esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Comparison with PFMI Experiments  
To have a better understanding of the influence of the spurious current in the simulation, the pore formation and mobility investigation (PFMI) experiment performed at the International Space Station is presented here. In the experiment, 10 mm width quartz tubes of 30 cm length were filled with SCN-0.24 wt% H\({}_{2}\)0 alloy, and bubbles of nitrogen were injected into the sample to ensure that porosity would be present during the microgravity experiments. During the solidification, a clockwise circulation of small bubbles was observed in front of the tip of dendrites as seen in Figure 12[61].  
Since the experiment took place in the space station, the only possible explanation for fluid flow is Marangoni convection. The average bubble velocity due to Marangoni convection in the experiment was in the order of 0.2 mm/s. In enhanced Shan-Chen simulation, the magnitude of spurious current for the density ratio of 40 was around 3.5 mm/s. This implies that the Shan-Chen models generate a high spurious current, which makes them useless to simulate Marangoni convection. Therefore, a phase field model should be utilized for this situation.  
However, the Shan-Chen model could successfully simulate elongated porosity based on the in situ X-ray tomography result of Ref. [62]. The experiment refers to directional columnar dendrites of Al-30 wt%Cu alloy contained between two plates as shown in Figure 13. The dendrites were growing downward at an angle of about 60\({}^{\circ}\) from the horizontal. The contrast of the image was proportional to the atomic number of the elements (i.e., low Cu concentration appear white in the image). The eutectic line was a nearly straight thin line, shown at about the middle of the Figure 13 images. During the solidification, the shape of the left bottom bubble changed from circular to worm-shape as it can be seen in Figure 13 from the left to right images. The elongated shape can be seen for both left and right bubbles. Since the model parameters were not the same as the experiment, it is hard to compare the results directly. However, the Shan-Chen models can predict that the shape of the bubble changes from a circular to an elongated elliptical shape due to adhesive force between the bubbles and dendrite.  
Figure 11: Velocity magnitude along the centerline in the width and height direction (**a**) X*=1/2 Shan-Chen model, (**b**) Y*=1/2 Shan-Chen model, (**c**) X*=1/2 phase field, (**d**) Y*=1/2 phase filed, at t = 0.0013 s.  
In conclusion, the Shan-Chen models were capable of modeling phenomena observed in experiments, such as the narrow and long worm-like bubbles in the interdendritic regions. However, they were unable to simulate phenomena that produce characteristic velocity in the order of 1 mm/s or lower since the spurious current masks the physical flow.
## Case Study  
### System Configuration  
The experimental microgrid is shown in Figure 1. Parameters of various microsources [31] are listed in Table 1. The WT and PV generation forecast data are obtained from a generation site in Shanghai as shown in Figure 2. The load forecast data are obtained from a residential building in Yangpu district, which is shown in Figure 3. Since the load prediction accuracy is up to 95% [32], this paper assumes that the actual load value is equal to the predictive value in order to simplify the model. The forecast errors of wind power and PV output power are assumed to be independent normal distribution with 35% standard deviation. The ESS data are scaled from a lead-acid battery system project in China [33] as shown in Table 2. The environmental parameters of DE and main grid are listed in Table 3 [34]. The time-of-use (TOU) electricity price of Shanghai, China is adopted and shown in Figure 4 [31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **DE** & **WT** & **PV** \\ \hline Lower limit (kW) & 12 & 0 & 0 \\ Upper limit (kW) & 120 & 60 & 60 \\ Ramp-up/ramp-down limit (kW/h) & 60 & - & - \\ Quadratic coefficient (yuan/kW\({}^{2}\)h) & 0.0029 & - & - \\ Monomial coefficient (yuan/kWh) & 0.1258 & - & - \\ Constant coefficient (yuan/h) & 2.72 & - & - \\ Operation and maintenance cost (yuan/kWh) & 0.04 & 0.07 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of various microsources.  
Figure 3: Forecast values of load.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Numerical Value** \\ \hline Battery capacity (kWh) & 100 \\ Maximum charging/ discharging power (kW) & 50 \\ Charging/discharging efficiency & 0.9 \\ Maximum SOC & 0.95 \\ Minimum SOC & 0.25 \\ Initial SOC & 0.5 \\ Final SOC & 0.5 \\ Operation and maintenance cost (yuan/kWh) & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of ESS.  
Figure 2: Forecast values of wind and photovoltaic power.
# Investigation of a Gravel-Bed River's Pattern Changes: Insights from Satellite Images
## 2 Materials and Methods
### Hydrological Data  
As described in the literature [8,46] the Tagliamento River is influenced by Alpine snows and precipitation regime with high peaks especially in spring and in autumn. In the present study the Villuzza gauging station, located at Pinzano gorge, that is around 2 km downstream of the examined river reach and with continuous records in the examined time period, has been considered. Only the recorded water depths are available. In fact, the definition of a stage discharge relationship in the Tagliamento river is rather difficult because of the absence of stable cross-sections. At Pioverno gauging station (basin area 1880 Kmq), in the period 1932-1973, the maximum and the mean discharge were estimated respectively of 81 m\({}^{3}\)s\({}^{-1}\) and of 4050 m\({}^{3}\)s\({}^{-1}\)[34]. [PERSON] and [PERSON] [46] estimated a 10-year flood of 2150 m\({}^{3}\)s\({}^{-1}\).  
Based on the aforementioned, the water depths registered the Villuzza gauging station in the time period between January 2002 and September 2012 have been considered for the analysis. From Figure 3a it is clear that frequent peaks ranging from 100 cm to 200 cm and sporadic peaks over 200 cm occur. This trend has suggested to assume for the analysis a low threshold value of the water depths equal to 100 cm. This is in line with previous [PERSON] et al.' [32] findings highlighting that the water level starts to attack bar surfaces at a stage of around 130 cm and a complete gravel islands inundation occurs at around 200 cm. Figure 3b reports the estimated values of the frequency of occurrence of water depths in the range 100 cm \(<h<\) 200 cm and of the frequency of occurrence of water depths higher than 200 cm during the examined time period. For both cases it is clear that during the year 2002 the frequency of occurrence is around of 20%, in the time period 2003-2007, the frequency of occurrence is very low; it suddenly increases in the year 2008 maintaining high values in the time period 2009-2012.  
Figure 3: (**a**) recorded data during time period 2002–2012 at Villuzza gauging station; (**b**) frequency of occurrence of events with water depth 100 cm \(<h<\) 200 cm and of events with water depth \(h>\) 200 cm.  
Thus, it could be summarized that, while the time period 2003-2007 is characterized by relatively frequent low-magnitude events with sporadic peaks (as an example the events occurring in August 2003, in November 2003 and in October 2004, as Figure 3a shows), the time period 2008-2012 is characterized by frequent high-magnitude events.
## 4 Experimental Results and Analysis  
The data of four real networks are used to analyze and verify the accuracy of the DDMF method. The Node2 vec algorithm [43] is a network-embedded learning algorithm that cannot be directly used to calculate the relative importance scores of nodes. Therefore, the NMF index is obtained on the basis of the improvement of the Node2 vec algorithm. The basic idea of the NMF index is as follows: first, the Node2 vec algorithm is adopted to generate the embedded vector of the network. Second, the multi-index fusion is made for the obtained vectors so as to calculate the relative importance scores of the nodes. The multi-index fusion method of the NMF index is consistent with proposed DDMF method.  
The comparative algorithms included the Ksmar index, PPR index, Katz index, NLD algorithm, and NMF index obtained on the basis of the Node2 vec algorithm improvement.
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Knowledge Representation
#### 2.2.2 Physical Knowledge Representation
This process also updates the state to \(h_{t}\). The entire process of updating the wave equation can be described as follows:  
\[ \begin{split}& h_{t}=\mathbf{A}h_{t-1}+P^{(i)}s_{t}\\ &\left[\mathbf{K}_{pt},\mathbf{K}_{st}\right]^{\mathsf{T}}=P^{( \mathbf{o})}h_{t}\\ &\mathbf{K}_{p}=\left[\mathbf{K}_{p0},\mathbf{K}_{p1},..., \mathbf{K}_{pT}\right]^{\mathsf{T}}\\ &\mathbf{K}_{s}=\left[\mathbf{K}_{s0},\mathbf{K}_{s1},..., \mathbf{K}_{sT}\right]^{\mathsf{T}}\end{split}  \tag{8}\]  
The symbols \(\mathbf{P}^{(i)}\) and \(\mathbf{P}^{(o)}\) represent the linear operations that sample a specific row or column of the variable. \(\mathbf{A}\) represents the operator matrix of the partial differential equations. \(\mathbf{K}_{p}\) denotes the knowledge representation of P-waves and \(\mathbf{K}_{s}\) denotes the knowledge representation of S-waves.
## 3 Proposed Concept  
### Design Considerations  
The following requirements are essential for producing a system for real-life ADL.  
* **User identification:** If a patient does not live alone, the measured activities in the house may be caused by other family members. Therefore, to collect the ADL of a particular user, it is necessary to identify whose ADL were measured. In this study, each wearable device worn by a user includes a unique ID, which is shared with adjacent devices as the user moves between indoor unit spaces. Consequently, the collected data in some areas can be assumed to represent activities performed by the user with that ID;
* **Unit space-based location awareness:** It is necessary to distinguish where the measured ADL occurred. In this study, using the wireless communication between the wearable device worn by a user and a stationary device installed in each unit space, it is possible to track the user's indoor location in real-time and determine where the measurement of ADL occurs;
* **Abnormal activity awareness:** The ADL measurement system should be able to determine and record whether the user's activities were finished successfully or abnormally and alert the user of abnormal or dangerous situations;
* **Privacy protection:** The data generated at home are mostly personal, so users are not likely to want to expose such data. Therefore, each device in the system is required to protect user privacy by processing collected user activity and ADL data appropriately before transmitting a message to other devices;
* **Low-power consumption:** The wearable and tag devices of the proposed system operate on batteries, so it is essential to consider a low-power implementation. We suggest how these devices can minimize power consumption and wake up wirelessly from a standby state.
## 5 Conclusions  
In conclusion, 3D DIC is a relatively new technique, that is quickly becoming a very valuable tool as a non-invasive method of measuring three-dimensional deflection, velocity, acceleration, strain and strain rate in materials. With several companies producing commercial software packages for the processing of sets of high-speed imagery, it is becoming even easier to implement and use 3D DIC for the characterization of material response in both ballistic and blast events. This technique has even been extended into the X-ray spectrum, allowing one to measure the internal deformation and strain. The following is a list of the observed benefits of using DIC in dynamic experiments:  
1. It is non-invasive and therefore a preferred method when 'in-material' gauges could affect the result of the test.
2. It provides full field measurements of strain and deflection, whereas 'in-material' gauges only provide point measurements.
3. By utilizing different spectrums of non-visible light, 3D DIC can be performed in regimes where heat or particulate matter may obscure or distort the image (X-ray imaging), or regions where an 'in-material' gauge would be destroyed (UV imaging), for example high temperature applications.
4. By utilizing X-ray imaging, 3D DIC can be performed on internal surfaces or inside of objects where it would be very difficult or impossible to wire up 'in-material' gauges without changing the structure.  
As in so many areas of engineering and science, our understanding of the behaviour of armour materials under impact loading conditions has been enhanced by analytical models and finite element simulations, coupled with laboratory testing techniques that probe the high strain-rate response. In particular, the DIC technique has demonstrated that it is an invaluable approach to couple with finite element techniques to provide hi-fidelity data, to validate numerical simulations.  
Conceptualisation, C.L.E.; review and summarising of papers, C.L.E.; writing--original draft preparation, C.L.E.; writing--review and editing, C.L.E., P.H. All authors have read and agreed to the published version of the manuscript. This research has been funded by Integrated Personnel Protection STC, Land Division, Defence Science and Technology Group, Australia, research agreement ID9028. The authors declare no conflict of interest.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Traction Power Supply Systems
However, it has the following distinct features:  
(1) The _general_ rule neuron is denoted by \(R_{(c,general)}\) and after application of the firing rule, the newly obtained spike has the potential value \(\beta=\theta*\omega*c\). (2) The _and_ rule neuron is represented by \(R_{(c,and)}\) and the newly obtained spike has the potential value \(\beta=[(\theta_{1}*\omega_{1}+\ldots+\theta_{k}*\omega_{k})/(\omega_{1}+\ldots +\omega_{k})]*c\), where the neuron is connected with \(k\) antecedent neurons. (3) Similarly, \(R_{(c,or)}\) represents the \(or\) rule neuron and the potential value of the newly obtained spike is \(\beta=max\{\theta_{1}*\omega_{1},\ldots,\theta_{k}*\omega_{k}\}*c\). Next the fuzzy production rules are modeled in the framework of WFRSNPs models. The fuzzy production rules/ fault diagnosis production rules in [88] are divided into four types. These rules have the following forms:  
Type 1 (Simple Rules) \(R_{i}:\) IF \(p_{i}(\theta_{i})\) THEN \(p_{k}(\theta_{k})(CF=c_{i})\),  
Type 2 (Compound _and_ Rules) \(R_{i}:\) IF \(p_{1}(\theta_{1})\) and \(\ldots\) and \(p_{k-1}(\theta_{k-1})\) THEN \(p_{k}(\theta_{k})(CF=c_{i})\),  
Type 3 (Compound _or_ Rules) \(R_{i}:\) IF \(p_{1}(\theta_{1})\) or \(\ldots\)\(p_{k-1}(\theta_{k-1})\) THEN \(p_{k}(\theta_{k})(CF=c_{i})\),  
Type 4 (Conditional _and_ Rules) \(R_{i}:\) WHEN \(p_{0}(\theta_{0})\) is true, IF \(p_{1}(\theta_{1})\) and \(\ldots\) and \(p_{k-1}(\theta_{k})\) THEN \(p_{k}(\theta_{k})(CF=c_{i})\). The corresponding fuzzy production rules in the framework of WFRSNPs models can be referred to [88]. WFRSNPs model is similar to FRSNPS with one exception. In case of FRSNPS, no weight was associated with the synapses connecting the neurons. However, in WFRSNPs, weights are associated with the synapses connecting the neurons. Moreover, the proposed model can express status information of protective relays and circuit breakers efficiently. Next, the reasoning algorithm based on WFRSNPs is performed in order to identify the faults. The reasoning algorithm is called as weighted matrix-based reasoning algorithm (WMBRA). The synaptic connections are also represented by weighted matrix. Unlike in [89], the entries of the truth value vectors are real numbers and using the values received by the input proposition neurons and the truth values of these neurons, the truth values of other proposition neurons without any truth values associated with them are obtained.
## 5 Evaluation and Control of Blasting Vibration Hazards in Tunnel  
There are two primary forms of structural damage under blasting vibration: first passage damage and cumulative damage. According to the above analysis, the hysteretic energy consumption can be used as an index parameter to reflect the cumulative damage, and the instantaneous input energy of the structure can be used for the first passage failure [32; 33; 34; 35; 36]. However, the instantaneous input energy cannot accurately describe the cumulative damage, and the hysteretic energy consumption cannot accurately describe the first passage damage [37; 38]. Therefore, it is more scientifically rigorous and comprehensive to use these two energies to evaluate vibration hazards. This paper presents an input-hysteresis energy criterion to determine whether the structure is damaged. To verify the validity and rationality of the input-hysteretic energy criterion, 10 sets of data at each position of the tunnel vault, arch waist, and sidewall are used to calculate energy responses, which are shown in Tables 2-4.  
Tables 2-4 show that the maximum instantaneous input energy of measuring point No. 7 of the vault is large, but its hysteretic energy consumption is small, as is that of measuring points No. 5 of the arch waist and No. 2 of the sidewall. The measuring points with damage have a common feature: the maximum instantaneous input energy is greater than 200 J, or its hysteretic energy is greater than 3000 J. Therefore, the initial lining of the tunnel causes the first passage damage when the maximum instantaneous input energy exceeds 200 J; once the hysteretic energy consumption exceeds 3000 J, plastic cumulative damage occurs. The input-hysteresis energy criterion explains the phenomenon that the destroyed structure near the blasting centre is easily destroyed, but it is safe according to Chinese safety regulations for blasting vibration. It can evidently supplement and improve  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Natural Period (s)** & \multicolumn{2}{c}{**Maximum Instantaneous Input**} & \multicolumn{2}{c}{**Hysteretic Energy**} \\  & & **Energy (J)** & **Consumption (J)** & **Initial Lining State** \\ \hline
1 & 0.1428 & 226.99 & 2245.44 & cracked \\
2 & 0.1462 & 173.57 & 1854.35 & intact \\
3 & 0.1394 & 127.73 & 1355.32 & intact \\
4 & 0.1428 & 210.3 & 2678.21 & intact \\
5 & 0.1378 & 153.84 & 1677.93 & intact \\
6 & 0.1394 & 154.61 & 1768.86 & intact \\
7 & **0.1445** & **193.06** & **1459.19** & **intact** \\
8 & 0.1512 & 150.05 & 1679.37 & intact \\
9 & 0.1478 & 157.22 & 1712.21 & intact \\
10 & 0.1378 & 169.17 & 3173.12 & damaged \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy response at the vault.  
Figure 26: Influence of the delay time on the hysteretic energy consumption.  
the current safety criterion. However, both the maximum instantaneous energy threshold and the hysteresis energy threshold must be studied in more detail and applied in more engineering applications to verify these results.
## 3 Results and Discussion
### Field Survey Results and Analysis
#### 3.2.3 Satisfaction-Comfort Matrix Results and Analysis  
Figure 16 shows the results of the Satisfaction-Comfort Matrix in the overall 14 target test points. The horizontal axis of the matrix corresponds to the level of each comfort (thermal, lighting, ventilation, and air quality) with the physical environment inside of the tubular space. The data was calculated into a comfort percentage obtained from the test results, and divided into six classes from 0 to 100%. The vertical axis of the matrix corresponds to the subjective analysis of occupancy space satisfaction, following with the \(-3\)-\(3\) score scale outlined in the above research method.  
The Satisfaction-Comfort Matrix is divided into four quadrants. The results located in the first quadrant indicate that the animate space has a positive effect in terms of both satisfaction and comfort. The results located in the second quadrant mean it has a positive effect in terms of satisfaction but a negative effect in terms of comfort. The results located in the third quadrant imply neither satisfaction nor comfort. The results located in the fourth quadrant mean it has a negative effect in terms of  
Figure 16: Test Point Satisfaction-Comfort Matrix results. (**a**) Middle tubular space; (**b**) Tube entrance (connection to building); (**c**) Tube entrance (connection to subway); (**d**) In subway station.  
satisfaction but a possible positive effect in terms of comfort [39]. Each quadrant's evaluation is divided into four grades (as shown in Figure 16).
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
###### Abstract
For the reasons given above, in this paper, a method of improved complementary ensemble empirical mode decomposition (ICEEMD) is proposed for the first time. In terms of feature extraction, time-frequency information entropy is utilized to reveal the complexity hidden in signals and can effectively reflect fault information of time-frequency characteristics. Thus, time-frequency information entropy is regarded as a feature characteristic parameter and is applied to realize the robust feature extraction [13]. For mechanical fault diagnosis, different fault classification methods directly affect the efficiency and accuracy of fault recognition. The algorithm of variable predictive model-based class discriminate (VPMCD) makes use of the inherent correlation of sample eigenvalues to establish a feature learning model [14], which can be effectively applied to multi-classification mechanical fault diagnosis with small samples [15]. In addition, the VPMCD algorithm avoids the problem of over-fitting of neural network classification and kernel function selection of SVM (Support Vector Machine) classification. At the same time, the computing time is reduced and the robustness of the algorithm is improved [16]. In the study presented in this paper, a fault diagnosis method of planetary gearboxes based on ICEEMD-time-frequency information entropy and VPMCD is first proposed. First, vibration signals of planetary gearboxes are collected by the accelerometer. Second, several IMFs are obtained by using the method of ICEEMD. The method proposed can eliminate noise on the basis of each IMF, which avoids the disadvantages of loss of feature information caused by overall noise reduction. Afterwards, each IMF is calculated by time-frequency information entropy as feature vectors. Then, by the method of principal component analysis (PCA), the dimension of feature vectors is reduced. In order to verify the effectiveness of the proposed method, the diagnostic performance of other two methods under various working conditions is also investigated in fault feature extraction. Compared with the other two methods, the ICEEMD-time-frequency information entropy method proposed in this paper has an advantage for feature extraction and allows the fault features to be effectively distinguished in the feature space. Finally, the VPMCD model is utilized to classify different fault modes. This paper is organized as follows: Section 2 introduces the relevant feature extraction methodology, which includes the ICEEMD, time-frequency information entropy, and principal component analysis (PCA); Section 3 presents the principle of classification about VPMCD. In Section 4, the scheme of the planetary gearbox based on ICEEMD-time-frequency information entropy and VPMCD are described. Section 5 describes the case study to validate the entire method; Conclusions are drawn in Section 6.