esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The numerical analysis and optimization process are based on the Ansys Workbench platform and Design Explorer module, [18]. Among available optimization methods are Nonlinear Programming by Quadratic Lagrangian (NPQL) [19], Mixed-Integer Sequential Quadratic Programming (MISQP) [20; 21; 22], and Genetic algorithm (GA) [23].  
The optimization framework used in the article is described in Figure 13 and is based on the genetic algorithm (GA).  
In the first step, the parametric model dedicated for optimization is defined. It ensures geometry feasibility for defined ranges and combinations between the accounted geometrical parameters. Metamodeling uses a parametric FEM model to establish the mathematical transfer function \(\hat{y}(x_{i})\) between geometrical characteristics (\(x_{i}\)) of the casing nozzle and output parameters (natural frequency and model area). At the same time, the formulated optimization problem for optimization task with GA. The algorithm uses a metamodel to assess each chromosome. The optimization process is completed when it reached the stability of the population based on mean and standard deviation of the output parameters  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Lower Bound, in** & **Upper Bound, in** \\ \hline Casing shell A & 0.080 & 0.120 \\ Casing shell B & 0.080 & 0.180 \\ Casing shell C & 0.080 & 0.120 \\ Forward hook thickness & 0.100 & 0.200 \\ Rear hook thickness & 0.090 & 0.120 \\ Nozzle shell thickness & 0.060 & 0.120 \\ Forward hook position & \(-\)0.050 & 0.050 \\ Rear hook position & \(-\)0.050 & 0.050 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical thickness and position parameters.  
Figure 13: Optimization framework with metamodel.  
(natural frequency and 2D model area). If both fell below the defined convergence stability percentage then the algorithm is converged (see Equations (25) and (26)).  
\[\frac{|Mean_{i}-Mean_{i-1}|}{Max-Min}<\frac{S}{100} \tag{25}\]  
\[\frac{|StdDev_{i}-StdDev_{i-1}|}{Max-Min}<\frac{S}{100} \tag{26}\]  
where:  
_Mean_;--frequency and area mean of the ith population,  
_StdDev_;--frequency and area standard deviation of the \(i\)th population,  
_S_--Convergence stability percentage,  
_Max_--maximum output value calculated from the first population from GA,  
_Min_--minimum output value calculated from the first population from GA.  
The algorithm will also be stopped in case of reaching the maximum number of iterations, however without fulfilling stability criteria will be not converged. The maximum number of candidates represents the number of the best chromosomes reported in the article.  
The last step in the framework is the validation aimed to confirm obtained the best chromosome from metamodel through FEM analysis and results. After validation process is considered completed.
###### Abstract  
As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both time-scale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity.  
Keywords:induction motor; broken rotor bar; wavelet packet signature analysis; fast Fourier transform; multi-layer perceptron neural network +
Footnote †: journal: Applied Sciences
# The Effect of Web Augmented Reality on Primary Pupils' Achievement in English
## 5 Analysis of Data and Discussion of Results
### Comparison between the Achievement of the EG and CG in the Second Posttest  
In order to verify the second hypothesis which states that there is no significant difference between the mean scores of the EG achievement and that of the CG achievement in the second posttest, the mean scores as well as the standard deviations of the two groups are obtained. Results show that the mean scores of the EG and CG achievements are 9.67 and 9.47, respectively. In order to determine whether there is a statistically significant difference between the acquired mean scores, the t-test formula for two independent samples is utilized. At 56 degrees of freedom and 0.05 level of significance, the computed t-value is determined to be 1.02, while the tabular t-value is 2.00, as shown in Table 12. These results indicate that there is a little improvement in the EG performance in the second posttest. It is worth pointing out that the obtained improvement is not significant thus, the second hypothesis is also rejected.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Question** & **Item/S** & **High** & **Low** & **DL** & **DP** \\ \hline \multirow{3}{*}{1} & 1 & 8 & 5 & 0.3 & 0.65 \\ \cline{2-6}  & 2 & 7 & 4 & 0.3 & 0.55 \\ \hline \multirow{3}{*}{2} & 1a & 8 & 3 & 0.5 & 0.55 \\ \cline{2-6}  & 2a & 9 & 6 & 0.3 & 0.75 \\ \cline{2-6}  & 1b & 8 & 5 & 0.3 & 0.65 \\ \cline{2-6}  & 2b & 8 & 4 & 0.4 & 0.6 \\ \cline{2-6}  & 3b & 8 & 5 & 0.3 & 0.65 \\ \hline \multirow{3}{*}{3} & 1 & 10 & 4 & 0.4 & 0.35 \\ \cline{2-6}  & 2 & 9 & 5 & 0.4 & 0.7 \\ \cline{2-6}  & 3 & 8 & 4 & 0.4 & 0.6 \\ \hline \multirow{3}{*}{4} & 1 & 9 & 3 & 0.6 & 0.6 \\ \cline{2-6}  & 2 & 10 & 4 & 0.6 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The Difficulty Level and Discrimination Power of Posttest Items.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**No. of Pupils**} & \multirow{2}{*}{**Mean Scores**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{**T-Value**} & \multirow{2}{*}{**DF**} & **Level of** \\ \cline{4-5}  & & & **Computed** & & & **Tabulated** \\ \hline EG & 30 & 9.133 & 1.479 & & & \\ \hline CG & 28 & 9.18 & 0.98 & 0.14 & 2 & 56 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The Mean Scores, Standard Deviations, and T-Values of the EG and CG in the First Posttest.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 5 Application to Roller Bearings Testing  
In this section, the proposed method is applied to analyze the outer ring fault signal of the Case Western Reserve University (CWRU) bearing data set [33] and the National Aeronautics and Space Administration (NASA) bearing data set [34], respectively. It is also compared with the FK, EMD and the modified empirical wavelet transform (MEWT) method proposed in [17]. The bearing with normal and degraded bearing [35] is shown in Figure 8.
## 3 Reliability-Based Topology Optimization
### Failure Probability  
The concept of structural safety is linked to the capacity of a given structure to withstand the various actions imposed on it during its service life, while also satisfying the functional conditions for its construction. Thus, the safety of a structure can be defined based on the probability that one of the failure states will occur during its service life. For a given design rule, the basic random variables are defined by their joint probability distribution associated with some expected parameters. The vector of random variables is here denoted by \(Y\). Thus, the probability of a structure failure can be written as  
\[P_{f}=P[G(Y)>0]=\int_{G(Y)>0}f_{Y}\big{(}y_{1},y_{2},\ldots,y_{j}\big{)}dy \tag{10}\]  
where \(f_{Y}\big{(}y_{1},y_{2},\ldots,y_{j}\big{)}\) is the joint probability density function (PDF) of the random variables, \(P_{f}\) is the failure probability, calculated by the probability density function's integral according to Equation (10), and \(P\) represents the constraints imposed on the probability. If \(G(Y)>0\), this indicates the failure region, and \(G(Y)=0\) indicates the failure surface as a limit state function. Figure 2 displays the probability integrations in Equation (10) for the joint PDF for random variables \(y_{1},y_{2}\) which is the two-dimensional case. The contours are projections of the surface of \(f_{Y}\) on the \(y_{1}\)-\(y_{2}\) plane.  
Ref. [46] defines the reliability index, \(\beta\), as the minimum distance from the source surface to the failure in the space of the standard normalized distribution, and the MPP as a point corresponding to the minimum distance. FORM is used to estimate the failure probability by approximating the limit state function. FORM involves the process of obtaining the MPP on the failure surface and the concept of  
Figure 2: Probability Integration, adapted from [45].  
function. If the curvature of the failure surface at MPP is not large, FORM can provide an accurate reliability index. Therefore, the failure probability can be expressed as  
\[P_{f}=P[G(Y)>0]=\Phi(-\beta) \tag{11}\]  
where \(\Phi\) is the standard normal distribution. The reliability index \(\beta\) can be determined by finding the minimum distance to the failure surface in the space of the standard normal distribution [47]. A transformation is introduced to map the original random vector X to a standard, uncorrelated normal vector, Figure 3.
# Carbon and Nitrogen Pools and Fluxes in Adjacent Mature Norway Spruce and European Beech Forests
## 3 Results  
### Carbon and Nitrogen Stocks  
#### 3.1.1 Carbon and Nitrogen in Vegetation Biomass  
In 2013, 540 individual spruce trees and 340 individual beech trees per hectare were present in the stands (Table 1). The age of the trees in the spruce stand was estimated as 80 years, and the age of the beech trees was approximately 150 years.  
Spruce stand total C biomass was 192 t\(\cdot\)ha\({}^{-1}\), of which 69% was stored in the stem, 9% in branches, 6% in needles and 24% in roots. The total biomass C in the beech stand was 258 t\(\cdot\)ha\({}^{-1}\), 1.34\(\times\) higher than in the spruce stand. The largest part of biomass C was stored in the stem (72%), with 15% in branches, 1% in leaves and 13% in roots (Table 2).  
The total biomass N pool in the spruce stand was 1447 kg\(\cdot\)ha\({}^{-1}\), of which 26% was in the stem, 16% in branches, 19% in leaves and 39% in roots. In the beech stand, the total biomass N pool was 1454 kg\(\cdot\)ha\({}^{-1}\), of which 34% was in stem biomass, 37% in branches, 7% in leaves and 22% in roots (Table 2). Due to the higher C pool in beech biomass and rather similar biomass N pools, the total biomass C/N ratio was higher in the beech stand. The lowest C/N ratio (g/g) was measured in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No. of Trees** & **DBH** & **Height** & **Crown Area** \\ \hline Spruce stand & ha & cm & m & m\({}^{2}\) \\ Avg & 540 & 37.9 & 25.0 & 29.5 \\ Median & & 37.7 & 25.8 & 24.6 \\ Min & & 14.7 & 14.9 & 4.0 \\ Max & & 59.2 & 30.0 & 105 \\ Beech stand & & & & \\ Avg & 340 & 40.1 & 29.9 & 37.8 \\ Median & & 42.0 & 31.2 & 32.6 \\ Min & & 16.9 & 20.2 & 4.4 \\ Max & & 55.6 & 34.8 & 123 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of trees (trees-ha\({}^{-1}\)) and their DBH (diameter at breast height), tree height and crown area (m\({}^{2}\)) characteristics in both stands.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**2013** & **Stem** & **Branches** & **Leaves** & **Roots** & **Abovegr** & **Total** \\ \hline Spruce stand & & & & kg ha\({}^{-1}\) & & \\ DW & 249,344 & 35,973 & 23,000 & 95,518 & 308,317 & 403,835 \\ C & 118,438 & 17,087 & 10,925 & 45,371 & 146,451 & 191,822 \\ N & 374 & 227 & 276 & 570 & 877 & 1447 \\ C/N & 317 & 75 & 40 & 80 & 167 & 133 \\ Beech stand & & & & & \\ DW & 390,095 & 79,084 & 4104 & 70,611 & 473,283 & 543,894 \\ C & 185,295 & 37,565 & 1949 & 33,540 & 224,809 & 258,350 \\ N & 495 & 543 & 96 & 320 & 1134 & 1454 \\ C/N & 374 & 69 & 20 & 105 & 198 & 178 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stocks (kg\(\cdot\)ha\({}^{-1}\)) of biomass (dry weight), carbon, nitrogen and the mass ratio of C:N in spruce and beech vegetation.  
needles (40) and leaves (20), whereas the highest C/N ratio was measured in stem biomass (317 in the spruce and 374 in the beech stand).
## 3 Numerical Analysis Results
### Reduced Shank Diameter Effect on SCF  
The straight bolt with 37.1 mm nominal diameter and neck-down bolt with reduced shank diameter of 28 mm were analyzed with two different nut types--standard and round nut type--as shown in Figure 12.  
Irrespective of nut types, the SCF of bolt with reduced shank diameter was reduced dominantly by 45% as listed in Tables 8 and 9.  
As shown in Tables 8 and 9, it also turned out that bolt joint with round nut type reduced the SCF by 15% compared with bolt joint with standard nut type. Results showed that the shank effect of the neck-down bolt is the most critical factor to reduce SCF.
## 3 System Overview
### Probabilistic Fingerprint  
During the offline phase, a probability density function for each cell is estimated based on the MVGMM. Most current work that exploits GMM to estimate the probability distribution tends to specify a fixed mixture component, while it is important to note that the mixture component is a variable that is acting together to determine the overall estimation. The Akaike's information criterion (AIC) measures the goodness of fit of statistical models [56] and it is applied in this paper to find the optimal number of components \(K\) of MVGMM. The authors decided that seven mixture components should be used in terms of optimum classification results and computation burden, as presented in the Optimal \(K\) Section 4.6. Then, an online matching process fits the online observation with the optimal parameters calculated during the offline process to identify the probability the observation belongs to each cell.
# Multimodal Biometric Template Protection Based on a
###### Abstract  
Authentication systems that employ biometrics are commonplace, as they offer a convenient means of authenticating an individual's identity. However, these systems give rise to concerns about security and privacy due to insecure template management. As a remedy, biometric template protection (BTP) has been developed. Cancelable biometrics is a non-invertible form of BTP in which the templates are changeable. This paper proposes a deep-learning-based end-to-end multimodal cancelable biometrics scheme called cancelable SoftmaxOut fusion network (CSMoFN). By end-to-end, we mean a model that receives raw biometric data as input and produces a protected template as output. CSMoFN combines two biometric traits, the face and the periocular region, and is composed of three modules: a feature extraction and fusion module, a permutation SoftmaxOut transformation module, and a multiplication-diagonal compression module. The first module carries out feature extraction and fusion, while the second and third are responsible for the hashing of fused features and compression. In addition, our network is equipped with dual template-changeability mechanisms with user-specific seeded permutation and binary random projection. CSMoFN is trained by minimizing the ArcFace loss and the pairwise angular loss. We evaluate the network, using six face-periocular multimodal datasets, in terms of its verification performance, unlinkability, revocability, and non-invertibility.  
cancelable biometrics system; multimodal biometrics; neural network model; security and privacy; fusion of features; random projection; authentication
###### Abstract  
Large Intelligent Surfaces (LISs) have emerged as a promising technology for enhancing spectral efficiency and communication capacity in the Sixth Generation of Cellular Communications (6G). Low-complexity receiver architectures for LISs rely on Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) receivers, often complemented by iterative detection techniques for interference mitigation. In this work, we propose a novel approach where a neural network replaces iterative interference cancellation, learning to estimate the transmitted signals directly from the received data, mitigating interference without requiring iterative cancellation. Moreover, this also eliminates the need for channel matrix inversion at each frequency component, as required for Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) receivers, reducing computational complexity while still achieving a good performance improvement. The neural network parameters are optimized to balance performance and computational cost.  
LIS systems; 6G; receiver types; neural networks Article
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 6 Conclusions  
Based on the conducted experiments, several significant points have been highlighted. The adopted signal-processing technique can generate a new visualization of images which differs from the original images. Enhanced feature generation of images can be achieved by adopting these technologies for image processing in medicine, which will make a significant contribution if AI/ML studies are applied for osteoporosis differentiation based on CT scan data. Some attribute indices highlight consistency in differentiating osteoporosis from normal bone in different scanning modalities (the grayscale, phase, and roughness attributes). Other attributes are consistently different within the same modalities; however, they show no contrast at different modalities (the contrast attribute). One attribute appears to be consistent only on CT scan images, but displays no difference between normal and osteoporotic bone on the DEXA images (the frequency attribute). More experiments on the different modalities should be conducted to test the robustness of these attributes before they are applied as a practical method utilized by the medical practitioner. Studies on the automation and classification of bone by implementing AI/ML technology can be inspired by other studies in different disciplines, especially for classification problems. Hence, the standard workflow for classification purposes in the signal-processing discipline can also be adopted to solve medical problems, especially for osteoporotic bone classification. Future research should be directed toward the implementation of this technique as part of an AI/ML study.
## 2 Study Area  
The Sistan and Baluchestan province is located in the east and southeast of Iran (58 deg55'-63 deg20' E longitude and 25 deg04'-31 deg25' N latitude), bordering Pakistan and Afghanistan, and its capital is Zahedan. This province is the second largest province in Iran with an area of 180,726 km\({}^{2}\) and a population of about 2.5 million. Figure 1A shows the location of this province on the Maphill Earth map. There is a depression in the study area known as the Hamun-Jaz-Murian basin, which is part of the central plateau basin. This basin is located in the southeast of Iran between 56 deg17' and 61 deg25' E longitude and 26 deg32' and 29 deg35' N latitude (Figure 1B). Its total area is about 69,930 km\({}^{2}\), of which 44% is mountains. The depression belongs to Kerman and Sistan-Baluchistan provinces [35]. Figure 1C shows a flooded region in IranShahr, which is one of the cities in this province.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Description** \\ \hline Orbit & LEO, \(-\)520 km, Nonsynchronous \\ Period & 95.1 min \\ Spatial Resolution & \(\sim\)25 km \(\times\) 25 km (incoherent), \(\sim\)0.5 km \(\times\) 5 km (coherent, theoretical) \\ Revisit Times & 2.8 h median, 7.2 h mean \\ Polarization of the reflectometry antennas & LHCP \\ Coverage & \(-\)38 \(\times\) Latitude \(<\) 38 \(\&\) -180 \(<\) Longitude \(<\) 180 \\ Type of Data which is relevant & Observe GPS L1 C/A signals and Delay Doppler Maps \\ \hline \end{tabular}
\end{table}
Table 1: The Cyclone Global Navigation Satellite System (CYGNSS) satellite parameters.  
Sistan and Baluchestan is one of the warmest regions in Iran, with a desert climate and an average daily temperature of 29 degrees centigrade. For several months of the year, it is warm at temperatures continuously above 25 degrees centigrade, and temperatures sometimes exceed above 40 degrees centigrade. Figure 2 illustrates the average precipitation per day over 20 years. As can be seen, 0.40 mm/day rainfall is normal during January in the province, but between 10 January and 12 January 2020, this amount is over 100 mm. Figure 3 shows the precipitation rate from 8 January to 13 January 2020.  
Figure 1: (**A**) The red region shows the location of the Sistan and Baluchestan province in the southeast of Iran, which is our study area. (**B**) The Hamun Jaz-Murian depression, which is located between the Kerman and Sistan and Baluchestan provinces. (**C**) IranShahr, one of the flooded cities in the study area.  
Figure 2: Average daily precipitation data collected from three meteorological stations in the Sistan and Baluchestan province based on the average values of the last 20 years [36].
## 4 Discussion
Although quantifying the individual effect of each impacting variable is difficult, we believe that continuous datasets provide the ability to explicitly examine the cause of changing PRI over time and space. This has been a criticism of studies using non-continuous PRI measurements such as those from satellite platforms, where their low temporal resolution cannot resolve short-term reflectance changes and instead changes primarily represent changing pigment pools [38; 39]. Both the sPRI and \(\alpha\) LUE models were able to track the overall progression of GPP. Still, some of the short-term dynamic changes observed in GPP during maturity were better captured by APAR the sPRI and \(\alpha\) derived LUE models. We can attribute this to the strong association between incident light and productivity during peak GPP months. During peak GPP months, canopy structure remains mostly unchanged; thus APAR variability is driven by PPFD dynamics. During leaf senescence, PPFD was no longer associated with GPP and lead to the differences seen between APAR and GPP trends. Overall, the \(\alpha\) driven LUE model showed to be the most accurate. The sPRI LUE model followed closely and was able to track seasonal changes in productivity. Evaluation of APAR, sPRI and LUE model results indicate that construction of LUE models needs to consider the changing contribution of the individual model variables through time and within different ecosystems. This follows [PERSON] et al. [14] concept suggesting that canopy efficiency can change between vegetation and environmental conditions. Exploration of this concept has been hard to test due to lack of continuous datasets that would allow mechanistic and comparative analysis within and across biomes. Continuous SRS-sPRI offer new opportunities to explore diurnal and seasonal changes in vegetation physiology.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 4 Experimental Setup and Results  
In this section, we discuss the experimental setup and procedures employed to evaluate the effectiveness of our proposed VAE-based real-time anomaly detection framework. Additionally, we present the results of our experiments, highlighting the model's performance in detecting various types of anomalies in V2X communications under realistic conditions.