esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
## 1 Introduction
3D reconstruction technique to determine the fiber orientation and length distribution in short glass fiber-reinforced phenolic foam composites. [PERSON] et al. [19] applied the C.T. 3D reconstruction technique to determine the fiber orientation distribution in short glass fiber-reinforced PA6 composites. Nondestructive measurement techniques can expose three-dimensional information in a variety of materials without causing damage to the sample, but they are restricted, low-resolution, or costly. Despite their promise, their current applications are restricted [20]. [PERSON] et al. [21] used a continuous sectioning method to trace the fiber curvature and orientation in a single-oriented carbon-fiber-reinforced thermoplastic composite. [PERSON] et al. [22] compared different methods for analyzing the orientation of fibers in composite materials using computed tomography image data. They found that the structure tensor-based method is the most effective, even for low contrast images. Nonetheless, the continuous sectioning method necessitates continual removal or polishing of the specimen after grinding to obtain a sequence of continuous cross-sectional photographs of the material's interior, making the entire procedure laborious, time-consuming, and labor-intensive. Due to the fracture surface's nonplanar character, numerous fields require assessing and analyzing items' three-dimensional structures. SEM 3D reconstruction technology based on binocular stereo vision has made a 3D reconstruction of material surface microstructure possible. The key advantage of the SEM-based technique is its ability to provide high-resolution images of the microstructure of composite materials. Many researchers have recovered the 3D geometry of material fracture surfaces using this technology [23; 24]. However, these research works are primarily focused on the field of metal and ceramic materials. The application of this technique to the analysis of fiber-reinforced composites has not been published. Informed by the aforementioned literature, this study aims to apply the SEM 3D reconstruction method to evaluate fiber orientation in short fiber-reinforcedcomposites. The analysis of SEM images can benefit from the application of image processing techniques, which allows for more accurate measurement of the fiber orientation distribution in the composite. This method permits the examination of a massive number of data, resulting in a statistically significant sample size that permits more precise conclusions to be reached. As a result, the design, optimization, and performance of composite materials across a wide variety of industries can be greatly improved.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 4 Performance Analysis
### Optimization of LT Code Parameters  
In this subsection, we propose a novel optimization model that aims to find parameters \(c\) and \(\delta\) (which define the RSD distribution of the LT codes) to maximize the DSP. Depending on the DSP obtained in the previous subsection, we propose a new optimization model to design the parameters of LT codes. The proposed optimization model is formulated in Equation (9).  
\[\underset{c,\ \delta}{\text{max}} \underset{\Omega(x),\ n}{DSP}\] s.t. \[n=k(1+\epsilon),\] \[\delta\leq 1,\] \[\delta>0, \tag{9}\] \[c>0,\] \[c\geq\frac{\sqrt{k}}{(1-k)\ln(k/\delta)},\] \[c\leq\frac{\sqrt{k}}{2\ln(k/\delta)},\]  
where the number of received symbols \(n\) is fixed. The last two constraints are obtained by sitting the spike position (i.e., \(k/R\)) between the two extreme cases. That is, \(2\leq\frac{k}{R}\leq k-1\)
## 2 Materials and Methods
### Synthesis and Characterization of the Electrolyte and Electrode Materials
The phase composition and crystal structure identification and calculation of the cell parameters was accomplished by employing the JCPDS base using MDI Jade 6 software. The XRD patterns are presented in the Supplementary Materials Figures S1-S5. The powders' specific surface area (S\({}_{\text{BET}}\)) was determined after the final ball-milling using a TriStar 3000 device (Micromerities, Germany). The characterization results are given in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Synthesis Method** & **Designation** & **XRD Data** &
\begin{tabular}{c} **S\({}_{\text{BET}}\), m\({}^{2}\)/g** \\ **(After Final** \\ **Ball-Milling)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Electrolyte BaCe\({}_{0.89}\)Gd\({}_{0.1}\)Cu\({}_{0.01}\)O\({}_{3\text{-}8}\)} & & \multicolumn{2}{c}{orthorhombic structure} \\  & nitrate combustion & & (sp. gr. Pmcn) & \\ BaCe\({}_{0.89}\)Gd\({}_{0.1}\)Cu\({}_{0.01}\)O\({}_{3\text{-}8}\) & method & BCGCCuO & a = 8.7929(21) Å & 3.01(7) \\  & & & b = 6.2333(13) Å & \\  & & & c = 6.2197(9) Å & \\ \hline \multirow{4}{*}{Electrode La\({}_{1.7}\)Ba\({}_{0.3}\)NiO\({}_{4\text{-}8}\)} & two-stage solid state reaction method & LBNO\({}_{\text{ss}}\) & tetragonal structure & \\  & & & (sp. gr. f4/mmm) & \\  & & & a = 3.8466(5) Å, & \\  & & & c = 12.7985(21) Å & \\ \cline{1-1} \cline{2-5}  & nitrate combustion & LBNO\({}_{\text{nc}}\) & tetragonal structure & \\  & & & (sp. gr. f4/mmm) & \\  & & & a = 3.8582(4) Å, & \\  & & & c = 12.8378(37) Å & \\ \hline \multirow{4}{*}{Electrode La\({}_{1.6}\)Fe\({}_{0.4}\)O\({}_{3\text{-}8}\)} & & \multicolumn{2}{c}{rhombohedral structure} \\  & modified Pechini method & LNFO\({}_{\text{P}}\) & gr. f4/mmm) & 6.50(13) \\  & & & a = 5.5053(13) Å & (as-prepared) \\ \cline{1-1}  & & & b = 5.5053(13) Å, & 1.06(2) (calcinated \\ \cline{1-1}  & & & c = 13.2727(31) Å & at 1180 \({}^{\circ}\)C) \\ \cline{1-1} \cline{2-5}  & two-stage solid state reaction method & LNFO\({}_{\text{NS}}\) & a = 5.5062(12) Å & 1.50(3) \\ \cline{1-1}  & & & b = 5.5062(12) Å & \\ \cline{1-1}  & & & c = 13.2501(31) Å & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characterization of the powdered electrolyte and electrode materials. The BCGCuO powder chemical composition was determined by an inductively coupled plasma optical emission spectroscopy by means of an Optima 4300 DV device (PerkinElmer, Boston, MA, USA) and was close to the nominal composition, namely: Ba-20.51, Ce-18.32, Gd-1.80, Cu-0.15 at.%.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 2 Fundamentals and Characteristics of Traffic Flow  
Because road traffic is determined by the flow of cars, the prediction system should reflect the information properly. Let \(f(x,t)\) be the traffic flow at the \(t\)-th time interval and the \(x\)-th location. The traffic flow consists of three components; speed \(v(x,t)\), flow \(q(x,t)\), and density \(d(x,t)\). They satisfy the following relationship:  
\[d(x,t)=\frac{q(x,t)}{v(x,t)}. \tag{1}\]  
Taking the changes of time and space into account, the traffic flow should meet the following conservation equation. \(g(x,t)\) below denotes the entering flow of the \(x\)-th location at \(t\):  
\[\frac{\partial q(x,t)}{\partial x}+\frac{\partial d(x,t)}{\partial t}=g(x,t), \tag{2}\]  
Although they can be computed and used in real life, traffic predictions and simulations may not make satisfactorily predictions for the following reasons.  
* On the real road, traffic flow can also be affected by non-traffic factors. For example, the traffic flow during heavy rain could exhibit unusual speed and density compared to a regular traffic flow. In [12], the authors verified the correlations between traffic flow and weather factors, including temperature, wind speed, and rainfall. Furthermore, authors of [22] examined that daily weather conditions also affected the daily traffic flow.  
* Congested traffic shows a different behavior to regular traffic. Authors of [8,10] proposed traffic predictions to cope with heavy traffic situations. Day of the week and the time of the day help forecast congestion since it usually happens at rush hours and holidays.
* The actual time and location interval typically do not match those of the numerical model. The authors of [23] presented the constraints of time and location intervals to verify the behavior of the model. However, the traffic data of some highways do not satisfy those constraints because of an insufficient number of installed sensors.
## 5 Experimental Evaluation
### Training Process of LSTM & DRL  
When performing training on the DRL offload decision model, it takes a longer time to explore and select the better result due to the initial random selection action of the agent. In this paper, we propose to predict the server load based on the edge server record history data. Based on the prediction results, the server predicted to be non-idle is selected with a certain probability as the offload choice for the next moment. This solution allows the agent to effectively avoid selecting servers with high loads, thus reducing task processing latency and task dropping rates. In this paper, we propose to use LSTM for load prediction and compare the impact of decisions with load prediction (LSTM & DRL) and without load prediction (DRL) on offloading performance. As can be seen in Figure 7, the traditional DRL is significantly slower than the LSTM & DRL for load prediction in the early stages of training decision making. As the number of training increases and the historical data keeps increasing, the LSTM & DRL can effectively make predictions about the load, and based on the prediction results, the agent can make full use of the best strategy that has been explored. Therefore, after certain training, the average delay, energy consumption, and the number of task throw volumes can be reduced rapidly by using LSTM for load prediction.
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 2 Domain Adaptation
### Unsupervised Domain Adaptation Techniques
#### 2.3.3 Deep CORAL
In our experiments, we apply CORAL loss in the same way, simplifying Equation (7) to become Equation (8) in the case where \(z=1\):  
\[L=L_{\text{classif}}+\lambda L_{\text{CORAL}}\,. \tag{8}\]  
More recently, further work has proposed a new approach built upon the Deep CORAL method. The authors of [60] argue that the Euclidean distance used in the original Deep CORAL proposal may not be the most appropriate way to measure the distance between the source and target domains. Knowing that covariance matrices are positive semi-definite, they can be seen as two points lying on a Riemman manifold, and the metrics defined therein should consider its non-Euclidean structure [61]. Therefore, the Euclidean distance as defined in Equation (6) may be seen as only suboptimal in such a space. Considering this information, the log-Euclidean distance is instead a Riemannian metric that better captures the manifold structure. This metric is defined according to the following equation:  
\[d_{\text{log}}(\mathbf{X},\mathbf{Y})=\|\log(\mathbf{X})-\log(\mathbf{Y})\|_{ F}\,, \tag{9}\]  
where \(\log(\mathbf{X})\) is the matrix logarithm of \(\mathbf{X}\)[62]. Similarly as the CORAL loss, the log CORAL loss can be obtained by replacing the Euclidean distance in Equation (6) with the log-Euclidean distance. This is shown in Equation (10). \[L_{\text{log CORAL}}=\frac{1}{4d^{2}}\|\log(\mathbf{C_{s}})-\log(\mathbf{C_{t} })\|_{F}^{2}\,. \tag{10}\]  
Figure 2: Schematic representation of Deep CORAL adaptation technique. Through the eigenvalue decomposition of matrices \(\mathbf{C_{s}}\) and \(\mathbf{C_{t}}\) in Equation (10) we obtain the final expression for Log Deep CORAL loss:  
\[\mathbf{C_{t}}=\frac{1}{M-1}(\mathbf{V}_{t}^{T}\mathbf{V}_{t}-\frac{1}{M}( \mathbf{1}^{T}\mathbf{V}_{t})^{T}(\mathbf{1}^{T}\mathbf{V}_{t}))\,, \tag{5}\]0  
where \(d\) denotes the dimension of the features whose covariances are intended to align, as previously explained; \(\mathbf{S}\) and \(\mathbf{T}\) are the matrices that diagonalise \(\mathbf{C_{s}}\) and \(\mathbf{C_{t}}\), respectively; and \(s_{i}\) and \(t_{i}\) are the corresponding eigenvalues. The final setup for the log CORAL loss is the same as the one explained for the original CORAL loss, being described in similar terms as the ones presented in Equation (7): a classification loss is combined with the log CORAL loss to obtain the global loss term.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 2 Research Area and Research Methods
### Data Sources and Preprocessing  
The land use data are based on the land use data produced by the [PERSON] team of the Institute of Geographic Sciences and Resources of the Chinese Academy of Sciences. The data sources used were Landsat TM (Thermal Mapper: TM) remote sensing image data from 2000 to 2010 and Jilin No. 1 remote sensing satellite mosaic image from Beijing in 2020. According to the land use classification standards, land types are divided into five categories: cultivated land, forestland, water bodies, urban construction land, and unused land.  
The digital elevation model (DEM) was derived from the Geospatial Data Cloud GDEMV2 30 m dataset accessed on 1 January 2021 ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))). The slope data of the study area were obtained through the analysis of elevation data using ArcGIS 10.4.1. Road-related data (such as highway, national highway, railway, and subway), river data, population spatial distribution data, and GDP spatial distribution data are all sourced from the Resource and Environment Data Cloud Platform accessed on 1 January 2021 (www.resdc.cn/). The required urban resident population, socioeconomic indicators, transportation condition indicators, and livability indicators are all sourced from the corresponding year's Beijing Statistical Yearbook.  
Figure 1: Location of the study area.  
Research Methods for Determining the Quantitative Characteristics of Urban Expansion 2.3.1. Urban Expansion Speed Index  
The speed of urban expansion represents the annual growth rate of urban built-up areas during a certain period of time and is generally used to measure the speed of built-up area expansion [12]. The formula is as follows:  
\[\mathrm{V_{u}=\frac{S_{b}-S_{a}}{T}} \tag{1}\]  
where \(\mathrm{V_{u}}\) represents the expansion speed index, \(s_{a}\) represents the area of urban construction land in the early stage of the study, \(s_{b}\) indicates the area of urban construction land at the end of the research period, and T indicates the research interval time period.
## 3 Challenges with Smart City Applications
Moreover, many smart city applications involve deploying battery-operated devices that should be efficiently managed. * Latency: The delay of communicated data represents challenges with modern communication technologies. Many smart city applications are classified as ultra-reliable low-latency communications (uRLLC), which requires considerable work on network design to meet the latency demands. These challenges necessitate introducing novel network structures and promising solutions to overcome such limitations and meet the demands of smart cities. The rest of the work considers introducing novel solutions and key technologies to face such challenges and meet these requirements. Figure 6 presents the leading key technologies that can assist the evolution of smart cities. Figure 6: Key technologies of future smart cities.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 5 Discussion  
This section discusses additional control techniques that can be considered for the target FOWT beyond power production and load and motion reduction. Large horizontal-axis floating wind turbines are typically not installed individually but are grouped to form wind farms. Consequently, it is necessary to consider techniques related to the Demanded Power Point Tracking (DPPT) control, which aims to mitigate wake effects caused by upstream wind turbines [29; 32; 33]. The DPPT control can be achieved by adjusting the set-points of the blade pitch controller and the generator torque controller according to the demanded power command. Figure 15 shows the block diagram of the proposed control algorithm incorporating the DPPT control. The set-points are extracted from steady-state curves as the target rotor speed and the target generator torque trajectories corresponding to the demanded power command. In the control algorithm depicted in Figure 11, the target rotor speed inputted into RBC and the target generator torque inputted into the baseline torque controller are replaced by these extracted set-points.  
To verify the DPPT control algorithm, steady-state and dynamic simulations were performed for the target FOWT under power curtailment conditions. The dynamic simulation was conducted over 1800 s under below-rated wind speed conditions with a mean wind speed of 8 m/s, considering a power curtailment scenario at 70% starting around 900 s. The steady-state and dynamic simulation results under the DPPT control for the power curtailment are presented in Figure 16. In the steady-state simulations, it was observed that the blade pitch control and the generator torque control were successfully carried out based on the rotor speed and generator torque set-points, enabling the FOWT to track the demanded power command. In the dynamic simulation, prior to the initiation of the power curtailment at 900 s, the blade pitch control and the generator torque control operated normally, resulting in a mean power production of 6.967 MW. After 900 s, when the demanded power command was set to 70%, the power output had a mean value of 4.925 MW. Consequently, during the period when the power curtailment was active, it was  
Figure 15: Block diagram of the proposed control algorithm with DPPT control. *Lookup Table1 and *Lookup Table2 represent the target trajectories of the rotor speed and the generator torque, respectively.  
found that the difference between the mean produced power (4.925 MW) and 70% of the mean available power (4.877 MW) was within 1%.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Design of Wind Turbines
#### 3.1.1 Design Process
Although there is not yet much scientific research on this topic, two examples can be cited. [PERSON] et al. [34] applied an innovative approach using AI to analyze design loads and optimize foundation systems for overturning resistance while complying with Eurocode guidelines. This approach can significantly reduce the amount of concrete and steel required and thus reduce construction costs by up to 70%. [PERSON] [35] developed a meta-model based on machine learning to optimize foundationsfor wind turbines in his master's thesis. Two multi-output machine learning algorithms, namely the multi-output random forest (RF) algorithm and the multi-output feedforward neural network (FFNN) algorithm, were selected and optimized using the proposed genetic algorithms (GA) method to determine the best model configuration and the best combination of input features. The author concluded that the multi-output RF model has better performance in terms of accuracy and computational time compared with other developed models. This approach replaces conventional finite element analysis (FEA) with machine learning models that predict optimal designs more effectively, reduce computational time and maintain the accuracy of structural performance estimates.
# Stochastic Generator of Earthquakes for Mainland France
## 1 Introduction
Mainland France is a low-to-moderate seismic area since it is localized on an intraplate domain, which is also called a stable region, i.e., far from tectonic plate boundaries. The spatial segregation of the zoning method induces a drastic reduction in data. In fact, available data in each region are far smaller than those observed in the whole studied area. This finding is even more critical in low-to-moderate seismic areas, where seismic data are scarce. Using this method calls for specific care regarding data completeness and representativeness. The smoothing method provides a good description of the seismicity but is not complete. In intraplate regions, where seismic frequencies are low, longer observation periods are needed. This is particularly true for extreme events (highest magnitudes) that may not have been seen during the period of observations. In fact, according to some authors, e.g., [14; 15], stable regions present a seismicity that is more diffuse and homogeneous than past observations. Finally, in intraplate domains, only few or even zero active faults are known. Moreover, a seismic cycle assumption could be questionable in stable regions [15]. According to these authors, intraplate seismicity is not driven by repeating tectonic loading and release energy but by transient crust deformations [15] caused by other origins than tectonics; see [16] (for a review in a French context). For this reason, the application of the active fault method is disputed. For example, for an application in the southeastern part of France [17], [PERSON] et al. used this method as a complement to others, but only for the Provence region, where active faults are better known, e.g., [18; 19]. In this paper, we propose a new stochastic generator to simulate the seismic catalogues of earthquakes that considers the specificities of the intraplate region (e.g., little data, various and complex origins of seismicity). We start by presenting the stochastic generator that simulates both main shocks (main and independent events) and aftershocks (correlated events). Then, we apply this methodology to mainland France.
# A Review of Blast Loading in the Urban Environment
## 3 Numerical Methods
### BlastFoam  
[PERSON] et al. [41] utilised BlastFoam, an open-source computational software, to investigate blast wave interaction with randomly positioned objects inside a crowded environment. The positioning of obstacles was not truly random in all tests; a predetermined bias (towards or away) was selected to allow for some form of controlled input--though the positioning of obstacles was still random within the bounds of the bias. Compared to the no-bias scenario, the object bias towards the blast increased the blast loading towards the centre of the domain while reducing the blast loading towards the outside of the model. The opposite can be said when the bias is away from the centre, see Figure 8. These results could be linked to the shielding and channelling behaviours discussed in Section 2.3, where the two are seen to somewhat counteract one another. BlastFoam compares well against other CFD-based software used within the field and has been previously validated in numerous cases [104; 105].