esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The Mudeungsan tuff provides evidence of volcanic activity by producing high-temSSperature minerals such as sanidine and cristobalite. In this section, we focus on cristobalite, which was first reported in the Mudeungsan area. Cristobalite, a crystalline polymorph at a high-temperature and low-pressure in the SiO\({}_{2}\) system, is a metastable silica phase in the surface environment. Nevertheless, it has been widely reported in extrusive rocks [11] and particularly in volcanic ash formed in dome-forming eruptions [12, 13].  
Our synchrotron X-ray diffraction results show that the Mudeungsan tuff contains mainly \(\alpha\)-cristobalite belonging to the P4\({}_{1}\)2\({}_{1}\)2 space group in the tetragonal system. \(\alpha\)-cristobalite appears when cooled to approximately 240 C or less, accompanied by a displacive transition from cubic \(\beta\)-cristobalite to tetragonal \(\alpha\)-cristobalite [11, 13, 14, 15]. In volcanic rocks that have recently erupted, \(\beta\)-cristobalite can be observed, and both \(\alpha\) and \(\beta\) types may also appear together [13]. However, no \(\beta\)-cristobalite is shown in the Mudeungsan samples, which may be due to the age of these rocks (\(>\)80 Ma). Thus, these samples were preserved for a long time after cooling and the \(\beta\)-\(\alpha\) cristobalite transition  
Figure 4: High-resolution synchrotron X-ray powder diffraction patterns of the Mudeungsan tuff samples.  
could be completed during the process. However, it is worth considering that such silica phases did not completely convert to quartz, a stable phase under ambient conditions.  
The \(\alpha\)-cristobalite content of the Mudeungsan twff samples (0.4 to 2.7%) is significantly lower compared to those reported in modern active volcanoes such as the Soufriere Hills (up to 11%) in Monserrat and Cordon Caulle (up to 23%) in Chile [11; 15]. It can be inferred that the columnar joints have progressed from \(\alpha\)-cristobalite to quartz to some extent. The time when cristobalite was converted to quartz at room temperature was proved to be less than 105 years [16]. However, it has been suggested that it could take up to tens of millions of years to cool slowly from cristobalite to quartz [17]. Some argue that hydrothermal fluids produce cristobalite through recrystallization of vapor in vesicles in relation to the explosive dome-forming eruptions [15; 18]. Volcanic cristobalite, in particular, has been proposed to be produced in the process of vapor-phase crystallization or devitrification in volcanic ash [12]. Sanidine and cristobalite were also reported in the Cretaceous Parana Magmatic Province (southern Brazil), indicating that they were mainly produced by the devitrification of dacites and rhyolites [19].  
Mudeungsan lacked discussion about its long-standing volcanic activities despite the distribution of volcanic rocks. Therefore, we would like to suggest that the existence of the first reported cristobalite in the columnar joint samples does not only report mineralogical information but also that Mudeungsan experienced an explosive eruption at that time.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Liquid Evaporation Rate Measurement  
Liquid evaporation measurement is fundamental in many industrial applications and scientific research, such as quantitative analysis, and physical and chemical reaction process monitoring. The common methods used to measure the liquid level variation, including the capacitive sensors, the fiber liquid level sensor, the laser triangulation, etc., are contact-based or have limited accuracy. [PERSON] [66] reported the application of real-time evaporation of the liquid measurement based on the laser feedback interferometer. The experimental system is shown in Figure 12. The structure of the instrument belongs to the quasi-common-path laser feedback interferometer [44]. A hollow arm with a right-angled prism fixed on the edge is installed at the interface of the interferometer, turning the laser beam 90 degrees and adjusting it incident perpendicularly onto the liquid surface. Four different transparent liquids, including distilled water, absolute alcohol, acetone, and ether, are measured in the system. The results show that real-time direct measurement of liquid evaporation and liquid level measurement with a nanometer order is realized.
###### Abstract
The most relative and straightforward approach was to predict the worst-case objective value from all scenarios and minimize their dose delivery uncertainty similar to robust optimization strategies in intensity-modulated radiation therapy and proton therapy [31; 32; 33; 34; 35]. Consideration of the worst-case scenario for dose delivery uncertainties in cervical brachytherapy is useful for assessing resilience and robustness. In practice, the band of DVHs for a given structure represents the range of possible dose variations. The evaluation of the band width on the DVH can be used as a quantitative measure of robustness. The median absolute deviation (MAD) is used as one of the well-known robustness estimators. It is not only often used as an initial value for computing more efficient robust estimators but also as a good robust estimator in regression problems [36]. To cope with the measurements of bandwidth at the defined dose-volume points on the DVH, a MAD robust estimator was applied to design a robust optimization algorithm in this study. In real-world optimization problems, objectives can conflict, so there cannot be a single solution [37]. Accordingly, the optimization of dwell times and dwell positions using an inverse optimization technique needed to be carefully considered to improve the target dose coverage and reduce the dose delivered to the organ at risk (OAR). The objective function for determining the dwell times at each of dwell position for the desired dose distributions is a multi-objective optimization problem requiring complex computation [38; 39]. Classical optimization techniques are efficient at finding local minimum points; however, they may not yield the global minimum point. To solve this problem, we should search for the set of all optimal compromises. The so-called Pareto set, containingall Pareto-optimal points based on objective function, has more than one local minimum. As previously mentioned, the feasible solution to a multi-objective problem in real-world optimization is to find a set of solutions. Therefore, the genetic algorithm is well-suited to solving multi-objective optimization problems due to the population-based search approach. Given the above, a preferred efficient genetic algorithm that can search global minimum points as a multi-objective optimizer was selected to overcome the complexity of global searches [39; 40; 41; 42]. The focus of this research study was to assess the feasibility of minimizing dose delivery uncertainty by potential applicator displacements of cervical brachytherapy treatment using a MAD-constrained robust optimization method with a multi-objective genetic algorithm.
## 4 Results and Discussion
In this work, three different cases, in which initial maximum voltage difference and the total voltage of the cells are approximate, are tested to consider the correlation between the voltage difference of modules and balancing time in estimating the balancing time of the circuit. In case 1, the voltage difference of two modules is 892 mV, the balancing time is 300 min, and each 1 mV difference needs a time of approximately 0.336 min for balancing. Similarly, cases 2 and 3 are 0.523 and 0.426 min, respectively. It shows that the voltage difference between the \(M_{1}\) and \(M_{2}\) increases, resulting in the  
Figure 14: Initial and balanced state voltages: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.  
increased balancing time. However, the balancing time will slightly decrease as shown in Figure 15. It can be estimated the balancing time in the range segment AC for other experiments with the initial maximum voltage difference and the total voltage of the cells like this experiment. In order to evaluate a voltage drop of the cells after balancing, the total voltage of before and after balancing can be evaluated by the percentage of the voltage drop, which can be expressed by the efficiency as follows. \[\eta_{V}=\frac{\sum_{j=1}^{N}V_{Cell\_start}-\sum_{j=1}^{N}V_{Cell\_end}}{ \sum_{j=1}^{N}V_{Cell\_start}}\times 100\%, \tag{21}\]  
where \(\sum_{j=1}^{N}V_{Cell\_start}\) and \(\sum_{j=1}^{N}V_{Cell\_end}\) are the total voltage of eight cells before and after balancing, respectively. \(N\) is the number of the cells in the SCBS. From (21) and experimental data and results, the percentage of the voltage drop of three cases can be plotted as shown in Figure 16. They are displayed in the time range from 0 to 150 min (i.e., balancing time of the case 2). The percentage of the highest voltage drop of three cases is 0.8% and there is a minor difference. The voltage drop is due to the transmission energy of the cells through the transformers, capacitor etc.
## 5 Gaps in Literature
### Testing of Multiple Datasets  
Enhanced reliability and generalizability of a proposed model can be achieved by evaluating a model with multiple datasets collected through the implementation of diverse recording equipment and laboratory practices. As a result, it guarantees the model's strength and effectiveness in handling various scenarios [106]. A considerable amount of the existing literature assesses models using a single dataset. However, a growing body of literature as highlighted in [60; 67; 96; 97; 99; 100] tested their models across multiple datasets with variations in data collection environments. Results from these studies highlight the critical role that the diversity in datasets and data collection methods plays in improving a model's robustness.
## 1 Introduction
### Research Contribution  
This study leverages the high reliability and fidelity of DT technology to establish a virtual-real hybrid framework based on the key thermal parameter identification, enabling thermal prediction from low-risk behaviors (low-speed, light-load) to high-risk behaviors (high-speed, heavy-load) in bearing thermal analysis. The core innovation lies in overcoming the limitations of static modeling and data isolation inherent in conventional thermal analysis methods, providing real-time and adaptive solutions for bearing thermal behavior analysis under complex operating conditions. This approach significantly enhances thermal safety and fault early-warning capabilities during long-term durability operation.  
Unlike some studies on bearing health monitoring or remaining useful life prediction based on vibration signals, the temperature prediction method in this research relies on traditional physical models, adopting a semi-physical and semi-data fusion approach. The training data does not serve machine learning purposes but rather refines the conventional physical model through parameter identification.  
The principal contributions, potential application domains, and manuscript structure are outlined as follows:  
* Principal contributions  
(1) A DT system for bearing thermal characteristics and a digital twin-driven virtual-real hybrid framework are proposed to achieve bearing thermal prediction under complex operating and long-term operation. (2) A key thermal parameter identification mechanism is introduced to dynamically calibrate the virtual thermal model, effectively addressing time-varying and ambiguous thermal parameter characteristics caused by material degradation and grease property evolution during thermal prediction. (3) An NOA-based optimization method is developed to enhance the efficiency of parameter identification processes.
* Potential application domains  
The primary research focus of this study is the traction motor bearings of high-speed trains. The proposed thermal prediction methodology enables precise determination of grease service lifecycles and provides risk warning capabilities for thermal faults under high-risk operational scenarios. In analogous industrial applications, this virtual-real framework can leverage digital twin-derived thermal predictions to reverse-optimize physical system control parameters (e.g., lubricant flow rates, cooling fan speeds), thereby advancing preventive maintenance and intelligent control strategies for industrial bearings.
* Manuscript structure  
Section 2 introduces the DT system for bearing thermal analysis and the virtual-real hybrid framework for thermal prediction, as well as the influence of input parameters on bearing temperature. Section 3 describes the construction of the LPTN model in VML-Section 4 introduces the NOA-based thermal parameter identification strategy. Section 5 presents the results and discussion of bearing thermal parameter identification and temperature prediction.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 1 Introduction
### A First-Year Student's Approach to the Vertex Cover Problem
#### 1.2. The Question of Why  
Algorithmic metatheorems, which this paper is about, help us in understanding _why_ the vertex cover problem behaves the way it does with respect to the divide-and-conquer approach. Why does the division phase fail? Why does the merging phase work? Answering the first question seems easy: general graphs do not have any \"decomposition property\" at all. On the other hand, if the graph is a tree, everything is fine, and it turns out that \"everything is _still_ fine\" when the graph is \"nearly\" a tree, namely a graph of bounded tree width (this concept will be explained in more detail later). Answering the second question seems harder: the answer \"because solutions can be assembled using a small trick\" does not generalize very well. It took the research community quite some time to find a better answer: In 1990, [PERSON] [1] found that the merging phase works \"because the vertex cover problem can be described in monadic second-order logic\" (this logic will also be explained in more detail later).  
In general, algorithmic metatheorems follow a fixed pattern. They state that problems that can be described in a certain logic (\"are amenable to merging\" for the right logic) and whose instances can be decomposed in a certain tree-wise fashion (\"are amenable to division\") can be solved within a certain amount of time or space. The just-mentioned result by [PERSON] is known as _Courcelle's Theorem_ (Proposition 6.4, p. 227, [1]) and states in full (phrased in modern terminology): _all monadic second-order properties of graphs of bounded tree width can be decided in linear time._ It was just the first in a long line of further theorems that have been discovered, which basically only vary the three \"parameters\" of algorithmic metatheorems: the logic, the instance structure, and the required resources. By weakening one of them, one can often strengthen another. For instance, when we use first-order logic instead of monadic second-order logic (which narrows that class of problems considerably since we can express much less in first-order logic compared to second-order logic), we can change the requirement on the decomposition property to, for instance, \"nowhere dense graphs\" [2] (a much larger class of graphs than those of bounded tree width) and still obtain a (near) linear time bound or, as a more familiar example, to planar graphs and still obtain a linear time bound [3]. In another direction, when we increase the time bound to polynomial (rather than linear) time, we can broaden the class of graphs to graphs of bounded clique-width (which is another generalization of bounded tree width) [4]. In yet another direction, which will interest us in the present paper, it has been shown [5] that [PERSON]'s Theorem also holds when \"linear time\" is replaced by \"logarithmic space\".
## 2 Background and Related Works
### Multi-Criteria Decision Aid  
Decision problems [6] are usually complex as they involve the evaluation of objects defined on several conflicting criteria. MCDA methods aim to help users to make a decision when confronted with a set of alternatives [29]. They are widely used in decision problems to find the best alternative. MCDA makes the process more explicit, rational, and efficient. It is a multidisciplinary field, derived from Operations Research, that uses mathematical approaches to deal with complex problems encountered in human activities. Nowadays, it also integrates Artificial Intelligence and economic welfare techniques.  
In this work we have used ELECTRE-III, a well-known outranking MCDA method, to compare and order the decision alternatives according to their performance in several features, taking into account the decision maker preferences. The interested reader may look at the details of this method in [30]. For the purposes of this paper, suffice it to say that the user of ELECTRE-III must provide the following parameters for each feature (criterion):  
* The weight of the criterion.
* Indifference threshold: if the difference between the values of the criterion in two alternatives \(A\) and \(B\) is lower than this indifference threshold, then both objects have the same interest for the user and one cannot be preferred to the other.
* Preference threshold: if the difference between the values of the criterion in two alternatives \(A\) and \(B\) is higher than this preference threshold, then \(A\) will be preferred to \(B\) in this feature.  
Veto threshold: if the difference between the values of the feature in two alternatives \(A\) and \(B\) is higher than this threshold, then \(B\) will never be preferred to \(A\), even if it has better values in the rest of features.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 2 Materials and Methods
### P-I Model Based on Play Operator  
The P-I model is a phenomenological model that relies on the relationship between the system's input and its output. Its core involves a weighted overlay of hysteresis units, such as play and stop operators. It possesses the characteristics of rapid calculation speed and broad applicability. This paper introduces a P-I model based on the play operator to simulate hysteresis characteristics. The relationship between the input signal, denoted as x, and the output signal, denoted as y, of the play operator is illustrated in Figure 3. And the arrow direction is the direction in which the output signal changes when the input signal changes.  
The equation for the play operator is:  
\[y(t)=F(x(t),y(t_{i-1}),r)=\max\{x(t)-r,\min\{x(t)+r,y(t_{i-1})\}\} \tag{1}\]  
where t \(\in[t_{0},t_{m}]\), \(t_{0}\leq t_{1}\leq...\leq t_{i-1}\leq t_{i}\leq t_{i+1}\leq...\leq t_{m-1}\leq t _{m}\); \(x(t)\) is the segmented monotonic input signal; and \(r\) is the play operator threshold value.  
Figure 1: Schematic diagram of PZT FSM.  
Figure 2: Characteristics of hysteresis curves: (**a**) The hysteresis curve when the frequency changes. (**b**) The hysteresis curve when the amplitude changes.  
The initial condition is:  
\[y(t_{0})=F(x(t_{0}),0,r)=\max\{x(t_{0})-r,\min\{x(t_{0})+r,0\}\} \tag{2}\]  
The P-I model is obtained through a weighted overlay of multiple play operators with different thresholds. The equation is:  
\[z(t)=\sum_{j=1}^{n}\omega_{j}F_{j}(x(t),y_{j}(t_{i-1}),r_{j})=\sum_{j=1}^{n} \omega_{j}\text{max}\{x(t)-r_{i},\min\{x(t)+r_{i},y_{i}(t_{i-1})\}\} \tag{3}\]  
where \(\omega_{j}\) is the weight of play operator; \(r_{j}\) is the threshold value of play operator, \(0=r_{1}<...<r_{n}<+\infty\); and \(n\) is the number of play operators.  
The P-I model is established using the above equations, and a simulation analysis is conducted. Figure 4 shows the hysteresis curve of the P-I model when inputs are triangular wave signals with both constant and changing amplitudes.  
According to Figure 4, the hysteresis curve described by the P-I model is consistent with the hysteresis characteristics observed in the PZT FSM test. Therefore, by adjusting \(\omega_{j}\) and \(r_{j}\) in the P-I model, we can model the PZT FSM hysteresis curve, and then it can be used to compensate and suppress the nonlinear errors caused by hysteresis. However, the model is a static nonlinear model, meaning that it is unaffected by the frequency of the input signal. As mentioned in the previous section, the hysteresis is influenced by the frequency of the input signal. Therefore, in order to meet the application requirements of the fine tracking system, it is necessary to establish a dynamic hysteresis model related to the input signal frequency.  
Figure 4: (**a**) The amplitude of the input signal remains unchanged. (**b**) The amplitude of the input signal changes.  
Figure 3: Play operator.
_Applied_  
_sciences_  
_Article_  
**Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters**  
[PERSON]  
Department of Biomedical Engineering, Jungwon University, 85 Mummu-ro, Goesan-eup,  
Goesan-gun 28024, Korea; [EMAIL_ADDRESS]; Tel: +82-43-830-8607  
**Abstract**: The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. In total, 126 subjects (63 elderly and 63 young) were obtained from the Saarbruecken voice database. The highest performance of 93.75% appeared when the skewness was added to the MFCC and MFCC delta parameters, although the fusion of the skewness and kurtosis parameters had a positive effect on the overall accuracy of the classification. The results of this study also revealed that the performance of FNN was higher than that of CNN. Most parameters estimated from male data samples demonstrated good performance in terms of gender. Rather than using mixed female and male data, this work recommends the development of separate systems that represent the best performance through each optimized parameter using data from independent male and female samples.  
**Keywords**: elderly voice analysis; deep learning classifier; skewness; kurtosis; medical IT