esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distributed Power Control for Spectrum Sensor Nodes
### Cooperative Optimal Solution
Thus, in order to ensure the convergence of optimal transmit power adjustment, we design the average efficiency-to-stability ratio \(\overline{\varpi}_{m}\) as a scaling coefficient in Algorithm 2 as follows:  
\[\overline{\varpi}_{m}=\left\{ \begin{array}{ll}\frac{\wp_{m}^{R}}{\sum_{m= 1}^{n_{IT}}\wp_{m}^{R}},&m\in\mathcal{N}_{IT}\\ \frac{\wp_{m}^{R}}{\sum_{m=1}^{n_{IT}}\wp_{m}^{R}},&m\in\mathcal{N}_{MT}\end{array} \right. \tag{37}\]  
It is worth remarking that the adjusted optimal transmit power will be updated with respect ro the scaling coefficient \(\overline{\varpi}_{m}\). In particular, the adjusted optimal transmit power for all IT and MT SSNs via OTPA algorithm can be made locally while guaranteeing that the condition of the average power constraint as given in Equation (3) is satisfied. ```
1:Input:\(\mathbf{P}^{NC}\) and \(\mathbf{P}^{C}\). 2:Output:\(\left\{\underbrace{\widehat{p}_{1}^{NC}(t),\cdots,\widehat{p}_{n_{IT}}^{NC}(t)}_{1 \to n_{IT}},\overbrace{\widehat{p}_{n_{IT}}^{NC}(t),\cdots,\widehat{p}_{n_{IT} +n_{MT}}^{NC}(t)}^{n_{IT}+1\to n_{IT}+n_{MT}}\right\}\) and \(\left\{\underbrace{\widehat{p}_{1}^{C}(t),\cdots,\widehat{p}_{n_{IT}}^{C}(t)}_{1 \to n_{IT}},\overbrace{\widehat{p}_{n_{IT}+1}^{C}(t),\cdots,\widehat{p}_{n_{IT }+n_{MT}}^{C}(t)}^{n_{IT}+1\to n_{IT}+n_{MT}}\right\}\). 3:Initialization:\(\left\{P_{1}^{\max},\cdots,P_{m}^{\max},\cdots,P_{|\mathcal{N}|}^{\max}\right\}\), \(\left\{\wp_{1}^{R},\cdots,\wp_{m}^{R},\cdots,\wp_{|\mathcal{N}|}^{R}\right\}\), \(\left\{\wp_{1}^{C},\cdots,\wp_{m}^{C},\cdots,\wp_{|\mathcal{N}|}^{C}\right\}\). 4:for\(m=1\to|\mathcal{N}|\)do
5:\(\widehat{p}_{m}^{NC}(t)\gets p_{m}^{NC}(t)\) using Equation (28), and \(\widehat{p}_{m}^{C}(t)\gets p_{m}^{C}(t)\) using Equation (34). 6:endfor
7:if\(\sum_{m=1}^{n_{IT}}\widehat{p}_{m}^{NC}(t)>n_{IT}P_{aw}^{IT}\) or \(\sum_{m=1}^{n_{IT}}\widehat{p}_{m}^{C}(t)>n_{IT}P_{aw}^{IT}\)then
8:repeat
9:for\(m=1\to n_{IT}\)do
10:\(\widehat{p}_{m}^{NC}(t)\leftarrow\left(\wp_{m}^{R}/\sum_{n_{IT}}^{n_{IT}}\wp_ {m}^{R}\right)\times\widehat{p}_{m}^{NC}(t)\) when \(\sum_{m=1}^{n_{IT}}\widehat{p}_{m}^{NC}(t)>n_{IT}P_{aw}^{IT}\). 11:\(\widehat{p}_{m}^{C}(t)\leftarrow\left(\wp_{m}^{R}/\sum_{m=1}^{n_{IT}}\wp_{m} ^{R}\right)\times\widehat{p}_{m}^{R}(t)\) when \(\sum_{m=1}^{n_{IT}}\widehat{p}_{m}^{C}(t)>n_{IT}P_{aw}^{IT}\). 12:end
```  
**Algorithm 2**Distributed Optimal Transmit Power Adjust Algorithm
# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
## 4 Results and Discussion
### Comparison between PSO and GA
As illustrated in Figure 7c,d, this even distribution likely arises from the algorithm's reliance on individual crossover, mutation, and selection processes, rather than inter-particle interactions. Nonetheless, these outcomes might improve with different schemes or enhanced GA methods. Therefore, it is important to note that these findings are based on a fundamental GA environment and might present a limited perspective. When examining Figure 7a,c, despite only four iterations being completed, we can assess the ability of these algorithms to move particles toward the final objective. In the case of Figure 7c, it is evident that even as the iterations progress, more than half of the particles are unable to converge to a solution, or they fail to gather and find the remaining solutions. It is also observed that this aspect does not improve significantly with repeated iterations. Based on the aforementioned results, the optimal findings can be summarized in Table 4. For both PSO and GA, given computational constraints, it is believed that 20 particles and 10 iterations can yield satisfactory outcomes. While parameters such as initial battery temperature or radiator glycol solution range between the upper and lower bounds, factors like pump displacement and response delay are intermediary values. Figure 7: PSO and GA particle movements for different iterations. (**a**) PSO—battery initial temperature. (**b**) PSO—pump displacement. (**c**) GA—battery initial temperature. (**d**) GA—pump displacement. It is suggested that further research should employ enhanced algorithms, rather than solely relying on the foundational PSO or GA algorithms.
# Ontology-Based Feature Selection: A Survey
## 6 Open Issues and Challenges
Most of the methods that exist in the literature rely on feature pre-selection or on feature selection without online classification [106; 107]. On the other hand ontologies encoded in trees or knowledge graphs may provide some benefits such as solid representations of the current relations between features, which can be used to predict any possible relation between the current available features and the ones that are expected to arise in real-time processing tasks. Therefore, to develop ontology-based feature selection methods for achieving real-time analysis and prediction regarding high-dimensional datasets remains a challenge. Finally, an important open issue to consider is scalability. Scalability quantifies the impact imposed by increasing the training data size on the computational performance of an algorithm in terms of accuracy and memory [105; 107]. The basics of feature selection and classification were developed before the era of Big Data. Therefore, most feature selection algorithms are not efficient in scaling high-dimensional data as their efficiency appears to reduce quickly. On the other hand, scaling-up favors the accuracy of the model. Therefore, there is a trade-off between finding an appropriate set of features and the model's accuracy. In this direction, the challenge is to define appropriate ontology-based relations between features in order to group them in such a way that the resulting set of features will be able to maintain acceptable model's accuracy.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 2 Materials and Methods
### Motivation  
Since its invention in 2014, GAN has been a preferred choice in research. The advent of deep learning has highlighted its dependency on large training datasets, which are usually rare in the biomedical field. The number of peer-reviewed papers on GAN with MRI-only workflow for brain imaging has increased considerably. For example, the number of research papers in this field published in Web of Science (WoS) and Scopus databases show an increasing trend (Figure 2). The study of related work in the literature highlights the following two limitations:  
* There is no existing SLR paper that describes the applications of GAN-synthesized brain MRI.
* There is no existing SLR paper that mentions the types of GAN loss functions.  
This SLR is going to contribute to the literature in the following manner:  
* The paper presents a clear categorical division of brain MRI applications of GAN-synthesized images as well as the details of the technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. No.** & **Year** & **Objective** & **Imaging Modality** & **DL Methods** & **Type** \\ \hline
[8] & 2022 & It summarizes GAN’s role in brain MRI. & MRI & GAN &
\begin{tabular}{c} Scoping \\ Review \\ \end{tabular} \\
[9] & 2018 & The paper gives GAN training, architecture, and a few application details. & All type & GAN & Overview \\
[10] & 2020 & It summarizes machine learning and DL classification methods. & MRI &
\begin{tabular}{c} CNN 1, RNN 2, \\ GAN, DBM 3 \\ \end{tabular} & Review \\
[11] & 2020 & It discusses GAN’s application in radiology. &
\begin{tabular}{c} CT, MRI, PET, and \\ Y-ray \\ \end{tabular} & GAN, CNN & SLR \\ \hline \hline \end{tabular}  
* CNN: Convolution neural network; RNN.2 Recurrent neural network; 3 DBM: Deep Boltzmann machine.  
\end{table}
Table 1: Prior research in MRI brain imaging.  
Figure 2: The number of research papers published in a particular year on GAN-based brain MRI.  
* The paper presents a concise account of distinct loss functions used in GAN training.
* The paper identifies software to preprocess brain MRI.
* The paper compares the various evaluation metrics available for the performance evaluation of synthetic images.
## 3 Experimental Results and Discussion
### UL-DLA and GLCM Exclusive Analysis  
We have tested the usability of our model without the hybrid feature space to ascertain the effectiveness of the hybrid feature set. In order to confirm that the improvement is not just from deep features but also textural features, a SoftMax classifier experimentation was carried out using UL-DLA for 20 epochs based on deep learning features only. To avoid overfitting in the model, the dropout layer delinks 5% of the neurons for its output and input functionality (Section 2.3 and Table 3). The quantitative performance corresponding to three datasets, namely CE-MRI, WT-CE-MRI, and A-CE-MRI is illustrated in Table 5. The best overall accuracy during the test phase for the SoftMax classifier was found to be 96.882% using A-CE-MRI. The augmentation relieves the class-imbalance problem, thereby improving the results. Similarly, wavelet transform-based decomposition (WT-CE-MRI) results were improved due to high contrast in comparison to the plane dataset (CE-MRI). On the other hand, another experiment was carried out using the GLCM features only with SVM (polynomial kernel with order 3), and the quantitative results have been illustrated in Table 6. We observed that the best performance observed was attributed to the A-CE-MRI dataset with an accuracy of 86.26%. The results were indicative of the fact that textural or deep features alone were not able to achieve the required accuracy, but it was the combination of both which was able to achieve the required performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **Tumor** & **TP** & **FN** & **FP** & **TPR** & **PPV** & **F-Measure** & **Acc.** & **Acc.** \\
**Type** & **Type** & & & & & & & **(Ind.)** & **(Avg.)** \\ \hline \multirow{3}{*}{A-CE-MRI} & Glioma & 551 & 19 & 32 & 96.67 & 94.51 & 0.96 & 95.82 & \\  & Meningioma & 246 & 37 & 19 & 86.93 & 92.83 & 0.90 & 95.43 & 96.88 \\  & Pituitary & 371 & 1 & 6 & 99.73 & 98.41 & 0.99 & 99.40 & \\ \hline \multirow{3}{*}{WT-CE-MRI} & Glioma & 276 & 9 & 17 & 96.84 & 94.20 & 0.96 & 95.70 & \\  & Meningioma & 118 & 24 & 10 & 83.10 & 92.19 & 0.87 & 94.44 & 96.15 \\  & Pituitary & 184 & 2 & 8 & 98.93 & 95.83 & 0.97 & 98.30 & \\ \hline \multirow{3}{*}{CE-MRI} & Glioma & 272 & 13 & 19 & 95.44 & 93.47 & 0.94 & 94.71 & \\  & Meningioma & 117 & 25 & 12 & 82.39 & 90.70 & 0.86 & 93.93 & 95.59 \\ \cline{1-1}  & Pituitary & 184 & 2 & 9 & 98.93 & 95.34 & 0.97 & 98.12 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: UL-DLA results using SoftMax classifier for augmented (A-CE-MRI), wavelet transform based (WT-CE-MRI), and intensity normalized (CE-MRI) images.  
Figure 7: The 2D scatterplot for Hybrid Feature Space (1037 features) mapped to 2 dimensions using the “t-SNE” technique.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### Point-Cloud Downsampling Experiment and Analysis Based on FFN Output Facets
#### 4.1.2 Experimental Setup and Evaluation  
Within the PlantNet dual-branch point-cloud segmentation framework [18], the influence of the FFN-based downsampling method is compared with three common strategies (FPS, random sampling, and voxelization + FPS) on semantic and instance segmentation tasks. First, each strategy is used to downsample the original point cloud to a fixed number of points (1024). The downsampled outputs are then input into PlantNet for training and testing. The network outputs both semantic and instance segmentation labels, which are evaluated according to the metrics described in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Semantic Segmentation Metric** & **Expression** & **Instance Segmentation Metric** & **Expression** \\ \hline Precision (P) & \(\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP+FP}}\) & \(mCov\) & \(mCov=\frac{1}{\mathrm{f}}\sum_{m=1}^{l}\max[\mathrm{IoU}(IG_{m},IP_{n})]\) \\ Recall (Re) & \(\mathrm{Re}=\frac{\mathrm{TP}}{\mathrm{TP+FN}}\) & \(mWCov\) & \(mWCov=\sum_{m=1}^{l}w_{mmax}[\mathrm{IoU}(IG_{m},IP_{n})]\) \\ F1-score (F1) & \(\mathrm{F1}=2\times\frac{\mathrm{F}\times\mathrm{Reg}}{\mathrm{F}+\mathrm{Reg}}\) & \(w_{m}\) & \(w_{m}=\frac{\mathrm{F}_{m}}{\sum_{i=1}^{l}IG_{i}}\) \\ IoU & \(\mathrm{IoU}=\frac{\mathrm{TP}}{\mathrm{TP+FP+FN}}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Semantic segmentation evaluation metrics.  
Figure 4: Overall workflow of the point-cloud downsampling strategy based on the FFN output facets. (**a**) Point cloud data within each facet. (**b**) Selecting downsampling points within each facet.  
Where, I denotes the number of instance labels in the current semantic class; IGm and IPn denote, within the current semantic class, the point set of the m-th ground-truth instance label and the point set of the n-th network-predicted instance label, respectively; \(w_{m}\) denotes, within the current semantic class, the proportion of points belonging to the m-th ground-truth instance label relative to the total number of points in that semantic class.  
During the experiments, in addition to using the same network architecture and hyperparameters, we ensured that weight initialization, data augmentation, and other stochastic components remained consistent in order to guarantee the fairness and reproducibility of the comparative results. After training, the above metrics were computed on test sets comprising 100 samples each for tobacco, tomato, and sorghum, and the average performance of each method was reported.
# Non-Stationary Stochastic Global Optimization Algorithms
## 2 Preliminaries
### Systematic Formalization Theory
#### 2.1.2 Measure and Probability Theory  
[PERSON] in [11] used probabilistic kernels to formalize SGoals. This work is an extension of [PERSON]'s work. We review some concepts that [PERSON] uses and that we are going to use to characterize adaptive SGoals.  
Definitions of Measure Theory  
Probability theory uses measure theory to formalize the concepts. Measure theory defines elementary events \(\Omega\
eq\emptyset\) and the system of observable events \(\mathcal{A}\subseteq 2^{\Omega}\), where \(\mathcal{A}\) is a family of sets. These concepts can be translated in the context of SGOAL where the set of elementary events contains all possible populations and, a family of sets of populations is a system of observable events.  
Probability theory operates over sets to measure the probability of some observable event. The structure used to measure subsets of \(\Omega\) is a \(\sigma\)-algebra that meets the following conditions:  
1. \(\Omega\in\Sigma\). \(\Omega\) is considered a universal set.
2. \(\Sigma\) is \(\overline{\mathbb{C}}\). \(\Sigma\) is closed under complement.
3. \(\Sigma\) is \(\overline{\mathbb{C}}\). \(\Sigma\) is closed under countable unions.  
When we deal with a continuous space, a topological space is used. Hence, \((\Omega,\tau)\) is a topological space, the sigma-algebra (\(\sigma\)-algebra) \(\mathcal{B}(\Omega)\equiv\mathcal{B}(\Omega,\tau)\equiv\sigma(\tau)\) is the Borel \(\sigma\)-algebra on \(\Omega\). Consider set \(\Omega=\mathbb{R}\), in that case \(\mathcal{B}(\mathbb{R})\)is the \(\sigma\)-algebra on \(\mathbb{R}\). In this paper, a tuple of the form \((\Omega,\Sigma)\), refers to a measurable space, where \(\Sigma\) is a \(\sigma\)-algebra.  
In probability theory, we generally need to operate between measurable spaces using certain functions, precisely measurable functions. Let \((\Omega_{1},\Sigma_{1})\) and \((\Omega_{2},\Sigma_{2})\) be two measurable spaces. A function \(f\) is defined as measurable if \(f\colon\Omega_{1}\to\Omega_{2}\), and \(f^{-1}(B)\) maps a subset of the domain. i.e., if \(B\in\Sigma_{2}\) then \(f^{-1}(B)\in\Sigma_{2}\).  
We call this function \(f\) measurable because we can define a measure \(\mu\colon\Sigma\to\overline{\mathbb{R}}\) on \((\Omega_{2},\Sigma_{2})\) in terms of \((\Omega_{1},\Sigma_{1})\). Where \(\mu\) has the following conditions:  
1. \(\mu(\emptyset)=0\).
2. \(\mu(B)\geq 0\) for all \(B\in\Sigma\).
3. \(\mu(\bigcup_{i\in I}B_{i})=\sum_{i\in I}\mu(B_{i})\) for all \(\{B_{i}\in\Sigma\}_{i\in I}\) Countable Disjoint Family.  
If \((\Omega,\Sigma)\) is a measurable space and \(\mu\) is a measure, we call \((\Omega,\Sigma,\mu)\) a measure space, if \(\mu\) is a probability measure i.e., \(\mu(\Omega)=1\), then \((\Omega,\Sigma,\mu)\) is a probability space.  
Let \((\Omega_{1},\Sigma_{1},Pr)\) be a probability space and \((\Omega_{2},\Sigma_{2})\) be a measurable space. If \(X\colon\Omega_{1}\to\Omega_{2}\) is a measurable function, then \(X\) is called a random variable with values in \((\Omega_{2},\Sigma_{2})\).
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 2 A Brief Review of MMC Topologies  
There are many topologies of the MMC. A comprehensive comparison among these topologies is presented in Table 1[24; 25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Topology** & **Configuration** & **Sub-Modules (SMs)** & **Remarks** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comprehensive comparison between Modular Multilevel Converter (MMC) topologies.  
For more explanation to state the motivation of this paper, the level of harmonics in the output voltages and currents of the MMC is very essential and has to be within the allowable limits. The harmonic content could be decreased without the need for any filters with the help of modern control methods [26; 27]. In order to obtain the output voltage with an acceptable level of THD in the AC side cascaded configuration, the switching frequency of the two-level converter (main converter) is adjusted to 400 Hz, while the frequency of carriers is taken as 2 kHz. Due to the possible mismatch of synchronization between the main converter and the active filter (H-bridge cascaded model), the fifth and seventh harmonic components may arise, which must be canceled using a number of filters.  
As example, reference [26] presented a comparison between the output voltage and the harmonic spectra generated from the single-phase AC cascaded configuration with two H-bridges on the AC side, which are presented in Figures 1 and 2, and the output voltage and the harmonic spectra generated from the single-phase AC cascaded configuration with 4H-bridges on the AC side, which are presented in Figures 3 and 4. It can be seen from Figure 2 that the third harmonic component has a large value and also that the value of THD is extremely high. That is because the method of third harmonic subtraction allows the multilevel converters to work independently of the load power factor and without the problems of capacitor voltage balancing [26]. The data presented in Figure 4 prove the superiority of the last configuration of 4H-bridges, as the profile of the output voltage harmonic is slightly better, but with an increase in the number of the power electronic devices and the cost of the system. Moreover, reference [26] concludes that the increasing of the series sub-modules (SMs) in the case of using two H-bridges or 4H-bridges will lead to a reduction in the harmonics and over-shoot in the output voltages of the MMC.  
The introduced discussion leads the authors to present an optimal design of the control system and the optimal capacitor value of the MMC for the STATCOM application, using the half-bridge SMs to decrease the number of devices and the cost of implementation.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 3 Results and Discussion
### Results from Tests with Concentrated Natural Sunlight  
The results of irradiation tests performed with concentrated natural sunlight are summarized in Table 5.  
Figure 9: (**a**) Solar radiation spectrum for direct light at both the top of the Earth’s atmosphere (represented by yellow area) and at sea level (red area); (**b**) unfiltered radiation spectrum of xenon arc lamp and the sunlight spectral irradiance at top of the atmosphere.  
As shown in Table 5, the irradiation power applied to the lenses tested in the solar tower are in the range between 200 and 314 W. We should note that tests performed with a xenon arc lamp covered the range between 180 and 852 W (Table 4). Comparing maximum temperatures in the vicinity of the lenses, we found higher temperatures with the test setup used in the solar tower than in the test setup used with the solar simulator using a xenon arc lamp. This discrepancy is explained due to the fact that in the solar tower the total irradiated area of the target (made of alumina foam) is quite large (approximately 30 cm in diameter, as depicted in the Figure 8b) and it is continuously being heated up by the heliostats during the 60 min of the irradiation test. However, in the solar simulator using a xenon arc lamp, the total irradiated area is only approximately 5.5 cm in diameter and most of the radiation beam just illuminates (irradiates) the lens. It should be emphasized that the values of irradiation power mentioned in Table 4 (for a xenon arc lamp) and Table 5 (for concentrated natural sunlight) pertain only to power incident on the lens surface.
## 4 Research Methodology
### Structural Model and Hypotheses  
A PLS-SEM bootstrapping approach was used to test the hypotheses with 10,000 bootstrap samples. The results are given in Table 6. First, the perceived justice-satisfaction model accounted for 66% of the explained variance of satisfaction in online recovery (R\({}^{2}\) = 63%). The levels of explained variance for perceived justice (R\({}^{2}\) = 47.5%) accounted for by the customer participation components are acceptable as well. Thus, the fit of the overall model is acceptable.  
Second, the path coefficients and confidence intervals (CIs) show that emotional participation has no positive effect on informational justice; thus, H1a is supported. Mental participation positively influences perceived justice; thus, H1b is supported. Physical participation positively influences perceived justice; thus, H1c is supported. Perceived justice positively influences customers' post-recovery satisfaction; thus, H3 is supported.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Item** & **EP** & **MP** & **PP** & **PJ** & **SAT** \\ \hline EP1 & **0.798** & 0.377 & 0.396 & 0.131 & 0.338 \\ EP2 & **0.850** & 0.362 & 0.475 & 0.161 & 0.245 \\ EP3 & **0.703** & 0.221 & 0.337 & 0.070 & 0.226 \\ \hline MP1 & 0.358 & **0.886** & 0.531 & 0.297 & 0.516 \\ MP2 & 0.404 & **0.883** & 0.569 & 0.293 & 0.541 \\ \hline PP1 & 0.422 & 0.512 & **0.761** & 0.180 & 0.491 \\ PP2 & 0.454 & 0.556 & **0.865** & 0.308 & 0.494 \\ PP3 & 0.405 & 0.422 & **0.771** & 0.228 & 0.427 \\ \hline PJ1 & 0.153 & 0.212 & 0.196 & **0.848** & 0.368 \\ PJ2 & 0.144 & 0.272 & 0.248 & **0.881** & 0.381 \\ PJ3 & 0.164 & 0.309 & 0.323 & **0.868** & 0.409 \\ PJ4 & 0.120 & 0.341 & 0.285 & **0.857** & 0.417 \\ \hline SAT1 & 0.280 & 0.509 & 0.424 & 0.379 & **0.839** \\ SAT2 & 0.234 & 0.457 & 0.490 & 0.399 & **0.852** \\ SAT3 & 0.374 & 0.556 & 0.570 & 0.387 & **0.854** \\ \hline EP & 0.766 & & & & \\ MP & 0.431 & 0.885 & & & \\ PP & 0.532 & 0.621 & 0.800 & & \\ PJ & 0.168 & 0.333 & 0.310 & 0.864 & \\ SAT & 0.348 & 0.597 & 0.583 & 0.458 & 0.848 \\ \hline \hline \end{tabular} Note: 1. Each item’s loadings are expressed in bold type. 2. Diagonal elements show the square root of AVE for each construct. Numbers below diagonal are correlations between constructs.  
\end{table}
Table 5: Standardized factor loadings, cross-factor loadings of study and inter-construct correlations.