esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### C-NMR Chemical Shifts Indicating Intermolecular Interactions of n-Hydroxylflavones
position on the phenyl B-ring, has no isomer, resulting in a reduced variability and the smallest RMSD among the B-ring-substituted flavones. Another key factor influencing the RMSD is the substitution pattern across the flavone structure. Unsubstituted flavone (0-HF) exhibited the lowest RMSD values, showing an excellent agreement with the experimental data. This was likely due to its structural rigidity, which minimizes conformational variability. In contrast, mono-substituted flavones on the A- and C-rings showed larger RMSD values. The flexibility of the hydroxyl group in these compounds introduces variability in the local chemical environment, as the hydroxyl group can rotate or \"flip,\" leading to deviations between the calculated and experimentally observed values [18]. Despite these discrepancies, an RMSD analysis provides a valuable tool for benchmarking computational predictions. The observed deviations emphasize the need to account for conformational flexibility, intramolecular interactions, and the inherent limitations of static computational models. These factors must be considered when interpreting RMSD values to ensure accuracy. The systematically calculated \(\delta_{\rm C}\) values of the n-HFs presented in Table 3 reveal an intriguing pattern. The \(\delta_{\rm C}\) values for the flavones ranged from 100.64 ppm (C8 in 7-HF) to 182.96 ppm (C4 in 5-HF), spanning approximately 82 ppm. This wide range reflects the diverse chemical environments of the carbon atoms. Notably, the carbonyl carbon (C4) consistently exhibited the largest \(\delta_{\rm C}\) (?170 ppm) across all isomers, with 3-HF showing the smallest \(\delta_{\rm C}\) (C4) at 172.22 ppm and 5-HF exhibiting the largest \(\delta_{\rm C}\) (C4) at 182.96 ppm. The carbon chemical shifts also revealed three consistently prominent carbons--C4, C2, and C9--that displayed significantly higher \(\delta_{\rm C}\) values compared to the others. The fourth largest \(\delta_{\rm C}\) corresponded to the carbon bonded to the hydroxyl group in each n-HF isomer
## 3 Machine Learning and Fault Diagnosis
### Simple Fault Diagnosis Models
#### 3.4.2 Support Vector Machine (SVM)  
SVM is a supervised learning algorithm based on statistical learning theory and the structural principle of risk minimization and was first presented in 1995 by [PERSON] and [PERSON] [90,91]. This model is used for classification and regression problems such as medical diagnosis, communication, biology, engineering, etc. The main goal of this model is to search for a hyper-plane in a high-dimensional space that best separates different classes of data with a large margin [92]. This model performs well when the separation margin between classes is observable with large-dimensional spaces. Nevertheless, it shows that the model detected the line-to-line fault with about 95% accuracy. However, it is not suitable for large amounts of data [93]. The structure of the SVM model is given in Figure 12.  
The hyperplanes \(H_{1}\)_et_\(H_{2}\) pass through the closest samples and are parallel to the hyperplane \(H\), respectively. Points located on hyperplanes \(H_{1}\)_et_\(H_{2}\) are the samples that have all the information used to design the SVM classifier. In the context of fault diagnosis, this model can make decisions on small quantities of data because a large amount of implicit data classification knowledge can be extracted [92]. Several authors have implemented the SVM algorithm successfully in many fields. For example, [PERSON] et al. used the SVM algorithm for open-circuit fault detection of the IGBT switch of an H-bridge multilevel inverter [94]. [PERSON] et al. proposed the SVM model to classify module crack defects and hot spot defects due to shading and dust accumulation. Intentionally created defects were detected and classified in real time with 97% accuracy [93].
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets
### Non-Destructive Quantum Verification
Moreover, as quantum money is expected to operate within space-based or fiber-based quantum networks, preserving state fidelity during verification is essential. Noise, decoherence, and other environmental factors can degrade the quantum states representing money. Thus, adopting robust non-destructive verification protocols ensures that quantum money remains functional and trustworthy across various storage and transfer scenarios, whether in terrestrial or space-based applications.
## 3 Mathematical Model of Gas-Liquid Separation
### Characteristic Parameters of Multiphase Flow in Production Profile Logging
#### 3.1.3 Gas-Liquid Separation Efficiency  
In addition to calculating the gas holdup, oil holdup, and water holdup, it is also necessary to calculate the gas-liquid separation efficiency and the oil ratio, for an area in which the volume method is used for measurement. For example, Equation (18) is the calculation formula for gas-liquid separation efficiency.  
\[\eta_{g}=(1-\frac{(1-Y_{w})*M_{a}}{Y_{sg}})\times 100\% \tag{18}\]  
where \(\eta_{g}\) is the gas collection rate, \(Y_{w}\) is the central pipe's water holdup, \(M_{a}\) is the central pipe's total flow, and \(Y_{sg}\) is the wellbore's total gas flow.  
\[\eta_{oil}=\frac{(1-Y_{w})M_{a}}{Y_{so}}\times 100\% \tag{19}\]  
where \(\eta_{oil}\) is the oil collection rate ratio, \(Y_{w}\) is the central pipe's water holdup, \(M_{a}\) is the central pipe's total flow, and \(Y_{so}\) is the wellbore's total oil. According to the actual engineering needs, \(\eta_{g}\geq 90\%\) and \(\eta_{oil}\geq 90\%\) should be satisfactory.
## 2 Geological Setting and Sample Descriptions  
### Geological Setting  
The Dabie-Sulu orogenic belt, resulting from the Triassic continental collision between the NCC and the YC (Figure 1), preserves one of the world's largest concentrations of UHP metamorphic rocks [1; 3; 4; 6]. Following the identification in eclogite of coesite and diamond, both serving as indicators of UHP conditions, in eclogites [28; 29; 30], this belt has emerged as a foremost site for studying UHP metamorphism associated with continental subduction. The Tanlu fault divides this orogenic belt into two distinct segments: the east-west-oriented Dabie orogen and the northeast-trending Sulu orogen (Figure 2).  
The Sulu orogen, located east of the Tan-Lu fault, is bordered by the Wulian-Yantai Fault, delineating its boundary with the NCC [32] (Figure 2). At the heart of the northern Sulu orogen lies the Jiaodong terrane, which is mainly characterized by its high abundance of orthoneiss. This terrane also contains smaller quantities of amphibolites, eclogites, and garnet peridotite, as well as a minor proportion of metamorphic supracrustal rocks. The precursor rocks of these orthoneisses and amphibolites are Neoproterozoic bimodal magmatic rocks, which can be traced back to the rifting event that led to the disintegration of the supercontinent Rodinia [22, 33, 34]. Furthermore, a minor quantity of Archean to Paleoproterozoic rocks is present within the core of the orogenic belt [6, 7, 20, 21, 22, 33, 35].  
Our samples were collected from the Weihai area, situated within the northern Sulu orogen zone, characterized predominantly by orthoneiss, with smaller occurrences of eclogite interspersed among Archean to Paleoproterozoic rocks. The presence of coesite within zircon crystals sourced from orthoneiss suggests that it underwent a uniform UHP metamorphic event [9, 10, 36]. Additionally, within the UHP orthoneiss, there are embedded blocks of Archean to Paleoproterozoic rocks, which include ultramafic to mafic rocks.  
Figure 1: Schematic geological map of the Dabie–Sulu orogenic belt (modified after [PERSON], 2015 [31]). NCC and YC in the insert denote the North China Craton and the Yangtze Craton, respectively. CCSD, the drill site of the Chinese Continental Scientific Drilling. HP—High pressure, UHP—Ultrahigh pressure.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 4 Experiment and Evaluation
### Experimental Result and Evaluation
#### 4.2.2 Evaluation of Defensive Efficiency  
In this section, the investigation emphasizes the evaluation of the model's efficiency predicated on its defense performance, targeting the future commercialization of AI security.  
According to the outcomes presented in Table 2, denoising ResNet required the shortest learning time among the defense models; however, it was confirmed to have the lowest defense performance, with a defense success rate of 31.7%. In contrast, the adversarially trained ResNet exhibited a relatively superior defense performance with a defense success rate of 71.8%; however, it required a lengthy learning time of 1 h 47 min and 40 s. The model that integrated denoising and adversarial training showed the most outstanding defense performance among the comparative models, with a defense success rate of 72.0%; however, it was verified that it required the longest learning time of 1 h 47 min and 40 s. Based on these results, this study ascertained that the technique proposed by [PERSON] et al. [48] can exhibit superior performance; however, the limitations associated with the learning time of adversarial training persist. The model utilizing knowledge distillation exhibited the best results in terms of parameters and learning time. However, its defense performance was less effective than DiNo-Net, with a defense success rate of 71.6%. The DiNo-Net proposed in this study exhibited the highest defense performance, with a defense success rate of 72.7%. In addition, by composing 11.24 million model parameters, the increase in parameters was minimized, and the learning time was reduced to 1 h 12 min 33 s. Therefore, integrating the feature denoising technique with knowledge distillation reduces the learning time and effectively improves the defensive performance. Furthermore, our method achieves the best robustness without directly employing adversarial training, demonstrating its efficiency and effectiveness in improving model robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Defense Method** & **Parameter** & **Learning Time** & **Defense Success Rate (\%, 1)** \\ \hline \multirow{6}{*}{ResNet8 [52]} & - & 11.17 M & 1:06:22 & 33.5 \\ \cline{2-5}  & DN [45] & 11.2 4M & 1:11:46 & 31.7 \\ \cline{2-5}  & ADT [20] & 11.17 M & 1:47:13 & 71.8 \\ \cline{2-5}  & DN + ADT [48] & 11.24 M & 1:47:40 & 72.0 \\ \cline{2-5}  & KD [41] & 11.17 M & 1:12:06 & 71.6 \\ \hline
**DiNo-Net (Proposed)** & **KD + DN** & 11.24 M & 1:12:33 & **72.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiency Performances of defensive models (DN: denoising, ADT: adversarial training, KD: knowledge distillation).  
Figure 6: Ablation study of Hessian traces from second derivatives on training stability.
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
## 2 Methods
### Empirical Mode Decomposition (EMD)  
In the previously introduced methods (PCA and SD), one set of vectors \(\{\boldsymbol{\phi}_{i}\}\) is used to generate every signal \(\mathbf{x}\) in the dataset, meaning that the chosen generators are optimal for a particular dataset. Other methods have been proposed to find signal specific set of components. This was the case of the Empirical Mode Decomposition (EMD), which was first described by [PERSON] et al. in [45] as a new method to analyze nonlinear and non-stationary signals. EMD is a data-based approach that decomposes any signal into a sum of so-called Intrinsic Mode Functions (IMF) plus a residual as illustrated in Figure 1d. Therefore, the original signal is modelled as a linear combination of amplitude and frequency modulation (AM-FM) functions. Every single function (IMF) is capturing specific information from a different frequency band present in the signal and is obtained in an iterative sifting procedure. Other variants have been defined since the introduction of EMD, but they all share the basic steps presented below. Let us suppose that we want to decompose a signal \(x(t)\) by means of EMD. Then its decomposition will be calculated as follows:  
1. Determine the local maxima and minima of the signal \(x(t)\).
2. Calculate the upper (lower) envelope by interpolating the local maxima (minima) points. The interpolation can be carried out in different ways (linear interpolation, spline interpolation, etc.), which could lead to slightly different results.
3. Calculate the local mean \(m(t)\) by averaging the upper and lower envelopes.
4. Calculate the first IMF candidate \(h_{1}(t)=x(t)-m(t)\).
5. Checks whether candidate \(h_{1}(t)\) meets the criteria to be an IMF: * If \(h_{1}(t)\) meets the criteria, define the first IMF as \(c_{1}(t)=h_{1}(t)\). * If \(h_{1}(t)\) does not meet the criteria, set \(x(t)=h_{1}(t)\) and repeat from step 1 The next IMF will be extracted using the same procedure on the signal \(r_{1}(t)\) that remains after subtracting the first IMF from the signal: \(r_{1}(t)=x(t)-c_{1}(t)\). The process stops when two consecutive IMFs are (almost) identical and the empirical mode decomposition of the signal \(x(t)\) is written as:  
\[\mathbf{x}(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t), \tag{5}\]  
indicating that the original signal \(x(t)\) has been decomposed in \(n\) IMFs plus a residual signal. This residual signal captures the trend (or the mean) of the original signal.
## 4 Case Study
### Lighting Sensors  
The overall assessment of the indoor illuminance is realized using of Luxmeters (Lux) installed inside the presence sensors, the IAQ sensor and the magnetic contact sensors for the doors/windows. As with the assessment of thermal comfort, the data are both aggregated and averaged to create a comprehensive and timely measurement based on the information collected in the environment. In addition, in the future, the data obtained in this way may be used for the evaluation of lighting comfort at work surface level (Standard UNI EN 12464-1 [45]) and for the dimming of luminaires (currently not supported by the lighting system).
_Algorithms_**2015**, \(8\), 336-365; doi:10.3390/a8020336  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**MAKHA--A New Hybrid Swarm Intelligence Global Optimization Algorithm**  
**[PERSON]. [PERSON] 1,\({}^{\dagger}\), [PERSON] 1,2,\({}^{\dagger}\) and [PERSON] 3,\({}^{\star}\)**  
\({}^{1}\) Department of Chemical Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt; E-Mails: [EMAIL_ADDRESS] (A.M.E.K.); [EMAIL_ADDRESS] (S.-E.K.F.)  
\({}^{2}\) Department of Petroleum and Energy Engineering, American University in Cairo, New Cairo 11835, Egypt  
\({}^{3}\) Department of Chemical Engineering, Aguascalientes Institute of Technology, Aguascalientes 20256, Mexico  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +52-449-910-5002 (ext. 127); Fax: +52-449-910-5002.  
Academic Editor: [PERSON]  
_Received: 2 April 2015 / Accepted: 3 June 2015 / Published: 19 June 2015_  
**Abstract:** The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.  
**Keywords:** global optimization; nature-inspired methods; monkey algorithm; krill herd algorithm; hybridization
## 1 Introduction  
Polycyclic aromatic hydrocarbons (PAHs) are compounds that contain two or more aromatic rings, such as naphthalene with two benzene rings, benzo[a]pyrene with five benzene rings, and benzolgh]perylene with six benzene rings. The US EPA has designated and manages 16 types of PAHs as high-priority substances. In the case of environmental media (soil, etc.) contaminated with PAHs, several types of PAHs are mixed in most cases. Due to their high carcinogenicity, recalcitrance, and bioaccumulation, PAHs have been subject to much research since the early 20 th century in relation to their behaviors and treatment technologies [1]. Most of the PAHs present in the environment are generated by the incomplete combustion of organic materials like dioxins, and some are caused by natural factors such as forest fires [2]. In recent years, a chemical method has commonly been used as a treatment method for PAHs owing to its low cost and fast treatment process. Typical chemical treatment methods include Fenton treatment [3], ozone oxidation [4], and H\({}_{2}\)O\({}_{2}\) + UV (Ultra Violet) [5]. These chemical treatment methods have a mechanism that decomposes recalcitrant materials such as PAHs using radicals (OH-) or UV light with strong energy. The kinds of radicals generated by the radical reactions also vary. Among them, the OH radical is the most reactive. Radicals are produced by various radical-generating reactions, causing chain reaction [6]. Many published previous studies have so far mainly dealt with the decomposition rates and pathways of PAHs during chemical treatment using radicals. The combined O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) treatment resulted in high removal rates of the selected PAHs: 89% for fluorene, 66% for phenanthrene, 71% for anthracene and 81% for dibenz[a,h]anthracene [7]. However, there is a lack of research on which positions of the PAHs are decomposed or oxidized by attacks of radicals. Recently, in some research papers, attempts have been made to interpret the reaction positions of various compounds based on radical reaction analysis using the DFT method [8], the calculation of bond dissociation energy [9], the method of ab initio transition-state theory calculation [10], and radical reaction position calculation using the semi-empirical method [11]. In this study, the transition state energy and atomic charge of OH radicals and PAHs were calculated using the DFT method. The accuracy of prediction of the radical reaction positions was evaluated by comparing with the radical products of PAHs reported in the literature.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 2 Research Background and Related Works
### kNN Search Using Single GPGPU  
In [9], [PERSON] and [PERSON] converted their Bi-Level LSH implementation from CPU to GPU, which uses KD-tree clustering (level 1) for the CPU and LSH search (level 2) for the GPU. They use one CUDA thread to process a single query in the GPU device, which demonstrated that the speed-up ratio could gain up to 40 times, compared to using CPU. In [15], the authors introduced a GPU-based kernelized locality-sensitive hashing method using a single GPU for satellite image retrieval, in which the search process of each query could also be processed by a single GPGPU thread. The methods in [9,15] presented a common bottle-neck problem when multiple queries have different processing times. In that case, the processing time for threads in the same CUDA block can differ greatly, which causes an increase in search time for most queries on the block. Thus, using a single thread for a single query cannot take full advantage of the power of a GPU. In our work, the DLSH algorithm divides the query into multiple small processes (probes), in order to balance the workflow of all CUDA threads. DLSH may have a lower number of concurrent queries; however, it can attain a significant average search speed by increasing the occupancy of the GPGPU.
# Multi-Objective Optimization of Three-Stage Turbomachine
## 6 Assessment of FEM and TMM with Optimized Design
### Natural Frequencies  
The 2D axisymmetric geometry was created as shown in Figure 17. By adding mass and bearing elements to this model, a general axisymmetric structure was obtained. Two types of analyses were performed on this model depending on the mesh quality. The first analysis used a 1 mm mesh size, while the second used a 10 mm mesh size. A comparison of the results obtained from these analyses with those from the TMM is shown in Table 6.  
Figure 16: Optimized 3D FEM model.  
Figure 17: Optimized 2D axisymmetric FEM model.  
Figure 15: Time consumption of each algorithm.  
The results obtained from the TMM are closely aligned with analytical results, indicating that improving mesh quality leads to the convergence of the FEM's results toward those of the TMM. This convergence occurs because the FEM model representing the rotor structure discretizes the system into stiffness matrix [K] and mass matrix [M]. Consequently, the mass distribution remains dependent on the mesh size due to this discretization. In contrast, the TMM does not perform such discretization; instead, it defines the mass and stiffness expressions within the element matrix in a sinusoidal continuous manner. This approach enables the attainment of results that are not only rapid but also closely approximate the analytical solution, regardless of the number of elements used. Thus, the increase in element quality contributes to the alignment of the FEM's results with the TMM's results.  
A significant difference in computation time is observed between the two methods. For an FEM model with a mesh size of 10 mm, the solution time differs by a factor of 24.29 compared to the TMM, while at a mesh size of 1 mm, this difference increases to a staggering 92.52 times. This duration will further increase during the examination of the frequency response function (FRF). The FRF analysis involved performing a series of modal analyses discretized according to defined frequencies. Since the natural frequency must be determined at each analysis step as one of the objective functions, this will cumulatively expedite the overall results. Furthermore, as the complexity of the model increases, more elements will be needed to accurately represent the rotor structure. In such cases, the solution speed advantage of the TMM becomes increasingly significant. It is important to note that in this comparison, the rotor was modeled in Ansys as a general axisymmetric structure. Although the solution time may vary for different FEM models, the TMM will maintain its superiority.
## 5 Discussion
### Improved Retention Rates  
Research on personalized feedback indicates that the higher retention rates measured in the AI-driven feedback group align with scientific documentation on knowledge retention practices. The analysis of student performance and engagement by AI systems helps identify students at risk so necessary interventions can be organized, which leads to increased retention rates [44].  
Research findings show that AI-driven feedback led the experimental group to retain 85% of learned concepts while the control group maintained only 65%. This indicates that AI-powered feedback promotes both short-term results and long-term memory maintenance.