esa-sceva/satcom-chunk-collection huggingface dataset:
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 3 The Theoretical Model of Cyber-Physical Supply Networks
### Physical Supply Network  
#### 3.2.1 Nodes and Links of the Physical Supply Network  
The physical supply network consists of suppliers, manufacturers, retailers, and logistics that generate, store, transform, and deliver the flow of physical products [28],  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Meaning** \\ \hline \(k_{v_{i}^{c}}\) & Degree for node \(v_{i}^{p}\) \\ \(k_{v_{i}^{c}}^{in}\) & In-degree of the node \(v_{i}^{p}\) \\ \(k_{v_{i}^{c}}^{out}\) & Out-degree of the node \(v_{i}^{p}\) \\ \(L_{v_{i}^{p}}(t)\) & Load for node \(v_{i}^{p}\) at time \(t\) \\ \(L_{v_{i}^{c}}(t)\) & Load for node \(v_{i}^{c}\) at time \(t\) \\ \(\hat{C}_{v_{i}^{c}}\) & Capacity for node \(v_{i}^{p}\) \\ \(C_{v_{i}^{c}}\) & Capacity for node \(v_{i}^{c}\) \\ \(\delta\) & Tunable parameter used to adjust the initial load in the cyber supply network \\ \(\varphi\) & Upper-bound capacity parameter of the node in the cyber supply network \\ \(\tau\) & Weight parameter of the link in the cyber supply network \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._all of which can be signified as nodes, and the contractual relationship between firms can be signified as links [12; 15]. Therefore, the physical supply network as a weighted directed network is \(G^{p}(V^{p},E^{p})\), where \(V^{p}=\left\{v_{1}^{p},v_{2}^{p},...,v_{n}^{p}\right\}\) is the node set and \(E^{p}=\left\{\left(v_{i}^{p},v_{j}^{p}\right)\middle|e_{ij}^{p}=0\text{ }or\text{ }1,i,j=1,2,3,...,n\right\}\) is the set of connectivity links. Here, \(e_{ij}^{p}=1\) signifies a directed connection from node \(v_{i}^{p}\) to node \(v_{j}^{p}\); otherwise, \(e_{ij}^{p}=0\). A weighted adjacency matrix \(W^{p}\) is constructed to represent the weights of the links, where \(W^{p}=[w_{ij}^{p}]\) is an \(N\times N\) asymmetric matrix and \(N\) is the total number of nodes in the physical supply network. The links between the nodes with greater values of tend to have a long distance.  
The transportations between firms are regarded as the weights of the links. The empirical studies have proposed that the weight of the links between two nodes is related to the node's degree [14]. Therefore, the weight of a link \(w_{ij}^{p}\) that connects \(v_{i}^{p}\) to \(v_{j}^{p}\) is assumed to be:  
\[w_{ij}^{p}=(k_{v_{i}^{p}}\ast k_{v_{j}^{p}})^{\theta}, \tag{4}\]  
where \(\theta\) is the weight parameter of the link in the physical supply network, \(k_{v_{i}^{p}}\) indicates the degree for \(v_{i}^{p}\), and \(k_{v_{i}^{p}}=k_{v_{i}^{p}}^{in}+k_{v_{i}^{p}}^{out}\). \(k_{v_{i}^{p}}^{in}\) is the in-degree of node \(v_{i}^{p}\) and \(k_{v_{i}^{p}}^{out}\) is the out-degree of node \(v_{i}^{p}\). The degree of node \(v_{i}^{p}\) is represented as:  
\[k_{v_{i}^{p}}=\sum_{j\in V^{p}}e_{ij}^{p} \tag{5}\]
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 1 Introduction
### Overview of X-ray Diffraction (XRD) Technique
The Fourier analysis of XRD patterns is a clever technique used for the determination of the local arrangement of atoms, which proved the noncrystalline nature of soda-silica glass [61]. Over the years, XRD instrumentation has undergone significant advancements. Traditional parafocusing instruments were developed during the 1950s and are mostly used in Bragg-Brentano geometry. However, this configuration can introduce significant systematic errors such as specimen displacement. Parallel-beam diffractometers minimize errors arising from sample displacement and transparency but have the disadvantage of poor particle statistics. Modern XRD systems offer high-resolution detectors, faster data acquisition rates, improved sample handling mechanisms, and portability for in situ and operando studies [62].
## 3 Error Modeling and Analysis  
This part mainly focuses on the modeling of the geometrical error of the CCR, after which the wavefront transmission error is analyzed. Based on this analysis, the energy distribution of the backscattered light spots from the CCR is simulated, and the influence of assembly error on beam-pointing accuracy is also analyzed.
# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
These techniques have improved the ability of DL models to achieve the goals for which they were designed, but only slightly, as they do not generate new and realistic data and may even be harmful by creating samples that are anatomically incorrect ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017). Conventional data augmentation does not fill all existing gaps in the dataset, and some generative models, for example, AutoEncoder and Variational AutoEncoder (VAE), cannot produce sufficiently realistic scans, and they are characterised by blur and low detail in each structure ([PERSON] et al., 2017). To deal with this situation, [PERSON] et al. (2014)([PERSON] et al., 2014)([PERSON] et al., 2014) introduced a new approach to improve generative models, which is called Generative Adversarial Networks (GANs). GANs are a type of deep neural architecture with two simultaneously trained networks, one generating the image and the other specialised in discrimination. The first aims to generate fake images from an input vector and to fool the discriminator, which is a classifier that evaluates whether the images generated by the generator are realistic. The discriminator gives a probability of truth each time it receives an image, with higher values corresponding to images that are closer to reality. A probability close to 0.5 is the optimal solution, as this means that the discriminator is not able to distinguish the real images from the fake ones ([PERSON] et al., 2017; [PERSON] et al., 2017). The whole process is illustrated in Figure 1.  
Given the results that GANs have achieved in various tasks ([PERSON], 2018; [PERSON] et al., 2017), it will be possible to disseminate a larger dataset than the original one without protection restrictions once GANs have the ability to anonymise ([PERSON] et al., 2017). Additionally, data augmentation with GANs is more robust than traditional data augmentation alone. This reduces the number of real magnetic resonance scans needed to train good medical DL models or even improve some existing models. The fact that the entire 3D MRI volume is used exponentially increases the training difficulty, but by using the entire scan, it is possible to save time and resources by reducing the number of samples required. The use of MRI for this study is advantageous from a practical and ethical point of view, as high-resolution images can be obtained in a non-invasive way, reducing animal suffering and allowing them to be observed over a longer period. As far as the authors are aware, the use of GANs in preclinical research is a little addressed topic, but it is necessary, as it is fully in line with the ethical 3R rule by reducing the number of scanning sessions. The use of synthetic data generated with GANs in this work improved a DL model for the segmentation of Grey Matter (GM), White Matter (WM), and CerebroSpinal Fluid (CSF), as can be seen in the Section 3.
## 2 Methodology
### Active Learning for Multi-Objective Optimization (ALMO)  
In this work, we employ an Active Learning-based Multi-Objective Optimization (ALMO) framework to enhance the efficiency of constrained evolutionary optimization algorithms like NSGA-II. By selectively querying expensive constraint evaluations using an active learning model, we can significantly reduce computational costs while maintaining optimization performance. The optimization process proceeds for a total of \(N\) generations, where an active learner is trained after the first 10% of these generations. The ALMO approach is structured as shown in the following steps, while the pseudo-code is shown in Algorithm 2, and the flowchart is shown in Figure 5:  
1. _Initial Setup_: The NSGA-II algorithm begins by initializing a random population of solutions, and true constraint and objective function evaluations are performed for these solutions for the first 10% of the \(N\) generations. 2. _Training the Active Learner_: After the initial 10% of generations are evaluated, the active learner (a Random Forest classifier) is trained on the available data from these early generations. This learner is then used to perform a binary classification by predicting the feasibility of new solutions in the subsequent generations. 3. _Prediction of Feasibility_: During the remaining generations, the active learner predicts the feasibility of the offspring solutions generated by the NSGA-II algorithm. If the learner's confidence in the feasibility prediction exceeds a defined threshold \(\beta\), the candidate solution is accepted or discarded based on the prediction alone, bypassing the expensive constraint evaluation. 4. _Selective Constraint Evaluation_: If the confidence score of the learner falls below \(\beta\), the true constraint function is queried for that solution. These newly evaluated solutions are collected, and the active learner is re-trained once we have \(m\) new queries. In this study, we set \(m=\mu\), where \(\mu\) is the population size, ensuring periodic updates while maintaining computational efficiency. 5. _Offspring Selection and Evolution_: Feasible offspring, along with selected parents, are carried forward to the next generation following NSGA-II's selection procedure, which is based on non-dominated sorting and crowding distance metrics. This process continues for the remaining generations. Figure 4: (**A**) The 106 queries issued by the learner; (**B**) confidence plot after including queries in the training dataset (accuracy: 99%). Final Pareto-front Solution: Once the optimization is complete, the final Pareto-front solutions are evaluated using true constraint functions to ensure their feasibility, while the infeasible solutions are discarded.
# Visual Analytic Method for Students' Association via Modularity Optimization
## 2 Related Work
### Detecting Communities via Modularity  
Identifying communities in networks, especially complex networks with huge numbers of nodes and edges, can offer insight on how a network is organized, and it has been a hot topic in various fields. There are many algorithms, which can be categorized by different criteria. In [16], the categories include spectral methods and methods based on statistical inference, optimization, and dynamics. Among them, optimization techniques have attracted the most attention. They aim to find an extremum of a quality function, which indicates the quality of clustering. Modularity is the most popular quality function. [PERSON] in [9] proposed an algorithm to partition a complex network into densely connected subnetworks using the gain value of modularity. They iteratively remove edges that are selected by \"betweenness\" measures, and define the strength of the community structure to provide an objective metric to determine the number of communities into which a network should be divided.  
However, tuning the resolution parameter is usually computationally intractable and time-consuming. To circumvent this issue, [PERSON] et al. [17] proposed the Louvain method to extract the community structure, which hierarchically performs a greedy optimization of modularity. Their experimental results show that the Louvain algorithm can accurately identify communities in network with millions of nodes in a short time, and can uncover the hierarchical community structure to facilitate observation at the desired resolution.  
Many papers [18; 19; 20; 21; 16] compare the performance of the wildly adopted community detection algorithms based on real-world and artificial datasets. Most conclude that the Louvain algorithm outperforms others or has similar detection results in terms of general metrics such as modularity, computing time, precision, and recall.  
Due to its advantages, the Louvain algorithm is used in many applications. The modified Louvain algorithm was used in an online social network to discover the opinion leader [22], in economics to delineate the regional economic geography [23], and in location-based networks to improve recommendation accuracy [24].
# Multi-Omics Approaches to Study Molecular Mechanisms in _Camanabis sativa_
## 4 Multi-Omics Approaches in Cannabis
The genetic controls of complex agronomic and biochemical traits in cannabis is still poorly understood [19], but research has been increasing in this area lately, also exploring how cannabinoid synthase genes influence the THC:CBD ratio and the overall abundance of cannabinoids. Some recent studies are starting to build a picture of these processes. This includes an investigation into two phenotypically distinct hemp cultivars ('Carmagnola' and 'USO31') [89], where GC-MS results suggested that in cv. 'USO31', divetol synthase is less active compared to cv. 'Carmagnola', and this could explain why 'USO31' contains a lower concentration of cannabinoids. Using WGS, around 70 QTL were mapped and related to variation in agronomic and biochemical traits. Results showed that differences between 'Carmagnola' and 'USO31' are mainly controlled by a small number of loci, since most of the QTLs are colocalized. Another study addressing this issue was carried out by [63] applying metabolomic, genomic and transcriptomic methods to explore the characterisation of 'CBDRx', a high-CBD cultivar. Cannabinoid analysis was performed with HPLC, a chromosome-resolved reference genome was generated with the MinIONONT platform, and QTL mapping was performed by a high-resolution linkage map from two populations involving 'Carmen' hemp crossed with 'Skunk#1' marijuana. RNA-Seq libraries were aligned to the reference and assembled into transcripts. Cannabinoid synthase paralogs are arranged in tandem arrays on chromosome 7. Although 'CBDRx' is predominantly of marijuana ancestry, it was found that its genome includes a CBDAS introressed from hemp and lacks a complete sequence for THCAS. Results underlined that variation among cannabinoid synthase loci can affect the THC:CBD ratio, and that variability in overall cannabinoid content among cultivars might also be correlated with chromosomes other than 7 [63]. Furthermore, two candidate genes possibly associated to cannabinoid QTLs were detected: the gene coding _Acyl-activating enzyme 1_ (_AAE1_), an enzyme of the hexanoate (a precursor for cannabinoid biosynthesis) pathway [90], and the gene coding for _4-hydroxy-3-methylbut-2-enyl diphosphate reductase_ (_HDR_), the last enzyme in the MEP pathway. Despite the breadth of the results to date, further investigations are necessary in order to completely elucidate the biological controls and mechanisms behind the enhancement of cannabinoid expression.
## 5 Conclusions  
This study evaluated the temporomandibular joint spaces and condylar dimensions in adults with various skeletal patterns using CBCT. The findings are summarized as follows:  
* The average values of sagittal TMJ spaces (AS, SS, and PS) and coronal TMJ spaces (MS, CS, and LS) differed significantly, respectively.
* The AS:SS:PS ratio was 1.00:1.61:1.18.
* The MS:CS:LS ratio was 1.00:1.15:0.79.
* Most measurements were significantly higher in males compared to females.
* The average value of APT was significantly higher in the late age group than in others.
* There was no statistical difference according to race.
* PS, CS, and LS values were higher on the left side compared to the right, while AS value was higher on the right side than on the left.
* In hypodivergent skeletal patterns, SS, CS, and MLT values were significantly higher than in hyperdivergent patterns.
* There were no significant statistical differences based on horizontal skeletal patterns.
* In the Class II horizontal skeletal group, SS, CS, and MLT values were significantly lower in hyperdivergent patterns compared to hypodivergent patterns.  
In summary, our study indicated statistical differences in condyle-fossa relationships based on gender, age, side, and skeletal patterns, while differences according to race were negligible.  
Conceptualization, A.C. and J.-M.C.; methodology, A.C. and J.-M.C.; software, C.B.; validation, A.C., J.-M.C. and J.H.P.; formal analysis, C.B.; investigation, A.C.; resources, A.C.; data curation, A.C.; writing--original draft preparation, A.C.; writing--review and editing, J.-M.C., J.H.P. and C.B.; visualization, A.C.; supervision, J.-M.C. and J.H.P.; project administration, A.C., J.-M.C. and J.H.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of A.T. Still University, Arizona (protocol code #2023-189, approved on 17 April 2024). The IRB determined that this project was exempt under 45 CFR $46.104(d)(4) due to the use of de-identified human data.  
**Informed Consent Statement:** Informed consent was waived as this study used preexisting CBCT data obtained from routine orthodontic screenings, and no identifiable personal information was used.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** We express our gratitude to [PERSON] for her valuable contributions in identifying eligible participants and creating Figure 2. We also appreciate the support from the staff at the Arizona School of Dentistry & Oral Health Postgraduate Orthodontic Program for their assistance with the CBCT imaging. This paper was supported by The Korean Orthodontic Research Institute Inc. This paper was supported by Wonkwang University in 2025.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Fault Location Principle  
Due to the distinct velocities of line-mode and zero-mode traveling waves in overhead lines versus cables, separate positioning formulas are required for single line and hybrid line scenarios, as illustrated in Figure 3. The voltage traveling wave ranging device is installed at section M, with MN representing a cable line section and MT representing a hybrid line section. Cable lines are shown in black solid lines, while overhead lines are shown in gray. The wave velocities are represented as follows: \(v_{0}\) and \(v_{1}\) indicate the zero-mode and line-mode traveling wave velocities in the cable lines, respectively, while \(v_{h0}\) and \(v_{h1}\) denote the corresponding velocities in the overhead lines.
## 2 AI Applications and Research in NAFLD
### Liver Fibrosis
The multitude of ultrasound elastography methods used for the identification of advanced liver fibrosis in patients with NAFLD and NASH clearly allow the quantitative characterisation of inflammation, steatosis and fibrosis, but a multistep algorithmic approach is certainly needed, with AI techniques being needed for distinct clinical decision-making scenarios [39]. The application of non-invasive ultrasound elastography techniques such as SWE is especially important for identifying the presence of significant fibrosis in paediatric patients, where liver biopsy should be avoided [40]. Various AI and ML approaches have already been used for the evaluation of liver fibrosis based on either RTE or SWE, as both methods are still difficult to interpret in a clinical setting [41; 42]. Thus, various ML classifiers (i.e., support vector machine, naive Bayes, random forest and K-nearest neighbour) have been employed to stage liver fibrosis in chronic B hepatitis, outperforming the original Liver Fibrosis Index developed through regression analyses based on RTE images [41]. Another stiffness value clustering and ML algorithm indicated that the classification of healthy versus chronic liver disease patients based on a support vector machine (SVM) model had 87.3% accuracy with sensitivity and specificity values of 93.5% and 81.2%, respectively [42]. Additionally, DL has been used to assess liver fibrosis in a recent prospective multicentric trial, showing high values of the area under the receiver curve of 0.97 (for F4), 0.98 (for \(\geq\)F3) and 0.85 (for \(\geq\)F2) [43]. Based on 398 patients and 1990 images included, this approach yielded a better overall performance in predicting liver fibrosis stages compared with 2D-SWE and biomarkers, at least for the patients with chronic HBV infection. Nevertheless, both RTE and SWE still have a significant bias with intra- and inter-observer variability, whilst automated frameworks such as SWE-Assist can help by checking SWE image quality, selecting a region of interest and classifying the ROI into fibrosis stages (over F2) [44]. This kind of automated AI and DL/ML analysis based on CNNs will certainly help doctors to easily classify and stage inflammation, steatosis and fibrosis based on non-invasive ultrasound techniques that are highly accurate, extremely cheap and largely available in the primary setting or at the point-of-care. A recent meta-analysis studied AI-assisted ultrasonography and elastography, but also CT, MRI and clinical parameters, showing high sensitivity, specificity, PPV, NPV and diagnostic odd ratios (DORs), especially for liver steatosis, but also for significant fibrosis, advanced fibrosis and liver cirrhosis [45].
## 3 Simulation Analysis of FSS
### Effect of Ambient Temperature Change on Support Height under Different Air Contents  
When the support shaft of the cylinder is from \(-1.0\) mm to \(1.0\) mm, the volume of the upper and lower chambers of the support cylinder can be calculated by Equations (3) and (4). By simulating the effect of temperature change on volume under different air contents, the volume change in the upper and lower chambers can be calculated when the ambient temperature changes. Finally, in combination with Equation (2), when the ambient temperature and air content in the hydraulic oil are different, the change in the height of the support shaft under different working pressures can be calculated. The error is expressed by the difference between the set value and the actual measured value, and the errors mentioned below are calculated in this way.  
The simulation results are shown in Figure 5. The smaller the working pressure of the FSS, the more sensitive the support height is to the change in air volume fraction and ambient temperature. There is no obvious stratification of the support height error caused by the pressure change, which indicates that the working pressure change has relatively little influence on the ambient temperature and air content of the support cylinder. When the ambient temperature and air volume fraction are both low or high, the support height error of the support cylinder is the most affected. When the air volume fraction is \(0.1\), the ambient temperature is \(30\)\({}^{\circ}\)C, and the support height can be reduced to \(-0.073\) mm in the negative direction owning to the influence of the ambient temperature and air  
Figure 4: Variation of volume of upper and lower chambers with temperature and air content. (**a**) Volume of upper chamber. (**b**) Volume of lower chamber.  
content. At the same time, when the air volume fraction is 0 and the ambient temperature is 10 \({}^{\circ}\)C, the support height can be increased to 0.083 mm, owing to the influence of ambient temperature and air content. Therefore, to ensure the accuracy of the FSS, it is necessary to ensure that the range of fluctuation of the ambient temperature and air content in the working medium during the working process is as small as possible.