esa-sceva/satcom-chunk-collection huggingface dataset:
# Integral Seismic Risk Assessment through Fuzzy Models
## 4 An Integral Seismic Risk Fuzzy Model  
The proposed model is composed of different modules: Aggravation Fuzzy Model, Physical Risk Fuzzy Model, and Total Seismic Risk Fuzzy Model. Figure 1 shows the structure of the whole model schematically. Each module is formed by different sub-modules that are the result of the aggregation of the set of indicators proposed by [PERSON] [24].  
We designed the aggregation of indicators employing a Fuzzy Inference System (FIS) type based on [PERSON] [27]. The aggravation fuzzy model (FIS-AGGRAVATION in Figure 1), includes two elements  
Figure 1: Conceptualization of the Seismic Risk Fuzzy Model. White rectangles represent indicators either describing social economic fragility or physical risk. Here: PD = Property Damage, LLSD=Life Line Sources Damage, ND = Network Damage, FIS = Fuzzy Inference System.  
representing the social fragility (FIS-FRAGILITY) and the resilience (FIS-RESILIENCE). Both elements are, in turn, fuzzy models that are based on their own indicators, represented by white rectangles in Figure 1. Each of these indicators are described in detail in the next sections. The outputs of each of these two models represent the fragility and resilience levels of a given region, respectively. These levels are used as inputs of the FIS-AGGRAVATION model to infer the aggravation level of that region. Likewise, the physical risk model (FIS-PHYSICAL RISK in Figure 1) receives the property damage (PD), lifeline sources damage (LLSD), and network damage (ND) levels as inputs to infer the physical risk level of the region under study. PD, LLSD, and ND are also fuzzy models based on their own indicators, introduced in the following sections and represented by white rectangles in Figure 1. Finally, the aggravation and physical risk levels inferred by the FIS-AGGRAVATION and FIS-FRAGILITY models are the inputs of the FIS-TOTAL SEISMIC RISK fuzzy model, which provides the total seismic risk level as an output.  
Therefore, using the original indicator's raw values, each of the outputs of each sub-model also acts as inputs for the next inference layer in the model's structure. In this sense, we guarantee that all variables entered in the model remain as fuzzy sets, giving the chance to connect them through a new FIS without losing consistency, thereby allowing model completeness.
## 4 Stocky RC Column Database  
The PEER Structural Performance Database provides the test data of 246 rectangular RC columns. Among them, only 47 columns have section-to-depth ratios less than or equal to 2.0. Only a limited number of cyclic tests were conducted for stock columns. In this study, 47 rectangular RC column test specimens with height-to-depth ratios (\(a_{0}\)/\(h\)) less than or equal to 2.0 (stocky columns) were collected from the PEER Structural Performance Database [35]. All the 47 specimens experienced a strength drop by more than 20%.  
The ranges of important parameters are summarized below:  
\begin{tabular}{l l} Height-to-depth ratio & \(1.0\leq a_{0}/h\leq 2.0\) \\ Section width (mm) & \(160\leq b\leq 500\) \\ Section height (mm) & \(160\leq h\leq 914\) \\ Center-to-center spacing of a transverse & \(20\leq s\leq 406\) \\ reinforcement (mm) & \(20\leq s\leq 406\) \\ Longitudinal reinforcement ratio (\%) & \(1.27\leq\rho_{l}\leq 6.94\) \\ Transverse reinforcement ratio (\%) & \(0.08\leq\rho_{l}\leq 1.64\) \\ Measured compressive strength of concrete & \(14\leq f_{c}\leq 118\) \\ Measured yield strength of longitudinal & \(323\leq f_{yt}\leq 510\) \\ reinforcement & \(258\leq f_{yt}\leq 1424\) \\  & \(0\leq v\leq 80.1\); \(v=P/\left(A_{g}f_{c}\right)\) \\ \end{tabular}  
To estimate the error between the measured and calculated values of the modeling parameters, absolute relative errors (_AREs_) were calculated using Equation (6):  
\[ARE=\left|\frac{\mathit{MP}_{\mathit{mass}}-\mathit{MP}_{\mathit{ASCE}}}{\mathit{ MP}_{\mathit{mass}}}\right|, \tag{6}\]  
where \(\mathit{MP}_{\mathit{meas}}\) is the measured modeling parameter value for \(a\) and \(b\) extracted from the first-cycle envelope curve and \(\mathit{MP}_{\mathit{ASCE}}\) is the parameter value for \(a\) and \(b\) calculated using Equations (1) and (3), respectively.  
Figure 3 shows the relationship of the calculated \(ARE\) and the shear span-to-section height ratio (\(a_{v}\)/\(h\)), in which 246 specimens with \(a_{v}\)/\(h\) ranging from 1 to 7.3 were included. The test data for these specimens were also obtained from the PEER Structural Performance Database [35]. The solid and dashed lines in Figure 3 represent the mean \(ARE\) values obtained from the moving windows analyses. With an increase in \(a_{v}\)/\(h\), the mean values of \(ARE\) for parameters \(a\) and \(b\) [\(\mu_{ARE(a)}\),\(\mu_{ARE(b)}\)] generally decrease. In the case of \(a_{v}\)/\(h\)\(\geq\) 2.0, \(\mu_{ARE(a)}\) and \(\mu_{ARE(b)}\) approach approximately 0.5 and 0.3, respectively. However, with the decrease in \(a_{v}\)/\(h\) within the range of \(a_{v}\)/\(h\) less than 2.0, \(\mu_{ARE(a)}\) and \(\mu_{ARE(b)}\) increase sharply, which indicates that the errors associated with the ASCE 41-17 equations for parameters \(a\) and \(b\) become more significant. Thus, it is necessary to propose a calibration factor for Equations (1) and (3) for columns with \(a_{v}\)/\(h\)\(\leq\) 2.0.
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
###### Abstract  
Cobalt-chromium (CoCr) alloy is widely used for medical implants such as for dental or joint replacements because of its strength and high corrosion resistance. By throwing a spherical media against a material surface, fine-particle shot peening can modify surface properties and, as a result, has been widely used as a low-cost and simple method to increase a metal's wear resistance. However, no recent literature has reported the effect of particle type and size on the surface properties of CoCr alloys. This study examined two different particle types (ceramic (alumina-zirconia composites) and silica (SiO\({}_{2}\))) and three different particle sizes to determine their effects on CoCr's surface properties after fine-particle shot peening. The surface properties, including morphology, roughness, hardness, residual stress, and cytotoxicity, were tested to evaluate the effect of the process. The larger size and higher hardness of the particle (ceramic) changed the surface microstructure more than particles with smaller sizes and lower hardness (silica). The results of the cytotoxicity test showed that the fine-particle shot peening on the CoCr material did not affect cell viability, an important fact when considering its potential use as a surface material for medical implants. The results showed that fine-particle shot peening on CoCr material can improve several surface properties and that the larger ceramic particle offers the best results.  
cobalt-chromium alloy; fine-particle shot peening; surface properties; surface hardness; surface roughness +
Footnote †: journal: Applied Sciences
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 3 Results
### Fourth DOE Configuration  
To eliminate the drawbacks inherent in the holographic waveguide structures considered above, an implementation of the holographic waveguide shown in Figure 9 was proposed and tested. Here, as in the first configuration shown in Figure 6, there are input DOE1, relay DOE2a, and DOE2b, and output DOE3 recorded separately and overlapped with an accuracy of \(\pm\)0.2 mm. DOE1 consists of two symmetric (with a vertical axis of symmetry) halves. Each of the halves has its own DOE1a and DOE1b with oblique directions of the lines at the angle of \(60^{\circ}\) in both structures. This configuration provides the in-coupling of the light in the direction of the relay DOE2a and DOE2b areas.  
Experiments with this configuration showed that there is no overlapping structure influencing the in-coupling of the light because this configuration is free of superimposed gratings. The optical efficiency of this configuration is higher than that of the above, but the fabrication requires precise alignment of DOE1 and DOE2 areas since possible overlaps or gaps could lead to dark stripes in the image. For the fourth configuration of the holographic waveguide the achieved level of brightness non-uniformity was about 18%. It should also be noted that five diffraction gratings need to be recorded separately to implement this configuration, which is the largest number among all previous cases.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 3 Curriculum Learning Based Modulation Classification  
### Architecture of Automatic Digital Modulation Classifier  
The diagram of our automatic digital modulation classifier is shown in Figure 2. The input of this classifier is an intermediate frequency signal-containing carrier, which is different from the baseband signal used in previous studies [22; 23; 24]. Then, the input signal is sampled and normalized to obtain a one-dimensional sequence. Next, the one-dimensional sequence is sliced into multiple short sequences, and a grayscale image is gained by arranging these multiple short sequences row by row. Finally, this grayscale image is considered as the input of StudentNet. In practical applications, the StudentNet needs to be trained under the supervision of MentorNet. Later, we would interpret the training of StudentNet in details.  
Figure 2: The diagram of the automatic digital modulation classifier.
## 4 Instrument Design
### Detailed Designs  
It can be seen from the system scheme that the high-energy particle detector consists of three identical high-energy particle probes and a shared electronics box. The electronics box mainly completes the functions of collecting the output signals of each probe, FPGA data processing, compression, packaging, and communication with the satellite bus interface. Each probe consists of a collimator system, a sensor system, and a front-end electronics system. Figure 4 below is the schematic diagram of the high-energy particle probe structure.
## 3 Methodology
### Generation of Input Data from Lidar Data  
Forest vertical structure is highly related to tree height. This is because the criteria for classification of canopy, understory, and shrub layers in a forest inventory are tree species and tree height. Two input layers were generated from the lidar data (initially 1 m GSD, but resampled to 0.48 m GSD): a height difference map and a height texture map. The height difference map was generated by subtracting the DTM from the DSM. The DSM is a kind of digital elevation model (DEM) that represents the Earth surface including features such as building, trees, and houses. The DTM is also a kind of DEM, but it only represents the Earth's terrain and excludes the natural and manmade features. Thus, the height difference map shows the height of the natural and manmade features, including the tree height Pixel values in the height difference map are not direct measurements of tree height, but a close approximation.  
The height difference map was used for the ANN analysis. Since the triple- or double-layered forests have various tree species, the variance of the tree height in a colony would be uneven and the standard deviation of height difference in a colony can be large. The calculation of the height texture map from the lidar data is performed by two processing steps. In the first step, the height difference map is smoothed using a NL-means filter with the kernel size of \(11\times 11\) pixels. The second step involves calculating the standard deviation of the difference between the height difference map and the NL-means filtered height difference map using a moving window of \(5\times 5\) pixels. This map is used for the ANN classification processing. Figure 5d shows the height difference map. The relative height difference among trees can be recognized from Figure 5d. Figure 5e shows the height texture map that indicates the spatial variance in tree height.  
Figure 6 shows the characteristics of the five input layers used for the ANN. The areas in the boxes labeled A, B, and C in Figure 6 are representative of single-, double-, and triple-layered forests, respectively. The dominant species in the area under box A is chestnut tree and the area is a broad-leaved and artificial forest. The area under box B includes nut pine trees as the dominant species and is a coniferous and artificial forest. The dominant species in the area under box C is oak tree and the area is a broad-leaved and natural forest. The nut pine (box B in Figure 6) was the brightest of the forests among the reflectance index maps. The reflectance texture maps in the single- and double-layered forests were smoother than the triple-layered forest. In box A of Figure 6d, there are some individual trees in the artificial forest that appear as bright patches. These trees show different reflectance values on the RGB and median-filtered index maps as compared with other trees in the same community and appear to be a different species. They show odd values on the maps from the lidar data as well. The brightest parts of the Height Texture Map (Figure 6e) are where the standard deviation was high.  
Figure 6: _Cont._
A reliability model of gas-electric coupling devices to quantify the interdependence of gas-electric systems is established in [12], evaluating the reliability of the distribution system based on the shortest path method. In the field of considering the impact of the natural gas system on the reliability of the power system, a reliability model for the joint operation of electrical and natural gas systems is established and the maximum power that the joint-cycle power plant in the system can provide is calculated in [13]. Fuel availability is considered and a reliability model for the joint operation of power and natural gas systems is established in [14]. However, neither of the above references quantified the impact of gas-transmission losses on the reliability of the power system in a high-load area with a high proportion of gas-fired units. The researchers above conducted extensive research in the two independent fields of natural gas supply chain and electric power system reliability assessment, but the impact of natural gas supply reduction on the power supply reliability in heavily loaded areas dominated by natural gas is not clear, and there are few relevant studies considering this impact. Therefore, the motivation of this paper is to propose a novel reliability-evaluation method for power systems that accounts for natural gas supply fluctuations and not only considers the impact of the natural gas supply but also considers the influence of external macro factors such as policy regulation and market economic fluctuations on the reliability assessment. The contribution of this paper lies in proposing a method that combines natural gas supply and electric power system reliability assessment for better evaluatingthe reliability of electric power systems in heavily loaded areas dominated by natural gas. At the same time, the method proposed in this paper considers the impact of external macro factors, which improves the accuracy and reliability of the evaluation. Firstly, the minimal cut set theory is used to construct the reliability model of the natural gas supply chain, and the influence of policy regulations and economic market factors on system components is quantified. Secondly, a reliability-evaluation model for a power system that considers gas loss is constructed, and a non-sequential Monte Carlo simulation is used to solve it. Thirdly, a reliability-evaluation method considering the power system reserve capacity is proposed. Finally, case studies on a natural gas system with a 14-node power system in a certain area illustrate the effectiveness of the proposed method in evaluating the reliability of the power system considering the natural gas loss.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 3 Proposed GDSS Scheme
### Gaussianity of GDSS Signals  
This section compares the distribution of the GDSS signals with the theoretical Gaussian distribution. The noise-free signal constellation of the GDSS scheme is shown in Figure 6. The figure shows that the signal constellation is sufficiently masked to appear as chaotic white noise to any adversary.  
The probability density function of a random variable \(Z\) with Gaussian distribution is given by  
\[f_{Z}(x)=\frac{1}{\sqrt{2\pi}}exp^{-x^{2}/2}. \tag{2}\]  
Using Equation (2) its moments can be computed using  
\[\mu_{k}=\int_{\infty}^{-\infty}x^{k}f_{Z}(x)dx. \tag{3}\]  
Figure 5: A collection of scattered constellation points of the proposed QPSK GDSS signal, with matching colours of QPSK DSSS in Figure 3.  
A closed-form expression of Equation (3) for even orders of moments of the zero-mean unit-variance normal distribution can be expressed as [40]  
\[\mu_{k}=\frac{k!}{2^{k/2}(k/2)!} \tag{4}\]  
The transmitted noise free signal is compared against a standard Gaussian distribution (SGD) in Figure 7 and in Table 1 using \(1\times 10^{6}\) bits with a spreading factor of 256. The probability distribution shown in Figure 7 closely matches the theoretical Gaussian distribution. The Gaussian distribution is further supported in Table 1 from the estimated moments of the waveform depicting acceptable levels of commonality with the theoretical moments (calculated using Equation (4)).  
A random white noise signal would have an ACF of zero at all lags except a value of unity at lag zero, to indicate that the signal is uncorrelated and does not exhibit repetitive time-domain features. The repetitive features of the GDSS signals were analysed using the ACF. The results obtained showed no identifiable features for either GDSS or DSSS noise-free signals provided each uses non-repeating spreading sequences. However, for the case when these systems use a fixed pair of spreading sequences, the ACF for DSSS shows several very strong identifiable components across multiple lags. Compared to this, for the GDSS, the correlation magnitudes are small and are overall at an insignificant level.  
Figure 6: A collection of signal constellations of noise-free transmitted GDSS signal.  
Figure 7: Distribution of the noise-free transmitted GDSS signal.
## 3 Results and Discussion  
### Microalgae Identification  
Firstly, it should be noted that the fountains in the Alhambra and Generalife undergo continual maintenance. In addition to general cleaning, all of the studied fountains are treated with algicide. Chlorine tablets are also added to many of them. In the specific case of the Fountain of the Lions, the water flowing through gets special treatment, since the circulating water is chlorinated and purified independently from the rest of the fountains of the monumental complex. This means that the algae colonising these fountains exist in conditions that are far from natural and that they are generally resistant to such treatment, since it does not manage to eliminate them completely. Fundamentally, as seen in previous studies [15; 16], the microalgae colonising the selected fountains largely belong to the large groups of green algae (Chlorophyta and Charophyta), cyanobacteria (Cyanobacteria) and diatoms (Bacillariophyta). Once in a while, some Euglenophyceae have been found in the \"guitar\" fountain of the Ladies Tower (Torre de la Damas, Granada, Spain), and a Phaeophyceaeae (_Pleuroladia_ sp.) sample was found in the southern \"guitar\" fountain of the Court of the Myrtles (Patio de los Arrayanes), but those species were only seen in spring in those two fountains. Table 2 shows a list of the fountains studied and the most common genera of microalgae in each. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fountain** & \multicolumn{3}{c}{**Most Common Genera**} \\ \hline  & & **Green Algae** & **Cyanobacteria** & **Diatoms** \\ \hline
1. & & _Bractaecoccus_ (3-2), & _Navicula_ (3-1), \\  & & _Chlorosarcinopsis_ (2-1) & _Nitzschia_ (2-2) \\ \hline
2. & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline
3. & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline
4. & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline
5. & & & \\  & & & \\  & & & \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The most common genera of microalgae in each of the fountains studied. For each fountain, the set of samples taken was considered, along with the different sampling points, and where applicable the different times of year. The genera that appear most often and in greater abundance were chosen. For each genus, the total number of samples in which it is present (first number) and the number of samples in which its index is 3 or higher (second number; see 2.2. Procedures) are shown in brackets.
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 4 Evaluation
### Model Complexity Comparison  
This article constructs a graph neural network model based on PyTorch. The cross-entropy loss function is used for loss calculation, and the Adam optimizer is employed to update the parameter matrices of the network. The learning rate is set at 0.003. The following figure presents a comparison of different approaches for model training. Graph WaveNet [17] incorporates two distinct modules to effectively capture spatial dependencies and temporal correlations in a separate manner, resulting in inferior training performance compared to STSGCN and PDASTSGAT. By introducing graph attention mechanisms, our model can achieve faster convergence.  
As shown in Figure 5, it can be observed that the loss value stabilizes when the number of iterations reaches 48. This indicates that the graph neural network architecture we constructed performs well in classifying feature subgraphs, with fast convergence speed and high training efficiency.  
Figure 4: Subflow fairness simulation results of the five algorithms. The value range of Jain’s fairness index is \([1/n,1]\). When the value is 1, it indicates absolute fairness. When the value is \(1/n\), the entire system is completely unfair. n denotes the number of subflow.  
Figure 5: Loss value—iteration number curve. In our study, we employ the cross-entropy loss function to compute the discrepancy between the predicted subflow and the optimal subflow. A smaller value of the loss function indicates a reduced disparity between the predicted results and the ground truth values, thereby indicating the superior performance of the model.  
In summary, PDASTSGAT outperforms round-robin, Average-RTT, FastRTT, RLDS and PDAA3C scheduling algorithms in MPTCP transmission simulations, achieving better transmission performance. It exhibits significant improvements in throughput and ensures a relatively fair allocation of transmission resources among links. In model training experiments, PDASTSGAT outperforms Graph WaveNet and STSGCN in the classification of feature subgraphs, thanks to its incorporation of a multi-head attention mechanism. Furthermore, PDASTSGAT exhibits fast convergence speed and high training efficiency.