esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation of Equivalent Stress
\[\sigma_{\rm eq}=\left\{\frac{C}{N_{T}}\left[\left(\left(\frac{n_{1}\sigma_{ 1}^{m}}{C}\right)^{(\frac{\sigma_{2}}{\sigma_{1}})^{0.4m}}+\frac{n_{2}\sigma_ {2}^{m}}{C}\right)^{(\frac{\sigma_{3}}{\sigma_{2}})^{0.4m}}+\cdots+\frac{n_{ i-1}\sigma_{i-1}^{m}}{C}\right]^{(\frac{\sigma_{i}}{\sigma_{i-1}})^{0.4m}}+\frac{n_{i} \sigma_{i}^{m}}{C}\right\}^{\frac{1}{m}} \tag{13}\]  
Define the exponent term \(a_{i}\) as follows:  
\[a_{i}=\left(\frac{\sigma_{i}}{\sigma_{i-1}}\right)^{0.4m} \tag{14}\]  
Equation (13) can be deduced as  
\[\sigma_{\rm eq}=\left\{\frac{C}{N_{T}}\left[\left(\left(\frac{n_{1}\sigma_{1} ^{m}}{C}\right)^{a_{1}}+\frac{n_{2}\sigma_{2}^{m}}{C}\right)^{a_{2}}+\cdots+ \frac{n_{i-1}\sigma_{i-1}^{m}}{C}\right]^{a_{i}}+\frac{n_{i}\sigma_{i}^{m}}{C} \right\}^{\frac{1}{m}} \tag{15}\]  
Based on the M-H model, Equation (15) gives the calculation formula of equivalent stress calculated by nonlinear cumulative damage theory. In the formula, parameter C is the constant corresponding to the S-N curve. Equation (15) for the calculation of the equivalent stress only considers the loading sequence effects and does not consider load interactions. Therefore, it is difficult to guarantee the accuracy of its prediction analysis. In order to improve the accuracy of the analysis further, the stress ratio is adopted to reflect load interactions. The modified M-H model method in reference [25] is introduced to modify the exponential parameter \(a_{i}\). After that, Equation (14) can be shown as  
\[a_{i}^{*}=\left(\frac{\sigma_{i}}{\sigma_{i-1}}\right)^{0.4m\cdot\min\left\{ \sigma_{i-1}/\sigma_{i}\sigma_{i}/\sigma_{i-1}\right\}} \tag{16}\]By substituting Equation (16) into Equation (15), the equivalent stress calculation formula based on the modified M-H model can be obtained as follows:  
\[N_{\text{eq}}=\frac{C}{\sigma_{\text{eq}}^{m}} \tag{8}\]0  
Based on the modified M-H model, Equation (17) is the calculation formula for the equivalent stress calculated by the nonlinear cumulative damage theory. In this model, if the load and damage change, the fatigue damage will accumulate with nonlinear characteristics. When the loading sequence changes, the cumulative damage also changes. Therefore, the equivalent stress obtained by Equation (17) takes both the loading sequence effects and load interactions into account.
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 3 Results and Discussion
The compositing derived bands had a negative effect on the probability of the pixel classifications. MahD is a direction-sensitive distance classifier that uses statistics for each class. Also, it is similar to the ML but assumes all class covariances are equal and therefore is a faster method [11]. The increase in classification accuracy shows that the MahD classifier seems to consider the derived band information for the classification.
## 2 Materials and Methods
### Mobility Indicator Design  
As mentioned above, the primary task of establishing a mobility model is to provide the mobility scenarios required for network simulation by correctly and reasonably describing the node mobility manners. Are there any differences in the scenarios provided by different mobility models? What are the unique aspects? Does network performance vary depending on how UAV nodes move? If so, how to measure its impact, and so on, all of these issues involve correctly measuring and evaluating the motion of mobility models. The speed of movement and communication radius of a node directly affects the establishment of a communication link. During the movement of a single node, not only the movement relationship between two nodes will change, but also the movement state of a single node will affect the global connectivity and the structure of the graph. Therefore, our research considers the motion relationship between nodes when describing the motion performance of a mobility model. At the same time, the motion nodes in a network can be described as points by using graph theory, the link between two nodes can be described as the edge, and global connectivity characteristics of the network can be abstractly described as a graph. By analyzing the graph's characteristics, we also establish global motion characteristics description indicators.  
Firstly, we show the definitions and symbolic expressions of the following variables:  
* \(\hat{\mathrm{Y}}\) Node \(s\) is the source node, node \(d\) is the destination node for forwarding, and \(R\) is the communication radius of the node.
* \(l_{x,y}(t)\) indicates the Euclidean distance between node \(x\) and node \(y\) at time \(t\).
* \(\vec{V}_{x}(t)\) represents the velocity vector of node \(x\) at time \(t\); and \(\left|\vec{V}_{x}(t)\right|\) represents the velocity value of node \(x\) at time \(t\).
* \(x_{x}(t)\) represents the \(x\) coordinate of node \(x\) at time \(t\); and \(y_{x}(t)\) represents the \(y\) coordinate of node \(x\) at time \(t\).
* \(RD\bigg{(}\vec{v}_{x}(t),\vec{v}_{y}(t)\bigg{)}\): the relative direction (RD), or cosine of the angle between two velocity vectors, calculated by \(\frac{\vec{v}_{x}(t),\vec{v}_{y}(t)}{\left|\vec{v}_{x}(t)\right|^{4}\left| \vec{v}_{y}(t)\right|}\).
* \(VR\bigg{(}\vec{v}_{x}(t),\vec{v}_{y}(t)\bigg{)}\): the velocity ratio (VR) between two vectors, calculated by \(\frac{\min\left\{\left|\vec{v}_{x}(t)\right|,\left|\vec{v}_{y}(t)\right| \right\}}{\max\left\{\left|\vec{v}_{x}(t)\right|,\left|\vec{v}_{y}(t)\right| \right\}}\).
* \(\hat{\mathrm{Y}}\) \(N\): number of mobile nodes.
* \(\hat{\mathrm{Y}}\) \(T\): time of nodes motion duration.  
Figure 3: Node movement in RPGM mobility model.
## 3 Experimental Results
### Effect of Different Factors on Blockage Type
#### 3.3.2 Effect of Slide Angle  
In reality, there are many different slope conditions for landslides that determine the movement process of landslides and the final type of river blockage. Although much research focuses on the influences of slope conditions on landslides, there is still a lack of precise conclusions on how slope conditions influence the process of dam formation and river blockage. Figure 14 shows that some cross-sections of landslides initiate on slopes with different angles. When the slide angle increases, the landslide velocity also increases significantly, so the landslide movement process is reduced and the type of river blockage is changed. The increase in slide angle reduces the height difference between \(H_{\text{min}}\) and \(H_{\text{max}}\) and makes the landslide dam more compact and more resistant to flushing.  
Figure 14: The variation in experimental river blockage under the influence of slide angle.  
Figure 13: The variation in the transversal location of the peak deposition.
## 3 Results and Discussion  
### Total Phenol and Flavonoid Content  
The total polyphenol contents for the selected propolis samples were found to be \(178.9\pm 5.7\), \(198.4\pm 4.1\), \(167.6\pm 7.8\), and \(246.3\pm 3.2\) mg GAE/g of dry extract for GUA-1, GUA-2, GUA-3, and GUA-4, respectively. It should be noted that the Folin-Ciolcateau reagent was reported in the literature as not specific to only phenols and could react with other reducing compounds that could be oxidized by the Folin reagent [21]. The total flavonoid contents were \(64.32\pm 5.75\), \(58.34\pm 2.8\), \(77.45\pm 6.9\), and \(87.5\pm 1.9\) mg QE/g of dry extract for GUA-1, GUA-2, GUA-3, and GUA-4, respectively. Flavonoids can form complexes with aluminum chloride to yield a yellow solution. [PERSON] et al. [22] has been reported values of \(629.6\pm 9.9\) mg GAE/g of dry extract for TPC and \(185.9\pm 3.2\) mg QE/g of dry extract and TF in a propolis from Sonora, Mexico. The most important flavonoids isolated in this propolis sample were pinochemrin, pinobanksin 3-acetate, and chrysin. The results also confirm the influence of the geographic region and the season of collection for the quality and properties of the propolis [2].
# Labeling Peptides with Radioiodine: An Overview of Traditional and Emerging Techniques
## 1 Introduction
Direct radioiodination involves the electrophilic substitution of radioiodine into specific amino acid residues, such as tyrosine or histidine, within the peptide structure. While this method is relatively straightforward, it may result in the modification of residues that are critical for the peptide's biological function, potentially altering its binding properties. Indirect radioiodination, on the other hand, employs prosthetic groups that are pre- or post-labeled with radioiodine and conjugated to the peptide at sites distant from functionally important residues. Although this approach introduces a larger chemical moiety, it is often better suited for preserving the native conformation and biological activity of the peptide, while also enabling site-specific and potentially more stable labeling [1; 2]. Recent advances in radioiodination methodologies have led to the development of innovative chemical strategies that enhance radiochemical yields (RCY), improve in vivo stability, and expand the range of applicable peptide substrates. These advancements include the use of novel prosthetic groups, click chemistry approaches, and optimized purification techniques that enhance the efficiency and specificity of the labeling process. Additionally, the integration of computational modeling and automation in radiolabeling procedures has further refined the synthesis of radioiodinated peptides, making them more accessible for clinical and research applications [14; 15]. Given the growing demand for radiolabeled peptides in nuclear medicine, it is essential to understand the chemical principles underlying their synthesis. This review is structured to first introduce the rationale and key principles of peptide radioiodination, followed by detailed discussions of direct and indirect labeling strategies, specific examples from the recent literature and purification techniques. To better guide readers in selecting the most appropriate radioiodination approach, we have compiled a comparative summary of the main iodine-incorporating methods discussed in this review. Table 1 provides an overview of these strategies, highlighting the key features, optimal use cases, and relevant references.
## 2 Results and Discussion
### SPME-GC/MS: Chemical Volatile Composition of the Spices
On the other hand, curcumene, also a sesquiterpene hydrocarbon and the main metabolite of ginger rhizomes, is known for its insecticidal [22] and antimicrobial properties [23]. These results show that both peppers can be used as a biorational source of zingiberene and curcumene. Lastly, considering the high content of phytochemicals in the investigated spices, these could also represent a source of bioactive compounds for possible future applications if encapsulated inside specific carriers such as hyper-cross-linked polymers (HCPs) that possess characteristics that also make them usable in the biomedical area as antimicrobials [24]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Component 1** & **LRI 2** & **LRI 3** & **Paprika (\%)** & **Baklouti** \\ \hline
1 & \(\alpha\)-pinene & 935 & 937 & 0.6 \(\pm\) 0.03 & 0.3 \(\pm\) 0.02 \\
2 & limonene & 1025 & 1028 & 8.6 \(\pm\) 0.07 & 3.5 \(\pm\) 0.04 \\
3 & linalool & 1081 & 1088 & 6.6 \(\pm\) 0.04 & 2.2 \(\pm\) 0.05 \\
4 & camphor & 1141 & 1139 & 0.9 \(\pm\) 0.03 & - \\
5 & estragole & 1190 & 1177 & 2.4 \(\pm\) 0.05 & 1.2 \(\pm\) 0.03 \\
6 & (-)-carvone & 1238 & 1243 & 11.9 \(\pm\) 0.10 & 6.0 \(\pm\) 0.02 \\
7 & anethole & 1255 & 1260 & - & 0.1 \(\pm\) 0.00 \\
8 & isobornyl acetate & 1270 & 1268 & - & 0.3 \(\pm\) 0.03 \\
9 & \(\alpha\)-limonene diepoxide & 1297 & 1294 & 0.1 \(\pm\) 0.01 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical composition (percentage mean value \(\pm\) standard deviation) of Paprika and Baklouti peppers, as determined by SPME-GC/MS.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 5 SMT-Based Scheduling Optimization for Fault-Aware Job Assignments  
In this section, we explain how Satisfiability Modulo Theories (SMT) is utilized to optimize job scheduling based on the fault probabilities predicted by the deep learning model. The objective is to minimize the risk of machine faults while ensuring that all jobs are completed within their deadlines and adhering to machine constraints.
# Exploring Variability of Visual Accessibility Options in Operating Systems
## 6 Case Study
### Results
#### 6.3.1 Omnipresent Features (RQ1)  
To find omnipresent features, we observe the top concept intents in the different lattices (Figure 9), which indicate the features that are present in all systems. We consider meaningful an attribute which is different from the root of an hypothetical feature model. For example, the Brightness lattice top concept intent (_Concept_Brightness_4_ in Figure 9d) introduces _BrightnessMechanism_ and _ManualDisplayBrightnessLevel_. _BrightnessMechanism_ would be the root feature in a feature model for the Brightness mechanism. As the root, it is trivially omnipresent, thus it is not considered as meaningful here.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concept Lattice** & **\#Objects (=\#OS)** & **\#Attributes (=\#Features or \#Relational Attributes)** & **\#Concepts** \\ \hline Brightness & 8 & 6 & 5 \\ \hline Text style & 8 & 38 & 21 \\ \hline Contrast and Color & 8 & 88 & 21 \\ \hline Central lattice & 8 & 47 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Metrics on the objects, attributes and concepts for the case study.  
The Central lattice top concept intent (Figure 9a) introduces relational attributes that point to the top concepts of Contrast, Brightness and Text style lattices respectively. This means that all operating systems provide options in the three mechanisms.  
The extraction shows that omnipresent features are very few. We learn that all systems propose manual setting for brightness, the possibility to set text size in various elements, a gallery, a screen saver option and the possibility to define a contrast type. This low number of shared features reveals a diversity in approaching the visual accessibility option design.
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 3 Results  
This chapter highlights the unique AIS challenges associated with problems in securing ships at sea. It presents findings on AIS vulnerabilities, some of the recent examples of AIS spoofing trajectories, and practical investigations of an AIS spoofing event. To understand the current research, it is important to understand the background, how AIS works, the liabilities, and the methods by which vulnerabilities to the system can be initiated. The nature of the cyber arena is changing today, and the maritime sector is experiencing sophisticated and complex attacks that no one could have predicted years ago.
## 4 Shape Optimization
### Sensitivity Analysis  
In shape optimization based on Equation (32), sensitivity analysis, i.e., stress sensitivity and mass sensitivity with respect to design variables, is essential for gradient-based optimization algorithms.  
For the level set function \(\Phi(\mathbf{x},\mathbf{d})\) of a physical domain \(\Omega\), the Dirac delta function \(\hat{\delta}(\mathbf{x},\mathbf{d})\) denotes the directional derivative of the Heaviside function \(H\) in the normal direction, i.e.,  
\[\hat{\delta}=\
abla H(\Phi)\cdot\frac{\
abla\Phi}{\|\
abla\Phi\|}=\frac{\mathrm{ d}H(\Phi)}{\mathrm{d}\Phi}\cdot\
abla\Phi\cdot\frac{\
abla\Phi}{\|\
abla\Phi\|}= \frac{\mathrm{d}H(\Phi)}{\mathrm{d}\Phi}\|\
abla\Phi\| \tag{33}\]  
Then, the mass sensitivity is calculated by  
\[\begin{array}{rl}\frac{\partial\mathbf{m}}{\partial\theta_{i}}&=\int_{\Omega} \rho\frac{\partial H(\Phi)}{\partial\theta_{i}}\mathrm{d}\Omega\\ &=\int_{\Omega}\rho\frac{\partial\Phi}{\partial\theta_{i}}\frac{1}{\|\
abla \Phi\|}\frac{\mathrm{d}H(\Phi)}{\mathrm{d}\Phi}\|\
abla\Phi\|\mathrm{d}\Omega \\ &=\int_{\Omega}\rho\frac{\partial\Phi}{\partial\theta_{i}}\frac{1}{\|\
abla \Phi\|}\delta\mathrm{d}\Omega\\ &=\int_{\Gamma}\rho\frac{\partial\Phi}{\partial\theta_{i}}\frac{1}{\|\
abla \Phi\|}\mathrm{d}\Gamma\end{array} \tag{34}\]  
in which \(\Gamma\) represents the boundary of the physical domain, i.e., \(\Gamma=\)\(\partial\Omega\).  
Figure 11: Flowchart of shape optimization based on the weighted B-Spline FCM and GCMMA.  
Based on the stress-strain relationship \(\sigma=D\epsilon=D\overline{B}\mathbf{U}\), the stress sensitivity is estimated by  
\[\frac{\partial\sigma}{\partial\theta_{i}}=D\overline{\mathbf{B}}\frac{\partial\mathbf{ U}}{\partial\theta_{i}}+D\frac{\partial\overline{\mathbf{B}}}{\partial\theta_{i}}\mathbf{U} \tag{35}\]  
where \(\mathbf{D}\) represents the elastic modulus, \(\mathbf{U}\) denotes the displacement field, and \(\overline{\mathbf{B}}\) is the strain-displacement matrix corrected by the weighted function.  
For the four topological configurations of the lifting lug are considered in Section 2.3. Since the design variables are all on the free boundary, \(\overline{\mathbf{B}}\) is independent of the design variables. Thus, the stress sensitivity is derived as  
\[\frac{\partial\sigma}{\partial\theta_{i}}=D\overline{\mathbf{B}}\frac{\partial\bm {U}}{\partial\theta_{i}} \tag{36}\]  
According to the equilibrium equation \(\mathbf{K}\mathbf{U}=\mathbf{F}\), the stress sensitivity is deduced as  
\[\frac{\partial\sigma}{\partial\theta_{i}}=D\overline{\mathbf{B}}\mathbf{K}^{-1}\! \left(\frac{\partial\mathbf{F}}{\partial\theta_{i}}-\frac{\partial\mathbf{K}}{ \partial\theta_{i}}\mathbf{U}\right) \tag{37}\]  
where  
\[\frac{\partial\mathbf{K}}{\partial\theta_{i}}=\int_{\Gamma}\overline{\mathbf{B}}^{ \mathrm{T}}D\overline{\mathbf{B}}\frac{\partial(\Phi)}{\partial\theta_{i}}\frac{ 1}{\|\
abla\Phi\|}\mathrm{d}\Gamma \tag{38}\]  
and  
\[\frac{\partial\mathbf{F}}{\partial\theta_{i}}=\int_{\Gamma}\omega\mathbf{M}^{\mathrm{ T}}\!\frac{\partial\Phi}{\partial\theta_{i}}\frac{1}{\|\
abla\Phi\|}\mathrm{d}\Gamma \tag{39}\]
## 4 Experimental Results and Analysis
### Comparative Analysis of Detection Experiments Using Different Algorithms  
Based on the given data, we can analyze and compare several mainstream object detection algorithms, including Faster-RCNN, SSD, YOLO-V3, YOLO-V5, and YOLO-V8. From a comparative analysis of overall performance, it is evident that with the evolution and optimization of these algorithms, newer generations exhibit significant improvements in processing speed and accuracy, as follows:  
* Faster-RCNN, though demonstrating good accuracy in earlier models, suffers from slower processing speeds, resulting in a lower overall efficiency rating.
* SSD offers a good balance between speed and accuracy and is particularly effective for scenarios where real-time processing is not the critical requirement.
* The YOLO series has progressively improved through its versions. YOLO-V8, the latest in this series, shows marked improvements in accuracy and speed over its predecessors, with an overall efficiency rating approaching perfection.  
After comparing and analyzing various object detection algorithms, we decided to base our improved network on YOLOv5. We chose YOLOv5 because it offers significant advantages in terms of stability, technological maturity, and broad applicability compared with other models. These characteristics align closely with the high standards required for production. YOLOv5 not only achieves a good balance between processing speed and accuracy but has also proven to be reliable in various complex environments, which is particularly critical for meeting the demands of production lines.  
On the other hand, while YOLOv8 excels in accuracy, its demand for computational resources is relatively high, which could be a disadvantage in time-sensitive or resource-limited application scenarios. High computational demands could lead to slower processing speeds, thus affecting production efficiency. This makes YOLOv5 a more suitable choice for applications where a balance of speed, accuracy, and computational efficiency is necessary, with the results shown in Table 1.
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
* Extensive experiments conducted on the publicly available SOTS-indoor, SOTS-outdoor, and Haze4K datasets demonstrate that our proposed HarmoDehazeDiffusion method outperforms the current state-of-the-art dehazing methods in its dehazing performance.
## 3 Verification and Validation  
### Verification at the Tube Section  
A simplified model is proposed to verify the present method for Hagen-Poiseuille solution in a non-Newtonian fluid: see Figure 4a. To get the fully developed flow domain, periodic flow condition is applied in both inlet and outlet boundaries, and the length \(\Delta z\) is set 0.5 mm. The corresponding mesh system is plotted in Figure 4b, which is the identical level of the meshes in Section 2.4.  
The classical solution of Hagen-Poiseuille [23; 25] shows that the axisymmetric velocity distribution in a pipe should be given as a function of radial position, \(r\):  
\[u(r)=\frac{n}{n+1}\bigg{(}-\frac{dp}{dz}\frac{1}{2m}\bigg{)}^{\frac{1}{n}} \bigg{(}R^{\frac{n+1}{n}}-r^{\frac{n+1}{n}}\bigg{)} \tag{9}\]  
where the pressure gradient \(dp/dz<0\) is specified, and the tube radius is \(R\). From the comparison of the present numerical analysis with Equation (9) is shown in Figure 5, the maximum relative error is within 1.4%, and the present numerical method is verified for the tube-side computational region.  
Figure 4: Verification at the tube section: (**a**) boundary condition, (**b**) grids system.
## 2 Research Methodology
### Content Transfer Protocols
#### 2.4.3. Signiant  
Signiant is a high-speed data transfer protocol designed for securely transferring large files and data sets over long distances. It uses patented acceleration technology to optimize transfer speeds, regardless of network conditions, while maintaining security and reliability. Overall, Signiant is a powerful and flexible data transfer protocol that is well-suited for large-scale, high-speed transfers over long distances. Its combination of speed, security, and reliability features make it a popular choice for a variety of industries and applications, including media and entertainment, healthcare, scientific research, and government agencies.
###### Abstract  
C6 volatiles are synthesized through lipoxygenase-hydroperoxide lyase (LOX-HPL) pathway and these volatiles play important roles in the aromatic quality of grape berries. This study investigated the evolution of both C6 volatiles and the key genes in the LOX-HPL pathway in different table grape cultivars during the berry development period, and further assessed the correlation between the accumulation of C6 volatiles and the expression of these genes in these cultivars. Results showed that hexanal, (_E_)-2-hexenal, (_E_)-2-hexen-1-ol and (_Z_)-3-hexen-1-ol were found to be the dominant C6 volatiles in these ripened grape cultivars under two consecutive vintages, and their flavor notes were incorporated in the overall aroma of these cultivars. The cultivar \"Xiangfei\" showed the most abundant level of C6 aldehydes and C6 acid, whereas the cultivar \"Tamina\" and \"Moldova\" possessed the highest C6 alcohol content. The \"Muscat of Alexandria\" cultivar was found to contain the highest level of C6 esters. C6 volatiles were grouped into three evolutionary patterns in these cultivars during berry development, and their evolution was consistent with the evolution of the LOX-HPL pathway genes' expression. [PERSON]'s correlation analysis indicated that the LOX-HPL-pathway-related genes were correlated to the accumulation of C6 volatiles in these cultivars, and _VvLOXA_ appeared to be an important gene that regulated the synthesis of all C6 volatiles.  
_Vitis vinifera_ grapes; C6 volatiles; gas chromatography-mass spectrometry (GC-MS); _VvLOXA_ expression; lipoxygenase-hydroperoxide lyase (LOX-HPL) pathway; multivariate analysis