esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
###### Abstract  
A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.  
rotating machinery fault diagnosis; single-channel monitoring; EEMD-based KICA; singular-value decomposition Article
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 2 Materials and Methods
### Data Analysis  
By analyzing the water consumption of households during the target periods, we can obtain an understanding of the differences in water consumption patterns among different households. These data are important for understanding the various water consumption patterns and behaviors displayed by various types of households. To evaluate water consumption, one-way ANOVA, and \(t\)-test analysis were used [38; 39]. The probability (p) is compared by the following metrics: \(p<0.05\), \(p<0.01\), and \(p<0.001\). These ratings indicate whether consumption data are robust or highly significant.
## 2 Experimental and Computational Section
### Experimental Section
#### 2.2.4 Batch Soil Experiments  
Cd-contaminated soil was collected from the surface layer (0-20 cm) in an industrial field in Changing city, Hunan Province, China (111 deg40'54.40'' E, 26 deg05''72.50'' N). The moisture soil was sampled by using an iron shovel, after which it was air-dried at room temperature and then sieved through a 1-mm nylon sieve prior to use. The basic soil properties are shown in Table 2. A series of amino acid solutions from 0.1 to 1 mmol/L with a pH of 6.5 was mixed with soil at a 10:1 liquid-to-soil ratio. The mixture solution was shaken for 48 h and separated by centrifugation at 8000 r/min for 10 mins. After that, the Cd concentration in the separated solution was tested by inductively coupled plasma  
\begin{table}
\begin{tabular}{c c c c} \hline
**Molecule** & **M-L** & **RM-L (calc)** & **RM-L (expt)** \\ \hline
[Cd(H\({}_{2}\)O)\({}_{6}\)]\({}^{2+}\) & Cd-O & 2.26 & 2.27 (\(\pm\)0.04) \\
[Cd(NH\({}_{3}\))\({}_{6}\)]\({}^{2+}\) & Cd-N & 2.35 & 2.37 (\(\pm\)0.03) \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between Computed and Experimental Average Metal-Ligand (M-L) Bond Distances (in Å).  
optical emission spectrometer (ICP-OES Agilent 5100). The BCR (a method proposed by the European Community Bureau of Reference) [28] sequential extraction method was employed to analyze the Cd transfer among different fractions in soil treated with amino acids. Cd is mainly distributed in four fractions: acid-extractable fraction, reducible fraction, oxidizable fraction, and residual fraction, which were extracted with four extractants by the following steps: (1) 0.1 mol/L acetic acid (OHAC) was added to the soil to extract exchangeable species and a weak acid soluble fraction for 16 h (HOAC-extractable fraction) at 295 K; (2) the solid in the first step was separated by centrifugation at 3000 r/min for 20 min and then washed by distilled water. After that, 0.5 mol/L hydroxylamine hydrochloride was adjusted to pH 2 with HNO\({}_{3}\) to extract the reducible metal species bound to Fe-Mn oxyhydroxides (reducible fraction) in the solid for 16 h at 295 K; (3) the solid of step 2 was obtained by centrifugation at 3000 r/min for 20 min and washed with distilled water. After that, 8.8 mol/L hydrogen peroxide was employed to extract oxidizable metal species bound to organics and sulfides in this solid (oxidizable fraction) for 1 h at 358 K and then shaken with 50 mL NH\({}_{4}\)OAC (1 mol/L) for 16 h at 295 K; and (4) the solid of step 3 was obtained by centrifugation at 3000 r/min for 20 min. Finally, the collected solid was immersed in Aqua regia (7.5 mL 6 mol/L HCl and 2.5 mL 14 mol/L HNO\({}_{3}\)) for 2 h to obtain the residual fraction in the solid. The Cd concentrations in the extractants were analyzed with ICP-OES (Agilent 5100).
## 6 Results  
This paper presents a low-cost, high engineering ease-of-use, and strong anti-jamming capability tool for the high-precision and real-time positioning requirements in challenging environments. Our algorithm is based on the LEO giant constellation. It has a realistic material basis and actual needs. Space X, OneWeb, and Telesat are intensively conducting research and subsequent prototype satellite launches, striving to achieve seamless internet interconnection in any corner of the world and high-precision positioning at a cm level.  
Through our research, we can find that, even in the scenario without an altimeter, our algorithm can provide basic location services; when combined with an altimeter, it can effectively overcome the divergence problem of INS and significantly improve the navigation and positioning accuracy in challenging environments, and when the altimeter deviation is small, the positioning accuracy can be further improved. With the advancement of modern electronic technology, it is not difficult to obtain an altimeter with a smaller deviation; in addition, we also verified from the perspective of simulation that the LEO constellation is more suitable for the implementation of the PNTRC scheme, which benefits from the advantages and potential advantages of the LEO constellation. Compared with the traditional single-satellite positioning algorithm, our algorithm is an implementation scheme of PNTRC, which eliminates the clock bias. Additionally, there is no need to exchange time redundancy for spatial redundancy for positioning, there is no positioning delay, and the positioning accuracy is significantly better than that of traditional algorithms.  
Therefore, there are various signs that our algorithm can satisfy the requirement for location services in challenging environments, such as lush forests, canyons, cities with towering buildings, etc.  
[PERSON] and [PERSON] conceived the conceptualization and algorithm [PERSON] completed the implementation of the algorithm and the writing of the paper, and supported the writing--review and editing. [PERSON] completed some preliminary simulations, and did preliminary research and summary. [PERSON] and [PERSON] review the experimental results and give formal analysis and suggestions. [PERSON] provides theoretical guidance and suggestions for revision of the paper. All authors have read and agreed to the published version of the manuscript.  
Funding was supported by the National Key Research and Development Program of China (Grant Nos.2017 YFC1500904, 2016 YFB0501301) and National 973 Program of China (Grant Nos.613237201506).
# A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation
## 2 Materials and Methods
### Clustering Accuracy Assessment  
Although previous works [8; 28; 29; 30] showed that the AP algorithm provided satisfactory results, it still could present a limitation due to the arbitrary choice of the initial preferences vector \(\mathbf{p}\). For this reason, we compared different clustering results obtained by using the minimum and the median of the similarity matrix. A commonly used approach to assess the quality of clustering results is the Silhouette index, which is a measure of how close each point in a cluster is to the points in its neighboring cluster. Given a cluster \(X_{j}\) (\(j=1,\ldots,c\)where \(c\) is the number of clusters found), the Silhouette index \(Sil_{i}\) (\(i=1,\,\ldots,\,n\) where \(n\) is the number of points in the cluster) of the \(i\)-th point in the cluster X\({}_{j}\) is given by:  
\[Sil_{i}=\frac{mavd(i)-avd(i)}{max\{avd(i),mavd(i)\}} \tag{10}\]  
where \(avd(i)\) is the mean distance between the \(i\)-th point and all the points in the cluster X\({}_{j}\), \(mavd(i)\) is the mean minimum distance between the \(i\)-th point and all the points in the cluster X\({}_{k}\) (\(k=1,\,\ldots,\,c\)), and \(max\) is the maximum operator. The Silhouette values range varies from \(-1\) to \(1\), where a value close to \(1\) could mean that the \(i\)-th point was accurately clustered to the optimum exemplar of the cluster. A value near to \(0\) could suggest that the \(i\)-th point could be attributed to the nearest cluster, whereas a value near to \(-1\) could mean that the \(i\)-th point was misclassified [47]. Thus, for characterizing the heterogeneity and isolation of a cluster Y\({}_{j}\) (\(j=1,\,\ldots,\,c\)) it is possible to calculate the cluster Silhouette \(S_{j}l\) given by:  
\[S_{ijl}=\sum_{i=1}^{m}S_{i}l(i)\]  
where \(m\) is the number of points in \(S_{ijl}\). The mean value of all the Silhouette indices of all the points in a partition \(U\leftrightarrow Y\,:\,Y_{i}U\ldots\,Y_{i}U\ldots\,Y_{c}\), is called Global Silhouette and it is given by the equation:  
\[GS_{u}(i)=\frac{\sum_{j=1}^{c}Sil_{j}}{c} \tag{11}\]  
In the present work, we calculated the \(GS\) of the clustering obtained by using the median and the minimum of the similarity matrix as preference values, for comparing their quality and assessing the optimal one.
## 4 Results and Analysis
Under identical vibration intensities, i.e., \(f_{t}=f_{r}=24\) GHz,  
Figure 11: The Doppler PSDs of the proposed A2A channel model with different degrees of UAV vibrations (\(f_{c}=20\) GHz). Figure 12: The Doppler PSDs of the proposed A2A channel model with different UAV vibration angles (\(f_{c}=20\) GHz, \(f_{t}=f_{r}=24\) Hz, \(a_{tm}=a_{tm}=0.01\) m). \(a_{tm}=a_{rm}=0.005\) m, the coherence time reduces as the carrier frequency increases. Specifically, for a given threshold, e.g., \(c=0.9\), \(T_{c}=\{6.81,3.14,1.58\}\) ms for \(f_{c}=\{5,10,20\}\) GHz, respectively. Furthermore, a larger vibration amplitude or frequency results in shorter coherence times of the channel, which means the channel experiences more rapid fluctuations. As a consequence, more frequent channel estimation is necessary to keep up with the variations in the channel, especially for systems operating at high-frequency bands.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 6 Melt Electrospinning  
Recent attempts have also offered the use of the melt electrospinning technique (Figure 9a) for the production of PLA-based nanofibers with desired features (Figure 9b) [35].  
This method has been presented as an eco-friendly approach that eliminates the cytotoxic effects of solvents in the solution electrospinning for utilization of medical-grade polymers. Electrospinning of a polymer melt has recently attracted scientific attention to fabricate highly porous nano- or micro-fibrous scaffolds for biomedical applications [59; 60].  
Figure 9: (**a**) A schematic illustration of the melt electrospinning apparatus, reproduced with permission from [57] (License number: 5236501189506), (**b**) Morphology of PLA melt-electrospun nanofibers Reproduced with permission from [57] (License number: 5236501189506), (**c**) Macroscopic view of PLA tubular model produce via melt electrospinning procedure, reproduced from an open access paper [58] distributed under the terms of the Creative Commons CC BY license.  
Morphology control of PLA fibers via melt electrospinning was performed by [PERSON] et al. [60]; they reported that the average diameter of melt-electrospun PLA fibers decreased by increasing the process temperature (from 200 \({}^{\circ}\)C to 250 \({}^{\circ}\)C); moreover, it was found that the average diameter of the electrospun PLA fiber reduced with enhancing the applied voltage, while the fiber diameter increased at higher spinning distances. In another study by [PERSON] et al. [59], the influences of processing parameters (applied voltage, spinning temperature, and nozzle to collector distance) on the morphology of melt-electrospun PLA-based fibers have been evaluated. Effects of process parameters on macromolecular orientation and crystallinity of melt-electrospun PLA fibers for future biomedical studies have also been investigated by [PERSON] et al. [57].  
In a research work by [PERSON] et al., to examine the solvent-free effect on cellular activities, melt-electrospun poly(lactic acid) (PLA) fibrous structures were successfully produced with a gas-assisted melt electrospinning set-up for bone tissue regeneration; they concluded that the possible residual solvent in solvent-based electrospun scaffolds decreased cell viability and osteogenic expression; hence, melt electrospinning can be employed as a suitable process for spinning PLA nanofibers [61]. [PERSON] et al. have also applied the melt electrospinning process to prepare PLA webs with tubular structures for potential application as cardiovascular implants (Figure 9c) [58,62].
## 1 Introduction  
Weyl semimetals, which have linear intersections at the isolated momentum in three-dimensional (3D) materials, have attracted great research interests [1, 2, 3]. The linear intersection points named as Weyl points (WPs) are stable in the face of small disturbances [4, 5, 6]. Weyl semimetals exhibit various intriguing features, such as chiral anomalies and topological Femi interface states [5, 6, 7, 8, 9].  
Researcher has introduced Weyl physics into sonic crystals [10, 11, 12, 13, 14], and the related topological surface states have also been extensively explored. Topological interface states usually occur at two insulator interfaces with different topological phases. Besides acoustic topological states, the fields of electromagnetic [15, 16], plasmas [17], and elastic waves [18, 19, 20, 21] have been investigated the topological states. However, the vast majority of Weyl crystals have a real 3D structure, which are complicated in manufacturing samples and detecting signals. The development of synthetic dimensions has been used to alleviate the problems encountered in real 3D space [22, 23]. There are many ways to create synthetic dimensions, and the development of synthetic dimensions even provides a corresponding way to explore systems beyond 3D systems [24].  
Different from previous studies [13, 25, 26, 27], synthetic dimensions are used to replace purely physical dimension. By constructing two independent structural parameters in Weyl Hamiltonian with wave vector, one-dimensional (1D) phononic crystal (PC) plates are used to explore interesting Weyl physics. Different from the Dirac points, the WPs interacting with other bands, are immune to interference since the wave vector maintains a good quantum number [28, 29]. The certain topological charge of each WP can be calculated by the Chern number [30]. Although the WPs are usually investigated in real 3D structures, which can also be studied theoretically in the synthetic dimension [31].  
In this paper, synthetic WPs are observed, the transmission spectra (TS) and the reflection phases (RPs) with changing geometrical parameters are studied. The intrinsic vortex structure of the RP is confirmed, which provides a new representation for the topologically robust WPs. It is proved that the RP of the truncated one-dimensional (1D) phononic crystal (PC) plates shows the vortex structure around the synthetic WPs, which carries the same topological charge as the corresponding WPs. This study may facilitate several operational applications. For example, the interface states under strong constraints can enhance the elastic energy and thus can be combined into directional elastic antenna structures, providing a technical means for communication and detection.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 2 Historical Overview of Post-Earthquake Fire (PEF) Events
### 1985 Mexico City PEF  
On 19 September 1985, Mexico City was severely shaken by a magnitude 8.1 earthquake, lasting for approximately one minute. This event is regarded as the most devastating earthquake to have occurred in Mexico and North America in the 20 th century. A magnitude 7.5 aftershock followed the next day, intensifying the destruction. The combined shocks resulted in over 10,000 fatalities, 30,000 injuries, and more than 50,000 people left homeless. Hundreds of low- and high-rise buildings collapsed, blocking numerous streets and severely damaging water distribution systems, leading to a complete loss of water supply in many districts. Additionally, electricity networks failed, causing a severe power outage in the city center, while telephone lines were extensively damaged, disrupting communication and preventing updates about the city's condition from reaching rescue teams.  
The earthquake triggered approximately 200 PEFs, but none developed into a major conflagration. The source attributes this to the widespread use of non-combustible building materials and the absence of gas supply systems, which limited fire spread [10].
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Combination of Geographic Parameter Data with PCA-Derived Data (DGP-DPCAP)  
Several algorithms were evaluated for combining geographic parameter data with PCA-derived data. Table 7 presents the performances of these algorithms in terms of different criteria. The results show that Random Forest has the best performance, with an accuracy of 55.29%, MCC of 45.12%, and execution time of 3.59 ms. The other algorithms, Gradient Boosting, and the effectiveness of combining geographic parameters and PCA data for classification tasks, particularly with the Random Forest algorithm, are thus demonstrated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 17.88 & 50.85 & 50.10 & 4.16 & 50.10 & 50.85 & 39.59 \\ XGBoost & 5.95 & 50.85 & 50.49 & 4.05 & 50.49 & 50.85 & 39.66 \\ Random Forest & 3.59 & 55.29 & 54.82 & 4.21 & 54.82 & 55.29 & 45.12 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the performance of combining geographic parameter data with PCA-derived data.  
Figure 11: Histogram of results for each type of geological data, based on parameter sets and classifiers used.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 2 Materials and Methods
### Proposed FRA6\(\sigma\) Approach--Define Phase
#### 2.1.4 Step 4: Sigma Level and Process Capability Determination
The \(\overline{R}\)-chart combined with the \(D_{4}\) constant provides an effective tool for monitoring transformer process variation using FRA data. It ensures that any abnormal changes in variation (rather than just mean response shifts) are detected, contributing to early fault detection and preventive maintenance. The \(D_{4}\) constant is used in statistical quality control to determine the upper control limit (\(UCL_{R}\)) for range values and is expressed as follows in Equation (10). \[UCL_{R}=D_{4}\times\overline{R} \tag{10}\]  
where  
\(UCL_{R}\)--upper control limit for the range of FRA measurements  
\(D_{4}\)--3.267 (for \(n=2\))  
\(\overline{R}\)--mean of the range of baseline FRA measurements  
The \(D_{4}\) value depends on the sample size (n) as tabulated in Table 5 and since FRA baseline vs. \(X_{latest}\) measurements involve two values per test period, the sample size is \(n=2\). These FRA deviations in different frequency bands correspond to specific transformer faults. The author proposes Table 7 which summarizes the key frequency ranges, likely faults, and physical interpretations based on a review of the existing and recent related literature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Observation in \(\overline{R}\)-Chart** & **Conclusion** \\ \hline \multirow{3}{*}{Stable process} & \multirow{3}{*}{\(R<\ UCL_{R}\)} & (1) Transformer process is stable. \\  & & (2) Variations are within acceptable limits. \\  & & (3) No significant mechanical changes detected. \\  & & (1) Excessive mechanical variation or an emerging defect \\ \multirow{3}{*}{Potential faults detected} & \multirow{3}{*}{\(R>\ UCL_{R}\)} & is indicated. \\  & & (2) Possible faults include winding displacement, insulation \\ \cline{1-1}  & & failure, core movement, or clamping pressure loss. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Interpretation of the \(\overline{R}\)-Chart in FRA Analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Frequency Range (Hz)** & **Category/Region** & **Likely Transformer Faults** & **Physical Interpretation** \\ \hline
[46; 47; 48; 49; 50] & 10–1000 & Low frequency & Core deformation, core movement, core looseness, \\  & & & magnetostitraction effects \\  & & & (1) Related to overall winding \\
[51; 52; 53; 54; 55] & 1000–10,000 & Mid–low frequency & Clamping pressure loss, bulk winding movement, mechanical displacement & movement and mechanical stress. \\  & & & (2) Affected by winding shifting, clamping pressure loss, and \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Proposed FRA frequency discretization based on literature (improve phase). \begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Size (\(n\))** & **D4 Constant** \\ \hline
2 & 3.267 \\
3 & 2.574 \\
4 & 2.282 \\
5 & 2.114 \\
6 & 2.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selection of \(D_{4}\) values for sample size (\(n\)).