esa-sceva/satcom-chunk-collection huggingface dataset:
# Plane Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network
## 2 Proposed Method
### Image Pre-Processing
* When \(|R|\) is small, which happens when \(\lambda_{1}\) and \(\lambda_{2}\) are small, these points belongto flat regions;
* When \(R<0\), if only one eigenvalue of \(\lambda_{1}\) and \(\lambda_{2}\) is bigger than the other eigenvalue, the region belong to edges;
* If \(R\) has a large value, the region is a corner. Figure 4a shows the input original images, and Figure 4b shows the corner detected by the HSV color conversion images using the Harris corner detector, and the corner points are still detected for non-flame objects. In order to only select the areas that were most likely to be flames among the corner points of the detected objects, this study additionally applied the appearance characteristics of flames in the flame. Therefore, this paper proposes a method to further filter only the corners with the top of the detected corners. \[\theta(x,\ y)=\left\{ \begin{array}{cc}(180^{\circ}/\pi\times arctan(I_{x}(x, \ y)/I_{x}(x,\ y))&if(I_{x}\geq 0)\\ 180-(180^{\circ}/\pi)\times arctan(I_{y}(x,y)/I_{x}(x,y)),&if(I_{x}<0)\end{array} \right. \tag{8}\]  
In order to only select the corner facing the top direction among the detected corners, the angle of the corner-facing direction was calculated using Equation (8). \(I_{x}\) and \(I_{y}\) are first-order derivatives in the \(x\) and \(y\) directions, respectively, and can be obtained via a convolution arithmetic using \(S_{x}\), the Sobel x kernel, and \(S_{y}\), the Sobel y kernel. \[ROI=\left\{ \begin{array}{cc}1,&45^{\circ}<\theta(x,y)<135^{\circ}\\ 0,&otherwise\end{array} \right. \tag{9}\]  
Figure 4: Corner detection using the Harris corner detector and top corner detection results. Corresponding to where the pixel where the green points are detected: (**a**) original flame image; (**b**) results of corner detection using the Harris corner detector; and (**c**) among the results using the Harris corner detector, only the corner point facing the top direction is displayed. Therefore, each Sobel filter value could be used to calculate the angle in the direction toward the corner point through the arctangent, of which only the corners from 45 to 135 degrees, as shown in Equation (9), are shown (Figure 4c). As a result of the preprocessing of these images, most of the non-flaming objects have been removed, but there are cases where the corners of some non-flaming objects remain. Therefore, the area where the corners are concentrated is designated as the ROI, and finally, the process of classifying flames or non-flames using the Inception-V3 CNN model was undertaken.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 4 WEC Control Using Estimated Instantaneous Frequency
### Fixed Parameter PTO Control
#### 4.5.2 Iteration Setup for Statistical Performance Analysis  
The key parameters for the simulation setup are summarized in Table 3.  
Figure 11: PTO control estimation block using estimated instantaneous frequency information.  
Figure 12: Saturation block tuning to mitigate excessive overshoot in instantaneous frequency estimation.  
A total of 1000 independent 20 min simulations were conducted for the statistical analysis of each control approach, using a time step of 0.1 s. This relatively low sampling rate was chosen to ensure that the proposed techniques remain compatible with practical measurement systems for future experimental integration.  
All simulations were performed using a MacBook Pro 16\({}^{\prime\prime}\) (2021) equipped with an Apple M1 Pro chip (Apple Inc., Cupertino, CA, USA), running MATLAB R2024b.  
The wave elevation signals were generated based on a JONSWAP spectrum with a peak enhancement factor of \(\gamma=3.3\), significant wave height \(H_{s}=2.4\) m, and peak period \(T_{p}=9\) s. These parameters were selected to ensure a consistent wave energy flux per unit width, thereby enabling a fair conditional CWR comparison across all simulations. Phase information for constructing the wave signals was assumed to be uniformly distributed in the probability domain.  
The excitation force was estimated by convolution with the IRF of the excitation force, precomputed from WAMIT hydrodynamic data.
## 2 Materials and Methods
### Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Type** & **Resolution** & **Year** & **Data Source** \\ \hline Land use/cover & Raster & 30 m & from 2000 to 2020 & [[https://data.caserth.cn](https://data.caserth.cn)]([https://data.caserth.cn](https://data.caserth.cn)), (accessed on 27 December 2021) \\ Population & Raster & 100 m & 2015 and 2020 & [[https://www.worldpop.org/](https://www.worldpop.org/)]([https://www.worldpop.org/](https://www.worldpop.org/)), (accessed on 8 November 2021) \\ Topographic & Vector & - & 2017 & [[https://www.webmap.cn/main.do?method=index](https://www.webmap.cn/main.do?method=index)]([https://www.webmap.cn/main.do?method=index](https://www.webmap.cn/main.do?method=index)), (accessed on 28 November 2021) \\ Elevation & Raster & 30 m & 2019 & [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), (accessed on 3 September 2021) \\ POI & Point & - & 2020 & [[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)), (accessed on 30 July 2020) \\ GDP & Raster & 1 km & 2015 and 2020 & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), (accessed on 8 November 2021) \\ Soil type & Raster & 1 km & 2015 & [[https://www.fao.org/](https://www.fao.org/)]([https://www.fao.org/](https://www.fao.org/)) soils-porl/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/, (accessed on 17 April 2022) \\ Nighttime light & Raster & 500 m & 2015 and 2020 & [[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), (accessed on 5 November 2021) \\ \hline \end{tabular}
\end{table} 
Table 1: Research data. Figure 1: Location of the Chongqing metropolitan area. consistent with NPP-VIIRS nighttime light data and data quality similar to NPP-VIIRS and solves the problem that DMSP-OLS and NPP-VIIRS nighttime light data cannot be used simultaneously. The soil texture type data are derived from the Harmonized World Soil Database (HWSD). The data are grid data with a resolution of kilometers, providing information, such as soil type, soil phase, and soil physical and chemical properties, of each grid point. To build a unified driving factor dataset, the research scope, spatial coordinate system, and resolution (100 m) of all the above datasets were processed the same (Figure 2).
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Node Centric  
In these protocols, sensing levels are predefined at the sensors; thus, decisions following an event are made by the end-nodes and gathered data would be sent to the sinks. Among these protocols, we find:  
* **Collaborative Event Detection and Tracking (CollECT) protocol** Based on three main procedures, this protocol ensures fast and reliable events detection [33] in different types of WSNs. Among the strengths of this protocol is that it represents a fully distributed scheme protocol, which results in its high-speed events detection.
* **Energy Efficient-Low Latency Express Routing Protocol(EELLER)** Based on the clustering mechanism, the EELLER protocol removes data redundancy and provides optimized routes to send data from end-nodes to sinks [34]. To send sensed data, EELLER builds the expressways based on a link factor, and then it sends aggregated data through formed clusters. In [35], researchers show that this protocol provides efficient energy management and precise even detection by dint of the data aggregation techniques, but it is less reliable.
* **Information Quality Aware Routing (IQAR) Protocol** As an energy-efficiency protocol, IQAR uses IQ constraints to find the least-cost routing tree [36]. It allows nodes to make independently a decision about an event to check and track it. Research proves that this protocol optimizes the QoS and helps in saving delay and energy in the network but presents high levels of overheads [37].
* **Event Reliability Protocol (ERP)** ERP is designed as a reliable and real-time event transmission protocol [38]. It uses a region-based selective retransmissions mechanism to minimize similar redundant data to avoid congestion. It offers reliable event transmission and avoids collision, but it has the disadvantage of being greedy in energy consumption.
* **Efficient Event Detection Protocol (EEDP)** EEDP is designed as a reliable data transmission protocol for event monitoring applications that require fast detection and transmission of data [39]. It allows nodes to take accurate decisions based on the two rules, namely the simple decision rule (SDR) and composite decision rule (CDR). Researchers show that EEDP offers accurate event detection, but it is less reliable [32].
## 1 Introduction
By constructing the density of intermittent failure, [PERSON] et al. proposed methods to establish maintenance policy [12]. [PERSON] et al. introduced the failure test profile method to construct an intermittent failure mechanism model and the number of intermittent failures is used to optimize the sensor threshold to reduce the false alarm rate [13]. Similarly, [PERSON] et al. proposed a method to estimate the using state of electrical connectors through the frequency of intermittent failure. Based on the previous literature, it is obvious that the most common feature for intermittent failure characterization includes the frequency, duration and amplitude of failures. However, a single feature is not sufficient for intermittent failure characterization. These features need to be integrated, forming a comprehensive feature that is suitable for the residual life prediction of electrical connectors. Except for intermittent failure features, the residual life prediction model for electrical connectors needs to be conducted. Currently, the life prediction model mainly includes a physical model and a data driven model. Considering the randomness of intermittent failures, the data driven model is more suitable for the residual life prediction of electrical connectors [14; 15; 16; 17; 18; 19]. Combined with the degradation processes of electrical connectors, it is helpful to improve the prediction accuracy of the residual life of electrical connectors by selecting a suitable model. In this paper, the dynamic properties of electrical connector intermittent failure are analyzed and a residual life prediction method for electrical connectors is studied. The residual contents are structured as follows: Section 2 builds a residual life prediction model of the electrical connector using the Hidden Semi-Markov Model (HSMM); Section 3 studies an extraction method for intermittent failure features and provides a definition of intermittent failure generalized \"severity\"; Section 4 investigates using state estimation and residual life prediction for electrical connectors; Section 5 conducts a test for residual life prediction; and Section 6 summarizes the work in this paper.
# Photodynamic Inactivation of Human Coronaviruses
## 3 Results
### PhytoQuin PDI of HCoV Virions Damages the Viral Lipid Envelope
SARS-CoV-2 S, like other CoV S proteins, is processed by host proteases into distinct molecules, namely, S1 and S2, with S0 being the full-length precursor [25]. We next generated additional lentivirusises pseudotyped with a mutant S that lacks the furin cleavage site (S(\(\Delta\)furin)), to better visualize differences in S by concentrating the detected protein into a single species, the precursor S0. Using the same experimental set-up, we again observed that photoactivated PhytoQuin caused the emergence of S protein isoforms with reduced electrophoretic mobility (Figure 5C). These observations, together with our data showing PhytoQuin-mediated effects on HCoV lipid envelopes and vRNA, demonstrate that PhytoQuin PDI can damage multiple components of HCoV particles. investigated whether emodin alone could recapitulate the PhytoQuin PDI antiviral effect against CoVs. From our HPLC analysis, we observed that the amount of emodin, as compared to a commercial emodin standard, equaled 29.3 mg per gram of extract (\(\sim\)3% \(w/w\)). To investigate whether emodin is a key PS required for PhytoQuin PDI against HCoVs, we treated HCoV-OC43 inoculum with a dose of photoactivated emodin (117 ng/mL) that matched its concentration in a 4 ug/mL PhytoQuin extract. In these experiments we observed a three-fold inhibition of HCoV-OC43 infectivity due to LED light treatment alone (Figure 6B), similar to the inhibition observed for HCoV-229E when exposed to LED light (Figure 2A). While emodin treatment in the absence of light had no impact on HCoV-OC43 titers, PhytoQuin treatment without photoactivation did have a small impact on HCoV-OC43 infectivity (Figure 6B). Photoactivated PhytoQuin resulted in 99.8% reduction in HCoV-OC43 titre compared to DMSO-treated virus exposed to light (Figure 6B). When treated with LED light, emodin caused a similar 98.5% reduction in HCoV-OC43 titer compared to the DMSO + Light control, where the inhibitory effect of emodin was indistinguishable from that of PhytoQuin (Figure 6B). Therefore, these data indicate that emodin is a significant contributor to the antiviral activity of PhytoQuin against human coronaviruses.
###### Abstract  
This study aimed to determine whether there are relationships between vertical facial patterns, cervical posture, and cervical curvature types. Ninety-two adult females with skeletal Class I relationships were retrospectively analyzed and divided into hypodivergent, normovergent, and hyperdivergent groups based on the mandibular plane angle to the nasion-sella line. Variables representing craniocerical posture (sagittal vertical axis, SVA; cervical inclination angle, CIA) and cervical curvature were measured. Differences in craniocerical posture among the groups were assessed. Cervical curvatures were classified into lordotic, straight, kyphotic, or sigmoid categories. The distribution of cervical curvature types among the groups was compared, and correlations between vertical facial patterns, craniocerical postures, and cervical curvature measurements were calculated. The results indicated that the hyperdivergent group exhibited increased SVA and decreased CIA compared to the normovergent and hypodivergent groups. Significant differences in cervical curvature types were observed among the groups. Lordotic curvature was most common in the normovergent group, while straight curvature was predominant in the hypodivergent and hyperdivergent groups. A significant correlation was found between an increased mandibular plane angle and a forward head position (increased SVA and decreased CIA). In conclusion, there are relationships between vertical facial patterns, cervical posture, and cervical curvature types. Therefore, careful assessment of craniocerical posture and cervical curvature is necessary in lateral cephalosporars for orthodontic evaluation. However, cervical curvature measurements show minimal correlation with the mandibular plane angle.  
craniocerical posture; cervical curvature; vertical facial pattern; mandibular plane angle +
Footnote †: journal: Applied sciences  
Article
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 1 Introduction
### Motivation and Novelty  
Traditional tensile testing methods involve significant time and cost due to preparatory work and equipment setup, limiting their usability in various environments. Therefore, a simplified and efficient method is required to enable usage without environmental limitations. Additionally, to improve strain prediction accuracy and minimize differences due to user proficiency, an automated process that minimizes user intervention is needed. Accurately predicting material behavior and fracture also requires identifying strain at the fracture point, not just at the gauge length.  
Thus, this paper proposes a circular grid marking pattern and a strain prediction algorithm using AI, which simplifies the preparation process and allows strain prediction with a single camera without the need for additional equipment. To shorten the preparation time, the circular grid pattern is engraved on the specimen using a laser marker, which requires minimal time. Applying this method to tensile testing demonstrated the advantages of this study's concept. The proposed circular grid marking pattern can be applied regardless of the specimen's shape. Strain is measured using an image-based AI model, allowing calculation regardless of the specimen's gauge length and size, and enabling the measurement of local strain as well as gauge-length strain. The strain predicted by the AI model is automatically used to calculate the stress-strain curve, elastic modulus, and elongation.  
The structure of this paper is as follows. Section 2 introduces the theoretical background of the strain measurement calculation and method that utilizes the circular grid, as well as the image preprocessing and AI model used to detect only marking images and determine strain. Section 3 describes the tensile test procedure, including the shape and formation method of the circular grid pattern proposed in this study. The circular grid pattern's strain prediction accuracy differences were analyzed according to specimen size and camera specifications. Section 4 explains the data generation method for training the proposed strain prediction algorithm and the AI training conditions. Section 5 covers the results and analysis of strain prediction accuracy with variations in the circular grid pattern, formation method, and AI training. Finally, Section 6 summarizes conclusions regarding the proposed circular pattern method and AI strain prediction.
## 5 Practical Challenges and Best Practices  
In what follows, a summary of the most important technical challenges encountered while conducting the experiments are provided. Furthermore, our experience and best practices are summarized, which in turn can facilitate the widespread adoption of this new evolving technology in various fields and applications.  
* As explained in Section 3, it is important to determine the suitable path loss model that can be used to estimate the theoretical results for the harvested power, thus being able to predict precisely the amount of harvested energy. This step of utmost importance for charging the nodes in WSN network that can result in improvement in the lifetime of the network;
* It is advised to follow Electrostatic Discharge (ESD) precautions during the use of the kit, as it is prone to damage from static charges produced by the body. We have used anti-static wrist strap and anti-static mat during the experiments. The devices were not placed on top of the mat while taking measurements as it did affect the readings, yet there is a need to discharge whenever one moves and carries the devices;
* The kit is sensitive and influenced by the environment too, as any obstacle on the way can affect the radio wave propagation. Furthermore, since the utilized frequency is 915 MHz, it may be interfered by other devices such as mobile phones operating in the same frequency. Thus, several measurements have to be recorded and averaged to get accurate values, which is a time consuming process;
* Another challenge we encountered was in measuring the current, especially for short distances using the multi-meter. It was not easy as the frequency of the pulsating voltage is high and the width of the pulse is very small.
## 5 Discussion  
We employed the Zhang Zhengyou calibration method to calibrate the digital camera. The sub-pixel method was used to extract the center coordinates of the ring marks by using our own program for image processing and recognition to obtain the monitoring results. The application of the method, accuracy of measurement, and measures to improve the accuracy of results are discussed below.  
1. The relative position of the camera and the slope changed because of the real-time displacement of the slope. The proportional coefficient in this study was the mean value of the two moments, and thus the calculated displacement of the slope had a  
Figure 9: Calculation results upon camera movement. (**a**) The result of the landmark moving 10 mm in \(X\) and \(Z\) axes. (**b**) The result of the landmark moving 100 mm in \(Y\) axes. (**c**) The result of the landmark moving 20 mm in \(X\) and \(Z\) axes. (**d**) The result of the landmark moving 10 mm in \(Y\) axes.  
certain error. Therefore, the time difference between the two proportional coefficients could be shortened to improve the accuracy and obtain more precise displacement values. This method is most suitable for monitoring the large displacement of a slope within a short time.
2. The accuracy of measurement was similar to the study of [PERSON] and [PERSON] et al. [19; 20], reaching the accuracy of millimeters. However, this study took into account the changing position of the digital camera, and the result was accurate to the millimeter level, too. The calibration of the digital camera was not considered in the study by [PERSON] [19]. For non-metric digital cameras, the camera calibration directly affects the accuracy of measurement results. The Zhang Zhengyou calibration method is convenient; however, its robustness is not as good as in the space resection method. Therefore, a better distortion parameter solution method and correction model could be used for camera calibration to improve measurement accuracy. On the simulated slope, when the distance was 20 m, the accuracy achieved approximately 2 mm on the X and Z axes and \(\sim\)10 mm on the Y axis.
3. During the temporary placement of the digital cameras and landmarks on the test day, the field error was large due to the influence of external weather factors. Several improvement measures can be taken for the test device: constructing a pan-tilt camera to ensure stability, using steel landmarks for the deeply buried slope of the concrete foundations, and using better image-processing algorithms to calculate the center coordinates of landmarks. In this study, the slope displacement value was obtained by processing the images. The temporal resolution could be increased by shooting a monitoring video, and the data could be transmitted and processed in real time. The specific displacement and velocity of the slope could thus be calculated, and an early warning device could be installed.