esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Diurnal Patterns of SIF  
For the diurnal patterns, wheat F760 (one-hour interval) averaged for each of the 15 days showed a typical hump shape with a steady increase in the morning and decline in the afternoon (Figure 5a). Averaged one-hour values of maize F760 across 15 days also showed similar patterns as that of hourly F760 of wheat (Figure 5d). As observed with seasonal patterns, diurnal patterns of F760 similarly followed the diurnal variations of SWR (Figure 5a,d), suggesting that SWR was also a main driver for the diurnal variation of F760. A similar effect of SWR in driving diurnal patterns of AF760 was also found from wheat measurements (Figure 5b). Nevertheless, the effect of SWR was indistinct for diurnal patterns of wheat Fy760 (Figure 5c). Such an indistinct effect of SWR was more prominent for diurnal patterns of maize AF760 and Fy760 (Figure 5e,f).  
Figure 4: Seasonal patterns of daily AF760 (first column) and Fy760 (second column) from wheat (left of dashed line in each sub-figure) and maize (right of dashed line in each sub-figure) in the four plots compared with the daily solar radiation (SWR). All measurements of wheat were collected from 28 March to 19 May 2017, while the measurements of maize were collected from July 29 to September 29 of 2017.  
Furthermore, the magnitude of diurnal F760, AF760, and Fy760 in each plot showed apparent fluctuations with the development of wheat and maize (Figure 5). Part of these fluctuations might have been due to the variation of SWR, such as the first two diurnal patterns (Figure 5). However, plant phenological stages might have also played an important role in driving the fluctuation because these values in the third diurnal patterns of wheat and fourth diurnal pattern of maize were much lower than that of previous patterns, even if homochronous SWR values were higher or approximate compared to SWR of previous patterns (Figure 5).  
We also found that the differences of wheat F760 and AF760 between each plot were indistinct in the first diurnal pattern but were distinct in the last two patterns (Figure 5a,b). Furthermore, the difference in wheat Fy760 between each plot were distinct for all three diurnal patterns. The differences of these diurnal SIF patterns showed distinguishable and decreasing hourly values in the order plot 1 \(>\) plot 2 \(>\) plot 3 \(>\) plot 4, particularly in the third 15-day period (12 to 21 May 2017) of wheat (Figure 5a-c). The decreasing order of diurnal SIF (F760, AF760, and Fy760) was consistent with the irrigation gradient applied over the four plots, thus suggesting that water stress also affected the diurnal SIF patterns. Without the influence of water stress, the magnitude of peak values of maize SIF declined from the second diurnal pattern as the season progressed. This continuous decrease of peak values agrees well with the progress of the phenological stages. On one hand, this further confirmed that plant phenological stages are another important factor driving the diurnal patterns of SIF. On the other hand, this revealed that peak SIF values at noon might provide meaningful information for SIF diurnal patterns.
## 1 Introduction
The PVA method, which was described above, is inefficient because it is difficult to apply; furthermore, it is inefficient owing to the influence of noise, according to [28]. For the direct sampling method, it is difficult to extract samples for a time variation; therefore, samples are artificially extracted. Although the FFT method uses kurtosis, skewness, crest factor, clearance, and shape factor as feature values, steady-state induction motors also exhibit three harmonic characteristics, which add some time burden to the NN [29]. Many papers on artificial intelligence (AI) technology have been reported to classify stator failures, rotor failures, and bearing failures, and studies on changes in speed or load should be conducted. Meanwhile, in order to minimize the time burden of AI learning and recognition, feature extraction methods such as LDA and PCA have been studied. [30] showed that the LDA method is superior to the stator failure diagnosis method and proved its usefulness. In [29], LDA is used for FFT as a diagnostic method according to the speed change, but in the method of LDA calculation, the LDA should be recalculated for each speed in the entire LDA operation. Furthermore, it causes a time burden on the application and recognition of AI's learning data. In addition, as many papers depend on FFT to apply LDA, a new method is needed. In this paper, a new method using the direct-quadrature (D-Q) synchronous min-max coordinate system is proposed for ITSC diagnosis, through which the effective LDA application method is explained. To demonstrate the performance of this method, the interaction between false acceptance rate (FAR) and false recognition rate (FRR) was verified graphically. This is a method for determining whether linear separation is well performed through the relational analysis of FAR and FRR; the effectiveness of this method can be proved when it is applied to future AI diagnosis technologies [31; 32]. The organization of this paper is as follows. The turn short, D-Q transformation, and min-max coordinate system are defined in Section 2, LDA is described in Section 3, and the FAR-FRR interaction graph is described in Section 4 to demonstrate the efficiency. Finally, experiments and results are discussed in Section 5.
## 5 Conclusions  
The paper focuses on the foreshortening effect of a rotating flexible link, which leads to the stiffening effect of the flexible link system. To study the rotating system in pick and place application, an SFF manipulator system including the effect of the flexible joint and the flexible link with tip payload is used as a research subject. The dynamic modeling is developed to analyze the features of natural frequency, which contributes to the model-based control of the SFF system.  
The Hamilton principle is applied to deduce the governing equations of motion, considering the stiffening effect as the potential energy. Under the assumption of the small deformation of each point in free vibration, the motion equation is simplified, and the feature matrix is obtained to determine the natural frequencies of the SFF system. The results are compared with those in the literature. The method presented in this paper is more precise since the lower frequency is identified. Moreover, the modal shapes corresponding to each natural frequency are illustrated in detail.  
Based on the constant rotational motion, it is concluded that the stiffening effect highly depends on the angular velocity. The stiffening effect of the flexible link becomes more prominent as a higher rotational velocity is adopted. Furthermore, the dynamic model of the SFF system, considering the stiffening effect, is studied using the finite element method with Hermite interpolation. The fundamental natural frequency results validate the conclusion that the system becomes stiffer when the foreshortening effect is considered in the modeling. Finally, the effect of three factors (i.e., angular velocity, mass of the payload, and length of the link) on the frequency are discussed respectively. The constant rotational velocity constraint used in the modeling will be dismissed to discuss the corresponding  
Figure 9: The relationship of the fundamental frequency and three factors with respect to the number of elements: (**a**) effect of rotational velocity, (**b**) effect of payload mass, (**c**) effect of length of flexible beam.  
features in the next study. Moreover, the geometrically-exact beam theory will be employed to study the stiffening effect of the flexible link system in the future.  
Conceptualization, L.T.; methodology, L.T.; investigation, L.T. and D.Z.; writing--original draft preparation, L.T.; writing--editing, D.Z.; supervision, L.T.; project administration, L.T.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. 531118010118).  
Not applicable.  
The authors declare no conflict of interest.
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 2 Design Methodology
### Optimization Algorithms  
Lately, metaheuristic algorithms have been used in numerous fields, such as engineering, economics, politics, and management, to solve complex real-life problems [16]. A proper balance between intensification and diversification elements is required to solve real-life problems effectively [17]. A carefully chosen optimization algorithm for a specific cause can effectively solve many nonlinear optimization problems [18].  
In this paper, four different optimization methods, SA [19], GA [20], PSO [21], and PS method [22], were used to optimize the remaining working element in the planar array. An optimization method that produces SNR similar to the initial radiation pattern was proposed at the end of the paper for a planar array failed radiation pattern correction.  
To optimize the weights, an objective function is created. In the objective function, SNR is calculated with (8) for a reference radiation pattern \(\eta_{G_{p}}\), and a current optimized pattern \(\eta_{G_{pnear}}\). The optimization will compare both SNR values and minimize the objective function. The objective function is defined as (10):  
\[f^{*}_{min}=\left(\eta_{G_{p}}-\eta_{G_{pnear}}\right)^{2} \tag{10}\]  
The planar array weights, \(w_{fx}\) and \(w_{fy}\), are vector quantities that require optimization techniques. Minimization in the objective function will return optimized weights of remaining working elements in the array that can generate an optimized beam pattern of the array in the presence of failure elements.
## 3 Case Analysis and Discussion
### Data Preprocessing
#### 3.2.2 Trajectory Data Preprocessing
vehicle \(i\) with continuous frames that can match the front and rear vehicle information is reserved as the research object of the car-following risk measurement. 5. For the lane-changing behavior, this study only chose vehicles with single lane-changing behavior as the research objects of the lane-changing risk measurement. 6. The Z-standardized scoring method was used to judge abnormalities of the screened data and remove the noise in the dataset. The Z-standardized scoring formula [43] is expressed by Equation (20). \[Z=\frac{y-\mu}{\sigma} \tag{20}\]  
where \(y\) represents the observed value, and \(\mu\) and \(\sigma\) represent the mean value and the standard deviation of the observed value, respectively.
# Seismic Damage Analysis of Box Metro Tunnels Accounting for Aspect Ratio and Shear Failure
###### Abstract  
We performed a series of inelastic frame analyses for single, double, and triple reinforced concrete box tunnels to investigate their unique damage mechanisms. We focused our interest on the influence of the aspect ratio of box tunnels and the occurrence of a shear structural failure. This is a follow-up study of [PERSON] et al. (2016), where damage analyses of box tunnels with an aspect ratio of unity were performed by considering only the flexural failure. We show that only flexural failures occurred in single box tunnels, whereas shear structural failures were produced at the inner column for double and triple box tunnels. The inner column failed in shear after flexural plastic hinges were formed at all four outer corners, and might cause a brittle collapse. A structural collapse was not observed in single box tunnels. An increase in the aspect ratio was demonstrated to cause associated increment in the seismic resistance. The moment and shear strains at which plastic hinges formed an increase by up to 5% and 20%, respectively. We proposed revised damage indices (DIs) corresponding to three damage states for single box tunnels, where DI is defined as the ratio of the elastic moment to the yield moment. The collapse damage state and corresponding DI for double and triple tunnels are newly presented.  
box tunnel; seismic damage; shear failure; aspect ratio; inelastic frame analysis Article
## 1 Introduction  
Leakage often occurs in operating tunnels due to the aging of sealing materials of tunnels' lining joints [1; 2; 3; 4; 5; 6]. Historically, leakages mainly start from joints of lining segments in tunnels supported by impervious concrete lining segments, since the whole tunnel lining consists of numerous longitudinal and circumferential joints, particularly, almost more than 90% occurs in circumferential joints [5; 6]. Once leakage develops, surrounding soils of a tunnel can be eroded by leakages of water, which leads to tunnel deformation and further exaggerated leakages [7; 8]. Consequently, continuous leakages have a significant potential impact on the operation of tunnels, which is especially adverse to the electrical equipment in it. Since leakages often happen at points of lining segments joints and have significant potential damage, the sealing and waterproofing capacity between joints of lining segments has attracted the extensive attention of researchers [6; 9; 10].  
In the past decades, some scholars have focused on the sealing performance of shield joints [11; 12; 13; 14; 15; 16; 17], having experimentally investigated the waterproof and mechanical properties of shield joints. [PERSON] and [PERSON] [18] presented a method to predict the water-leakage pressure of segmental joints in shield tunnels using a finite element solver. Meanwhile, a series of researches [5; 6; 10; 19; 20] were conducted on tunnel leakage and its induced hazards. All of those studies, to some extent, brought insights into the mechanisms that control the sealing performance of materials between joints, and presented hazards due to leakages through in-situ monitoring and numerical simulation. However, an analytical approach to describe the water -leakage failure process and reflect the main influencing factors of gasketed joint leakage is rarely reported.  
In order to understand the underlying process of shield joint leakage and the influential law of factors on it, a recently developed multi-scale contact mechanical model (the Persson model) was introduced to predict the leakage of shield lining joints. The model can reflect the joint gap (i.e., contact stress) and the offset (i.e., the length of the leak path), the service time (i.e., rheological properties of sealing materials), the roughness of sealing materials, and groundwater pressure in its parameters. Then, the model was developed to derive the calculation process of the self-sealing stress and critical leakage pressure of the sealing interface. Then, the mathematical model was applied to study the case of the second Dapu Road Tunnel in Shanghai. Finally, the affecting degrees of factors were evaluated and arranged, and the application conditions, advantages and disadvantages of this computed model were also elaborated.
# Soliton Content of Fiber-Optic Light Pulses
## 4 Methods to Verify Soliton Content
### The Inverse and Direct Scattering Transform
#### 4.1.3 Applications and Limitations of DST
Adaptations of IST to periodic boundary conditions (periodic IST, also called finite gap theory) have been given in [75; 76; 77] where the obtained eigenvalues are not necessarily interpreted as solitons, but rather as the representation of linear and nonlinear modes [78; 79; 80]. Like the nonperiodic IST, this periodic case gained renewed interest in recent years. Different theoretical and numerical schemes have been developed with a view toward the application of nonlinear data transmission [63; 66; 67; 81; 82; 83]. A variant of the periodic DST based on the artificial periodization of  
Figure 10: Energy and frequency eigenvalues of a fundamental soliton obtained using the DST method. The soliton was perturbed by Raman self-frequency shift (blue, solid, Raman response time: \(T_{\mathrm{R}}=0.03\ T_{0}\)), third-order dispersion (orange, solid, TODparameter: \(\beta_{3}=0.5\ T_{0}\beta_{2}\)), and linear loss (green, dashed, loss length: \(L_{\alpha}=200\ L_{\mathrm{D}}\)). field components was used to analyze rogue events and numerically to find the eigenvalue spectra of the breather solutions of Section 2.2[84]. Another approach to characterize rogue events via DST was taken in [85; 86]. We have here concentrated on pulse-like initial conditions. Recently, the scope was extended to integrable turbulence [87] with a view towards understanding rogue waves. Initial conditions for the NLSE were generated by superimposing random perturbations in both amplitude and phase on a continuous wave; the resulting field was characterized by its eigenvalue spectrum.
## 2 Materials and Methods  
### Dynamic Model of Prestressed Modal Analysis  
A mode is the intrinsic vibration characteristic of a mechanical structure. To alter the mode of the structure, one needs to focus on aspects such as the mass, damping, and stiffness distribution of the structure. As gas turbines must operate under conditions requiring high reliability, where the entire rotor system must function within the elastic range, the entire RSS base is treated as an elastic body, and any nonlinear dynamic characteristics are omitted in the analysis. The typical dynamic equations are as follows [28]:  
\[[M]\big{\{}\ddot{x}\big{\}}+[C]\big{\{}\dot{x}\big{\}}+[K]\big{\{}x\big{\}}=[F (t)], \tag{1}\]  
where \([M]\) represents the mass matrix; \([C]\) represents the damping matrix; \([K]\) represents the stiffness matrix; \([x]\) represents the displacement vector; \(\big{\{}\dot{x}\big{\}}\) represents the velocity vector; \(\big{[}\ddot{x}\big{]}\) represents the acceleration vector; and \([F\{t\}]\) represents the time-dependent external force vector.  
In conventional modal analysis, F(t) = 0, and the damping effect is ignored. The typical modal equation is as follows:  
\[[M]\big{\{}\ddot{x}\big{\}}+[K]\big{\{}x\big{\}}=0, \tag{2}\]  
As the RSS base carries bearing loads, which can affect the natural frequency of the support plate base, the influence of bearing loads must be taken into account during modal analysis. This research treats the bearing load as preloading force and conducts a prestressed modal analysis. The steps for performing prestressed modal analysis are as follows:  
1. First, a static linear analysis is conducted according to Equation (3): \[[K]\big{\{}x\big{\}}=\{F\},\] (3) where \(\{F\}\) represents the bearing load vector.
2. Next, the stress stiffness matrix is determined through structural analysis and used as the additional stiffness matrix, as shown in Equation (4): \[[\sigma_{0}]\rightarrow[S],\] (4)
3. Lastly, the additional stiffness matrix is incorporated into the stiffness matrix in the modal analysis, as shown in Equation (5), and conventional modal analysis is conducted: \[[M]\big{\{}\ddot{x}\big{\}}+[K+S]\big{\{}x\big{\}}=0,\] (5)  
In order to account for the effects of preload and bearing loads in modal analysis, static finite element analysis was performed prior to modal analysis to extract the stresses caused by displacement and bearing loads. The stress stiffness matrix in Equation (4) was then adjusted, meaning that prestress was applied to strengthen the modal analysis approach. As a result, the structural mode shapes and their corresponding natural frequencies were determined.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods
### Calculation of the Value of the Variables Involved in the Charging Process  
This section will show how to calculate the values of the resistance and capacitance of the charger, (R\({}_{1}\) and C\({}_{1}\)), resistance and inductance of the smoothing inductor, (R\({}_{\text{L}}\) and L) and the initial voltage of the charger, U\({}_{01}\). It will be assumed that, upon arrival of the vehicle at the charging station, the bank of SCs of the charger presents it maximum voltage, U\({}_{01}\), and is ready for charging. For this to occur, the estimated time that will elapse between charging one vehicle and the next one must be carefully studied, so that the charger has time to reach its maximum voltage. This time will depend on other variables, which are not the subject of this study.  
The variables of the vehicle's bank of SCs, (R\({}_{2}\) and C\({}_{2}\)), will be assumed to be known, as well as its rated voltage, U\({}_{2N}\); however, its initial voltage, U\({}_{02}\), may take any value less than or equal to U\({}_{2N}\). Following the recommendation of the SCs' manufacturers, who advise that the cells should never be discharged below 50% of their rated voltage, it will be considered that the initial voltage of the vehicle's SC bank will take values within the following range: \(0.5\text{U}_{2N}\leq\text{U}_{02}\leq 0.8\text{U}_{2N}\). The values of R\({}_{2}\) and C\({}_{2}\) shall be assumed to have been selected by the vehicle's manufacturer so that, with one prior tour at the time of charging, their voltage is never above \(0.8\text{U}_{2N}\). This assumption is very conservative as it implies charging when only 20% of the available energy has been consumed, something that is very unlikely.
## 4 Results and Discussion
[49] studied the effect of the thermosonic treatment (24 kHz, 400 W, 120 \(\upmu\)m amplitude at 63 \({}^{\circ}\)C for 30 min) on the microstructure of fat globules in whole milk and found that ultrasound reduced the size of milk fat globules by up to \(<\)1 \(\upmu\)m, promoting the fusion of casein and serum proteins. The ultrasound treatment reduced the size of the ice crystals while whipping the emulsion, resulting in a faster formation of the three-dimensional lattice structure of the cream [50]. The application of ultrasound in ice cream production is useful in crystallization processes because it can control crystal growth. In addition, since ice cream contains air up to 50% of its volume, ultrasound can be an effective degasser that can change the texture of ice cream [51]. The thermal treatment used in the ultrasound application results in less organoleptic and nutritional changes in the products compared with the traditional heat treatment, and the reduction in thermal energy consumption lowers the cost of the product [52]. However, long duration, high temperature, and inadequate ultrasound amplitude may lead to a reduction in the physicochemical and textural properties of ice cream; therefore, further research on the selection of appropriate conditions for ultrasound pasteurization of ice cream mixtures (time, temperature, and ultrasound amplitude) is needed to improve the quality of ice cream.