esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Trends of High-Voltage Porcelain Insulators Depending on the Constituents of the Porcelain
## 4 Conclusions  
An insulators must withstand rigorous stress as it is an outdoor device. Due to this, the aging cycle is hastened in insulators, which affects the mechanical and electrical characteristics of the insulators. Although an initial visual inspection is possible, almost all cases require experimental and statistical studies for a proper prediction of failure. This paper studied insulator samples that were collected from different power utilities in South Korea and had a weight of 36,000 lbs and operated at 154 kV. It was observed from load test and hardness test results that with later manufacturing years, the load at which failure occurred increased by 14.7% in average. K increased by 90.9% on average in the case of sample C compared to A. Characterizations using XRF and SEM were performed to investigate the reason for this. The XRF and SEM analysis showed there was a 8.93% and 73.17% decrease in the weight percentage of SiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) and a 16.23% increase in the weight percentage of Al\({}_{2}\)O\({}_{3}\) from sample A to C. The mechanical and electrical properties improved, as depicted by an increase in the K value of sample C. The Weibull distribution was implemented on the results to predict the probability of failure for the insulators. The goodness of fit was evaluated before the implementation of the Weibull analysis, as depicted in Figures 5 and 6. The Weibull estimates \(\alpha\) and \(\beta\) were calculated. The probability of the load at which failure occurred was observed to increase on average by 14.7% with a new trend line of the weight percentage of the porcelain body in sample C. The probability of failure was calculated for all insulators. The probability of failure decreased by 91.5% on average from sample A to C. Hence, it can be concluded from this work that the life expectancy of a porcelain insulator is dependent on the weight percentage of SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), and Al\({}_{2}\)O\({}_{3}\) of the porcelain body of an insulator where sample A was the most deteriorated and sample C was the least deteriorated sample among the three samples.  
Data curation, J.-B.K., J.-A.S., and I.-H.C.; formal analysis, S.S. and T.K.; project administration, J.-B.K., J.-A.S., and I.-H.C.; supervision, J.Y. and I.-H.C.; writing--original draft, S.S.; writing--review and editing, T.K. All authors have read and agree to the published version of the manuscript.  
This research was funded by the Korea Electric Power Corporation R16 TA29.  
The authors declare no conflict of interest.  
Figure 8: Probability of failure of samples A, B, and C based on the weight percentage of the porcelain constituents.
# Numerical Study of Wave Effect on Aircraft Water-Landing Performance
## 2 Computational Method
### Case Setup of Aircraft Water-Landing Problem  
A full-scaled amphibious aircraft is employed to investigate the wave-landing performance, the nacelle and tip float beam of this aircraft are removed due to these two parts have negligible effects on hydrodynamic and aerodynamic forces. Figure 1b shows the Cartesian grid of the numerical model as well as the cross-section grid around the flap and step in the forward and spanwise directions. Only half of the physical domain is numerically simulated due to the symmetry of the aircraft configuration with respect to the \(xz\)-plane. Therefore, three degrees of freedom of the aircraft are released during the simulation, including the translation of the horizontal (\(x\)) and vertical (\(z\)) directions and  
Figure 1: Computational case setup: (**a**) boundary condition; (**b**) simulation grid.  
the rotation of the spanwise (\(y\)) direction. This half model is 36 m in the \(x\) direction, 18 m in the \(y\) direction and 49,000 kg in weight. The computational domain is about 5 times of half aircraft model size. The total cell of the baseline numerical model is 18.4 million for calm water and wave landing simulations. Three refined grid regions along the \(x\) direction, namely, front, aircraft and wake refinement, are defined to accurately capture the flow features and dynamic of free-surface. Over 14 million cells are used in the aircraft refinement region combing with 15 grid layers in the viscous boundary area.  
As shown in Figure 1a, the flow field of a three-dimensional rectangular computational domain is divided into an air zone and a water zone, denoted in the VOF function by 0 and 1, respectively. At the inlet boundary of the computational domain, a velocity-inlet wavemaker is established to specify the wave elevation and the horizontal and vertical water particle velocities, as presented in Section 2.2. At the outlet boundary, the pressure outlet boundary condition with hydrostatic pressure and zero wave elevation is applied. The free-slip condition is specified at the sidewalls and top face of the computational domain. The wall condition is adopted at the bottom surface to simulated the finite-depth water tank.  
Figure 2 shows the initial flow field of calm water and wave surface landing simulations. The surface tension at the air-water interface is assumed to be negligible. Initial hydrostatic pressure is assigned in the water zone, and the reference pressure is initially imposed on the entire computational domain. The initial landing speed \(V_{x}=-40\) m/s and \(V_{z}=-4\) m/s with pitch angle \(\alpha=6\) degree is selected. The wave height \(H=2\) m, wave length \(L=40\) m and wave period \(T=5.06\) s are employed to study the wave landing performance. With these numerical setup parameters, we simulated aircraft landing into calm water and wave field with a time step size of \(2\times 10^{-4}\) s.
## 7 RF-EH Circuit
### Storage Element
#### 7.5.2 Batteries  
Besides supercapacitors, batteries can be used as an alternative option to store energy or supply rechargeable energy in energy harvesting circuits. A variety of batteries are currently available, including Lithium-Ion (Li-ion), Lithium (Li), Sealed Lead Acid (SLA), Nickel Metal Hydride (NiMH), Nickel Cadmium (NiCd), and Nickel Metal Hydride (NiMH). Li and lithium alloy batteries have the edge over other batteries due to their high efficiency. The same battery model could be configured as a series connection of an internal resistance and an ideal voltage source [164]. As with supercapacitors, batteries are treated as part of the load in the energy harvesting circuit. Their charging efficiency varies as a function of the amount of current applied to the batteries during charging. In order to achieve maximum battery charging efficiency, it was necessary to identify the optimal charging current [165].  
Contrary to supercapacitors and conventional capacitors, batteries have the advantage of exhibiting higher energy density. Nevertheless, the downside of batteries is their low power density and low lifespan compared to supercapacitors. A novel energy storage device, known as the supercapacitors, was recently introduced. The electrode in supercapacitors combines the most effective components in supercapacitors and batteries, carbon nanotubes, and redox materials [166].
## 2 Multi-Dimensional User Importance Indicator System and Ranking Model
### Brief Introduction to the Proposed User Importance Indicators
#### 2.1.1 Direct Economic Loss of Users \(C_{\text{Y1}}\)  
The economic benefit created by unit electricity in a given area and year is measured by the electricity production ratio. Thus, the direct economic loss for users can be calculated by multiplying the amount of lost electricity during the outage period by the electricity production ratio of the users.  
Using the start and end times of the outage and the average daily load curve of the user, the amount of lost electricity during an outage can be determined. Multiplying this loss by the electricity production ratio of the user's industry provides the direct economic loss during the outage period as follows:  
\[C_{\text{Y1}}=Q_{\text{loss}}.\frac{F_{\text{total}}}{Q_{\text{total}}} \tag{1}\]  
\[Q_{\text{loss}}=\int_{t_{\text{stm}}}^{t_{\text{end}}}w\text{d}t \tag{2}\]  
where \(C_{\text{Y1}}\) is the direct economic loss of the user (in CNY 10,000), \(Q_{\text{loss}}\) is the electricity lost during the outage (in 10,000 kW h), \(F_{\text{total}}\) is the gross product of the industry of the region (in CNY 10,000), \(Q_{\text{total}}\) is the total electricity consumption of the industry in the region (in 10,000 kWh), \(t_{\text{st}}\) is the start time of the outage, \(t_{\text{end}}\) is the end time of the outage, and w is the load value of the customer's typical daily load profile at a given point in time (in kW).  
Figure 1: Multi-dimensional user importance indicator system.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 4 Numerical Results and Discussions
### Frequency Analysis  
The effect of train speed on the frequency component of ground response is very important to understand. For this reason, time history response and frequency spectrum of the ground acceleration at the center of track for selected cases given in Table 5 are studied. Time history and frequency spectrum of ground acceleration from case 01 (train moving only on the right track) are shown in Figure 10. In case of a train speed of 30 m/s the sharp peaks appear at 2.9 Hz and 11.05 Hz (Figure 10b), which are in close agreement with the results computed using the numerical model proposed by [PERSON], [PERSON], et al. [36]. The given numerical model shows that the peaks at certain frequencies in the ground response spectrum are the function of train geometry (distance between loads or position of wheels) and speed of the train. Similarly, at the train speed of 60 m/s dominant peaks of ground acceleration appear at 5.7 Hz and 22 Hz, as shown in Figure 10d.  
Figure 7: Maximum vertical track response at the center of the track at different speeds (train moving only on right track): (**a**) left track, (**b**) right track.  
Figure 8: Vertical track response at the center of the track at different speeds (trains on both tracks moving in the opposite direction): (**a**–**d**) left track, (**e**–**h**) right track.  
that for the ground response at the train speed of 30 m/s a group of peaks appear near the frequencies that are deemed to be induced from the geometry of train against the train speed. Moreover, at the train speed of 60 m/s not only various peaks at low frequencies but also significant peaks at high frequencies are clearly visible in the frequency spectrum (Figure 11d). A significant increase in the number of peaks at different frequencies in frequency spectrum when trains are traveling on both tracks can be attributed greatly to the fact that vibration radiating from one track due to moving train significantly affect the nearby track.
## 2 Materials and Methods
### Assessment of Segmentation Accuracy  
Multiple methods exist to assess the accuracy of segmentation. segmentation errors can be identified through visual inspection, but it is time consuming, especially when assessing large areas and comparing numerous segmentation outputs. Additionally, visual interpretation is subjective, as the results produced by the same or different operators may not be reproducible. On the other hand, several quantitative methods for assessing the quality of image segmentation exist and grouped into two main categories: supervised and unsupervised methods [22]. Supervised methods essentially compare segmentation output to a reference dataset and measure the similarity or discrepancy between the two representations. Ideally, there should be a one-to-one correspondence between the reference objects and segments. In turn, supervised methods can be divided into two  
Figure 7: Density of regions obtained by segmenting the PlanetScope NDVI multitemporal dataset with two different combinations of scale level and merge level: in panel (**a**), the two parameters were set respectively to 10% and 97.5%; in panel (**b**), they were set to 40% and 97.5%.  
main categories: geometric and non-geometric. Geometric methods rely on quantitative metrics that describe geometric and topologic similarities between reference objects and segments [23]. Non-geometric methods essentially leverage the similarity in the content of the objects, considering, for instance, their spectral values [24] or topographic parameters (e.g., slopes, curvatures) [25]. Finally, unsupervised methods measure some desirable properties of the segmentation outputs (e.g., an object's spectral homogeneity) without comparing them with a reference, thus measuring their quality [26].  
In the present study, a supervised geometric method was selected to assess the accuracy of the four segmented parcel maps derived from the use of four different scale-level thresholds. It involved the following steps:  
1. Sampling of 100 reference parcels (ground truth) from the cadastral dataset provided by the Italian Tax Agency (AE).
2. Selection of 100 corresponding segmented parcels from each of the segmented parcel sets.
3. Definition, at the local level, of geometric metrics to compare reference and segmented parcels.
4. Definition, at the global level, of the accuracy of each segmented parcel set.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 3 Resource Profiling and Performance Modeling
### Resource Profiling and Prediction Models
#### 3.2.2 Moving Average (MV)  
Moving average (MV) is widely used especially when data is highly variable. Profiling information close to the recent \(n\) points are typically more relevant than data further away from the latest because computing resources in a distributed scientific computing system dynamically changes over some periods of time possibly due to addition of new resources, removal of old resources or even failures.  
Moving average follows the recent trend of the data, not an actual average of the data and it is useful for smoothing out the noisy raw data. Suppose the sequence of data \(X=(x_{1},x_{2},\ldots,x_{n})\), a _w-moving average_ (\(w\) interval) is defined from \(X_{i}\) by taking the arithmetic mean of sub sequences of \(w\) terms (as we can see from Equation (12)).  
\[MV_{w+i}=\frac{1}{w}\sum_{j=i}^{w+i-1}X_{j}\;\textit{where}\;i=(1,2,\ldots,n-w +1) \tag{12}\]  
For instance, if our window size is 3, then on day 4, the moving average is generated by taking the simple average of the preceding three days' available CPU cores for that specific CE, and the procedure is repeated. If we have six days of data (201, 202, 230, 213, 180, and 190), then the \(MV\) on day 4 will be 211, on day 5 will be 215 and so on.
## 5 Results and Discussion
### Discussion: Future Model Improvements  
There are some deficiencies in predicting the ultimate axial capacity \(N_{u}\) of square CFST short columns through the proposed PSVM model, which need to be resolved in future research. (1) The amount of data used to establish the PSVM model is small, with only 180 groups of experimental data. Although the test accuracy of the trained model is over 90%, the generalizability and robustness of the model based on a small amount of data are insufficient. More high-quality experimental data needs to be collected to improve the overall performance of PSVM. (2) Some other properties that affect the ultimate axial capacity of square CFST short columns are not treated as input variables, such as steel grade, concrete age, and concrete pouring method. When many input variables are considered, feature selection is also required. (3) Four supervised learning methods (DT, GP, MLR, and SVM) and a metaheuristic optimization algorithm (PSO) were used for nonlinear regression modeling in this paper. Other advanced MLAs can be introduced to predict the ultimate axial capacity, such as multilayer perceptron [65; 66] and random forest [67; 68; 69]. Some new optimization algorithms can also be used to improve model performance while reducing the operation time. The combined model is the development trend of the regression prediction method. (4) The proposed PSVM model is essentially a  
Figure 9: Sensitivity analysis results of seven input variables on the output \(N_{u}\).  
black box. In other words, the complex relationship between input and output variables is difficult to explain. The interpretability of the model needs to be studied urgently. Furthermore, feature importance analysis is also imperative before the model is applied to practical engineering, which makes it easy to adjust the geometric, steel, and concrete properties of square CFST short columns.
## 4 Experimental Evaluation
### Battery Reliability  
The battery reliability has been defined through an experiment using actual motes (nodes). The relationship between the reliability and the battery level is determined by averaging errors occurring in a given voltage. To adjust the voltage, a source of DC voltage was used. It is worth observing that the decrease of the battery over time was not considered in this experiment, because voltage does not decrease along the time like battery. It is necessary to change the voltage manually using the source of DC voltage. Additionally, this experiment used the IRIS mote, which works properly between 3.3 V and 2.7 V (MEMSIC, 2014) [31], _i.e._, it is able to send a packet without failures.  
This experiment used a sensor node to periodically send messages to a desktop that detects whether an error occurred in the sensor node or not. An application was developed and deployed in the mote to collect the temperature and send it to the sink node, which forwards it to the desktop. The communication between the sensor node and the sink node is direct and the distance is short enough to avoid interference. Any error in the packet is only due to the sensor node.  
The aforementioned application was evaluated with different battery levels, starting from 3.3 V (battery level equal to 100%) to 2.7 V (representing 81.81% of the battery level). It is worth noting that these levels are informed by the IRIS manufacturer for the proper functioning of the mote. Additionally, the voltage was also adjusted from 2.7 V until the sensor node stops working. It was observed that the sensor node also works correctly from 2.7 V (81.82% of battery level) to 1.78 V (53.94% of battery level), _i.e._, the sensor node did not fail in this voltage interval.  
With these initial results, we created a strategy to define battery reliability using proportion, because it is interesting to work with the possibility of failure due to the battery level. For that reason, the reliability of the battery is 1.0 when the battery level ranges from 100% (3.3 V) to 81.81% (2.7 V) and set to 0.0 when the battery level is below 53.94% (1.78 V). When the battery level ranges from 81.81% to 53.94%, we will use a proportion of the battery reliability ( \(\dfrac{R}{1}=\dfrac{Level-Min}{Max-Min}\)).  
Figure 17: General overview of the three scenarios.  
Thus, the battery reliability is configured according to Equation (9):  
\[R_{battery}(Level)=\left\{\begin{array}{ll}1&\mbox{$if\ Level\ ranges\ from\ 100\%\ to\ 81.81\%$}\\ \\ \frac{Level-Min}{Max-Min}&\mbox{$if\ Level\ ranges\ from\ 81.81\%\ to\ 53.94\%$}\\ \\ 0&\mbox{$if\ Level\ is\ below\ 53.94\%$}\end{array}\right. \tag{9}\]  
where \(Max\) and \(Min\) are equal to 0.8182 (81.82% of battery level) and 0.5394 (53.94%), respectively; and _Level_ is a value of the battery level. For example, if the battery level is equal to 70% (0.70), its reliability is 0.57. It is worth observing that, as shown in this equation, the reliability of the battery itself is not influenced by any other component of the sensor node, e.g., radio, hardware, application, middleware and operating system.
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 6 Discussion  
In this study, deep learning is applied to the field of linear block code scrambling-type identification. A linear block code scrambling-type identification method based on correlation feature extraction and a correlation feature extraction network model is proposed.  
Figure 16: Influence of number of samples on the recognition rate under the Condition of Gaussian White Noise.  
Figure 17: Influence of generator polynomial of scrambling code on the recognition rate.  
First, the cross-correlation of the scrambled sequence symbols is deduced according to the scrambling principle, and then a biased autocorrelation function that reflects the correlation is further constructed. To effectively extract the relevant features of the sequence, a correlation feature extraction network model is constructed, and the correlation and autocorrelation features are used as model input for training, which finally achieves the purpose of identifying the type of linear block code scrambling. The simulation results show that, compared with the traditional feature extraction method, the algorithm proposed in this study has a better recognition rate under the BER, and it can recognize the linear block code scrambling-type under the conditions of BER and noise. The identification of scrambling code parameters lays a foundation for future scrambler parameter identification and has significant practical engineering value. Future research will focus on classification recognition with convolutional code scrambling.  
Conceptualization, J.T. and L.Z.; methodology, J.T.; software, Z.Z.; validation, Z.Z., J.T. and L.Z.; formal analysis, Z.Z.; investigation, L.Z.; resources, L.Z.; data curation, Z.Z.; writing--original draft preparation, J.T.; writing--review and editing, Z.Z.; visualization, J.T.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (91538201), the Taishan Scholar Special Foundation (ts201511020), and the Chinese National Key Laboratory of Science and Technology on Information System Security (6142111190404).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data is contained within this article.  
We thank LetPub (www.letpub.com, accessed on 27 June 2022) for its linguistic assistance during the preparation of this manuscript.  
The authors declare no conflict of interest.