esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Continuous Mapping Relationship  
Accurate constitutive relationship is necessary for the accurate numerical simulation. In this work, the constitutive relationship for Inconel 718 superalloy was expressed as a new style: continuous mapping relationship. Due to outstanding generalization capability of the ANN model, it can undoubtedly be employed to predict the true stress of Inconel 718 superalloy outside of the experimental conditions. In this investigation, the true stress-strain data under the temperature range of 1103-1403 K, the strain rate range of 0.01-10 s\({}^{-1}\), as well as the strain range of 0.05-0.9 were predicted. The predicted results were expressed as continuous mapping relationship among strain, strain rate, temperature and stress (illustrated in Figure 10). In Figure 10, the \(X\)-axis, \(Y\)-axis, \(Z\)-axis and \(V\)-axis respectively represent deformation temperature, strain rate, true strain and true stress. The values on \(V\)-axis, that is, the predicted true stress values under different deformation conditions are indicated by different colors. Figure 10a is a global expression of the continuous mapping relationship. It covers all the deformation conditions and homologous predicted true stress values. Therefore, any true stress values in the deformation condition scope can be read and accessed directly. Figure 10b-d, which were constructed by cutting Figure 10a into slices along with the \(X\)-axis, \(Y\)-axis, \(Z\)-axis, respectively intensively reflect the continuous mapping relationships under fixed temperatures, strain rates and strains. It can be clearly observed that, just as the experimental stress-strain data, the stress increases with the strain rate increase and deformation decrease. In addition, the greater color spans under lower temperatures and more modest ones under higher temperatures also indicate DRX and DRV dominated deformation mechanisms. So, such continuous mapping relationship has successfully articulated not only the external quantitative relationships among process parameters but also the intrinsic deformation mechanisms.  
Figure 10: _Cont._  
Figure 9: The comparisons between the experimental and predicted true stress by the ANN model at different temperatures and the strain rates of (**a**) 0.01 s\({}^{-1}\); (**b**) 0.1 s\({}^{-1}\); (**c**) 1 s\({}^{-1}\) and (**d**) 10 s\({}^{-1}\).  
The continuous mapping relationship plays an essential part in finite element method (FEM). As is known, the stress-strain data are the most fundamental data to predict the deformation behaviors of materials with FEM. On the basis of the continuous mapping relationship, it is realizable to perform accurate numerical simulations of various hot forming processes. In this way, more reasonable design and optimization of the process parameters such as the deforming temperature, the shape of die cavity, deformation velocity, etc., can be conducted.
## 2 Data and Preliminary Process
### Input Value Selection  
For machine-learning models including ANN models, the accuracy of the machine-learning model can vary greatly depending on the selection of input variables. Therefore, the input variables were selected by examining the mathematical theory of the variables to be predicted and comparing them with the system operation data items. The coefficient of performance of the heat pump system is a value indicating the performance of the heat  
Figure 1: Heat pump System. (**a**) Indoor unit, (**b**) Outdoor unit.  
Figure 2: Diagram of heat pump system and sensor.  
pump and is the ratio of the effect that can be obtained by refrigeration with respect to the supply under certain conditions. The cooling performance coefficient can be obtained through Equation (1), and the heating performance coefficient can be obtained through Equation (2). The system performance of the heat pump system can be obtained through Equation (3), and the amount of heat is obtained through Equation (4).  
\[COP_{c}=\frac{Q_{c}}{W_{c}} \tag{1}\]  
\[COP_{h}=\frac{Q_{h}}{W_{c}} \tag{2}\]  
\[COP_{sys}=\frac{\mathsf{Q}}{W_{c}+W_{p}+W_{f}} \tag{3}\]  
\[\mathsf{Q}=m_{c}\mathsf{c}\left(t_{cap,j}-t_{cap,o}\right) \tag{4}\]  
where \(COP_{c}\) is coefficient of performance in cooling mode, \(W_{c}\) is power consumption in the compressor [kW], \(W_{p}\) is power consumption in the circulation pump [kW], \(W_{f}\) is power consumption in the fan [kW], \(Q_{c}\) is heat transfer rate in cooling mode [kW], \(t_{cap,i}\) is water inlet temperature [\({}^{\circ}\)C], \(t_{cap,o}\) is water outlet temperature [\({}^{\circ}\)C], \(m_{c}\) is water flow rate and is measured by a flow rate meter [kg/s], c is specific heat [kJ/kgK], \(COP_{h}\) is coefficient of performance in heating mode, \(Q_{h}\) is heat transfer rate in heating mode [kW], \(COP_{sys}\) is coefficient of performance in heat pump system.  
Regression analysis, analysis of variance (ANOVA) and unstandardized coefficients, etc. It is shown in Table 3. F is the ratio of between variance to within variance.  
R2 is the explanatory power of the variable, and the higher the explanatory power, the better the estimation. The \(p\)-value value is an index that judges statistical significance. The heat source side temperature has a high correlation with the load side temperature, and the geothermal inlet temperature R2 is the most. It shows a high number, and it can be confirmed that it has the highest correlation with the dependent variable. The \(p\)-value of the four independent variables (heat source side inlet/outlet temperature, load side inlet/outlet temperature) was less than 0.05, which was a significant independent variable, indicating a high influence with the dependent variable, and the significance was less than 0.001, indicating statistical stability.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 5 Conclusions  
In this paper, the stability of the DSOGI-PLL-based grid-connected inverter under the unbalanced grid condition is investigated. Considering the frequency-coupling effect, frequency-coupled impedance models of the DSOGI-PLL-based grid-connected inverter under balanced and unbalanced grid conditions are derived. To analyze the stability under the unbalanced condition, a simplified stability analysis method for the MIMO system  
Figure 15: Nyquist curves of the DSOGI-PLL-based inverter with different parameters: (**a**) The DSOGI-PLL with different damping coefficients. (**b**) The DSOGI-PLL with different \(B_{\text{PLL}}\)s.  
is proposed and the stability analysis is carried out based on this method. Based on the proposed impedance model and analysis method, the influences of system parameters on the system stability are investigated. According to the analysis results, parameter design recommendations are summarized to provide guidance for practical engineering and other researchers. Finally, the accuracy of the proposed frequency-coupled impedance model and the effectiveness of the simplified stability analysis method have been well validated. The system's critical instability points and oscillation frequencies can be accurately predicted. The proposed impedance modeling method and stability analysis method have broad application prospects for modeling and analyzing other power electronic devices. The research in this paper can provide a general theoretical basis for the stability analysis of inverters under balanced and unbalanced grid conditions.  
Conceptualization, L.Y.; methodology, J.S.; software, J.S.; validation, J.S. and H.Y.; formal analysis, J.S.; investigation, J.S.; resources, L.Y.; data curation, H.Y.; writing--original draft preparation, J.S.; writing--review and editing, L.Y. and H.Y.; visualization, H.Y.; supervision, L.Y.; project administration, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Related Methods  
In order to avoid the Hughes Phenomenon caused by redundant information in HSIs, a common method is to process hyperspectral images by means of dimensionality reduction. Band selection for hyperspectral data should consider three factors [19]:  
1. The information contained in the band or the band combination;
2. The correlation among the bands;
3. The spectral response of the ground objects to be identified.  
Therefore, the optimal band combination is one with a large amount of information, the small correlation between bands, a large difference in reflectance values of ground objects and good separability between ground objects. The following introduces the IOIF algorithm based on the amount of information as the selection criterion and the GA-BPSO with the inter-class distance as the objective function.
## 4 Discussion
### Influence of BDS MEO Satellites Distribution on Positioning performance  
We analyzed the contribution of MEO satellites distribution on influencing BDS positioning performance due to currently incomplete constellation distribution, which will be fulfilled in 2020. With the same strategy of data processing in Section 2, we reprocessed BDS observations without four MEO satellites. The mean and RMS of baselines differences between BDS positioning results with and without MEO satellites are shown in Figures 8 and 9 in red dots. Comparing to the impact from different ephemeris, the contribution from four MEO satellites is significant on influencing BDS positioning performance. The variation of baseline differences is amplified linearly with the increasing length of baselines, which is insignificant in the ephemeris comparison, thus indicating that the impact on the accuracy of BDS positioning results from ephemeris is stochastic. The RMS of baselines differences also linearly increase with the increasing length of baselines, which shows a similar pattern in the case of different ephemeris, but with larger amplification. The mean and RMS of baselines in differential position time series between BDS results with and without MEO satellites are shown in Table 4. The mean values are less than 1 mm for both north and up components, while it is 5.1 mm on the east component, which is considerable compared to the case of different ephemeris. All baselines are moving eastward without including MEO satellites, especially for baselines longer than 2500 km. The RMS of baselines differential time series is 12.0, 14.1, and 42.9 mm for north, east and up components, respectively. The average value of RMS without MEO satellites is almost two times larger than the case with MEO satellites included, and two times larger than the accuracy of BDS positioning too. The results indicate the utmost importance of MEO satellites in improving the accuracy of BDS positioning, and thus infer that the accuracy of BDS positioning may be improved with increasing the number of MEO satellites.
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 4 Backpropagation Neural Network  
The backpropagation neural network (BPNN) is a feed forward neural network, which has a backpropagation process. The backpropagation process repeatedly adjusts the weights of connections in the network, to minimize the difference between the actual output vector and the desired output vector [28].  
The structure of the classic the BPNN usually consists of three layers, namely, the input layer, the hidden layer, and the output layer. The log-sigmoid function and the tan-sigmoid function are usually used as activation functions [18; 35; 36]. The BPNN has been widely used for its advantages of simple structure, stable performance, and easy to implement.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 3 The Proposed CSO-Based Algorithm
### The Proposed CSO-Based Approach
#### 3.1.2 Initialization of the Agents (Cats)  
Since the UTRP is a very difficult problem to solve (see Section 2.3), we decided not to generate the initial population of route sets at random but to construct them based on some specific logical principles. Each route set is created by defining the number of routes it consists of. The initialization procedure, which has been adopted in this contribution, has been mainly based on the approach presented in [31] by [PERSON] and [PERSON], partly changed using the make-small-change procedure, as described by [PERSON] and [PERSON] [37] and [PERSON] et al. [15] and as it was also modified by [PERSON] and [PERSON] [43]. In these approaches, an initial node set (INS) with K nodes is created, from which the first node is picked with a probability proportionate to its activity level. The value of parameter K, which is user defined, was chosen equal to 14 for the [PERSON]'s Swiss network, in line with the above works. Considering that [PERSON]'s network has 15 nodes, setting a value of 14 for the K parameter means that, besides node 14 which does not attract or generate any transfers (see Table 1), all other nodes are possible first nodes. For the four Mumford's instances [13], all the nodes in each instance are considered as possible first nodes.  
To maximize the number of nodes in each route, [PERSON] and [PERSON] in [43] propose repetitive creation of a route 10 times to hold the one with the largest number of nodes. In the present approach, many original agents are created (limited by memory issues that are mainly affected by the size of the road network). Then, the initial whole population is filtered, and a smaller number of active agents remains (also limited by execution time issues). The filtering criterion is either the total number of nodes in the agent's route set (we pick agents with as many as possible total number of nodes) or its fitness value (we pick agents with as bigger as possible fitness value). The first criterion has been used in trials focusing on customers' interest, and the second criterion has been used in trials concerning the provider's interest.  
The initialization procedure satisfies the following demands:  
* Establishment of a consistent route set.
* The duration of each route does not exceed the allowed limit.
* The minimum and maximum number of nodes on each route of the route set is within the predefined limits.
* There are no circles or inversions on each route in the route set.  
The above requirements are ensured throughout the evolution of the algorithm. Initialization also determines the initial mode of each cat (if it is in tracing or in seeking mode) according to the mix ratio specified by the user (see Section 3.1.5).
## 4 FE Analysis and Result
### Material Model  
#### 4.2.1 Structural Steel, Rebar, and Shear Connectors  
The stress-strain linear model proposed by [PERSON] and [PERSON] [4] and [PERSON] [21] was used as the material model for the steel members. The stress-strain curves for the structural steel (H beam) and rebar were modeled as two-line curves as shown in Figure 13a. Complete  
Figure 11: Definitions of failure load and slip displacement [21].  
plastic behavior occurs after yielding in these models, and the mechanical behaviors with regard to tension and compression are both assumed to be symmetric in the same way. The steel beams were SM490A structural steel, and they had an elastic modulus of 210 GPa and a yield strength of 316.5 MPa. However, the rebar steel type was SD40, with an elastic modulus of 192.8 GPa and a yield strength of 542.6 MPa. A Poisson's ratio of 0.3 was used for both. The material model of the welded headed studs was modeled as a three-line stress-strain curve as shown in Figure 13b. This model considers three-stage linear behavior consisting of an elastic region, a strain softening region, and a general yield region.  
Table 4 shows the material property values of each of the steel members that were used in the FE analysis. The values obtained from actual coupon tests were used for the welded studs, stud bolts, and rebar. Regular nominal values were used for the H beams. During the FE analysis, the material damage and failure options were not utilized on the material models for each steel material, unlike the concrete material model.
## 3 Results and Discussion:  
### Compressive Strength Results  
Figures 5 and 6 show the results of the compressive strength tests. Each curve represents the result of one specimen. Six repetitions of each mixture were performed, and valid results were drawn in different colors to identify each specimen clearly. These graphs show load-displacement curves for all the specimens tested with the same composition. Higher compressive strength is obtained when ZnO nanoparticles are added. However, the incorporation of ZnO nanoparticles as a cement substitute reduces the compressive strength of this type of LWC. Figure 6 includes only four results because two of the six specimens tested failed. Because of the heterogeneity in and high porosity of LWC, a minimum of four samples was required.  
Figure 5: Force–displacement results for LWC with 1% of ZnO nanoparticles added.  
The average values of compressive strength were determined for each type of LWC. Table 4 includes average values for all the samples tested under compression following standard UNE-EN 12390-3 [29].  
The control LWC samples reached 30 MPa in compressive strength. However, the compressive strength of the LWC with ZnO reduced for all the samples. When ZnO was incorporated as a cement substitute, compressive strength was significantly reduced. Percentages of ZnO above 1 wt% led to non-structural LWC with compressive strengths below 20 MPa. However, when ZnO was incorporated by addition, the LWC samples presented compressive strengths above 20 MPa for ZnO percentages of 1.5 wt% and below. Although the incorporation of ZnO nanoparticles reduces mechanical properties, they are still suitable for structural LWC applications with a percentage of incorporation up to 1.5 wt% as an additive. The reduction in mechanical properties when ZnO is incorporated as a cement substitute is related to the lower quantity of cement in the mixes. The lower the quantity of cement, the lower the compressive strength.  
Figure 7 shows one of the samples tested in the compressive strength testing machine. Figure 7b shows satisfactory failures for the cube specimens after the test, according to standard UNE-EN 12390-3 [29].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Mix** & **Specimen 1** & **Specimen 2** & **Specimen 3** & **Specimen 4** & **Specimen 5** & **Specimen 6** \\ \hline LWC\_control & 32.56 & 34.21 & 26.55 & 26.85 & 29.15 & 30.32 \\ Zn-S-0.5 & 21.63 & 22.17 & 18.81 & 16.05 & 18.50 & 18.83 \\ Zn-S-1.0 & 25.08 & 18.31 & 23.91 & 17.00 & 20.47 & 17.22 \\ Zn-S-1.5 & 16.85 & 15.04 & 16.00 & 13.28 & 17.62 & 15.04 \\ Zn-S-2.0 & 14.33 & 14.25 & 12.98 & 14.15 & 17.35 & 13.34 \\ Zn-A-0.5 & 23.64 & 26.37 & 22.58 & 18.41 & 20.65 & 22.19 \\ Zn-A-1.0 & 21.42 & 21.77 & 26.13 & 24.89 & 20.68 & 22.09 \\ Zn-A-1.5 & 28.94 & 27.09 & 16.10 & 20.09 & 19.65 & 19.61 \\ Zn-A-2.0 & 18.35 & 16.26 & 21.22 & 18.78 & 16.62 & 15.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of compressive strength.  
Figure 6: Force–displacement results for LWC with 1% of ZnO nanoparticles as a substitute for cement.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
### Problem Statement  
Satisfying QoS requirements is the most important issue in the disaster-monitoring application domain, and the medium access control (MAC) layer plays a critical role. However, WSNs exhibit dynamic network environments, heterogeneous network traffic, and resource-constrained nodes. Disaster monitoring involves three main metrics: delay, throughput, and reliability. Alert messages should be transmitted as quickly as possible, and this delay plays an important role. The MAC layer can obtain minimum delay by minimizing queuing and transmission delays. Sufficient throughput is required for multimedia transmission in WSNs. Because wireless communication is based on a shared medium, all co-instantaneous transmissions in the coverage of wireless signals interfere with each other, and this interference causes frame loss. This affects transmission delay and network throughput. The reliability of disaster monitoring is vital, and WSNs must provide reliable transmission, even in abnormal situations.  
In general, three types of data are available for disaster-monitoring applications. One is ordinary data, such as sound, pictures, or video data, which require high throughput, such as data rates of several hundred kb/s for 480p H.265 video streaming [15], and can endure some degree of frame loss. The second type of data, seismic data, simultaneously require a high throughput of several kb/s and a low delay [11]. The third type is alert data, which demand a low delay of tens of milliseconds and high reliability. In a disaster-monitoring system with WSNs, the MAC layer of the WSNs must simultaneously satisfy the requirements of the above three types of data, which is a difficult and vital issue.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
###### Abstract  
In the present work, modifications were made in the typical synthesis of KIT-6 mesoporous material. The molar ratio of P123 and its dissolution time, the type of alcohol, the aging time, and the heat treatment time were varied. The materials obtained were characterized by X-ray diffraction (XRD), thermogravimetry and differential thermogravimetry (TG/DTG), Fourier-transform infrared spectroscopy (FTIR), adsorption and desorption of N\({}_{2}\) and transmission electron microscopy (TEM). It was observed that the modifications interfered directly in the ordered structure of materials, displaying materials with cubic structures _la3d_ (KIT-6 mesoporous material) and hexagonal structures _P6 mm_, (SBA-15 mesoporous material). The type of alcohol probably acts on generation of the micelle, influencing the formation of the porous system and ordered structure. The results obtained indicate that the cubic structure of KIT-6 can be formed with a reduction of 30% in the P123 concentration, and decreases of 2, 6 and 12 h in times of P123 dissolution, aging and heat treatment, respectively. The modifications carried out in the synthesis procedure have resulted in ir being possible to employ materials with different characteristics, such as mesoscopic ordering and textural properties, in applications in the areas of catalysis and adsorption.  
mesoporous silica; KIT-6; synthesis parameters; cubic structure +
Footnote †: journal: Applied