esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 2 Modeling of System Components  
### Modeling of Wind Generator  
In general, the performance of a wind turbine is characterized by the power output curve, which is typically employed to estimate the electric output as a function of the wind speed at hub height. The approaches for modeling the wind turbine power curve can be broadly classified into parametric and non-parametric techniques [38; 39; 40]. The former include piecewise linear functions [41; 42; 43] and polynomial expressions [44], as well as models that are based on logistic functions with four or five parameters [38; 45], while the latter include ANNs [46], fuzzy logic methods [47], and data mining methods [45].  
The ideal aerodynamic system model to describe the mechanical output power of the wind turbine is given below:  
\[P_{w,I}(v)=\frac{1}{2}\rho\cdot A\cdot C_{p}(\lambda,\beta)\cdot v^{3}, \tag{1}\]  
where \(P_{w,I}\) is the theoretical power that is captured by the rotor of the wind turbine, \(v\) is the wind speed, \(\rho\) is the air density, \(A\) is the swept area of the rotor, and \(C_{p}(\lambda,\beta)\) is the dimensionless power coefficient as a function of the tip speed ratio \(\lambda\) and blade angle \(\beta\). Taking into account that the wind speed near to the ground varies with height, the wind speed \(v\) at hub height \(h\) is adjusted as follows:  
\[v=v_{0}(h/h_{0})^{\alpha}, \tag{2}\]  
where \(v_{0}\) is the reference wind speed at height \(h_{0}\) and the power law exponent \(\alpha\) denotes the ground surface friction coefficient which depends on the roughness of the terrain.  
In this work, the wind generator power output is approximated by interpolating the data provided by the manufacturer. The fitting equation of the output characteristic of the wind turbine is described by a sigmoid curve that can be expressed as follows:  
\[P_{w}(v)=\left\{\begin{array}{cc}0&0\leq v\leq v_{ci}\\ A+\frac{B-A}{1+\exp((C-v)/D)}&v_{ci}\leq v\leq v_{co}\\ 0&v_{co}\leq v\end{array}\right., \tag{3}\]  
where \(P_{w}(v)\) is the output power of the wind generator at wind speed \(v\), while \(v_{ci}\) and \(v_{co}\) are the cut-in and cut-out speeds, respectively, \(A\) and \(B\) are the horizontal asymptotes, \(C\) refers to the inflection point of the curve, and \(D\) is the scale parameter.
# Portable Raman Spectrometer for In Situ Analysis of Asbestos and Fibrous Minerals
###### Abstract
Raman spectra are very sensitive to variations in chemical composition and structure, allowing the discrimination of the different mineral phases [31; 32; 33; 34]. Micro-Raman allows the synergic exploitation of the structural discriminating power and the small portion of the investigated sample to extend the use of Raman spectroscopy to micrometric fiber bundles and/or single micrometre-sized fibers (fiber of 1-2 \(\upmu\)m in diameter; [35; 36]). Such exceptional instrumental properties were also exploited in the detection of mineral fibers in histological specimens [37; 38]. However, delicate optics, hazardous high-power laser sources, and a heavy anti-vibration table and electronics characterize benchtop setups. To take advantage of the highly diagnostic power of Raman spectroscopy during in situ surveys, portable instruments were tested in the identification of asbestos and asbestos-like minerals at the outcrop [36; 39]. However, a work devoted to comparatively test the performance of portable Raman spectroscopy (pRS) under different environmental and geological conditions is still lacking. This work aims to evaluate the capabilities of pRS in the identification and discrimination of asbestos and asbestos-like minerals in three mining sites differing in the geological setting and associated minerals. A comparison with benchtop high-resolution micro-Raman spectrometer was carried out to estimate the performance, advantages, and instrumental limitations of the portable Raman. The use of a portable Raman may help professional geologists involved in the health and safety assessment of mining sites to protect workers and the environment better.
## 2 Materials and Methods
### SOS, POS, EOS Analysis in the 2001-2021 Period
#### 2.2.1 Study Areas and Meteorological Time Series  
The analysis of the period 2001-2021 was performed across different grassland sites in Europe. Study areas were identified from WEKEO ([[https://www.wekeo.eu/services](https://www.wekeo.eu/services)]([https://www.wekeo.eu/services](https://www.wekeo.eu/services)) accessed on 27 July 2022), the EU Copernicus DIAS reference service for environmental data, virtual processing environments and skilled user support. From the specific layer that identifies the category \"grasslands\", we selected 31 different sites (Figure 2) from which the MODIS-kNDVI annual patterns to retrieve SOS, POS and EOS were elaborated and extracted. Sites were selected to include areas with different altitudinal and latitudinal conditions to increase the representativeness of the dataset (Table 2).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Site ID** & **Country** & **Latitude** & **Longitude** & **Altitude** & **Meteo Station** & **Meteo Years** \\ \hline AUS1 & Austria & 46.7782\({}^{\circ}\)N & 14.9746\({}^{\circ}\)E & 1740 & Feistritz Ob Bleiburg & 2008–2021 \\ \hline AUS2 & Austria & 47.0373\({}^{\circ}\)N & 11.2085\({}^{\circ}\)E & 1931 & Obergurgl & 2002–2021 \\ \hline BOS & Bosnia & 44.2219\({}^{\circ}\)N & 16.5075\({}^{\circ}\)E & 1092 & Livno & 2001–2021 \\ \hline BUL & Bulgaria & 42.1875\({}^{\circ}\)N & 23.2977\({}^{\circ}\)E & 2434 & Mussala Top Sommet & 2001–2021 \\ \hline CZE & Czech Republic & 48.5850\({}^{\circ}\)N & 14.3765\({}^{\circ}\)E & 642 & Budejovice Roznov & 2001–2021 \\ \hline DEN & Denmark & 56.1995\({}^{\circ}\)N & 10.5378\({}^{\circ}\)E & 40 & Aarhus & 2001–2021 \\ \hline FRA1 & France & 45.5949\({}^{\circ}\)N & 6.6931\({}^{\circ}\)E & 1812 & Bourg St Maurice & 2001–2021 \\ \hline FRA2 & France & 45.1842\({}^{\circ}\)N & 2.7997\({}^{\circ}\)E & 1169 & Aurillac & 2001–2021 \\ \hline FRA3 & France & 42.2984\({}^{\circ}\)N & 9.0294\({}^{\circ}\)E & 1125 & Ile Rousse & 2001–2021 \\ \hline GEO & Georgia & 42.6512\({}^{\circ}\)N & 44.601\({}^{\circ}\)E & 2258 & Pasanauri & 2004–2016 \\ \hline \hline \end{tabular}
\end{table}
Table 2: European grassland sites selected for phenological analysis during the period 2001–2021.  
Figure 2: Distribution of study areas across Europe.  
Meteorological data were extracted from the National Centers for Environmental Information (NCEI) stations of the National Oceanic and Atmospheric Administration (NOAA) dataset ([[https://www.ncei.noaa.gov/maps/daily/](https://www.ncei.noaa.gov/maps/daily/)]([https://www.ncei.noaa.gov/maps/daily/](https://www.ncei.noaa.gov/maps/daily/)) accessed on 29 July 2022). Wherever possible, mean daily temperatures were collected from stations located in the closest proximity to the grassland site coordinates.
# Size-Dependent Mechanical Behaviors of Defective FGM Nanobeam Subjected to Random Loading
## 2 Theory of FGM Timoshenko Nanobeam
### Nonlocal Elastic Theory for FGM Timoshenko Nanobeam  
To consider the size-dependent effect, we analyze the FGM Timoshenko nanobeams by nonlocal elastic theory, which assumes that the stress at one point in a continuum is related to the strain of all points in the continuum [43]. Furthermore, the nonlocal stress tensor \(\sigma\) at \(x\) point was provided by [PERSON] [5], as follows:  
\[\sigma_{ij}(x)=\int_{V}\alpha(\left|x^{\prime}-x\right|,\tau)t_{ij}(x^{\prime })\mathrm{d}x^{\prime} \tag{2}\]  
where \(t_{ij}(x^{\prime})\) is the stress tensor at \(x\) point; \(\alpha(\left|x^{\prime}-x\right|,\tau)\) is the kernel function that denotes the nonlocal modulus; \(\left|x^{\prime}-x\right|\) presents the distance in Euclidean space; \(\tau\) is a material constant. However, there is a tricky problem in Equation (2), where the integral constitutive relation is difficult to solve. To deal with the issue, in [PERSON]'s work [5], the integral constitutive relation can be converted to the differential equation as follows:  
\[L\sigma_{ij}=t_{ij} \tag{3}\]  
in which \(L\) is a linear differential operator. It is written as:  
\[L=1-(e_{0}a)^{2}\
abla^{2} \tag{4}\]  
where \(e_{0}\) is a parameter to modify the model to coincide with the experimental results, and \(a\) is the internal characteristic length. To simplify the formulas, a nonlocal factor \(\mu=(e_{0}a)^{2}\) is defined to describe the size-dependent effect. Thus, the constitutive relation can be presented as:  
\[\Big{(}1-\mu\
abla^{2}\Big{)}\sigma_{ij}=t_{ij} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Unit** & **Aluminum** & **Aluina (Al\({}_{2}\)O\({}_{3}\))** \\ \hline Young’s modulus/\(E\) & Gpa & 70 & 393 \\ Shear modulus/\(G\) & GPa & 26 & 157 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of FGM constituents.  
Figure 2: Material properties through the thickness with the profile parameter \(n\): (**a**) Young’s modulus; (**b**) shear modulus.
## 7 Processing Architecture
### CHP2 Physical Layer
#### 7.1.3 Reference Bank ToA Estimation  
The reference bank ToA estimator defined in Section 4.2 is implemented using a computation-efficient structure. This estimator performs a complex matrix multiplication, searches for the maximum in the resulting vector, and reports the corresponding index. This method is efficient but requires loading a large bank of coefficients into the block memory of the programmable logic, which is unrealistic.  
To reduce the number of coefficients that must be loaded into memory, we exploit the structure of the matrix and save only a subset of the rows. For an oversampling rate of 2 GHz and a sampling rate of 40 MHz, 50 micro-shifts correspond to 1 sample shift, meaning that every subsequent set of 50 rows is identical to the first 50 shifted by some integer number of samples. Instead of performing the full matrix multiplication, we can test only 50 rows against several sample-shifted versions of the received signal to emulate the output of the full multiplication without any loss. The structure of this reference bank is depicted in Figure 17.  
The polynomial fit described in Section 4.3 may be applied to improve the precision of the reference bank correlator. We extract 7 samples before and after the reported maximum and build a least-squares estimator for the coefficients of a second-order polynomial. Label the indices of these samples \(x_{-7},x_{-6},...,x_{6},x_{7}\) and the corresponding correlator outputs \(y_{-7},y_{-6},...,y_{6},y_{7}\). These 15 test points are used to estimate the polynomial fit to  
\[y=\beta_{0}+\beta_{1}x+\beta_{2}x^{2}+\epsilon. \tag{17}\]  
The corresponding least-squares estimator takes the form  
\[\begin{bmatrix}y_{-7}\\ \vdots\\ y_{6}\\ y_{7}\end{bmatrix}=\begin{bmatrix}1&x_{-7}&x_{-7}^{2}\\ \vdots&\vdots&\vdots\\ 1&x_{6}&x_{6}^{2}\\ 1&x_{7}&x_{7}^{2}\end{bmatrix}\begin{bmatrix}\beta_{0}\\ \beta_{1}\\ \beta_{2}\end{bmatrix}, \tag{18}\]  
\[\hat{\mathbf{\beta}}=\left(\mathbf{x}^{\dagger}\mathbf{x}\right)^{-1}\mathbf{x}^ {\dagger}\mathbf{y}\, \tag{19}\]  
where \(\mathbf{y}\), \(\mathbf{X}\), and \(\mathbf{\beta}\) are the matrixes defined in (18). For a 4 \(\times\) 4 MIMO system with 16 transmit-receive pairs, upon reception of the joint waveform, a receiver estimates the ToA of all navigation sequences on all receive channels in parallel. The hardware implementation of these 16 correlators is depicted in Figure 18.
# A Computational Study of Polymer Solutions Flow Regimes during Oil Recovery from a Fractured Model
## Materials and Methods
### Problem Statement and Boundary Conditions  
The computational study was carried out in a two-dimensional isothermal formulation. The geometry of the computational domain is shown in Figure 7a. This geometry was obtained by digitizing the image of a fractured surface. In Figure 7, the channels in which fluid flow occurs are filled with green, and the areas of solid rock are filled with white. A computational mesh with a total number of computational nodes of 700,000 has been constructed for the obtained geometry of the fractured medium. A fragment of the computational mesh in the channels is shown in Figure 7b.  
A constant flow velocity, corresponding to the required capillary number, was set at the inlet. Neumann boundary conditions were set at the outlet. The wetting angle at the walls of the computational domain was determined experimentally. Experimentally determined interfacial tension coefficients and wetting angles for the system consisting of displacing agents, oil, and rock were also introduced into the computational model. The displacing agents considered in the experiments were water and polymer solutions.  
Rheological parameters of the polymer solutions were determined experimentally. The viscosity of all polymer solutions used is described by a power rheology model with good accuracy.  
At the initial time point, the entire computational domain was filled with oil. In this work, for definiteness, we used the approximation of zero initial water saturation. We carried out a detailed study of the influence of initial water saturation on displacement characteristics earlier [27]. The calculations resulted in determining the oil-recovery factor and pressure loss while injecting the polymer solutions into the fractured structure.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Overview of Traditional Methods of Wake Detection  
Traditional methods of wake detection exploit the common features of ship wakes to discriminate them from other phenomena present in image acquisitions of the sea surface. The main characteristic feature of wakes that has been traditionally used in this context is their linear wedge-like shape. Therefore, the traditional approach to wake detection includes several methods and transforms able to detect straight lines in images. Other methods have been developed and tested over the decades. The following subsections serve to highlight these different methodologies for both SAR and optical imagery.
## 5 Conclusions  
In this study, a novel CIWHO-ORC technique was developed to effectively identify the presence of multiple static and dynamic objects, such as vehicles, pedestrians, signboards, etc., for autonomous driving systems. The CIWHO-ORC technique encompasses two major processes, namely multi-scale Faster RCNN-based object detection and OSRR-based classification. Additionally, the KH and WHO algorithms were applied to tune the parameters involved in the multi-scale Faster RCNN and OSRR techniques, respectively. The simulation analysis of the CIWHO-ORC technique was examined against benchmark datasets, and the results were inspected under several evaluation measures. Detailed comparative results demonstrated the promising outcome of the CIWHO-ORC technique's maximum detection accuracy of 0.9788 on the test of the Bdd100k dataset. In future, the proposed model can be extended to identify dangerous objects and the intended actions of detected pedestrians on roads. In addition, the proposed model can be tested on a  
Figure 7: Comparative Classifier Results of CIWHO-ORC technique on KITTI MOD dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mAP** \\ \hline MODNET & 0.630 \\ DS-ODFR & 0.550 \\ CIWHO-ORC & 0.692 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative mAP analysis of CIWHO-ORC technique under KITTI MOD dataset.  
large-scale heterogeneous dataset in the future. Moreover, the proposed model can be implemented in a real-time environment.  
Conceptualization, N.N. and O.A.; methodology, E.A.; software, A.M.H.; validation, J.S.A., E.A. and R.M.; formal analysis, A.M.; investigation, R.M.; resources, A.M.H.; data curation, R.M.; writing--original draft preparation, J.S.A., E.A. and R.M.; writing--review and editing, O.A. and A.M.; visualization, A.M.H.; supervision, E.A.; project administration, J.S.A.; funding acquisition, E.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (71/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R161), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4340237 DSR17). The Authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No. R-2022-176.  
This article does not contain any studies with human participants performed by any of the authors.  
Not applicable.  
Data Availability StatementData sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written with contributions from all of the authors. All of the authors have given approval to the final version of the manuscript.  
References
## 3 System Model Description
### Preliminary  
First, we give a brief introduction to the compress-and-forward scheme [13] in two BSs case (depicted as Figure 1). BS A acts as central node to execute joint decoding. BS B acts as cooperative node, forwarding its information via an unidirectional backhaul link from BS B to BS A. The specific operations are as below:  
BS B compresses and encodes its received signal waiting to be transmitted via a backhaul link, which is a mapping from a received sequence \(\{\mathbf{y}_{B}^{n}\}_{n=1}^{N_{s}}\in\mathcal{Y}_{B}^{N_{s}}\) to a codeword of length \(N_{s}\), \(r_{BH}\) is the compression rate:  
\[f:\mathcal{Y}_{B}^{N_{s}}\rightarrow\{1,2,\ldots,2^{N_{s}r_{BH}}\} \tag{1}\]  
BS A reconstructs the compressed signal with its received signal \(\{\mathbf{y}_{A}^{n}\}_{n=1}^{N_{s}}\in\mathcal{Y}_{A}^{N_{s}}\) as side information. It could be expressed using a mapping function:  
\[g:\{1,2,\ldots,2^{N_{s}r_{BH}}\}\times\mathcal{Y}_{A}^{N_{s}}\rightarrow \mathcal{Y}_{B}^{N_{s}} \tag{2}\]  
Then BS A jointly decodes users' information from \((\mathbf{y}_{A}^{N_{s}},\mathbf{\hat{y}}_{B}^{N_{s}})\).  
This compression and reconstruction process has been modeled as a Gaussian test channel:  
\[\hat{y}_{B}=y_{B}+q\quad q\sim\mathcal{NC}\left(0,\eta\right) \tag{3}\]  
where \(q\) is referred to as compression noise, with variance \(\eta\). Under this model, the optimum compression code parameterized by compressed noise is designed based on maximizing the sum rate criteria in [13]. We will verify, later in the paper, that it is also applicable in more widely used multicarrier systems.  
In the following part, we will give the modeling of our proposed scenario using the notations, equations and models presented above.
## 2 Data and Method
### Flux Tower Measurements  
Ground measurements of surface upward and downward shortwave radiation are available from the AmeriFlux and Surface Radiation (SURFRAD) networks (Figure 1). Data from AmeriFlux sites are measured and recorded every half hour. SURFRAD sites record data every minute. Ground albedo data from these networks are calculated as the ratio of upward to downward radiation averaged within the \(\pm\)30 min window of HJ satellite overpass.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **HJ CCD** & \multicolumn{2}{c}{**MODIS**} \\ \hline
**Band No.** & **Spectral Range (\(\mu\)m)** & **Band No.** & **Spectral Range (\(\mu\)m)** \\ \hline
1 & 0.43–0.52 & 3 & 0.46–0.48 \\
2 & 0.52–0.60 & 4 & 0.55–0.57 \\
3 & 0.63–0.69 & 1 & 0.62–0.67 \\
4 & 0.76–0.90 & 2 & 0.84–0.88 \\ \hline \end{tabular}
\end{table}
Table 1: Spectral bands (0.4–0.9 \(\mu\)m) of HJ CCD cameras and MODIS.  
Figure 1: Location of the flux sites from AmeriFlux and SURFRAD networks.  
After a cross-check of the HJ data with the ground measurements, clear-sky data from 26 sites in the AmeriFlux network and six SURFRAD sites were included in the validation. These sites cover a variety of land cover types and climate regions.  
To match the footprints between the ground measurements and satellite pixels, the actual footprints of the flux towers were calculated, taking into account the instruments' nominal height and field-of-view [31, 32].
# Collaborative Planning in Non-Hierarchical Networks--An Intelligent Negotiation-Based Framework
## 4 Application Case--Analysis and Results
### Multicriteria Optimization  
The multi-objective optimization module was implemented in MatLab version R2015a [MatLab]. The optimization problem was coded in MatLab language and solved using the intlinprog function provided in the Optimization Toolbox version 7.2. This function implements the branch-and-bound algorithm for linear integer programming problems. Different uniformly varied combinations of weights allow obtaining different nondominated solutions that approximate the Pareto-optimal set.  
The nondominated solutions found during the multi-objective optimization process correspond to different tradeoffs of the objectives. Each one is an alternative plan that has to be chosen. The multicriteria analysis allows selecting one of these solutions by the computation of value function according to the weights established with partners. For illustration purposes, in Figure 8, the nondominated solutions obtained on the fifth iteration of the negotiation are plotted in terms of cost, collaboration level, and quality certification.  
It should be noted that the partner selection problem has a combinatorial nature, and there are a large number of different feasible solutions. The multi-objective optimization process allows finding the nondominated set. Since this is, in general, a small set, the decision-making process is simplified. Moreover, it is assured that the decision-making process is restricted to optimal solutions, and the tradeoffs among solutions can be inspected. It can be observed there are three solutions for the quality certification level 2, two solutions for level 3, one solution for level 1, and one solution for level 0.  
It can also be seen that these solutions represent different compromises between cost and collaborative degree. For each solution, the score is indicated in terms of value function computed with the weights \(W_{1}=0.7\), \(W_{2}=0.2\), and \(W_{3}=0.1\), as well as the corresponding order. The selected solution is that on the bottom left side of the graph with a score of 93.91%. The cost, collaboration level, and quality certification values are 1955, 250 and 2, respectively.  
Figure 8: Multicriteria analysis of nondominated solutions.