load suddenly decreases. Additionally, the maximum drop rate reduced from 1.00% to 0.70%, indicating an enhancement in stability. Figure 10c showcases the Bode diagram of the DAB system, with the gain margin increasing from 25.8 dB to 26.2 dB after optimization, further highlighting the enhanced stability achieved by the optimized parameters.  
In summary, although the settling time slightly increases due to the reduced voltage drop, the use of GA to optimize the control parameters of the DAB results in improved control of the output voltage and reduced steady-state error. Therefore, GA exhibits great  
Figure 10: The optimization results for MPC control parameters: (**a**) The output voltage of the DAB; (**b**) the evolution of control parameters; (**c**) Bode diagram of the closed-loop system with pre- and post-optimized control parameters.  
potential in optimizing controller parameters, effectively enhancing the performance and stability of control systems.
## 3 Analysis of Experimental Results  
### Uniaxial Compression Process and Experimental Results  
The experimental design and physical parameters of the siltstone are provided in Table 1, where RS-1, RS-2 and RS-3 are uniaxial compression tests and the rest are cyclic disturbance loading tests. The stress-strain curves of uniaxial compression tests and the comparison of the specimens pre- and post-failure are shown in Figure 4, where the peak stress in the stress-strain curve is the compressive strength. The uniaxial compression stress-strain curve exhibits four stages: microfracture compaction stage (I), elastic deformation stage (II), yielding stage (III), and post-peak stage (IV).  
It can be seen from Figure 4 that during uniaxial compression, elastic modulus increases first and then decreases as the axial stress increases, indicating that when the axial stress is applied at the beginning of the experiment, the cracks inside the specimen are compacted, leading to an increase in elastic modulus. When the axial stress is applied to a certain extent, the cracks inside the specimen begin to propagate and then the main cracks coalesce, leading to a decrease in elastic modulus. Afterward, the axial stress continues to increase until it exceeds the bearing capacity of the specimen and then the specimen is ruptured.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs  
In the case of conventional substrates (e.g., glass, silicon), the process temperature and the effects of a high water vapor transmission rate and bending strain need not be considered. In contrast, all these effects will have to be considered on flexible substrates, especially the thermal expansion of the substrates during each process step. Temperature change will result in a mismatch between films and substrates, which can degrade the materials and the devices. This mismatch is contributed to by the large differences in the coefficients of thermal expansion (CTE), elastic modulus and toughness between polymer substrates and functional films. Three types of polymer substrates are shown in Figure 2[149], which are categorized by their maximum process temperature: conventional polymers (T\({}_{\text{g}}\) < 100 \({}^{\circ}\)C), common high-temperature polymers (100 \(\leq\) T\({}_{\text{g}}\) < 200 \({}^{\circ}\)C) and high-temperature polymers (T\({}_{\text{g}}\)\(\geq\) 200 \({}^{\circ}\)C). Polyimide (PI), polyethylene naphthalene (PEN), polyethylene terephthalate (PET), polydimethylsiloxane (PDMS) and polycarbonate (PC) are the most commonly used flexible substrates. Hence, flexible oxide-based TFT fabrication on such substrates is discussed respectively in this section.  
Figure 1: Typical device configurations of oxide semiconductors: (**a**) back channel etch (BCE) structure, (**b**) etch stop (ES) structure, (**c**) top gate (TG) structure, (**d**) dual gate (DG) structure, (**e**) co-planar (CP) structure.
# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 4 VM Placement Optimization Method
### Chaos-Elite NSGA-III
#### 4.1.5 Elite Guidance  
To accelerate its convergence to a global optimum of the algorithm, integrating elite solutions to guide population evolution can enhance the algorithm's global search capability [30]. Therefore, an elite-guided algorithm is proposed to guide the population toward the Pareto optimal solutions. This ensured that the algorithm continuously explored new  
Figure 4: The schematic of distance \(d_{j,1}(y)\) and \(d_{j,2}(y)\).  
non-dominated solutions while maintaining the discovered high-quality solution set and accelerating convergence.  
The process to guide the population evolution toward the Pareto front is as follows. First, randomly select individuals from the generated set of all the elite individuals, and add them to the leader set. Next, generate a random component index set, and extract individuals \(t_{1}\) and \(t_{2}\) from the current population and the leader set, respectively. Finally, replace the components of \(t_{2}\) at the current index positions with the corresponding components of \(t_{1}\), place the modified individual \(t_{1}\) back into the population, and return the updated population. Here, the population corresponds to the set of possible VM placement optimization schemes generated during the genetic evolution process. As a result, summarized the above process, the elite-guided algorithm is proposed, which is depicted as Algorithm 2.  
```
Input: Initialized population \(P\) of size \(N\), Archive \(A\) of elite individuals Output: The updated population \(P^{\prime}\)
1 Initialize an empty list \(L\) for leaders
2 Instantiate a random number generator RandomGen
3 for\(i=0\) to \(N-1\)do
4 Temp \(\leftarrow\) RandomGen.nextInt(0, \(|A|-1\)) \(L_{\text{Append}}(A[\text{Temp}])\)// Randomly select individuals from Archive
5
6 end for
7 Set crossover probability \(\rho=0.05\)
8 Calculate crossover length \(cLen=\lceil\rho\times d\rceil\), where \(d\) is the dimensionality
9 for\(i=0\) to \(N-1\)do
10 Generate \(cLen\) distinct random indices \(R\) from \([0,d-1]\)
11\(t_{1}\gets P[i]\)// Take \(t_{1}\) from the current population
12\(t_{2}\gets L[i]\)// Take \(t_{2}\) from the leader set
13 for each index \(\in R\)do
14\(t_{1}\).variables\([index]\gets t_{2}\).variables\([index]\) // Replace the variable at index of \(t_{1}\) with the corresponding index of \(t_{2}\)
15\(P[i]\gets t_{1}\)// Put \(t_{1}\) back into the population
16
17 end for
18
19 end for return\(P^{\prime}\)// Return the updated population \(P^{\prime}\)
```  
**Algorithm 2**Elite-guided algorithm  
The time cost of Algorithm 2 mainly yields from lines 11-18, which involve iterating over the individuals in the population and replacing components at index positions. If the number of individuals in the population is assumed to be \(N\) and the length of the index set is \(L\), then the time complexity of Algorithm 2 is \(O(NL)\).
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Solution to the Optimization Problem  
This section discusses the methodology for solving the UAV flight path optimization problem described above. Since the objective function of this problem exhibits significant variations in shape due to terrain and multipath effects, optimization using gradient information is challenging. Moreover, due to the enormous computational cost associated with radio wave propagation simulations, heuristic methods, which provide good approximate solutions without guaranteeing optimality, are more suitable.A strategy that refines heuristic solutions to further approach optimal solutions is known as a metaheuristic. In metaheuristics, the solution space is explored by iteratively performing the following two operations: (1) generating new solutions using past search history and (2) evaluating the generated solutions and feeding back the necessary information for subsequent searches. In this study, we focus on two metaheuristic methods: particle swarm optimization (PSO) and genetic algorithms (GAs). By utilizing these methods in simulations, an optimized UAV flight trajectory that enables high-accuracy localization can be determined.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 3 Performance Evaluation
### The Relevance of Service Awareness  
Objective: This experiment was conducted to assess the relevance of service awareness by comparing the SAC algorithm with the family of energy-aware LEACH algorithms in terms of energy dissipation per round of the experiment and total energy dissipation for the whole experiment. LEACH and LEACH-C were considered for these experiments.  
Network: Two network models were used in this experiment: a 50-node network and a 250-node network each with 80% of CSN nodes and 20% of HSN nodes randomly distributed in a 100 m \(\times\) 100 m sensing area. The initial energy of each node was 1 J, and the simulation time (round) varied depending on the experiment. The energy dissipation of the nodes was taken every 10 s.  
Results: The results presented in Figures 10 and 11 reveal that SAC outperforms both LEACH algorithms in terms of energy consumption per round and total energy consumption. By showing that LEACH performs worse than both the SAC and LEACH-C algorithms, these results reveal the optimality of centralized routing processes where the central node uses global knowledge to compute more optimized network configurations. Note that though the total energy consumption is the same  
Figure 9: SAC performance. (**a**) SAC, breadth-first search (BFS) and service-blind clustering (SBC); (**b**) SAC-MIXand SAC.  
for all algorithms at the end of the experiment, in general, the SAC algorithm performs better than the two other algorithms when considering the energy consumption per round of the 10-s interval.