esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Miniscrew Insertion
#### 3.2.2 Bicorticalism Evaluated According to Group, Clinical Experience and Digital Knowledge  
* Regarding the achievement of bicorticalism, 40% of the dental students, 50% of the orthodontics students and 60% of the orthodontists obtained it for both miniscrews in T1, but this difference was not revealed to be statistically significant (\(p\) = 0.670). Obtaining bicorticalism during miniscrew positioning did not show a significant variation in T2 (\(p\) = 0.133).
* Analyzing the difference considering the clinical experience of the operators, it was observed that 100% of those who achieved bicorticalism had clinical experience (\(p\) = 0.017).
* No relationship was observed between obtaining bicorticalism and digital knowledge for any of the study groups at T1 and T2 (T1: \(p\) = 0.671; T2: \(p\) = 0.713).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Study Group**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-8}  & & & **Dental** & **Orthodontics** & **Orthodontist** & & \(p\)**-Value** \\ \hline \multirow{4}{*}{Clinical experience} & No & Count & 3 & 2 & 1 & 6 \\  & & \% within Study Group & 30.0\% & 20.0\% & 10.0\% & 20.0\% & \\ \cline{2-8}  & Yes & Count & 7 & 8 & 9 & 24 \\  & & \% within Study Group & 70.0\% & 80.0\% & 90.0\% & 80.0\% & \\ \hline \multirow{2}{*}{Total} & Count & 10 & 10 & 10 & 30 & \\  & & \% within Study Group & 100.0\% & 100.0\% & 100.0\% & 100.0\% & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical experience by Study Group.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Study Group**} \\ \cline{3-8}  & & & **Dental** & **Orthodontics** & **Orthodontist** & **Total** & \(p\)**-Value** \\ \hline \multirow{4}{*}{Digital Knowledge} & No & Count & 7 & 2 & 4 & 13 \\  & & \% within Study Group & 70.0\% & 20.0\% & 40.0\% & 43.3\% & \\ \cline{2-8}  & Yes & Count & 3 & 8 & 6 & 17 \\  & & \% within Study Group & 30.0\% & 80.0\% & 60.0\% & 56.7\% & \\ \hline \multirow{2}{*}{Total} & Count & 10 & 10 & 10 & 30 & \\  & & \% within Study Group & 100.0\% & 100.0\% & 100.0\% & 100.0\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Digital knowledge by study group.  
3.2.3. Proximity to the Incisive Foramen and to the Incisive Root
* Only one operator positioned one of the miniscrews in proximity to the incisive foramen in T1 but not in T2.
# Evaluating the Impact of Drone Signaling in Crosswalk Scenario
## 3 Materials and Methods
### Assumptions  
To emphasize the drone contribution to road safety in the scenario described below, several assumptions are taken into account.  
It is considered that only one car is about to arrive to the crosswalk. Hence, in this work, we do not take into account any traffic model. In addition, for problem simplification purposes, we consider that, at each time instant \(t\), there is a pedestrian intending to cross. This means that the probability of pedestrian presence at the crosswalk is equal to 1.  
In addition, the infrastructure is assumed to be equipped with enough number of relaying sensors, such that the drone receives instantly reliable information about the scene. Moreover, the drone is equipped with sensors that correctly exchange information with both the car and the infrastructure. Hence, in this paper, we do not focus on the way how the information flows transit, and we do not take into account the transmission delays from and to the drone. Moreover, all the metrics of interest can be expressed as functions of the distance \(d_{c}\) (instead of time \(t\)).
## 3 Computation of the principal components
### Distributed estimation of the principal components
#### 3.4.5 Scalability analysis  
Highest network load:At each iteration, the computation of the product \(\mathbf{Cv_{t}}\) requires a node \(i\) to send one packet and to receive \(|\mathcal{N}_{i}|\) packets. The highest network load for this operation is therefore \(O(|\mathcal{N}_{i^{*}_{\mathcal{N}}}|)\). The normalization step implies one A and one F operations (as defined in 2.1.3), and the orthogonalization step implies \(k-1\) operations of type A and F respectively, where \(k\) is the index of the principal component computed. The highest network load for the computation of the \(q\) first principal components amounts to \(O(q|\mathcal{N}_{i^{\star}_{\mathcal{N}}}|+q^{2}|\mathcal{C}_{i^{\star}_{ \mathcal{C}}}|)\).  
The highest network load related to the power iteration method is therefore quadratic in the number of principal components computed. Depending on the constraints of the network, it may be more interesting to retrieve the approximated covariance matrix from the network at the base station for a centralized computation of the eigenvectors. The tradeoff depends here on the communication and computational resources available in the network, and will be discussed more in detail in Section 3.5.  
Computational and memory costsFor a node \(i\), the cost of the computation of \(\mathbf{C}\mathbf{v}_{\mathbf{t}}\) is \(O(|\mathcal{N}_{i}|)\). The cost of the normalization step is \(O(|\mathcal{C}_{i}|)\) for node \(i\), and the orthogonalization step is \(O(k|\mathcal{C}_{i}|)\). The overall highest computational cost therefore amounts to \(O(q(|\mathcal{N}_{i^{\star}_{\mathcal{N}}}|+|\mathcal{C}_{i^{\star}_{\mathcal{ C}}}|)\). Regarding memory costs, each node \(i\) needs to maintain variables for storing its local \(w_{ik}\) and its neighbors parameters \(w_{jk},j\in\mathcal{N}_{i}\). The complexity of the highest memory cost is therefore \(O(q+|\mathcal{N}_{i^{\star}_{\mathcal{N}}}|)\).
# Forced Convection Heat Transfer for Stratospheric Airship Involved Flight State
## 4 Conclusions  
A numerical survey is conducted to explore the forced convection heat transfer of a full-scale ellipsoidal airship. The effects of angle of attack, Reynolds number and length to diameter ratio on heat transfer are investigated. An empirical correlation describing the heat transfer of an ellipsoidal airship was developed. Several conclusions were drawn after analyzing the results:  
1. The change of flow field structure around the airship is the root reason for the change of forced convection heat transfer.
2. The average Nusselt number increases with the increase of \(Re\) (\(1\times 10^{7}\) to \(2.0\times 10^{8}\)), decreases with the increase of \(e\) (2 to 6), and increases with the increase of \(\alpha\) (0 to \(20^{\circ}\)).
3. In the present study, the results show a similar performance trend with the existing correlations, and at higher Reynolds number, the results of the present study are higher by 30 percent than those of [PERSON] with \(\alpha=20^{\circ}\).
4. \(K\) increases with the increase of \(e\) and \(\alpha\), and the results with \(\alpha=20^{\circ}\) have about a 6 percent difference than that without angle of attack. Flight state is an important factor to be considered in the thermal design period of an ellipsoidal airship.  
Conceptualization, Y.J.; Methodology, H.S. and H.P.; Software, B.K; Validation, H.P.; Formal analysis, H.P.; Investigation, H.P.; Resources, B.K.; Data curation, B.K.; Writing--Original draft preparation, B.K.; Writing--Review and editing, H.P.; Visualization, H.P.; Supervision, H.S.; Project administration, Y.J.; Funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
I am pleased to acknowledge [PERSON] for her guidance in English writing.  
The authors declare no conflict of interest.
# A Study on a Modified Model for the Hold Time of a Clean Extinguishing Agent
## 4 Results
It can be seen that the modified outflow volumetric flow \(\dot{V}_{mo}\) expresses the measured outflow volumetric flow \(\dot{V}_{o}\) better than the existing outflow volumetric flow \(\dot{V}_{mo}\). This can be confirmed quantitatively in Figure 5:3 In Table 3, it is shown that the value of \(\dot{V}_{o}/\dot{V}_{mo}\) is closer to 1 compared to \(\dot{V}_{o}/\dot{V}_{vo}\). The closer to 1 it is, the better it represents the measured outflow volumetric flow \(\dot{V}_{o}\). The same results are shown for both _IG-01_ and _HFC-125_. In particular, the larger the leakage exponent \(n\) is, the better \(\dot{V}_{mo}\) can express \(\dot{V}_{o}\) than \(\dot{V}_{vo}\). For \(n=0.526\), the difference between \(\dot{V}_{vo}\) and \(\dot{V}_{mo}\) is not significant for both _IG-01_ and _HFC-125_. However, when \(n=0.834\), _IG-01_ shows an improvement of approximately 4.5%, and _HFC-125_ shows an improvement of approximately 8.6%. In other words, the larger the agent-air mixture's density \(\rho_{mix}\) is, the greater the improvement effect of \(\dot{V}_{mo}\) is. In Figure 10, it can be seen that the hold time observed in the test enclosure is located above the dotted line. This means that the observed hold time is longer than the predicted hold time because the hold time is inversely proportional to the outflow volumetric flow. Therefore, the results for hold time are similar to those for outflow volumetric flow. The modified hold time \(t_{ml}\) represents the observed hold time \(t_{h}\) better than the previous hold time \(t_{vh}\). Also, the larger the leakage exponent \(n\) is and the larger \(\rho_{mix}\) is, the greater the improvement effect is by \(t_{ml}\). For \(n=0.526\), the difference between \(t_{vh}\) and \(t_{mh}\) is not significant (\(-0.4\%\) and \(-0.7\%\) for _IG-01_ and _HFC-125_, respectively). However, when \(n=0.834\), _IG-01_ shows improvement of approximately \(-5.5\%\), and _HFC-125_ shows improvement of approximately \(-10.7\%\). The comparison of these quantitative results shows that the modified theoretical equation more accurately reflects the volume outflow and hold time than the previous model's theoretical equation. In particular, the larger the leakage exponent \(n\) is and the larger the density of the agent-air mixture is, the larger the effect becomes.
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 3 Optimized Local Region Growth Algorithm Considering Polarization and Texture Information
### Automatic Selection of Seed Points Based on DWI  
#### 3.2.1 Rough Extraction of DWI Water Based on Otsu  
The key to region growth algorithms lies in the determination of seed points (i.e., the initially selected pixels). Traditional region growth algorithms require the manual selection of seed points, which cannot meet automation requirements. However, in the process of automatically selecting seed points, if the seed points are selected in a non-water body area, the final segmentation result may be significantly impacted. Therefore, we first use the Otsu method to extract the rough water bodies in the study area, which is then taken as the selection range for seed points.  
[PERSON], inspired by NDVI, proposed the SDWI (Sentinel-1 Dual Polarized Water Index) based on the VV and VH dual polarization data of Sentinel-1 satellite. This index combines the db values of the two polarization methods to expand the differences between water bodies and other features [7]. The GF-3 satellite image used in our experiment also includes two polarization methods. According to the idea of SDWI, the Dual Polarized Water Index (DWI) is used for coarse extraction of water bodies in the study area. The formula for the DWI is  
\[\text{DWI}=\ln(10\times\text{HH}\times\text{HV}). \tag{1}\]  
The Otsu maximum between-class variance method (also known as the Otsu algorithm) is currently considered to be the best method for automatic threshold selection. This method introduces between-class variance to describe the difference between the target terrain and the background. When the between-class variance between the two is maximal, it is considered that the feature difference between the two is the greatest. The formula is expressed as follows [19]:  
\[e^{2}(K)=P_{A}(\alpha-\alpha_{A})^{2}+P_{B}(\alpha-\alpha_{B})^{2}, \tag{2}\]  
where \(\alpha\) denotes the mean of the image DWI, and \(\alpha\)A and \(\alpha\)B are the mean values for the target A and background B, respectively. When the value of \(e^{2}(K)\) is maximal, \(K\) is the optimal threshold. When the variance difference between the region of interest image and the background image is maximized, theoretically speaking, the difference in elements between the two types of images is the greatest, the confusion between categories is the smallest, and the misclassification error of the segmentation results is also the smallest. The Otsu method can quickly extract the approximate range of water bodies and remove a large portion of non-water areas; however, its results often contain noise and non-water surface features with the same back-scattering characteristics as water bodies (e.g., airport runways, smooth roads, etc.). Therefore, in this experiment, the Otsu results were only used as the selection range for seed points.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 2 Materials and Methods
### Dynamic Saliency U-Net
#### 2.2.4 Loss Function  
We employed two loss functions while training our AMU-Net, which includes principal loss, auxiliary-agreement loss. Our principal loss consists of cross-entropy and Dice loss. The principal loss is calculated as follows:  
\[L_{PL}=L_{ce}+L_{\text{dice}} \tag{36}\]  
We proposed an auxiliary-agreement loss function to improve the learning efficacy of our auxiliary head and also ensure an equivalence between the predictions made by the primary and auxiliary components of the architecture.  
The logits coming from both the main and auxiliary heads are applied to the softmax operation to yield probability distributions over classes which can be defined as  
\[P_{\text{main}}\,=\,\text{softmax}(X_{\text{main}}\,) \tag{37}\] \[P_{\text{aux}}=\,\text{softmax}(X_{\text{aux}})\]  
where \(X_{\text{main}}\) and \(X_{\text{aux}}\) represent the logits from the main and auxiliary heads, respectively. The level of agreement or consistency between the main and auxiliary predictions is quantified using the Kullback-Leibler divergence, formally defined as:  
\[L_{\text{agree}}\,=\,D_{KL}(P_{\text{aux}}\,\|P_{\text{main}}\,) \tag{38}\]  
where \(D_{KL}\) is the Kullback-Leibler divergence measuring the difference between two probability distributions. The classification loss is then computed via the softmax cross-entropy between the auxiliary probability map and the ground truth labels as follow.  
\[L_{\text{class}}\,=\,\text{SoftCrossEntropyLoss}\,(P_{\text{aux}}\,,Y) \tag{39}\]  
Figure 6: An illustration of the overall framework of the SFFH.  
where \(Y\) denotes the ground truth labels. The final composite loss, \(L_{\text{class}}\), integrates the agreement loss and the classification loss using a weighting parameter w, which balances their relative contributions:  
\[L_{\text{aux}}=w\cdot L_{\text{agree}}+L_{\text{class}} \tag{40}\]  
The weight \(w\) is a tunable hyperparameter that modulates the importance of agreement relative to classification accuracy.
## 4 Application of AI in Food Integrity
### AI and ML in Food Supply Chain Traceability
[69] described the development of a refined ML classifier to detect anomalies such as contaminated food items along the supply chain that may need to be recalled. The monitoring system was enhanced by integrating internet-of-things (IoT) and the Bayes classifier algorithm with the Food Supply Chain Management (FSCM) system, which resulted in a secure framework for effective monitoring, tracing, and administration of the entire food supply chain, encompassing producers, exporters, and buyers. [PERSON] et al. [70] described an enhanced traceability system for food quality assurance and evaluation basedon AI and ML technologies. Their proposed system integrates fuzzy classification and ANN models that allow for both forward tracking and varied tracing along the supply chain to offer real-time evaluations of food quality along the entire supply chain. The researchers conducted a case study with a pork producer and the findings revealed the system's efficacy in guaranteeing food quality and delivering precise assessments. Using an open-source IoT-enabled spectrometer, [PERSON] et al. [71] demonstrated an AI-powered approach for tracking the geographical origin of Italian Extra Virgin Olive Oil (EVOO). Their trained ANN model had a 94.6% accuracy in the classification of the oil samples.
## 4 Experimental Simulation and Results Analysis
### Performance Comparison Between MSEChoA and Other Algorithms
#### 4.3.3 Time Complexity Analysis  
Unmanned delivery vehicles need to plan paths quickly in dynamic and complex environments to ensure efficient logistics operations. To ensure that unmanned vehicles can plan paths in real time in complex dynamic environments, the computation time and stability of each algorithm must be rigorously tested and compared. Table 5 shows the average runtime and standard deviation of each algorithm on unimodal, multimodal, and fixed-dimension multimodal functions, thereby evaluating their time complexity in path planning.  
As shown in Table 5 and Figure 6, the runtime of the ALO algorithm is significantly higher than that of other algorithms, indicating its higher computational cost when handling path planning problems in complex environments. In contrast, the EO and GWO algorithms exhibit higher computational efficiency. The average runtime of the MSECHOA algorithm is moderate; although it is slightly higher than EO and GWO, it is lower than the original ChOA and other improved algorithms. Combined with the algorithm performance results discussed earlier, MSECHOA demonstrates a good balance between computational efficiency and path planning performance.  
Further analysis of the standard deviation shows that the MSECHOA algorithm has a lower standard deviation when dealing with multimodal functions and fixed-dimension multimodal functions, indicating better operational stability. This stability is especially important for the path planning of delivery unmanned vehicles in dynamic environments, as it means the algorithm can maintain consistent and efficient performance across different scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Algorithm Name** & **GWO** & **EO** & **ALO** & **MSGWO** & **ChOA** & **ICHOA** & **WChOA** & **AWChOA** & **MSECHOA** \\ \hline Unimodal Function & 0.1840 & 0.1658 & 26.8618 & 0.6503 & 0.6324 & 0.0391 & 0.6649 & 4.9864 & 0.2147 \\ Multimodal Function & 0.1873 & 0.1538 & 25.9900 & 0.6485 & 0.6322 & 0.0375 & 0.6190 & 4.8141 & 0.2034 \\ Fixed-Dimension & 0.1949 & 0.1610 & 25.8121 & 0.6499 & 0.6467 & 0.0509 & 0.6355 & 4.7628 & 0.2155 \\ Multimodal Function & 0.1887 & 0.1602 & 26.2213 & 0.6495 & 0.6371 & 0.0425 & 0.6398 & 4.8544 & 0.2112 \\ Average Standard & 0.0068 & 0.0148 & 0.5746 & 0.0282 & 0.0231 & 0.004 & 0.0411 & 0.1186 & 0.0115 \\ Deviation (s) & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average Execution Time and Standard Deviation of Different Algorithms on Various Benchmark Functions.
## 4 Application of Cyclodextrin-Based Adsorbents
### Removal of Heavy Metals
The high-performance membranes, modified with biomaterial/nanoparticles composite, had excellent efficiency in adsorbing heavy metals from water [106]. Nano-porous carbons are getting research attention and are being frequently applied in adsorption applications, due to their various essential characteristics. Metal-organic framework (MOF) is a new class of nanoarchitecture particles with excellent stability and sensitivity [107; 108]. [PERSON] et al. prepared a g-CD-MOF composite by carbonizing the framework with potassium ions and \(\gamma\)-cyclodextrinrin. Figure 7 represents the SEM, TEM, and elemental mapping of the prepared adsorbent. The \(\gamma\)-CD-MOF-based adsorbent exhibited an adsorption capacity of 140.85 mg/g for Cd(II) ions that the Langmuir model calculated. The adsorption followed the ion exchange mechanism due to oxygen-comprising functional groups [109]. These studies revealed that CD has a remarkable ability to remove metal ions from water under laboratory conditions, making it a solid foundation for industrial research. Furthermore, Table 2 summarizes the adsorption capacity and removal efficiency for several heavy metals on CD-based adsorbents. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multirow{2}{*}{**Dye**} & **Adsorption** & \multicolumn{2}{c}{**Removal**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**References**} \\  & & **Capacity** & & & & \\  & & **(mg/g)** & & & & \\ \hline Poly-CD/PVA & Cu(II) & 48.15 & 85.00 & 8 & [101] \\ \(\beta\) –PMDA & Pb(II) & 272 & 95.00 & - & [102] \\ Pb(II) & 330.90 & - & 5 & \\ \(\beta\)-CD-CS-EDTA & Cu(II) & 161 & & 5 & [103] \\  & Ni(II) & 118.9 & 95.00 & 5 & \\ Fe\({}_{3}\)O\({}_{4}\)/ & Cu(II) & 0.94 mg/g & 93.14 & 6–7 & [110] \\ GO (\(\beta\)-MGO) & \(\alpha\)-CD MOF-NPC & Cd(II) & 140.85 & 90.00 & 4 & [109] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adsorption capacities of various CD-based adsorbents for metals. Figure 6: Schematic representation for the preparation of \(\beta\)-CD-Chitosan adsorbent [103].
## 3 Results and Discussion
### Effect of Different Extraction Methods on Stevioside and Rebaudioside A Yield
In another study, a saponin extraction from Chinese white ginseng (I) and fine root of Panax ginseng C. [PERSON] (II) by microwave-assisted extraction were reported by [19]. The raw materials were milled to 250 \(\upmu\)m particle size. The results confirmed that the extraction yield of the target compounds by microwave-assisted extraction at 72.2 \({}^{\circ}\)C for two minutes was similar to the conventional reflux (75.1 \({}^{\circ}\)C for 12 h). Unfortunately, the authors did not conduct a conventional reflux extraction for a similar period of 2 min as used in microwave extraction, to have a fair comparison. It is unlikely that a microwave could reduce extraction from 12 h to 2 min. [PERSON] et al. [42] have studied the influence of different processing factors such as solvent type, temperature, particle size, ultrasound power, and number of cycles on the phenolic compounds extraction from pomegranate peel using a combination of two extraction methods (UAE, PLE) [42]. The results showed that samples with small particle size (680 \(\upmu\)m) show higher phenolic compounds extraction as compared to samples with larger particle sizes (1050 \(\upmu\)m). However, the authors, due to the constant clogging of the processing line, did not recommend samples with small particle sizes. Moreover, [PERSON] et al. found a similar finding [43], showing that the smaller the size of particles, the faster the solvent can travel through it, which will indirectly shorten the extraction time. Excellent previous work has been done in the literature to obtain a better yield of the target compound/s using different methods. The aim in most of the previous work has been to compare the different extraction methods [34; 44; 45]. However, none of the researchers conducted the measurement of the bioactive soon after the powder produced by grinding is exposed to the solvent (zero time). The current study suggests doing further investigation to address the effect of grinding on the release of bioactives from plant leaves by conducting zero time extraction, as this has not done in the literature. Figure 6: Temperature change during UAE for stevia powder suspension (750 W, 12.75 mm, 20 kHz, 100% amplitude).