esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Renewable Energy Grid Code Specifications
### Comparison with International Grid Codes  
This study focused primarily on the renewable energy grid code (REGC) specifications of South Africa (SA), as regulated by the National Energy Regulator of South Africa (NERSA) and Electrical Supply Commission (ESKOM). Recognizing that REGCs for renewable power plant (RPP) integration vary across different electrical power systems worldwide is significant. Nonetheless, most international REGCs share fundamental operational requirements concerning continuous operating voltage and frequency ranges, active power control capabilities, reactive power support and control functions, fault ride-through (FRT) capabilities, frequency response, and stability requirements. For instance, the German  
Figure 10: Reactive power control functions for a renewable power plant [24; 28].  
BDEW Medium Voltage Directive, the UK National Grid Code, and the IEEE 1547 Standard for Interconnection of Distributed Energy Resources in the USA set comparable technical requirements for voltage and frequency tolerance, reactive power capability, and grid fault behavior [29; 30; 31].  
However, differences typically arise in specific voltage ride-through curves, the permissible ranges of power factor adjustment, the exact frequency thresholds and response times, and the operational control modes, such as voltage versus power factor versus reactive power control priority.  
The LSWPP model developed in this study was validated against REGCs of SA, which serve as a representative framework for Category C LSWPP. Given the similarity in core requirements across most international standards, the model can be adapted for other regions by adjusting the reactive power control ranges and setpoints, modifying the acceptable frequency and voltage operation limits, tuning the FRT capability curves and thresholds according to the REGCs of the target region.  
This adaptability is possible because the model was built using flexible, parameterized control components within the Real-time Simulation Computer-Aided Design (RSCAD) simulation environment. Therefore, while the present study validated compliance based on SA standards, the methodology and simulation framework remain applicable for evaluating LSWPPs against other regional grid codes with minor parameter adjustments.
## 4 Problem Formulation
### Outer Loop of Dec-MPMAB Framework
#### 4.1.2 Dec-MPMAB Formulation of RIS and RB Selection Problem  
Assume that, at time slot \(t\), the allocation of an RIS and an RB to a specific device-to-device (D2D) pair is decentralized. Let the set \(A=[a_{1},a_{2},\ldots,a_{M}]\) denote the arm set for the Dec-MPMAB framework, where \(M\) represents the total number of RIS, \(J\) is the total number of RB, and M represents the set of all possible reconfigurable intelligent surfaces (RIS). \(M\in{1,2,\ldots,M}\), where M is the total number of available RIS. Each element in setM corresponds to a unique RIS. J represents the set of all possible resource blocks (RB). \(J\in 1,2,\ldots,J\), where J is the total number of available RB. Each element in set J corresponds to a unique RB. Mathematically, it can be expressed as  
\[M\otimes J=(m,j)|m\in M,j\in J \tag{9}\]  
Here, \((m,j)\) represents a specific pair consisting of an element from set M (RIS) and an element from set J (RB). \(a_{n}\in\mathbf{M}\otimes\mathbf{J}\), and \(\otimes\) signifies the Cartesian product of the RIS set and RB set. Therefore, the Cartesian product of sets M and J represents all possible pairs of RIS and RB. Each \(a_{n}\) corresponds to a combination of a specific RIS and RB allocated to a device-to-device (D2D) pair in the decentralized allocation process.  
In this setup, multiple players make decisions simultaneously, and each player selects an arm from the common set without having information about the choices made by other players. The set of arms can be expressed as a combination of RIS and RB choices, with each player having independent rewards. It is important to note that more than one player can choose the same arm without consideration of collision situations, as the reward is defined for each specific player, and any influence of collisions can be captured in the resulting rewards. Further clarification and illustration will be provided below.
# Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data
## 2 Methods
### Numerical Modeling  
Numerical simulations were carried out to investigate the relationship between ECV and diffusion tensor quantities. Tensor invariants and eigenvalues were computed from the simulated diffusion of particles, and ECV was computed based on the generated synthetic cell structures. Individual cardiomyocytes were represented by cylinders with 9 to 20 \(\upmu\)m diameter and 100 \(\upmu\)m length. Cells were then connected along the axial direction to form a cellular tree with branching added between trees. Cells were added in a \(0.5\times 0.5\times 0.5\) mm\({}^{3}\) voxel until a target ECV was reached. Therefore, different ECVs corresponded to different cell densities and cell-to-cell distances. The displacements due to the diffusion of 20,000 water molecules were simulated in each voxel using a random walk approach [29] with a time step \(\Delta\)t of 10 \(\upmu\)s for a total duration of 51 ms, which correspondsto the duration of the diffusion encoding in vivo [30]. The native diffusion coefficient D\({}_{0}\) of the water molecules was 3 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s and 2.2 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s in the extra- and intracellular compartments, respectively [31]. The intracellular and extracellular compartments were kept impermeable.  
The diffusion of water molecules resulted in an intra-voxel displacement distribution that was projected in 12 directions to mimic a diffusion tensor acquisition. Subsequently, the diffusion tensor corresponding to the simulated signal was reconstructed and its mean diffusivity, fractional anisotropy, eigenvalues, and radial diffusivity were calculated.  
Five cell structures were generated per each target ECV. Nine target ECVs ranging from 20% to 100% were simulated, resulting in 45 different cell structures. For a given ECV and cell structure, each simulation was repeated five times, resulting in 225 diffusion simulations across ECVs and cell structures. To best approximate the simulated diffusion signal to the one acquired in the MRI experiment used in this work, only the extracellular water displacements were considered.  
The code to perform the diffusion simulations described above is freely available on GitHub at [32].
## 3 Experimental Results and Discussion
Additionally, in long-term measurements, the heat generated by the photoelectric detector causes the internal temperature of the shielding layer to rise (measured \(\Delta\)T \(\approx\) 0.1 K/h), introducing extra thermal noise and thus affecting the measurement precision.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 6 Classification Techniques
### Logistic Regression  
Logistic regression (LR) corresponds to a probabilistic statistical model widely used in classification problems. It aims at defining the connotation among a dichotomous dependent attribute and numerous independent attributes of the dataset [83]. The use of the logistic function \(f(z)\) (given with Equation (36)) mainly contributes to the wide acceptance of the LR classifier as its values always lie in the range of 0 to 1 [84]. In addition, the logistic function gives an attractive S-shaped representation of the cumulative outcome (\(z\)) of the independent attributes on the dependent attribute \(f(z)\) (Figure 10) [85]. It is clear  
Figure 8: Simple diagram of Naïve Bayes classifier.  
Figure 9: Graphical representation of (**a**) linear and (**b**) non-linear support vector machines.  
from Figure 10 that the logistic regression is different from the linear regression in the case of its ability to limit the output in the range of 0 to 1.  
\[f(z)=\frac{1}{1+e^{-z}} \tag{36}\]  
where \(z\) is the cumulative effect of the independent variables, and \(f(z)\) is the dependent variable.  
Equation (36) can be written as  
\[f(z)=\frac{e^{z}}{1+e^{z}} \tag{37}\]  
Let us take \(p=f(z)\).  
\[e^{z}=\frac{p}{1-p},\;and\quad z=\ln\biggl{(}\frac{p}{1-p}\biggr{)} \tag{38}\]  
where \([p/(1-p)]\) indicates the odds of the event occurring.
###### Abstract
The method is able to capture non-linear relationships and provide interpretable insights by analysing the significance of features. In study [24], the effectiveness of XGBoost in predicting the density of Ti-6 Al-4V alloy parts produced by selective laser melting (SLM) was demonstrated. The model achieved high accuracy despite a small data set and showed better values for error metrics compared to artificial neural networks and support vector regression models. In this paper, the data set includes experimental runs using different LPBF parameter settings. By analysing the importance of process features, the results identify and quantify the impact of each parameter on product density, thus providing a data-driven framework to improve the efficiency of the LPBF process in the production of Mg parts. This approach addresses the challenges of traditional optimisation while harnessing the power of ML with the aim of supporting the development of high-density Mg parts suitable for advanced applications. Although previous studies have applied ML to AM, this study focuses specifically on pure magnesium, a reactive and under-researched material in LPBF, and quantitatively demonstrates how parameter interactions dominate density results.
## 5 Discussion  
The new design is clearly amplitude-sensitive, and at large motions, is able to control engine motions. Yet, at small motions, it acts like a vibration isolator, unlike present hydraulic engine mount designs on the market employing a decoupler. Figures 10 and 21 clearly indicate that at small motions (\(\pm 0.1\) mm), the addition of a bilinear or cubic spring underneath the bottom piston had no impact on the location and depth of the notch frequency compared to using a linear spring. Tables 2 and 3 provide summaries of the numerical simulations results.  
Table 2 shows that when the engine operates near the notch frequency, if the engine displacement amplitudes increase from \(\pm 0.1\) mm to \(\pm 0.4\) mm, the new engine mount dynamic stiffness increases by 52% to 165%, thus controlling engine motion when shocks are present. The bilinear spring that was chosen in this paper appears to be more effective in controlling large engine motions. Table 2 also indicates that the peak frequency is greatly attenuated by 3.2 times for the bilinear spring and 4.45 times for the cubic spring. Table 3 below shows the chamber pressures and bottom piston displacements. Table 3 indicates that the top chamber pressures and the bottom piston displacements are higher in value by 2.7 to 3.5 times and 2.43 to 3 times, respectively, when a linear spring is used.  
Table 3 indicates that even though the bottom chamber pressures are higher when bilinear and cubic springs are used, fortunately, the maximum pressures remain below 0.82 MPa (120 psi), which most rubbers can tolerate.
## 2 Materials and Methods
### Overall SESADRN Architecture  
The dilated residual network (DRN) [33] has a good classification performance. In this paper, we used DRN-D-54 as the backbone to build a classification network with the squeeze-and-excitation (SE) and spatial attention (SA) modules, named as SESADRN. The overall SESADRN architecture is shown in Figure 5. The size of the original image is adjusted to \(224\times 224\times 3\) as the input image of the model. The outputs (feature maps with a size of C \(\times\) H \(\times\) W) of SE and SA modules are concatenated to feed into a \(1\times 1\) convolution layer to obtain a global feature map \(\mathbf{A}^{\text{C}\times\text{H}\times\text{W}}\). Finally, the global average pooling and fully connected layers are used to implement the butterfly classification.  
Gradient-weighted Class Activation Mapping (Grad-CAM) [34] can provide visual explanations for classification decision. For the global feature map \(\text{AC}\times\text{H}\times\text{W}\), and the gradient \(y^{s}\) of the predicted score of the target category \(s\), the weight \(w^{s}_{t}\) of the target category s corresponding to the \(t\)-th feature map \(\mathbf{A}^{t}\) (\(t\in[1,C]\)) can be calculated by Equation (7). After performing the ReLU operation on the weighted combination of the feature map, a Grad-CAM heatmap is obtained:  
\[L^{s}_{\text{Grand-CAM}}=ReLU\left(\sum_{t}w^{s}_{t}\times\mathbf{A}^{t} \right)w^{s}_{t}=\frac{1}{H\times W}\sum_{i}^{H}\sum_{j}^{W}\frac{\partial y^ {s}}{\partial A^{t}_{ij}} \tag{7}\]
## 3 Results and Discussion
### Nutritional Analysis
dealbeta_, AC—_A. cyclops_, AR—_A. retinodes_, AMs—_A. marmsii_, AP—_A. pycrantha_, AL—_A. longifolia_, AS—_A. saligna_. Different letters in the same column indicate statistically significant differences by the L.S.D. test (_p_ < 0.05) for each analytical determination. Figure 2: Nutritional value of acacia leaf species, based on dry matter (mean \(\pm\) standard). AMy—_A. melanoxylon_, AD—_A. dealbeta_, AC—_A. cyclops_, AR—_A. retinodes_, AMs—_A. marmsii_, AP—_A. pycrantha_, AL—_A. longifolia_, AS—_A. saligna_. Different letters in the same column indicate statistically significant differences by the L.S.D. test (_p_ < 0.05) for each analytical determination. the highest water content. Substantial differences were also observed among the other species, with the AMs species displaying the lowest values. Considering that the main components of the plant cell wall are cellulose, hemicelluloses, pectin, and lignin, and recognizing the potential use of leaves as animal feed, we also assessed the content of NDF, ADF and ADL in this study. These components are critical for understanding the digestibility and nutritional value of plant material for ruminants, as they directly influence how efficiently animals can utilize the feed. However, their quantities vary depending on the species and the plant's stage of development [34]. The concentration of cellulose, hemicelluloses, and lignin (the primary constituents of the plant cell wall) are included in the NDF measurement. The NDF contents varied significantly for the AP and AL species and ranged from 36.61% to 49.98%, respectively (Table 2). The average NDF among the different species was observed to be relatively higher compared to the NDF contents (24.6%) reported by [PERSON] et al. [35] and values reported by [PERSON] et al. [21] (18.6-62.1%) and [PERSON] et al. [24] (25.0-50.5%). The hemicellulose content ranged from 13.05% for AR to 21.90% for AC (Table 2), higher than that reported by [PERSON] et al. [19] (40-98 g/kg DM) for _A. rubica_ and _A. brewispica_ from Kenya. ADF represents the cellulose and lignin fractions of the plant cell wall [27]. For the different varieties of _Acacia_ species, ADF ranged from 21.34% (AC) and 46.55% for AMy, respectively. The average cellulose content of the different _Acacia_ species ranged from 7.65% for AD to 16.03% for AMy. As far as we know, no studies have presented values for this parameter in this species. The digestibility of cell walls is highly variable and depends on the degree of lignification, which is expressed as ADL [34]. The highest ADL content was recorded for Amy, at 30.52%, while the lowest was observed for AC, at 9.31%. Table 2 shows that the NFC content ranged from 18.42 to 41.23%. The highest values were found in the AP species, while AMy was the species with the lowest values.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Degradation of Material Properties by Cl\({}^{-}\) Corrosion  
#### 3.2.1 Effect of Cl\({}^{-}\) Corrosion on Concrete Materials  
After 30 days of electrochemical erosion under dry-wet cycles, content of Cl\({}^{-}\), which was detected at the concrete surface, 10 mm away from the concrete surface, and 30 mm away from the concrete surface in plastic hinge region after corrosion, was measured to be 0.85%, 0.60%, and 0.38%, respectively, which indicates that content of Cl\({}^{-}\) decreased with distance increasing from concrete surface. The measured content of Cl\({}^{-}\) was essentially the same due to the identical thickness of the protective layer of concrete and similar corrosive conditions of specimens under all working conditions. During the test, cracks on the specimens corroded by Cl\({}^{-}\) appeared earlier with larger length and width. Obvious rust appeared on the concrete surface at the bottom of the test column. There even were obvious cracks owing to rust expansion on part of protective layer of concrete, which resulted in severe concrete spalling and decline of the bonding effect between concrete cover and steel reinforcement. It was indicated that Cl\({}^{-}\) corrosion has an effect on strength and volume stability of concrete. The phenomenon of concrete damage and spalling is represented in Figure 21. The main reason for the effect of Cl\({}^{-}\) on performance of concrete is that Cl\({}^{-}\) will combine with calcium ions in concrete during the hydration process to produce strength-free calcium chloride, leading to the mixture of calcium chloride without bond strength in cured concrete, which affects the strength of concrete to a great extent.  
Figure 20: Schematic diagram of the column model.
# Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography
###### Abstract  
Practical entanglement distillation is a critical component in quantum information theory. Entanglement distillation is often utilized for designing quantum computer networks and quantum repeaters. The practical entanglement distillation problem is formulated as a bilevel optimization problem. A fuzzy formulation is introduced to estimate the quantum state (density matrix) from pseudo-likelihood functions (i.e., quantum state tomography). A scale-independent relationship between fuzzy relations in terms of the pseudo-likelihood functions is obtained. The entanglement distillation optimization problem is solved using the combined coupled map lattice and dual annealing approach. Comparative analysis of the results is then conducted against a standard dual annealing algorithmic implementation.  
practical entanglement distillation; bilevel optimization; fuzzy formulation; quantum state tomography; coupled map lattices; dual annealing
## 3 Analytical Tuning of Series PIDA Controller
### Prefilter Design  
If we again introduce the controller time constants  
\[T_{D}=K_{d}/K_{p}=0.4168T_{d};\ T_{A}^{2}=K_{a}/K_{p}=0.0484T_{d}^{2} \tag{25}\]  
Figure 3: Series PIDA controller (20) with two degrees of freedom: a third-order filter \(Q_{n}(s)\), \(n=3\) (16) of the measured output \(y_{m}(t)\), the automatic reset with time constant \(T_{i}\), with proportional, derivative and accelerative controller gains \(K_{p}\), \(K_{d}\) and \(K_{\rm a}\), with the reference setpoint \(w(t)\) and a prefilter \(F_{p}(s)\).  
we can further introduce a pre-filter \(F_{p}(s)\), the task of which is to cancel the zeros from the \(F_{c}(s)\) (21)  
\[F_{p}(s)=\frac{b_{3}s^{3}+b_{2}s^{2}+b_{1}s+1}{(T_{A}^{2}s^{2}+T_{D}s+1)(T_{i}s+1)} \tag{26}\]  
and possibly also some of the closed-loop poles \(s_{o}\) (time constants \(T_{o}\)). In the simplest and the most robust case, one can choose  
\[b_{3}=0;\ b_{2}=0;\ b_{1}=0; \tag{27}\]  
Setpoint step responses can be accelerated by selecting the \(F_{p}\) numerator so that it cancels one closed-loop time constant \(T_{o}=-1/s_{o}=T_{d}/2\) (23)  
\[b_{3}=0;\ b_{2}=0;\ b_{1}=T_{o}; \tag{28}\]  
Two closed-loop dominant time constants \(T_{o}\) can be cancelled by the numerator tuning  
\[b_{3}=0;\ b_{2}=T_{o}^{2};\ b_{2}=2T_{o}; \tag{29}\]  
Three closed-loop dominant time constants \(T_{o}\) could be cancelled by the numerator tuning  
\[b_{3}=T_{o}^{3};\ b_{2}=3T_{o}^{2};\ b_{2}=3T_{o}; \tag{30}\]  
We will expect the highest parametric sensitivity from this solution, which will manifest itself at least at shorter values of the equivalent delay \(T_{e}\) used when setting the low-pass filters \(Q_{n}(s)\) (16).
# Proposal of a Design Procedure for Steel Frames with Viscoelastic Dampers
## 5 Application to a Case Study  
The procedure illustrated in the previous section is applied to design a set of twelve residential buildings with 4, 6, and 8 stories, located in a medium or high seismicity zone with reference PGA \(a_{\text{g,R}}\) equal to 0.25 g or 0.35 g founded on rock soil (type A soil according to Eurocode 8 Part 1) or soft soil (type C soil).  
All buildings have the same squared floor plan whose axes of symmetry lay in the X and Y directions, as shown in Figure 8. The structure consists of two sets of four steel MRFs arranged in two orthogonal directions. The length of the bays is equal to 8 m in both directions, and the inter-story height is equal to 3 m at all levels. VEDs are located at all stories in the central bay of each perimetral frame, sustained by a couple of braces in the chevron configuration and connected to the bottom flange of the beam. Columns of the MRF are denoted with the letter \"C\" and a number that is 0 for columns belonging to the braced bay, 1 for corner columns, and 2 for columns of the central part of the building. Columns C0 are oriented with the strong axis perpendicular to the plane of the braced bay, columns C1 are oriented with the strong axis parallel to the X direction, and columns C2 are oriented with the strong axis parallel to the Y direction. HEB profiles are used for columns and braces, whereas IPE profiles are used for beams. To minimize the weight of the structure, different steel grades are used, i.e., steel grade S355 is used for columns, S275 is used for beams, and S235 is used for braces. Bi-directional slabs are used in order to equally distribute gravity loads on the beams. It is assumed that slabs are rigid in their plan and that masses are equally distributed over the floor surface.  
Characteristic values of dead loads \(g_{\text{k}}\) and live loads \(q_{\text{k}}\) acting on the slabs are assumed to be equal to 4.40 kN/m\({}^{2}\) and 2.00 kN/m\({}^{2}\), respectively. The design gravity loads in the non-seismic and seismic design situations are equal to 9.16 kN/m\({}^{2}\) and 5.00 kN/m\({}^{2}\), respectively. The values of the line loads applied to the beams and the values of the axial forces applied to the columns are determined as a function of tributary areas. The value of the floor masses is equal to 146.8 t at all floors.  
In the following sections, the application of the proposed procedure is developed in detail for the 4-story building founded on rock soil (A) in an area characterized by medium seismic hazard (PGA = 0.25 g).