esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase,** & **a, b, c** & \(\alpha\), \(\beta\), \(\gamma\)** & \\
**Symmetry** & **(Å)** & **(Degree)** & **Wyckoff Position** \\ \hline \(\gamma\) & 9.874. 7.769. 7.818 & 90.00, 90.00, 90.00 & Fe 8d 0.000 0.193 0.313 \\ CmCa & & & Si 16g 0.128 0.274 0.550 & Si 16g 0.373 0.448 0.727 \\ \hline \(\beta\)-1400 & & & Fe 4g 0.000 0.243 0.000 \\ C2/m & 9.612. 7.669. 4.068 & 90.00, 101.33, 90.00 & Fe 4i 0.309 0.000 0.646 \\  & & & Si 8j 0.630 0.287 0.546 \\  & & & Si 4i 0.421 0.000 0.203 \\  & & & Si 4i 0.149 0.000 0.059 \\ \hline \(\beta\)-1800 & & & Fe 4i 0.188 0.000 0.721 \\ C2/m & 6.889. 3.834. 6.520 & 90.00, 118.70, 90.00 & Si 4i 0.405 0.000 0.126 \\  & & & Si 4i 0.830 0.000 0.644 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Structures of the final structures identified during FPMD simulations of \(\beta\)-FeSi\({}_{2}\) at different temperatures (T). tions occurred due to the isotropy of the elastic properties of this orthorhombic phase. The results of the FPMD simulations showed that the unstable cubic \(\gamma\)-phase transforms into the orthorhombic \(\beta\)-phase at temperatures lower that 400 K, and at T \(\geq\) 400 K, it transforms into a C2/m structure. The final structures of the FPMD simulations of the \(\beta\)-phase at temperatures up to 2000 K were the 6- and 12-atom C2/m structures. Our results also predict that, at temperatures higher than 800 K, \(\alpha\)-FeSi\({}_{2}\rightarrow\alpha\)-Fe\({}_{y}\)Si\({}_{2}+\varepsilon\)-FeSi decomposition should occur with an optimum value of y = 0.8. Given these findings, it was suggested that structural transformations in the \(\beta\)-phase should occur via deformation of its unit cell, for example, through twinning, accompanied by the diffusion of the Fe atoms towards interfaces. As a result, the new \(\alpha\)-Fe\({}_{y}\)Si\({}_{2}+\varepsilon\)-FeSi system with y = 0.8 should be in agreement with the experiments, and this mechanism would not favor direct \(\beta\)-FeSi\({}_{2}\)-to-\(\alpha\)-FeSi\({}_{2}\) transformation. Conceptualization, P.E.A.T. ; methodology, P.E.A.T., V.I.I., V.I.S., L.G. and J.L. ; formal analysis, P.E.A.T., V.I.I., L.G. and J.L. ; investigation, P.E.A.T., V.I.I., V.I.S., L.G. and J.L. ; writing--original draft preparation, P.E.A.T., V.I.I., V.I.S., L.G., J.L. and A.P. ; writing--review and editing, P.E.A.T. and A.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study could be available on request from the corresponding author. The authors are grateful to the directorate of the Summer Institute at Jackson State University, USA, for the opportunity to perform large-scale calculations. Work by A.P. was performed under the auspices of the U.S Department of Energy by Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07 NA27344 and was supported by the Laboratory Directed Research and Development (LDRD) Program under project tracking code 22-SI-007. LLNL Release No. : LLNL-JRNL-843253. The authors declare no conflict of interest.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 3 Proposed Methodology
### Segmentation Network
#### 3.1.3 Loss Function  
The segmentation network is trained as a binary segmentation problem, and the binary cross-entropy loss function can be directly used for training the network. The training dataset is defined as \(S=\left\{X,Y\right\}\), where \(X=\left\{x_{i}^{(n)},i=1,...,M\right\}\) is the input image and \(Y=\left\{y_{i}^{(n)},i=1,...,M\right\}\) is the defect annotation map. \(M\)\(M\) represents the number of pixels in each image. The loss function is defined as follows (Equation (3)):  
\[L_{seg}=-\frac{1}{N}{\sum_{i=1}^{M}}\begin{pmatrix}y_{i}^{(n)}\cdot log\Big{(} F\left(x_{i}^{(n)};w\right)\Big{)}+\\ 1-y_{i}^{(n)}\Big{)}\cdot log\Big{(}1-F\left(x_{i}^{(n)};w\right)\Big{)}\end{pmatrix} \tag{3}\]  
where \(W\) presents the trainable parameters of the neural network model, and \(F(x_{n};w)\) is the predicted probability that the sample is a crack.  
The combined loss is used to separately train each decoder layer by generating predictions at each decoder layer and comparing them with ground truth. The loss function for each layer is referred to as the guidance loss for that layer (\(L_{seg}\)). The total combined loss function is computed as follows (Equation (4)):  
\[CL_{seg}=\alpha\cdot L_{seg}1+\beta\cdot L_{seg}2+\gamma\cdot L_{seg}3+\delta \cdot L_{seg}4+\theta\cdot L_{seg}5 \tag{4}\]  
where \(\alpha\), \(\beta\), \(\gamma\), \(\delta\), and \(\theta\) are the weights for the guidance losses of the first, second, third, and fourth layers and the combined output of the four encoder layers, respectively. Higher weights are assigned to the fourth layer and the final output, as they constitute the primary outputs of the prediction model. Different values were experimented with, and \(\alpha=\beta=\gamma=0.125\) and \(\delta=\theta=0.25\) were chosen.
###### Abstract
[24] conducted the dynamic simulation of water invasion in fractured gas reservoirs based on the orthogonal experimental method to study the effects of fracture permeability, fracture penetration, and water body multiples on indexes such as recovery rate and water-gas ratio. [PERSON] et al. [25] carried out physical simulation experiments of water invasion performance using fractured-porous full-diameter cores, converted the experimental results into evaluation parameters of water invasion performance by numerical inversion, and then studied the variation law of water invasion dynamics in fractured-porous water-bearing gas reservoirs. [PERSON] et al. [26] selected natural cores for series and parallel combinations and proposed a physical simulation experiment method for multi-layer commingled production edge water gas reservoirs. Based on this method, they conducted an experiment on four-layer commingled production in one well under the three conditions of gas reservoirs without water invasion, with water invasion without flow, and with water invasion with flow, and studied the law and influencing factors of water invasion in multi-layer edge water gas reservoirs. Although many research results have been obtained regarding the water invasion law of fractured edge and bottom water gas reservoirs through physical simulation experiments, most studies have focused on single-well experiments, and the overall water invasion law during the simultaneous development of multiple wells has not been adequately studied. Meanwhile, the conventional-scale core experiment can only collect gas and water production data from the holder outlet without capturing dynamic parameters like pressure and saturation distribution inside the reservoir. Furthermore, due to the influence of model dimension, the experimental results only reflect the trending influence law, mainly qualitative knowledge. It is difficult to characterize the actual production performance of gas wells accurately, and these results cannot be directly applied to the field [27; 28]. Therefore, based on the previous research [29; 30], this paper takes the typical water-producing well area in the southwest of the Kela 2 gas field as the research object, develops a large-scale three-dimensional physical simulation model, and establishes a physical simulation experiment method for the water invasion law of multi-well development in sandstone gas reservoirs with a strong water drive. Based on this, the water invasion physical simulation experiments of multi-well development under different water body multiples and production systems were conducted. These experiments aimed to reveal the law of water invasion in the multi-well development of sandstone gas reservoirs with strong water drives and to clarify residual gas distribution characteristics. The findingsprovide experimental and theoretical bases for the rational and efficient development of this type of gas reservoir.
###### Abstract
Consequently, it is crucial to enhance our understanding of the dynamics and influencing factors of the UHI effect, especially in the context of carbon neutrality and global warming. It is imperative to conduct comprehensive studies and investigations in this field to facilitate timely measures for UHI mitigation with all possible techniques. The existing literature on the urban heat island (UHI) effect in Zhengzhou is based on information up until 2019. In order to provide more up-to-date insights, this study extends the available information by including data from two additional years (2012-2021). The primary goal of this study is to use remote sensing data to analyze the spatiotemporal variations in the thermal environment and investigate the distribution properties and evolution law of the UHI effect in Zhengzhou City (China). The focus was not only to measure the UHI in the selected regions but also the land types (forest, shrub, grassland, cropland, built-up area, bare soil, wetland, and water body). The inter-annual and seasonal distribution of LST of different land use types in Zhengzhou during the period 2012-2021 was analyzed. This research uses two products of MODIS (MYD11A2 and MCD12Q1) LST data and land use data, using 3S technology (remote sensing (RS), geographic information system (GIS), satellite positioning system (SPS)) and Zhengzhou LST and UHI to study the land cover (LC)and the distribution characteristics and evolution law of the UHI effect in Zhengzhou City and to provide a reference for the next step of urban planning and design. This research focuses on three heat island indicators: (1) ground surface temperature, (2) heat island intensity, and (3) local heat island intensity through four aspects: (i) status, (ii) day and night change, (iii) seasonal change, and (iv) annual change. The goal of this study was to offer a thorough and comparative understanding of the spatiotemporal patterns of UHI intensity by using MODIS.
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
###### Abstract  
A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Meth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPTI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.  
Arithmetic Optimization Algorithm; fractional-order Predictive PI; Harris Hawks Optimization; industrial wireless mesh networks; optimal node placement; pressure process; real-time control +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Optimization and Comparison of the \"Base\" Schemes
#### 2.4.1 Implications of the \"Base\" Parameters  
Implementation of the \"base\" parameters shown in Table 1 in the \"base\" flow schemes for Cases 1 to 4 (illustrated in Figure 1) has an impact on the flow scheme design in some cases. In Case 1, a cooling temperature of 25 \({}^{\circ}\)C and a maximum temperature of 150 \({}^{\circ}\)C requires one fewer stages in each of the three compressors compared to the \"base\" flow scheme illustrated in Figure 1a. In Case 2, the CO\({}_{2}\) feed compressor also required one fewer stages for the 8.9 bara case compared to the scheme shown in Figure 1, but the NH\({}_{3}\) compressor remained unchanged compared to the original design. In Case 3, due to a lower CO\({}_{2}\) feed pressure, an extra stage is needed in the CO\({}_{2}\) feed compressor. The low-pressure NH\({}_{3}\) refrigeration compressors associated with Case 3 also require an additional stage to meet the 150 \({}^{\circ}\)C limit, but the high-pressure compressor is unchanged. In Case 4, the CO\({}_{2}\) feed compressor also needs an additional stage, but the scheme is otherwise unchanged. Each of these  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline Modelled (kWh/ton) & 106 & 101 & 79.9 & 86.6 \\ \hline Reported (kWh/ton) & 104 & 102 & 80.3 & 87.0 \\ \hline Difference (\%) & 1.9 & \(>\)1 & \(>\)1 & \(>\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the reported and modelled power consumptions for the “base” flow schemes.  
modifications was implemented in the models developed for the \"base\" case flow schemes when making the comparisons of process performance for each case in this part of the study.
# Cross Sensor Simulation of Tomographic SAR Stacks
## 2 Wavenumbers in SAR Acquisitions
### Wavenumbers in Airborne and Spaceborne SAR Surveys  
The much greater distance from sensor to target makes spaceborne SAR surveys rather different from airborne ones. Main differences are due to the significant atmospheric layer through which the signal propagates and to the similarity between near and far range. This section focuses on this latter issue and assumes that the propagation has been properly taken into account thus achieving a good azimuth compression.  
Airborne systems usually flight between \(\sim\)2 km and \(\sim\)10 km of altitude thus resulting in a wide range of look angles within the swath; as a general rule, near range targets are associated with look angles of about \(15^{\circ}\) reaching \(55^{\circ}\) in far range, this values being mainly determined by the antenna beamwidth in the elevation direction. On the contrary, the swath width of a spaceborne system shall be set according to the need of avoiding undesired ambiguous echos and nadir returns. These constraints together with the large distances at stake lead to a very small range of look angles; as a rule of thumb, it may change by \(3^{\circ}\) from near to far range. These features impact on the spectral region illuminated by the two systems in the \(\left\{ky,kz\right\}\) domain. Figure 4 shows the wavenumbers excited by an airborne system (in green) and a spaceborne one (in purple). With respect to the airborne, the spaceborne system here considered has a higher carrier frequency but a much lower bandwidth. The dashed line in Figure 4 shows the wavenumbers associated with the carrier frequency as the acquisition moves from near to far range. Its distance from the origin is constant and expressed by Equation (3) whereas its rotation depends exclusively on the geometry of acquisition. According to this, a polar system of coordinates turns out to be particularly helpful in this context; the relationship between the two coordinate systems are expressed by Equations (8)-(10).  
\[\mathbf{k}=\left[\begin{array}{cc}ky&kz\\ \end{array}\right]^{T} \tag{8}\] \[ky=\left|\mathbf{k}\right|\sin\left(\vartheta\right)\] (9) \[kz=-\left|\mathbf{k}\right|\cos\left(\vartheta\right) \tag{10}\]  
The wavevectors pointed out by the dashed line in Figure 4 obey to the relationship expressed by Equation (11).  
\[\left(\frac{4\pi}{c}f_{0}\right)^{2}=ky^{2}+kz^{2} \tag{11}\]  
A comparison between the impulse response functions (that is, the point spread functions) of airborne and spaceborne systems is shown in Figure 5 for difference range values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Central frequency & 400 MHz \\ Cross range resolution & 20 m \\ Number of images & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of a typical P-band tomographic system.
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
## 3 Materials and Methods  
### Finite Element Model  
The finite element model is employed to investigate the influence of the energy absorption characteristics and peak crushing force of four cellular parameters systematically. For honeycomb impact resistance studies, the number of cells in the transverse and longitudinal directions is required to be more than nine, so the finite element mode shape in this paper uses a \(10\times 10\) array of periodic cell structures. The concave I-shaped honeycomb structure which uses a commonly used aluminum alloy with density \(\rho=2.7\times 10^{-9}\)t/mm\({}^{3}\), elastic modulus \(E=7\times 10^{4}\)MPa, Poisson's ratio \(v=0.33\), and yield stress \(\sigma_{y}=76\)MPa is between the two impact plates. Assuming that the overall member satisfies the Mises reliability criterion and the ideal elastoplastic requirements, the specimen cell element is the S4R shell unit. Two steel plates, 240 mm long, 30 mm wide, and 3 mm thick, one fixed as a base plate and one as a velocity impact plate, are used at a distance of 1 mm from the cellular structure. These two plates are made of steel material with density \(\rho=7.8\times 10^{-9}\)t/mm\({}^{3}\), [PERSON]'s modulus \(E=2.07\times 10^{5}\)MPa, and Poisson's ratio \(v=0.3\). In the software, these two steel plates are endowed with rigid body attributes, and the unit attributes are selected as R3D4. To balance the computing time and accuracy, the cell mesh size of the honeycomb structure is 0.5 mm, and the cell mesh size of the steel plates is 1 mm. Five integration points are taken along the thickness direction of the honeycomb structure to ensure convergence of the results and to reduce the computational effort. The contact type is defined as general contact, and the friction coefficient is taken as 0.02 to simulate the frictional contact between the components of the model during the impact process. The model is shown in Figure 8.  
A particular velocity is applied to the velocity impact plate. The honeycomb sandwich reduces the energy of the impact on the base plate through the deformation. For the inves  
Figure 8: Impact on negative Poisson’s ratio honeycomb core materials.  
tigation of the effects of different geometric parameters, the origami-inspired honeycomb sandwich panels were grouped as group A, group B, group C, and group D. Each group consists of five samples, as shown in Table 1. The influence of these parameters on specific energy absorption and peak crushing force is studied. Nowadays, lightweight materials are sought, and the mass should also be considered.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 2 System Model
### Oma  
For this subsection, we analyzed the expressions for the received signal and the SINR with the OMA transmission scheme, which were regarded as benchmarks. The BD sent signal \(c(t)\) modulated on the data signal x(t). Under this assumption, the received signal at the \(d\)-th user is shown as  
\[y_{d}=h_{d}x(t)+\beta fg_{d}x(t)c(t)+n_{d}(t). \tag{5}\]  
According to the OMA guidelines, the received SINR expressions for the user and the BD can be obtained from (5). Hence, the received SINR of the \(d\)-th user can be expressed as  
\[\gamma_{d}^{OMA}=\frac{\rho|h_{d}|^{2}}{\rho|\beta|^{2}|f|^{2}|g_{d}|^{2}+1}. \tag{6}\]  
The SINR of the BD can be given by  
\[\gamma_{BD}^{OMA}=\frac{\rho|\beta|^{2}|f|^{2}|g_{D}|^{2}}{\rho|h_{D}|^{2}+1}. \tag{7}\]
## 3 System Model Description
### Channel Model  
**Satellite-to-user link:** The channel model between satellites and users is represented by the Weibull channel model, mainly considering rainfall attenuation, which has been utilized in numerous existing works [31; 32]. The distance between them can be regarded as the height \(H_{S}\) of the LEO satellite. Due to channel attenuation, the power attenuation of the satellite to user link is expressed as  
\[h_{SU}^{2}=\frac{G_{ST}G_{UR}\lambda^{2}}{\left(4\pi H_{S}\right)^{2}}10^{- \frac{F_{train}}{10}}\,, \tag{1}\]  
where \(\lambda\) is the wavelength of the carrier wave, \(G_{ST}\) and \(G_{UR}\) denote the antenna gains of LEO satellite and user satellite terminals, and \(F_{rain}\) represents the rainfall attenuation following the Weibull distribution [33]. Based on the power attenuation of the channel, it is easy to use the Shannon equation to calculate the maximum transmission rate between LEO satellites and users as  
\[R_{SU}=B_{S}\text{log}_{2}\left(1+\frac{P_{S}\cdot h_{SU}^{2}}{B_{S}\sigma_{S}^{2} }\right), \tag{2}\]  
where \(P_{S}\) is the transmission power of the LEO satellite, \(B_{S}\) is the bandwidth allocated to LEO satellites, and \(\sigma_{S}^{2}\) denotes the background noise power.  
**Inter-satellite link:** Due to there being less scattering in the space environment, the large-scale fading of electromagnetic waves in propagation is mainly caused by the free space propagation effect [34]. Hence, the power attenuation of the link between satellites can be expressed as  
\[h_{SS}^{2}=\frac{\text{G}_{ST}\text{G}_{SR}\eta_{\text{T}}\eta_{\text{R}}\lambda ^{2}}{(4\pi d_{SS})^{2}}, \tag{3}\]  
where \(G_{ST}\) and \(G_{SR}\) are the transmitter gain and receiver gain of the LEO satellite, respectively; \(\eta_{T}\) is the optics efficiency of the transmitter, and \(\eta_{R}\) is the optics efficiency of the receiver. Utilizing the power attenuation of the channel, the maximum transmission rate between LEO satellites can be calculated as follows  
\[\text{R}_{SS}=\text{B}_{S}\text{log}_{2}\left(1+\frac{\text{P}_{S}\cdot h_{SS }^{2}}{\text{B}_{S}\sigma_{S}^{2}}\right), \tag{4}\]  
Figure 1: Architecture diagram of integrated satellite–terrestrial remote sensing satellite networks.  
It is assumed that the link rates of the satellite-to-user and inter-satellite links are time-varied functions denoted by \(R_{su}(t)\) and \(R_{ss}(t)\), respectively, which are associated with Equations (2) and (4). The link rates \(R_{su}(t)\) and \(R_{ss}(t)\) are randomly generated within the ranges \([\frac{1}{3}R_{SU},R_{SU}]\) and \([\frac{1}{3}R_{SS},\ R_{SS}]\), respectively. In addition, temporary interruptions may occur on each link, at which time the link rate is 0.
## 2 Materials and Methods  
### Materials  
Tetraethyl orthosilicate (TEOS) (99%), triblock copolymer Pluronic 123 (P123, M\({}_{\rm n}\)\(\approx\) 5800), ammonium fluoride (NH\({}_{4}\)F) (99.99%), 1,3,5-trisopropylbenzene (TIPB) (95%), (3-aminopropyl)triethoxysilane (APTES) (99%), 2-bromo-2-methylpropionyl bromide (BMPB) (98%), 2,2\({}^{\prime}\)-bipyridine (BiPy) (\(\geq\)99%), copper (I) chloride (CuCl) (97%), styrene-4-sulfonic acid sodium salt (SSNa) (\(\geq\)90%), _n_-dodecane (nDD) (99%), acetic acid (AA) (\(\geq\)99.85%), _n_-heptanol (nHTL) (98%), trimethylamine (TMA) (\(\geq\)99%), dichloromethane (DCM) (\(\geq\)99.8%), and toluene (99.6%) were purchased from Sigma Aldrich (Taufkirchen, Bavaria, Germany). Methanol (MeOH) (99.5%) and ethanol (EtOH) (96%) were obtained from Fisher Scientific (Loughborough, England, UK). All chemicals were used as received unless otherwise stated in the text.