esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Test Results  
### General Behavior  
Figure 3 shows the load versus central deflection curves for the tested RC T-section beam specimens strengthened by CFRP sheets. It can be observed that there are three stages. The first is the elastic stage that started with initial loading until the first crack developed in the bottom tensile fiber at mid-span. The resistance of the T-section beam corresponds to the concrete cracking is denoted \(P_{\sigma}\) (Figure 3a). Subsequently, in the stage II, the applied load was redistributed. Both strains in the main flexural reinforcement and CFRP sheet increased rapidly. At the end of this stage, the RC T-section beam behaved elastically and reached it yield strength, \(P_{y}\) (Figure 3a). \(P_{\sigma}\) was improved significantly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & \(E_{s}\) **(GPa)** & \(f_{y}\) **(MPa)** & \(F_{u}\) **(MPa)** & \(\delta_{u}\) **(\%)** \\ \hline \(\mathcal{O}8\) HPB235 & 200 & 308 & 538 & 30.5 \\ \(\mathcal{O}12\) HRB335 & 200 & 365 & 550 & 28.5 \\ \(\mathcal{O}14\) HRB335 & 200 & 388 & 605 & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of materials involved in test program.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & \(f_{y}\) **(MPa)** & \(E_{F}\) **(GPa)** & \(\delta_{u}\) **(\%)** & \(\tau_{c}\) **(MPa)** \\ \hline CFRP & 3168 & 242 & 1.71 & 3.5 \\ JGJ-20 & 43 & 2.59 & - & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CFRP and cohesive materials.  
Figure 2: Test setup and measurements on T-section beams.  
when bonding layers increased. However, it showed obvious brittle failure (Figure 3a,b). Figure 4a,b illustrate the strain versus resistance curves of the main flexural reinforcement and CFRP sheet at mid-span, respectively. It can be observed that the flexural reinforcement reached its yielding strain. The stiffness of the beam in stage II was smaller than that in stage I due to flexural cracking of the T-section beam. Figure 5a-f display the observed cracks due to the flexural bending moment. In stage III, there were two distinct structural behaviors for the tested specimens. Beams B1-B3 exhibited ductile behavior, whilst the T-section beams strengthened with CFRP sheets exhibited sudden drop in P-\(\delta\) curves as shown in Figure 3. This implies that different types of failure modes occurred in the test specimens with and without CFRP-strengthening.  
Figure 4: Load-strain curves of RC T-section beams retrofitted by CFRP. (**a**) Strain in steel reinforcement at mid-span; (**b**) Strain in CFRP at mid-span.  
Figure 3: Load-central deflection curves of RC T-section beams retrofitted by CFRP. (**a**) \(P\)-\(\delta\) curves for B1, C1, C4-5; (**b**) P-\(\delta\) curves for B2-3, C2-3.  
Figure 5: Cracks developed in RC T-section beams strengthened by CFRP. (**a**) B1 (**b**) B2. (**c**) B3 (**d**) C1. (**e**) C2 (**f**) C3. (**g**) C4 (**h**) C5.
# Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning
## 3 Problem Formulation  
### Convex Constraints  
In this section, we describe the trajectory planning problem as an optimization problem. Therefore, we define an objective function that we aim to minimize, a variable that may be optimized freely, simple dynamics equations that describe the evolution of the trajectories, and constraints that must be met regarding inequalities and equalities. The proposed method can generate trajectories for vehicles of any arbitrary nature.  
Imposing several constraints while minimizing accelerations is necessary to achieve the desired objective. The initial and final positions of all the vehicles are known. Additionally, to accommodate the physical constraints of an agent, velocity, acceleration, and jerk may be restricted. Finally, we need to ensure that the generated trajectories maintain a minimum distance of R between each other at all time steps, thereby confirming collision avoidance.  
Next, we will establish and express these constraints in matrix form. To simplify the representation, we analyzed one vehicle at a time and then combined these constraints to form the constraints for all the agents.
# Experimental & FEM Analysis of Orthodontic Mini-Inplant Design on Primary Stability
## 4 Conclusions  
This study focused on the impact of the mini-implant design on primary stability. For examined specimens, cortex thicknesses were sufficient enough for successful implant insertion, which was important for conducting pull-out tests.  
The obtained results show that the value of generated pull-out force, and therefore primary stability, depends on mini-implant geometry. The Tomas(r)-pin mini-implant attributed with greater core diameters, greater conicity and shorter thread length, regarding Perfect Anchor with a greater TSF factor, which showed the capability of managing greater pull-out forces. Therefore, core diameters, conicity and pitch of a thread have more impact on primary stability then thread length.  
FEM analysis results show that the Perfect Anchor implant type in the implant-bone system generates greater stresses, although it reaches lower pull-out forces, compared to the Tomas(r)-pin implant type. In order to achieve favourable primary stability of the mini-implant (temporary anchorage device), careful selection of the implant system combined with adequate bone quality and a proper insertion protocol are strongly suggested to minimize the destructive influence of loading forces on the surrounding dental implant. This study integrated medical imaging, engineering design and analysis to develop an important tool for clinical orthodontics.  
Further research should be based on optimizing the process of mini-implant geometry, which would result in increased primary stability, especially in managing greater loads and reducing generated stresses on the contact of the implant thread surface with surrounding tissues.  
Conceptualization, L.R.-V. and E.M. ([PERSON]); methodology, E.M. ([PERSON]) and L.R.-V.; investigation, E.M. ([PERSON]), E.M. ([PERSON]) and L.R.-V.; data curation, N.P.; software, E.M. ([PERSON]); visualization, M.D. and M.G.; writing--original draft preparation, E.M. ([PERSON]) and E.M. ([PERSON]); writing--review and editing, A.J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, because there was no animal sacrifice. Animal bones (pig ribs) from the food industry were used.  
Figure 10: Comparison of pull-out diagram obtained with FEM analysis with the experimental diagram for Tomas®-pin and Perfect Anchor mini-implants.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data underlying this study will be available on reasonable request to the corresponding author.  
**Acknowledgments:** We wish to express our gratitude to the Faculty of Dentistry with Clinics and the Faculty of Mechanical Engineering of the University of Sarajevo for providing laboratory measuring equipment and assistance in the implementation of the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
###### Abstract
However, since the satellite moves only in a fixed orbit, it is difficult to obtain the image at the desired time. In addition, the resolution of multispectral satellite images such as Landsat ETM + has limitations in studies that require very precise analysis. Therefore, in the case of small urban areas, UAV research that can quickly take an image at a desired time is being used a lot. In particular, since the UAV system can be equipped with a multispectral sensor, it is easy to conduct various vegetation studies. Previous studies on UAV-based vegetation monitoring include the following. [PERSON] et al. used UAVs to map maritime Antarctic vegetation based on NDVI [38] while [PERSON] et al. developed a method of detecting sediment linked to vegetation using high-resolution UAV imagery for ecosystem management [39]. [PERSON] et al. developed an object-oriented vegetation classification method based on fusion of UAV and satellite images [40]. In particular, there have been various studies analyzing vegetation and landscape in urban areas using UAV technology [41; 42; 43; 44; 45; 46]  
Korea has recently become very interested in urban regeneration projects. Public operators have a strong interest in the realization of urban regeneration programs with the aim of encouraging the physical, social, and economic development of cities in both in the medium and long term [47; 48]. In order to promote urban regeneration projects, data are needed to effectively support decision-making. Vegetation provides a comfortable living environment and a respite from the heat waves and urban heat island phenomenon that are exacerbated by climate change [49; 50]. This study sprang from a fortuitous accident. In the process of making vegetation drawings using vegetation indices such as NDVI, long-run steel roofing, waterproof coated roofs, and urethane-coated areas were misclassified as vegetation. Therefore, this study aimed to develop a novel vegetation index that accurately reflects the characteristics of urban land cover, such as long-run steel roofing, waterproof coated roofs, and urethane-coated areas, using UAV-based multispectral images.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation
### Speedup vs. Number of Worker Nodes  
Figure 6c illustrates the speedup achieved by increasing the number of worker nodes, and provides a deeper understanding of the algorithm's scalability and its ability to leverage additional computational resources. Speedup is a critical metric in distributed computing, as it reflects the degree to which the algorithm can reduce execution time relative to the smallest cluster size. Speedup is calculated relative to the execution time using the smallest number of worker nodes, and the results show a clear trend, i.e., as the dataset size increases, the speedup approaches linearity, particularly for the largest datasets.  
For example, the dataset sized at 10,854.4 MB exhibits a nearly linear growth in speedup as more worker nodes are added, indicating that the algorithm efficiently utilizes the additional computational power provided by the larger cluster. This linearity suggests that the algorithm has low overhead and high parallel efficiency, which are crucial characteristics for scalability in distributed systems. In practical terms, this means that the algorithm is capable of scaling effectively across a wide range of cluster sizes, making it suitable for deployment in large-scale, data-intensive applications where maximizing computational efficiency is paramount.  
The ability to achieve near-linear speedup is particularly important in edge computing and big data environments, where the ability to scale efficiently across multiple nodes can significantly improve processing time and overall system performance. However, the plot also shows that, for smaller datasets, the speedup curve begins to flatten as the number of worker nodes increases, indicating that the benefits of adding more nodes are diminishing. This flattening suggests a point where adding more nodes does not contribute significantly to improving performance and may even introduce unnecessary overhead. This highlights the need to carefully assess the trade-off between the size of the problem to be addressed and the number of distributed nodes, where an unnecessary number of nodes results in computational overhead dedicated to managing communications and distribution across the computing cluster.
# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 4 Performance of Parametric Wind Models
Our results suggest, however, that these wind speeds are generally not overestimated, and might better represent the wind decay as a function of the radius compared to other formulas;
* the results are generally much better when considering a family of profiles with two characteristic lengths, as proposed by [PERSON] et al. [38]. For example, as stated above, the performance of the Holland model, H80, is significantly improved when both radii at 34-kt and 64-kt are prescribed; and
* models, such as W06 or M16 (which decay exponentially or as a power-law outside the eye), perform well in the outer region when the 34-kt radii are prescribed properly, which is consistent with the findings of [PERSON] et al. [38] and [PERSON] et al. [37]. The consistency of these results with the findings of past studies further suggests that the CYGNSS and ASCAT products are relatively good proxies for high and moderate surface wind speeds, respectively. The only exceptions are regions close to the eyewall, for which the spatial resolution is too low to capture the strong wind speed gradients (and where errors on NHC data may reduce the accuracy of wind speeds estimated using parametric models). To further build confidence in our results, we perform numerical hindcasts of hurricane Maria (2017) in the following section (Section 5), and compare computed significant wave heights with real in-situ data. The aim is to investigate the potential of the results presented in Figures 3 and 4 to objectively identify suitable parametric models.
## 1 Introduction
Consequently, it could improve the taste and nutritional value of food products and prolong their shelf-life [19]. There are widely available food products that contain nanomaterials. For instance, titanium dioxide and silica nanoparticles are now used as food additives in the food sector [20]. Additionally, in Europe, carbon black and silica dioxide nanomaterials have been already used as food contact materials [21]. Some particles were used for the destruction of microorganisms such as silver, nickel, nickel oxide, gold, zinc, metal oxides, etc. [22]. Gold nanostructures, quantum dots (QD), carbon nanotubes, and other active nanostructures have been used as sensors of microbes for food safety [23; 24]. Previous studies revealed that nickel nanoparticles could exhibit excellent antimicrobial effects in comparison with other metal nanoparticles under the same condition [25]. Additionally, _S. aureus_ was highly sensitive to nickel and nickel oxide nanoparticles (NiO NPs) as a prospective antibacterial agent [26; 27]. Nevertheless, the toxicity of using NiO NPs in food is not fully understood; however, the use of such particles has been assessed in previous studies such as that of [PERSON] & [PERSON] [26] who added nickel nanoparticles to bovine milk and examined its effect on the survivability of _S. aureus_. They found that _S. aureus_ was highly sensitive to Ni NPs. Because nanoparticles have never been used in cheese, the current study investigated the effect of NiO NPs on the growth and survival of enterotoxigenic _S. aureus_ (harboring SEA gene) during the manufacture and storage of Domiati cheese. Besides, the inhibitory effect of yoghourt starter culture on _S. aureus_ and enterotoxin production was examined during the processing and ripening of such cheese.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 7 Result
A 59-year-old man with heart disease, high blood sugar, hypertension, and a history of smoking is classified as high risk in Case 3. These outcomes demonstrate the model's capacity to evaluate several factors and accurately forecast the risk of stroke from patient data. Figure 8: Post-augmentation accuracy comparison. (**a**) Accuracy of RNN model, (**b**) accuracy of LSTM model, (**c**) accuracy of CNN model, (**d**) accuracy of hybrid model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RNN Model & 0.94 & 0.95 & 0.94 & 0.95 \\ \hline LSTM Model & 0.95 & 0.96 & 0.96 & 0.96 \\ \hline CNN Model & 0.70 & 0.76 & 0.73 & 0.75 \\ \hline Proposed Hybrid Model & 0.96 & 0.94 & 0.97 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics of deep learning models and hybrid approach after data augmentation. Figure 9 shows the ROC curve measures the performance of a hybrid model in a multi-class classification test. The \(X\)-axis indicates the false positive rate (FPR), while the \(Y\)-axis reflects the true positive rate as depicted in Figure 9. Each curve represents a class (class 0, class 1, class 2), with the following AUC (area under curve) scores: class 0 = 1.00, class 1 = 0.99, and class 2 = 1.00. The curves' proximity to the top-left corner and high AUC values suggest good model performance and discriminatory abilities across all classes. The hybrid model's results demonstrate outstanding accuracy in predicting stroke risk levels, at 92%. The model has well-balanced accuracy, recall, and F1-scores, surpassing individual RNN, LSTM, and CNN models. The ROC curve demonstrates the hybrid model's capacity to accurately distinguish across risk levels, with AUC scores near to one for all classes. These findings support the model's robustness and promise for improving stroke risk prediction and early intervention options. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Parameter** & **Case 1** & **Case 2** & **Case 3** \\ \hline Gender & Male & Male & Male \\ \hline Age & 35 & 56 & 59 \\ \hline Hypertension & No & Yes & Yes \\ \hline Heart disease & No & No & Yes \\ \hline Ever married & Yes & Yes & Yes \\ \hline Work type & Private & Private & Private \\ \hline Residence type & Urban & Urban & Urban \\ \hline Average glucose level & 98 & 100 & 189.9 \\ \hline BMI & 20.3 & 21.67 & 25.7 \\ \hline Smoking status & Never smoked & Never smoked & Smokes \\ \hline Result stroke risk level & Low & Medium & High \\ \hline \end{tabular}
\end{table} 
Table 6: Predicted Stroke Risk Levels Based on Health Parameters. Figure 9: ROC curve for hybrid model with AUC scores.
## 2 Background
### Generative Adversarial Network  
GAN (generative adversarial network) [17; 18] consists of a generator/generative model and a discriminator/discriminator model.  
Figure 2 shows the learning structure in a GAN. GANs are often described with examples of banknote counterfeiters and the police. The generator is likened to a banknote counterfeiter, while the discriminator is compared to the police. In this analogy, the criminal (generator) attempts to trick the police (discriminator) with fake banknotes, and the police strive to identify the authenticity of the counterfeit bills. Through this competitive process, each model improves its ability to distinguish between genuine and counterfeit banknotes. As a result, the discriminator (D) tries to make accurate judgments using original data, while the generator (G) generates fake data to prevent the discriminator (D) from making accurate determinations. Through this process of adversarial learning, both models iteratively improve their performance [19].  
Eventually, the discriminator (D) becomes less able to distinguish between the original data and the fake data generated by the generator (G).  
Figure 2: Learning structure of GAN.  
Since GANs are unsupervised, they do not require manual labeling, and by learning the data, they can acquire the internal representation of the data. GANs are capable of generating audio, video, text, and image data that is indistinguishable from real data. As a versatile technology, they find applications in marketing, e-commerce, gaming, advertising, and various other industries.
## 4 Results and Evaluation
### Effect of Penetration Rate  
To evaluate the effect of CV agents, models were implemented with variable penetration rates. We compared 5 scenarios with penetration rates of 20%, 40%, 60%, and 80%, respectively, and with a platoon of size 3. The input flow and model results are shown in Table 5.  
The cumulative negative reward and delay time in the learning process is shown in Figure 14 below.  
Figure 13: Results of MOE evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \begin{tabular}{c} **CV** \\ **Penetration** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Platoons** \\ **(Size = 3)** \\ \end{tabular} & \begin{tabular}{c} **RVs** \\ **Negative** \\ **Reward** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Speed (m/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Waiting** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Delay Time (s)** \\ \end{tabular} \\ \hline
20\% & 160 & 1920 & \(-\)28,529 & 8.8 & 35.8 & 61.6 \\ \hline
40\% & 320 & 1440 & \(-\)15,144 & 10.19 & 18.8 & 35.2 \\ \hline
60\% & 480 & 960 & \(-\)9603 & 11.2 & 10.6 & 24.1 \\ \hline
80\% & 640 & 480 & \(-\)6987 & 11.7 & 8.0 & 21.2 \\ \hline
1000\% & 800 & 0 & \(-\)4886 & 12.1 & 5.6 & 19.0 \\ \hline \end{tabular}
\end{table}
Table 5: Traffic simulation results with different CV penetration rates.  
From Figure 14, it can be seen that when the CV penetration rate increases, the efficiency of the DQN model is improved. However, this improvement is only achieved when CV penetration is at a significant level. The model's improvement is quite insignificant when the CV penetration rate is low. Because at this rate, the amount of information collected is not enough for the learning process. The model is only truly improved when the CV penetration rate is greater than 20%. With a high degree of penetration, platoons can merge into longer platoons during movement or at an intersection area. This will improve the model significantly in terms of both throughput and waiting time.  
In addition, when the CV penetration rate is low, the fluctuation degree of the reward curve is large. At a rate of 20%, although the cumulative negative reward curve seems to converge, the reward fluctuates between \(-\)30,000 s and \(-\)50,000 s, which is very unstable. As the CV rate increases, this fluctuation also decreases. It is clear that the higher the CV rate, the more data the agent gets in each episode, improving the learning model's efficiency. The model is the most effective when the CV penetration rate is 100%, i.e., all vehicles in the model are CVs. With this rate, the model efficiency is improved more than 6 times compared to the scenario with a 20% penetration rate.