esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present work aimed to search for constellation configurations for the 8-, 24-, 60-, and 120-satellite 2D-LFCs that could provide optimal spatial and temporal distributions of GNSS-R observations over the globe. The navigation signals from the four GNSS systems including GPS, GLONASS, BDS, and Galileo, were exploited in the simulation. The PSO algorithm was applied with a fitness function, which presents the length of time for the percentage coverage of the reflection observations to reach 90% of the globe. The visited and the revisited coverages of the GNSS-R observations obtained by the optimal LEO constellations are presented. The performances of the optimal constellations were further compared using the spatial and temporal distributions of the reflections (i.e., the latitudinal and longitudinal variations of the average numbers of the GNSS-R specular reflection points and the spatial distribution of the mean revisit time).  
The results show that all four optimized constellations can observe GNSS reflections with proper temporal and spatial distributions. We recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of GNSS-R missions.  
[PERSON] and [PERSON] conceptualized the initial idea and experimental design; [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the main manuscript text; [PERSON] reviewed and edited the paper. This research was funded by the National Natural Science Foundation of China (grant nos. 41774032 and 41774033), the National Key Research and Development Program of China (grant no. 2018 YFC1503502), and the National Basic Research Program of China (973 Program) (grant no. 2013 CB733302). The authors declare no conflicts of interest.
## 4 Results of an Experimental Study with Power-Line Communication and Selected End-User Equipment
Figure 11: Summary: spectra of the voltage in the connection point of the load and energy meter for mixture cases representing results of the analysis of PRIME PLC system operation with **(a)** LED; **(b)** CFL; and **(c)** induction motor with PWM converter; under the different contribution of low-frequency distortion in the supply voltage (pure sinusoid (brown), EN 50160:2015 [3] (yellow), IEC 61000-4-13 [30] (orange)) with relation to no-load condition (grey) and PRIME transmission (black) (uL—line voltage in the point of the equipment connection, which is also the point of energy meter connection with PRIME PLC). Details of the representative parameters of the transmission signal and the non-intentional emission in the transmission band 42-89 kHz of the investigated PRIME PLC system were collected in Table 4. It can be noticed that the condition of the transmission was consequently worse when the distortion of the supply voltage was increased. The impact of the supply voltage distortion on the signal-to-noise ratio (SNR) in the transmission band 42-89 kHz of the investigated PRIME PLC system is presented in Table 4 and illustrated in Figure 12. In particular:  
* With LED load the SNR coefficient decreased from 30.06 dB (\(SNR_{band}\))/17.04 dB (\(SNR_{local}\)) for the sinusoidal normal condition to 7.61 dB(\(SNR_{band}\))/3.14 dB (\(SNR_{local}\)) for margin distorted condition, which made PLC transmission more difficult;
* With CFL load the SNR coefficient decreased from 26.31 dB (\(SNR_{band}\))/20.61 dB (\(SNR_{local}\)) for the sinusoidal normal condition to 9.73 dB (\(SNR_{band}\))/2.94 dB (\(SNR_{local}\)) for margin distorted condition, which made PLC transmission more difficult;
* With induction motor powered by PWM converter, the SNR coefficient decreased from 18.97 dB (\(SNR_{band}\))/10.11 dB (\(SNR_{local}\)) for the sinusoidal normal condition to 2.49 dB (\(SNR_{band}\))/\(-0.79\)dB (\(SNR_{local}\)) for margin distorted condition, which made PLC transmissions impossible.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### ETC Gantry Data Completion Method Based on Improved Tensor Decomposition
#### 3.2.6. Construction of the Objective Function  
The method adopted for ETC gantry data completion, rooted in Tucker decomposition, addresses the associated optimization problem.  
\[f\Big{(}G,U^{(1)},U^{(2)},U^{(3)}\Big{)}=argmin\frac{1}{2}\Big{\|}P_{\Phi}-G \times_{1}U^{(1)}\times_{2}U^{(2)}\times_{3}U^{(3)}\Big{\|}_{2}^{2} \tag{22}\]  
To minimize the function, the core tensor \(G\) and factor matrices \(U^{(1)},U^{(2)},\text{ and }U^{(3)}\) are the factor matrices to be completed. The current function can be minimized by obtaining the core tensor and factor matrix.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 2 Preliminaries
### Problem Statement  
In conventional LEACH-based algorithms, DATA messages are transmitted under power control. However, the _control messages_ including ADV, Join-REQ, and TDMA are assumed to be transmitted with the fixed power enough to reach over a maximum distance of \(2L\) in the field. This fixed transmission range, \(R=2L\), would be inappropriate for IoT based on large-area WSN. Furthermore, this non-optimized, max-distance transmission radius, \(R=2L\), for control messages have caused us to set the number of clusters, \(k\), to be a sub-optimal value \(k=5\)[3], which is used as the rule of thumb thereafter. It is observed that the sub-optimal value \(k=5\) also significantly shorten the network lifespan especially when \(L\) becomes large.  
We now write the problem statement as follows. Assume we have \(N\) sensor nodes over a circular-shaped sensor field with radius \(L\gg\delta\) (e.g., \(L>200\) m). Our goal in this paper is to jointly optimize the average number \(k\) of CH nodes and transmission radius \(R\) (\(L<R<2L\)) for the control messages to minimize overall energy consumption in a round or to maximize the sensor network lifespan.
## 2 Related Works
### Model-Based Paradigm  
Model-based paradigms have become a powerful driver for systems engineering and the launching of system thinking, in which processes and activities of the system life cycle shape the context of systems engineering practice, and M&S constitutes the core mechanism and is beneficial to helping connect the stages of MBSE [7]. The essence of the transformation of MBSE is a continuous shift to a system development process supported by the continuity and traceability of models. When referring to models, we highly advocate executable models for simulation in an Experimental Frame (EF). Therefore, advanced M&S technologies and methods have become the key enabler driving the life cycle of systems engineering.  
MBSE relies on the trend of adopting a unified formal model throughout the system's life cycle process activities, and has been struggling to seek an appropriate means to connect the blueprint models that describe the system architecture in an iterative and incremental way, and then be used to validate these stakeholders and verify by its specifications, even early in prototyping development. In order to support the multidisciplinary practice in SE, the communities in systems engineering have been adopting many modeling approachesand tools, which range from mission or business analysis to requirement definition, system structure, and interfaces, and even to system behavior, and so on. We need a formal modeling language to combine visual graphics for human communication and a metamodel with constructs and rules needed to build specific models within a domain of interest, and a specification to be exchanged spanning diverse modeling paradigms and tools. The Unified Architecture Framework (UAF) for SoS, System Modeling Language (SysML) for systems, and its collections of visual diagrams, which are derived from the Unified Modeling Language (UML), have been standardized and continuously updated by the Object Management Group (OMG) and International Council on Systems Engineering (INCOSE) at the initiative of MBSE.  
Furthermore, from the perspective of system paradigm evolution, a CPS is defined as an autonomous, adaptive, and intelligent system in which Communication, Computing, and Control (C3) components dominate physical behavior. M&S will involve multiple levels of concept, specification, and operation in CPS Engineering (CPSE) to use formal methods to express basic concepts (such as structures, states, events, concurrency, etc.) and their relationships, to represent the system studies (problem) in the real world as a model, and to verify the implementation of system behaviors and functions (solution) by executing various simulation instructions through a simulation engine [8]. Therefore, a CPS is an advanced hybrid form of system and can be modeled in some very distinctive ways, such as computational elements for discrete modeling; physical elements for continuous modeling; communication networking for probabilistic scenarios; and even game theory for operations research to support planning and decision making [9].
## 4 Service Scheduling Algorithms
### Design of Service Scheduling Algorithm
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method of** & **Advantages** & **Disadvantages** & **Main Methods** \\ \hline \multirow{4}{*}{Subjective Method} & Weights are based on personal & \multirow{2}{*}{Ignore the distribution of objective data; highly influenced by personal subjectivity} & \multirow{2}{*}{AHP (Analytic Hierarchy Process), BWM (Best-Worst Method), Delphi Method} \\  & & & \\ \cline{1-1}  & Based on the distribution of objective data; easy to reproduce & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of subjective and objective weighting methods in MCDM problems. \[d_{i}^{-}=\sqrt{\sum_{j=1}^{n}(r_{j}^{-}-r_{ij})^{2}},\quad i\in m \tag{9}\]  
\[d_{i}=\frac{d_{i}^{-}}{d_{i}^{-}+d_{i}^{+}},\quad i\in m \tag{10}\]  
Based on the algorithm described above, the scheduling algorithm based on TOPSIS is outlined in Algorithm 1:  
```
0: Requests with demand (\(U_{\text{delay}}\), \(U_{\text{success rate level}}\)), network nodes with computing clusters' service information (\(S_{\text{delay}}\), \(S_{\text{success rate level}}\)), \(V_{\text{B}}\), \(V_{W}\)
0: An optimal cluster
1:if inputs are not null then
2:for all service in each network node do
3: Classify service according to delay level \(S_{\text{delay}}\) and success rate \(S_{\text{success rate level}}\)
4:for all service in each category do
5: Calculate normalized matrix \(Z\) according to Formulas (1)-(4)
6: Calculate weights using BWM
7: Calculate distances according to Formulas (5)-(10) and sort them
8: Choose top \(S_{\text{success rate}}\) candidate computing clusters
9:endfor
10:endfor
11:endif
12:for all demand in each service request in each network node do
13:if\(U_{\text{delay}}=S_{\text{delay}}\)then
14:if\(U_{\text{success rate level}}\geq S_{\text{success rate level}}\)then
15: Randomly select a cluster in top \(U_{\text{success rate level}}\) candidates
16:else
17:\(k\leftarrow\min S_{\text{success rate level}}\)
18: Choose all computing clusters with \(k\) success rate as candidates
19: Randomly select a cluster node from the top \(U_{\text{success rate level}}\) candidates
20:endif
21:else
22:if current network node is not the top-level node then
23: Forward request to the main node of the current node and repeat steps 12-25
24:else
25: Scheduling failed
26:endif
27:endif
28:endfor
```  
**Algorithm 1** Scheduling algorithm.
## 3 Methodology
### Evaluation Metrics  
Given the imbalanced nature of our dataset, accuracy alone is not a suitable metric for evaluating model performance [41; 42]. Therefore, we prioritize precision, recall, and F1 score. Precision measures the proportion of true positive results among all positive predictions, recall measures the proportion of actual positives correctly identified, and the F1 score provides a balance between precision and recall. This approach provides a more accurate and reliable evaluation of the model's effectiveness in handling imbalanced data. We consider that true positive (TP) is the number of positive cases predicted as positive, true negative (TN) is the number of negative cases predicted as negative, false negative (FN) is the number of positive cases predicted as negative, and false positive (FP) is the number of the negative cases predicted as positive. Here are the metrics upon which our evaluations rely.  
* Precision measures the percentage of accurate positive predictions compared to the total number of samples classified as positive.
* Precision = TP/(TP + FP)
* Recall measures the percentage of the accurate positive predictions compared to total number of actual positives.
* Recall = TP/(TP + FN)
* F1 score is a trade-off between precision and recall, which combines precision and recall to assess the performance of the model. It represents a better metric in the case of imbalanced classes.  
F1 score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall)
## 4 Results and Discussion
### Extraction of \"Weak\" FIV
#### 4.1.2 Frequency Range Identification  
CCA of the vibration and sound pressure signals was conducted to detect the correlated components. Figure 4 shows the spectrum of the CCF calculated from the two types of signals. Considering that the frequency of \"weak\" FIV was usually up to thousands of hertz, the frequency above 2000 Hz was displayed in the spectrum. As shown in Figure 4, the correlated frequency components of the vibration and sound pressure signals were distributed within 5500 Hz. In addition, owing to the influence of the high-value spectral line lumped at approximately 2000 Hz, its sideband frequency also had a large amplitude. Thus, the frequency band of 2500-6000 Hz was set as the preliminary target and extracted by HWPT.  
Figure 4: Spectrum of the CCF of the vibration and sound pressure signals.  
Figure 3: Time-frequency characteristics of the original signals: (**a**) waveform of the vibration signals, (**b**) spectrum of the vibration signals, (**c**) waveform of the sound pressure signals, and (**d**) spectrum of the sound pressure signals.  
The mean value of the frequency amplitude from 2500 to 6000 Hz was set as a threshold to determine the frequency distribution of \"weak\" FIV, i.e., the frequency whose amplitude exceeded the threshold was considered the potential \"weak\" FIV. Figure 5 shows the statistical results. It was evident that the frequency whose amplitude was higher than the threshold was mainly distributed in two prominent frequency bands: 2500-3700 Hz and 3800-5000 Hz. The proportion of the points in the above frequency band to the entire measured data was calculated to further judge the frequency range of the \"weak\" FIV. Because the ratio was as high as 78.1% (far larger than the others), the frequency band of 3800-5000 Hz was considered the dominant component of the \"weak\" FIV in the experiment. The frequency domain of the original signals was decomposed into 256 adjacent bands using an eight-layer HWPT. Continuous bands numbered 96-125 were selected to reconstruct the target frequency components.  
The time-domain waveforms and spectra of the extracted signals are shown in Figure 6. It can be seen that the extracted signals had uneven amplitudes and continuous spectra, which was consistent with the characteristics of the \"weak\" FIV [10]. Meanwhile, the Pearson cross-correlation coefficients were 0.60, 0.47, 0.46, and 0.46, respectively, which demonstrated that the extracted vibration and sound pressure signals had a high correlation at each time. Moreover, a positive coefficient indicated that the extracted vibration and sound pressure signals had a positive correlation. Consequently, the high-correlated frequency components submerged in original vibration and sound pressure signals could be detected and extracted by using CCA and HWPT.
# Energy Consumption Analysis of a Rolling Mechanism Based on a Five-Bow-Shaped-Bar Linkage
## 2 Trajectory Planning of the Mechanism's CoM
### Mechanical Structure of the Rolling Mechanism  
As shown in Figure 1, the rolling mechanism is composed of five identical bow-shaped modules connected by revolute joints to form a closed chain linkage. The usage of the bow-shaped module can not only ensure the continuity of the rolling locomotion, but also prevent the mechanism from being subjected to impact force from the ground. The bow-shaped module mainly consists of left and right bow-shaped plates, support attachments, motors, transmission devices, and others. By introducing a counterweight, the CoM of each module can be located at the midpoint of the line that connects adjacent joints [8]. Additionally, the rolling locomotion is then generated by controlling the trajectory of the mechanism's CoM to offset the grounded point in the horizontal direction.
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 2 Extraction of Multi-Scale Recurrence Quantification Measurements (MRQMs)  
In this work, the glottal signal as the source signal was studied to achieve the purpose of intelligent detection and classification of vocal fold diseases [27].  
Figure 1 illustrates the framework of pathological voice detection and classification system. Most pathological voice detection and classification methods use voice signals. The glottal signal is directly produced by the vibration of vocal folds caused by the airflow from lungs. Glottal waveforms can directly reflect the difference between normal vocal folds vibration and pathological vocal folds vibration [4]. Therefore, the glottal signal is closer to the vibration and sound mechanism of the vocal folds as the source signal. The human auditory perception experiment found that the human auditory system is a unique nonlinear system. Glottal signals are decomposed into 24 channels at the equivalent rectangular bandwidth (ERB) scale, which is a multi-scale analysis that satisfies the auditory characteristics. Signals in each frequency channel is embedded in the high-dimensional phase space to construct an RP according to the recurrence characteristics. RQMs are used to quantify the non-stationary recurrence characteristics of the signal in each channel. A total of 312-dimensional features consists of recurrence characteristics for pathological voice detection and classification.  
Figure 1: The framework of MRQMs extraction for pathological voice detection and classification.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 3 Experiments and Results  
### Experimental Settings and Data  
The CCSM was implemented by using the Open Cascade library given that its data structure is compatible with ISO 10303-42 guidelines. ISO 10,303 is known as the Standard for the Exchange of Product Model Data as published by the International Organization for Standardization (ISO) and the Initial Graphics Exchange Specification [38]. This standard can guarantee a certain level of generality of our proposed method. The experiment was conducted on a computer equipped with an Intel Core i5-6300U 2.40 GHz processor. As shown in Figure 8, the 1:50,000 topographic map of the northeastern part of Huimin, Chongqing, China, was downloaded from Google Earth and was used as our experimental contour dataset. Google Earth uses the Shuttle Radar Topography Mission as its digital elevation model data, which are mainly measured by NASA and the National Imagery and Mapping Agency. The digital elevation model data in China has an accuracy of 90 m [39].  
Figure 7: Ideal relationship between scale and similarity for continuous multi-scale simplification.
###### Abstract  
This paper presents a low-voltage ride-through (LVRT) control strategy for grid-connected energy storage systems (ESSs). In the past, researchers have investigated the LVRT control strategies to apply them to wind power generation (WPG) and solar energy generation (SEG) systems. Regardless of the energy source, the main purpose of the LVRT control strategies is to inject reactive power into the grid depending on the grid-code regulations using the grid-side inverter; the proposed LVRT control strategy for grid-connected ESSs also has the same purpose. However, unlike the WPG and SEG systems having unidirectional power flow, grid-connected ESSs have a bidirectional power flow. Therefore, the charging condition of the grid-connected ESSs should be considered for the LVRT control strategy. The proposed LVRT control strategy for grid-connected ESSs determines the injection quantity of the active and reactive currents, and the strategy depends on the voltage drop ratio of the three-phase grid. Additionally, in this paper, we analyzed the variations of the point of common coupling (PCC) voltage depending on the phase of the reactive current during the charging and discharging conditions. The validity of the proposed LVRT control strategy is verified and the variations of the PCC voltage of the grid-connected ESS are analyzed by simulation and experimental results.  
low voltage ride through; wind power generation system; solar energy generation system; grid-connected; energy storage system +
Footnote †: journal: Applied Sciences
## 3 Related Works
### Combined watermarking algorithms  
SVD has been used also in combination with other transforms, like DCT [35; 43-50]. The scheme proposed in [15] decomposes the original color image (in RGB format) into _YUV_ components and splits the \(Y\) component into non-overlapping blocks. To embed the watermark bits, the largest SV of each block is quantized. The security is then improved by performing a torus permutation on the watermark image. The proposed scheme is robust to most attacks and does not need the original image in the detection process. A similar scheme is proposed in [35], where authors add the SVs of the watermark to the SVs of the approximation image, i.e., the image formed by the DC coefficients from each block. To avoid the false positive problem, the watermark matrix of the right singular vectors is embedded into the 2 nd and 3 rd AC coefficients of each block, as a control parameter. This information is later used in the extraction process, which requires the original image; however, the watermark cannot be extracted with zero bit error rate. In 2006, the paper [32] showed that the detection process of the scheme proposed in [43] is subject to the false positive problem. The same considerations can be applied to the algorithm proposed in [46].  
The SVD transform was also used to devise semi-fragile watermarking schemes [15; 51; 52], which are used for data authentication.