esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Temperature Calculation in Practice  
Figure 2 shows practical calculation of the node's temperature. Assume that node \(X\) has neighbors \(A,B,C,D\) and \(E\) and knows their temperatures (_i.e._, \(\phi_{A}\), \(\phi_{B}\), \(\phi_{C}\), \(\phi_{D}\) and \(\phi_{E}\)). Then, node X creates a list of neighbors sorted descending by their temperature. Based on the Algorithm 1, the node's temperature is calculated. From the Figures 2(b) and 2(c) is clear that node's temperature value and also number of alternative paths varies with the node's own thermal conductivity value \(\xi_{X_{NORM}}\). In case of high thermal conductivity value, the calculation stops after two steps and only first two neighbors (\(C\) and \(B\)) are contributing to the node's own temperature value and only these two neighbors are potential data forwarders (column P shows priority of the next hop forwarder). In case of low thermal conductivity value, \(\xi_{X_{NORM}}=0.3\), the node's temperature calculation consists of three steps. The final temperature value is much lower, because of node's low thermal conductivity value. There are three neighbors contributing to the temperature of node \(X\). Thus, more alternative paths is available when one of those neighbors is disconnected. From the Equation (1) we can see that temperature of the node depends on the two factors (virtual physical attributes), namely on the temperature of the neighbors and as previously noted, on the node's thermal conductivity value. The former one enables end-to-end path selection and the latter one depends on the characteristics of node. The characteristics of node could be mapped to node's energy, location, importance of generated data, position in hierarchy (e.g., clusterhead), priority, transmission power or any other.
## 3 Results and Discussion
### Mechanical Properties of GN/PSt Composites  
To be useful in practical applications, the porous material used for oil/water separation processes must have excellent mechanical properties [53]. Figure 4 shows that the overall trends in the compressive stress-strain curves measured for the GN/PSt composites with different GN contents were similar, and the curves could be divided into three stages. At strains less than 10%, the stress increased linearly with the applied strain. Between 10% and 60% strain, the measured stress plateaued, indicating that the porous material absorbed a large amount of compressive energy and suggesting that the skeleton of the structure was highly deformed. At strains greater than 60%, the material began to fracture under the applied pressure and underwent a densification process. At this time, the stress increased sharply with the increase in the strain [54]. Materials prepared by HIPE polymerization have poor mechanical properties, for example, they are highly brittle and easily pulverized, due to the use of rigid monomers and the high crosslinking densities of the final materials [44]. Here, we see that as the amount of GN added during the HIPE polymerization increased, the compressive strength of the resulting composite materials first decreased and then increased. Small amounts of added GN increased the flexibility of the material, but the addition of excessive amounts of GN resulted in uneven dispersion and agglomeration of the nanosheets, which concentrated the stress and reduced the flexibility of the final composite material. From the present studies, the addition of 4% GN was the optimal amount to effectively improve the flexibility of the porous material while also increasing its hydrophobicity. Therefore, GN4/PSt was selected for the subsequent tests.  
Figure 3: WCAs of the GN/PSt composite porous materials.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## 3 Results
### Morphology and Spectroscopy of the Samples  
The modulation in the third-order nonlinear optical effects in the laser annealed ZnO nanostructures was obtained with no observation of changes in optical absorption nor in their morphology. Microscopy evidences for the nanostructured morphology exhibited by the ZnO thin solid film samples are shown in Figure 3. Statistical measurements indicate that the grain size corresponds to particles with a distribution from 100 to 500 nm. The pyramidal shape of the columnar nanostructures seems to be a signature for the defects and dislocation in these nanostructures [23].  
In Figure 4 is shown a representative XRD pattern measured in the ZnO samples studied in this work. No differences were identified in the as-prepared sample in comparison to the laser annealed sample. The ZnO nanostructures correspond to an indexation related to zinc oxide hexagonal (wurzite) phase consistent with the standard values for ZnO bulk (JCPDS Cardno. 36-1451). No evidence of presence of Ag in the films was detected by exhaustive XRD exploration measurements.  
High transparency in the films was revealed by spectroscopy observations. As it could be expected, from Figure 5 can be seen a monotonic increase in the absorption in the UV region of the electromagnetic spectra. However, only a small change in the optical absorption for the comparative samples at the 532 nm wavelength can be clearly visualized.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Sample & \(\alpha_{0}\) [cm\({}^{-1}\)] & Bandgap [cm\({}^{-1}\)] & \(n_{0}\) [eV] & \(n_{2}\) [m\({}^{2}\)/W] & [m/W] & [cm\({}^{-1}\)] \\ \hline As-prepared nanostructures & \(1.0541\times 10^{4}\) & 3.1813 & 1.95 & \(1.7\times 10^{-15}\) & \(4.4\times 10^{-9}\) & \(2.54\times 10^{-9}\) \\ \hline Laser annealed nanostructures & \(1.0735\times 10^{4}\) & 3.2059 & 1.95 & \(5.9\times 10^{-15}\) & \(11.9\times 10^{-9}\) & \(8.80\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 1: Optical parameters.  
Figure 2: Nanosecond probe transmittance vs. angle of polarization of the interacting beams in the thin films studied.  
Figure 4: XRD pattern showing the principal planes measured in the studied ZnO sample.  
Figure 5: Representative UV–vis absorbance spectra of the ZnO samples.  
Figure 3: Typical SEM micrograph of the ZnO thin solid film.
## 2 Materials and Methods
### Methods
#### 2.2.3 Artificial Neural Networks  
An artificial neural network (ANN) is a collection of artificial neurons organized in the form of layers [25]. Neurons are connected by weighted connections to form a NN. The most widely used ANNs in time series prediction are the multilayer perceptrons with an input layer, an output layer, and a single hidden layer that lies between the input and output layer. The most common structure is an ANN that uses one or two hidden layers, as a feed-forward neural network with one hidden layer is able to approximate any continuous function. Supervised learning algorithms and historical data can be used for the learning process of ANNs. The output of each neuron can be calculated based on the following formula:  
\[X(t)=F\left(\sum_{j=1}^{m}X_{j}(t)\cdot w_{j}+b\right) \tag{10}\]  
where \(X_{j}(t)\) is the value of the \(j\)th input signal, \(t=1,\ldots,N_{tr}\), \(N_{tr}\) is the number of training records, \(w_{j}\) is the synaptic weight, \(m\) is the number of input signals, \(b\) is the bias, and \(F\) is the sigmoid activation function. Training a neural network needs the values of the connection weights and the biases of the neurons to be determined. There are many neural network learning algorithms. The most popular algorithm for ANN learning is the back-propagation method. In this learning method, the weights change their values according to the learning records until one epoch (an entire learning dataset) is reached. This method aims to minimize the error function, described as follows [78; 14; 79]:  
\[MSE_{tr}(l)=\frac{1}{2N_{tr}}\sum_{t=1}^{N_{tr}}{e_{t}}^{2} \tag{11}\]  
where \(t=1,\ldots,N_{tr}\), \(N_{tr}\) is the number of training records, \(l\) is the number of epoch, \(l=1,\ldots,L\), \(L\) is the maximum number of epochs, and \(e_{t}\) is the one-step-ahead prediction error at the \(t\)th iteration, which is equal to:  
\[e_{t}=Z(t)-X(t) \tag{12}\]  
where \(X(t)\) is the output value of the ANN, and \(Z(t)\) is the desired value.  
The modification of the weights in the back-propagation algorithm can be calculated by the formula:  
\[\Delta_{w_{kj}}(l)=-\gamma\frac{\partial J(l)}{\partial w_{kj}(l)} \tag{13}\]  
where \(\Delta_{w_{kj}}(l)\) is a change of the weight \(w_{kj}\) at the \(lth\) epoch, \(\gamma\) is a learning coefficient.  
Backpropagation algorithm with momentum modifies the weights according to the formula:  
\[\Delta_{w_{kj}}(l)=-\gamma\frac{\partial J(l)}{\partial w_{kj}(l)}+\alpha \Delta_{w_{kj}}(l-1) \tag{14}\]  
where \(\alpha\) is a momentum parameter.
# Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
); anders.mikelsen@sljus.lse (A.M.);  
Centre for X-ray and Nano Science (XNS), Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Germany;
and high surface to volume ratio of the NWs result in higher piezoelectric effects, which influence the electron-hole recombination. The nanostructures are commonly analyzed by the laboratory table-top equipment employing different techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), laboratory X-ray diffraction (XRD), etc. In most of the cases, these methods have limitations, which do not allow investigation of the local structure of a single object along the whole NW with high resolution. Modern 3 rd generation synchrotron facilities allow investigation of individual nanostructures with intense, highly-coherent, focused down to sub-micrometer scale X-ray beams. Various coherent X-ray diffraction techniques, such as coherent diffraction imaging (CDI), Bragg CDI, and ptychography may provide an important information about the three-dimensional (3D) strain field and deformation of a single nanostructure with a high spatial resolution [10; 11; 12; 13; 14]. The Bragg CDI is a lensless imaging technique that makes use of the coherence properties of the X-ray beam. When a finite object is illuminated by a coherent X-ray beam in Bragg geometry, interference between the incoming wavefront and the scattered beams generate a diffraction pattern, which can be recorded in the far-field by a 2D detector with proper sampling [15]. By rotating the sample in the vicinity of the Bragg angle and recording 2D diffraction patterns, one can measure a full 3D diffraction pattern and then use iterative algorithms [16; 17] to determine the 3D shape and strain field in a sample. A complex amplitude object is reconstructed, whose modulus is directly related to the sample's crystalline shape and its phase is given by the projection of the displacement field on the scattering vector [18]. This allows to determine unique information about the strain field in nanosamples under different external conditions [19; 20; 21]. In our previous study [22], we analyzed the NWs of the 350-400 nm diameter with one type of contacts. We have demonstrated that deposition of the Au contacts, as well as the applied voltage bias deform single GaN nanowires leading to their bending. It was also demonstrated that the arching of the nanostructures may reach the critical value, when the NW is broken. In our current study, we analyzed NWs of the two different diameters of 200 nm and 350 nm, as well as we investigated the influence of the various type of contacts and applied voltage on the strain field and structure of these NWs.
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 2 Conceptual Background  
### Inspections  
In the construction phase, inspections are one of the important processes that ensure that work and workers comply with predetermined codes, standards, and development progress [18]. Meanwhile, at the operational stage, inspections are a way or strategy to reduce the risk of failure, where failure occurs due to damage [19]. In addition, another study stated that inspections were carried out to collect information about the conditions of structural components during their operational periods. This needs to be carefully considered and planned to optimize structural life cycle costs. Apart from the economic consequences associated with structural failure, social and environmental aspects can also be considered in the context of making decisions regarding inspections [20].
## 1 Introduction
The two-color quantum correlation between twin beams was usually obtained using NOPO with doubly or triply resonant configurations [13; 14; 15; 16; 17]. However, stable operation and frequency tuning continuously without mode hopping are difficult to achieve using doubly or triply resonant NOPOs because simultaneous cavity resonance conditions should be fulfilled. In this case, the stability of system will be reduced, and the level of quantum correlation between twin beams will be limited. Two-color nonclassical states can be achieved experimentally using a singly resonant OPO (SRO) in which only the signal is resonant in the cavity [29; 30]. Using this configuration of SRO, continuous tuning without mode hopping can be realized by locking the transmission peak of intra-cavity etalon to the oscillating mode of SRO [31; 32; 33; 34]. SRO is a good choice to generate two-color quantum correlated twin beams that are large frequency separated. In this paper, down-conversion beams with a telecommunication wavelength at 1.5 \(\upmu\)m and a mid-infrared wavelength at 3.3 \(\upmu\)m were generated experimentally from an SRO operated above the pump threshold with a magnesium-oxide doped periodically-poled lithium niobate (MgO:PPLN) crystal in the cavity. The two-color quantum correlation between the generated down-conversion beams was detected using home-made high-gain broad bandwidth detectors at 3.3 \(\upmu\)m and 1.5 \(\upmu\)m. A theoretical model for the two-color quantum correlation between the twin beams was given using a semi-classical approach. Then, the influence of the analysis frequency and pump parameter on the quantum correlation between twin beams was discussed theoretically and experimentally.
## 3 Experiments and Simulation
### Simulation Method  
Based on finite element simulation, this paper predicts the fatigue life of cracked carbon-fiber structures by writing a UMAT material subroutine, providing a model for the uncertainty analysis of their fatigue life. The main computational process of the UMAT subroutine is as follows:  
1. At the start of the increment step, the ABAQUS main program transfers the initial material properties, stress, strain, strain increment, and state variables to the UMAT;
2. The UMAT subroutine calculates the stress at the end of the increment step based on the user-provided material constitutive model and updates the stress, strain, and state variables accordingly;
3. At the end of the increment step, the UMAT transfers the stress, strain, and state variables back to the main program and provides the Jacobian matrix of the constitutive relationship to the ABAQUS main program to form the global stiffness matrix of the structure and determine whether the system has reached equilibrium;
4. If the system is not balanced, the iteration is considered non-convergent, and the next iteration is performed until convergence is achieved, at which point the next increment step calculation begins.  
The three-dimensional modeling was performed using solid elements selected based on the design dimensions. The simulation of composite laminate typically employs a layer-by-layer approach, cutting the carbon-fiber structure into eight layers along the thickness direction, with each layer representing a single element. For each layer, the material properties are identical except for the ply angle. The model and its boundary conditions are shown in Figure 7, where the red area indicates the pre-existing crack. The specific settings of the model include: one end fully fixed and the other end subjected to a periodic sinusoidal fatigue load. The UMAT subroutine is used to perform stress analysis, fatigue damage assessment, material parameter degradation, and fatigue life prediction of the carbon-fiber structure, as illustrated in the flowchart in Figure 8.  
Figure 7: Finite Element Model Setup.  
Using the above fatigue simulation analysis method, the fatigue life of carbon-fiber structures with different initial crack lengths under a fatigue load of 9.5 KN was simulated. As shown in Figure 9, with the increasing number of load cycles, the extent of the crack propagation continues to expand, and the stress level at the critical points also increases.
## 2 Network Virtualization
### NV Enabling Technologies  
The virtualization's key enabling strategies include SDN (Software Defined Networking) and NFV (Network Functions Virtualization). NFV and SDN have similarities, but they are not identical. Both are based on network abstraction, but the way the two technologies separate functions and abstract resources make the difference.  
* Software Defined Networking [7]: The traditional hardware-based networks cannot meet the computing and storage needs for the user's service requests in an unstable and a changing environment. SDN provides a better fit in such situations by including simplified and enhanced network control, flexible and efficient network management, and improved network service performance. Moreover, the SDN separates the control plane from the data plane through a well-defined programming interface, such that the centralized controller can have a complete view of the entire network as shown in Figure 2. Thus, SDN ensures a new virtualization level through open protocols, such as OpenFlow (OF) protocols, and standardized interface between the control plane and the data plane.
* NFV: The NFV paradigm aim is to decouple network functions from dedicated physical equipment by using virtualization technology, and runs the virtual network functions (VNFs) in the general purpose physical or virtual network appliances. To conclude, the main difference between the SDN and NFV architectures is the overall focus network management responsibilities, and the standards controlling the architectural and functional deployment.
* SDN-enabled NFV architecture: Several recent approaches [8, 9, 10] were aimed at integrating the SDN into NFV architecture to enhance the network performance by combining the benefits of the two paradigms described by Figure 3. This revolutionary architecture concept is dedicated to adapt the increasing on-demanding services for the next-generation mobile networks such as the 5G [11] technologies and other applications, by providing more flexibility and more scalability.  
Figure 2: SDN layered architecture.  
One of the most critical challenges in network virtualization is the service provisioning optimization. The efficient NFV approaches must address many objectives such as optimizing the resource utilization, ensuring load balancing, and reducing the congestion. Game theory is one of the promising solutions for the optimization problems in the network virtualization paradigms.
## 3 Results
### Spatial Concentration Mapping Using NN-PSO + EBK
Figure 10: Physicochemical parameters map of Calapan City (**a**) Temperature, (**b**) pH, (**c**) EC, and (**d**) TDS. The highest Ba concentration was measured in Brgy. Canubing I, where it was 7.9 times more than the background value for Ba measured in the research area. The average Ba concentration across all sample sites was 5.6 times more than the background value in the area of study. The highest concentrations of Cu were measured at several locations and recorded to be three times greater than the background concentration of copper. The mean concentration of Cu was found to be 2.1 times that of the background concentration. The Fe concentrations were found to be highest in Brgy. Gutad where it was found to be 6.1 times greater than the background value reported for the research region. Moreover, multiple sites were observed to exceed the WHO standards for Fe. These sites include Brgy. Canamishan, Brgy. Ibaba East, Brgy. Masipit, Brgy. Parang, Site 2 of Brgy. Persons, Brgy. San Vicente East, Brgy. Sta. Cruz, Brgy. Sta. Rita, and Brgy. Sto. Nino. Meanwhile, the mean concentration in Calapan City was just 0.5 percent more than the background level. Mn and Zn concentrations were highest in Brgy. Sto. Nino and Brgy. Gutad, respectively. However, these concentrations were still below the background concentration in the research region. The heavy metal concentration trend in the study area was observed to be Mn < Cu < Ba < Zn < Fe. Generally, these concentrations are within the WHO, USEPA and PNSDW limit except for Fe in several areas.
# Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters
## 2 Materials and Methods
### Analytical Model
Coil resistance is depending on the cross-section area and material of the wires (copper resistivity \(\rho_{Cu}\)), number of turns, and mean diameter of the coil (6). \[R_{coil}=\frac{4\rho_{Cu}}{d_{wire}^{2}}\times d_{coil,mean}\times N_{turns} \tag{6}\]  
An increasing number of turns results in a higher coupling coefficient but also greater damping, thus modifying the moving magnet speed and the variation of flux linkage in time. The coil axial length and location along the tube play a critical role in power generation because they affect the flux linkage variation during the magnet oscillation. The optimum load resistance value depends not only on the coil resistance but also on the electromagnetic coupling coefficient and viscous damping, hence on the moving magnet dimensions [29]. \[R_{load,opt}=R_{coil}+\frac{k_{em}^{2}}{c_{vis}} \tag{7}\]  
Equations (1)-(5) are implemented in a MATLAB/Simulink model for dynamic simulations of the system to obtain the moving magnet position, speed, acceleration, induced voltage, current, and generated power on the load for each time step. The Simulink block scheme is shown in Figure 3 and needs the stiffness and damping characteristics to be defined for each harvester configuration. Ansys Maxwell software for FEM analyses is essential for this study as it allows us to numerically evaluate the characteristics mentioned above. The stiffness and damping data obtained from Ansys simulations are thus inserted in the Simulink model as 1D Look-up Table blocks.