esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Water Retention of Coal Dust by Different Fluorine-Containing Solutions  
The fluorine-containing surfactants can interact with the surface of the coal dust particles through weak van der Waals forces, hydrogen bonding, and electrostatic interactions. When these surfactants are added to water, they form micelles, which are tiny clusters of molecules that can solubilize and emulsify hydrophobic substances, such as coal dust.  
In the case of non-ionic fluorinated solutions, the surfactants contain hydrophobic and hydrophilic regions, allowing them to form stable micelles in water. These micelles can adsorb onto the surface of coal dust particles, reducing the surface energy and making the coal dust particles more hydrophilic. This leads to an increase in the wettability of the coal dust particles, allowing them to absorb water and retain moisture more readily. On the other hand, anionic fluorinated solutions contain surfactants that have a negative charge in water. These surfactants can also adsorb onto the surface of coal dust particles, but their negative charge can repel water molecules, leading to a decrease in wettability and water retention. This is because the negative charges on the surfactant molecules create an electrostatic barrier that prevents water molecules from interacting with the surface of the coal dust particles.
# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 2 Related Work
### Spatial Context for Semantic Matching  
Although semantic matching can be established by performing a winner-takes-all operation on each point, it ignores spatial contextual information from the neighborhood, resulting in the reduction of matching accuracy. In other words, considering context helps to improve the performance of semantic matching. To this end, a direct way is to explicitly add neighborhood continuity constraints to loss, such as smoothness and geometric consistency constraints [34; 35]. Another strategy is to consider the spatial context when extracting semantic features so that the features can perceive local information. For example, [PERSON] et al. propose the local self-similarity (LSS) descriptor [36] to capture the self-similarity structure. It is then extended to some deep learning versions [37; 38]. More recently, some methods [22; 33; 39] cast LSS as a CNN module, computing local self-similarity with a learnable sampling and convolution pattern. However, all of these methods ignore an essential problem, that is, the correspondence for each pixel or patch is still determined independently via variants of the nearest neighbor assignment so that the estimated semantic matching field struggles to guarantee continuity. NCNet [31] provides a new idea that considers the contextual consistency of semantic matching hidden in correlations because correlation is the direct cue for matching decision. Our method retains this idea and uses 4D convolution to transform the correlation space to explore the spatial context consensus.
## 3 Methodology
### System Model  
The MGOA-WAVLCN system model is comprised of sensor nodes and several lighting sources to transmit and receive the data packets. The lighting infrastructure and the sensor nodes in the MGOA-WAVLCN model are equipped with transmitter and receiver for data transmission. In this scenario, the VLC transmitter and receiver were connected using a free space optical communication channel. In VLC, the light emitted from the transmitter is comprised of data packets, which are transmitted by using wireless medium. The channel model of MGOA-WAVLCN is mathematically indicated in Equation (1).  
\[MGOA-WAVLCN=\left\{\begin{array}{cc}\frac{(B+1)M}{2\pi dist^{2}}cos^{B}(\varphi )O(\psi)q(\psi)\cos(\psi)&0\leq\psi\leq\psi_{l}\\ 0&\psi>\psi_{l}\end{array}\right\} \tag{1}\]  
where \(q(\psi)\) indicates concentrator gain, \(\psi_{l}\) represents receivers field of view, \(\psi\) represents angle of impedance, \(\varphi\) indicates angle of irradiance, \(O(\psi)\) indicates optical band pass filter, \(dist\) denotes distance between one node to another node, \(M\) represents detector area, and \(B\) denotes order of the Lambertian emission. The workflow of MGOA-WAVLCN is represented in Figure 2.  
Figure 1: Overview of the VLC system.
Various methods have been used to increase the number of CPs extracted from the images, such as image division [21; 22] and histogram equalization [22], and GCP sampling optimization [17]. Other methods have been used to eliminate the mis-matched CPs such as random sample consensus (RANSAC) [17; 19], etc. Thesemethods are commonly used for images with rich textures at low- and mid-latitudes. However, due to the high reflectance of ice and snow surfaces at high latitudes, the texture of images in polar regions is rarely observed. It is necessary to explore methods for correcting the geolocation errors of the images of polar regions. This study aims to develop a geolocation method for polar images from BNU-1. The BNU-1 images for several regions of Antarctica and Greenland were used to demonstrate the effectiveness of this proposed geolocation method to deliminate the geolocation errors. This paper is organized as follows. Section 2 describes the data and the study area. Section 3 describes the geolocation method of the BNU-1 images in detail. Section 4 shows the performance of the geolocation method. The discussion of the results is shown in Section 5. Conclusions are given in Section 6.
## 3 Results and Discussion
### Microstructure  
Figure 14 shows the SEM images of the specimens using silica fume and quartz powder according to the C/S molar ratio. In all specimens, the presence of C-S-H hydrates and portlandites was confirmed. Quartz powder specimens, in particular, were found to contain more hydrates generated by the pozzolanic reaction of silica fume compared with the plain specimen [40]. This result suggests that silica fume exhibits high fineness and pozzolanic reactivity. The C-S-H formed by the pozzolanic reaction of silica fume fills the interior of the matrix, reducing pores and contributing to the improvement of compressive strength.  
Figure 15 and Table 5 show the images and results, respectively, obtained by EDS point analysis. In both QP0.9 and SF0.9 specimens, in addition to the plain specimen, the presence of portlandites and C-S-H hydrates was confirmed, as shown in Figure 15. The generally known C/S molar ratio of C-S-H hydrates ranges from 0.8 to 1.6 [16]. Specim QP0.9, with the C/S molar ratios of the points measured at 0.82, 0.88, and 0.96, respectively, was found to have the molar ratio of the C-S-H hydrate tobermorite [41]. Specim SF0.9 showed a lower C/S molar ratio than specimen QP0.9 and the plain specimen, with the C/S molar ratios of its internal hydrates ranging from 0.5 to 0.8. These hydrates were identified as Z-phase and gyrolite [42], C-S-H hydrates with low C/S ratios, which are known to be more easily synthesizable in a mixing design containing crystalline silica rather than amorphous silica [43; 44].  
Figure 14: SEM image of the specimens.  
Figure 15: SEM images using EDS analysis of the specimens.
## 8 Conclusions and Future Perspectives  
Genetic predispositions or any unfavourable epigenetic modulations might trigger NADs. The possible interplay between previous infections or morbidities has also been considered. The immune response to an autoantibody requires information about cascade signalling and successive metamorphic changes in the numerous immune targets such as B cells, T cells, interleukins, and other proteins. The identification of unique biomarkers strengthens NMOSD and MS disease differentiation. Proteomic analysis of serum samples of NMO, NMOSD, and MS subjects suggests the use of composite biomarkers to discriminate among these diseases. Metabolomic studies reveal NMOSD's pathophysiology and aid in its diagnosis and treatments.  
Due to differences in the serotypes and clinical manifestations of NMOSD, developing therapy methods is complicated, and further in-depth studies are necessary. Except for the pathogenesis due to autoantibodies against AQP, the autoantibodies against the MOG antigen and other blockers of interleukins might also be given more importance clinically to achieve therapeutic efficacies. In addition to the immunosuppressive therapies targeting the complement cascade system, B cells, interleukins, other inflammatory cytokines, and anti-AQP4 and anti-MOG antibodies, more scientific evidence is needed for personalized therapies for NMOSD. Protecting the BBB functions through enhancing immune tolerance in immunosuppressive patients could facilitate the development of neuroprotection and prevent the invasion of inflammatory cells from the CNS. Studies on combinatorial therapies using immunosuppressive drugs, plasmapheresis, or complement inhibition in animal models might reveal potent treatment strategies to manage NMOSD. In addition to the clinical application of advanced stem cell therapies, the pros and cons of stem cell transplantation and the key problems arising due to different sources of donor cells are also tough strategies. Thus, optimized stem cell therapy might be one of the prominent future treatment options for NMOSD.  
Damage to the intestinal barrier, gut dysbiosis, and inflammation are the primary pathological events in NMOSD. The imbalance in the mucosal microbiota produces inflammatory responses, damaging the gut barrier and allowing pathogens, endotoxins, and microbial metabolites to cross the barrier and reach the BBB. Understanding the role of gut microbiota in NMOSD might provide insights into employing microbial intervention (for example, probiotics) as an added therapeutic strategy for NMOSD.  
Conceptualization, C.C., B.S.S. and P.K.; methodology, S.T. and B.S.S.; validation, C.C., B.S.S. and P.K.; formal analysis, S.T. and P.K.; investigation, S.T. and B.S.S.; resources, C.C.; data curation, S.T. and A.R.; writing--original draft preparation, S.T., B.S.S., A.R., P.K. and C.C.; writing--review and editing, S.T., B.S.S., P.K. and C.C.; supervision, C.C. and B.S.S.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This project was supported by Chiang Mai University, Chiang Mai, Thailand.
## 4 Feature Extraction and Control Methods
### Feature Extraction
#### 4.2.1 Tracking-Filter Method  
The tracking filter method is also called the correlation filtering method. Its working principle is mainly based on the orthogonality of the triangular function [24]. The initial vibration signal can be expressed by  
\[x(t)=A_{0}\cos(2\pi f_{0}t-\varphi_{0})+\sum\limits_{i=1}^{N}A_{i}\cos(2\pi f _{i}t-\varphi_{i}) \tag{20}\]  
where \(f_{0}\) is the working frequency of the rotor system.  
One sinusoidal signal \(y_{1}\) and one cosine signal \(y_{2}\) are provided, and their frequencies are equal to \(f_{0}\), that is,  
\[y_{1}(t)=\sin(2\pi f_{0}t),\ y_{2}(t)=\cos(2\pi f_{0}t) \tag{21}\]  
Multiplying Equation (20) by Equation (21) gives:  
\[\begin{array}{ll}x(t)y_{1}(t)&=\frac{1}{2}A_{0}\sin\varphi_{0}+\frac{1}{2}A _{0}\sin(4\pi f_{0}t-\varphi_{0})\\ &+\frac{1}{2}\sum\limits_{i=1}^{N}A_{i}\sin[2\pi(f_{0}+f_{i})t-\varphi_{i}]+ \frac{1}{2}\sum\limits_{i=1}^{N}A_{i}\sin[2\pi(f_{0}+f_{i})t+\varphi_{i}]\\ \\ x(t)y_{2}(t)&=\frac{1}{2}A_{0}\cos\varphi_{0}+\frac{1}{2}A_{0}\cos(4\pi f_{0} t-\varphi_{0})\\ &+\frac{1}{2}\sum\limits_{i=1}^{N}A_{i}\cos[2\pi(f_{0}+f_{i})t-\varphi_{i}]\ + \frac{1}{2}\sum\limits_{i=1}^{N}A_{i}\cos[2\pi(f_{0}+f_{i})t+\varphi_{i}]\\ \end{array} \tag{22}\]  
DC components in Equations (22) and (23) are extracted with the application of a low-pass filter, and the extraction results can be obtained by:  
\[V_{x}=\
icefrac{{1}}{{2}}A_{0}\sin\varphi_{0},V_{y}=\
icefrac{{1}}{{2}}A_{0} \cos\varphi_{0} \tag{24}\]  
Multiplying Equation (24) by Equation (21) again, the unbalance vibration of the rotor system is expressed by:  
\[\begin{array}{ll}x_{0}(t)&=2\times\left(\frac{1}{2}A_{0}\sin\varphi_{0}\sin( 2\pi f_{0}t)+\frac{1}{2}A_{0}\cos\varphi_{0}\cos(2\pi f_{0}t)\right)\\ &=A_{0}(\sin(2\pi f_{0}t)\sin\varphi_{0}+\cos(2\pi f_{0}t)\cos\varphi_{0})=\ A_{0}\cos(2\pi f_{0}t-\varphi_{0})\\ \end{array} \tag{25}\]  
where \(A_{0}\) and \(\varphi_{0}\) are the amplitude and phase of unbalance vibration, respectively.
# Isogeometric Schwarz Preconditioners with Generalized B-Splines for the Biharmonic Problem +
###### Abstract  
We construct an overlapping additive Schwarz preconditioner for the biharmonic Dirichlet problems discretized by isogeometric analysis based on generalized B-splines (GB-splines) and analyze its optimal convergence rate bound that is cubic in the ratio between subdomains and overlap sizes. Our analysis is validated through a set of numerical experiments that illustrate good behavior of the proposed preconditioner with respect to the model parameters.  
domain decomposition methods; overlapping Schwarz methods; biharmonic problems; effective preconditioners; isogeometric analysis; generalized B-splines; finite element method 65N55; 65N30; 65F10
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 4 Analysis of State-of-the-Art Approaches
### Cybersecurity Standards  
Given the continuous evolution of cyber threats, many cybersecurity frameworks such as the _NIST Cybersecurity Framework_ (NIST CSF) and the _MITRE Cybersecurity Criteria_ have gained wide adoption. NIST CSF [38] focuses on sharing best practices for risk management and cyber flexibility, whereas MITRE ATT&CK focuses on a comprehensive classification of adversarial tactics and methods. Various studies proved that the integration of these frameworks enhances threat detection and mitigation across different industries (e.g., botnet detection). In addition, _CIS Critical Security Controls_ and _ISA/IEC 62443_ are other standards that reinforce cybersecurity. Challenges in adoption persist despite advancements, as continuous updates are needed, which requires skilled personnel to manage emerging threats effectively.  
Yet another research line consists of, for example [39], which explores the novel role of _Large Language Models_ (LLMs) in cybersecurity, focusing on their emerging capabilities such as _threat detection, cryptographic applications_, and _adversarial learning_. It also shows how LLMs increase security by automating cyber defense processes, detecting trends in criminal activity, and using ML to enhance encryption approaches. It further extends to _AI-driven intent-based networking_, where AI automates network security policy and _zero-touch Network Security_ for 5G/6G infrastructures to enable self-adaptive and resilient security mechanisms. Additionally, the discussion extends to how _Blockchain_ will offer decentralization in LLM for secure AI collaboration and adversarial training for developing automated _attack-defense strategies_. That means it aligns with existing frameworks such as the _NIST Cybersecurity Framework_ and the _MITRE ATT&CK_ for identifying challenges: adversarial robustness, secure AI governance, and the ethics of autonomous cyber defense systems. These place _AI-driven cybersecurity_ as a frontier for securing digital ecosystems against evolving threats such as _botnet detection_, as addressed in this paper.
## 3 Results and Discussion
### Optimization Process  
3.4.1. Fitting the Models  
Experimental results of the responses, including the DPPH values and sensory characteristics of biscuits fortified with RPC, were fitted to the CCD, and the least-squares technique was used for calculation of the regression coefficients of the individual linear, quadratic, and interaction terms (Table 4). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Response Variables**} & \multicolumn{5}{c}{**Model Coefficients**} \\  & **Intercept** & **RPC** & **SAFA** & **RPC2** & **SAFA2** & **RPC \(\times\) SAFA** \\ \hline DPPH\({}_{\text{CE}}\) [\(\mu\)mol TE/100 g] & 2227.6 ** & 148.3 ** & \(-\)383.3 ** & 1.7 * & 12.7 ** & 2.2 \(\times\) 10\({}^{-2}\) \\ DPPH\({}_{\text{UAE}}\) [\(\mu\)mol TE/100 g] & 1909.4 * & 102.7 * & \(-\)325.7 * & 9.5 \(\times\) 10\({}^{-1}\) & 11.3 * & 7.4 \(\times\) 10\({}^{-1}\) \\ Color & 6.2 *** & \(-2.5\times 10^{-2}\) * & \(-4.8\times 10^{-2}\) & \(-4.1\times 10^{-4}\) * & 1.4 \(\times\) 10\({}^{-3}\) & 4.4 \(\times\) 10\({}^{-4}\) \\ Odor & 5.9 *** & \(-8.7\times 10^{-2}\) *** & \(-6.2\times 10^{-2}\) * & 9.7 \(\times\) 10\({}^{-4}\) ** & 2.8 \(\times\) 10\({}^{-3}\) ** & \(-6.1\times\) 10\({}^{-4}\) * \\ Texture & 6.9 *** & \(1.7\times 10^{-2}\) & \(-1.8\times 10^{-1}\) * & \(-1.0\times\) 10\({}^{-3}\) * & 6.2 \(\times\) 10\({}^{-3}\) * & 4.6 \(\times\) 10\({}^{-6}\) \\ Flavor & 6.8 *** & \(-9.2\times 10^{-2}\) ** & \(-1.2\times 10^{-1}\) * & 4.9 \(\times\) 10\({}^{-4}\) & 4.2 \(\times\) 10\({}^{-3}\) * & \(-3.5\times\) 10\({}^{-4}\) \\ Overall acceptability & 6.9 *** & \(-9.6\times 10^{-2}\) ** & \(-1.2\times 10^{-1}\) * & 4.5 \(\times\) 10\({}^{-4}\) * & 3.9 \(\times\) 10\({}^{-3}\) * & \(-1.7\times\) 10\({}^{-4}\) \\ Purchase intent & 3.3 *** & \(-4.1\times 10^{-2}\) ** & \(-2.4\times 10^{-2}\) & 8.5 \(\times\) 10\({}^{-5}\) & 9.4 \(\times\) 10\({}^{-4}\) * & 7.5 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression coefficients of quadratic polynomial models for the studied response variables. The second-order polynomial equations generated were used to predict the responses (Tables 2 and 3). The ANOVA results for the predicted response quadratic models are listed in Table 5. ANOVA test revealed that the quadratic polynomial models adequately represent responses of DPPH for extracts obtained by conventional extraction and UAE as well as sensory properties due to an insignificant lack-of-fit (F ranged between 2.89 and 18.83, \(p\) > 0.05) for each estimated response (Table 5). Moreover, high F-values ranged between 154.89 and 1281.56, and the probability values less than 0.05 and 0.001 indicated that the empirical models were significant (Table 5). For this reason, these proposed mathematical models are valid and convenient for predicting the antioxidant potential and sensory properties of functional biscuits prepared under any combination of RPC amount and fat type.
# The Effects of Acidic, Alkaline, and Neutral Anolytes on Electrochemical Seawater Deoxygenation
## 3 Results and Discussion
### Physical Characterization of Cathode and CEM after Test  
Iridium oxide is the state-of-the-art anode used in the chlor-alkali industry for chlorine production, and it is the best anode material for oxygen or chlorine evolution [21]. Iridium oxide has high durability and is not affected at the low current used in the deoxygenation cell. Due to the reported high stability for iridium oxide, only the silver mesh and cation exchange membrane were analyzed in SEM with EDS after experiments in Cell A. Figure 6 shows the images of the silver mesh before and after experiments in the deoxygenation cell using acidic, basic, and neutral analytes. The insets in Figure 6 show the morphology of electroplated silver at 2500\(\times\) magnification. The images at different parts of the mesh were different as the electroplated silver had different appearances (formation of structures similar to bubbles on the silver surface); however, the silver layer was intact and there was no observation of significant change in the amount of silver before and after the experiments. The mass of the silver mesh was unaltered after the test, suggesting that there was no loss of electroplated silver during the operation.  
Figure 4: Cell voltage and resulting cathode potential (vs. reversible hydrogen electrode (RHE)) in different buffer solutions.  
Figure 5: Cell voltage and current output in the buffer solutions.  
The SEM images of CEM before and after the test in acidic, basic, and neutral analytes are depicted in Figure 7. The membrane was found to be working properly, and the results obtained in the deoxygenation cell were as expected. However, the specification for the membrane stated that the stability of the membrane might be affected by the use of solutions at very low or at very high pH values. Thus, it was recommended to use solutions with pH values in the range of 2-10. However, during these short experiments, the membrane was not found to be affected by acidic or neutral analytes as determined by SEM examinations. However, the EDS results indicated an increase in F/C ratio of the elemental composition of the membrane tested. This was less pronounced in the alkaline electrolyte compared to the other electrolytes (more details in Supplementary Materials).  
Figure 6: SEM images of (**a**) unused silver mesh, and after tests with (**b**) acidic analyte, (**c**) neutral analyte, and (**d**) alkaline analyte.  
Figure 7: SEM images of (**a**) unused membrane, and after tests in (**b**) acidic analyte, (**c**) neutral analyte, and (**d**) alkaline analyte.
###### Abstract  
Background: In recent years, there has been remarkable growth in AI-based applications in healthcare, with a significant breakthrough marked by the launch of large language models (LLMs) such as ChatGPT and Google Bard. Patients and health professional students commonly utilize these models due to their accessibility. The increasing use of LLMs in healthcare necessitates an evaluation of their ability to generate accurate and reliable responses. Objective: This study assessed the performance of LLMs in answering orthodontic-related queries through a systematic review and meta-analysis. Methods: A comprehensive search of PubMed, Web of Science, Embase, Scopus, and Google Scholar was conducted up to 31 October 2024. The quality of the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST), and R Studio software (Version 4.4.0) was employed for meta-analysis and heterogeneity assessment. Results: Out of 278 retrieved articles, 10 studies were included. The most commonly used LLM was ChatGPT (10/10, 100% of papers), followed by Google's Bard/Gemini (3/10, 30% of papers), and Microsoft's Bing/Copilot AI (2/10, 20% of papers). Accuracy was primarily evaluated using Likert scales, while the DISCERN tool was frequently applied for reliability assessment. The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42-0.44]. No heterogeneity was observed between the experimental group (ChatGPT-3.5, Gemini, and Copilot) and the control group (ChatGPT-4). However, most studies exhibited a high PROBAST risk of bias due to the lack of standardized evaluation tools. Conclusions: ChatGPT-4 has been extensively used for a variety of tasks and has demonstrated advanced and encouraging outcomes compared to other LLMs, and thus can be regarded as a valuable tool for enhancing educational and learning experiences. While LLMs can generate comprehensive responses, their reliability is compromised by the absence of peer-reviewed references, necessitating expert oversight in healthcare applications.  
artificial intelligence; deep learning; machine learning; large language models; orthodontics; clear aligners; knowledge; information +
Footnote †: journal: Applied Sciences