esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Non-Ideal Discussion  
The terminal voltage and current relationships of the non-ideal \(i^{th}\) MO-CCCCTA can be rewritten as \(V_{Xi}=\beta_{i}V_{Yi}+I_{Xi}R_{Xi}\), \(I_{Zi}=\alpha_{i}I_{X}\), \(I_{ZC1i}=\eta_{i}I_{Xi}\), \(I_{O1i}=\gamma_{i}g_{mi}V_{Zi}\) and \(I_{O2i}=\lambda_{i}g_{mi}V_{Zi}\) for \(i\) = 1, 2, where \(\beta_{i}\), \(\alpha_{i}\). \(\gamma_{i}\), \(\eta_{i}\) and \(\lambda_{i}\) represent the tracking errors of the MO-CCCCTA voltage and current, respectively [20]. The characteristic equation of the proposed circuit in Figure 3 can be rewritten as:  
\[s^{3}C_{1}C_{2}C_{3}R_{X1}R_{X2}+s^{2}C_{2}C_{3}R_{X2}+\alpha_{2}\beta_{2}\eta _{1}sC_{2}+\alpha_{1}\alpha_{2}\beta_{2}\gamma_{1}g_{m1}=0 \tag{20}\]  
In this case, the CO and FO of the non-ideal oscillator can be expressed as:  
\[\text{CO: }g_{m1}R_{X1}=\frac{\eta_{1}C_{2}}{\alpha_{1}\gamma_{1}C_{1}} \tag{21}\]  
\[\text{FO: }\omega_{o}=\sqrt{\frac{\alpha_{1}\alpha_{2}\beta_{2}\gamma_{1}g_{m1 }}{R_{X2}C_{2}C_{3}}} \tag{22}\]  
The active and passive sensitivities of the non-ideal oscillator parameters are shown as:  
\[S_{\alpha_{1},\alpha_{2},\beta_{2},\gamma_{1}}^{\omega_{0}}=S_{g_{\text{n}1}} ^{\omega_{0}}=-S_{R_{X2},C_{2}C_{3}}^{\omega_{0}}=\frac{1}{2} \tag{23}\]  
From Equation (23), the active and passive sensitivities are equal to 0.5, which satisfy the sensitivity performance of the circuit.  
The parasitic terminal impedances of the \(i^{th}\) non-ideal MO-CCCCTA are (\(R_{Yi}\)//\(C_{Yi}\)) of terminal \(Y_{i}\), (\(R_{Zi}\)//\(C_{Zi}\)) of terminal \(Z_{i}\), (\(R_{ZC1i}\)//\(C_{ZC1i}\)) of terminal \(Z_{C1i}\), (\(R_{ZC2i}\)//\(C_{ZC2i}\)) of terminal \(Z_{C2i}\), (\(R_{O1i}\)//\(C_{O1i}\)) of terminal \(O_{1i}\), and (\(R_{O2i}\)//\(C_{O2i}\)) of terminal \(O_{2i}\)[20; 33]. The \(X\), \(Z\), and \(O_{1}\) of the first MO-CCCCTA terminals connect to the external \(C_{1}\), \(C_{2}\) and \(C_{3}\) capacitors in parallel, respectively, as shown in Figure 3. If \(C_{1}>>C_{Z2}\), \(C_{2}>>C_{Z1}\) and \(C_{3}>>(C_{O11}+C_{Y2}+C_{ZC11})\), the effects of the parasitic capacitances, \(C_{Z2}\), \(C_{Z1}\) and \(C_{O11}\), can be ignored. Hence, to minimize the effects of the MO-CCCCTA parasitic terminal impedances, the external capacitor values should be restricted by  
\[\frac{1}{s(C_{1}+C_{Z2})}<<R_{Z2} \tag{24}\]  
\[\frac{1}{s(C_{2}+C_{Z1})}<<R_{Z1} \tag{25}\]  
\[\frac{1}{s(C_{3}+C_{O11}+C_{Y2}+C_{ZC11})}<<R_{O11}//R_{Y2}//R_{ZC11} \tag{26}\]
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 2 MIMO LS-SVR-Based Vibration Response Prediction under Unknown Uncorrelated Loads
### Theoretical Inference of the Relationship between Multi-Point Vibration Responses
The solution of LS-SVR problem can be solved by constructing Lagrange function to solve its corresponding linear Karush-Kuhn-Tucker (KKT):  
\[\left[ \begin{array}{cc}\rightarrow\\ \overset{\rightarrow}{b}_{0}(\omega)&\mathbf{B}(\omega)\end{array} \right] \left[ \begin{array}{cc}0&\mathbf{I}_{P}^{T}(\omega)\\ \mathbf{I}_{p}(\omega)&K(\omega)+\gamma(\omega)\mathbf{I}_{p}(\omega)\end{array}  \right]=\left[ \begin{array}{cc}0&\mathbf{S}_{yy\_ums}(\omega)\end{array} \right] \tag{9}\]  
where \(\mathbf{I}_{p}(\omega)\) is a \([P\times n_{2}]\) vector of ones, \(\gamma(\omega)\) is a weight vector, and \(\mathbf{B}(\omega)\) represents regression vector. It can be seen from Equations (8) and (9) that LS-SVR has two hyper-parameters \((\sigma(\omega),\gamma(\omega))\). The \(\left[ \begin{array}{cc}\rightarrow\\ \overset{\rightarrow}{b}_{0}(\omega)&\mathbf{B}(\omega)\end{array} \right]\) in LS-SVR can be obtained by training the model with a training set, and then LS-SVR can be used to predict the response under a real-work condition. The procedure for building and applying the MIMO LS-SVR-based multi-point vibration response prediction in the frequency domain is shown in Figure 2.  
1. With the measured multi-point vibration responses as the input and the unmeasured multi-point vibration responses as the output, the multi-point vibration response prediction model can be established using the historical training dataset and MIMO LS-SVR. 2. After training, the model can be used to predict the unmeasured vibration response under a real-work condition.
## 3 Methodology
### Hyperparameters Used for Feature Selection Methods and Machine Learning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Selection** & **Hyperparameter** & **Explanation** & **Parameter Value** \\ \hline \multirow{4}{*}{SelectKBest} & \multirow{4}{*}{score\_func} & Function to calculate the score for each feature & ANOVA _F_-value \\  & & & 100, 500, 1500, 2000, \\  & K (EEG Emotion) & Number of top & 2500 \\  & K (DEAP) & features to select & 50, 100, 150 \\ \hline LASSO & alpha & Regularization strength & From \(10^{-6}\) to \(10^{1}\) \\ \hline \multirow{4}{*}{GA} & Population Generation & Size of population Number of generations & 30 \\  & & Probability of & 20 \\  & Crossover rate & crossover occurring between individuals & Starts at 0.8, decays to \\  & & Probability of & 0.2 \\  & Mutation rate & mutation occurring in an individual & Starts at 0.2, increases \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters that were used for various feature selection methods. and potentially high-quality solutions early in the optimization process. As generations progressed, the crossover rate increased exponentially, reaching 0.8. A higher crossover rate in later generations promotes exploitation, in which the algorithm focuses on refining and combining the best solutions. By starting with a lower crossover rate and increasing it gradually, we ensure that the algorithm does not converge to suboptimal solutions and has a higher chance of finding the optimum. This adaptive strategy helps maintain a good balance between diversity and convergence throughout the evolutionary process. Table 2 lists the hyperparameters for the classification algorithms used in the experiment. As we introduced above, we applied hyperparameter tuning using BO across several machine learning models, including random forest, logistic regression, XGBoost and SVM. For RF, the number of trees (n_estimators) is set to an integer value between 50 and 200, the maximum depth of a tree (max_depth) is set to a categorical choice of 10, 20, or 30 and the minimum number of samples required to be split is set to an integer value between 2 and 10. For logistic regression, the penalty parameter is set to a categorical choice of I1 and 12, c is set to a real number between \(10^{-3}\) and \(10^{3}\), and the solver parameter is set to 'liblinear'. For XGBoost, the number of gradient-boosted trees is set to an integer value between 50 and 200, the learning rate is set to a real number between \(10^{-3}\) and 1, and a maximum tree depth (max_depth) is set to an integer value between 3 and 9. For SVM, the kernel parameter is set to a categorical choice of 'linear' and 'rbf', and the c parameter is set to a real number between \(10^{-3}\) and \(10^{3}\). The BO process is iterated through 30 trials for each model to tune hyperparameters. For the baseline model, we implemented the models with the default hyperparameter values provided by scikit-learn. Figure 2: Flowchart of GA.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 7 Clinical Applications of Dental Adhesion  
Dental adhesion is crucial in various restorative procedures, including direct restorations, indirect restorations, and immediate dentin sealing (IDS).  
In direct restorations, adhesive techniques bond composite materials to tooth structures, enhancing esthetics and function. Studies such as those by [PERSON] et al. explore stratification concepts for anterior teeth using predefined enamel thickness and dentin shades [335,336], while [PERSON] addresses substrate considerations for direct composites [337]. Indirect restorations involve bonding pre-fabricated restorations like crowns and inlays to prepared tooth structures. Research on indirect restorations includes studies on the fracture resistance of different materials and preparation designs, such as the investigation by [PERSON] et al. of lithium disilicate and polymer-infiltrated ceramic network restorations [338], and the comparison by [PERSON] et al. of factory-crystallized silicates for monolithic metal-free restorations [339]. Immediate dentin sealing (IDS) is another critical application of dental adhesion. IDS involves applying an adhesive resin immediately after cavity preparation and before the placement of an indirect restoration. This technique helps to protect the dentin from dehydration and bacterial contamination, improving the overall BS and longevity of the restoration. IDS is supported by various studies that emphasize its role in enhancing adhesive performance and ensuring a better seal against microleakage [340; 341; 342].  
These clinical applications underscore the importance of adhesion in ensuring the success and durability of different restorative techniques.
## 4 Test Results and Analysis
### Validation Tests  
As shown in Table 5, considering the adjustable conditions of the harvester parameters, some adjustments are made to the recommended values of the optimized parameters. The drum rotation speed (\(A\)) is 550 r/min, the operating speed (\(B\)) is 3 km/h, and the spiral angle of the straw guide board (\(C\)) is set at 28\({}^{\circ}\) to conduct three repeated verification tests.  
The verification test results are shown in Table 6. The results show that the predicted value of the optimized _PLR_ is 1.18%, the verified value is 1.26%, the relative error is 6.78%, and the absolute error is 0.08%; however, the field _PLR_ of the 17 treatments is decreased by 8.31%. The predicted _PIR_ value is 0.72%, the verified _PIR_ value is 0.73%, the relative error is 1.39%, and the absolute error is 0.01%; however, the verified _PIR_ value is 50.04% lower than the average _PIR_ value of the original 17 treatments. On the other hand, the _PBR_ predicted value is 0.54%, the _PBR_ verified value is 0.61%, the relative error is 12.96%, and the absolute error is 0.07%; however, the _PBR_ verified value is 60.30% lower than the field average value of the original 17 treatments. From the optimization results, the relative error of some indexes is large; however, the absolute error is generally not high, and the values of the three indexes are low.
## Appendix A Packet Format  
In this section, we explain the packet format used to realize the routing mentioned in Section 5.1. The data packet needs to include an application identifier, a source node address, an identifier of the \(i\)-th-tier module to which the source node belongs (\(1\leq i\leq N\)), a destination node address, an identifier of the \(i\)-th-tier module to which the destination node belongs and the sender and receiver node addresses of the data packet. These pieces of information are included in the packet by encapsulating a packet recursively and adding the identifiers for the source and destination module in each tier. The packet format is shown in Figure 11. The source node encapsulates the data packet. When the \(N\)-th-tier packet reaches the destination module in the \(N\)-th tier, it is unpacked, and the \((N-1)\)-th-tier packet is then routed.  
The field \"long-distance link mode\" is a flag that shows whether the data packet exists on the path assigned to a virtual long-distance link and is routed to the end node of the virtual long-distance link. The field \"inter module mode\" is a flag that shows whether the data packet exists on the path assigned to a virtual link connecting modules in the first tier or higher. The field \"hop count on long-distance link\" is the hop count to the source node of a virtual long-distance link, and the field containing the \"path on long-distance link\" is a list of node addresses for forwarding this packet along the virtual long-distance link. These fields are used for the maintenance of long-link tables.  
If the data packet reaches the source node \(a\) of a virtual long-distance link and node \(a\) decides to forward it to destination node \(b\) of the virtual long-distance link, then node \(a\) encapsulates the packet to indicate that the packet is in long-distance link mode. Then, node \(a\) queries its long-link table and determines the next-hop node \(c\) for delivering the packet to node \(b\) along the virtual long-distance link. The packet format for this situation when the data packet exists on a virtual long-distance link is shown in Figure 12.
# Laser Transverse Modes with Ray-Wave Duality: A Review
## 1 Introduction
[PERSON] and [PERSON] further [43] proved that the use of a pair of astigmatism lenses can lead to elliptical precession, forming a Lissajous dot pattern under reentrant conditions. Herriott-type multiple-pass cells have been used in various experiments, such as optical delay lines [44], absorption spectroscopy [45], Raman conversion [46; 47; 48], and high-power laser systems [49]. Moreover, an analysis was developed by [PERSON] et al. [50] to illustrate the Lissajous dot pattern on a mirror [51]. We previously used the SU(2) quantum theory to derive the Lissajous geometric mode to be a superposition of different order HG modes combined with different longitudinal-order modes subject to the degeneracy [39; 41]. Recently, we have exploited the generalized wave-packet state representation to construct the ray-wave connection for the multiple-pass Lissajous dotted patterns [52]. Another important aspect of the wave-packet representation is to explore the damping effect that causes the asymmetric structure of the laser mode [53]. Asymmetric laser modes [54] have been employed in manipulating biological cells [55] and generating entangled states of quantum communication [56]. In this article, we systematically summarize the laser transverse modes with ray-wave duality in spherical resonators with and without astigmatism. We start from the eigenfrequency spectrum in an ideal spherical cavity to comprehend the important role of degeneracy in the formation of geometric modes. We then review the Gaussian wave-packet representation for unifying the HG eigenmodes and the planar geometric modes. We further extend the wave-packet representations to the case related to elliptical modes carrying orbital angular momentum. Next, we present an overview the fine structure of the characteristic frequency in a spherical cavity subject to astigmatism to obtain the parametric ray equations for Lissajous geometric modes. Based on the parametric ray equations, we clearly review the modification of the wave packet representation for considering the astigmatism effect. Finally, we review the damping effect on the generation of asymmetric geometric mode from the Gaussian wave-packet representation. This review is believed to provide important insights into the ray-wave duality in mesoscopic optics and laser physics. For example, the astigmatism of the laser cavity can lead the eigenfrequency spectrum to be topologically identical to the 2D commensurate harmonic oscillators. Consequently, the astigmatic degenerate cavities can be exploited to generate the geometric modes with transverse patterns to manifest the quantum wave functions.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 5 Experiments and Performance Measures
### Performance Measures
#### 5.2.1 Papr  
One of the key parameters for analyzing the performance of the transferred signal is its peak-to-average power ratio (PAPR), which indicates how extreme the peaks are in a waveform. This can be determined as the ratio of peak power to the average power of the transmitted signal [37]. In a multicarrier system, PAPR occurs when the different subcarriers do not correspond to a phase with each other at each point. It means that these subcarriers are different relative to each other for different phase values. The value of PAPR depends on the number of subcarriers involved as well as on the modulation scheme. It should be mentioned that high PAPR requires a high power consumption for transmitting a signal. In other words, it can be said that the efficiency of the power amplifier will be very low. On the other hand, a lower value of PAPR results in an increase in the flight time (battery life) of the vehicle. The PAPR of the transmitted signal is defined in the units of dB and it can be expressed as follows:  
\[PAPR_{dB}=10\log_{10}\left(\frac{\left|X_{max}\right|^{2}}{X_{ms}{}^{2}}\right), \tag{3}\]  
where \(X_{max}\) is the maximum value and \(X_{ms}\) is the mean square value of the signal. Here, PAPR is equivalent to the crest factor, as it is defined in decibels. Now, if we consider that \(X_{max}\) denotes the crest factor, it can be written by:  
\[X_{max}=\underset{k=0,1,\ldots,\ N-1}{max}\ X_{k}. \tag{4}\]  
The cumulative distribution function (CDF) of \(X_{max}\) is the probability that \(X_{max}\) will take a value less than or equal to \(x\). The CDF of \(X_{max}\) is described by [38]:  
\[\begin{array}{l}F_{X_{max}}(x)&=P(X_{max}\leq x)\\ &=\int_{0}^{x}f_{Y_{k}}(y)dy\\ &=\int_{0}^{x}\frac{y}{\sigma^{2}}e^{\frac{-y^{2}}{2\sigma^{2}}}dy,\\ \end{array} \tag{5}\]  
\[CDF=1-e^{\frac{-x^{2}}{2\sigma^{2}}}. \tag{6}\]  
Since we have the Equations (5) and (6), we can characterize the PAPR by using a complementary cumulative distribution function (CCDF). The CCDF of the PAPR is the probability of the PAPR which is higher than a certain PAPR value and it can be calculated as:  
\[CCDF=1-P(PAPR\leq x)=1-\left(1-e^{\frac{-x^{2}}{2\sigma^{2}}}\right)^{N}. \tag{7}\]
## 6 Therapeutic Approaches of Dentin Hypersensitivity  
The treatment of patients with DH entails a structured series of steps, comprising six key points [2]:  
1. Accurate diagnosis subsequent to recording the patient's dental history and initial clinical examination.
2. Identification of etiological and predisposing factors, with particular attention to dietary and brushing habits and their potential impact on erosion and abrasion.
3. Differential diagnosis to rule out other conditions presenting similar pain symptoms.
4. Therapeutic intervention for any coexisting conditions manifesting symptoms similar to DH.
5. Removal or mitigation of etiological and predisposing factors, accompanied by guidance on appropriate dietary habits and oral hygiene practices.
6. Dispensing patient instructions or implementing office-based treatments in accordance with the patient's requirements.  
Addressing the potential etiological factors contributing to DH requires comprehensive patient education. The patient should be instructed in proper brushing techniques, advised against using abrasive toothpaste (such as whitening dentifrices) [60] and firm toothbrushes, cautioned against excessive brushing force and advised to abstain from brushing for at least one hour after consuming acidic foods or beverages. Acidic substances, whether from dietary intake or gastric reflux, have been identified as triggers or exacerbators of DH [61]. Following the consumption of acidic foods or beverages, the consumption of alkaline drinks like milk or neutral options such as water is recommended. Practicing proper oral hygiene can also play a substantial role in maintaining healthy periodontal tissues and preventing gum recession, which can lead to root exposure and potentially trigger DH [61].
## 3 Monthly and Seasonal Variation Characteristics
### Seasonal Variation Characteristics  
During the daytime, the average hourly changes in MLH and SMEs across different seasons are shown in Figure 5. It can be seen in Figure 5a that the highest average hourly MLH (2.98 km) appeared at 17:00 BJT in spring and at 16:00 BJT in other seasons. Taking 16:00 BJT as the center point, the MLH presents a unimodal distribution, and the MLH decreases more rapidly in the afternoon than it increases before noon. The MLH is the highest in spring and summer, followed by autumn, and the lowest in winter.  
Figure 4: During the daytime, the average hourly change in the mixing layer height and various surface meteorological elements in different months. (**a**) Mixing layer height. (**b**) Atmospheric temperature. (**c**) Relative humidity. (**d**) Horizontal visibility. (**e**) Near-surface wind speed. The data at 8:00 BJT represent the monthly average of the whole hour, and the data of other hours are the same.  
In spring, firstly, the cold air activity is frequent and intense (maximum wind speed 2.68 m/s, 17:00 BJT). Secondly, it can be found in Figure 5b that the temperature rises rapidly from winter to spring after the underlying surface is heated by solar radiation, which can accelerate the freeze-thaw alternation of soil. Finally, the combination of the two roles can accelerate the exchange of land-air material and energy, resulting in rapid development of the ML (Figure 5a). Due to frequent sand-dust weather, visibility is the lowest in spring (Figure 5d). In summer, the precipitation in the study area is the most concentrated, and the surface evaporation is high, so the relative humidity at 15:00-18:00 BJT is similar to that in winter (Figure 5c). The characteristics of dust underlying the surface in summer have the most significant heating effect on the atmosphere (32.31 \({}^{\circ}\)C, 17:00 BJT), and the distribution characteristics of MLH in summer are similar to those in spring (Figure 5a). Fall is the transition season, and this season has the best visibility (Figure 5d). As the near-surface layer cools in fall, the atmosphere becomes more stable, and the development of the ML is gradually suppressed. In winter, the rapid cooling and unique basin topography enable the lower desert atmosphere to form an inversion layer [49], and coupled with soil freezing and wind speed slowing, the average MLH is the lowest in each season.