esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiment
### Feasibility Analysis of Uncertainty Modeling  
Currently, the uncertainty of both carbon tax rate and capacity has not been formulated in existing relevant studies. Therefore, it is necessary for us to demonstrate whether considering twofold uncertainty can improve GIMRP optimization. To verify such feasibility, we compare the uncertainty modeling of the GIMRP with deterministic modeling. We thereby built the following three deterministic scenarios. A deterministic model can be established by replacing the interval fuzzy parameters of the model proposed in Section 3 with their deterministic settings. The planned intermodal route in a deterministic scenario can be obtained by solving the deterministic model using the same software and algorithm.  
1. The receiver holds an optimistic decision-making attitude in the GIMRP. In this case, the minimum carbon tax rate (i.e., \(p^{-}\)) and the maximum capacities (i.e., \(\eta_{ijr}^{+}\) and \(\eta_{j}^{rs+}\)) are used by the IMO to make a plan. The TCs of the PIMR in this deterministic scenario are CNY 214,027. This is identical to the result of the uncertainty modeling when the optimization level is 0, which means that it yields an extremely low reliability in the actual transportation environment where uncertainty exists and results in unreliable transportation;
2. The receiver holds a pessimistic decision-making attitude in the GIMRP. In this situation, the maximum carbon tax rate (i.e., \(p^{+}\)) and the minimum capacities (i.e., \(\eta_{ijr}^{-}\) and \(\eta_{j}^{rs-}\)) are used by the IMO to make a plan. The TCs of the PIMR in this deterministic scenario are CNY 315,148, and this is identical to the result of the uncertainty modeling when the optimization level is 1.0;
3. The receiver holds a compromised decision-making attitude in the GIMRP. Therefore, the mean values (i.e., \(\frac{p^{-}+p^{+}}{2}\), \(\frac{\eta_{ijr}^{-}+\eta_{jr}^{+}}{2}\), and \(\frac{\eta_{j}^{rs-}+\eta_{jr}^{rs+}}{2}\)) of the upper and lower bounds of the corresponding interval fuzzy parameters are used by the IMO to plan the intermodal route. The TCs of the PIMR in this deterministic scenario are CNY 248,093, and this is the same as the result of the uncertainty modeling when the optimization level is 0.5.  
Above all, uncertainty modeling can present a series of optimization-level-sensitive intermodal routes that fully reflect the receiver's different decision-making attitudes in the GIMRP, and thus significantly improves the flexibility of the problem optimization and helps the IMO to plan the most suitable transportation scheme to match the receiver's requirements, which might change in the planning stage. Moreover, uncertainty modeling can help to avoid plans that yield a low RT by restricting the optimization level. Consequently, uncertainty modeling is able to improve problem optimization, and should be highlighted in transportation planning studies.
# Validation of Seismic Performance of Stainless Press-to-Connect Piping System under Cyclic Loadings
## 5 Validation and Verification of FE Large Scale Piping System Model
It can be observed that the high-fidelity simulation FE model was identical to the experimental test. Figure 9: High fidelity FE model of large-scale press-to-connect piping system. Figure 11 presents a validation of the deformation angles of the large-scale press-to-connect piping system. However, because the interference slip issue between the opening and closing modes during the cyclic test was not considered in the analytical FE model of the large-scale piping system, an insignificant error in the validation between the analytical and experimental results was observed in this study. This issue could also be caused by the springback phenomenon, which is not reflected in the analytical model. Even with this small issue, it was revealed that the characterization of the parameters and material properties of the high-fidelity FE model were extremely reliable. Additionally, it is interesting to note that the seismic sway standard (0.02 h) corresponding to KBC 2016 [15] and IBC 2015 [16] was satisfied in the press-to-connect elbow system of the large-scale piping system in both the experimental and analytical results. In addition, to verify the  
Figure 11: Validation of the deformation angles of the large-scale press-to-connect piping system: (**a**) lower elbow; (**b**) upper elbow. Figure 10: Validation of the von Mises stress of the large-scale press-to-connect piping system: (**a**) lower elbow; (**b**) upper elbow; (**c**) maximum and minimum stress distributions. seismic performance of the piping system, the following equations of the ASME boiler and pressure vessel code section III, subsection NB [17] for service level \(D\), can be applied. \[B_{1}\frac{P_{E}D_{o}}{2t}+B_{2}\frac{D_{o}}{2t}M_{E}\leq 3S_{m} \tag{3}\]  
where \(B_{1}\) and \(B_{2}\) are the primary stress indices for the product under investigation, \(D_{o}\) is the outside diameter of the pipe, \(I\) is the moment of inertia, \(M_{E}\) is the amplitude of the resultant moment owing to the weight and inertial loading obtained from reversing the dynamic loads, \(P_{E}\) is the pressure that is coincident with the reversing dynamic load, and \(S_{m}\) is the allowable design stress intensity value at a temperature consistent with the loading under consideration. The allowable design stress (\(S_{m}\)) for the stainless-steel material (SUS304) was 137 MPa, and \(3S_{m}\) was considered to be 410 MPa. Finally, considering ASME B and PV section III [17] for service level D of the press-to-connect piping system based on the NFPA 13 [10] condition, the stress level mentioned above can be experimentally and analytically satisfied. Therefore, the high-fidelity FE model of the piping system related to the data from the experimental test was verified according to seismic demands, and the FE model has a high application value for further research.
## 2 Experimental Program
### Concrete Core Components  
To manufacture the SCS slabs, three concrete mix designs are presented in Table 1. To evaluate the compressive and tensile strength of concrete in each SCS specimen, six cylinders with a diameter of 150 mm and a height of 300 mm were manufactured and tested under a hydraulic jack. For each mix, the average compressive and tensile strength of three of those cylinders were evaluated. The tensile and compressive concrete strength has been determined according to ASTM C293-08, BS EN 12390-1, 2 and 3 [33; 34; 35; 36]. The obtained outcomes are presented in Table 2. In Tables 1 and 2, SCS, NWC, LWC, and SLWC indicate steel-concrete-steel, normal weight concrete, lightweight lika concrete and steel fibre reinforced lightweight lika concrete, respectively.  
The normal weight aggregates were characterized according to the standards: ASTM C136 [37] for grading size analysis, ASTM C29 [38] for particles dry density, ASTM C127 [39] and ASTM C128 [40] for saturated surface dry density, determination of bulk density and the specific gravity of sand and gravel. In this study, lightweight and normal-weight aggregates were used in the SCS slabs. The used lightweight is illustrated in Figure 3. Furthermore, the physical and chemical properties of both kinds of aggregates are presented in Tables 3 and 4, respectively.  
To manufacture one of three SCS slabs, steel fibres with two bent ends were used in concrete as illustrated in Figure 4. The elastic modulus, density, the tensile strength, length and diameter of the fibres are 200 GPa, 7610 kg/m\({}^{3}\), 809 MPa, 50 mm and 1 mm, respectively. In this study, the steel fibres added to the concrete composition at a volume percentage of 1%.
## 5 Conclusions  
A novel integrated AMB controller consisting of parallel co-frequency ANF and cascaded multi-frequency improved DTNFs is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method for the ANF based on displacement stiffness perturbations is designed. This addresses the issue of the phase-lag angle \(\varphi(\varepsilon)\) introduced at the open-loop cutoff frequency by an ANF with an inappropriate choice of bandwidth factor \(\varepsilon\), which can adversely affect the phase stability margin \(\gamma\) of the MSR system. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method for cascaded improved DTNFs based on linear normalization is developed. This method addresses the problems of an excessive notch (resulting in too large a phase-lag angle) and an incomplete notch (insufficient notch depth).  
The effects of the bandwidth factor \(\varepsilon\), depth factor \(\zeta\), and width factor \(k\) on the stability of the MSR system are comprehensively simulated and analyzed in detail. Simulation and experimental results validate the effectiveness of the proposed method. The proposed algorithm is compared with an existing algorithm. At rotational speeds of 12,000 rpm, 15,000 rpm, 18,000 rpm, and 21,000 rpm, the spectrum only includes fundamental and harmonic components, with the suppression of co-frequency synchronous vibration maintained between \(-30.94\) dB and \(-30.56\) dB, exhibiting minimal fluctuation across different rotor speeds. At the rated speed of 24,000 rpm, the value of the rotor displacement converges from 0.08 mm (ANF-PSF algorithm) to 0.03 mm (proposed algorithm), a reduction of 62.50%. The convergence time of the algorithm decreases from 3.67 s (ANF-PSF algorithm) to 2.85 s (proposed algorithm), a reduction of 22.34%.  
Data curation, K.W.; Formal analysis, J.T.; Resources, J.Z.; Software, M.Z.; Validation, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62073010 and Grant 62103022, and the Beijing Municipal Natural Science Foundation under Grant 4222048.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Analysis of Organic Acids and Betaine  
Figure 2 shows the analysis results of malic acid, lactic acid, citric acid, succinic acid, total organic acids, and betaine in four dried and fresh oyster species. Organic acids are essential to oyster taste, and the presence of malic, citric, lactic, and succinic acids has been reported in live oysters [6; 8]. Lactic acid is an important indicator of the energy metabolism of living organisms and also contributes to taste [6; 8]. Malic acid has a slightly fruity, sour taste [6]. Succin acid contributes a strong salty and bitter taste at different concentrations in addition to its sourness [6; 17]. Citric acid adds a mild, crisp acidity that contributes to the taste of oysters [6]. As shown in the graph (Figure 2), various drying ways had an effective (\(p<\) 0.05) impact on organic acids in oysters, with malic acid (9.06 mg/g) exhibiting a significantly higher effect on the HAD group than on the other groups (Figure 2A). The VD group had the highest concentration of succinic acid but was not statistically different from  
HAD (Figure 2D). Meanwhile, the NSD organization has the most citric acid (Figure 2C) content (19.29 mg/g), and lactic acid tended to increase after oyster drying (Figure 2B). Drying significantly increased the total organic acid content of oysters compared with the control group (CK), with the HAD group having the highest total organic acid content of 25.68 mg/g and the VFD group having the least change in total organic acid content. This result was similar to a previous one in which organic acids in Lentinula erodes significantly increase after HAD [23]. In the early drying stages, variations in organic acids were relative to the activity of pyruvate kinase and pyruvate dehydrogenase enzymes associated with respiratory metabolism. The increase in temperature activated the relevant enzymes in the samples and increased the organic acid content through enzymatic reactions [23]. Organic acids as precursors may be involved in enzymatic reactions, thermal degradation, Maillard reactions, and heating oxidation, which are intimately relative to the synthesis and metabolism of aromatic components, amino acids, and esters [8; 24].
## 3 Results
### PCA Analysis
Figure 5: Biplot relative to the principal component analysis performed on marketable yield, yield quality indicator (YQ), water productivity for yield quality (WP\({}_{\text{YQ}}\)), irrigation water use efficiency for marketable yield (IWUE) and blue water requirement. The red crosses indicate the centroids of the categorical variable, irrigation scheduling methods (FarMan: irrigation scheduling managed by the farmer; IriMan: irrigation scheduling based on “Irriframe” model; PlaSoMan: irrigation scheduling based on the smart system that monitors both plant water content and soil moisture). The black crosses indicate the centroids of the experimental year (2005 and 2006). Red and blue symbols are relative to 2015 and 2016, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Selected Quantitative Variables** & **PC1** & **PC2** \\ \hline Marketable yield & 0.67 ** & \(-\)0.52 * \\ YQ & 0.51 * & 0.75 *** \\ WP\({}_{\text{YQ}}\) & \(-\)0.12 ** & 0.98 *** \\ IWUE & \(-\)0.93 *** & \(-\)0.12 ** \\ BWR & 0.94 *** & \(-\)0.03 ** \\ \hline Percentage explained variation & 50.0\% & 36.7\% \\ \hline Total percentage explained variation & 86.7\% \\ \hline YQ: yield quality indicator: WP\({}_{\text{YQ}}\): water productivity for yield quality; IWUE: irrigation water use efficiency for marketable yield; BWR: blue water requirement. Significance codes: *** = \(p\) < 0.001; ** = \(p\) < 0.01; ** = \(p\) < 0.05; ** = not significant. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation coefficients between quantitative variables and the first two principal components (PC), with indication of the explained variance.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data
### Algorithms for Material Data Fusion
#### 3.4.4 AI Algorithms for Material Data Fusion  
AI algorithms have already been studied in material knowledge discovering, e.g., predicting new materials by using massive sample data from experiments. [PERSON] et al. [60] proposed a factor-based framework that incorporated a-priori knowledge as constraints to solve the pattern decomposition problem. They also proposed a new pattern decomposition algorithm that aimed at solving a sequence of (mixed integer) quadratic programs. [PERSON] et al. [61] devised an attribute-based approach to recognition in X-ray scattering images, and demonstrated applications to image annotation and retrieval, which is helpful to the design of automatic analysis techniques for probing the physical structure of materials. [PERSON] et al. [62] provided the first publicly available dataset for the phase-map identification problem, along with a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation. They fulfilled the goal of stimulating the development of new computational techniques for the analysis of material data. [PERSON] et al. [63] introduced a novel deep regression network with individual residual learning that placed shortcut connections after each layer, so that each layer learned the residual mapping between its output and input. They showed that the proposed approach was able to provide significantly better prediction performance than state-of-the-art machine learning approaches.  
Actually, AI algorithms are the main-stream techniques, not only in material knowledge discovery, but also in other areas. However, this review focuses on material data fusion, and we have to clarify that material data fusion is the foundation of material knowledge discovery, because it determines the quality of material data that are processed by material knowledge discovery. On the other hand,many AI algorithms, such as classification models and deep learning models, can be considered in feature-level fusion and decision-level fusion.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 5 Simulations and Experimental Results
### Practical Implications  
Increasing the switching frequency does not have the desired effect of decreasing the necessary filter attenuation. The attenuation of a filter can be improved by around 10 dB by increasing the switching frequency from 10 kHz to 80 kHz. However, the switching losses will increase by a factor of 8. Therefore, it is desirable to reduce the noise generated. The noise produced by the input capacitor is proportional to the current ripple. Noise can be reduced by lowering the current ripple in the boost inductor. Continuous mode operating produces the least amount of noise. Another method of noise reduction is variable frequency operation. This spreads the noise spectra over a wider frequency range, reducing the peak loudness of the noise and thus the amount of filter attenuation required. Furthermore, it will complicate the control method in its practical implementation.  
We settled on a fixed switching frequency of 10 kHz to tackle this issue in the experimental work. This represents a compromise between the switching losses and the attenuation caused by the filter.  
In a large number of power factor applications, the voltage error amplifier cannot be used to compensate for input supply voltage fluctuations. This is because the output of the PFC is not pure DC; a small quantity of ripple still exists on top of the DC signal, which cannot be eradicated with a realistically sized bulk capacitor at high voltage and current levels. This ripple was slightly out of phase with the main signal that had been half-wave corrected. Therefore, the voltage error amplifier's input must be low-pass filtered to get rid of the ripple.  
Typically, in experimental work, this filter is designed to have a crossover frequency of approximately 20 Hz to eliminate sufficient experimental noise and enable the error amplifier to function properly.
## 2 Related Techniques
### Strategies for Surrogate Model  
#### 2.2.1 Polynomial Regression Model  
Polynomial regression (PR) [26] is prominently recognized as a favored surrogate model in the domain of multidisciplinary design optimization. Its core mathematical articulation is presented as follows:  
\[\mathrm{f(x)}=\beta_{0}+\sum_{i=1}^{m}\beta_{i}*x_{i}\ldots+\sum_{i=1}^{m} \sum_{j>=i}^{m}\beta_{j}*x_{i}x_{j}\;(i,j=1,2,...,m) \tag{1}\]  
where the unknown parameters are methodically organized to compose a column vector. \(x_{i}\) represents the \(i\)-th design variable, and \(f(x{i})\) symbolizes the predicted response associated with the \(i\)-th design variable. Furthermore, \(\beta_{i}\) stands for the undetermined weight coefficients. The encompassing equation can be structured as follows:  
\[\hat{f}=x\hat{\beta} \tag{2}\]  
To tackle this intricate scenario, the least squares approach is employed, culminating in the subsequent equation.  
\[\hat{\beta}=\left(x^{T}x\right)^{-1}x^{T}f \tag{3}\]  
The polynomial response method stands out for its robust continuity and consistency, making it adapt at diminishing the effects of digital noise. Moreover, it enhances the efficiency of the optimization search trajectory. Through the analysis of the coefficients of individual components in the polynomial, one can gauge the impact of each parameter on the cumulative system response. Notably, while higher polynomial orders can provide more nuanced fits, they might lead to overfitting. This occurs when models become excessively intricate in the pursuit of accommodating data, and it will potentially lead to misrepresentative assumptions. For example, in the context of a Taylor series expansion, while integrating more terms can refine the model's approximation, it simultaneously runs the risk of overfitting to anomalous data, thereby jeopardizing the fidelity of genuine response values.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 4 Experimental Results
### The Results
CSK and MCSK were also subjected to phase correction using PLL after downconverting the signal. Figure 16b shows the comparison between the artificial Doppler factor and the Doppler factor measured based on the obtained data. The input SNR measurement in the real communication channel was \(-4.7883\) dB. The performance in the simulation was somewhat good in the SNR environment of approximately \(-5\) dB, but the overall BER performance in the actual communication channel was substantially lower. The Doppler spread was shown to have a much greater impact on system performance. \[SNR_{input}=10\log\frac{P_{payload}-P_{guard}}{P_{guard}} \tag{19}\]  
where \(P_{payload}\) indicates the payload, that is, the power of the data part, and \(P_{guard}\) indicates the guard, that is, the power of the noise part. The performance of our proposed method, MCSS, is better than other signals, as shown in the experimental results in Tables 5 and 6. It performs similarly to the conventional MSS signal on a channel with artificial Doppler added. In terms of BER outcomes, the results are comparable. However, because the proposed MCSS approach has twice the data rate of MSS, experiments and simulations have shown that the proposed method is a better UWA communication method.