esa-sceva/satcom-chunk-collection huggingface dataset:
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 2 The Model of Correlated Wind Speeds  
### Nataf Transformation  
The Nataf transformation and Cholesky decomposition are used to convert non-normal relevant variables to independent standard normal ones.  
For the input speed wind vector \(\mathbf{V}=[v_{1},v_{2},\dots,v_{m}]^{T}\), whose probability density function (PDF) \(f_{i}(v_{i})\) and marginal cumulative distribution function (CDF) \(F_{i}(v_{i})\) are known, using marginal transformations, [PERSON] and [PERSON] [14] obtained the jointly standard normal variate vector \(\mathbf{X}=[x_{1},x_{2},\dots,x_{m}]^{T}\), which is expressed as:  
\[x_{i}=\Phi^{-1}[F_{i}(v_{i})]i=1,2,\cdots,m, \tag{1}\]  
where \(\Phi(\cdot)\) is the CDF of standard normal variable (SNV), and \(\Phi^{-1}(\cdot)\) is the inverse CDF of SNV.  
The relationship between correlation coefficient \(\rho_{ij}\) of vector \(\mathbf{V}\) and correlation coefficient \(\rho_{0,ij}\) of vector \(\mathbf{X}\) can be expressed as  
\[\begin{array}{ll}\rho_{ij}&=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty} \left(\frac{v_{j}-\mu_{j}}{\sigma_{i}}\right)\cdot\left(\frac{v_{j}-\mu_{j}}{ \sigma_{j}}\right)f_{v_{i}v_{j}}(v_{i},v_{j})dv_{i}dv_{j}\\ &=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}\left(\frac{v_{j}-\mu_{j}}{ \sigma_{i}}\right)\cdot\left(\frac{v_{j}^{-}\mu_{j}}{\sigma_{j}}\right)\times \phi_{2}(x_{i},x_{j},\rho_{0,ij})dx_{i}dx_{j}\end{array}, \tag{2}\]  
where \(\rho_{ij}\) of \(\rho_{0,ij}\) of are, respectively, the elements of correlation matrix \(\mathbf{\rho}\) of vector \(\mathbf{V}\) and the \(\mathbf{\rho}_{0}\) of vector \(\mathbf{X}\), \(\mu_{i}\) and \(\sigma_{i}\) are the mean and standard deviation of wind speed \(v_{i}\), respectively; \(\phi_{2}(x_{i},x_{j},\rho_{0,ij})\) is the bi-dimensional standard normal PDF of zero means, unit standard deviations, and correlation coefficient \(\rho_{0,ij}\).  
In most engineering applications, \(\mathbf{\rho}_{0}\) is a positive definite, so it can be decomposed by Cholesky decomposition.  
\[\mathbf{\rho}_{0}=\mathbf{L}_{0}\mathbf{L}_{0}^{\mathrm{T}}, \tag{3}\]  
where \(\mathbf{L}_{0}\) is an inferior triangular matrix.  
Then, the correlated standard norm vector \(\mathbf{X}\) is transformed into an independent standard normal vector \(\mathbf{U}=[u_{1},u_{2},\ldots,u_{m}]\) by using \(\mathbf{L}_{0}\).  
\[\mathbf{U}=\mathbf{L}_{0}{}^{-1}\mathbf{X} \tag{4}\]  
Through Equations (1)-(4) above, the correlated non-normal wind speed vector \(\mathbf{V}\) is transformed to the independent standard normal vector \(\mathbf{U}\), and this is the positive process of Nataf transformation.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 3 Description of an Isolated Fractional-Order Microgrid Model
### The Diesel Engine Generator Model  
The diesel engine generators (DEGs) have a lot of advantages, such as their fast speed in the start, low maintenance, and high efficiency; hence, they have been a good option for backup in isolated MGs [32]. By precisely regulating the DEG, the changes of load in the MGs can be instantaneously tracked. In addition, the diesel power element can compensate for the fluctuations of renewable DGs (such as WTG, PV, etc.) effectively.  
The transfer function of the DEG is illustrated in Figure 2, which describes the control relationship of the DEG output power and the LFC action. As presented in Figure 2, the elements of the governor and generator are represented by the first-order inertia models of inertia term.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Computational Domain Discretization
#### 1.2.3 Polyhedral Mesh  
The polyhedral (POLY) mesh (Figure 1--centre) was introduced in order to overcome the above-mentioned disadvantages of HEX and TETRA meshes and simultaneously combine the advantages of both types of the control volumes (low numerical diffusion of HEX and rapid semi-automatic generation of TETRA) [17; 27; 28; 29].  
The major benefit of POLY is that each individual cell has many neighbours (in contrast only four neighbours in case of TETRA elements), thus gradients can be approximated much better in comparison to TETRA. POLYs are also less sensitive to stretching than TETRAs which results in better numerical stability of the model. In some cases, POLY can even achieve better accuracy than HEX due to larger numbers of neighbouring elements allowing for an interchange of mass over a larger number of faces, reducing numerical diffusion effects caused by flows not perpendicular to any of the cell faces. This proves to be advantageous in situations where no prevailing flow direction can be identified and leads to a more accurate solution achieved with a lower cell count [17; 29].  
POLY mesh generation is a simple conversion of TETRAs to POLYs by decomposition of cells into multiple sub-volumes as illustrated in Figure 2. In order to do so, new edges are created on each face between the face centroid (dots in Figure 2) and the centroids of the edges of that face (diamonds in Figure 2). Subsequently, new faces are created within the cell by connecting the cell centroid to the new edges of each face. The newly-created faces may be adjusted and merged with the neighbouring faces during the agglomeration process in order to minimize the number of faces on the resultant polyhedral cell.  
Figure 1: Hexahedral (**left**), polyhedral (**centre**) and tetrahedral (**right**) control volumes.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 3 System Model  
In this section, we present the system model of BAN and the optimization problem formulation. It is interesting to note that the BAN optimal design problem presents many connections with general wireless network design problem (see e.g., [42; 43; 44]). We consider a body area network, consisting of a personal device and \(N\) lightweight sensors, such as those shown in Figure 1. Let \(N=\{1,2,3,\cdots,N\}\) denote the set of sensors. The system time is partitioned into time slots of constant duration, denoted by \(t\). Let \(T=\{1,2,3,\cdots,T\}\) denote the set of time slots. Each sensor has two queues: data queue and energy queue. Data queue is used to describe data collection and data transmission. Energy queue is used to describe energy consumption and energy harvesting.
# Local Stiffness Assessment of Geogrid-Stabilized Unbound Aggregates in a Large-Scale Testbed
## 5 Results and Discussion
### LWD Testing  
LWD tests were conducted on the surface of the unbound aggregate layer according to the ASTM E2835 test method after the shear wave velocity measurements. A portable LWD (ZORN ZFG 3.0), which has a 300 mm (11.8 in.)-diameter loading plate, was utilized. In addition to the center of the testbed, four additional LWD test locations were selected 61 cm (2 ft) apart from the center of the testbed to north, south, west, and east, respectively. Table 6 summarizes the average values of the resilient moduli, which were measured using the LWD device for each test. The highest and lowest moduli were observed from Test 5 and Test 3, respectively, and neither test section was geogrid stabilized. The average modulus value for the stabilized test sections and non-stabilized sections were 24.3 MPa and 24.2 MPa, respectively. Therefore, LWD test results could not detect the effects of geogrid stabilization. This could be attributed to the influence depth of LWD, which is 0.5 to 1.8 times the loading plate diameter, depending on testing conditions [62,63]. The thickness of the unbound aggregates above the geogrid was \(\sim\)1.2 times the load-plate diameter, which might be thicker than the influence range of LWD. As shown in Figures 10 and 11, the stiffening effect from the geogrid stabilization is a localized phenomenon in the vicinity of the geogrid. Considering the measurement of the LWD is more influenced by the layer near the surface, LWD test data do not properly reflect the local stiffness enhancement in the vicinity of the geogrid. Further, previous studies have reported no measurable benefits of geogrid stabilization observed from LWD modulus measurements [8,21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test No.** & **Surface Modulus (MPa)** & **Geogrid Placement** \\ \hline
2 & 26.2 & GG2-stabilized \\ \hline
3 & 18.2 & No geogrid \\ \hline
4 & 23.4 & GG2-stabilized \\ \hline
5 & 27.3 & No geogrid \\ \hline
6 & 23.3 & GG2-stabilized \\ \hline
7 & 27.1 & No geogrid \\ \hline \hline \end{tabular}
\end{table}
Table 6: Surface modulus results from LWD tests.  
Figure 11: DCP test results: (**a**) CBR profiles from DCP tests; and (**b**) Accumulated DCP blow counts and the geogrid-stiffened zone.
## 2 Background
### Problem Statement  
This study aims to develop a TL-based fault diagnosis algorithm, for the PACK model discussed, that leverages knowledge from a source domain with sufficient labelled data collected at one operating condition to accurately predict the health state labels for a target domain with no labelled data taken at a different operating condition than that of the source domain.  
The problem represents a common real-life scenario in which, after training a fault diagnosis model with labelled data under known conditions, systems such as the ECS are likely to be operated under a vast variety of conditions, generating massive amounts of unlabelled data under new conditions that are not considered in the initial training stage. Because labelling new data under every new condition is expensive or even impossible, there is a practical need to develop a fault diagnosis algorithm that can handle unlabelled data under new operating conditions.
## Materials and Methods
### Analytical Methods  
The composition in sugars (glucose, xylose, galactose, arabinose, and mannose) and inhibitory compounds (acetic acid, formic acid, furfural, and HMF) in the hydrolysate's before and after detoxification were measured by high performance liquid chromatography (HPLC) using a Waters 2695 liquid chromatograph (Mildford, MA, USA) equipped with a refractive index detector (Waters 2414). Sugar quantification was performed with a Transgenomic CHO-782 carbohydrate analysis column (70 \({}^{\circ}\)C and 0.6 mL/min ultrapure water as the mobile phase), while the inhibitors were analysed with a Bio-Rad HPX-87H column (65 \({}^{\circ}\)C and 0.6 mL/min, 5 mM H\({}_{2}\)SO\({}_{4}\)). The Folin-Ciocalteu method was employed to quantify the total phenolic compound concentration in the hydrolysate [18].  
Sugars, ethanol, and xylitol concentrations contained in fermentation samples were also measured by HPLC using the Bio-Rad HPX-87H column mentioned previously. In order to analyse the cell content in the fermentation samples, they were filtered using 0.2 \(\upmu\)m cellulose nitrate filters (Sartorius stedimBiotech, Gottingen, Germany). The biomass concentration was determined as the ratio between the mass of dried biomass and the volume of filtered inoculum [19]. All analytical determinations were performed in triplicate, and the average results and relative standard deviations were below 1%.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Experiment Description
#### 4.1.4 Configuration  
To cope with the high computational complexity of the transformer model, integration of the FMU state was achieved with the help of the Adams-Bashforth-Moulton variable step-size solver, as implemented in the Sundials sub-library [29]. All experiments used an initial step-size of 10 ms and iteratively located state events until a precision of 1 ms was reached, using relative and absolute integrator tolerances of \(10^{-4}\). Synchronization and prediction step sizes were chosen such that little delay was accumulated and real-time deviations quickly decayed to zero. In particular, a synchronization step size of 100 ms for periodic synchronization and a prediction horizon of 1 s supported by locally stored intermittent results every 100 ms for predictive event mapping were selected.  
The simulation outcome in terms of model in- and out-put variables was recorded whenever an event was exchanged. Each event record contained the nominal event time and every associated variable value. Alternative instrumentation points may include the external hardware or the software PLC, but to gain a consistent view according to the reference clock of the experiment, only the FMITerminalBlock records were taken into account. Since each experiment covered multiple switching operations and numerous event emissions, synchronization was inherently repeated within one experiment run.  
Both the software PLC and the virtual component were executed with a high process priority on a Windows 7 PC featuring 6 GB of RAM and an Intel Xeon dual core CPU W3505, clocked at 2.53 GHz. All software PLC configurations were designed and executed by Eclipse 4 diac [30]. The hardware OLTC controller was based on a development board featuring an ATmega2560 microcontroller from Microchip, running at 16 MHz, and a WIZnet W5100 Ethernet-TCP/IP driver, which implemented both the control logic and the Modbus TCP/IP interface.
## 3 Proposed Method
### MSR Generation Using T-Spline Local Refinement  
After segmenting the T-spline surface into a set of sub-regions using watershed technology, the boundary of each sub-region can be described by means of sampling points, which is inaccurate to describe the boundaries of sub-regions and is troublesome for toolpath generation of sub-regions. The concept of Manufacturing-Suitable Region (MSR) is introduced to construct the boundary for sub-regions, which has been demonstrated for region-based T-spline toolpath generation in our previous work [7]. The MSR is a regular region that is composed of a set of T-spline faces. Toolpath continuity near boundaries of MSR-based sub-regions is guaranteed by the properties of T-spline.  
Figure 4: Feature recognition using watershed technology.  
The MSR is constructed using T-spline local refinement based on segmented _2 DCM_, which can insert control points locally without changing the surface. MSR generation is an expanding method starting from one sub-region to another one, convex and concave sub-regions are commonly considerable for freeform surface toolpath generation. Hence, the construction of MSRs extends from convex or concave sub-regions to flat sub-regions in the paper. MSR generation generally includes two stages to execute T-spline local refinement, the topological stage and the geometrical stage.
## 3 The Proposed Approach
### Cause-Effect Graph  
#### 3.1.1 Related Concepts  
A cause-effect graph is a graphical tool that describes the combinations of input conditions and the output corresponding to each combination. It is mainly applicable when there is some interconnection between various input conditions, or when the output depends on the combination of various input conditions.  
The basic components of a cause-effect graph include variables and arrows. Two variables that are connected by arrows indicate that there is a causal relationship between the two variables for at least one individual in the total. If there is no arrow connection between two variables, it means that there is no causal relationship between the two variables for any individual overall. Figure 1 shows an example of a cause-effect diagram.  
1. Confounding factor  
In Figure 1, there are two arrows pointing to D and Y from X. X is called the common cause of D and Y, and X is also called the confounding factor of D affecting Y;  
2. Common result  
In Figure 1, there are two arrows pointing to Y, indicating that Y is a common result of D and X;  
3. Path  
A path is formed by a series of arrows that connect two variables. For example, there are two paths connecting D and Y in Figure 1, including D\(\rightarrow\)Y and D\(\leftarrow\)X\(\rightarrow\)Y. The former is called a causal path, and the latter is called a backdoor path. A backdoor path [24; 25] is an on-causal path that connects the cause variable (D) and the result variable (Y). It has an arrow pointing to the cause variable (D).  
In addition, other unobserved perturbations affecting a variable in the graph may not appear in the causal graph if they are all independent of the other variables.  
Figure 1: The basic components of a cause–effect graph.
# Simulation of the Marangoni Effect and Phase Change Using the Particle Finite Element Method
## 2 Governing Equations and Solution Methods
### Solution Procedure  
The continuity Equation (34) and momentum Equation (35) are strongly coupled and are solved using a monolithic approach. Since the system of equations is non-linear and an implicit time integration was chosen, each time step is solved iteratively. We used the fixed-point iteration, also known as Picard iteration.  
The heat Equation (36) is significantly more non-linear due to the latent heat term (Equation (20)), and therefore required a non-linear algorithm with better convergence in order to preserve stability. We chose the well-known Newton-Raphson method, accompanied by a line search algorithm that is described in more detail by [PERSON] et al. [31]. The convergence was assessed using the normalized residual of the heat equation, which must fall below a chosen threshold.  
The fluid flow is governed by the strongly coupled momentum (35) and continuity (34) equations, together referred to as incompressible Navier-Stokes equations (NSE). The heat Equation (36), which governs the temperature field and the NSE, is solved in sequence in each time step. First, the heat equation is iteratively solved while the domain is rigid until the convergence criterion is reached. Then, the NSE are iteratively solved, deforming the domain until convergence while the temperature remains constant. This can be described as a weakly coupled staggered scheme.
## 2 Materials and Methods
### Acquisition of Spectra  
The QCL spectrometer was used for reflectance (R) acquisitions across the wavenumber range of 990 to 1600 cm\({}^{-1}\). The samples were placed in metal holder wells (2.54 cm diameter and 10 mm deep), and the soil surface was flattened to create a smooth surface for accurate measurements. Spectra were acquired at various points on the surface, resulting in 100 reflectance spectra per petroleum concentration in the soil samples. To obtain QCL spectra, an initial background spectrum from a rough gold surface or KBr powder was acquired due to the lack of MIR reflected/backscattered signals from the solid matrices used in this study.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 3 Methodology
### Energy Consumption and Indicators  
This section explains the consumption and indicator computations. The annual electrical energy consumption (kWh/year), E, was computed using measurements and Equation (1) for all devices except air conditioners. The air conditioner's electricity consumption was determined by subtracting the appliance's consumption from the total consumption based on the bill.  
\[\text{E}=\text{C}\times\text{T}\times\text{O}\times\text{OL} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Application and Location** & **System, Capacity (kW\({}_{\text{P}}\))** & **SPBP (Years)** & **LCOE (§kWh)** & **References, Date** \\ \hline Mutla and Al-Wafra, Kuwait & Grid-connected PV systems, 100.0 & 15.00 & 0.100 & [PERSON] et al. [31], 2012 \\ \hline Sohar University campus in & Grid-connected PV systems, 3.08 & - & 0.158 & [PERSON] and [PERSON] [32], 2013 \\ \hline At Sulaiman Al-Rajhi & Grid-connected PV systems, 1200.0 & & & \\ University, KSA & and 5300.0 & & & \\ \hline Colleges in KSA & Hybrid RE system, 62.0 & 17.00 & 0.0688–0.0753 & [PERSON] [34], 2021 \\ \hline \multirow{3}{*}{Jubail Industrial City, KSA} & On-off-grid-connected & & & \\  & PV/wind/DG, 685.0, 463.0, & - & 0.183–0.244 & [PERSON] et al. [35], 2019 \\ \cline{1-1} \cline{2-5} Electric vehicle charging stations in & Grid-connected PV systems, & & & \\ \cline{1-1} \cline{2-5} Hail City, KSA & 1047.35 & & & \\ \cline{1-1} \cline{2-5} Mosques in Qassim region, KSA & Grid-connected PV systems, 12.0 & & & \\ \cline{1-1} \cline{2-5} Mosques in Hail City, KSA & Grid-connected PV systems, 18.0 & & & \\ \cline{1-1} \cline{2-5} Mosques in Hail City, KSA & and 72.0 & & & \\ \hline School in Arar City, KSA & Grid-connected PV systems, 150.0 & 8.26 & - & \\ \hline Islamic University of Madinah, KSA & PV system, 1500.0 & 18.60 & 0.051 & [PERSON] et al. [40], 2022 \\ \hline Power plant in Qassim region, KSA & Grid-connected PV systems, 1000.0 & 13.70 & 0.036 & [PERSON] [41], 2017 \\ \hline \end{tabular}
\end{table}
Table 1: System capacity, SPBP, and LCOE of PV systems.  
where C (W) is the capacity of each item, T (h/year) is the total yearly operation time, O is the degree of ownership (item), and OL is the operating level (%).  
Figure 1: Flow chart of the research methodology.  
The EUI per floor area was calculated using this equation:  
\[\text{EUI}=\text{E}/\text{A} \tag{2}\]  
where E represents the total electricity consumed per year (kWh/year) and A is the floor area for all floors (m\({}^{2}\)).  
The CPC per person was calculated using this equation:  
\[\text{CPC}=\text{E}/\text{P} \tag{3}\]  
where P is the number of students and staff in each school.
# Theory and Design of Blink Jamming
## 4 Designing Formation Jamming Techniques
### Synchronous Blink Jamming  
A more effective attack comes in the form of blink jamming, where the two sources \"blink\" on and off 180\({}^{\circ}\) out of phase. This is accomplished by using an amplitude modulated jammer signal, synchronized between the two platforms, as shown in Figure 19. The result is to isolate the two angle measurements from each other in time, while still operating a frequency low enough that the two platforms' attacks can be synchronized in physical isolation.  
Figure 18: The azimuth lag for target A in a two target scenario with both sources providing a continuous wave jamming signal at different relative powers.  
The effect of this blink attack in the monopulse receiver is shown in Figure 20 in the form of an offset between the true target angles and the estimated angle. The frequency of the \"blinking\" is evident in the oscillations of the tracking signal which are related to the transfer function that governs the angle tracking system.  
The metric generally associated with the effectiveness of an electronic attack while conducting self-defence jamming is miss distance. In this scenario the miss distance, shown in Figure 21, is defined as the distance between the tracking point at the target range and the actual target location. This is calculated as  
\[d_{m}=R\tan\left(\theta_{e}\right) \tag{21}\]  
where \(R\) is the target range, and \(\theta_{e}\) is the error in tracking angle.  
Figure 19: The jammer amplitude modulation waveforms from sources \(A\) and \(B\) operating at a 4 Hz blink and 180\({}^{\circ}\) out of phase.  
Figure 20: The azimuth to targets \(A\) and \(B\) compared to the antenna’s pointing angle during the blink jamming scenario.  
In a blink jamming scenario \(d_{m}\) oscillates between the two targets at the frequency of the blinking but depends primarily on the geometry of the two jammers relative to the antenna pattern of the monopulse antenna. In order to maximize this distance the two jammers should be separated by as large a range as possible, while staying within the main lobe of the radar.