esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiducial Lower Confidence Limit of Reliability for a Power Distribution System
## Materials and Methods
### Fiducial LCL of Reliability for an Exponential-Type Component
```  
**Algorithm 1**Fiducial algorithm procedure for exponential distribution. Next, we use a simulated example to show how to derive the LCL of reliability for an exponential-type component. **Example 1**. : _In the satellite intelligent PDS, the failure rate of the voltage detector, current detector and sense resistor are approximately 0.2 \(\times\) 10\({}^{-9}\), 0.5 \(\times\) 10\({}^{-9}\) and 0.4 \(\times\) 10\({}^{-9}\), respectively. The simulated lifetime data are listed in Table 1. In practice, these data are usually obtained by an accelerated lifetime test._  
For the voltage detector, current detector and sense resistor, the simulated pivotal quantity, whose sample size was 2000, was generated by Monte Carlo simulation. Simulations were conducted in statistical software R. Through Equations (2) and (3), the estimators and the CIs of parameter \(\lambda\) were obtained after generating 2000 random numbers which followed \(\chi^{2}\) distribution with 20 degrees of freedom. However, the estimate changed if another 2000 Chi-square random numbers were generated. To avoid this change, the estimates were averaged by 1000 simulation trials. The results are listed in Table 2.  
In Table 2, the estimators of the parameters for the voltage detector, current detector and sense resistor lifetime distributions were 0.241 \(\times\) 10\({}^{-9}\), 0.505 \(\times\) 10\({}^{-9}\), and 0.407 \(\times\) 10\({}^{-9}\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Datasets (h)** \\ \hline Voltage Detector & 749,318,825, 249,122,143, 3,041,561,966, 1,290,709,269, 9,207,672,141, \\  & 346,452,730, 898,416,864, 1,921,665,561, 832,302,119, 22,978,273,342 \\ \hline Current Detector & 2,976,355,068, 1,614,412,014, 1,596,199,429, 4,385,031,242, 343,767,602, \\  & 1,394,727,033, 1,699,963,564, 1,395,646,527, 4,092,186,674, 303,092,873 \\ \hline Sense Resistor & 3,249,590,569, 2,144,799,183, 4,865,614,194, 1,061,070,057, 2,052,784,459, \\  & 946,101,390, 5,840,861,755, 3,188,572,801, 955,939,901, 260,937,877 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulated lifetime datasets of the voltage detector, current detector and sense resistor. respectively. It is shown that the fiducial estimators were close to the true value. In the same way, with Equations (4) and (5), the estimators and the LCLs of reliability at \(t_{0}\) = 87,600 h (10 years) with a confidence level 0.8 are listed in Table 3.  
From Table 3, we find that the estimator and the LCL of reliability for the voltage detector were 0.9999789 and 0.9999736 at 10 years, respectively. These two numbers were both very close to one since the failure rate was very small. In fact, if \(t_{0}\) is taken as \(8.76\times 10^{7}\) h, the reliability is approximately 0.97, which is still very high. The analyses of the current detector and sense resistor were similar. We could treat them as \"non-failed\" components.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 3 The Proposed Model
### Model Construction
#### Auxiliary features  
For better modeling real-world correlations among users and trust relationships and for the proposed method to work properly in the model's probabilistic inference, certain auxiliary edge features are built and attached to the model. They are called auxiliary in this paper because the proposed model and the trust inference approach will still work without adding them, though inefficiently.  
We build the auxiliary edge features through the inspiration of the _Ising model_[46] (or more generally the _Potts model_) in statistical mechanics. These models imply that two directly connected nodes of the same type tend to be in the same state. This inspires us that the state-state pair of two directly connected nodes of different types might also follow certain statistical rules. In this paper, two categories of auxiliary edge features are proposed as follows.  
1. One category of auxiliary edge features will be attached to each edge between a user node and a trustRelation node. Their labelnames are, respectively, prefixed with \"u2 TT\" and \"T2 uT\" for features on a _trustor-trustRelation edge_ and features on a _trustRelation-trustee edge_. This setting matches the construction of our probabilistic graphical model where edges between user nodes and trustRelation nodes have different types. Such an setting allows the model to distinguish how differently a trustor or a trustee affects a trust relationship's formation.
## 2 Materials and Methods
### Methods
#### 2.2.2 Laboratory Work  
The samples were dried (65 \({}^{\circ}\)C) and sieved; thus, only the grain-size fraction smaller than 2 mm was processed during the separation. The separation procedure was performed on 50 g samples. If during the sieving, larger MPs (2-5 mm) or mesoplastics (\(\geq\)5 mm) were found, they were counted and noted. Based on our previous experience, the silt and clay content of the samples makes it difficult to separate the MPs during the zinc chloride treatment; therefore, the fine-grained particles were removed by wet sieving (90 \(\upmu\)m). Then, the inorganic sediment fraction was separated using 50 mL zinc chloride solution (1.8 g/cm\({}^{3}\)) following [PERSON] et al. [44] and [PERSON] et al. [29]. In the next step, the organic material was decomposed in 30 mL hydrogen peroxide (30%) for 24 h. The MP samples were finally washed into Petri dishes.  
The identification and counting of MP particles were performed with an Ash Inspex II digital microscope at 60\(\times\) magnification [45] following [PERSON] et al. [20]. An item was identified as MP if (1) it did not have a characteristic structure of organic matter; (2) it melted or shrank on contact with a hot needle [46]; and (3) if it retained its rigid shape when moved. In addition, clear microscopic characteristics related to anthropogenic origin were considered as well [20; 45], such as special color (e.g., red, blue) and shape (i.e., sphere, irregular fragmented). Though [PERSON] et al. [47] claimed that the hot needle test does not provide precise data in the case of some plastic types, no other method was available to check the accuracy of the identification. However, the identification of the MP was performed by a microscope expert (palynologist) to minimize the misidentification. During the identification, fibers (colored and colorless), fragments and spheres were separated. The MP content of the dry sediment was expressed in item/dry kg. As the study aimed to evaluate the sedimentary environment of MPs rather than to evaluate the accumulation of various plastic types, no FTIR and Raman spectroscopy was applied.  
To compare the changes (%) between subsequent spatial and temporal data, the enrichment ratio (\(E\)) was calculated as  
\[E=100-\frac{100\times n_{2}}{n_{1}} \tag{1}\]  
where the \(n_{1}\) is a preceding MP concentration data (e.g., of an upstream section, or 2019 data or of coarse-grained sediments), and \(n_{2}\) is subsequent MP data (e.g., of a downstream section, or 2020 data or of fine-grained sediments). The positive numbers refer to the enrichment of MP in the sediment, while negative values refer to declining MP content.  
The grain size of the dried samples was determined by dry sieving and laser particle sizer FritschAnalysette 22 MicroTecPlus. The mean grain size (\(d_{50}\)) was calculated for each sample.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 2 Study Area and Problems  
The study targeted two sites in southern Saudi Arabia along the Sarawat Mountain, constituting a natural obstacle to communication between cities and population centers above the mountains and those scattered in the plains and valleys [19]. The first site (AqabatShaar) is located on the road linking the cities of Abha and Mahayel Asir, and this obstacle extends to a length of 14 km on mountain ranges with a height of 2160 m. The second site (Aqabat Dhala) is located on the road linking the cities of Abha and Jazan. This obstacle extends 11 km along a steep mountain at a height of 2220 m. These two sites have many bends and tunnels, crowded with high traffic intensity, which increases the possibility of exposed cars at the moment of a rock fall.  
One of the most important reasons for the fall of rocks in this area is the constant rainfall throughout the summer on the mountain range, which causes an absolute nightmare for passers-by in Aqabat Shaar and Dhala. It is stable and becomes suitable for the fall of rock masses. Furthermore, one of the causes of rock fall in this area is the nature of the rock formation in some locations, where it consists of large rocky blocks interspersed with rocky rubble, in addition to other areas permeated by a limestone layer. This formation makes it weak against natural factors. The difference in temperatures between night and day characterizes these areas. These temperature differences, in addition to ground movements, cause rock stress, which leads to cracks. These cracks expand with time, and rainwater retracts into the cracks, causing pressure on the rocks, which reduces cohesion strength and results in successive rock slides [20].  
The construction work in the area has led to the emergence of edges with sharp sloping angles, which has reduced the gravity of the rock masses and made it easier to roll them downward, increasing the possibility of rocks falling at any moment.
## 3 Multi-Objective Optimization
### Uniform Latin Hypercube Sampling and Sensitivity Analysis  
In order to make the data sampling coverage more comprehensive, the uniform Latin hypercube sampling method was selected for data collection, and ultimately 134 sets of data were extracted for sensitivity analysis. The sensitivity concept is the Pearson correlation coefficient, which ranges from \(-1\) to \(+1\). Negative values indicate a negative correlation with the response value, whereas positive values indicate a positive correlation with the response value. The absolute value of the coefficient is compared with 1, and the closer it is to 1, the greater the correlation. Or else, the opposite is true. [PERSON]'s correlation coefficient is denoted by and is commonly used to measure the degree of correlation between a pair of random variables (\(A\), \(B\)). The Pearson correlation coefficient is calculated [30] as shown in Equation (7).  
\[S=\frac{\sum_{i}{(a_{i}-\overline{a}_{i})(b_{i}-\overline{b}_{i})}}{\sqrt{ \sum_{i}{(a_{i}-\overline{a}_{i})}^{2}(b_{i}-\overline{b}_{i})^{2}}} \tag{7}\]  
where, \(\overline{a}_{i}\) and \(\overline{b}_{i}\) are the average of \(A\) and the average of \(B\), respectively.  
As shown in the sensitivity analysis of the rotor topology parameters in Figure 13, the absolute Pearson correlation coefficients of DM2 and LM3 for torque ripple and average torque are both less than 0.1, indicating that these two parameters have little effect on the average torque and torque ripple. Therefore, removing these two parameters in the subsequent multi-objective optimization can improve the efficiency of multi-objective optimization.
## 3 Research Methodology
### Author and Publication Metadata Mapping
#### 3.3.1 Cosine Similarity Measure for Contextual Mapping  
The most popular and efficient methods of determining similarities include the angular distance between two vectors, given to us by the cosine of the angle between the two vectors [18; 19]. It is not only an angle-based measure but is also scale invariant, that is it does not account for the actual values of the compared vectors instead emphasizes on their relative relationships. For two vectors V1 and V2, the formula to determine their cosine similarity is shown is Equation (1), where V1 indicates the embedding vector of the publications' metadata and V2 represents the embedding vector of the LA-domain-specific keywords [20]. Furthermore, we used the threshold value to label each publication as either an LA paper or not:  
\[cos\;(\theta)=\frac{\sum_{i=1}^{n}\text{V1}_{i}\;\text{V2}_{i}}{\sqrt{\sum_{i =1}^{n}\text{V1}_{i}{}^{2}}\sqrt{\sum_{i=1}^{n}\text{V2}_{i}{}^{2}}} \tag{1}\]  
This yielded for each author their highly cited papers that showed the presence of keywords from the list of seed keywords, as in Table 1.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline
**SCOPUS Papers** & **Seed Keywords** \\ \hline
2011 to 2019 [12] & Learning analytics, students, e-learning, teaching, learning systems, computer-aided instructions, data mining, engineering education, educational data mining, education computing, curricula, big data, visualization, higher education, learning management systems, social networking (online), artificial intelligence, computer programming, forecasting, learning environments, massive open online course, information management, MOOCs, learning process, data visualization, online learning, learning, online systems, human, self-regulated learning, decision making, educational technology, blended learning \\ \hline \end{tabular}
\end{table}
Table 1: Top keywords used in research articles on learning analytics.
# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 2 Materials and Methods  
### Dataset Description  
In this research, three publicly available breast cancer datasets have been used to assess the effectiveness of the proposed method and validate the experimental results. These three datasets include CBIS-DDSM, MIAS, and BCDR.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 4 Experimental Results
### Experiment 1: Memory Pattern Formation Experiment without Pre-Memory Process
As a result, there are some cases where a small number of images are stored as separate memories, and there are many cases where they are morphologically similar but stored as different memories. We can consider the main reason for these phenomena is initial memory  
Figure 17: Results of stored memory pattern in an experiment using 20% SDR overlap without the pre-memory process; \(x\)-axis represents the memory patterns, \(y\)-axis describes the number of members belonging to each memory pattern, and a vertical color code indicates a ground-truth label ratio belonging to each memory pattern. Figure 18: Results of stored memory pattern in an experiment using 20% SDR overlap without the pre-memory process; \(x\)-axis represents a ground-truth label, \(y\)-axis describes the number of members belonging to each label, and a vertical color code indicates a memory pattern ratio belonging to each ground-truth label. problem. When forming a memory pattern while gradually feeding an image, if the data set has the same label but slightly different morphological feature, it is initially classified as a different memory. Therefore, in order to prevent such a problem, it is necessary to collect morphologically similar images in the beginning stage and perform pre-learning at once. The images of the data set used for the experiment show some morphological diversity even with the same label. However, it is necessary to assume that the ground-truth labels of the dataset are human-determined morphological similarities and create an average SDR of the images for each label and use it as a prior memory. In the second experiment, assuming that there is a prior memory, a new image is gradually fed to form a memory.
## 3 Results
### \({}^{13}\)C Phantom MR Spectroscopy Experiments  
Figure 6 demonstrates the \({}^{13}\)C MR spectra of the corn-oil phantom. Figure 6a is a \({}^{1}\)H T\({}_{2}\)-weighted reference image with an 8 \(\times\) 8 \({}^{13}\)C spectral grid, which was the result in Section 3.3. The SNR for both case 1 and 2 was calculated at the selected \({}^{13}\)C spectral grid, which is described as a red broken line in Figure 6a. Figure 6b,c are the \({}^{13}\)C MR  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** \\ \hline Configuration for \({}^{1}\)H MR imaging of the ACR phantom & Body coil & Body coil + 16-leg \({}^{13}\)C FFT & Body coil + 16-leg \({}^{13}\)C birdcage RF coil \\ \hline PIU of \({}^{1}\)H MR images [\%] & 85.0\% & 83.5\% & Cannot be measured \\ PIU reduction when compared & 0\% & \(-\)1.50\% & Cannot be measured \\ with body coil [\%] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: \({}^{1}\)H MR experimental results for verifying the performance of the proposed CPSCs. The data are then compared with the FDTD simulation results shown in Table 2.  
Figure 5: ACR phantom images using body coil transmission and reception without a PFT \({}^{13}\)C birdcage RF coil: (**a**) and with a PFT \({}^{13}\)C birdcage RF coil: (**b**) in a 3.0 T MRI system, showing almost identical uniformity as (**a**). \({}^{1}\)H MR images of the corn-oil phantom using body coil transmission and reception with a 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil with CPSCs: (**c**) and with a 16-leg low-pass \({}^{13}\)C birdcage RF coil without CPSCs: (**d**) in a 3.0 T MRI system. \({}^{1}\)H MR image of corn-oil phantom in (**d**) shows inhomogeneity compared with (**c**), which verifies that proposed CPSCs act appropriately for \({}^{1}\)H MR imaging.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** \\ \hline Configuration for \({}^{1}_{1}\) mapping of the uniform fat phantom & Body coil & Body coil + 16-leg \({}^{13}\)C FFT & Body coil + 16-leg \({}^{13}\)C birdcage \\ Uniformity of fat phantom [\%] & 85.0\% & 84.9\% & 75.3\% \\ Uniformity reduction when compared with case 1 [\%] & - & \(-\)0.10\% & \(-\)9.70\% \\ Intensity reduction when compared with case 1 [\%] & - & \(-\)0.43\% & \(-\)64.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the uniformity of B\({}_{1}^{+}\) mapping FDTD simulation from the body coil to verify the performance of the proposed CPSCs.  
spectra of corn-oil phantom, which is surrounded by a 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil with CPSCs and a 16-leg low-pass \({}^{13}\)C birdcage RF coil without CPSCs, respectively, corresponding to the 8 \(\times\) 8 \({}^{13}\)C spectral grid in Figure 6a. Table 4 lists the SNR and the standard deviation of the corn-oil phantom at the selected \({}^{13}\)C spectral grid. The SNR of the selected \({}^{13}\)C spectral grid in case 1 and case 2 was 33.80 and 35.76, respectively. There was a 5.5% of SNR drop in Figure 6b compared with Figure 6c.
## 1 Introduction
The cell phone captures the images with visible light and evaluates the four descriptors of the ABCD rule. A Gaussian model was used to estimate its descriptors; the dataset contained different skin tones. Achieved efficiency average was 85%. The application is designed to detect only one melanoma for each image. [PERSON] et al. [29] propose a supporting system for specialists focused on this area. The system works with simple images, performs segmentation, and calculates color (RGB as color space) and texture. The specialists provide a set of visual attributes, and according to the results of the descriptors, a decision is taken, so it can thus be regarded a semi-automatic system. The ranking is obtained by the majority of votes in the predictions. They reported an efficiency of 81%. [PERSON] et al. [30] worked with the Daugman Transformation for the extraction of image characteristics and achieved better efficiency in the detection of melanoma. They do not evaluate symmetry; they only estimate the shape, color, and texture. During the evaluation of the different descriptors, the image is processed in both RGB and CIELAB color spaces. The method focusedon dermatological images, used Support Vector Machine (SVM) for classification, and achieved an efficiency close to 96%. [PERSON] et al. [31] use a double classification to achieve greater effectiveness using dermatological images. They reached an efficiency between 75% and 92%. [PERSON] et al. [32] use dermatological images. They use several color spaces to improve the efficiency in the detection of melanoma; some are RGB and CIELAB. The study focuses on contour detection and color dispersion. They achieved a Jaccard Index of 0.765. Table 1 shows relevant research in the literature focused on the detection of melanoma using image processing. The aforementioned studies have an efficiency between 70% and 96%, and they are only designed for one type of image ( dermatological or simple), mostly working with RGB and CIELAB color space, and symmetry evaluation is performed in a similar way or the descriptor is not considered. In this research, an efficient and novel method for the detection and classification of melanoma is developed. The algorithm can run using simple images, which are acquired with an RGB camera, as well as dermatological images, which are acquired with professional equipment. It was designed to estimate four descriptors (symmetry, edge, color, size), which are subsequently used as inputs for the classifier. A multilayer perceptron was used to classify between malignant and benign melanoma. Finally, k-fold cross-validation, an analysis of variances and a \(t\)-student test were used to validate the robustness and reliability of the method. Moreover, the method developed is capable of detecting more than one case of melanoma in the same image, even if there is some hair present in the picture. The paper is organized as follows: Section 2 contains the main stages of the proposed method. Section 3 includes the results and discussions. Finally, in Section 4, the conclusions of the article and future work are mentioned.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 2 Settlement Calculations for Existing Tunnels  
### Model Assumptions  
The existing tunnel will sink and distort as a result of the excavation of the new tunnel, which will put extra strain on the earth above it. In this study, considering the potential for additional loads and stratum loss, the existing tunnel is modeled as an elastic foundation beam. This approach forms the basis for developing a theoretical model to examine the settlement deformation of existing tunnels induced by tunnel penetration [18], as illustrated in Figure 1. The following assumptions are proposed to facilitate the calculation: (1) The existing tunnel is approximated as an Euler-Bernoulli beam, characterized by an equivalent longitudinal flexural rigidity (\(EI\))\({}_{\text{eq}}\); (2) The interaction between the tunnel and the surrounding soil is modeled using the Pasternak foundation approach; (3) The additional stresses in the strata are calculated by using [PERSON]'s solution and the three-dimensional [PERSON]'s formula; and (4) No consideration is given to the soil arch effect and drainage consolidation effect.