esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis
Using Equation (5) in Equation (6) gives  
\[H_{z1}(s)=\frac{\frac{s^{2}}{\omega_{2}^{2}}+\frac{c_{2}s}{k_{1}}+\frac{k_{2}}{k_{ 1}}}{\frac{s^{4}}{\omega_{1}^{2}\omega_{s}^{2}}+\frac{c_{2}s^{3}}{k_{1}\omega_{ 1}^{2}}+\left[\frac{1}{\omega_{2}^{2}}+\left(\frac{k_{2}}{k_{1}}+1\right)\frac{1 }{\omega_{1}^{2}}\right]\mathrm{s}^{2}+\frac{c_{2}s}{k_{1}}+\frac{k_{2}}{k_{1}}} \tag{7}\]  
When the initial state is zero, the Laplace transform of \(\ddot{z}_{1}\) is  
\[\ddot{z}_{1}(s) =s^{2}z_{1}(s)=s^{2}H_{z1}(s)a(s)\] \[=\frac{\frac{s^{4}}{\omega_{2}^{2}}+\frac{c_{2}s^{3}}{k_{1}\omega_ {1}^{2}}+\frac{k_{2}s^{2}}{k_{1}}}{\frac{s^{4}}{\omega_{2}^{2}}+\frac{c_{2}s^{ 3}}{k_{1}\omega_{1}^{2}}+\left[\frac{1}{\omega_{2}^{2}}+\left(\frac{k_{2}}{k_{ 1}}+1\right)\frac{1}{\omega_{1}^{2}}\right]s^{2}+\frac{c_{2}s}{k_{1}}+\frac{k_ {2}}{k_{1}}}{\frac{a(s\sin\theta+\omega\mathrm{cos}\theta)}{s^{2}+\omega^{2}}} \tag{8}\]  
Because \(\frac{1}{\omega_{1}^{2}\omega_{2}^{2}}\), \(\frac{c_{2}}{k_{1}\omega_{1}^{2}}\), \(\left[\frac{1}{\omega_{2}^{2}}+\left(\frac{k_{2}}{k_{1}}+1\right)\frac{1}{ \omega_{1}^{2}}\right]\), \(\frac{c_{2}}{k_{1}}\), and \(\frac{k_{2}}{k_{1}}\) are all positive, the Routhcriterion is satisfied, and the characteristic equation becomes  
\[\frac{s^{4}}{\omega_{1}^{2}\omega_{2}^{2}}+\frac{c_{2}s^{3}}{k_{1}\omega_{1}^{ 2}}+\left[\frac{1}{\omega_{2}^{2}}+\left(\frac{k_{2}}{k_{1}}+1\right)\frac{1}{ \omega_{1}^{2}}\right]\mathrm{s}^{2}+\frac{c_{2}s}{k_{1}}+\frac{k_{2}}{k_{1}}=0 \tag{9}\]  
The necessary and sufficient condition for all roots having negative real parts is  
\[\ddot{z}_{1}(s)=\sum_{i=1}^{4}\frac{A_{i}}{s+p_{i}}+\frac{Bs+C}{s^{2}+\omega^{ 2}} \tag{10}\]  
where \(A_{i}\), \(p_{i}\), \(B\), and \(C\) are all constants related to \(\omega_{1}\), \(\omega_{2}\), \(c_{2}\), \(k_{1}\), \(k_{2}\), and \(\omega\). Thus, the inverse Laplace transformation is given by  
\[\ddot{z}_{1}=\sum_{i=1}^{4}A_{i}e^{-p_{i}t}+D\cos(\omega t+\varphi) \tag{11}\]  
For Equation (11), the first item is an exponential attenuation function independent of frequency, and, after a long time (\(p_{i}t<<1\)), the second term gives \(\ddot{z}_{1}\) for zero phase angle between the two wheels on the same axle (\(\varphi=0\)). Based on Equation (11), it can be concluded that the frequency of the axle-box acceleration \(\ddot{z}_{1}\) is the same as the characteristic frequency of the wheel polygon, which shows that it is reasonable to analyze and identify wheel polygon defects based on axle-box vibration.
###### Abstract  
A novel method for determining the electro-optic (EO) coefficient \(\gamma_{22}\) of lithium niobate and its dispersion using photoelastic modulation is presented. A spectroscopic polarimetry was constructed with the photoelastic modulator (PEM), and a monochromator was selected to automatically scan the wavelength of a light source. Phase retardation induced by an EO sample was loaded into the modulation signals to demodulate the EO coefficients. The PEM and data processing were controlled in the same field programmable gate array (FPGA), and the DC and harmonic terms were extracted simultaneously by employing digital phase-locked technology. An experimental system was built to analyze the principle of this scheme in detail. After the modulation phase retardation amplitude of the PEM was precisely calibrated, the EO coefficient \(\gamma_{22}\) of a Y-cut lithium niobate crystal plate was measured in the spectral range from 0.42 to 0.8 \(\upmu\)m. The experimental results demonstrated that the measurement sensitivity of the system was \(1.1\times 10^{-14}\) m/V for a sampling time of 198.9 ms. Plotting the measured results against the light wavelength, the dispersion of the EO coefficients was obtained similar to the Cauchy dispersion formula \(\gamma_{22}=5.31\,\times\,10^{-12}+\frac{4.071\,\times\,10^{-13}}{\lambda^{2} }+\frac{7.184\,\times\,10^{-14}}{\lambda^{4}}\) in the visible light range. This method is suitable for studying dispersion of the EO coefficients of crystals as well as of thin films and two-dimensional materials.  
electro-optic (EO) coefficient; dispersion measurement; polarization modulation; digital phase-locked technology +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Phenolic Acids Composition  
Five phenolic acids such as chlorogenic, gallic, caffe, \(p\)-coumaric, and ferulic acid were found in the flesh and seeds of all the investigated organic rosehip species (Table 2). The statistically verified high content of total phenolic acid was identified in RRA seeds and in RC species flesh at concentrations of 177.03 mg 100 g\({}^{-1}\) and 121.81 mg 100 g\({}^{-1}\) DW, respectively. The rosehip flesh showed a higher \(p\)-coumaric content, while gallic acid dominated in the seeds. Research has shown that the variation of total phenolic acids was significantly influenced by the genetic characteristics of the species, and essential differences in the percentages of acids in the flesh were identified among all the tested species.  
Figure 2: Total polyphenols content (mg 100 g\({}^{-1}\) DW) of different organic rosehip flesh (**a**) and seed (**b**) samples species. Different small letters represent significant differences between flesh samples and seed samples species, respectively (\(p<0.05\)). RRA: _Rosa rugosa’_ Alba’; RC: _Rosa canina_; RR: _Rosa rugosa_; RRR: _Rosa rugosa’_ Rubra’; RV: _Rosa villosa_.  
[PERSON] [19] reported that the content of gallic acid was 94.3 mg 100 g\({}^{-1}\) DW in _Rosa rugosa_, 41.0 mg 100 g\({}^{-1}\) DW in _Rosa villosa_, and 43.5 mg 100 g\({}^{-1}\) DW in _Rosa canina_ rosehip flesh species. The gallic acid levels were 61% higher in _Rosa rugosa_, 11% higher in _Rosa villosa_, and 49% higher in _Rosa canina_ compared with our results.  
The data demonstrated that the significantly higher concentrations of caffeic (24.54 mg 100 g\({}^{-1}\) DW) and \(p\)-coumaric (48.22 mg 100 g\({}^{-1}\) DW) acids were identified in RRR and RC flesh species, compared with those of [PERSON] [19]. The causes for these variations may be related to different climatic and environmental conditions (light, temperature, soil nutrients) and the maturity of the fruit, which may influence the accumulation of phenolic compounds [14]. Moreover, some enzymes and genes can also contribute to the biosynthesis and accumulation of these compounds. The activity of these enzymes and genes varies in different genotypes and species [31].  
[PERSON] et al. [32] found that the predominant phenolic acids in wine obtained from _R. rugosa_ and _R. canina_ fruits were gallic acid (144.02 mg L\({}^{-1}\)) and chlorogenic acid (40.91 mg L\({}^{-1}\)), respectively. Moreover, wines from _Rosa canina_ L. contained higher levels of total polyphenols and flavonoids.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 5 Open Issues
### Full-Scale Performance Evaluation  
Until now, research on FL integration in maritime environments has mainly considered limited network topologies, statistical propagation environments, and a small number of participating devices in the FL process. This work aimed to present experimental results with real datasets from the maritime industry and involved a varying number of participating vessels. Moreover, it was shown that the application of the proposed FL method can be generalized to other maritime applications by appropriately defining the objective function, e.g., to conduct accurate fault detection. Still, experimental results with a massive number of edge devices are needed to reveal the impact of high-dimensional and high-volume data, heterogeneous networks, and vulnerabilities on FL performance.
###### Abstract  
Large earthquakes are followed by a sequence of aftershocks. Therefore, a reasonable prediction of damage potential caused by mainshock (MS)-aftershock (AS) sequences is important in seismic risk assessment. This paper comprehensively examines the interdependence between earthquake intensity measures (IMs) and structural damage under MS-AS sequences to identify optimal IMs for predicting the MS-AS damage potential. To do this, four categories of IMs are considered to represent the characteristics of a specific MS-AS sequence, including mainshock IMs, aftershock IMs (i.e., \({IM}^{\text{MS}}\) and \({IM}^{\text{AS}}\), respectively), and two newly proposed IMs through taking an entire MS-AS sequence as one nominal ground motion (i.e., \({IM}_{1}^{\text{MS}-\text{AS}}\)), or determining the ratio of \({IM}^{\text{AS}}\) to \({IM}^{\text{MS}}\) (i.e., \({IM}_{2}^{\text{MS}-\text{AS}}\)), respectively. The single-degree-of-freedom systems with varying hysteretic behaviors are subjected to 662 real MS-AS sequences to estimate structural damage in terms the Park-Ang damage index. The intensities in terms of \({IM}^{\text{MS}}\), \({IM}^{\text{AS}}\), and \({IM}_{1}^{\text{MS}-\text{AS}}\) are correlated with the accumulative damage of structures (i.e., \({DI}_{1}^{\text{MS}-\text{AS}}\)). Moreover, the ratio (i.e., \({DI}_{2}^{\text{MS}-\text{AS}}\)) of the AS-induced damage increment to the MS-induced damage is related to \({IM}_{2}^{\text{MS}-\text{AS}}\). The results show that \({IM}_{2}^{\text{MS}-\text{AS}}\) exhibits significantly better performance than \({IM}^{\text{MS}}\), \({IM}^{\text{AS}}\), and \({IM}_{1}^{\text{MS}-\text{AS}}\) for predicting the MS-AS damage potential, due to its high interdependence with \({DI}_{2}^{\text{MS}-\text{AS}}\). Among the considered 22 classic IMs, Arias intensity, root-square velocity, and peak ground displacement are respectively the optimal acceleration-, velocity-, and displacement-related IMs to formulate \({IM}_{2}^{\text{MS}-\text{AS}}\). Finally, two empirical equations are proposed to predict the correlations between \({IM}_{2}^{\text{MS}-\text{AS}}\) and \({DI}_{2}^{\text{MS}-\text{AS}}\) in the entire structural period range.  
Keywords:damage potential; mainshock-aftershock sequence; correlation analysis; single degree of freedom systems; intensity measures +
Footnote †: journal: Applied Soft. 2020, 10, 6795; doi:10.3390/app10196795
# An Improved Cutting Force Model for Ultrasonically Assisted Grinding of Hard and Brittle Materials
## 1 Introduction
[25] extended the cutting force model to the face milling and proposed a mathematical model for cutting forces in the rotary ultrasonic face milling. However, the models established by either [PERSON] or [PERSON] only considered the influence of the diamond grains in the end face of the tool. [PERSON] et al. [26; 27] considered that the effective diamond grains in the lateral face of the tool generated a continuous contact in a sinusoidal trajectory and established a grinding force model based on the brittle fracture theory. However, in the case of grinding at a cutting depth larger than the protrusion height of the abrasives, the material removal is realized by the abrasive grains not only at the end face but also at the lateral face. Therefore, on the basis of the above existing cutting force model, with the consideration of the diamond grains in both the end and lateral faces of the tool, [PERSON] et al. [28] proposed a cutting force model for the rotary ultrasonic milling of dental zirconia ceramics. The existing studies of the cutting force model of UAG only considered the influence of the diamond grains in either the end face or the lateral face of the tool. In the existing studies, the cutting force was the primary cause responsible for edge chipping and surface integrity. From the study of [PERSON] [29; 30] and [PERSON] [31], the maximum stress was found in the transition fillet and the surrounding surfaces of the tool. No studies to date have considered the influence of diamond grains in the transition fillet surface. In an exploratory grinding experiment of SiC, the wear of a diamond grinding wheel was identified mainly in the transition fillet and the surrounding surfaces, as shown in Figure 1, which implies that the diamond grains on the transition fillet surface have a great influence on the cutting force. In this paper, an improved mechanistic model for predicting cutting forces based on the K9 optical glass in UAG was developed by considering the influence of the diamond grains in the transition fillet surface for the first time. Firstly, the force analysis of the effective abrasive grains in the end face, lateral face and the transition fillet surface of the tool was carried out. Secondly, the cutting forces of a single abrasive grain in the end face, lateral face and the transition fillet surface of the tool are calculated separately. Then, the cutting force model was established by integrating the forces on all effective abrasive grains. In modeling the cutting forces caused by the abrasive grains in the transition fillet surface, variations of the normal and tangential forces on the contact surface between the grains and workpiece were analyzed. Finally, the unknown parameters of the cutting force model were obtained through experiments and calculations, and the model was verified through slot grinding experiments.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
### Organization  
The rest of the paper is organized as follows. Section 2 provides the details of the network architecture. In Section 3, details of the mobility algorithms are presented. Section 4 provides the performance assessment results along with relevant discussion. Section 5, finally, concludes this work and highlights some future research directions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Ideology** & **Approach** & **LoRaWAN** & **Relay-Aided** & **Resilience** &
\begin{tabular}{c} **Wireless Support** \\ \end{tabular} \\ \hline
[28] & \
\begin{tabular}{c} Surveillance system based \\ on the cognitive IoT \\ \end{tabular} &
\begin{tabular}{c} Amateur surveillance \\ system \\ \end{tabular} & No & Yes & - & Yes \\ \hline
[33] &
\begin{tabular}{c} Multi-UAV surveillance \\ \end{tabular} & Motion planning & No & Yes & No & Yes \\ \hline
[34] &
\begin{tabular}{c} Multi-constraints in a 3D \\ environment \\ \end{tabular} & UAV path planning & Yes & Yes & - & No \\ \hline
[35] & Network centric systems & Urban surveillance & No & Yes & - & Yes \\ \hline
[30] & LoRa wireless network &
\begin{tabular}{c} Meteorological \\ information display \\ \end{tabular} & Yes & No & - & Yes \\ \hline
[36] &
\begin{tabular}{c} Low-cost, low-power and \\ long-range visualization \\ \end{tabular} & Visual surveillance & Yes & Yes & - & Yes \\ \hline
[37] & \begin{tabular}{c} Key points matching \\ problem \\ \end{tabular} & \
\begin{tabular}{c} Feature detection of \\ nonconforming \\ objects \\ \end{tabular} & No & Yes & No & No \\ \hline This work &
\begin{tabular}{c} UAV-enabled LoRa \\ network \\ \end{tabular} & Proposed approach & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the state-of-the-art and the proposed Unmanned Aerial Vehicle (UAV)-enabled Long Range (LoRA) network.
## 2 Materials and Methods
### Methods
#### 2.3.1 Country-Scale Processing of Annual Water Index Images  
The creation of annual water-index composites was performed within the cloud-processing framework of GEE. To define the area of interest (AOI), an Open Street Map (OSM) global shoreline from 2020 was clipped to the country boundaries of Vietnam and buffered by 10 km. The resulting polygon was then simplified and subdivided into tiles with a maximum of 20 km\({}^{2}\) for batch processing. All images between 1986 and 2021 were cloud-masked based on the pixel quality band (_pixelqa_), provided with each Landsat acquisition. The pixel quality band was generated from the _CFMask_ algorithm developed by [PERSON] et al. (2017) [60] and contains pixel quality attributes including bitmasks for clouds and cloud shadows. The cloud-masking processing step was preferred over the immediate generation of annual composites, e.g., done by [PERSON] et al. (2018) [29], since the high cloud coverage of tropical Vietnam is expected to cause cloud residuals [61]. A water index was calculated for each cloud-masked image using the Modified Normalized Difference Water Index (MNDWI) developed by [PERSON] (2006) [62]. The index is calculated as follows (Equation (1)):  
\[\mathrm{MNDWI}=(\mathrm{Green}\,-\,\mathrm{SWIR})/(\mathrm{Green}\,+\,\mathrm{ SWIR}) \tag{1}\]  
The calculation results in pixel values ranging from \(-1\) to \(+1\), where high values are associated with water bodies and low values with land features [62]. The MNDWI has been proven to provide more accurate results than the traditional NDWI, which uses the NIR band instead of SWIR [33; 63; 64]. After having calculated the MNDWI for all images, they were combined into annual image stacks. To reduce the effect of tidal and seasonal fluctuation, a 75 th percentile composite was calculated for each year. The 75 th percentile  
Figure 3: Schematic workflow of the coastline detection method.  
is expected to capture the mean high-water level, assuming that different water levels are represented within one annual image stack. Finally, a band containing the number of valid (cloud-free) observations per pixel (NOPS) was calculated for each image stack and exported with the final water index composite as a quality layer. Low-quality pixels with only one observation were masked to avoid a single acquisition bias of the local coastline detection result.
###### Abstract  
This study presents six degrees of freedom (6-DoF) pose estimation of an object from a single RGB image and retrieval of the matching CAD model by measuring the similarity between RGB and CAD rendering images. The 6-DoF pose estimation of an RGB object is one of the important techniques in 3D computer vision. However, in addition to 6-DoF pose estimation, retrieval and alignment of the matching CAD model with the RGB object should be performed for various industrial applications such as eXtended Reality (XR), Augmented Reality (AR), robot's pick and place, and so on. This paper addresses 6-DoF pose estimation and CAD model retrieval problems simultaneously and quantitatively analyzes how much the 6-DoF pose estimation affects the CAD model retrieval performance. This study consists of two main steps. The first step is 6-DoF pose estimation based on the PoseContrast network. We enhance the structure of PoseContrast by adding variance uncertainty weight and feature attention modules. The second step is the retrieval of the matching CAD model by an image similarity measurement between the CAD rendering and the RGB object. In our experiments, we used 2000 RGB images collected from Google and Bing search engines and 100 CAD models from ShapeNetCore. The Pascal3D+ dataset is used to train the pose estimation network and DELF features are used for the similarity measurement. Comprehensive ablation studies about the proposed network show the quantitative performance analysis with respect to the baseline model. Experimental results show that the pose estimation performance has a positive correlation with the CAD retrieval performance.  
6-DoF pose estimation; similarity measure; deep learning; CAD model retrieval 2025151501  
1
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 1 Introduction
### Related Work
However, the approximation of the outage probability will bring a larger error compared with the exact outage probability result. All these energy-efficient HARQ protocols are aimed at terrestrial communication systems and do not consider the complex link transmission characteristics between the LEO satellite and the ground user, especially in higher frequency bands.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 2 Materials and Methods
### Data Collection  
A research account was created for the investigators to access the patients' records through the University Dental Hospital health record system (CareStream R4 Clinical+ software, Jeddah, Saudi Arabia), and to retrieve the required BW radiographs. The inclusion criteria for the BW radiographs were: (1) for patients under treatment at the University Dental Hospital with fully erupted permanent dentition, and (2) the BW was either premolar or molar view. The exclusion criteria included: (1) records with findings that might affect the machine learning process such as severe periodontitis that cannot be assessed by a horizontal BW, removable partial dentures components, fixed retainers, provisional crowns, and defective direct/indirect restorations that hinder the contacts assessment, and (2) certain acquisition errors such as double exposure, patient movement, or extremely low/high kVp that resulted in non-distinguishable enamel, dentin, or pulp, and cannot be corrected. The R4 Clinical+ system was searched for patients' records from 2017-2021 that fulfilled the study inclusion criteria.
## 1 Introduction
### Pothole Detection
The motivation of this paper is to provide a comprehensive overview of the different models available and to compare their performance in pothole detection in a complex environment through experiments. To the best of our knowledge, a comparison of pothole-detection-based systems concerning heterogeneous weather conditions is currently lacking. The main contributions of this paper are summarized as follows:  
* An overview of contemporary neural networks employed in the pothole detection task and evaluation of the effectiveness of Fast-, Faster-, Mask-, Cascade-, Sparse R-CNN, and YOLO versions 3, -v4, -v5, -v6, and -v7 computer vision models for the pothole detection. The results of the study can provide valuable information for future research and can aid in the development of more accurate and reliable pothole detection systems. * Comparison of models' performance in terms of detection accuracy under different weather conditions and exploring the capability of models dealing with adverse weather and light conditions. Although YOLO architectures perform detection with significant accuracy and speed, R-CNN models may handle the very low-visibility detection more successfully. * Determining the limitations of existing deep neural networks for pothole detection and identifying techniques by which future research could improve pothole detection performance under adverse visual conditions. Detecting potholes in adverse conditions and achieving a high level of accuracy at the same time is quite a difficult task, as indicated above. The main challenge in solving this problem is to work with the data collected under adverse conditions and to select an appropriate algorithm for detection. This work was carried out in six steps: firstly, a survey of existing methods for detecting road damage is reviewed and discussed in Section 2. Section 3 reviews the state-of-the-art algorithms for object detection. Section 4 describes the methodology used in this study, the data acquisition method, and the evaluation metrics. Section 5 discusses the experimental results and performs a comparative analysis between the state-of-the-art systems. In Section 6, we discuss the experiments, the benefits and limitations of the research, and recommendations for future research.