esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Energy Requirements
#### 3.4.2 Satellite Constellation  
As previously discussed, some relay satellite constellation must be in place to have continuous contact with the vehicle during a mission. This can be achieved with a set of three relay satellites orbiting Mars at a radius of approximately 10,000 km and an inclination of 42.5\({}^{\circ}\). Since these satellites are only needed for required communication purposes and not for tracking, three satellites are sufficient.
# Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition
## 5 Conclusions  
The proposed method mainly focuses on solving the challenge of low-SNR horizontal components of microseismic data. Among the different components, there is no correlation for random noise, and there is a linear relationship for the signal. For most field three-component microseismic data, the SNR of the vertical component is better than that of the two horizontal components. Therefore, by adding a new constraint to the VMD and modifying the new updated formulas, we propose the re-constrain VMD. The new constraint is used to represent the characteristics of linear relationships for signal and random noise among the three components of the data.  
According to the denoising results for the synthetic data and field microseismic data, we found that the proposed method could suppress random noise to a certain extent, and the effect was better than that of VMD. The subsequent polarization analysis also showed that the proposed method could obtain better polarization characteristics.  
Each author contributed to the present paper. [PERSON] conceived the method and directed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the article; [PERSON] supervised the experiments and the article. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the state key program of National Natural Science Foundation of China, grant No. 42030805 and the Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, grant No. K2021-19.  
Not applicable.  
The authors declare no conflict of interest.
# An Approach to the State Explosion Problem: SOPC Case Study
## 4 Preliminaries  
### Temporal Logic  
Temporal logic (temporal logic) identifies the temporal order in which events occur in a system and categorizes them according to a time model. Linear temporal logic (LTL) and computational logic (CTL) are commonly used languages for concurrent system specification and verification. Different temporal logics have their corresponding operators and their corresponding semantics. There are many branches of temporal logic, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) are classical [29; 30]. Temporal logic in the real-time design industry has evolved from early proprietary property languages such as Sugar from IBM [31] and Spec from Intel [32]. At the same time, there are a series of industry standards, including Accellera's Open Verification Library (OVL) [33], Property Specification Language (PSL) [34], and most recently the IEEE standard SystemVerilog Assertion (SVA) [35], which is the property language of a real-time system.  
The IEEE standard temporal logic program SystemVerilog Assertion (SVA) is a combination of regular expressions and LTL formulas known as suffix implication formulas. A suffix implication formula is of the form \(\mathtt{r}\Rightarrow\phi\), where \(\mathtt{r}\) is a regular expression and \(\phi\) is either an LTL formula or another suffix implication formula. Intuitively, this formula states that whenever a prefix of a given computation path matches the regular expression \(\mathtt{r}\), the suffix of that path must satisfy \(\phi\). The formal definition of suffix implication is taken from [36]. Anne F in the SystemVerilog IEEE 1800-2012 language reference manual [34] gives the syntax and formal semantics of SVA, including the core operators for RE and LTL, rewriting rules for derivative forms and language sugaring, extensions including clocking,  
Figure 3: The resources occupied by the FPGA software.  
and weak/strong semantics, etc. SVA formulas are built up from sequences. The core sequence syntax is:  
R::= b|R ## R| R ##0 R| R or R| R intersect R|R[*0]|R[*1:S]  
where b is a Boolean expression, ## stands for the concatenation operation and intersects the conjunction operation.
## 3 Results  
In this section, we perform DEM-based, MC3-based (hereafter referred to as MC3), and CTS methods on several images to illustrate the effectiveness of our proposed algorithms. The detection results are evaluated qualitatively and quantitatively. Three conventional shadow detection algorithms, [PERSON]'s [20], [PERSON]'s [22], and [PERSON]'s [18], as well as two deep learning-based methods, [PERSON]'s [34] and ECA [35], are used as comparisons to demonstrate the superiority of the proposed methods in accurately recognizing shadows and avoiding shadow misdetection. For the DEM-based method, we focus more on visual evaluation because it is difficult to obtain the ground truth. The experiment images are from GF07 with a resolution of 2.6 m, containing RGBNIR four bands. The DEM used has three resolutions: 5 m, 12.5 m, and 30 m.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 2 Materials and Methods
### Method
#### 2.2.3 Alignment
The second adjusts the point cloud through the minimization of the distance between the point in the point cloud of reference and the tangent plane to its corresponding point in the point cloud to register. Studies shown that the validity of point-to-point ICP is more robust to Gaussian noise than point-to-plane ICP [60]. However, the latter undergoes fewer iterations, and it is more effective [60]. Since the point clouds are cleaned and filtered from noise before starting the registration process, the point-to-point ICP method was chosen for this work. The main advantage of the double step Harris 3D/PFH and point-to-point ICP registration is that there is no need of user interaction, and there is no need to place any targets in the scene to serve as points of interest, because the points of interest in the method proposed are naturally present in the scene.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 2 PMF Overview
### PMF Propagation Characteristics  
The key measures of a PMF's electrical performance are the attenuation and dispersion. The attenuation reflects the power losses inside the dielectric, while the dispersion reflects the phase distortion of the propagating modes.
## 4 Discussion  
To the best of our knowledge, the current study is the first experimental study focused on ACL injury prevention in female amateur rugby players. The main finding of this study was that significant effects of the KneeRugbyWomen program were found only in RSI, while leg stiffness indicators and LESS scores showed unclear results. Therefore, observed changes in knee joint neuromuscular control indicators and landing biomechanics only partly evidence the effectiveness of a short-term ACL prevention program and do not support the proposed hypothesis.  
Rebound-based testing was used in the current study to assess how leg stiffness and RSI elicits pre-activation and a stretch reflex in the lower limbs, which are the fundamental prerequisite of efficient SSC behavior. In other words, higher values of both leg stiffness and RSI require efficient SSC action [16]. Therefore, they are considered relevant jump, speed, and endurance performance indicators [52; 53; 54]. Simultaneously, lower RSI values  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Median (IQR)**} & \multirow{2}{*}{**MD [95\% CI]**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**r-Value [95\% CI]**} \\ \cline{3-4} \cline{6-7}  & & _Pre-Intervention_ & & & & & \\ \hline \multirow{2}{*}{RSI} & EG & 0.58 (0.30) & 0.86 (0.30) * & \(-\)0.19 [\(-\)0.31— 0.03] & 0.012 * & 0.70 [0.27–0.88] \\  & CG & 0.63 (0.33) & 0.59 (0.19) * & 0.04 [\(-\)0.07–0.14] & 0.531 & 0.19 [0.01–0.70] \\ \hline \multirow{2}{*}{ALS (kN\(\cdot\)m\({}^{-1}\))} & EG & 25.98 (5.75) & 23.72 (5.29) & 1.58 [\(-\)0.65–3.11] & 0.110 & 0.48 [0.07–0.89] \\  & CG & 23.04 (4.92) & 22.60 (1.34) & 0.78 [\(-\)0.94–4.35] & 0.339 & 0.29 [0.02–0.82] \\ \hline \multirow{2}{*}{RLS} & EG & 32.67 (7.86) & 30.48 (7.69) & 2.07 [\(-\)0.13–3.98] & 0.052 & 0.57 [0.11–0.89] \\  & CG & 27.58 (6.92) & 26.92 (4.34) & 1.68 [\(-\)0.22–5.76] & 0.052 & 0.57 [0.07–0.89] \\ \hline \multirow{2}{*}{LESS (errors)} & EG & 5.00 (2.00) * & 4.33(1.33) * & 0.50 [\(-\)0.83–1.66] & 0.366 & 0.27 [0.00–0.82] \\  & CG & 7.33 (0.67) * & 6.17(1.33) * & 0.17 [\(-\)1.16–1.67] & 0.906 & 0.05 [0.00–0.69] \\ \hline \multirow{2}{*}{LESS (errors)} & EG & EG—experimental group, CG—control group, SD—standard deviation, CI—confidence intervals, IQR—interquartile ranges, MD—Hodges–Lehmann estimate of the median differences. * Significant difference between groups according to the Mann–Whitney U test. * Significant difference within the group according to the Wilcoxon matched-pairs signed-rank test. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Reactive Strength Index (RSI), Absolute Leg Stiffness (ALS), Relative Leg Stiffness (RLS) and Landing Error Scoring System (LESS) mean scores pre- and post-intervention.  
show ACL injury risk [18]. In connection with this knowledge, it seems to be reasonable to increase values of leg stiffness and RSI.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Energy Prediction
#### 3.2.2 Energy Consuming Model
It should be noted that the total amount of data that the sending node \(i\) must encode is \(D^{i}_{\text{data}}\), which contains not only the data it has sensed (\(D^{i}_{\text{sensing}}\)) but also the data received from other nodes (\(D^{i}_{\text{relay}}\)). Again, it is important to note that of the total energy \(E^{i}_{\text{consume}}\) (Equation (4)) used by node \(i\), the terms related to the error recovery rate of FEC (i.e., the parity length of the RS block) are \(E^{i}_{\text{Tx}}\) (Equation (7)) for data transmission and \(E^{i}_{\text{FEC\_sysy}}\) (Equation (8)) for data encoding and decoding with the RS scheme.
## 1 Introduction
To reduce the model mismatch between the theoretical EPS models and EPS plant, this paper identifies each EPS parameter using three different algorithms and then designs a controller based on the system-identification experiments. In addition, this paper investigates sampling rate, which significantly affects the identification results, and finds the optimal sampling rate based on experiments. Based on evaluated the estimation criterion in this paper, the LSSVF algorithm is the simplest method for system parameter estimation. The IVSVF algorithm reduces the bias of the LSSVF algorithm, whereas the SRIV algorithm is the optimal estimation method. Hence, the SRIV algorithm is adopted for estimating the parameters of the control plants. In this algorithm, an adaptive procedure is employed. The SRIV algorithm is shown to be highly robust in practical applications. Hence, the model can provide optimal system parameters considering the measurement noise, thus making the nominal model of the EPS system highly accurate. However, the estimated model of EPS system is the nominal mode. The EPS system controller is challenging design problem. The EPS system controller must provide good tracking performance to ensure system stability and good steering feel. The proposed loop-shaping controller in this paper satisfies these several objectives. This paper is organized as follows. Section 2 introduces the operational principle and mathematical model of EPS system. Section 3 introduces the mathematical basis used to implement the estimation algorithms. Section 4 presents an identification analysis of the motor parameters. Section 5 includes both the simulation identification and experiment identification of the proposed EPS system. Section 6 presents a design of the loop-shaping controller. Section 7 shows the EPS test bench. The verification of the EPS control algorithms is presented in Section 8.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 1 Introduction
Section 3 analyzes the constraints and optimization objectives of the hardware resource scheduling problem in satellite data transmission ground stations and, based on this, proposes a satellite data transmission task scheduling model. Section 4 presents the design of the ITGA, along with the designs of the task set partitioning method and the conflict resolution approach. Section 5 validates the effectiveness of the ITGA based on heuristic rules through simulation experiments. Finally, Section 6 concludes the paper.
## 2 Materials and Methods  
### Experimental Procedures  
#### 2.1.1 Sample Preparation  
The fine-grained red sandstone, characterized by its hardness and brittleness, was selected for testing. The rock sample was extracted from Sichuan Province, China, and processed into a standard cylinder shape with a diameter of 50 mm and a height of 100 mm, as depicted in Figure 1. The dimensions, parallelism, and perpendicularity of the rock sample adhered to the ISRM standard (International Society of Rock Mechanics) [18]. Measurements of the physical parameters of the rock samples were conducted. The average density of the red sandstone samples, without ultrasonic vibration excitation, is 2.7 g/cm\({}^{3}\), and the average P-wave velocity is 4110 m/s. The fundamental mechanical parameters of the rock samples were derived from the uniaxial compression test. The average uniaxial compressive strength and elastic modulus stand at 92.5 MPa and 5.4 GPa, respectively. The natural frequency of the rock samples, determined by the tapping method, is 11,625 Hz [15].  
The three red sandstone samples were meticulously chosen and subjected to a saturation treatment for a duration of 48 h prior to undergoing nuclear magnetic resonance testing. As illustrated in Figure 2, the average porosity of the fragile red sandstone, before ultrasonic vibration excitation, was 6.4%. This finding can be employed subsequent to this study to corroborate the precision of the method of extracting rock pores via digital image processing techniques.  
Figure 1: Red sandstone specimens.
# Cost-Effective CNNs for Real-Time Micro-Expression Recognition
## 4 Experiments
### CNN Input Study  
In this subsection, we study impacts of optical flow representations on ME recognition performance. Two types of CNN have been investigated, one with 1-channel input (Vx, Vy, or M) and the other one using the 2-channel Vx-Vy pair. Due to the fact that off-the-shelf CNNs typically take 3-channel inputs and are pretrained accordingly, applying transfer learning to adapt to our models would have been a nontrivial task. Instead, we created custom CNNs and trained them from scratch. Table 4 shows the recognition accuracies of different configurations using a small number of CNN layers.  
We can observe that the Vx-Vy pair and Vy alone gave the best results, both representations achieving 60.17% accuracy. On the other hand, using Magnitude alone leads to a similar accuracy to those of Vy and the Vx-Vy pair with a score of 59.34%. Vx got the worst results overall, with a maximum  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 CL** & **2 CL** & **3 CL** & **4 CL** \\ \hline Vx & 52.24\% & 54.34\% & 53.92\% & 53.50\% \\ Vy & 58.09\% & 59.34\% & **60.17\%** & 60.17\% \\ Vx-Vy & 58.51\% & 59.75\% & **60.17\%** & 58.09\% \\ M & 58.09\% & 58.92\% & 59.34\% & 59.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracies under various CNN architectures and optical flow representations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Nb. of CL** & 17 & 15 & 13 & 11 & 9 & 7 & 5 & 3 & 1 \\ \hline
**Nb. of param.** & 10,670,932 & 5,400,725 & 2,790,149 & 1,608,965 & 694,277 & 398,597 & 178,309 & 104,197 & 91,525 \\
**Accuracy** & 57.26\% & 57.26\% & 60.58\% & 59.34\% & 60.17\% & 61.00\% & 58.51\% & 60.17\% & 58.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracies varied by the number of Convolutional Layers (CLs) and associated number of learnable parameters.  
score of 54.34%. This observation indicates that the most prominent features for ME classification might indeed be more dominant in vertical movement rather than the horizontal movement. This assumption is logical when thinking about the muscle movements happening in each known facial expression.  
To better visualize the difference in the high-level features present in Vx, Vy and the Magnitude, we did an averaging on all the different samples according to their classes. The result can be seen in Figure 6. We observed that Vx exhibits a non-negligible quantity of noise. Magnitude and Vy, on the other hand, had clear regions of activity for each class. The regions of activity were aligned with the muscles responsible of each facial expression.
# An Experimental Analysis of Gas Reduction in Multiphase Flow with a KMS Helical Static Mixer
## 7 General Remarks  
It is important to remark that the main motivation of this work was the requirement of reducing the gas from the mixture in the current operation of the Nuvoil company, to decrease the foaming and emulsion phenomenon in the horizontal separator of a real hydraulic ALS in oil wells, letting the injection pump operate more efficiently. Since the presence of gas represents difficulties in the current operation, the main goal is the gas's isolation before the mixture enters the horizontal separator.  
From the results of the presented cases, it can be noticed that the KMS implementation as the inlet device contributes to the gas separation: 29.2%, 11.3%, 7%, and 0% of gas for Cases A, B, C, and D, respectively, in an average performance under controlled conditions.  
On the one hand, the KMS implementation results in gas reduction from the mixture, which improves the efficiency of the injection pump by enabling it to operate with almost liquid phases. On the other hand, the sedimentation time required to create these liquid phases, which is correlated with higher operation costs, is increased. This is a crucial topic to consider while extracting oil from an oil well since businesses need to determine what will best meet their production targets. Table 4 summarizes the main criteria to make decisions on the configuration; the current implementation in the real application refers to Case A, but it has the issues already discussed; therefore, Case D may be an alternative following this study regarding the gas reduction.  
Recalling Figure 2 and since the identified bubble and the bottom end of the KMS have a geometry that slightly varies in time because of the rotation, traces of the liquid phase could also be trapped in the physical implementation. Several orifices are distributed in the horizontal section to let liquid fall into the reservoir. In addition, a minimum amount of gas could also fall into the reservoir within the liquid phase and should be separated by sedimentation in the usual way, influencing at the same time the sedimentation times.  
Regarding the models of turbulence in Table 1 for the CFD code, it was shown that \(k-e\) is a fast model to obtain a solution; however, it is not necessarily the most accurate for multiphase flows and transient state simulations. The BLS model, in comparison, produces good results, but the eddies at the end of the KMS are not appreciated as in the SST model, which finally was selected for the experiments.
# Skin Cancer Disease Detection Using Transfer Learning Technique
## 1 Introduction
Deep learning, specifically convolutional neural networks (CNNs) powered by sophisticated computer techniques and massive datasets, has become one of the most potent and popular ML approaches in image identification and classification [31] and has been used to categorize skin lesions [32; 33]. The preliminary information and complex image preprocessing methods required for image classification using traditional ML methods are no longer in demand. Some deep-learning-based classifiers have demonstrated the ability to classify skin cancer images with the same accuracy as dermatologists [33]. As a result, CNNs can assist in developing computer-aided rapid skin lesion classifiers at the level of dermatologists. However, high-quality medical imaging datasets for training are still scarce. It is predominantly related to the absence of annotated/labeled images for abnormal classes [34]. CNN with simple architecture is more likely to overfit on limited training datasets. Some researchers use extremely deep CNNs models (e.g., Resnet152 contains 152 layers) [35]. Although this improves network classification performance and increases computing costs, that is a major problem for clinical applications [36; 37]. Moreover, researchers are using pre-trained CNNs to classify skin lesions [38; 39; 40; 41; 42], which prevents the issue of overfitting, and pre-trained CNNs use features learned from real-world image datasets (such as ImageNet). The present study proposes a deep transfer learning technique for melanoma classification based on MobileNetV2. For melanoma detection and recognition, pre-processing and heavy augmentation methods are used for the first level to overcome the imbalanced class problem in the ISIC-2020 challenge dataset. In the second stage, the transfer learning MobileNetV2 architecture is used for auto feature extraction and classification as benign or malignant. The remaining of the article is organized as follows. In Section 2, a detailed related work of the existing approaches is discussed. The materials and methods are discussed in Section 3. In Section 4, the results and discussion are presented. Finally, Section 5 describes the conclusion and future work, followed by the references.
###### Abstract  
Apart from constituting a topic of high relevance for transport planners and policymakers, support technologies for traffic have the potential to bring significant benefits to mobility. In addition, there are groups of \"high potential\" users, such as young adults, who constitute an essential part of the current market. Notwithstanding, and especially in low and middle-income countries (LMICs), their knowledge and acceptance remain understudied. This study aimed to assess the appraisal of intelligent transport systems (ITS) and other technological developments applicable to mobility among Dominican young adults. Methods: In this study, we used the data gathered from 1414 Dominicans aged between 18 and 40, responding to the National Survey on Mobility in 2018 and 2019. Results: Overall, and although there is a relatively high acceptance, attributed value, and atttitudinal predisposition towards both intelligent transportation systems and various support technologies applicable to mobility, the actual usage rates remain considerably low, and this is probably exacerbated by the low and middle-income status of the country. Conclusions: The findings of this study suggest the need to strengthen information and communication flows over emerging mobility-related technologies and develop further awareness of the potential benefits of technological developments for everyday transport dynamics.  
technology; mobility; intelligent transport systems; perception; low and middle-income countries; Dominican Republic +
Footnote †: journal:  
could be expected. However, recent studies suggest that opinions, intentions, and usage willingness of ITSs could be rather split, especially among users who currently have minute information about them [15].