esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Results
#### 5.2.9 Changes in Network Density vs. End-to-End Packet Delivery Delay  
The end-to-end packet delivery delay for different node densities is shown in Figure 15. Here, the average packet delivery delay is calculated in seconds; only one packet is sent during the entire simulation runtime, and the number of nodes varies from 100 to 1000. Moreover, a sensor field of 1000 m \(\times\) 1000 m was used with this considerable number of nodes to demonstrate the performance of the protocols with scalability. The performances for duty cycles of 0.5 and 1 (always active node) are shown in Figure 15a and Figure 15b, respectively. According to the graphs, the delay increased with increasing density in both setups. As the number of nodes increased, the probability of an increase in active nodes simultaneously increased, resulting in channel congestion.  
Figure 14: Comparison of the network lifetime with varying packet rates and non-uniform densities.  
Channel congestion resulted in end-to-end packet delivery delays. Moreover, the delay of the proposed protocol was mostly greater than that of existing protocols. This results from the additional delay as the packet travels through low-density areas, which may not be the shortest path toward the sink node.
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 3 Noniterative Simultaneous Rigid Registration Method (NSRR)
### Optimality Conditions  
In this section, we analyze the conditions under which our approximation algorithm will obtain optimal solutions. Assuming that all the sections suffer from rigid deformations, because \(\mathbf{X}_{i,\mathbf{i+1}}\) and \(\mathbf{X}_{i+1,i}\) are one-to-one correspondences, we can consider them as the same point set \(\hat{\mathbf{X}}_{i}\) centered at the origin under different rigid transformations \((\hat{\mathbf{R}}_{i},\hat{\mathbf{T}}_{i})\). Let \(\mathbf{X}_{i,i+1}=\hat{\mathbf{R}}_{i}\hat{\mathbf{X}}_{i}+\hat{\mathbf{T}}_{i}\) and \(\mathbf{X}_{i+1,i}=\dot{\mathbf{R}}_{i+1}\dot{\mathbf{X}}_{i}+\dot{\mathbf{T}}_{i+1}\). If the position of the first and last image have been correctly adjusted, we have \(\dot{\mathbf{R}}_{1}=\dot{\mathbf{R}}_{n}=\mathbf{I},\dot{\mathbf{T}}_{1}= \dot{\mathbf{T}}_{n}=\mathbf{0}_{2\times 1}\). Hence, Equation (2) can be rewritten as:  
\[\begin{array}{l}\operatorname*{arg\,min}\limits_{\mathbf{R}_{i}}\sum \limits_{i=1}^{n-1}\left\|\left(\mathbf{R}_{i}\dot{\mathbf{R}}_{i}-\mathbf{R} _{i+1}\dot{\mathbf{R}}_{i+1}\right)\dot{\mathbf{X}}_{i}\right\|_{F}^{2}\\ s.t.\left\{\begin{array}{l}\mathbf{R}_{i}^{T}\mathbf{R}_{i}=I\\ \det(\mathbf{R}_{i})=1\\ \mathbf{R}_{1}=\mathbf{R}_{n}=I\end{array}\right.\end{array} \tag{10}\]  
Here, we have a trivial solution to Equation (10):  
\[\mathbf{R}_{i}=\dot{\mathbf{R}}_{i}^{-1}\quad(i=2,\ldots,n-1) \tag{11}\]  
Substituting Equation (11) into Equation (8), Equation (8) is simplified as  
\[\mathbf{Th}_{i}=-m_{i}^{-1}\mathbf{Z}_{i}\mathbf{e}_{i}^{T} \tag{12}\]  
Substituting Equation (12) into Equation (1), we obtain Equation (2), proving that, under ideal condition, the result is equivalent to using Equation (2) to replace Equation (1).  
Therefore, to obtain the optimal solution, two conditions must be satisfied: first, all the serial sections must be rigidly deformed, and, second, the positions of the first and last images must be correctly adjusted, meaning that they have no relative displacement. Although our algorithm is intended to handle rigid deformations, it still offers robust initial estimates for nonrigid registration algorithms.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 2 Materials and Methods
### Workflow and Data-Processing Algorithm
#### 2.3.2 The Positioning Data Processing Algorithm of PIT
Third, we extract key positioning data for the drift trajectory of drowning personnel through a small-angle turning feature. The vertical distance \(d_{las}\) from \(P_{las-u}\) to the historical drift trajectory is calculated in the X-Y coordinate system. The algorithm extracts key positioning data by comparing \(d_{las}\) with the distance threshold \(d_{thr}\) set by the system. The method for extracting key positioning data from small angles is as follows:  
In the \(x\)-\(y\) coordinate system centered on \(P_{ori}\), the expression for the latest historical drift trajectory is  
\[f_{(x_{tra})}=(\frac{y_{dir}}{x_{dir}})x_{tra} \tag{8}\]  
The shortest actual offset distance from \(P_{las-u}\) to \(f_{(x_{tra})}\) in this coordinate system is expressed as  
\[d_{las}=\frac{\left|kx_{P_{las-u}}-y_{P_{las-u}}\right|}{\sqrt{1+k^{2}}} \tag{9}\]  
where k is the slope of \(f_{(x_{tra})}\), and \(x_{P_{las-u}}\) and \(y_{P_{las-u}}\) denote the actual offset distance of \(P_{las-u}\) relative to \(P_{ori}\) in the longitude and latitude directions, respectively. If \(d_{las}\) is larger than \(d_{thr}\), this indicates the occurrence of small-angle turning features. \(P_{pre}\) are the key positioning data, which are stored in the \(array_{KPD}\) of key positioning data. Otherwise, they will be discarded. Compress The Key Positioning Data: \(array_{KPD}\) contains multiple key positioning data, which are calculated and stored using floating-point data types. This article focuses on the continuous features of key positioning data in spatial distribution and designs an improved differential encoding method to reduce the space occupied by key positioning data. The compression method is as shown in Formula (10). \[\left[ \begin{array}{c}\triangle jp_{i}\\ \triangle wp_{i}\end{array} \right]=\left(\left[ \begin{array}{c}jp_{i+1}\\wp_{i+1}\end{array} \right]-\left[ \begin{array}{c}jp_{i}\\wp_{i}\end{array} \right]\right)\times 10^{7} \tag{10}\]where \(jp_{i}\) and \(wp_{i}\) represent the latitude and longitude of the \(i\)th key positioning data stored in the current cycle, while \(\Delta jp_{i}\) and \(\Delta wp_{i}\) represent the latitude and longitude increment information of the \(i\)th key positioning data, and \(i\) increases by 1 in each cycle. At the time of communication cycle arrival, the main control unit sequentially calculates the adjacent increment of key positioning data in longitude and latitude, and converts the increment information into the short data type for storage. It is worth noting that the incremental information of the latest key positioning data is obtained through calculating of the latest effective positioning data and the latest key positioning data. This encoding method can compress 50% of the storage space of key positioning data while ensuring the accuracy of positioning data.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Huperzine A  
Huperzine A (Hup), a lycopodium alkaloid originally extracted from _Huperzia serrata_ (Figure 1), is an effective inhibitor of acetylcholinesterasease; thus, it has attracted intense attention as a therapeutic drug for [PERSON]'s disease [95]. However, production of Hup from plants in large quantities is currently insufficient because the producing plants are rare and their content of Hup is extremely low. Multiple studies have shown that this compound is produced by various endophytic fungi [96]. In 2014, genome of the endophytic fungus _Shiraia_ sp. SIf14 was sequenced, disclosing the presence of a putative Hup biosynthetic gene cluster [72].  
De novo RNA sequencing of transcriptome of the Hup-producing endophytictive fungus _C. gloosporoides_ ES026 has revealed a gene encoding copper amine oxidase (CAO), responsible for the conversion of cadaverine to 5-aminopentanal in Hup biosynthesis [73]. This gene was also found in roots, stems, and leaves of _H. serrata_. A correlation was observed between the expression of _CAO_ and the quantity of Hup extracted from strain ES026. Thus, it was postulated that _CAO_ plays a key regulatory role in the biosynthesis of Hup. The next attempt of de novo RNA-seq of ES026 transcriptome enabled annotation of two key genes for Hup biosynthesis, encoding lysine decarboxylase (LDC) and again CAO, for conversion of L-lysine to 5-aminopentanal [74].  
Whole genome and transcriptome analysis of two other Hup-producing endophytes, _Penicillium polonicum_ hy4 and _C. gloosporoides_ Cg01, was performed to elucidate Hup biosynthesis [75]. Based on the proposed HupA biosynthetic pathway proposed in plants, 1 LDC gene, 6 CAO genes, 27 PKS genes, 2 BBE genes, 111 P450s, and 35 2 OGDs were identified in hy4, while 2 LDC genes, 12 CAO genes, 47 PKS genes, 4 BBE genes, 280 P450s, and 39 2 OGDs were found in Cg01. Orthologous and phylogenetic analyses between hy4 and Cg01 were performed. The presence of differentially expressed genes was observed between a group where production was induced with added extracts of _H. serrata_ and the control group. Among these genes, those displaying a similar expression pattern in Cg01 and hy4 were selected. This enabled identification of 2 _LDCs_, 1 _CAO_, and 3 _PKS_ genes in Cg01 as candidates for further validation.  
Strain hy4 of _P. polonicum_ was also subjected to the mitochondrial genome sequencing [97]. The study of _P. polonicum_ hy4 mitogenome enabled detailed phylogenetic analysis and provided valuable information on mitochondrial evolution.
# Changed Detection Based on Patch Robust Principal Component Analysis
## 2 Preprocessing
### Intensity Correction  
Many studies have presented intensity correction techniques inside an image, such as color normalization [21; 22], contrast enhancement [11; 23], and nonuniform illumination correction [24]. As shown in Figure 1, the original image taken as the reference image was that from DRIVE. DRIVE was established to enable comparative studies on the segmentation of blood vessels in retinal images and acquired using a Canon CR5 non-mydriatic 3 CCD camera with a 45-degree field of view (FOV) [25]. After correcting the intra-intensity of each image, the image pair is adjusted into two different intensity levels as shown in Figure 1a,b. The global illumination variation is still obvious, and hence we take the intensity normalization to correct this kind of illumination variations for the image pair.  
Suppose that there are two original images \(I_{1}\) and \(I_{2}\) denoting the reference image and current image, respectively, which will be enhanced by some intensity correction techniques such as IRISF [11] and denoted by \(\tilde{I}_{1}\) and \(\tilde{I}_{2}\) separately. Then, \(\tilde{I}_{1}\) is adjusted to the intensity of \(\tilde{I}_{2}\) to obtain the normalized images \(\tilde{I}_{12}\), and \(\tilde{I}_{2}\) is adjusted to the intensity of \(\tilde{I}_{1}\) to obtain the normalized image \(\hat{I}_{21}\)[10]. The formulas of \(\hat{I}_{12}\) and \(\hat{I}_{21}\) are given as follows:  
\[\hat{I}_{12}=\frac{\sigma_{2}}{\sigma_{1}}\{\tilde{I}_{1}-\mu_{1}\}+\mu_{2} \tag{1}\]  
and  
\[\hat{I}_{21}=\frac{\sigma_{1}}{\sigma_{2}}\{\tilde{I}_{2}-\mu_{2}\}+\mu_{1} \tag{2}\]  
where \(\mu_{i}\) and \(\sigma_{i}\) are the mean value and standard deviation of \(\tilde{I}_{i}\), \(i=1,2\).  
Figure 1 illustrates the result of intensity normalization for a pair of fundus images. In Figure 1, (c) is closer to (b) than (a) for the intensity, and (d) is closer to (a) than (b) for the intensity. The image pair showing in (a) and (b) is adjusted to two image pairs with different intensity levels: a brighter image pair (a) and (d) and a darker image pair (b), (c).  
Figure 1: Intensity normalization of the image pair. The original image taken as the reference image is from DRIVE. A small noise patch is attached on the reference image and the intensity is adjusted which is taken as the current image. (**a**,**b**) are the grayscale images of the reference image and current image, and (**c**,**d**) are results of intensity normalization for (**a**,**b**).  
In fact, after intensity correction and normalization, the comparison of \(\tilde{I}_{1}\) and \(\tilde{I}_{2}\) is converted into the comparison of two image pairs at two different intensity levels: the image pair \(\hat{I}_{1}\), \(\hat{I}_{21}\) and the image pair \(\hat{I}_{2}\), \(\hat{I}_{12}\). In order to filter the distraction of illumination variations and keep the background more robust, we take the linear interpolation to obtain more comparisons of the image pair on different intensity levels.
# Chitosan Films with Caffeine and Propolis as Promising and Ecofriendly Packaging Materials
## 3 Results and Discussion  
### Antiradical Activity of Film-Forming Solutions  
Examining the antioxidant properties of materials with potential applications in food packaging is of significant importance, especially in the context of their ability to mitigate food oxidation during storage [3]. In order to prevent food oxidation, new solutions which will improve the antioxidant properties of packaged food are sought in the packaging industry. The solution may be provided by packaging materials made of natural polymers with additives showing proven antioxidant properties [44; 45; 46].  
The antiradical activity was investigated in the chitosan-based solution with CAF and EP prepared with two acids, acetic and citric, and the results are presented in Figure 1.  
The outcomes of the antiradical activity tests revealed that chitosan solutions lacking the inclusion of natural active components (EP and CAF) exhibited no ability to capture free radicals, regardless of the acid used. The addition of CAF resulted in a minor elevation in the antioxidant activity of the tested film-forming solutions--sample AC (8.07%) and sample CC (17.47%). The value of antiradical activity in chitosan solutions was found to increase after the addition of CAF and EP to the solutions. Moreover, when comparing the activity of the chitosan-based solutions with both natural additives, it was observed that the acid used to dissolve the chitosan also affected the antioxidant activity of the final solutions. Statistical analysis indicates that the greatest antiradical properties were exhibited by a solution consisting of chitosan prepared with citric acid and the addition of CAF and EP. This ability to scavenge free radicals was higher (99.42%) than for standard antioxidant Trolox (92.82%). This high antiradical activity of the chitosan/citric acid film with propolis and caffeine may be due to the well-known antioxidant properties of caffeine and propolis. The high antioxidant activity of propolis extracts has been confirmed in numerous literature reports [47; 48; 49]. The antioxidant effect of caffeine is related to its ability to neutralize free radicals [28; 32], while ethanolic propolis extracts (EP) comprises numerous compounds, notably flavonoids and phenolic acids, known for their potent antioxidant properties [47; 50; 51; 52].  
The use of additives such as caffeine and propolis is extract exhibiting antioxidant effects to chitosan-forming solutions may be an effective method to mitigate oxidative stress in food packaging.
## 2 Background
### Opposition-Based Learning
#### 2.3.2 Disadvantages of OBL  
Some disadvantages of OBL are:  
1. **Increased computational complexity:** The introduction of opposite solutions adds computational overhead, as it requires additional fitness evaluations and solution comparisons.
2. **Sensitivity to parameters:** The performance of OBL can be sensitive to the choice of specific parameters, such as the method of generating opposite solutions or the selection criteria between original and opposite solutions.
3. **Limited exploration:** Although OBL can enhance exploration, it may not always guarantee exploration of the entire search space, especially in complex and high-dimensional optimization problems.
4. **Lack of universally optimal opposite generation strategy:** The choice of method for generating opposite solutions depends on the problem domain and algorithm used, and there is no universally optimal strategy applicable to all scenarios.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 2 Materials and Methods
### Triangle-Grid
#### 2.3.3 Triangle-Grid: Code Implementation
The result is displayed in the _TGView_ object, allowing the user to switch to source selection mode and choose the edges of the model where the input source will be placed. The _PortSettingsForm_ class allows for precise adjustments to the input source location through a graphical user interface. Since the excitation source according to (51) must be  
Figure 10: UML sequence-diagram of the Triangle-Grid. located inside the area covered by the mesh and edge elements, which may be absent in the original antenna geometry, the _PortSettingsForm_ class implements the functionality for modifying the geometry of the simulated object. By interacting with the antenna model, the user specifies the coordinates of points \(a_{1}\) and \(a_{2}\), which determine the height of the conductive plate (Figure 11a). The plate width is automatically calculated based on the specified mesh discretization step \(\Delta\). Subsequently, the procedure of combining the computational meshes of the antenna and the port is performed, followed by the selection of an edge to specify voltage \(V_{\text{in}}\) (red dashed line in Figure 11b). The refined source location is then used to modify the antenna model, resulting in an updated display of the antenna structure in the _TGview_ widget. The next step is to generate a mesh representation of the model. This is accomplished by the _CGALmesher_ class, which utilizes powerful triangulation algorithms provided by the CGAL library. The user can specify mesh generation settings through the _MeshSettingsForm_ window, including elements such as the maximum edge length and the meshing angle. The _CGALmesher_ class integrates seamlessly with the main program, providing a smooth workflow for generating high-quality meshes. Additionally, the CGAL library offers robust error handling and adaptivity features, ensuring that the generated mesh accurately represents the antenna model. The user has the option to refine the mesh, using the provided settings, to achieve the desired level of accuracy for the simulation results. Figure 11: Setting the length (**a**) and width (**b**) of the conductive plate used as an excitation source. The pre-computation of computer resources required to calculate of the antenna pattern is an important aspect of Triangle-Grid. The module provides this information to the user through a separate window of the standard Qt _QMessageBox_ class. This feature allows users to plan and manage their computing resources effectively, ensuring that they have enough resources to perform their calculations efficiently. In the final step of the antenna modeling process, the user has the option to select the type of a diagram they want to plot, such as gain or field strength, using the _PostForm_ class. This choice is then reflected in the plotted diagram, which is displayed in the _FieldDisplayForm_ object using the powerful visualization capabilities of the VTK classes.
## 2 Methodology
### Unsupervised Anomaly Detection Using Spherical Detectors and Local Neighborhood Sampling
#### 2.4.3 Algorithm Description  
To assess whether or not the example \(x\) is an anomaly, the algorithm generates random points in the vicinity of \(x\) and evaluates how many of these points are detected as anomalies by the spherical detectors. The underlying assumption is that if a significant proportion of the points in the neighborhood of \(x\) are detected as anomalies, then \(x\) is likely to be an anomaly as well.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 6 Future Directions and Challenges
### Channel Coding Optimization  
Another crucial area for future research is the optimization of channel coding schemes within VLC systems. Current coding schemes, such as RS, LDPC, and polar codes, have demonstrated strong error correction capability, but further improvements are needed, to enhance their performance under varying environmental conditions. Investigating new and advanced coding techniques tailored to the unique characteristics of VLC, such as flicker mitigation and dimming control, will be essential. Researchers should also focus on evaluating how distinct photodiodes perform under different lighting conditions and coding schemes. For example, combining photodiode performance analysis with optimized channel coding could result in better data integrity, especially in scenarios where the SNR is reduced, due to ambient light interference. The use of simulation tools, such as NI cDAQ programmed in LabView, as verified by previous studies, should continue to be used for physical system validation and to assess real-world performance [86].  
Figure 4: VLC-based monitoring of multi-parametric data in intensive care units (ICUs).
## 3 Results and Discussion
### Structural Response
#### 3.2.4 Load-Crack Opening Curves  
The cracking response of a concrete structural member plays a significant role in its survivability state. The serviceability phase is related to the crack propagation stage when the service loading is less than the yielding one of the UHPC elements. Figure 13 displays the load vs. the maximum crack width for all investigated UHPC beams, after the initiation of the crack propagation stage. As expected, the load-cracking response of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Beam**} & \multicolumn{4}{c}{**Deflection Ductility**} & \multicolumn{4}{c}{**Curvature Ductility**} \\ \cline{2-11}  & \multicolumn{2}{c}{\(\delta\) **(mm)**} & \multicolumn{2}{c}{\(\mu_{D}\)} & \multicolumn{2}{c}{\(M_{u}\) **(kNm)**} & \multicolumn{2}{c}{\(\varnothing\) (\(\times\)**10\({}^{-6}\)/mm)**} & \multicolumn{2}{c}{\(\mu\)} \\ \cline{2-11}  & \(\delta_{y}\) & \(\delta_{u}\) & \(=\frac{\delta_{u}}{\delta_{y}}\) & **Average**\(\mu_{D}\) & \(M_{u}\) & **Average** & \(\varnothing_{y}\) & \(\varnothing_{u}\) & **Equation (15)** & **Average** \\ \hline B1 & 1.094 & 3.882 & 3.548 & & 16.5 & 16.7 & 28.0 & 105.2 & 7.429 & & \\ B1-R & 1.040 & 3.020 & 2.904 & 3.226 & 16.9 & 16.7 & 28.5 & 79.4 & 5.167 & 6.3 \\ B2 & 1.385 & 4.41 & 3.184 & 3.340 & 21.9 & 23.1 & 27.2 & 178.3 & 13.948 & 9.3 \\ B2-R & 1.440 & 5.035 & 3.497 & (+3.5\%) & 24.3 & (+38.1\%) & 76.2 & 195.6 & 4.660 & (+47.7\%) \\ B3 & 1.178 & 4.295 & 3.646 & 3.749 & 24.8 & 25.1 & 29.8 & 92.8 & 5.923 & 4.2 \\ B3-R & 1.324 & 5.099 & 3.851 & (+16.2\%) & 25.3 & (+50.3\%) & 34.6 & 57.9 & 2.567 & (\(-32.6\%\)) \\ B4 & 1.641 & 6.181 & 3.767 & 3.293 (+2.1) & 29.8 & 30.3 (+81.7) & 39.3 & 122.5 & 5.941 & (+20.5\%) \\ B5 & 1.838 & 17.556 & 9.552 & 7.460 & 41.7 & & 34.2 & 149.7 & 8.868 & 12.2 \\ B5-R & 2.053 & 11.02 & 5.368 & (+131.2) & 43.1 & 42.4 (+154.1) & 28.9 & 209.9 & 15.626 & (+94.5) \\ B6 & 2.051 & 27.201 & 13.262 & 14.536 & 50.7 & 48.6 & 30.5 & 83.1 & 5.028 & 6.1 \\ B6-R & 1.885 & 29.803 & 15.811 & (+350.6) & 46.5 & (+191.0\%) & 38.7 & 140.3 & 7.119 & (\(-3.6\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ductility analysis of the reinforced UHPC beams.  
Figure 11: Ductility calculated by **(a)** deflection (\(\mu_{D}\)) and **(b)** moment-curvature area (\(\mu\)).  
UMPC beams becomes very similar to the descending branch of the load-deflection curve (Figure 7). Additionally, this figure demonstrated that increasing \(\rho\) notably decreases the crack opening width of the UHPC beams at the same service loading. It is worth noting that this finding is inconsistent with that reported by [PERSON] et al. [52]. Another significant observation is that increasing the bar reinforcement ratio contributed to stabilizing the crack propagation, as the slope of the UHPC beam's load-crack curve increases as its bar reinforcement increases. In other words, the initiation and development of cracking are considerably delayed as the tensile continuous reinforcement of the beam increases. A similar pattern of results was obtained in [53].