esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Eliminating B-O LID during Cell and Module Fabrication  
Although p-type Cz wafers are subject to B-O related LID, a key strength of p-type cell technologies is that they naturally incorporate processes that can enable the elimination of the LID [139]. For industrial p-type solar cells, hydrogen is introduced throughout the silicon bulk during conventional cell processing. This natural introduction of hydrogen allows for a relatively simple modification to the production process to enable the mitigation of B-O related LID during cell or module fabrication. The processing sequence for the fabrication of typical passivated emitter, rear contact (PERC) solar cells is shown in Figure 3. The use of PECVD SiN\({}_{\mathrm{x}}\):H on the front surface as an antireflection coating creates a source of hydrogen in the device structure that can be manipulated with subsequent processing. In addition, rear surface passivation layers such as PECVD hydrogenated aluminium oxide (AlO\({}_{\mathrm{x}}\):H) and SiN\({}_{\mathrm{x}}\):H provide additional hydrogen sources. However, it should be noted that even though the AlO\({}_{\mathrm{x}}\):H layer may contain hydrogen, it can inhibit the diffusion of hydrogen from the overlying, hydrogen-rich SiN\({}_{\mathrm{x}}\):H layer into the bulk silicon and retard the regeneration rate [102].  
The co-firing process for forming the screen-printed metal contacts is another crucial process for the hydrogenation of B-O defects. This process acts to release hydrogen from the dielectric layers [103] and can enable the diffusion of hydrogen into the silicon and throughout the bulk [140, 141, 142, 143]. The high temperatures used for co-firing, typically in the vicinity of 700-900 \({}^{\circ}\)C, overcome issues with trapping, significantly increase the generation of H\({}^{0}\) and enhance the diffusivity of hydrogen [111].
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 2 Proposed Unit Cell Configurations
Although the FSS unit cell reduces the phase range, it should be included to the design since it enhances the reflectarray antenna performance by reducing the mutual coupling effect and providing isolation between frequency bands. The phase analysis of the 20 GHz unit cell is performed by including the FSS and 30 GHz reflectarray unit cell. As shown in Figure 3, 330\({}^{\circ}\) phase range is obtained at 20 GHz by varying the \(b\) parameter in the single reflectarray unit cell. It reduces to 260\({}^{\circ}\) when the analysis is performed with the multilayer unit cell consisting of FSS as well. This range reduces to 220\({}^{\circ}\) when the 30 GHz reflectarray unit cell is included. Corresponding phases of the proposed unit cells are obtained for a very small periodicity value (\(D=6\) mm). Although the inclusion of the FSS reduces the phase range of the unit cells, they are still at acceptable levels. Figure 3: Phase variations of the unit cells proposed as the function of a and b parameters: (**a**) 20 GHz; (**b**) 30 GHz.
###### Abstract
The antenna gain is not high; considering the use of the antenna in the actual scenario, an AMC structure is designed to reduce the backward radiation to increase the forward radiation by introducing the same phase reflection property of the AMC structure, avoiding the increase of antenna profile brought by using PEC plates. Finally, we process and test the sample, and the experimental result indicates that the design has a more stable gain in the working band and that the maximum gain is 11.3 dBi. The measured impedance bandwidth and AR bandwidth can realize 77.76% (2.74-6.22 GHz) and 65.16% (3.0-5.9 GHz), respectively. The antenna has certain application values in space-limited environments.
## 2 Materials and Methods
### Test Preparation
#### 2.1.2 Sample Preparation and Saturation  
The soil was initially prepared with an optimal moisture content. Then, it was mixed with fibers until the mixture was homogeneous. Subsequently, the sample was prepared. A mixture of rubber fibers and expansive soil was prepared via layered compaction to improve the random and uniform distribution of fibers in the sample. During the compaction process, the layers were scraped to ensure good bonding between adjacent layers of the mixture. The prepared samples were saturated using the pumping saturation method, placed in a vacuum saturation cylinder for 2 h, and then saturated by immersion in water for 48 h.  
In this study, the rubber fiber content was defined as the ratio of the total mass of rubber to that of the rubber fiber-reinforced expansive soil. This is shown in Equation (1). [PERSON] et al. [28] found that when the rubber content exceeds 10%, the performance of the specimen changes greatly. Therefore, 0, 5,10, 15, and 20% rubber contents were selected for this study.  
\[f=\frac{m_{R}}{m_{s}+m_{\omega}+m_{R}}\times 100\% \tag{1}\]  
where \(m_{R}\) is the mass of rubber fibers, \(m_{s}\) is the mass of soil, and \(m_{\omega}\) is the mass of water in the mixture.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Training Process  
Initially, the model is trained with no noise. The defined autoencoder model is compiled with an optimizer and loss function and trained on the data for 15 epochs with a batch size of 50. The Adam optimizer is used with a learning rate of 0.001. Since the last deconvolutional layer of the decoder has a sigmoid activation function, the output of the final layer is a number between 0 and 1 for each node, which is why binary cross-entropy is chosen as the loss function. To train only the decoder, the encoder's predicted data must be used as the decoder's input data. Since the encoder and decoder layers are defined independently in the autoencoder model, a new model can be created that only contains the encoder layers. Then, the encoder model can predict the data for the training dataset. After obtaining the encoder output data for the training dataset, the data are modified thereafter by the addition of different noises. In the proposed system, 80,000 training latent vector sets are created for each BER considered. Listed below are the different BERs for which the latent vectors are created. Based on the hyperparameter selected, BERs of 0.0%, 0.625%, 1.25%, 1.875%, 2.5%, 3.125%, 3.75%, and 4.375% are considered in the rest of the paper. However, it should be noted that any other BERs can be selected for training the AE based on external parameters. After obtaining those eight datasets, each containing \(80,000\) latent vectors, eight different decoders are individually trained.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line Condition Monitoring Intelligent Sensing Design
#### 2.3.3 Optimal Sensor Configuration Design for High Voltage Transmission Line  
Drop Monitoring  
The monitoring of the droop can be determined by other parameters, but the tension is easily accessible and better reflects the change in the droop. Because the transmission line is suspended between two towers, a static model of the transmission line is usually used to describe its mechanical droop due to its weight, \(W\). Deriving the relationship for a single horizontal span configuration and extending it to any number of towers, the horizontal span configuration to measure transmission line sag is shown in Figure 3.  
According to the tension measurement, the drap \(D\) can be expressed as:  
\[D_{i}=\frac{(F_{i}-H_{i})}{W}, \tag{19}\]  
where \(F_{i}\) and \(H_{i}\) are the vertical and horizontal components of the line tension, respectively, and \(i\) is the span number of the horizontal span model.  
With the change in line temperature \(T\), the line length changes from \(L_{1}\) to \(L_{2}\):  
\[L_{2i}=L_{1i}(1+\alpha_{A}(T-T_{\text{crefi}})), \tag{20}\]  
where \(\alpha\) is the linear thermal expansion coefficient and \(T_{\text{crefi}}\) is the conductor temperature at length \(L_{\text{cref}}\).  
The droop corresponding to the new length \(L_{2i}\) can be expressed as:  
\[L_{2i}=\frac{8D_{i}^{2}}{3S_{i}}+S_{i}. \tag{21}\]  
Figure 3: Horizontal span configuration measurement of transmission line sag.  
Then the relationship between arc drape and tension is:  
\[D_{i}=\sqrt{\frac{3S_{i}(L_{2i}-S_{i})}{8}}, \tag{22}\]  
where, \(S\) is the span of the transmission line between the two towers.  
In the Figure, the drape-tension relationship of the towers A and B2 is:  
\[\left\{\begin{array}{ll}WD_{i}+\frac{WS_{i}^{2}}{8D_{i}}=F_{1i}\\ WD_{i}+\frac{WS_{i}^{2}}{8D_{i}}=F_{2i}\end{array}\right. \tag{23}\]  
In order to obtain the location of the sensors during the elimination process, while maintaining the observability of the line, the problem is expressed as a linear integer programming, which yields:  
\[f(X)=\min\sum_{m,n=1}^{N}\Xi_{mn}x_{m}, \tag{24}\]  
where \(f(X)\) is a function with non-zero terms, \(m\) and \(n\) are two consecutive towers, \(\Xi\) is the set of connections, and \(x\) is the decision variable.  
Furthermore,  
\[\Xi_{mn}=\left\{\begin{array}{ll}1,&\mbox{if }m=n\\ 1,&\mbox{if }m\&\mbox{$\kappa n$ is connected}\\ 0,&\mbox{if Other}\end{array}\right. \tag{25}\]  
And  
\[x_{m}=\left\{\begin{array}{ll}1,&\mbox{if Configuring Sensors}\\ 0,&\mbox{Other}\end{array}\right. \tag{26}\]  
With the proposed optimal sensor configuration method, the need for sensors is greatly reduced. As a result, the measurement data required for droop estimation is also reduced, and the accuracy of the estimation is not affected.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 1 Introduction
The inherent independency of vendor equipment obtained in the aforementioned projects is mainly based on using a specific modelling language, namely, the Yet Another Next Generation (YANG) one [17]. YANG offers the possibility to consolidate in a common language and a catalogue of rules the definition of the main characteristics of device models that can be used in software agents. In SDN-based DONs the communication between the agents generated based on YANG models and the SDN controller (via South Bound Interface, SBI, in the canonical SDN architecture) usually relies on the NETCONF protocol [18]. The YANG/NETCONF symbiosis provides the ideal breeding ground not only for the development of optical devices that facilitate vendor interoperability but also for the development of software tools that emulate the real behaviour of such devices, which is basic in the early stages of the development of new devices and network planning. Further details on the YANG/NETCONF usage for optical networks and additional alternatives for SBIs can be found in [19].
# Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials
###### Abstract  
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a \(k\)-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are \(\approx\mathcal{O}(10^{6})\) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.  
**Keywords:** fracture; graph theory; \(k\)-nearest neighbors; machine learning; failure paths; damage statistics; brittle failure; fracture process zone +
Footnote †: journal: Applied Soft. 2019, 9, 2706; doi:10.3390/app9132706
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 4 Solution Approach
### Variable Neighborhood Search for the 2E-EVRP-TW
#### 4.3.2 Removal/Destroy Operators  
One of the most critical aspects of a destroy operators is deciding on the number of nodes in the solution to be removed. Limiting the amount of destruction too much may cause a poor exploration performance of the algorithm. On the contrary, repairing a largely destroyed solution can be time-consuming and may result in a poor quality solution depending on the utilized repair procedure [53]. Therefore, we used a random removal rate between a lower and an upper bound to determine how many nodes (or routes) will  
Figure 4: An illustration of the cyclic exchange operator with \(\zeta=3\). Note that the route at the top and the route at the bottom are the same in order to show the cyclic nature of the move.  
be removed from the current solution. Well-working upper and lower bounds are decided via parameter tuning (Section 5.2) and fixed for each group of instances.  
_Random customer removal:_ First, a random number \(\rho\) is drawn from the interval \([rr1_{Lb},rr1_{Ub}]\). This number is the fraction of customers to be removed from the solution, henceforth called the removal rate. Note that \(rr1_{Lb}\) and \(rr1_{Ub}\) are the lower and upper bounds for the removal rate. Finally, a randomly chosen number of \(\max\{1,\,\left\lfloor\rho*n_{c}\right\rfloor\}\) randomly chosen customers are removed from the current solution and added to a removal list \(L_{r}\).  
_Random route removal:_ Similar to the random customer removal operator above, a random number \(\rho\) is drawn from the interval \([rr2_{Lb},rr2_{Ub}]\). This number is the fraction of routes to be removed from the solution. Assume that solution \(S\) has \(n\) routes in the second echelon. After drawing number \(\rho\), a number of \(\max\{1,\,\left\lfloor\rho*n\right\rfloor\}\) randomly chosen routes are removed from the current solution and all customers from these routes are added to a removal list \(L_{r}\).  
_Close satellite:_ This operator closes a randomly chosen satellite and adds all the customers served through this satellite to a removal list \(L_{r}\).
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.4 Forecasting System State Variables with Lyapunov-Based Closed-Loop Control  
After establishing a closed-loop system based on the Lyapunov function, the next step involves determining the vector of future state variables of the system to generate an MPC signal. This signal's length is equal to the prediction horizon (Np). To achieve this, the previously established closed-loop system, a mathematical model with nonlinear differential equations, is numerically solved. The results obtained from this solution, as per the following equation, constitute the predicted state variables for the future of the system.  
\[\widetilde{x}(t_{k})=\hat{x}(t_{k}) \tag{32}\]  
where \(\widetilde{x}(\mathbf{t}_{k})\) refers to the predicted state variables of the system's future.
# Joint Energy Supply and Routing Path Selection for
## 3 A Joint Algorithm of Energy Supply and Routing Selection
### Property Analysis and Implementation Guidlines
In the process of solving the shortest travel path, The number of external loops under temperature control (i.e., the maximum number of iterations) is assumed to be \(a\), and the number of internal loops at each specific temperature is \(b\). Then, the time complexity of the entire algorithm is not \(O(a*b*n)\). To sum up, the time complexity of our proposed algorithm in a worst-case scenario is \(O(n^{2})\). Next, we provide simple implementation guidelines of our algorithm. In order to prolong the network lifetime as much as possible, the algorithm burden borne by the sensor nodes should be minimized. Therefore, some tasks with high computing demands such as charging plan formulation are processed by the centralized computing infrastructure, while the routing selection can be executed locally. In addition, the load energy consumption of the nodes remains unchanged for a certain period of time. Therefore, it is possible to appropriately extend the communication interval that the ordinary nodes report their status to the sink node, so that reduce the communication energy consumption.
## 2 Materials and Methods
### Methodological Validation  
Selectivity was assessed by comparing LLOQ samples (5 ng/mL of enavogliflozin) prepared from six different mice to the corresponding blank plasma samples. The ratios of analyte peak areas to IS peak areas was plotted against the concentration of enavogliflozin. The linearity of the calibration curve over the range of 5-3000 ng/mL was evaluated using least-squares linear regression with 1/concentration2 as weighting factors. Acceptance criteria for calibration standard curve was as follows: non-zero calibration standards were \(\pm\)15% of nominal concentrations, except at LLOQ (\(\pm\)20% of the nominal concentration) in each validation run. Moreover, 75% and a minimum of six non-zero calibration standard levels met the above criteria in each validation run. Six replicates of the four QC levels (5, 15, 250, and 2000 ng/mL) were analyzed on the same day to assess intra-day precision and accuracy. Inter-day precision and accuracy were determined by measuring the four QClevels (5, 15, 250, and 2000 ng/mL) over 5 consecutive days. Precision was represented as the coefficient of variance (CV, %), whereas accuracy was expressed as the percentage of the measured QC concentration to the nominal QC concentration.  
Matrix effects for enavogliflozin were evaluated at three QC levels (15, 250, and 2000 ng/mL) using the six different mouse plasma by dividing the peak areas of post-extraction spiked samples by those from neat solutions of corresponding concentrations. Extraction recovery was determined at three QC levels (15, 250, and 2000 ng/mL) by comparing the mean peak areas of pre-extraction spiked samples with those of post-extraction spiked samples. The matrix effects and extraction recovery of IS was determined using the same procedure, with an IS concentration of 200 ng/mL. Post-treatment stability was assessed by placing processed QC samples at the three levels in the autosampler at 10 \({}^{\circ}\)C for 24 h. Freeze-thaw stability was evaluated by subjecting QC samples at the three levels to three freeze-thaw cycles, each consisting of freezing overnight at \(-80\)\({}^{\circ}\)C and thawing completely at 25 \({}^{\circ}\)C. Bench-top stability was assessed by keeping QC samples at the three levels at 25 \({}^{\circ}\)C for 5 h.
## 3 Results  
### Generated Urban Land Use Suitability Map  
Based on the criteria utilized, a literature review, and survey results, the binary comparison matrix resulting from the AHP for the suitability analysis of urban land use within the Mersin Metropolitan Area is presented in Table 2, while the weights of the criteria and sub-criteria according to their importance are provided in Table 3.  
Consistency analysis was conducted on these results to verify whether the binary comparison matrix and the weights of the criteria were consistent. As a result of the calculations, the CR value was obtained as 0.08. Since the CR value was lower than 0.10, it was determined that both the comparison matrix and the criteria weights were at an acceptable level of consistency. According to the calculated criteria weights, land use, lithology, and distance to settlement were identified as the most important criteria. Distance to bus stops and distance to road network were of secondary importance, while proximity to coastal areas, soil suitability, distance to port, slope, distance to rivers, land elevation, and aspect were considered to be less important criteria. Subsequently, each layer was overlaid to obtain the urban land use suitability index map. In this map, the suitability values of pixels were classified into five categories (very low, low, marginally, moderately, and highly suitable) using the natural breaks method (Figure 10). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Criteria** & **S** & **E** & **A** & **SC** & **LULC** & **RE** & **TI** & **ST** & **BS** & **RO** & **P** & **C** & **Li** \\ \hline S & 1 & 1/2 & 3 & 1/3 & 1/7 & 1/5 & 1/4 & 2 & 1/5 & 1/6 & 1/2 & 1/3 & 1/4 \\ E & 2 & 1 & 2 & 1/2 & 1/9 & 1/7 & 1/5 & 1 & 1/5 & 1/4 & 1/2 & 1/4 & 1/3 \\ A & 1/3 & 1/2 & 1 & 1/4 & 1/9 & 1/8 & 1/5 & 1/2 & 1/5 & 1/4 & 1/3 & 1/4 & 1/2 \\ SC & 3 & 2 & 4 & 1 & 1/6 & 1/7 & 1/5 & 1/2 & 1/4 & 1/5 & 2 & 1/2 & 1/4 \\ LULC & 7 & 9 & 9 & 6 & 1 & 5 & 2 & 8 & 1 & 2 & 5 & 3 & 3 \\ RE & 5 & 7 & 8 & 7 & 1/5 & 1 & 2 & 6 & 1 & 1 & 3 & 2 & 2 \\ TI & 4 & 5 & 5 & 5 & 1/2 & 1/2 & 1 & 4 & 1/3 & 1/4 & 2 & 1 & 1/3 \\ ST & 1/2 & 1 & 2 & 2 & 1/8 & 1/6 & 1/4 & 1 & 1/5 & 1/5 & 1/2 & 1/4 & 1/2 \\ BS & 5 & 5 & 5 & 4 & 1 & 1 & 3 & 5 & 1 & 1 & 3 & 2 & 1/5 \\ RO & 6 & 4 & 4 & 5 & 1/2 & 1 & 4 & 5 & 1 & 1 & 3 & 2 & 1/4 \\ P & 2 & 2 & 3 & 1/2 & 1/5 & 1/3 & 1/2 & 2 & 1/3 & 1/3 & 1 & 1/2 & 1/4 \\ C & 3 & 4 & 4 & 2 & 1/3 & 1/2 & 1 & 4 & 1/2 & 1/2 & 2 & 1 & 1/2 \\ Li & 4 & 3 & 2 & 4 & 1/3 & 1/2 & 3 & 2 & 5 & 4 & 4 & 2 & 1 \\ \hline \end{tabular}  
* S: Slope, E: Elevation, A: Aspect, SC: Soil capability, LULC: Land use land cover, RE: Distance to residential areas, TI: Distance to trade and industrial zones, ST: Distance from streams, BS: Distance to bus stops, RO: Distance to roads, P: Distance to port, C: Distance from coastline, Li: Lithological structure.  
\end{table} 
Table 2: The binary comparison matrix resulting from the AHP for the suitability analysis of urban land use within the Mersin Metropolitan Area.