esa-sceva/satcom-chunk-collection huggingface dataset:
# A Kriging Model Based Finite Element Model Updating Method for Damage Detection
## 2 Kriging Based Model Updating Method
### Construction of Kriging Model
According to the maximum likelihood function method, one can get:  
\[\hat{\mathbf{\beta}}=(\mathbf{F}^{\mathrm{T}}\mathbf{R}^{-1} \mathbf{F})^{-1}\mathbf{F}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{Y} \tag{5}\] \[\hat{\mathbf{\sigma}}^{2}=\frac{(\mathbf{Y}-\mathbf{F}\mathbf{ \beta})^{\mathrm{T}}\mathbf{R}^{-1}(\mathbf{Y}-\mathbf{F}\mathbf{\beta})}{n}\]  
Based on this, the logarithmic form of the maximum likelihood function can be written as:  
\[\ln(L)\approx-\frac{m}{2}\ln(\hat{\mathbf{\sigma}}^{2})-\frac{1}{2}\ln\left| \mathbf{R}\right| \tag{6}\]  
The maximum value of the function above is solved by the genetic algorithm to determine the value of the decay rate \(\theta_{k}\) on different dimensions. At this point, a Kriging model linking the sample point and the response is constructed. The next step is to predict the value of new points. For any point \(\mathbf{x}_{0}\), following the principle that the predicted value for the point continue to maximize the augmented likelihood function of both the sample point and the new point, the predicted response value can be obtained by:  
\[\hat{y}(\mathbf{x}_{0})=\mathbf{f}^{\mathrm{T}}(\mathbf{x}_{0})\hat{\mathbf{ \beta}}+\mathbf{r}^{\mathrm{T}}(\mathbf{x}_{0})\mathbf{R}^{-1}(\mathbf{Y}- \mathbf{F}\hat{\mathbf{\beta}}) \tag{7}\]  
And the mean squared error (MSE) of the predictor can also be calculated to estimate the accuracy of the predicted value, which is denoted by \(\hat{s}^{2}(\mathbf{x})\):  
\[\hat{s}^{2}(\mathbf{x})=\sigma^{2}\left[1-\left\{\mathbf{f}^{\mathrm{T}}( \mathbf{x}),\mathbf{r}^{\mathrm{T}}(\mathbf{x})\right\}\left[ \begin{array}{cc }0&\mathbf{F}^{\mathrm{T}}\\ \mathbf{F}&\mathbf{R}\\ \end{array} \right]^{-1}\left\{ \begin{array}{cc}\mathbf{f}(\mathbf{x})\\ \mathbf{r}(\mathbf{x})\\ \end{array} \right\}\right] \tag{8}\]  
where \(\mathbf{r}^{T}(\mathbf{x}_{0})\) is a row vector of correlation function between the new point and each sample point:  
\[\mathbf{r}^{\mathrm{T}}(\mathbf{x}_{0})=\left[R(\mathbf{x}_{0},\mathbf{x}_{1} ),\cdots,R(\mathbf{x}_{0},\mathbf{x}_{n})\right] \tag{9}\]  
It is worth noting that when the value of the \(i\)th sample point is predicted, since \(\mathbf{r}^{\mathrm{T}}(\mathbf{x}_{i})\mathbf{R}^{-1}\) equals the \(i\)th order unit vector (whose elements are all zero except the \(i\)th element which is one), Equation (7) becomes:  
\[\hat{y}(\mathbf{x}_{i})=\mathbf{f}^{\mathrm{T}}(\mathbf{x}_{i})\hat{\mathbf{ \beta}}+y_{i}-\mathbf{f}^{\mathrm{T}}(\mathbf{x}_{i})\hat{\mathbf{\beta}}=y_{i} \tag{10}\]  
which shows that the Kriging model predicts the real response value at the sample point. That is why it can be considered an interpolation technique. Interested readers can refer to references [37; 38] for a more detailed presentation of the theory, and to reference [36] for a more detailed formulation, based on the idea of best linear unbiased predictor.
## 3 Model Predictions
### Sensitivity Study Regarding the Recycled Aggregate Morphology  
So far, we considered that the aggregates are distributed evenly among the three recycled aggregate classes. Herein, we study the sensitivity of the strength predictions regarding changes in the volume fractions of the three aggregate classes. First, we consider that 60% of recycled aggregates (by volume) are attributed to one class, while the remaining 40% are equally shared by the remaining two classes. Related strength results are very insensitive with respect to changes in the class volume fractions. Noticeable differences occur only at very high hydration degrees (see Figure 8a). This shows that the quantities of class volume fractions are of minor importance.  
The observed insensitivity calls for yet another study, whether or not it is necessary to consider all three recycled aggregate classes. Therefore, the following three limit cases are studied:  
1. The volume fraction of old plain aggregates (class I) amounts to 65%, allowing for maintaining 35% of old cement paste (according to composition of the benchmark concrete), which is then attributed to old mortar aggregates (class II), i.e., the class volume fractions read as \(f_{\rm I}=0.65\), \(f_{\rm II}=0.35\), \(f_{\rm III}=0\).
2. All aggregates are considered to be old mortar aggregates (class II), i.e., \(f_{\rm I}=0\), \(f_{\rm II}=1\), \(f_{\rm III}=0\).
3. All aggregates are considered to be old covered (class III), i.e., \(f_{\rm I}=0\), \(f_{\rm II}=0\), \(f_{\rm III}=1\).  
For pastes with high \((w/c)_{ncp}\)-ratios, and, at early and medium ages, respectively, the modeled strength for all three cases is very similar (see Figure 8b). Compared to the strength evolutions of the benchmark concrete, the strength of recycled concrete is almost identical when maximizing the content of old plain aggregates (case 1), it is slightly higher when considering covered recycled aggregates only  
Figure 7: Uniaxial compressive strength of the benchmark recycled concrete as function of the ITZ porosity factor \(F_{por}\) for different hydration degrees: (**a**) for \((w/c)_{ncp}=0.35\), and (**b**) for \((w/c)_{ncp}=0.5\). The solid lines represent the material strength, and the dashed lines represent the theoretical strength in case of \(Z_{opa}^{ncp}\)-failure only.  
(case 3), and it is notably higher in case of considering only mortar aggregates only (case 2). However, for mature concretes, in particular for small \((w/c)_{ncp}\)-ratios, remarkable differences are observed. Morphology case 2 results in the smallest macrostrength, and case 1 results in the highest one. The micromechanical reason for the latter is related to the fact that, at such mature stages, when the old paste is much more compliant than the new paste, the ITZs around old covered aggregates and old mortar aggregates are prone to failure (see the previous sections). However, old covered aggregates do not exist in case 1 and old \"mortar\" aggregates consist of old cement paste only; thus, \(\mathcal{I}_{\text{ocn}}^{\text{ocp}}\)-failure and \(\mathcal{I}_{\text{ocn}}^{\text{ocp}}\)-failure is impossible.
## 4 The Development of 3 SMVI Software and Integration
### The Algorithm for Edge Labelling  
To compute the roundness error of each circular part individually, it is necessary to first identify the edge pixels of each part from the captured images. This can be achieved through the use of an edge labeling algorithm, which generates a labeled image from the edge image and assigns a unique color label to the pixels of each circular part. The algorithm follows a general process described in the reference and comprises several steps [43], including the following steps:  
1. The edge-pixel image is encoded using the run-length algorithm;
2. After scanning the runs, each run is given a preliminary label. The label equivalent is then entered into a local equivalent table;
3. The classes that are resolved have equivalence;
4. Finally, based on the resolved equivalence classes, the runs are given labels.
# A Disc-Type High Speed Rotary Ultrasonic Motor with Internal Contact Tenth
## 6 Test of the USM's Lifting Force  
The experiment setup for the rotor system lifting force measurement is shown in Figure 17. The lift-weight ratios of six different propellers were tested under the input voltage of 20 V\({}_{\text{PP}}\), and the result is shown in Table 3. In this table, total mass refers to the mass of the propeller and the USM. The propeller needs to match the load characteristic of the motor to gain the biggest lift force. Propeller no. 6 provides the biggest lift-weight ratios among them. The lifting force of it under different voltage is shown in Figure 18. The maximum lifting force generated by this USM was measured as 46.1 mN, which is 2.35 times bigger than its weight. It proves this rotor system is capable to carry additional load. Figure 19 shows a hover flight test result of the piezoelectric rotor system when installed into a linear guide rail, which indicates that the piezoelectric rotor system has good attitude stability. When the drive voltage is close to 45 V\({}_{\text{PP}}\), the whole device starts to take off, and when the drive voltage reaches 50 V\({}_{\text{PP}}\), the whole rotor system can hover stably in the air. Power consumption of the rotor system was 435 mW.  
Figure 16: Experiment setup for USM’s output measurement.  
Figure 15: (**a**) Rotational speed of the USM versus preload; (**b**) stalling torque of the USM versus driving voltage.  
Figure 19: Hover flight test of the piezoelectric rotor system. (**a**) Stationary state. (**b**) Hovering state.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Propeller No.** & **Total Mass/g** & **Rotation Speed/rpm** & **Lifting Force/mN** & **Lift-Weight Ratio** \\ \hline
1 & 2.36 & 2145 & 0.245 & 0.0106 \\
2 & 3.17 & 1910 & 0.519 & 0.0167 \\
3 & 2.89 & 2112 & 0.696 & 0.0246 \\
4 & 3.06 & 1879 & 1.303 & 0.0435 \\
5 & 2.4 & 2081 & 0.353 & 0.0150 \\
6 & 2.95 & 1760 & 3.910 & 0.1350 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The lift–weight ratios of different propellers.  
Figure 17: Experiment setup for the test of rotor’s lifting force.  
Figure 18: Rotor’s lifting force versus driving voltage.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 3 Simulation and Realization of the System
### Simulation Results  
We simulated the quadrature modulation and coherent demodulation using the MATLAB software, where an optimized signal was used for the baseband symbol. Figure 8 presents the simulation results, where A represents the baseband symbol signal transmitted through the in-phase channel, B is the baseband symbol signal transmitted via the orthogonal channel, and C is the quadrature-modulated output signal. The envelope of the output signal after the modulation was found to be constant, and in agreement with the expected results.  
Figure 6: Block diagram of the quadrature modulation system.  
Figure 7: Block diagram of the partial-respond coherent demodulation. NCO: numerical control oscillator.  
Figure 9 plots the simulation results of the coherent demodulation, where A indicates the transmitted signal corrupted with additive white Gaussian noise (AWGN), which is the input of the receiver, B is the baseband signal demodulated in the in-phase channel, and C is the baseband signal demodulated in the orthogonal channel. The black solid and green dotted lines in D represent the baseband signal demodulated in the in-phase channel and the baseband signal through the matched filter, respectively, while the arrows show the best sampling time [1; 15; 16].  
Figure 10 compares the BER performance of the optimized baseband signals achieved with and without minimum cross-correlation function [17]. We may notice from this figure that for small \(E_{b}/N_{0}\)s, the BER performances of the optimized baseband with and without minimum cross-correlation function restriction are close. However, when \(BER=10^{-3}\), the \(E_{b}/N_{0}\)s of the optimized baseband signal with and without minimum cross-correlation function restriction are 6.8 dB and 7.0 dB, reducing 0.2 dB in the requirement of \(E_{b}/N_{0}\). For, \(BER=10^{-4}\), the \(E_{b}/N_{0}\)s of the optimized baseband signal with and without minimum cross-correlation function restriction are 8.3 dB and 8.6 dB, respectively, which reduces 0.3 dB in the requirement of \(E_{b}/N_{0}\). The results show that the optimized baseband  
Figure 8: Simulation results of the partial-respond quadrature modulation. (**A**): The baseband symbol signal transmitted through the in-phase channel; (**B**): The baseband symbol signal transmitted via the orthogonal channel; (**C**): The quadrature-modulated output signal.  
Figure 9: Simulation results of partial-respond demodulation system. (**A**): The transmitted signal corrupted with additive white Gaussian noise; (**B**): The baseband signal demodulated in the in-phase channel; (**C**): The baseband signal demodulated in the orthogonal channel; (**D**): The baseband signal through the matched filter and best sampling time.  
signals with minimum cross-correlation function have superior spectral efficiency and the BER performance can be improved by introducing the minimum cross-correlation function restriction.
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 3 Results and Discussion  
### Effect of Different Vibration Stimulation Impulses  
In the acoustic vibration test, choosing a proper vibration stimulation impulse to induce vibration or generate sound waves is one of the key parameters to be considered. Three steel balls, with diameters of 3.5, 4.0, and 4.7 mm, and masses of 0.17, 0.25, and 0.45 g, respectively, were chosen to be dropped at the same height. The test results are shown in Figure 3.  
In Figure 3, it can be observed that for both the hemp/epoxy and ramie/epoxy composites, when the mass of the impact steel ball increased, the acoustic wave spectra were highly similar in shape, but the amplitudes of the peaks were proportionally increased. This is because, with the increased masses, the impact energy applied to the specimen was raised.  
Figure 3: Frequency spectra induced by different impulses in composites: (**a**) hemp; (**b**) ramie. Acoustic vibration performance parameters under different vibration impulses: (**c**) hemp; (**d**) ramie.  
By analyzing the peak frequencies of the spectra, the average values of the acoustic vibration parameters, the specific dynamic modulus \(\mathrm{E_{d}}/\rho\), the acoustic radiation damping coefficient R, and the acoustic impedance Z were obtained, as shown in Figure 3c,d. The three acoustic vibration parameters of either the hemp- or the ramie-reinforced epoxy composites, obtained by stimulation with the steel balls of different masses, were virtually the same. This is because the peak positions did not change with the change in the impact energy, which means that one may use almost any reasonable mass of the impactor for this type of test, without significant variation in the resultant acoustic vibration parameters.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 5 Radiotherapy Resistance  
CSCs may escape death from radiotherapy exposures through different mechanisms: the control of the cell cycle, elevated free-radical scavenging, more efficient DNA repair, the activation of autophagy, and protection by the microenvironmental niche [109; 110; 111; 112; 113]. Different biological factors may also enhance the radioresistance in HNSCC: tumor size, high density of CSCs, negative HPV status, and other parameters such as tumor hypoxia [114].  
CD44 is a well-known CSC marker, and it has been suggested that it is responsible for radiotherapy resistance and poor overall survival [115]. It was reported previously that the CD44 high/EGFRlow phenotype is associated with therapy resistance [116; 117]. Other well-known proteins that have been associated with radioresistance in HNSCC are integrin \(\beta\)1 [118], Oct4 [119], and ALDH [120].  
We will focus here on the recent studies reporting novel mechanisms of radiotherapy resistance. [PERSON] et al. recently reported that CD98 expression might be a predictive marker of resistance to radiotherapy in HNSCC as the response to chemoradiotherapy or bioradiotherapy was lower in the high CD98 expression group [121]. Also, CD98 heavy chain has been shown to be a regulator of HNSCC radiosensitivity as its high expression levels increase radioresistance in vitro and in vivo [122].  
Another interesting study on the radioresistancestant SQ20B cell line highlighted CSCs characteristics: low EGFR expression, cetuximab-resistant, and highly migratory. However, carbon irradiation in combination with cetuximab strongly inhibited the invasion of SQ20B CSCs signifying that carbon ion irradiation is a better potential choice of therapy for CSCs than photon irradiation [123]. Another study involving the same cell line revealed that the combination of irradiation, cetuximab, and Bcl-2 inhibitor can be a prosperous, novel CSC-targeted therapy. This drug combination inhibits cell proliferation, invasion/migration, and resistance to apoptosis in both 2D and 3D models. Also, this combination therapy effectively delayed tumor growth and improved in vivo lifespan in a nude mouse model with a heterotopic tumor xenograft [124].  
Recently, Glycoprotein nonmetastatic melanoma protein B (GPNMB)-positive cells in HNSCC have been shown to enhance sphere formation, invasion, and migration and were more resistant to chemoradiation and bio-radiotherapy, indicating that this protein might be involved in radiation resistance acquisition [125]. Also, a high expression of GPNMB in patients was associated with poor prognosis.
# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 4 Experimental Results
### Precision Analysis  
Delving into the intricacies of trimodal emotion detection, this study extends its focus to the precision aspect in the evaluation of text, audio, and video components within the IEMOCAP and CMU-MOSI datasets. Precision in this context is a measure of how accurately the trimodal emotion identification model discerns and categorizes emotions, taking into account the variances in emotional expressions manifesting through visual, auditory, and textual forms.  
A crucial part of this comparative analysis involves assessing the diversity, quality, and annotation methodologies of the datasets. These elements are instrumental in understanding the nuances and the resulting precision in sentiment recognition.  
The detailed comparison of precision levels achieved by the models using IEMOCAP and CMU-MOSI datasets is systematically presented in Table 7 and Figure 9. The findings indicate a marginally superior precision in the CMU-MOSI dataset (0.94%) as compared to IEMOCAP (0.93%).  
This precision comparison not only highlights the subtle yet significant differences in the datasets but also underscores the importance of detailed and nuanced evaluation in TERsystems. An analysis such as this is vital to developing more accurate and reliable sentiment recognition models.  
The comparative analysis of precision metrics significantly highlights the effectiveness of different computational approaches in emotion recognition. In Table 8 and Figure 10 we present the precision for both the proposed and existing methods. The CA-WGNN achieved a remarkable 92%, in comparison with the existing methods of SVM, which attained 76%, GBM attained 72%, and VC(LR-SGD) attained 78%. This demonstrates that the suggested process achieves higher precision rates than the existing methods, underlining the superiority of the CA-WGNNapproach in accurately categorizing emotional states. The results reinforce the notion that leveraging advanced algorithms like CA-WGNN significantly enhances the precision of emotion recognition, setting a new benchmark for future research in this domain.
## 1 Introduction  
Permanent magnet generators these days are manufactured in a wide range of sizes and for numerous applications. They have a wide range of applications including as auxiliary generators mounted on the engines in automobiles [1], motorcycles [2], tidal [3] and aircraft [4]. These types of generators are electronically commutated compared to wound field generators, making power control feasible. Also, the development of high-energy rare earth magnets and NdFeB magnets adds an advantage towards improvement in generating capability electro-magnetically [5]. New types of rotary [6; 7; 8] and linear machines [9; 10] are feasible today due to advancements in permanent magnets and the advanced control strategy. However, the biggest challenge in power generation is that the electrical parameters are highly limited by load fluctuations. The use of a high-power generator such as the double-stator generator [11; 12] and double rotor [13] machines increases the efficiency even for short-duration applications [14]. Double-stator machines as in Reference [11] with reduced weight to decrease the losses to a greater extent through the removal of heat caused by high density in the electric loadings. References [12; 13; 14] introduce the double rotor more typical for short-duration characteristics which shows better performances. Magnetic circuit design variations such as in References [14; 15; 16] are possible, but are developed without dynamic load considerations. The idea of the power line estimator for dynamic operation as used in solar tracking is the motivation of this research. There are other estimators in the literature such as those used in thermo-electric power generation [17], hybrid power generation systems [18; 19], improved hybrid with fault studies [20; 21; 22], solar tracking [23], and wind operations [24; 25]. This proposed estimator is slightly different, as the case where it depends on the load (dynamic) is formulated through a current equation that brings the best fit curve characteristics of the estimator.  
In this work, the power characteristics of the double-stator generator developed [15] using the design procedure [16] together with the load condition are simulated to derive the line of trace (estimator). The values derived from the power mapping are modeled as a mathematical equation. The new estimator control strategy method we presented in this work demonstrates that the estimator used in the dynamic loading fluctuation is highly beneficial, particularly for systems with short-duration generation requirements [14]. The power generation using the double-stator generator, the mathematical formulation of the control strategy used in this approach, its experimental implementation and the result analysis are presented in the subsequent sections.
## 2 Materials and Methods  
### Preliminaries  
#### 2.1.1 Tensor Decomposition  
Tensor can be said to be a higher-order extension of matrix. In this paper, we use \(\mathrm{Y}\in\mathbb{R}^{n_{1}\times n_{2}\cdots\times n_{d}}\) is the tensor, where \(\mathrm{d}\) is the order. We use \(y_{i_{1}i_{2}\cdots i_{d}}\) to represent the component of the third-order tensor \(\mathbf{Y}\), where \(i_{m}=1,\;2,\cdots,n_{m}\), and \(m\) is called the module of tensor \((m=1,2,\cdots,d)\). The more commonly used tensor decomposition method is CPD, where the tensor \(\mathbf{Y}\) can be further expressed as the sum of \(\mathrm{r}\) ranks, for example, \(\mathbf{Y}\approx\sum_{j=1}^{r}Q_{j}^{1}\circ Q_{j}^{2}\circ\cdots\circ Q_{j}^{d}\). Each component can be expressed as \(y_{i_{1}i_{2}\cdots i_{d}}\approx\sum_{j=1}^{r}q_{i_{1}i}^{1}q_{i_{2}j}^{2} \cdots q_{i_{d}i}^{d}\), where \(Q_{j}^{m}=\left(Q_{1j}^{m},\cdots,Q_{n_{d}j}^{m}\right)^{T}\). For non-convex problems in tensor processing [26], we mainly solve them by regularization method.  
\[\xi\Big{(}Q^{1},\cdots,Q^{d}\Big{|}\mathbf{Y}\Big{)}=\sum_{(i_{1}i_{2}\cdots i_{d} )\in\Omega}\big{(}y_{i_{1}i_{2}\cdots i_{d}}-\hat{Y}_{i_{1}i_{2}\cdots i_{d}} \big{)}^{2}+\theta\sum_{k=1}^{d}\parallel Q^{m}\parallel_{F}^{2} \tag{1}\]  
where \(\hat{Y}_{i_{1}i_{2}\cdots i_{d}}=\sum_{j=1}^{r}q_{i_{1}i}^{1}q_{i_{2}j}^{2} \cdots q_{i_{d}i}^{d}\) is the estimated value of tensor component \(\parallel\cdot\parallel_{F}\) represents Frobenius parameterization. In our research, tensor decomposition technology is mainly used to analyse the internal relationship of hydrological longitudinal data.