esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Target Component  
The converter failure analysis was performed following the recommendations of VWF operating technicians. A review of the maintenance records and SCADA alarms confirmed that the converter has the highest failure rate at 89.3% (see Figure 2a). This percentage differs from the 23% mentioned in [14] for converter failures, since the cited article includes failures of other components of the WT, such as the generator, gearbox, pitch system, among others. In our case, the WT is relatively new, so no failures in these other components have been recorded, resulting in a higher percentage of failures attributed to the converter.  
Additionally, SCADA system alarms on the converter account for 86.4% of the total, significantly outnumbering those on the pitch system (see Figure 2b). In this study, only the converter and pitch components were analyzed because both required replacement as critical parts of the WT.  
In addition, it is worth noting the conceptual difference between failures and alarms. A failure involves the shutdown of the WT, and generally requires the replacement of the affected component, while an alarm may cause a temporary shutdown of the wind turbine, but does not necessarily lead to the replacement of the component. Failures can stop production and cause significant losses. It is thus essential to predict when a failure will occur, allowing preventive measures to be taken to minimize the impact on production.
## 2 The Basis of Derivation
### Joint Strength of Loess  
The loess area is generally a semi-arid area in the middle temperate zone. The groundwater level is deep, the water content of the loess is low, and the liquid index of the natural loess is generally 0.19 [29]. Natural loess is in a hard plastic state, with high hardness and high strength. In the process of tunnel excavation, the surrounding rock of the loess shows a certain degree of self-stability. After tunnel excavation, the stress of the surrounding rock is redistributed. In the tensile shear-stress zone, the tensile stress and shear stress of a loess with high strength do not exceed the limit.  
The M-C strength line of loess can be determined according to conventional triaxial shear strength test and the three failure stress circles, as shown in Figure 2, where \(\sigma\) is normal stress and \(\tau\) is shear stress. By determining the point of intersection of M-C strength line and horizontal axis, the value of tensile strength corresponding to M-C strength can be obtained.  
In order to unite the tensile strength and shear strength of loess, the strength that can take the tensile characteristics and shear characteristics into consideration is called the joint strength, and the corresponding failure strength curve is expressed in the form of hyperbola. Here \"joint\" means a kind of united consideration.  
Figure 1: Stress component of surrounding rock in plane-strain analysis.  
To fit a hyperbolic curve of the joint strength line of structural loess on the \(\sigma\)-\(\tau\) plane, [PERSON] et al. assumed that the horizontal axis intercept of the curve represents the tensile strength \(\sigma_{\rm t}\), and that the M-C strength envelope was regarded as the asymptote [26]. Then, the joint strength formula corresponding to the dotted line, which can describe the tensile and shear strengths of structural loess comprehensively and reasonably, was derived on the basis of the tensile and conventional triaxial shear strength tests.  
\[\tau_{\rm f}^{2}=\left(c+\sigma\tan\varphi\right)^{2}-\left(c+\sigma_{\rm t} \tan\varphi\right)^{2} \tag{2}\]  
Figure 2 shows that the tensile strength determined by the M-C strength is more than twice that of the tensile test. The results show that the M-C strength criterion obviously overestimates the tensile strength of the loess, whilst the joint strength overcomes the shortcoming of the M-C strength exaggerating the tensile strength of the loess. Therefore, the joint strength can reasonably evaluate the stress and displacement of the surrounding rock of the loess tunnel.
## 3 Results
### Relationship between Forest Ecosystem NEP Values and Meteorological Factors in Zhejiang Province  
Figure 9 shows the temperature, precipitation, relative humidity, and radiation data of Zhejiang Province from 1985 to 2015. As shown in Figure 9, the temperature of Zhejiang Province shows a significant increasing trend, with an increasing rate of 0.43 \({}^{\circ}\)C every 10 years, ranging from 17.24 to 19.16 \({}^{\circ}\)C. The average temperature from July to December is 5.7 \({}^{\circ}\)C higher than the average temperature from January to June. On the other hand, the trend of increasing precipitation is not obvious. In recent years, precipitation has begun to fluctuate greatly, overall ranging from 1124.14 to 1910.14 mm. The total precipitation in January-June was 222 mm higher than the total precipitation in July-December. Relative humidity shows a decreasing trend, with a range of 70.58-80.01%. There was no significant difference in the relative humidity data between January-June and July-December. The radiation data generally showed a decreasing trend, with a range of 81.43-91.84 w\(\cdot\)m\({}^{-2}\) s\({}^{-1}\). The daily radiation data from July to December was 9.78 w\(\cdot\)m\({}^{-2}\) s\({}^{-1}\) higher than the daily radiation data from January to June.  
After 1999, more than 97% of forests in Zhejiang Province were carbon sinks, and NEP increased to a relatively stable value. Therefore, this study used 1999 as the boundary to analyze the relationship between NEP and temperature, precipitation, relative humidity, and radiation in forest ecosystems in Zhejiang Province at two different stages: 1985-1999 and 2000-2015. It can be seen from Figure 10 that the correlation between NEP and relative humidity in forest ecosystems in Zhejiang Province is weak, and the correlation with precipitation is the strongest. From 1985 to 1999, the correlation coefficients of NEP with temperature, precipitation, relative humidity, and radiation were 0.39, 0.47, \(-\)0.18, and \(-\)0.45, respectively, and the correlations were weak. From 2000 to 2015, NEP and precipitation showed a significant positive correlation; the coefficient reached 0.85. NEP was negatively correlated with temperature and radiation; the correlation coefficient was \(-\)0.56. NEP was slightly negatively correlated with relative humidity, and the correlation coefficient was \(-\)0.18.  
Figure 9: Changes in (**a**) temperature, (**b**) precipitation, (**c**) relative humidity, and (**d**) radiation data in Zhejiang Province from 1985 to 2015.
## 5 Discussion  
In this article, we have carried out a comparative analysis between the data obtained by radiation dosimeters in several experiments in space and computer simulations of predictive models used in aerospace engineering.  
On average, the models used in the simulations show a factor of 3 as the smallest difference between the measurements and the modeling, and a factor of 30 as the highest. Computational models provide an ideal solution for manned missions with a wide safety margin and a worst-case scenario approach, as well as for missions with critical design specifications. If radiation models overestimate the radiation that components may receive during a mission, and an additional design factor (RDM) of 1.2 to 2 is applied, we may greatly limit the use of high-reliability COTS components in non-critical space applications.  
A different approach is necessary to assess the utilization of COTS electronic components for space applications, relying on analytical data rather than depending exclusively on engineering judgment and best-practice experience [42].  
The use of reusable launchers greatly reduces the cost-per-kilogram placed in orbit, which makes it possible to launch more missions that can update previous ones. Currently, satellite constellations such as Starlink or Oneweb use COTS components in their satellites, allowing for significantly cheaper devices and the use of proven technologies on Earth to improve performance and provide better services to their customers.  
Currently, NASA and ESA are betting on hybrid technologies that combine a COTS electronic system with typical supervision based on space-specific design devices. It is a bet that can guarantee complete system reliability, but leads to designs that increase costs and move away from the New Space paradigm and the use of mature and sufficiently tested technologies in terrestrial applications.  
The use of disruptive technologies in the space environment and the analysis of their performance will determine their qualification for the New Space business model that will allow science and technology to advance by leaps and bounds in the future.
# Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
## 5 RUL Prediction Method of Hydraulic Pump Based on KPCA and JITL
To illustrate the RUL prediction efficiency of the JITL method proposed in this paper, the results are compared with those of the traditional JITL method based on k-NN, which are shown in Figure 12. Table \(8\) compares the MREs and AREs of both methods. Table \(8\) indicates that both MRE and ARE of the gear pump RUL prediction method proposed in this paper are better than those of the traditional k-NN-based RUL prediction algorithm. The k-VNN method considers both the Euclidean distance and angle relationship between two vectors, while k-NN mainly refers to the Euclidean distance as the basis for modeling, which is insufficient for fully exploring the internal relationships among the input and nearest data. Therefore, sample selection method based on k-VNN of the RUL prediction method significantly improved the prediction accuracy of the RUL prediction model. This shows that the RUL prediction model based on KPCA and JITL proposed in  
Figure 11: RUL prediction of gear pump 4 based on JITL method proposed in this paper. Figure 12: RUL prediction of gear pump 4 predicted by JITL method based on k-NN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MRE** & **ARE** \\ \hline Proposed JITL method & 0.6184 & 15.1547 \\ JITL method based on k-NN & 0.8962 & 16.2983 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of MRE and ARE of both methods. this paper has higher prediction accuracy than the traditional RUL prediction model based on k-NN.
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
###### Abstract  
As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR's reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs' damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.  
n _Applied_  
_sciences_
## 2 Materials and Methods
### Vacancy Flux Model
The driving forces for atomic migration in microstructures like hotplates are a function of the electric field, \(\overset{\rightarrow}{E}\), hydrostatic stress gradient, \(\
abla\sigma_{hyd}\), and/or temperature gradient, \(\
abla T\). Vacancy migration is a specific type of atomic migration where an atom moves to a nearby vacancy. As the atoms move, the vacancy moves in the opposite direction. The following vacancy fluxes can be distinguished due to various driving forces [20]:  
* A vacancy flux due to the electric field: \[PFlux=\frac{\big{|}z^{\star}\big{|}e}{kT}Dc_{v}\overset{\rightarrow}{E}\] (7) where \(z^{\star}\) is the effective charge number and \(e\) is the elementary charge. * A vacancy flux due to the temperature gradient: \[TFlux=\frac{Q^{\star}}{kT^{2}}Dc_{v}\
abla\mathrm{T}\] (8) where \(Q^{\star}\) is the heat of transport. * A vacancy flux due to the hydrostatic stress gradient: \[SFlux=-\frac{f\Omega}{kT}Dc_{v}\
abla\sigma_{hyd}\] (9) The total vacancy flux is present in the form [20] Figure 3:0 (10)  
In this equation, the first term describes a diffusion effect and results in a flow from high to low vacancy concentration, while the other terms represent driven fluxes: the second term represents electromigration, the third term represents the material transport, which occurs due to thermal gradients, and the last term accounts for the atomic migration caused by gradients of hydrostatic stress [20]. The driving force behind atomic migration-induced failure stems from the accumulation of vacancies leading to void formation and the accumulation of atoms leading to hillock formation [22]. Hillocks can grow, causing short-circuits, while vacancies can grow and eventually result in open circuits [21].
## 3 Results and Discussion
### Sem-Eds  
Figure 7 shows the microstructures of PC00 and PC05 at 28 days of water curing. Major hydration products from cement and fly ash without CNTs were mainly C-S-H, C-A-H, and Ca(OH)\({}_{2}\). In order to investigate the effects of CNT addition on the changes in hydration products, SEM-EDS analysis was conducted. Figure 7 shows the main components from cement composites with CNTs.  
It appears that the CNTs were similar to Ettringite, which were needle-like in shape; however, the CNTs had smaller diameters and curvatures as shown in Figure 7b. It can also be considered that the CNTs dispersed evenly around the hydration products and acted as a bridge between hydration products. Owing to the large surface area of the particles, the long-term strength for the cement composites can be developed with the use of fly ash.  
Figure 6: Normalized heat of paste samples compared to PC00 and PC01 as a function of time, (**a**) PC02, PC03, (**b**) PC04, PC05.  
Figure 7: SEM images for PC00 and PC05, (**a**) PC00, (**b**) PC05.  
EDS analysis was conducted to determine the components of hydration products, and major elements such as Ca, Si, Al, O, and C were semi-quantified in mass percent as shown in Table 6. The results in Table 6 represented average values for points dotted in the same color.  
The blue dot (#1) represents the C-S-H, produced by Ca(OH)2 from the cement hydration reaction and the supply of SiO2 from fly ash. The red dot (#2) indicates the plate crystals that are commonly called Portlandite (Ca(OH)2), which is formed from CaO and water. However, most crystals in the SEM image were C-S-H. CNT particles were dispersed evenly as shown in the SEM image (yellow dots).
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 3 Methodology
### Experimental Setup
Figure 1: Block diagram of the laboratory-based FSO testbed for evaluating visible-wavelength laser diode performance.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 4 The Finite Element Models
### Simulated Sensor Layout  
The pipeline is assumed to be instrumented with a set of four optic fibers arranged as depicted in Figure 3. This is compatible with state of the art practices in the field; e.g., a system of distributed fiber optic sensors was used, e.g., in [51] to perform strain measurements. The four optic fibers are labeled according to their angular coordinate, as expressed in the legend of Figure 2. We considered 200 measurement points per fiber, totaling 800 output channels. These were equally spaced from each other by steps of \(\Delta\mathrm{z}=0.10\) m along a 20 m-long tract of the pipeline length. To emulate the readings from Fiber Bragg grating (FBG) sensors [52], the axial strains (along the z-direction in the model) were considered as the quantity of interest.  
Figure 3: The layout of the simulated optic fibers.
## 3 Metasurfaces
### Metasurface Lenses  
Metasurfaces can also be employed to fabricate ultrathin lenses. In order to focus the electromagnetic energy at a distance \(d\), a metasurface needs to have a phase profile given by Equation (3) [16].  
\[\Phi_{L}(x,y)=\frac{2\pi}{\lambda}(\sqrt{x^{2}+y^{2}+d^{2}}-d) \tag{3}\]  
This profile also alters the shape of the wavefront from planar to spherical, a condition necessary for focussing. High numerical aperture efficiency can be achieved as long as the electromagnetic wave strikes the surface at normal. However, when the angle of incidence is not perpendicular, a phenomenon known as 'coma' occurs, which can cause significant degradation in the numerical aperture efficiency. The effects of 'coma' can be reduced by placing the surface on a curved piece of dielectric [16]. Metasurface consisting of v-shaped antennas have been shown to focus energy at telecom frequencies [56]. Another metasurface lens based on reflect-arrays is presented in [57]. The lens was designed for the near infrared region and was designed on a metal-insulator-metal structure. Gold bricks were deposited on silicon dioxide and the individual elements were optimized in order to engineer the desired reflection response. This lens showed a theoretical efficiency of 78%. A metasurface lens based on rectangular dielectric resonators (RDRs) has been shown to focus energy at a single focus for three different frequencies. This was made possible by making the sum of the phase transversed by the wave and the phase jump imparted by the metasurface constant at three distinct frequencies. The resulting metasurface ended up being completely aperiodic [58].
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 2 Multi-Functional Energy Storage System Application Development
### Open Issues  
A closer analysis of the abovementioned design and engineering process results in the following issues which need to be addressed:  
* _Flexibility:_ The integration of new and different actors into the power distribution grid such as Electrical Vehicles (EV) and EMO motivates evolving services focused on BESS participation. Thus, EMS should be flexible enough to implement those services and future ICT requirements.
* _Complexity:_ The complex-nature of EMS is based on the manifold of services provided by BESS and the deployment of them in the ICT and power system infrastructure. The implementation of such services may be centralized or decentralized. In case of a decentralized configuration, where services are deployed in different hardware controllers, more than one control engineer may be required for the EMS design. Thereby, the lack of a common vocabulary and design language may lead to errors during EMS design and later on operation in the field.
* _Overlapping:_ An EMS focused on multi-services for BESS is vulnerable to conflicts between UCs. This overlapping across them could cause a non-expected function behavior. For instance, an EMS that operates UC2 and UC3 would need to setup mechanisms to align both use cases in the event of a simultaneous operation. Otherwise, those UCs would not be attained.
* _Interoperability:_ The realization and validation of smart grid and BESS-based applications entails the use of a wide range of power system, automation and communication tools. This goes from power system emulators/design, network simulators to co-simulation frameworks. However, a method to interoperate those tools during the whole chain of the engineering process is missing.
## 2 Basic Principles
### Conventional Methods and Their Deficiencies in Boundary Deformation Measurement
Although the half-subset method can extend the deformation measurement area towards the AOI boundary, the accuracy and precision of the calculated displacements with half subsets are generally lower than those calculated using full subsets, owing to the lower amount of pixels used for correlation coefficient optimization. Theoretically, the number of valid points contained in a subset should be substantially greater than the number of unknown parameters to be determined; otherwise, the displacements at the centre point may not be obtained.
## 3 Experiments
### Experimental Results and Grey Relational Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Normalized Value for SR** & **Normalized Value for RA** & **Normalized Value for ACD** \\ \hline
1 & 0.8294 & 0.9808 & 0.6994 \\
2 & 0.4960 & 0.9511 & 0.3151 \\
3 & 0.9563 & 0.9478 & 0.1930 \\
4 & 0.8571 & 0.9090 & 0.1341 \\
5 & 0.8333 & 0.8054 & 0.1964 \\
6 & 0.9722 & 1.0000 & 0.8565 \\
7 & 0.7579 & 0.7737 & 0.1085 \\
8 & 0.9802 & 0.9503 & 0.2972 \\
9 & 0.7857 & 0.3591 & 0.0666 \\
10 & 0.7222 & 0.0000 & 0.0359 \\
11 & 0.8135 & 0.9912 & 1.0000 \\
12 & 1.0000 & 0.9837 & 0.6251 \\
13 & 0.7222 & 0.2923 & 0.0000 \\
14 & 0.0000 & 0.3048 & 0.1623 \\
15 & 0.7857 & 0.9106 & 0.4740 \\
16 & 0.8492 & 0.9215 & 0.3971 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized data results. Figure 9: Machined specimens. Second, grey relational coefficients are determined to reveal the relationship between the reference sequence and comparison sequence, which can be calculated as follows:  
\[\Delta_{0i}(k)=\min_{\forall i}\min_{\forall k}|x_{0}(k)-x_{i}^{*}(k)| \tag{5}\]  
where \(x_{0}(k)\) is the reference sequence \(x_{0}(k)\) = 1, \(k\) = 1, 2..., m; \(x_{i}^{*}(k)\) is the comparison sequence; and \(\Delta_{0i}(k)\) is the deviation between the reference sequence and the comparison sequence, and  
\[\gamma_{i}(k)=\frac{\Delta\min+\zeta\times\Delta\max}{\Delta_{0i}(k)-\zeta \times\Delta\max} \tag{6}\]  
where \(\gamma_{i}(k)\) is the grey relational coefficient; \(\Delta\min\) is the minimum value of sequence \(\Delta_{0i}\); \(\Delta\max\) is the maximum value of sequence \(\Delta_{0i}\); and \(\zeta\) is a distinguishing coefficient, which is adjusted according to the practical needs of the system and falls within the range \(0\leq\zeta\leq 1\). In this paper, the value of \(\zeta\) is 0.5. Finally, the grey relational grade can be calculated by averaging the grey relational coefficients:  
\[\overline{\gamma}=\frac{1}{i}\frac{n}{i}\gamma_{i}(k) \tag{7}\]  
where \(i\) is the number of comparison sequences and \(\overline{\gamma}\) is the grey relational grade for the \(k\)th experiment. The resultant grey relational coefficients (GRCs) and grey relational grades (GRGs) are shown in Table 8.  
Based on the data shown in Table 8, the sixth parameters obtained the highest grey relational grade. The parameters of A\({}_{2}\)B\({}_{2}\)C\({}_{1}\)D\({}_{4}\) are the best combination of the 16 experiments. However, the number of combinations with four factors and four levels is 256 groups. The orthogonal experiment only provided 16 groups of parameter combinations. In the remaining 240 combination groups, there might be a better parameter combination than A\({}_{2}\)B\({}_{2}\)C\({}_{1}\)D\({}_{4}\). Thus, it is necessary to analyze the influence of each parameter on the GRG and to find the best combination. The mean GRG for the applied voltage at level 1 can be calculated using the average GRG of experiments 1, 2, 3, and 4. The mean GRG for the electrode feed rate at level 1 can be calculated using experiments 1, 5, 9, and 13. By using this method, the mean GRG for each parameter at each level is calculated.