esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Converting a Pyramidal Horn Antenna into a Circularly Polarized Antenna
### Radiating Systems with Circular Polarization Consisting of a Horn Antenna and FSS Layers
#### 3.2.2 Multiple-Layer FSS Structures  
The distances between successive layers were calculated in a similar manner to when designing a Yagi-Uda array, using a dedicated online calculator. The resulting distances between the five layers of the FSS structure are given in Table 2. Based on the analysis presented in Section 3.2.1, the FSS structure consists of 4 \(\times\) 4 radiators. Three geometries of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Elements of the** & **Overall Gain,** & \
\begin{tabular}{c} **Gain Difference between** \\ **Types Circular Polarization,** \\ **\(\mathbf{G}\) [dB]** \\ \end{tabular} &
\begin{tabular}{c} **Axial Ratio** \\ **\(\mathbf{G}_{\text{{RHCP}}}-\mathbf{G}_{\text{{LHCP}}}\) [dB]** \\ \end{tabular} \\ \hline
2 \(\times\) 2 & 5.2 & 0.3 & 58 \\ \hline
3 \(\times\) 3 & 11.34 & 1.74 & 10 \\ \hline
4 \(\times\) 4 & 13.02 & 2.3 & 7 \\ \hline
5 \(\times\) 5 & 12.7 & 2.8 & 6 \\ \hline
6 \(\times\) 6 & 8.64 & 2.36 & 7.38 \\ \hline
7 \(\times\) 7 & 12 & 2.5 & 6.86 \\ \hline \hline \end{tabular} \(G_{\text{{RHCP}}}\)–right hand circular polarization gain; \(G_{\text{{LHCP}}}\)—left hand circular polarization gain.  
\end{table}
Table 1: Figures of merit for the radiating system consisting of a horn antenna and a single-layer FSS structure with \(N\times N\) radiators.  
Figure 10: FSS structures with \(N\times N\) elements in the aperture of the horn antenna.  
FSS structures were investigated (Figure 11) based on the three types of unit cell analyzed in Section 2.2 by varying the width of the radiators (1.5 mm, 2 mm, 2.5 mm and 3 mm), but also the number of layers of FSS structures placed in front of the aperture of the pyramidal horn.  
The simulations for the horn antenna with the structures presented in Figure 11 were performed by rotating the FSS structures at \(\pm\)45\({}^{\circ}\). The best figures of merit were obtained for the versions 1, 2, 5 and 8 as displayed in Table 3. The criterion for choosing the best version was based on a tradeoff between axial ratio and the difference between the gain figures for cross-polarization and co-polarization, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. \#**} & **FSS Structure** & **Number of Layers** & **Radiator** & **Overall Gain, \(G\)** & **Gain Difference System Types of Polarization, \(G_{\text{RHCP}}-G_{\text{LHCP}}\)** & **Axial** & **Gain Difference between Types of Linear Polarization, \(G_{\text{co}}-G_{\text{cross}}\)  
Figure 12 shows the physical FSS structures for each selected version. To make it easier to follow, we will designate them from now on as denoted in Figure 12: type 5 (a), type 1 (b), type 2 (c), and type 8 (d). The distance between the physical FSS layers was kept as in Table 2 by using 3D-printed carbon fiber spacers.
## 3 Results
### Correlations between Radiomics MRI Features and BMD  
In Table 2, correlations between radiomics MRI features and BMD are shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **T1** & **T2** & **T2 Fat-Sat** \\ \hline \multicolumn{4}{c}{**First Order**} \\ \hline Volume & 0.226 (0.005) & N.A. & N.A. \\ \hline Surface & 0.228 (0.004) & N.A. & N.A. \\ \hline Compacity & 0.179 (0.026) & N.A. & N.A. \\ \hline Conv. Min. & \(-\)0.173 (0.032) & N.S. & N.S. \\ \hline Conv. Max & N.S. & \(-\)0.166 (0.039) & N.S. \\ \hline Conv. Kurtosis & \(-\)0.261 (0.001) & \(-\)0.265 (0.001) & N.S. \\ \hline Conv. Excess Kurt. & \(-\)0.340 (\(<\)0.001) & \(-\)0.265 (0.001) & N.S. \\ \hline Disc. Standard Dev. & N.S. & 0.224 (0.005) & 0.344 (\(<\)0.001) \\ \hline Disc. Q2 & N.S. & 0.183 (0.022) & 0.169 (0.014) \\ \hline Disc. Q3 & N.S. & 0.224 (0.005) & 0.253 (0.001) \\ \hline Histogram Energy & \(-\)0.205 (0.010) & \(-\)0.260 (0.001) & \(-\)0.247 (0.002) \\ \hline Histogram Entropy & N.S. & 0.199 (0.013) & 0.265 (0.001) \\ \hline \multicolumn{4}{c}{**GLCM**} \\ \hline Homogeneity & \(-\)0.205 (0.011) & \(-\)0.289 (\(<\)0.001) & \(-\)0.212 (0.008) \\ \hline Contrast & N.S. & 0.169 (0.036) & 0.187 (0.020) \\ \hline Entropy & 0.250 (0.002) & 0.280 (\(<\)0.001) & 0.258 (0.001) \\ \hline Dissimilarity & N.S. & 0.231 (0.004) & 0.227 (0.005) \\ \hline \multicolumn{4}{c}{**GLRLM**} \\ \hline SRE & 0.167 (0.038) & 0.239 (0.003) & 0.173 (0.031) \\ \hline LRE & \(-\)0.169 (0.035) & \(-\)0.241 (0.003) & \(-\)0.170 (0.034) \\ \hline HGRE & N.S & 0.176 (0.028) & 0.247 (0.001) \\ \hline SRHGE & N.S & 0.177 (0.028) & 0.275 (0.001) \\ \hline LRHGE & N.S & 0.174 (0.031) & 0.272 (0.001) \\ \hline GLNU & N.S & N.S & \(-\)0.160 (0.047) \\ \hline RP & 0.168 (0.037) & 0.240 (0.003) & 0.172 (0.032) \\ \hline \multicolumn{4}{c}{**GLZLM**} \\ \hline SZE & \(-\)0.176 (0.028) & 0.216 (0.007) & 0.159 (0.048) \\ \hline HGZE & N.S & 0.169 (0.036) & 0.271 (0.001) \\ \hline SZHGE & N.S & 0.170 (0.034) & 0.270 (0.001) \\ \hline ZLNU & N.S & N.S & 0.171 (0.033) \\ \hline ZP & 0.160 (0.046) & 0.233 (0.004) & 0.173 (0.032) \\ \hline \multicolumn{4}{c}{**NGLDM**} \\ \hline Contrast & 0.237 (0.003) & 0.282 (\(<\)0.001) & 0.228 (0.004) \\ \hline \multicolumn{4}{c}{**PET/CT**} \\ \hline Agatston & N.S & N.S & \(-\)0.358 (0.013) \\ \hline Peak Sphere 0.5 mL & N.S & N.S & 0.266 (0.001) \\ \hline Peak Sphere 1.0 mL & N.S & N.S & 0.298 (\(<\)0.001) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlations between radiomics MRI features and BMD by the significance (\(p\)) in parentheses. N.A. = Non Available, redundant; N.S. = Non significative.
###### Abstract  
In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder-evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning-variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios.  
rolling bearing; sample imbalance; labeled-sample deficiency; generative adversarial network; fault diagnosis +
Footnote †: journal: Applied sciences
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 2 Theoretical Models
### Basic Theory
#### 2.1.3 Equation of Solid Mechanics  
Although infrared temperature measurement has good spatial and temporal resolution, infrared glass can undeniably impact temperature measurement. When ultrasound waves propagate in tissues, only the influence of longitudinal waves needs to be considered. However, there are still shear waves in infrared glass, and their effects cannot be ignored.  
The control equation is given below [17].  
\[-\rho\omega^{2}X=\
abla\sigma+F_{\mathrm{v}}e^{i\Phi} \tag{4}\]  
In the above equation, \(\omega\) is the angular frequency, \(X\) represents the displacement generated by the structure, \(\sigma\) is a stress tensor, and \(F_{\mathrm{v}}\) is an external force. The first term on the right side of the equation is the surface load, and the second is the body load.  
The boundary load equation is:  
\[\sigma\overset{\rightarrow}{n}=F_{\mathrm{v}}e^{i\Phi} \tag{5}\]  
In the above equation, \(\overset{\rightarrow}{n}\) is the surface normal vector.  
The symmetric boundary constraint conditions are given below.  
\[\overset{\rightarrow}{n}\cdot X=0 \tag{6}\]  
The boundary control equation can be expressed in the following fashion:  
\[\
u_{\mathrm{fluid}}=i\omega\chi_{\mathrm{solid}} \tag{7}\]  
In the given equation, \(\
u_{\mathrm{fluid}}\) represents the fluid velocity. \(\chi_{\mathrm{solid}}\) represents the displacement of a solid, ensuring continuity on the stress boundary. Moreover, the temperature can comprise adiabatic or isothermal conditions.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Resource Management Decisions  
The action space for each agent in our resource scheduler is defined by the available radio resource pool, denoted as \(C^{k\times m}\), where k represents the number of subchannels (each consisting of groups of resource blocks (RBs)), and m represents the number of slots. Furthermore, the total channel bandwidth is 20 MHz, operating in the 5.9 GHz ITS band. We also incorporate a reselection counter \(C_{Resel}\) as per ETSI specifications, with a range uniformly distributed between 5 and 15 [20]. This counter determines the duration for which a vehicle holds the selected resources for future transmissions. The system operates under design constraints such as channel bandwidth (20 MHz), V2X transmission power (0 to 23 dBm), and an RSRP threshold of \(-\)128 dBm. These constraints ensure that radio resources are efficiently allocated and that the signal strength is sufficient for reliable communication.  
Each vehicle, with a mean speed of 140 km/h (and standard deviation of 3 km/h), selects one radio resource from \(C^{k\times m}\) and one category of the \(C_{Resel}\) value. The action selection mechanism aims to achieve the following:  
* Select a radio resource and decide the duration for using the same resource for future transmissions, based on the V2X data traffic pattern forecasted by the GRU layers;
* Detect potential resource collision risks and re-evaluate the resource selection within the PDB;
* Avoid scheduling the same resource for subsequent transmissions after detecting collisions in one of the intended slots and inform other agents to avoid the same resource;
* Adapt \(C_{Resel}\) based on the V2X data traffic pattern to ensure that reserved resources are utilized efficiently.  
The actor network selects a radio resource based on the softmax probability values. The critic network evaluates the impact of the selected action by computing the state value after the environment transitions to a new state. This continuous evaluation by the critic network provides feedback to the actor network, helping it to refine its policy and make more effective resource allocation decisions.
## 3 Proposed Vision-Based Arching Detection Method
### Data Conversion and Augmentation  
In our initial data-based arching detection, a basic technique is to plot and compare the LLI segments of different months manually, and then determine whether the slab arching has occurred or not. To imitate this process, we firstly convert original series data into images, rather than directly inputting the series to the detection model. The arching waveforms in the data have obvious graphical spatial characteristics, which can be more completely preserved in 2D images, thus facilitating clear detection.  
The series-image conversion method is displayed in Figure 8. Combining the LLI sequence data of high-temperature month (e.g., July in the northern hemisphere) and low-temperature month (e.g., January in the northern hemisphere), the sliding window is utilized to construct a 2D-image dataset. To ensure the arching characteristics can be fully captured, the window length is set to 20 m, and the overlapping length is 10 m. The size of each image is 250 pixel \(\times\) 100 pixel, the ordinate range is \(-\)4 mm to 4 mm, and the abscissa range is 0 m to 20 m. In the images, the blue line and orange line denote the LLI of the low-temperature month and high-temperature month, respectively. On account of determined axis ranges, the coordinates are invisible because labeled values are not required for the CNN model to learn the arching features. Furthermore, the naming of image files contains arching detection information, including the inspection date, mileage section, and inspection item (LLL/RRL), which is convenient for locating damaged segments.  
Following the maintenance information and manual inspection, the images are labeled with \"Normal\" or \"Arching\". We can observe that part of the normal and arching images have some similar characteristics, which can readily be confused.  
In actual HSR service, the probability of slab arching is much lower than the normal conditions. The number of arching samples and the number of normal samples are extremely imbalanced, which may cause over-fitting to the normal pattern and under-fitting to the arching pattern. Thus, it is necessary to carry out data augmentation on the arching images to expand the dataset. Common augmentation operations including rotation, flipping, cropping, translation, and scaling. Due to the particularity of the images formed by series data, the arching images are only flipped horizontally to avoid the generation of unnecessary features or the increase of image category, as shown in Figure 9. Moreover, the normal images are randomly downsampled to reduce the normal set. In this way, feature extraction can be better achieved.  
Figure 7: Flow chart of slab arching detection.
## 2 Second Messengers in Cyanobacteria
### Cyclic Guanosine 3',5'-Monophosphate, cGMP  
Cyclic GMP (cGMP) is a fairly recently confirmed second messenger in bacteria [81], although it has been well characterized in eukaryotic systems. Cyclic GMP is synthesized from GTP by guanylyl cyclases (GC), which are homologous to the class III adenylate cyclases. Class III AC and GC enzymes share similar catalytic domains and are thought to have evolved from a common ancestor [82]. Cyclic GMP is hydrolyzed to GMP by cGMP-specific phosphodiesterase (PDE) (Figure 5). Cyanobacteria contain higher levels of cGMP compared to other bacteria [83]. To date the only confirmed bacterial GC reported is the protein Cya2 from _Synechocystis_[84]. Mutational analysis of _cya2_ indicated that the encoded protein contributes to intracellular cGMP levels, but does not impact cAMP levels [84]. Cya2 has higher specific activity for synthesizing cGMP from GTP than for the production of cAMP from ATP [85]. This cGMP specificity has been attributed to faster turnover of GTP than ATP by Cya2 rather than preferential affinity for GTP [85]. The only cyanobacterial phosphodiesterase (PDE) known to degrade cGMP is encoded by the gene _slr2100_ in _Synechocystis_[86]. This cGMP-specific PDE is required for the adaptation of the cells to UV-B radiation. Intracellular cGMP levels decreased in wild-type after exposure to UV-B radiation, but not in the _slr2100_ mutant. The _slr2100_ mutant exhibited reduced transcripts of genes encoding components essential for PSII repair [86].
## 1 Introduction
In [42], a new hybrid method is proposed that allows for improving the accuracy of short-term load forecasting, which is based on the use of an artificial neural network and an artificial bee colony algorithm. This method allows for taking into account historical data and weather conditions. The considered methods of power consumption analysis are used to improve the economic indicators of industrial enterprises by enhancing the efficiency of power use, reducing environmental fines, planning the expansion of power systems, enhancing the operation of the electricity market, as well as shedding load at the dispatcher's command in the event of emergencies [43; 44]. The studies discussed do not take into account the specific features of the technological process, but this is essential to predict the possibility of its completion in order to fulfill the production plan in accordance with the contract. The aims of the study are (1) to analyze the features of technological processes at industrial enterprises, (2) to analyze the possibilities for fulfilling the plan for production output under conditions of changing speed of the technological process and power consumption, (3) to study the characteristics of the random function of power consumption and the law of distribution of emissions of the random process, and (4) to develop an algorithm and methods for analyzing the modes of power consumption of industrial enterprises taking into account the features of technological processes for their use in automatic power consumption control systems. The paper is organized as follows. Section 2 examines the characteristics of non-stationary conditions of producing products and power consumption by industrial enterprises and discusses methods for analyzing and making decisions on power consumption management. Section 3 presents an algorithm designed to analyze the current state of the technological process and power consumption behavior. This section also justifies the need to factor in potential fluctuations in the system parameters when managing power consumption and outlines the conditions for specifying power consumption patterns and parameters. Section 4 focuses on the findings and potential areas of research in the field of power consumption management. The conclusion encapsulates the key results of the study.
# Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia
## 2 Methodology
### Estimation of Seismic Hazard by Using a Logic Tree
Using ArcGIS(r) software, all ground motion variables were represented to create hazard maps to visualize the results. In particular, isoacceleration seismic hazard maps (for a Vs,30 value of 760 m/s) were created for the B/C NEHRP site class. These maps show the average PGA and SA values for the 475- and 975-year return periods at 0.2 and 1.0 s (assuming a 5% damping ratio). The uniform hazard spectrum (UHS)'s general shape was represented by the two oscillation periods (0.2 and 1.0 s). Furthermore, under various site conditions, hazard curves and UHS are provided for well-known cities in southwest Saudi Arabia. It has been suggested that a design response spectrum (based on both SA values for 0.2 and 1.0 s) might be used for some cities as an alternative to the present seismic design regulations, which only take into account PGA values. This method is based on the approach in [99] and is extensively explained in [100]. These suggested design spectra are outlined for the two site conditions taken into consideration. The current seismic hazard assessment's outputs provide designers and engineers with revised hazard data, such as PGA and SA, UHS, design spectra, and hazard curves for two distinct soil classes and return periods.
# Accounting for Round-Off Errors When Using Gradient Minimization Methods
## 4 On Increasing Computational Complexity  
Note that in the gradient method under consideration, starting from \(s=2\), the most computationally intensive operation in each iteration is the operation that calculates \(q^{(s)}\). It requires \(M(2N-1)\) arithmetic operations to compute the vector \(A\,p^{(s)}\) and \(N(2M-1)\) arithmetic operations to compute the final value \(q^{(s)}\). That is, the total number of arithmetic operations for computing \(q^{(s)}\) is \(4 MN-M-N=O(MN)\). The remaining operations in each iteration of the gradient method make a much smaller contribution to the total number of arithmetic operations, so we estimate the computational complexity of each iteration of the gradient method as \(O(MN)\).  
Once again, we also emphasize the advantage of the recurrent calculation of the \(r^{(s)}\) residual in all iterations, starting from \(r^{(s)}\). If we do not use the recurrent notation and then calculate the residual \(r^{(s)}\) in each iteration in the same way as in the first iteration, then the number of arithmetic operations required to complete the iterative process will increase by about two times.  
An important question arises: does the computational complexity of the algorithm increase when using the new stopping criterion?  
The first way to calculate \(\sigma^{2}_{r^{(s)}_{n}}\) requires about \(O(MN)\) additional arithmetic operations in each iteration of the considered gradient method. One can accurately calculate all arithmetic operations; in order to achieve this, one needs to consider the most computationally intense part--the calculation components of \(\sigma^{2}_{n}\). This operation requires \(M(3N-1)\) arithmetic operations to compute the elements of the inner sum, and then \(N(3M-1)\) more arithmetic operations to compute the outer sum. In total, \(6 MN-M-N=O(MN)\) operations are obtained, which increases the computational complexity of the considered gradient method by about 2.5 times.  
The second way to calculate \(\sigma^{2}_{r^{(s)}_{n}}\) requires only about \(O(N)\) additional arithmetic operations. This means that the computational complexity of the considered gradient method will not change in order.  
The use of both approaches in solving a large number of applied problems has shown that both methods give approximately the same results. In this regard, in the test calculations in Section 6, we will use only the second method as it is the most economical in the computational sense. However, we will first formulate this version of the conjugate gradient method with an improved stopping criterion of the iterative process.
## 3 Methods
### Regression Models
#### 3.3.2 Decision Tree  
In the context of regression, the decision tree is based on recursively partitioning the input feature space into regions and then assigning a constant value to each region. This constant value serves as the prediction for any data point that falls within that region. Assume that \(X\) is the input data, \(Y\) the target variable, and \(\theta\) represents the parameters that define the splits in the decision tree [27]. Let \(h(X;\theta)\) be the predicted value for \(Y\) given input \(X\) and parameter \(\theta\). Given a set of \(n\) training samples \((X_{i},Y_{i})\), where \(i=1,2,\ldots,n\), the decision tree regressor seeks to find optimal split \(\theta\) that minimizes the sum of square differences between the predicted value and the actual target value. The prediction for a given \(X\) can be represented as  
\[h(X;\theta)=\sum_{i=1}^{N}c_{i}I(X\in R_{i}) \tag{4}\]where \(N\) is the number of leaf nodes (regions) in the tree, \(c_{i}\) is the constant value associated with the leaf node \(R_{i}\), and \(I(X\in R_{i})\) is an indicator function that equals 1 if \(X\) falls within the region \(R_{i}\) and 0 otherwise.
## 2 Study Area and Data Sets
### Data Sets
#### 2.2.2 Landsat 8 Images  
The Landsat 8 satellite (NASA, Washington DC, USA) carries the Operational Land Imager (OLI) (Ball Aerospace & Technologies Corporation, CO, America) and Thermal Infrared Sensor (TIRS) push-broom imager, and the OLI consisted of nine bands with a spatial resolution of 30 meters and a full-color band of 15 meters. In this study, bands 2-4 in the Landsat 8 OLI image without clouds at four monitoring times were selected. As shown in Table 2, the images were collected from 30 October 2014 to 11 December 2018, which were used to calculate the building area of the southern region.