esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Rotor Optimization Results
#### 4.2.3. Blade Thickness Optimization Results  
Figure 23a shows the blade thickness distribution at hub section for T-100 rotor. It can be seen that the peak value of T-100 thickness is larger than the baseline design, and the location moves backward. The new thickness distribution results in a reasonable maximum stress of 649 MPa, as indicated in Figure 24a.  
Figure 21. Change of trailing edge sweep along with equivalent stress. (**a**) T-100, (**b**) T-100 re.  
Figure 20. Change of blade trailing edge height along with total-to-static efficiency. (**a**) T-100, (**b**) T-100 re.  
It can be seen from Figure 23b that the peak value of T-100 re thickness is smaller than that of the baseline design, and the location also moves backward. The maximum stress in Figure 24b is larger than that of T-100 optimal design because of higher rotational speed, but still lower than the allowable value. A high-stress zone indicated by a red circle in Figure 24b appears on the suction surface near the rotor outlet just under the mid-span. This is due to the high blade turning on the mid and tip sections. It means this area is exposed under bending force which is also a reflection of the conflicting relationship between aerodynamics and stress.  
Figure 23: Thickness distribution at hub section. (**a**) T-100, (**b**) T-100 re.  
Figure 22: Change of total-to-static efficiency along with trailing edge blade angle. (**a**) T-100, (**b**) T-100 re.
## 2 Materials and Methods
### Prediction and Optimisation Models
#### 2.3.2 Ref  
RF is a parallel ensemble learning technique that enhances the model's accuracy and stability by generating several decision trees and aggregating their outputs [48]. A collection of autonomous decision trees is generated utilising distinct training datasets and random attributes. Each tree is trained independently to partition the data according to different attributes and establish prediction rules, as seen in Figure 3.  
In the application of RF for prediction, each tree generates a prediction, and for regression tasks, the final result is obtained by calculating the mean of these predictions. The fundamental concept of RF is that by amalgamating several decision trees, the model's prediction bias and variance can be diminished, enhancing its accuracy and generality [49].  
Table 2 delineates the spectrum of values for the RF model's public hyperparameters. This paper employed two hyperparameter optimisation strategies, GridSearchCV and RandomizedSearchCV, to enhance the RF regression model's performance by exploring all combinations of designated hyperparameter values. The hyperparameters, precisely, the maximum tree depth and the number of decision trees, were determined to influence the model performance significantly. Subsequently, the RF predictions for each performance type are examined.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Number of estimators & 1–300 \\ Maximum depth & 1–300 \\ Minimum sample split & 2 \\ Bootstrap & False \\ Warm start & True \\ Maximum features & sqrt \\ \hline \hline \end{tabular}
\end{table}
Table 2: RF hyperparameter ranges.  
Figure 3: Schematic representation of the RF model.
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 3 Results and Discussion
### Characterization of the Essential Oil by GC-MS  
In order to assess the chemical composition and quality of the fennel EO encapsulated by electrostatic extrusion, the initial fennel EO and the EO hydrodistilled from the fennel EO microbeads obtained under optimal conditions (1.5% alginate/0.75% whey protein/freeze-drying) were analyzed by GC-MS. The comparison of their chemical profiles is shown in Table 5. The GC-MS analysis identified a total of 18 compounds (Figure 3) in both samples, categorized into monoterreborocarbons (11), oxygenated monoterreps (4), phenyl-propanoids (2), and aromatic aldehydes (1). The results indicate that electrostatic extrusion followed by freeze-drying did not affect the qualitative composition of the fennel EO. Both oils exhibited a comparable composition of chemical groups, with phenylpropanoids being the most prevalent (68.96 and 81.09%), followed by oxygenated monoterreps (16.22 and 13.19%) and monoterre hydrocarbons (12.78 and 4.63%). Aromatic aldehydes were the least abundant, accounting only for 2.04 and 1.09% of the oils, respectively.
# Synthesis, Characterization, and Self-Assembly of a Tetrathiafulvalene (TTF)-Triglycyl Derivative
## 1 Introduction
In addition, conductive gels have also been fabricated by simple mixing and mechanically grinding neutral TTF and an acceptor (i.e., tetracyanoquinodimethane) in ionic liquids [72]. More recently, gels made of TTF-dipeptide and tripeptide conjugates have been reported [73; 74]. In this communication we describe the synthesis, characterization, and gelation properties of a new TTF-triglycyl-based LMW organogelator (Figure 1). As far as we are aware, a triglycyl peptide has not been used to fabricate TTF-based gelators.
## 3 Experimental Results and Analysis
### Verification of Proposed Model  
Three types of watermarking algorithms have been chosen because their embedding methods are nearly consistent with or easily adapted to satisfy the proposed watermarking principles. The first watermarking algorithm denoted as Algorithm A is based on scale-invariant feature transform, where embedding methods can be referred to [11]. Algorithm B is based on discrete cosine transformation, referred to [8] and Algorithm C is based on statistic index, referred to [10]. In addition, repeat embedding strategy and watermark synchronizations have been adopted in the watermarking algorithms in accordance with the proposed principles. The verification is conducted with three sets of experimental parameters and pseudo-random binary sequence are used as the watermark information. The parameters of the experiments are listed in Table 1.  
In the experiments, the attacking strength means that the robustness index will be calculated on each interval of \(S\) (\(S\in[0,1]\)). In addition, the threshold of \(BER\) is set to 0.2 empirically, means that when \(BER<0.2\) the extracted watermark is regarded as the same as the original. For each interval of \(S\), watermarking algorithms are executed 1000 times on each image with the cropping area randomly chosen. Finally, the robustness results from watermarking algorithms are compared with the theoretical ones, which are calculated by the proposed model.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 2 Li\({}^{+}\)/Na\({}^{+}\) Storage Mechanism  
Li\({}^{+}\)/Na\({}^{+}\) storage mechanisms for different Co-based materials are shown in Figure 1. Co-based electrode materials can be roughly divided into three types: Co-based alloys [19; 34]; compounds of cobalt and nonmetals (Co\({}_{\rm x}\)A\({}_{\rm y}\), A=O, P and S... ) [18; 35; 36; 37; 38] and other composites fabricated by cobalt and metal nonmetals [39]. Unlike graphite-based materials, Li\({}^{+}\)/Na\({}^{+}\) storage mechanisms of cobalt-based electrode materials consist mainly of alloying and conversion reactions. During the processes of alloying and conversion reactions, atomic rearrangements occur along with structural evolution and phase transformation, thereby yielding severe volume fluctuations during battery cycles.
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Dataset Preprocessing  
Data preprocessing is crucial for ensuring that the input medical images are optimal for subsequent segmentation and enhancement processes [84]. The preprocessing steps address image artifacts, enhance image quality, and simplify image representation [85]. In this section, we provide a comprehensive description of the data preprocessing pipeline, which includes the following key steps: image conversion to 8-bit, grayscaling, image enhancement with histogram equalization (HE), and image adjustment. These methods can improve the quality of images without any information being deformed or lost [86].
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 3 Experiments
### Ablation Experiments
#### 3.3.4 Ablation Experiments of IoU Methods  
In this paper, we address the limitations faced by traditional IoU metrics in detecting objects with large aspect ratios, such as hair follicles. To this end, we employ the EIoU as a loss function, which is designed to more accurately reflect the actual loss associated with these challenging detection scenarios. In our experiments, we also evaluate the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Class1** & **Class2** & **Class3** & **Class4** & **mAp** & **FPS** \\ \hline Coupled + Reg & 83.06 & 82.86 & 79.61 & 78.92 & 82.22 & 36.69 \\ Decoupled + Reg & 86.11 & 85.57 & 82.27 & 81.33 & 85.02 & 37.63 \\ Coupled + Cls & 84.13 & 83.71 & 80.89 & 79.96 & 83.22 & 38.03 \\ Decoupled + Cls & 87.98 & 87.13 & 84.01 & 81.36 & 86.66 & 40.93 \\ Coupled + Cls (GCL) & 84.98 & 84.27 & 81.13 & 80.65 & 83.83 & 41.57 \\ Decoupled + Cls (GCL) & 88.49 & 87.31 & 84.31 & 81.89 & 86.98 & 43.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on the effect of different modules in the feature detection network on detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Class1** & **Class2** & **Class3** & **Class4** & **mAp** & **FPS** \\ \hline FPN & 85.26 & 85.13 & 81.69 & 80.03 & 84.40 & 46.96 \\ FPN + FAM (Conv) & 85.11 & 85.02 & 81.53 & 80.06 & 84.27 & 45.71 \\ FPN + FAM (DCN) & 88.36 & 87.41 & 84.33 & 82.76 & 87.01 & 43.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments on the effect of different modules in the feature fusion network on detection accuracy.  
three most commonly used IoU variants--Generalized IoU (GIoU), Distance IoU (DIoU), and Complete IoU (CIoU)--as the loss functions within the YOLO-OHFD framework for oriented hair follicle detection.  
As shown in Table 5, the experimental results show that CIoU and EIoU are relatively effective. Compared to GIoU and DIoU, CIoU and EIoU take the loss of the bounding box aspect ratio into account when calculating the loss, which is suitable for the task of detecting large aspect ratio objects such as hair follicles. Meanwhile, EIoU splits the influence factor of the aspect ratio of the predicted and real frames on the basis of CIoU, so as to capture the changes in the shape of the hair follicle more accurately and further improve the accuracy of the localization of the hair follicle. According to the experimental results, it can be seen that EIoU effectively improves the loss calculation accuracy of YOLO-OHFD.
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 5 Computational Comparison
### Random Graphs  
Figure 7 shows the average CPU times of our implementations using random graphs with \(n\in\{100,1000,10000\}\) and \(k\in\{3,10,20\}\). In all cases, five graphs were generated for each density \(d\in\{0,0.02,0.04,\ldots,1.0\}\) and algorithms were run using each of the \(n\) vertices as a source in turn. Each point in the figure is therefore a mean of \(5n\) different values. As an additional means of comparison, we also ran a second (slightly slower) implementation of [PERSON]'s algorithm on Kirby-Potts expanded graphs, available in the Python package NetworkX (v. 2.4).  
As might be expected, Figure 7 shows that run times grow for increases in \(n\), \(m\), and \(k\). In general we see that using [PERSON]'s algorithm with Kirby-Potts graphs gives the shortest run times, with Extended-Dijkstra-V2 a close second. The differences in these two algorithms seems due to our choice of data structures. As noted, in Kirby-Potts graphs all information is encoded via adjacency lists hence,during execution, [PERSON]'s algorithm is able to access this information directly when iterating through each list. When using Extended-Dijkstra-V2, on the other hand, the overheads involved in using hash tables for storing the transfer penalties makes accessing each value of \(t(u,i,j)\) slightly more expensive. (Indeed, in preliminary tests we found that our implementation of Extended-Dijkstra-V2 could be improved for small instances by using a different data structure. This was achieved by maintaining an \(n\times k\times k\) array in which an element \((u,i,j)\) referred to \(t(u,i,j)\). However, in many problem instances this array wasted a lot of space because transfers between various pairs of colours did not exist at vertices. For some of the larger real-world problem instances used in Section 5.3, for example, the memory requirements of this array exceeded 40 GB.)  
Figure 7 also shows that the gap in the time requirements of Extended-Dijkstra-V1 compared to Kirby-Potts and Extended-Dijkstra-V2 widens for increases in \(n\), \(k\), and \(m\). Indeed, the most extreme difference occurs with graphs with \(n=10,000\), \(d=1.0\), and \(k=20\), where a difference of over twenty seconds per shortest-path tree is observed. That said, the effects of the asymptotic growth rate intersections (as seen in Figure 6) are also apparent with small sparse graphs, where Extended-Dijkstra-V1 has slightly shorter running times.  
Figure 7: CPU times required to produce a shortest path tree from a single source using random graphs of differing densities. Rows show \(n\) values of 100, 1000 and 10,000 respectively; columns consider \(k\)-values of 3, 10 and 20 respectively. The number of edges in a graph is determined by multiplying the density by \(n(n-1)\).
## 6 Adsorption Energy Distribution (AED) Determination  
The Polanyi potential theory [39; 40; 41; 42] provides a means for deducing the distribution of adsorption energies at different sites. According to this theory, we rely on the adsorption energy (\(\varepsilon\)), which is related to the concentrations of the substances at half saturation, (C\({}_{1}\)) and (C\({}_{2}\)), to determine the site energy distribution using the following formula:  
\[C=C_{\varepsilon}e^{\frac{-\varepsilon}{(\mathrm{K}\cdot\mathrm{I})}} \tag{8}\]  
where C\({}_{\varepsilon}\) is the solubility of the adsorbate, \(R=8.314472\) J/mol. K is the ideal gas constant, and \(T\) is the absolute temperature.  
Furthermore, by integrating Equation (8) into the multilayer saturation model, equation (M3), the isotherm \(Q_{a}=f(C)\) can be expressed as a function of \(\varepsilon\). Upon differentiating the equation model with respect to \(\varepsilon\), the site energy distribution F(\(\varepsilon\)) can be derived using Equation (9):  
\[F(E)=\frac{\partial Q_{a}}{\partial E} \tag{9}\]  
Figure 5 depicts the adsorption energy distributions (AEDs) for hydrocortisone and dexamethasone on the surfaces of the two adsorbent composites, the MNIPs and the MMIPs, in accordance with Equation (9). It is important to note that all the curves follow a similar normal distribution pattern but vary in the intensity of the energy associated with their respective peak values. In particular, the AED for the MMIP adsorbent's surface exhibits a broader range of adsorption energies compared with the AED for the MNIPs in the adsorption of hydrocortisone and dexamethasone. This indicates that binding sites with low adsorption energies become less accessible for the adsorption of the two adsorbates on the MNIP adsorbent. Indeed, the energy distribution plot shifts along the \(x\)-axis, revealing a singular peak that likely represents the average energy of the distributions of the two adsorbate molecules on the surfaces of both adsorbents (the MNIPs and MMIPs). Consequently, the MNIPs and MMIPs exhibit heterogeneous surfaces, with disparities in their maximum peak intensities attributed to variations in the functional activated groups of both the molecules and the adsorbent surfaces. Notably, the adsorption energy values are confined to 12 kJ/mol for hydrocortisone and 17 kJ/mol for dexamethasone adsorption. This observation solidifies the notion that this phenomenon primarily involves physisorption processes.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Adsorbate** & **Adsorbent** & \(\Delta\mathbf{E_{1}}\) **(KJ/mol)** & \(\Delta\mathbf{E_{2}}\) **(KJ/mol)** \\ \hline \multirow{2}{*}{**Hydrocortisone**} & **MNIPs** & 11.617 & 7.656 \\ \cline{2-4}  & **MMIPs** & 11.992 & 8.035 \\ \hline \multirow{2}{*}{**Dexamethasone**} & **MNIPs** & 15.397 & 11.120 \\ \cline{2-4}  & **MMIPs** & 15.770 & 11.531 \\ \hline \end{tabular}
\end{table}
Table 3: Adsorption energy values of hydrocortisone and dexamethasone on dual-template MMIPs and MNIPs at 25 \({}^{\circ}\)C.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 2 System Description
### Security Strength  
The security strength of the encryption algorithm is related to the encryption overhead, so knowing the strength of the encryption algorithm can help to better select the appropriate encryption algorithms in the actual system. Table 1 illustrates the minimal security key sizes for different algorithms. The common symmetric encryption algorithms are AES, CAMELLIA, ARIA, etc. The common factoring modules algorithms include RSA, and the common algorithms for elliptic curves include ECDSA. Hash functions are implemented in TLS with SHA, for example. The key size of the algorithm is mostly reflected in the name of the algorithm, and some key sizes of the algorithms that are not clearly reflected can be found in [22].
# Experimental Study on Mechanical Properties of Mask-Improved Calcareous Sand
## 2 Laboratory Testing Program
### Penetration Test  
This test was conducted following the procedures outlined in ASTM D2434 [27], using a permeameter for the constant head permeability test. The pressure hole spacing of the permeameter is 10 cm, the diameter of the seepage bucket is 100 mm, and the depth of the bucket is 345 mm. To facilitate lateral comparison with the results from triaxial tests, this test ensures that all specimens have the same relative densities as those of the triaxial samples. The test procedure is divided into the following steps: assembling the apparatus, saturating the samples, stabilizing the pressure tube, measuring the seepage volume, and timing.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen Name** & **Mask Mass Content (\%)** & **Pressurization (kPa)** & **Pre-Shear Pore Ratio** \\ \hline C0P80 & & 80 & \\ C0P200 & 0 & 200 & 0.879 \\ C0P500 & & 500 & \\ \hline C03P80 & & 80 & \\ C03P200 & 0.3 & 200 & 0.945 \\ C03P500 & & 500 & \\ \hline C06P80 & & 80 & \\ C06P200 & 0.6 & 200 & 1.020 \\ C06P500 & & 500 & \\ \hline C09P80 & & 80 & \\ C09P200 & 0.9 & 200 & 1.065 \\ C09P500 & & 500 & \\ \hline \end{tabular}
\end{table}
Table 2: Summary of specimen information.  
Prior to testing, the pressure-measuring orifices were inspected and cleared of any obstructions to prevent erroneous readings due to clogging. To ensure homogeneity and full saturation, the specimen was compacted in successive layers (2-3 cm per layer). After each layer was placed, the water-stopping clamp was slightly opened to allow for saturation before proceeding with the next layer. Upon completing the final layer, a 2 cm gravel buffer layer (4-6 mm grain size) was placed atop the specimen to minimize internal structural disturbance caused by direct water flow. After the samples were loaded, water injection was conducted at a constant head. It is important to note that, in order to eliminate the effect of temperature on the viscosity coefficient of the water, a sufficient volume of water was prepared and stabilized in a temperature-controlled environment before each set of tests. A thermometer was used to continuously monitor the water temperature until it stabilized across all tests (with an error tolerance of \(\pm\)1 \({}^{\circ}\)C), ensuring that the test environment remained at a constant 20 \({}^{\circ}\)C [28]. Once steady flow was established, as indicated by a stable reading on the gauge tube, seepage measurements were recorded. After initial data collection, the regulating tube outlet was repositioned to the middle and lower third of the specimen to alter the hydraulic gradient, with parallel tests conducted following the same protocol. Fine particles transported by the seepage flow were collected and analyzed to assess the erosion resistance of the mask fibers within the mixture.
## 4 Discussion  
Although studies have mentioned the bur eccentricity of dental high-speed handpieces [8; 18], no studies have evaluated the cutting efficiency of high-speed handpieces compared to the bur eccentricity. In this study, the correlation between bur eccentricity and cutting performance of three high-speed handpieces was measured.  
The cutting performance of three dental high-speed handpieces was evaluated and S-MAX M600 showed a higher performance that was statistically significant compared to TG-98 and TRAUS ATN-400. On the other hand, S-MAX M600 showed the lowest bur eccentricity, while TG-98 and TRAUS ATN-400 showed a progressively higher bur eccentricity. The findings indicate that bur eccentricity and cutting efficiency tend to be inversely proportional to each other, that is, the lower the bur eccentricity, the higher the cutting efficiency. Since the rotational speed, bur type, type of cut specimen, and size of the load were fixed in the study, the eccentricity of the high-speed handpieces probably affects the cutting efficiency. Eccentricity influences the cutting efficiency because it changes the tooling case and the local cutting conditions (cutting speed and angle), which implies that there is no cutting position at one or several cutting edges. It also degrades the surface quality and reduces the stability of the process. In addition, the centrifugal force generated by eccentricity may result in the failure of high-speed handpieces [19; 20; 21].  
Figure 6: Eccentricities of the three kinds of high-speed handpieces.  
Figure 7: Correlation between cutting efficiency and bur eccentricity.  
The vibration caused by bur eccentricity may cause abrasion and fatigue of the bur, as well as of the connected bearings, shafts, and gears, and reduces the machine life, eventually rendering it defective [11]. Therefore, eccentricity should be carefully considered when selecting a high-speed handpiece. Although our findings suggest that S-MAX M400 showed the best performance, all three high-speed handpieces were clinically acceptable.  
Therefore, when selecting high-speed handpieces, the design, price, brand, maximum bur speed, vibration, and noise must be considered. However, more specific criteria are needed for the selection of high-speed handpieces used in the field of prosthetic dentistry.  
The present study has limitations. Using the ceramic block with similar mechanical strength to human teeth without studying the actual oral environment, the experimental conditions did not match the actual oral environment. In future studies, in vivo or ex vivo studies should be conducted. In addition, one type of diamond bur was used, which cannot identify the effects of various types of burs. Further research also needs to be done to evaluate the effects of various types of burs.