esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Volatile Compounds (VOCs) and Sensory Attributes
#### 2.2.1 GC-MS and Sensory Attributes
Sensory defects that were observed in the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compounds** & **Class** & **Confirmed by** & **F-Ratio** & & **Class** \\ \hline  & & & & **Monovarietal** & **Commercial/Blended** \\ \hline Ethyl acetate & Ester & Std; MS & 95.76 & 0.638 & 5.465 * \\ Methyl acetate & Ester & Std; MS & 80.37 & 0.436 & 2.994 * \\
3-Ethyl-1,5-cataline III & Hydrocarbon & MS & 64.32 & 4.260 * & 1.853 \\
2-Methylbutanal & Aldehyde & Std; MS & 38.87 & 0.181 & 0.715 * \\
3-Ethyl-1,5-cataline I & Hydrocarbon & MS & 37.64 & 0.957 * & 0.511 \\ (_Z_)-2-Hexenal & Aldehyde & Std; MS & 27.14 & 1.696 * & 0.591 \\
3-Methylbutanol & Alcohol & Std; MS & 22.79 & 0.477 & 1.229 * \\
2-Phenylethanol & Alcohol & Std; MS & 18.50 & 0.181 & 0.376 * \\
3-Ethyl-1,5-cataline IV & Hydrocarbon & MS & 17.12 & 1.434 * & 0.473 \\ (_Z_)-3-Hexenal & Aldehyde & Std; MS & 15.79 & 0.844 * & 0.320 \\ Octane & Alkane & Std; MS & 14.06 & 1.197 & 2.912 * \\
1,4-Pentalene & Hydrocarbon & MS & 12.88 & 2.889 * & 1.582 \\
4.8-Dimethyl-1,3,7-nonatriene & Hydrocarbon & Std; MS & 11.60 & 0.677 * & 0.235 \\
1-Penten-3-one & Ketone & Std; MS & 11.26 & 6.508 * & 1.772 \\ (_E_)-2-Hexen-1-ol & Alcohol & Std; MS & 7.79 & 4.145 & 6.825 * \\ (_E_)-2-Hexenal & Aldehyde & Std; MS & 7.79 & 72.229 * & 46.644 \\ (_Z_)-2-Penten-1-ol & Alcohol & RI & 4.78 & 2.413 * & 1.570 \\ (_E_)-2-Hexenyl acetate & Ester & Std; MS & n.s & 0.249 & 0.100 \\ (_E_)-2-Pentenal & Aldehyde & Std; MS & n.s & 0.708 & 0.547 \\ (_Z_)-3-Hexen-1-ol & Alcohol & Std; MS & n.s & 9.490 & 8.718 \\ (_Z_)-3-Hexenyl acetate & Ester & Std; MS & n.s & 3.184 & 4.829 \\ 1-Hexanol & Alcohol & Std; MS & n.s & 6.859 & 7.009 \\ Hexanal & Aldehyde & Std; MS & n.s & 6.535 & 6.086 \\ Hexyl acetate & Ester & Std; MS & n.s & 0.812 & 0.814 \\ Nonanal & Aldehyde & RI & n.s & 0.414 & 0.149 \\ \hline \hline \end{tabular} Identification: the volatile aroma compounds were identified on the basis of the comparison of their mass spectra and retention time with those of pure standards or with mass spectra from a mass spectral database (Std, MS), as well as by retention indexes (RI) matches on a similar phase column (NIST Chemistry WebBook SRD 69, VCF Volatile Compounds in Food 16.1) or by comparing only the mass spectra (MS). An asterisk (*) in a row represents significant differences between mean values at \(p<0.05\) obtained by one-way ANOVA and least significant difference (ISD) test.  
\end{table} 
Table 1: List of volatile aroma compounds found in monovarietal Protected Designation of Origin (PDO) and commercial-blended extra virgin olive oils by headspace solid-phase microextraction combined with gas chromatography/mass spectrometry (HS-SPME-GC-MS) sorted by descending Fisher _F_-ratio, compound class, type of identification, and semi-quantitative values in mg/kg relative to internal standard. commercial-blended EVOOs most probably, at least partly, originated from the elevated concentrations of fermentation and oxidation derived volatiles, such as linear alcohols and esters (Figure 2).
# Improved Design of LNG Marine Loading Arm Docking Method Based on TRIZ Theory
## 4 Innovative Design of Problem-Solving Method Based on TRIZ
### The Method of Numerical Analysis Is Adopted for Innovative Design
#### 4.6.1 Determination of the Location of Optimization  
The finite element model was imported into Fluent and defined, and the SST K-omega turbulence model [28] was selected. It is worth noting that ground objects can significantly impact the movement of air flow, leading to a reduction in wind speed due to air flow friction. The effect of ground roughness on wind speed diminishes with increasing height as the frictional force becomes weaker. Consequently, the average wind speed changes exponentially with the ground's roughness and height, following the expression:  
\[\frac{\overline{v_{1}}(z)}{\overline{v_{b}}}=\left(\frac{z}{z_{b}}\right)^{\alpha} \tag{11}\]  
where \(z\) represents the height of arbitrary wind, \(z_{b}\) represents the standard value of the reference height, \(\overline{v_{1}}(z)\) and \(\overline{v_{b}}\) represent the wind speed at heights \(z\) and \(z_{b}\), respectively, and \(\alpha\) represents the coefficient of ground roughness, which was set to 0.12 according to the working environment in this paper [29; 30].  
Following the Chinese national standard, we set the reference height \(z_{b}\) to 10 m and \(\overline{v_{b}}\) to 50 m/s for the calculations. We developed a UDF velocity inlet that conforms to the exponential distribution for the model's velocity inlet. The operating pressure was set to standard atmospheric pressure, with a temperature of 20 \({}^{\circ}\)C, and dynamic viscosity of \(\mu=1.42\times 10^{-5}\) m\({}^{2}\)/s, and a density of \(\rho=1.20\) kg/m\({}^{3}\). The model selected the velocity inlet and pressure outlet. The SIMPLE solver was used to solve the model.  
Figure 15 depicts the velocity cross-section of the external flow field in the discharge arm after being solved. The figure demonstrates that the air flow experiences an obstruction in the marine loading arm, resulting in a disturbance and reduction in air flow speed, consequently creating a vortex at the rear. As a result, the air flow velocity through the LNG marine loading arm weakens to varying degrees.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **No. 21 Power** \\ \hline No. 5 area of moving object & 19, 10, 32, 18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Contradiction matrix table of Plan 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **No. 13 Stability of Object’s Composition** \\ \hline No. 11 stress of pressure & 35, 33, 2, 40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contradiction matrix table of Plan 1.  
Figure 16 depicts the velocity distribution of the transverse section of the LNG feeder arm at 2 m distance intervals. The results demonstrate that the obstruction caused by the marine loading arm leads to a significant reduction in the flow velocity at Y = 2 m, Y = 4 m, Y = 6 m, and Y = 14 m. The corresponding areas of the marine loading arm include the column, the counterweight block and its support, and the upper and lower rope wheels.
## 2 Non-mechanical Remote Center Motion  
### System Configuration for Remote Center Motion  
In general laparoscopic surgery, the surgical instrument requires four-degrees-of-freedom motion: Pan (Roll), Tilt (Pitch), Spin (Yaw) rotational and linear movements (Forward/Backward), as shown in Figure 2a. In this study, a two-degree-of-freedom end-effector is mounted on a six-degree-of-freedom cooperative robot, and a virtual remote center is defined by a control algorithm to achieve RCM. A six-degree-of-freedom cooperative robot embodies Pan and Tilt rotational movements in the four-degree-of-freedom RCM while maintaining the remote center of three-dimensional space to form a conical trajectory. Surgery assistance is performed in such a small space that the scrub nurse has to hold an endoscopic camera between the sides of a surgeon. This robot, which maintains a non-mechanical remote center, mimics the movement of a human wrist, as shown in Figure 2a. In this case, a small device mechanism can have the effect of human grasping surgical tools in a narrow space and minimize collisions when collaborating with surgeons. However, the robustness of the controller is very important for safe surgery. As the two-degree-of-freedom motion of an end-effector can be controlled independently, this paper focuses research on the pan, tilt RCM control of the cooperative robot.
## 3 Results and Discussion
### 3.1. Sorption of Zn(II), Cu(II), and Pb(II) by the Bean Peel
A more efficient sorption at higher metal concentrations can be attributed to boosting the driving force and accelerating the mass transfer [39]. These findings can be observed, for example, for Zn(II) and Cu(II) for the contact time of 6 h and at pH 5.6, when initial concentrations increased from 100 \(\upmu\)g/L to 500 \(\upmu\)g/L, and the removals increased from 91% to 95% and 68% to 77%, respectively. For the Pb(II), it can also be observed that at pH 4.0 and after 4 h of contact time with the peel, the removal increased from 74% to 90% from 100 \(\upmu\)g/L to 500 \(\upmu\)g/L, respectively. Regarding the influence of pH on the removal, the results show that for Zn(II), the increase of pH from 4.0 to 7.0 did not affect the removal, while for Cu(II), the increase of pH decreased its removal (e.g., for Cu(II) with an initial concentration of 250 \(\upmu\)g/L, the removal decreased from 51% to 23% and from 90% to 53%, in relation to a contact time of 2 h and 6 h, respectively; Figure 1b). Moreover, the results highlight that at pH of 5.6 and 7.0, higher removal percentages were obtained for Zn(II) than for Cu(II) (\(p<0.05\)), which suggests a greater affinity of the biomaterial for sorption of Zn(II), possibly due to Cu(II) being able to precipitate as carbonate (CuCO\({}_{3}\)) or form weaker complexes (CuOH\({}^{+}\)) reducing the sorption.
## 3 Results and Discussion  
### Characterization of Soil Samples and Soil Colloids  
The basic physicochemical characteristics of soil samples and soil colloids are shown in Table 1[33]. It is obvious that the pH of the soil colloids was lower than the soil samples, indicating that the colloid extraction process changed the pH of the soil. Moreover, the cation exchange capacity (CEC) was 34 meq/100 g for soil samples. The main mechanical composition of the soil samples was in the range of 0.02-0.002 mm since the proportion  
Figure 1: Schematic illustration of experimental setup **(a)** batch experiments **(b)** column experiments; 1� experimental solutions; 2� a three-way valve; 3� peristaltic pump; 4� a column containing the porous medium; 5� fully automatic partial collector.  
was more than 50%. There is a difference in the average particle sizes between soil colloids and soil samples, but the Zeta potential of the two samples was similar. Moreover, the mass concentration of the soil colloids was detected to be 220 mg\(\cdot\)L\({}^{-1}\). The total organic carbon of the soil samples was higher than that of the soil colloids, indicating that the colloid extraction process led to the loss of organic carbon, which may influence the adsorption capacity of the two kinds of materials for contaminants in groundwater.  
As shown in Figure 2, the EDX result indicates that the composition of soil colloids was complicated and mainly contained Si, Ca, Fe, Ti, Ag, etc. The shapes of the single colloidal particles are diverse in the SEM image, e.g., circular, irregular polygon, and aggregates of nanoparticles. Generally, the soil colloidal particles were considered to be mineral particles coated with organic matter. The embedded tables in Figure 2b exhibit the main element compositions and the main ion content in the soil samples. It is obvious that the contents of Fe and Mn in soil are higher than in other elements. Moreover, some toxic heavy metals (i.e., Cr, Ni, Cu, Zn, Cd, Pb) were detected in the soil, indicating that the soil was polluted by heavy metals, which cannot be ignored. It is obvious that HCO\({}_{3}\)' is dominant in all the anions. The detection of Ca\({}^{2+}\) was consistent with the EDX result.
## 3 Results and Discussions
### Yeast Films Characterization
#### 3.3.6 FTIR Spectra of Yeast Films Functionalized with CLEO  
The FTIR spectra of both functionalized YF and CLEO are presented in Figure 7a.  
The YF spectra are very similar to other reported infrared spectra of yeast films [10; 27], indicating the presence of characteristic yeast functional groups and their interaction with the plasticizer and emulsifier in the film-forming emulsion. Relevant bands for YF components were identified at 1027 cm\({}^{-1}\) as C-O groups of polysaccharides, 1540 cm\({}^{-1}\) for amide II (proteins, N-H bending, and C-N stretching), 1624 cm\({}^{-1}\) for amide I (proteins and C=O stretching), and 2923 cm\({}^{-1}\) CH2 (lipids) [49]. The comparative analysis of FTIR spectra for YFs indicated a decrease in the intensity for amide I and II with increasing concentrations of the plasticizer, which was previously reported by [27] and explained by the lower protein content and N-H group interaction with glycerol. Moreover, due to the sensitivity of the amide II N-H group to hydrogen bonding, the absorption intensity of amide II decreased by 1.12 times compared to that of the amide I band. The interaction between YF and emulsifying agent was depicted by the 1734 cm\({}^{-1}\) peak corresponding to C=O stretching, present in Tween 80 [50], while the broad, weak peak at 3275 cm\({}^{-1}\)  
Figure 6: Heat map showing the retention rates of VOCs by the YFs in comparison with the CLEO fingerprint. Red indicates a low retention rate, green indicates a good retention rate, and orange and yellow indicate a medium retention rate.  
characteristic of O-H stretching, indicated the presence of glycerol and polysaccharides [49] in YFs.  
The FTIR spectra of the CLEO mixture (Figure 7b) had relevant peaks from both lavender (LEO) and cinnamon EO (CEO) spectra. The peaks in the CLEO spectra correlated well with LEO-specific peaks at 836 cm\({}^{-1}\) and 1450 cm\({}^{-1}\), corresponding to the vibrational modes of monoterpenes, and at 1739 cm\({}^{-1}\), corresponding to the vibrations of C=O group, characteristic of lavender [51]. In addition, the spectra showed CEO-specific peaks at 688 cm\({}^{-1}\), characteristic of alkenes vibrations; 1121 cm\({}^{-1}\), specific for terpenoid components; and 1677 cm\({}^{-1}\) and 1734 cm\({}^{-1}\), corresponding to the C-O carbonyl bond of aldehyde vibration, an indicator of cinnamaldehyde, a marker for cinnamon [52]. The increased peak intensity at 1624 cm\({}^{-1}\) of composites YFs in comparison with CLEO showed that a higher proportion of carbonyl groups was present in the films. The FTIR spectra of LEO, CEO, and CLEO are available in Table S1.  
PCA analysis indicated the clustering of YF1 and YF2, while the higher glycerol-Tween concentration (1.50%) made YF3 different. CLEO was indicated by PCA to have a intermediary profile in comparison with LEO and CEO. PC1 explained 77% of total variance, and only 22% of the variance was explained by PC2 (Figure 7c).
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 1 Introduction
Pix2 Pix GAN can learn color transfer from color cards to silk printed fabrics to facilitate the design process. The method incorporated the color and texture features of silk printed fabric images into a neural network to predict the color effect, accounting for fabric style and systemic color variation during the printing process. Finally, the model generates predicted silk printed fabric images based on the input of a designed image. Designers and manufacturers can directly visualize colored images of predicted print textiles of a given fabric structure in their particular manufacturing setting, potentially skipping the lengthy proofing processes.
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
###### Abstract  
The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population's diversity and accelerates the algorithm's convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.  
chimp optimization algorithm; refraction learning; tent chaos; global optimization; engineering design optimization
###### Abstract
For thin film deposition, the effects of sputtering power and deposition pressure on the electrical and structural properties of dc magnetron sputter-deposited copper films on p-type silicon was investigated by [14]. Experimental results showed that the deposition rate of the copper films increased proportionally with the sputtering power. Sputtering power also affected the structural properties of the copper films through the surface diffusion mechanism of the adatom. The influence of the substrate temperature during film deposition is studied by means of scanning electron microscopy and X-ray scattering analysis [15]. A survey on recent advances in the development of copper diffusion-proof materials was presented by [16]. The literature research did not provide the copper seed layer quality at different sections of the trenches, and the uniformity based on various material properties was not studied. This paper considers sputtering process parameters and material properties to enhance the copper seed layer quality, including step coverage, uniformity, bottom height, necking, and alloy percentage of the copper seed layer, using molecular dynamics simulations. The results of this study provide comprehension of various mechanisms of copper thin film growth and a good reference for physical vapor deposition of copper seed layers during the interconnect metallization process.
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 4 Experiment
### Experimental Settings
#### 4.3.1 Evaluation Metrics  
For the alignment of social networks, we randomly divided the labeled anchor links into two parts, the training set and the test set. As introduced in Section 3.3, with a pair of node \((v_{i}^{s},v_{j}^{t})\) as input, DCSNA aims to predict whether they are a pair of potential anchor nodes in two networks or not. In the context of our testing, each pair of anchor nodes \((v_{i}^{s},v_{j}^{t})\) can be classified into one of three categories based on its prediction status, true positive (TP), false positive (FP), or false negative (FN).  
A pair of anchor nodes \((v_{i}^{s},v_{j}^{t})\) is classified as TP if it is predicted as a pair of anchor nodes in the test and is indeed a pair of anchor nodes. On the other hand, if \((v_{i}^{s},v_{j}^{t})\) is not a pair of anchor nodes in the test but it is predicted as a pair of anchor nodes, it is classified as FP. Lastly, a pair of anchor nodes \((v_{i}^{s},v_{j}^{t})\) are classified as FN if it is a pair of anchor nodes in the test but it is not predicted as such.  
Under this prediction status, we have the following indicator, Precision = TP/(TP + FP), Recall = TP/(TP + FN), F1 = 2 * (Precision * Recall)/(Precision + Recall). Since each  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\# Nodes** & **\# Edges** & **\# Anchor Links** & \(p_{r}\) \\ \hline \multirow{2}{*}{Dataset.1} & 9872 & 39,561 & \multirow{2}{*}{6352} & \multirow{2}{*}{0.86} \\  & 9916 & 44,808 & & \\ \hline \multirow{2}{*}{Dataset.2} & 3583 & 14,485 & \multirow{2}{*}{985} & \multirow{2}{*}{0.80} \\  & 1905 & 6097 & & \\ \hline \multirow{2}{*}{Dataset.3} & 5120 & 164,919 & \multirow{2}{*}{1609} & \multirow{2}{*}{0.73} \\  & 5313 & 76,972 & & \\ \hline \multirow{2}{*}{Dataset.4} & 38,310 & 591,124 & \multirow{2}{*}{38,287} & \multirow{2}{*}{0.82} \\  & 38,310 & 591,694 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics details of the datasets.  
pair of anchor nodes can be seen as a class, we use three frequently used metrics, Macro Precision, Macro Recall, and Macro F1, as measurement, that is, the arithmetic mean of each statistical indicator value across all classes.
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### MCDA-Based Decision-Making Framework for PM Implementation
#### 3.3.5 Validation and Comparative Analysis  
During the study, validation and comparative analysis was conducted to ensure the reliability, robustness, and practical applicability of the PM framework. This phase involved testing the framework against historical maintenance data, expert evaluations, and real-world case studies. The PM suitability rankings generated by the framework were compared to documented maintenance outcomes, such as failure rates, costs, and downtime, to confirm alignment with real-world performance trends.  
Expert validation was also performed, where industry professionals reviewed the framework's criteria, weights, and outcomes to ensure they reflected practical aviation priorities. Additionally, the sensitivity analysis tested the framework's stability by adjusting criteria weights, identifying the most influential factors and ensuring consistency across various scenarios. Comparative analysis further demonstrated the framework's advantages over traditional maintenance decision-making approaches, highlighting its ability to incorporate safety, regulatory, and economic considerations comprehensively.  
The results of this phase validated the framework's effectiveness and established its practical relevance, confirming its utility as a robust tool for optimizing predictive maintenance strategies in aviation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Median** & **Std Dev** & **Weight** \\ \hline Flight Safety Impact & 9.20 & 0.38 & 0.15 \\ \hline Reliability Predictability & 8.80 & 0.42 & 0.10 \\ \hline Degradation Progression & 8.50 & 0.45 & 0.10 \\ \hline Data Sufficiency & 8.70 & 0.43 & 0.10 \\ \hline Economic Feasibility & 8.90 & 0.41 & 0.10 \\ \hline Regulatory Compliance & 9.10 & 0.39 & 0.05 \\ \hline Technological Integration & 8.50 & 0.45 & 0.05 \\ \hline Environmental Influence & 8.30 & 0.47 & 0.05 \\ \hline Operational Impact & 8.80 & 0.42 & 0.10 \\ \hline Scalability & 8.40 & 0.46 & 0.05 \\ \hline Workforce Training & 8.70 & 0.43 & 0.05 \\ \hline Data Privacy & 8.60 & 0.44 & 0.05 \\ \hline End-of-Life Management & 8.20 & 0.48 & 0.05 \\ \hline Stakeholder Acceptance & 8.40 & 0.46 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics for predictive maintenance evaluation.
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 3 The Proposed NLOS Mitigation and SRUKF-Based Location Estimation Method for 5G Positioning
### SRUKF-Based Location Estimation Method for NLOS Mitigation
The square root of the predicted measurement covariance can be derived according to Equation (41). \[R_{y,y}[t_{k}]=\Big{(}\text{qr}\Big{\{}\big{[} \begin{array}{cc}y^{*}[t_{k}] &\sqrt{U[t_{k}]}\end{array} \big{]}^{T}\Big{\}}\Big{)}^{T} \tag{49}\]  
\[y^{*}[t_{k}]=\big{[}(y[t_{k}])_{0}-\hat{y}[t_{k}]\cdots(y[t_{k}])_{i}-\hat{y}[ t_{k}]\cdots(y[t_{k}])_{2L}-\hat{y}[t_{k}]\big{]}\times\text{diag}\Big{(}\sqrt{W^{(c)}} \Big{)} \tag{50}\]Here, L represents the dimension of the receiver. \((y[t_{k}])_{i}\), where \(i\) ranges from 0 to 2 L, signifies the propagated sigma point, and \(y[t_{k}]\) denotes the predicted measurement results. \(U[t_{k}]\) represents the Gaussian measurement noise matrix at time \(t_{k}\). The term \(\sqrt{W^{(c)}}\) represents the weight of the sigma points. The state covariance derived from QR decomposition can be calculated as follows:  
\[R[t_{k}]=\left(\text{qr}\Bigg{\{}\left[\chi^{*}[t_{k}]-y^{*}[t_{k}]\quad K[t_{ k}]\sqrt{U[t_{k}]}\right]^{T}\Bigg{\}}\right)^{T} \tag{51}\]  
\[\chi^{*}[t_{k}]=\left[(\chi[t_{k}])_{0}-\delta[t_{k}]\cdots(\chi[t_{k}])_{i}- \delta[t_{k}]\cdots(\chi[t_{k}])_{2n_{x}}-\delta[t_{k}]\right]\times\text{diag} \Big{(}\sqrt{W^{(c)}}\Big{)} \tag{52}\]  
where \((\chi[t_{k}])_{i}\), where i ranges from 0 to 2 L, denotes the propagated sigma points, and \(K[t_{k}]\) represents the filtering gain.