esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Human Element
#### 3.1.2 Research Status  
In addition to these definitions, Figure 3 illustrates the connotation and extension of the human element. It indicates that the human element comprises six parts: people factors; ship factors; external influences and environment; working and living conditions; shore-side management; and organization on board. The details of these six parts are discussed elsewhere [25]. Some of these segments, such as people factors, shore-side management, and organization on board, can directly affect human behavior. According to Section 3.2, these segments can be viewed as human-related factors; the remaining categories indirectly affect human behavior.  
To thoroughly analyze and study the human element, the IMO adopted the Human Element Analyzing Process (HEAP) to help regulatory bodies consider the human element when developing regulations. The details of the HEAP can be found elsewhere [25].  
Few academic papers on the human element have been published in the maritime safety field. We collected seven articles (five journal articles and two conference papers) with \"human element\" in the title. The details are listed in Appendix A. [PERSON] [26] introduced performance management systems, crew management, and human contribution to shipping companies. [PERSON] et al. [27] addressed how human error influences shipping casualties, fluctuating the risk homeostasis in the shipping market. Furthermore, [PERSON] et al. [28] developed the semi-SRPM model to analyze human factors by studying maritime accidents. Other work has focused on the potential impacts of autonomous technology and human roles in maritime operations [29; 30]. The authors of [31] studied the influence and benefits of the IMO member states and agencies on maritime safety and human factor issues. [PERSON] et al. [32] presented a thorough human element analysis from the perspective of its definition, connotation, and denotation.
###### Abstract  
Phase measuring deflectometry has been widely studied as a way of obtaining the three-dimensional shape of specular objects. Recently, a new direct phase measuring deflectometry technique has been developed to measure the three-dimensional shape of specular objects that have discontinuous and/or isolated surfaces. However, accurate calibration of the system parameters is an important step in direct phase measuring deflectometry. This paper proposes a new calibration method that uses phase information to obtain the system parameters. Phase data are used to accurately calibrate the relative orientation of two liquid crystal display screens in a camera coordinate system, by generating and displaying horizontal and vertical sinusoidal fringe patterns on the two screens. The results of the experiments with an artificial specular step and a concave mirror showed that the proposed calibration method can build a highly accurate relationship between the absolute phase map and the depth data.  
system parameter calibration; fringe reflection; phase matching; specular surface measurement; direct phase measuring deflectometry
## 3 System Architecture
### Deep Learning for Detecting and Tracking LEDs  
In the OCC system, RoI algorithms are well-established. In most RoI methods with real-time processing for object detection, both object and feature-based detection approaches are deployed. As previously noted, with the rolling-shutter effect, LEDs are shown in images represented by black and white strips corresponding to \"0\" and \"1\" bits. Each  
Figure 1: MIMO C-OOK architecture based on a deep learning neural network.  
image includes many strips, which creates problems for RoI detection, particularly as it concerns the mobility effect. Deep learning neural networks are well-established methods for computer vision applications, e.g., object detection, image classification, localization, and image reconstruction. CNNs have emerged as good candidates for deep learning-based computer vision applications. The CNN-based YOLO algorithm is a state-of-the-art, real-time object detection system. In this paper, we proposed the customization and training of YOLO models for LED detection and tracking considering the rolling-shutter and mobility effects.  
The experimental dataset was collected using real traffic scenes to verify the performance of our LED detection and recognition method. We recorded daytime and nighttime video footage with the mobility effect and generated 1000 blurry and clear images at different exposure times. Using this image dataset, we labeled images and trained the YOLOv5 model, which was modified with 5/7 convolution layers. Only one detection class was included. The corresponding number of filters in the final number of convolution layers was 40.
## 3 Approach
### Salient Region Localization Module
We also calculate a self-adaptive threshold, \(a\), using the OTSU algorithm [32]. With the derived threshold, we can turn a saliency map into a binary map; the equation is as follows:  
\[B_{x,y}=\left\{ \begin{array}{ll}1&\quad A_{i_{x,y}}\geq a\\ 0&\quad A_{i_{x,y}}<a\end{array} \right.. \tag{2}\]  
For the processed binary image \(B\), we perform a scan, and mark all pixels' connection areas according to the four-neighborhood rule. It is assumed a pixel is represented by \(f(x,y)\), where the \(f\) produces the binary value of the pixel by \(x\), \(y\), which stand for the pixel's location in image. And we assume the connectivity domain tag of pixel \(f(x,y)\) is represented by \(m(x,y)\). When scanning \(f(x,y)\), the scanning process is already done for \(f(x-1,y)\) and \(f(x,y-1)\), so their marks, \(m(x-1,y)\) and \(m(x,y-1)\), are already known. Hence, the connected area mark \(m(x,y)\) of the pixel \(f(x,y)\) is only relevant to the connected area marks of pixel \(f(x-1,y)\) and \(f(x,y-1)\), which are \(m(x-1,y)\) and \(m(x,y-1)\). The equation is as follows:  
\[m(x,y)=\left\{ \begin{array}{ll}m(x-1,y)&\text{if }f(x,y)=f(x-1,y)\quad \text{and}\quad f(x,y)\
eq f(x,y-1)\\ m(x,y-1)&\text{if }f(x,y)\
eq f(x-1,y)\quad\text{and}\quad f(x,y)=f(x,y-1)\\ m(x,y-1)&\text{if }f(x,y)=f(x-1,y)\quad\text{and}\quad f(x,y)=f(x,y-1)\\ \text{\emph{Newlabel}}&\text{if }f(x,y)\
eq f(x-1,y)\quad\text{and}\quad f(x,y) \
eq f(x,y-1)\end{array} \right.. \tag{3}\]  
In the equation above, when the condition to the right of Function (3) holds, the marker number of the connection mark is the same. At the same time, in the final case of Function (3), if the condition is true that pixel \(f(x,y)\) belongs to the new connection domain, \(\text{\emph{Newlabel}}=\text{\emph{Newlabel}}+1\). Additionally, we set the value of \(m(x,y)\) to the value of the new connected area mark _Newlabel_. To better visualize the effect of the salient region localization module, we plot the bounding box based on the resulting detected saliency regions. As shown in Figure 5, we visualize the effect of the module on different fine-grained datasets. We can see that even for different fine-grained images, all the regions of focus are detected accurately. Especially, most parts of the response will be in the foreground of the target to be classified, and only a minority will be on the background of the target to be classified.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data  
The Kriging algorithm is a spatial interpolation method based on geostatistics, which is a minimum variance estimation and linear unbiased method for unknown data of similar characteristics in a region according to certain characteristic data of several information samples.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.6 DJI Matrice 210  
The DJI Matrice 210's maximum flight time depends on its payload, which can reach up to 2.3 kg with TB50 batteries and up to 1.57 kg with TB55 batteries. Additionally, it is equipped with an obstacle sensing system, making it useful for advanced agricultural missions. The use of the Matrice 210 is featured in the literature in various agricultural applications, such as mapping and monitoring [82; 83; 84], and plant health assessment [85]. For example, the authors in [83] used the DJI Matrice 210 to capture aerial images of sweet corn fields at the University of Florida's Tropical Research and Education Center in Homestead, Florida, United States. From these images, digital surface and terrain models were generated. Using machine-learning techniques, the authors successfully estimated various crop parameters, such as plant height, biomass, and phenotype, contributing to improved agricultural productivity.
## 4 Flight Load Prediction Case Study
### Genetic Algorithm Enhanced ELM  
Given 100 neurons of the hidden layer, the weight matrix **W** and offset **b** are optimized by a genetic algorithm. With the sigmoid activation function and parameter setting of the genetic algorithm in Table 1, the optimization process based on the training dataset is shown in Figure 9. In addition, a sensitivity analysis of the population size, crossover probability, and mutation probability is conducted, which confirms that the parameter setting in Table 1 is suitable for solving the proposed optimization problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Functions** & **Mean Relative Errors** & **Maximum Relative Errors** & **Mean Squared Errors \(\left(\text{KN}\cdot\text{m}\right)^{2}\)** \\ \hline Sigmoid function & 1.0546\% & 6.3417\% & 2.9702 \\ Sine function & 1.3321\% & 8.2345\% & 4.3269 \\ Hard-limit function & 6.3217\% & 32.0980\% & 86.852 \\ Hyperbolic tangent function & 1.1769\% & 4.5714\% & 3.1849 \\ Morlet wavelet function & 8.3282\% & 61.873\% & 144.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy of wing bending moments under different activation functions.  
Figure 8: Wing bending moment prediction based on ELM (\(\varphi\): sigmoid function; \(K\) = 100): (**a**) predicted value series; (**b**) the correlation coefficient between measured and predicted values.  
Figure 7: Demonstration of five investigated activation functions: (**a**) sigmoid function, hard-limit function, and hyperbolic tangent function; (**b**) sine function and Morlet wavelet function.  
In Figure 9, the mean and minimum fitness values of each generation based on the training dataset are plotted, and they gradually converge to the same value. After 200 generations, the optimal weight matrix **W** and offset **b** are obtained, and based on the testing dataset, the mean relative error, mean squared error, and maximum relative error between the predicted wing bending moments and measured ones are shown in Table 3.  
In addition, based on the same optimization procedure, the weight matrix **W** and offset vector **b** are also optimized for other activation functions, whose mean relative error, mean squared error, and maximum relative error based on the testing dataset are presented in Table 3 as well. In comparison with Table 2, the mean relative and mean squared errors are all decreased, which manifests that the prediction accuracy of the wing bending moment has been improved by using the GA-enhanced ELM method. In this case, it shows that the optimization of **W** and **b** by using the genetic algorithm can effectively boost the performance of the ELM.
## 3 Results and Discussion
### Cybersecurity and Privacy in Energy Management Systems
#### 3.5.4 AI-Based Threat Prediction and Blockchain for Secure Transactions  
The application of AI for threat prediction focuses on improving the resilience of energy management systems. AI models analyze network traffic, identify anomalies, and predict cyberattacks before they occur, enabling preventive defense strategies [15; 31].  
At the same time, blockchain technology is being explored as a decentralized method to secure energy transactions in microgrids [23; 32]. By recording transactions in an immutable and transparent manner, blockchain facilitates secure peer-to-peer energy trading without reliance on central intermediaries.  
Future research should examine how AI-based threat detection can be integrated with blockchain platforms to develop automated cybersecurity solutions for microgrids. Defining standardized protocols for secure data exchange and transaction verification will be essential to support reliable and scalable deployment of these technologies.
## 3 Results and Discussion  
### Comparison of Numerical Simulation and Experimental Results  
PLA-based cellular samples were fabricated using MEX 3D printing technology and their mechanical properties investigated. During the experimental work, cyclic compressive loading was applied to PLA samples, and the ES was altered through combined elastic and plastic deformation. The outcome of the experimental work showed that, under cyclic compressive loading, the sample alters its elastic stiffness. In the present study, the first five cycles of the experimental procedure were simulated via ABAQUS finite element code. To obtain the elastic stiffness results from numerical simulation, the true stress-strain diagram was plotted with the force-displacement diagram data, and the elastic stiffness was calculated from the slope of this diagram. The simulation results show that the elastic stiffness of the model changes, which is consistent with the results obtained from previous experimental research. Tables 2 and 3 summarize the results obtained from both studies (experimental and numerical simulation). It can be seen that the results obtained from the numerical simulation and experimental research are in good agreement, and the percentage of discrepancies in results is reasonably low. This outcome can be considered a validation of both parts of the research. Comparing the results of each cycle, it is clear that the first cycle's elastic response varied significantly from the subsequent ones. In both studies, there is a significant increase in the elastic stiffness in the first cycle, but in the following cycles, the amount of alteration decreases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Cycles** & **1** & **2** & **3** & **4** & **5** \\ \hline Elastic stiffness in the experiment [35] (MPa) & 2800 & 2970 & 2998 & 3005 & 3015 \\ Elastic stiffness in the numerical simulation (MPa) & 2732.8 & 2936.8 & 3005.0 & 3036.5 & 3057.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the ES of the sample in experiments and model in the numerical simulation, induced by cyclic compressive loading.  
Figure 9: PLA 3D-printed model mesh.  
The results of the experiment and numerical simulation research in each cycle are presented in Figure 10. As is evident, the numerical simulation results and experimental results are significantly close.  
In addition to the above observations, the hysteresis loop shown in the force-displacement diagram of the numerical simulation (Figure 11) is also fully consistent with the experimental results. The hysteresis loop demonstrates the mechanism of cyclic hardening within the model.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 2 Materials and Methods
### Cell Viability Measurement by the MTT Assay  
In this paper, only the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) assay method was used to evaluate the cell viability after the CAP treatment as a preliminary study. On the one hand, the reliability of the MTT assay for evaluating the cell viability has been discussed by [PERSON] and [PERSON] with a conclusion that the results obtained by MTT assay were comparable with those of the Lactate dehydrogenase (LDH) release assay [30]. On the other hand, the advantages of ease of operation, sensitivity, rapidity and low cost have made the MTT assay as one of the most widely used methods for measuring the cytotoxic effects and for evaluating the cell viability [31; 32; 33; 34; 35].  
After CAP treatment, the 96-well plates were put into the cell incubator to cultivate for a certain time (e.g., 0, 6, 12, 18 or 24 h), then, 10 \(\upmu\)L MTT solution was added to each well. After another 4 h incubation, the formed formazan crystals were dissolved in 110 \(\upmu\)L dimethyl sulfoxide (DMSO) in each well. Finally, the absorbance was detected at 570 nm by a microplate reader (Tecan Trading AG, Mannedorf, Switzerland). Since the cell numbers had a linear relationship with the absorbance, the cell viability was calculated by:  
\[\frac{A_{\text{i}}}{A_{\text{control}}}\times 100\% \tag{2}\]  
where \(A\) was the value of the absorbance, the subscript \(i\) represented the treatment time, i.e., \(i=2,3,3.5,4,4.5,5,7,10\).
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
## 5 Conclusions  
In this paper, we presented new oscillation and non-oscillation criteria for the NDDE with two time delays by defining two functions, wherein the difference between the two functions was the corresponding characteristic equation. These criteria provide the number and sign of the real roots of the NDDE. We designed a state feedback controller that makes the NDDS exponentially stable. Moreover, the controller gains were easily and analytically determined using the Lambert W function. Finally, we also included several examples to demonstrate the main results.  
**Author Contributions:** Conceptualization, B.K.; Methodology, B.K.; Software, J.K.; Validation, J.K., S.C.; Resources, S.C.; Writing-Original Draft Preparation, B.K.; Writing-Review & Editing, J.Y., B.K.; Visualization, J.K.; Supervision, B.K.; Funding Acquisition, B.K.  
**Funding:** This work was supported by the Gyeongsang National University Fund for Professors on Sabbatical Leave, 2019.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(r\)=\(\frac{\pi}{\sqrt{3}}+\frac{1}{20}\), \(c\)=\(\frac{\sqrt{3}}{2}\) & \(\frac{1}{25}\)=0.2356 [31] & \(r\)=\(\frac{\pi}{\sqrt{3}}+\frac{1}{20}\), \(c\)=0.56 & \(r\)=\(\frac{\pi}{\sqrt{3}}+\frac{1}{20}\), \(c\)=0.63 \\ \hline
**without Feedback** & **with Feedback** & **without Feedback** & **with Feedback** & **without Feedback** & **with Feedback** \\ \hline \(-0.1656\) & \(-2.5687\) & \(-0.2777\) & \(-2.6174\) & \(-0.2936\) & \(-2.5194\) \\ \(-8.2685-131.2548\) & \(-3.7482\) & \(0.4315-131.2442\) & \(-3.8654\) & \(1.6161-131.2448\) & \(-3.6338\) \\ \(-8.2685+131.2548\) & \(-6.9049+i0.6305\) & \(0.4315+i31.2442\) & \(-7.1044-i63.0651\) & \(1.6161+i31.2448\) & \(-6.7092+i63.0384\) \\ \(-8.2321+194.1912\) & \(-6.9049-i0.6305\) & \(0.4281-194.1908\) & \(-7.1044+i63.0651\) & \(1.6070+i94.1908\) & \(-6.7092-i63.0384\) \\ \(-8.2321-194.1912\) & \(-6.9247+i1.2577\) & \(0.4281+94.1908\) & \(-7.1260+i125.7820\) & \(1.6070-94.1908\) & \(-6.7272+i125.7683\) \\ \hline \end{tabular}
\end{table}
Table 1: Dominant poles of population model (Equation (40)) with different \(c\).
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Construction Method of Entry-Side Filling Wall  
According to the actual engineering application on site, the construction methods of filling walls in China's GSER application include wooden stack support, gangue filling, fly ash filling, concrete filling, (ultra) high-water material filling, paste material filling, roof-cutting, and entry retaining. The entry-side construction method of the filling body affects the selection of the overall process system for GSER and its effectiveness. The selection should consider the geological conditions of the GSER engineering projects and the structure and deformation characteristics of the surrounding rock of the retained entry. Among 587 cases of GSER engineering projects, 463 cases have identified the construction method of entry-side filling walls. Figure 8 shows the application of different entry-side filling structures in GSER engineering projects.  
Figure 7: Statistical chart of the GSER engineering projects with different coal seam dip angles.  
As shown in Figure 8, concrete filling beside the entry is the most common construction method applied in China's GSER engineering projects, comprising a total of 160 cases and accounting for 34.56%. After 2010, with the proposal and development of GSER with roof-cutting, the application of GSER with presplitting and roof-cutting in on-site engineering has gradually increased. The statistical results of its engineering cases are 160 cases, accounting for 27.61%. The engineering cases of GSER with entry-side filling of high-water materials, gauge materials, and paste materials are 74, 59, and 28, respectively, accounting for 15.98%, 12.74%, and 6.05%, respectively. The construction methods of other main entry-side filling structures, such as GSER with wooden stack support and GSER with fly ash filling, are less commonly used due to their limited usage conditions. Based on the curve in Figure 9, it can be seen that GSER with concrete filling and GSER with presplitting and roof-cutting are the most commonly used methods. However, regardless of the construction method of the filling body, the combination of stress optimization technology in the retained entry in practical applications can improve the effectiveness of GSER to a certain extent.  
Figure 8: Statistical chart of the GSER engineering projects with different construction methods of the filling body.  
Figure 9: Statistical chart of the GSER engineering with different filling widths.