esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### Elements of Molecular Imprinting
#### 5.3.2 Functional Monomers  
In MIPs, the functional monomers play an important role in the creation of the pre-polymerization complex with the template that interacts through a specific functional group. Thus, the selection of the necessary functional monomer is quite important. It should be selected such that the template:functional monomers interact strongly and construct specific antigen-antibody or donor-receptor complexes for polymerization. Figure 5 shows some typical functional monomers used for the synthesis of MIPs.  
Among these functional monomers, methacrylic acid (MAA) is the \"universally\" used functional monomer because of its stability for the creation of hydrogen bonds. It is further shown that a high molar fraction of MAA causes a large MIP pore size which plays an important role in the enhancement of the binding capacity of the resulting MIPs [108].  
The ratio of functional monomer:template (M/T) is also one of the important parameters which affect the number and stability of MIP binding sites [109; 110]. Low M/T ratios lead to the creation of fewer template:monomer complexes due to an insufficient amount of functional monomer. This results in the creation of less imprinted sites and hence poor MIP binding ability, while for high M/T ratios, a limited number of functional monomer binds with the template and the remaining amount of monomer would remain scattered within the MIPs. This results in the non-selective rebinding of the templates with retained monomers, referred as the extreme case of non-imprinted polymer [111].
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 2 Fundamentals of the Model
### Piecewise Chaos Mapping  
Chaos, a nonlinear dynamic phenomenon characterized by deterministic stochasticity and ergodic behavior [31], has been widely adopted in global optimization algorithms due to its inherent advantages of population diversity preservation and phase space traversal. Specifically, Piecewise Shaos Mapping--based on the principle of adaptive linear segmentation--constructs nonlinear global functions through concatenated linear sub-functions, enabling the controlled perturbation of search trajectories. By incorporating this mapping mechanism, optimization algorithms can enhance the global exploration and local exploitation balance via the dynamic adjustment of the chaos intensity, maintain population diversity through ergodic sampling in high-dimensional spaces, and accelerate convergence by avoiding premature stagnation in local optima.  
The formula for Piecewise Chaos Mapping is as follows:  
\[x_{i+1}=\begin{cases}\frac{x_{i}}{p},&0\leq x_{i}<p\\ \frac{x_{i}-p}{0.5-p},&p\leq x_{i}<0.5\\ \frac{1-p-x_{i}}{0.5-p},&0.5\leq x_{i}<1-p\\ \frac{1-x_{i}}{p},&1-p\leq x_{i}<1\end{cases} \tag{23}\]  
where \(p\) is the segmentation control factor, which is taken as 0.3.  
Given the segmented linearity feature of Piecewise Chaos Mapping, specific segmented regions may demonstrate local loops and unstable points. Such phenomena eventually result in the degradation of the traversal performance and a deviation from the optimal solution. To address this, this study innovatively introduces a perturbation term, \(\sin\tau r\times\frac{1}{N}\), on the basis of the original formula. The refined formulation is as follows:  
\[x_{i+1}=\begin{cases}\frac{x_{i}}{p},&0\leq x_{i}<p\\ \frac{x_{i}-p}{0.5-p},&p\leq x_{i}<0.5\\ \frac{1-p-x_{i}}{0.5-p},&0.5\leq x_{i}<1-p\\ \frac{1-x_{i}}{p},&1-p\leq x_{i}<1\end{cases}+sin\tau r\times\frac{1}{N} \tag{24}\]  
where \(N\) denotes the population size, \(r\) represents a random number within the interval \([0,1]\), and \(sin\) is the sine function. The incorporation of the perturbation term serves to en hance the initial randomness and traversal capability of the population while circumventing localized loops. The population initialization formula is defined as follows:  
\[X[i,j]=k\cdot(ub[j]-lb[j])+lb[j] \tag{25}\]  
where \(X[i,j]\) denotes the initialization position at row \(i\) and column \(j\) in \(X\), and \(i=0,1,\cdots,n-1\); \(j=0,1,\cdots,d-1\), and \(k\) is \(x_{i+1}\) in Equation (23). \(ub\) and \(lb\) represent the upper and lower bounds of the search space, respectively. This paper employs the improved Piecewise Chaos Mapping to initialize the SSA population, aiming to elevate the randomness and traversal property of the population's initial positions, strengthen the global search ability, and prevent deviations from the global optimal solution caused by the uneven population distribution.  
Additionally, the flowchart illustrating the process of utilizing the final ISSA to optimize the LSTM network parameters is depicted in Figure 2.
## 3 UV Aging Law of Asphalt
### Analysis of Test Results
#### 3.2.1 Penetration, Softening Point and Ductility
In addition to high-grade base asphalt, the differentiation of softening point in the process of UV radiation was not very large, and the testing error in the process of softening point experiment made it more difficult to blur the differentiation, so the softening point index was not suitable as an index to evaluate the UV aging performance of asphalt. Asphal ductility is the total ability of the plastic deformation that the asphalt can bear when it is stretched by external forces, and it is a measure index of the cohesion of asphalt. The ductility of asphalt is closely related to the rheological properties of asphalt. In engineering, ductility is generally used as an important index of the low-temperature performance of asphalt, and its value is closely related to the low-temperature crack resistance of asphalt pavement. Figure 8 shows the asphalt ductility at the UV irradiation intensity of 157.5 W/m\({}^{2}\) under different UV irradiation times. Figure 9 shows the ductility loss rate of asphalt under different UV irradiation times. Figure 8: The ductility of asphalt with different UV irradiation time (the test temperature of base asphalt is 10 \({}^{\circ}\)C while that of SBS modified asphalt is 5 \({}^{\circ}\)C). Figure 7: The softening point loss rate of asphalt with different UV irradiation time. With the extension in UV irradiation time, the ductility of asphalt decreased gradually. The grade of base asphalt had little effect on the ductility loss rate. For SBS modified asphalt, when the UV irradiation time was within 24 h, the loss rate of the ductility of SBS modified asphalt was basically equal to that of base asphalt. However, when the UV irradiation time was over 24 h, the absolute decrease in the ductility of SBS modified asphalt was significantly smaller than that of matrix asphalt, although the test temperature of this data was only 5 \({}^{\circ}\)C. This suggests that that the anti-UV performance of SBS modified asphalt was better than that of base asphalt. In addition, the decrease of ductility of SBS I-D was lower than that of SBS I-C. In the process of UV radiation, the light components in asphalt, such as aromatics, volatilize, resulting in the loss of asphalt quality, so the mass loss rate is generally used as an index to evaluate the aging degree of asphalt. Figure 10 shows the asphalt mass loss rate at the UV irradiation intensity of 157.5 W/m\({}^{2}\) under different UV irradiation times. Figure 10 shows that, with the extension of UV aging time, the mass loss of asphalt basically presented a gradually increasing trend, which reflected the aging degree of asphalt from one side. However, due to the large error in the data acquisition process of this experiment, generally, the data were more inclined to be used for qualitative research.
## 4 Discussion-Error Influences  
The aim was to demonstrate the possible link between terrestrial static or kinematic GNSS measurement and data derived from the GLAS device onboard the IceSat-1 satellite and, next, to verify the reliability and accuracy of the digital surface model based on IceSat-1 satellite measurements. The measured values were examined for random jumps using the difference quotient. There are jumps of a maximum of 1.2 cm, so that jump measured values cannot be identified as a significant source of error. Moreover, these are eliminated as much as possible by Gaussian filtering.  
After PPP evaluation, the position accuracy is \(+/-3\) cm, and the height accuracy is \(+/-5\) cm for a single data set. For the investigations performed here, the elevation component is most relevant. Of course, there are some defined outliers; during measurement at these locations, a lower number of GNSS satellites occurred (PDOP). This influence is also visible in the dispersion of the elevation component (Figure 11, comparison km 680) around kilometer 680. This is classified as not trustworthy.  
Regarding the measured antenna heights, a deviation of \(+/-1\) cm is to be expected when it is attached to the pulkas. Likewise, variations of nearly 0 cm (in good conditions) to \(+/-5\) cm (in uneven terrain) due to the ground conditions can be seen.
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
###### Abstract  
Sign languages are complex, but there are ongoing research efforts in engineering and data science to recognize, understand, and utilize them in real-time applications. Arabic sign language recognition (ArSL) has been examined and applied using various traditional and intelligent methods. However, there have been limited attempts to enhance this process by utilizing pretrained models and large-sized vision transformers designed for image classification tasks. This study aimed to create robust transfer learning models trained on a dataset of 54,049 images depicting 32 alphabets from an ArSL dataset. The goal was to accurately classify these images into their corresponding Arabic alphabets. This study included two methodological parts. The first one was the transfer learning approach, wherein we utilized various pretrained models namely MobileNet, Xception, Inception, InceptionResNet, DenseNet, and BiT, and two vision transformers namely ViT, and Swin. We evaluated different variants from base-sized to large-sized pretrained models and vision transformers with weights initialized from the ImageNet dataset or otherwise randomly. The second part was the deep learning approach using convolutional neural networks (CNNs), wherein several CNN architectures were trained from scratch to be compared with the transfer learning approach. The proposed methods were evaluated using the accuracy, AUC, precision, recall, F1 and loss metrics. The transfer learning approach consistently performed well on the ArSL dataset and outperformed other CNN models. ResNet and InceptionResNet obtained a comparably high performance of 98%. By combining the concepts of transformer-based architecture and pretraining, ViT and Swin leveraged the strengths of both architectures and reduced the number of parameters required for training, making them more efficient and stable than other models and existing studies for ArSL classification. This demonstrates the effectiveness and robustness of using transfer learning with vision transformers for sign language recognition for other low-resourced languages.  
A Article  
17 October 2023  
18 September 2023  
17 October 2023  
23 October 2023  
Arable sign language; transfer learning; VGG; ResNet; MobileNet; Xception; Inception; DenseNet; InceptionResNet; ViT; Swin; BiT
# Demand Analysis of Lubricating Oil in Spur Gear Pairs
###### Abstract  
Theoretical calculation and numerical simulation are used to investigate the lubricating oil demand of spur gears. In accordance with the function of lubricating oil during the meshing process, oil demand is regarded as the superposition of oil for lubrication and cooling. Oil for lubrication is calculated in accordance with meshing and elastohydrodynamic lubrication (EHL) theories. Oil for cooling is obtained from friction heat. The influence of different meshing positions on lubricating oil demand is analysed, and the effects of modulus, tooth number, transmission ratio, input speed and input torque on lubricating oil demand is investigated using a control variate method. Simulation results indicated that oil for lubrication and oil for cooling have two maxima each during a meshing circle. The influences of different gear parameters and working conditions on lubricating oil demand are compared. The results showed that the oil volume for lubrication increases and oil volume for cooling decreases as the modulus, tooth number and transmission ratio of the gear increase, the oil volume for lubrication and oil volume for cooling increases as the input speed and input torque increase.  
EHL; spur gear; lubrication oil; simulation; parameters +
Footnote †: journal: Applied Sciences
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Outsourcing Process  
The process of constructing the encrypted outsourced data will be illustrated based on the original database structure. For each keyword \(w\) related to a certain POI type and each set of description files containing the keyword \(w\), the LBSP uses the pseudo-random function with the key \(k\) (i.e., \(prf_{k}\)) and \(AES\) to form the encrypted retrieval index set \(prf_{k}(W)=\{prf_{k}(w_{1}),prf_{k}(w_{2}),\ldots,prf_{k}(w_{\theta})\}\) and the ciphertext set \(C(DS_{w\in W})=\{c(ds)_{w_{1}},c(ds)_{w_{2}},\ldots,c(ds)_{w_{\theta}}\}\). Moreover, to support the query result verification, the verification objects need to be constructed. Similar to the scheme proposed in [9], our scheme also adopts the Bloom filter to generate verification objects.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 2 Materials and Methods
### Principal Component Analysis  
Principal component analysis (PCA) is a powerful method in solving multi-collinearity or eliminating potential collinearity hidden in spectral data. In this step, PCA is applied to decompose raw spectra into a series of new variables named principal components (PCs) which are orthogonally projected. Then, the first few PCs with high interpretation representing most of the samples' information will be considered. Data variability will be observed to analyze the distribution of sample data. PC loading lines are also commonly used to investigate the relationship among the variables.  
In current study, PCA was first employed to visualize the spectral data in different groups. Characteristic PC loading lines were drawn to see the useful wavelengths in identifying Chinese walnut varieties. In addition, PCA was also applied to the hyperspectral images to select the corresponding characteristic images by implementing PC transformation to the spectra of each pixel. After that, transformed PC score values of each pixel were obtained, and thus pixel-wise PC score images were formed. As a result, similarities, and differences among different samples were observed in effective PC score images. Spectra PCA was conducted in Matlab (Vision 2013b, The Mathworks Inc., Natick, MA, USA), and PC transformation was carried out in the ENVI software.
# Route Risk Index for Autonomous Trucks
## 2 Literature Review
### Deployment Risk Assessments  
The literature on autonomous trucking deployment risk assessment spans various methodologies aimed at safe and efficient integration into transportation networks. Road roughness poses a risk to AV operation because they must accurately assess roughness levels to adjust actuations such as speed and acceleration to maintain operational stability [13]. Climate change has also emerged as a catalyst for road deterioration, particularly rutting damage caused by extreme weather changes [14]. Precipitation poses challenges because AVs must automatically assess the levels of skidding and hydroplaning on wet surfaces to adjust for safe operation [6]. Rain that leads to flooding can also challenge the performance of autonomous navigation systems. Advanced sensor fusion and more advanced machine learning methods become necessary to enhance operational reliability in adverse weather conditions [15].  
Studies have also shown that truck operation is associated with higher crash risks relative to cars [16]. High traffic variability also challenges the safe operation of AVs because their sensors and actuators must react quickly [17]. For example, in congested situations, erratic human driving behavior, like sudden lane changes or aggressive maneuvers, can be unpredictable for AVs to handle, potentially causing crashes. Studies have also shown that crash risk is a dynamic quantity that has considerable spatial and temporal variations [18]. These studies highlight the importance of tailored risk assessment frameworks to address the unique challenges of ATs. Frameworks leveraging driving simulators [19], trajectory planning [20], liability considerations [21], and cybersecurity [22] illustrate the diverse application and critical nature of comprehensive risk assessments. Collectively, this body of work highlights the need for interdisciplinary approaches to ensure the safe integration of ATs into the transportation ecosystem.
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Auxiliary Datasets  
The identification of haze pixels based on satellite remote sensing data is still in the research stage, and the corresponding official products have not released yet, therefore, there are still great difficulties in quantitative validation of haze recognition. The real-time PM\({}_{2.5}\) monitoring data released by the national ground air quality monitoring stations can be taken as the true ground-based observation values, which usually reflect the air quality conditions at the stations, and the larger the value is, then the more serious the pollution is. There were 1670 monitoring stations nationwide on 22 October 2020. As shown in Figure 1, most of the stations are located in eastern China, with a few in the Xinjiang and Yunnan provinces [35]. The Ministry of Environmental Protection classifies the value of PM\({}_{2.5}\) into six ranks, with a value below 35 \(\upmu\)g/m\({}^{3}\) being excellent, a value larger than 35 \(\upmu\)g/m\({}^{3}\) and smaller than 75 \(\upmu\)g/m\({}^{3}\) (define as 35-75) being good, a value of 75 \(\upmu\)g/m\({}^{3}\)-115 \(\upmu\)g/m\({}^{3}\) being classified as mild pollution, a value of 115 \(\upmu\)g/m\({}^{3}\)-150 \(\upmu\)g/m\({}^{3}\) being classified as moderate pollution, a value of 150 \(\upmu\)g/m\({}^{3}\)-250 \(\upmu\)g/m\({}^{3}\) being classified as severe pollution and a value of larger than 250 \(\upmu\)g/m\({}^{3}\) being classified as serious pollution, which can be used as a data source to verify the identification accuracy of this study.  
Figure 1: The study domain and overlapped digital elevation model from MERSI-II Level 1 GEO1K. The sea surface is shown in white. The national PM\({}_{2.5}\) monitoring stations employed by the Ministry of Environmental Protection on 22 October 2020 are depicted as the magenta-colored dots.  
Additionally, [PERSON] (2022) mentioned in his research on aerosol retrieval using MERSI remote sensing data that some haze areas could be recovered by modifying the recognition conditions of inland water bodies ([PERSON], 2022). In this paper, the method proposed by [PERSON] (2022) were replicated and cross-compared with the results of this study.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 4 Numerical Experiments
### Parameter Study  
In this section, the influence of the parameter \(\beta\) on the optimization result is determined using a parameter study. For this purpose, the bracket is used as an optimization case and the parameter \(\beta\) is varied in the three levels 0, 0.25 and 0.5. The remaining optimization parameters are identical to the previous section.  
Figure 10 shows the optimization results for the three values of beta. It is noticeable that the optimization with \(\beta=0\) (partial mapping a) differs greatly from the other two optimizations (partial mapping b and c). Material 2 in the optimization with \(\beta=0\) consists of one component, whereas it consists of two components in the other two optimizations. In the optimization with \(\beta=0\), the interface between the materials is such that it is primarily subjected to shear. This is logical, as this optimization with \(\beta=0\) does not take into account the shear components in the objective function. The two optimization results with consideration of the shear components (\(\beta=0.25\) and \(\beta=0.5\)) differ only in the shape of the left interface. When increasing \(\beta\) (partial figure c), two additional struts develop and the area of the interface is also reduced.  
Figure 11 illustrates the normal and shear stress density distributions on the interfaces of the three optimization results. When examining the shear stresses, it is noticeable that, analogous to the previous section, the shear stress \(\sigma_{ns}\) is higher and is therefore focused on by the optimization method. It is evident that the maximum shear stress decreases with an increase in \(\beta\). Furthermore, it can be seen that the variance of the density distribution also decreases with an increase in \(\beta\).  
When considering the normal stress, it can be seen that although the majority of the density distribution is lower for the lowest \(\beta\) value than for the other two density distributions, there are individual surfaces with higher normal stress. This is probably due to the fact that the interface behavior is considered as an objective function and not as a hard boundary condition.  
In summary, it can be stated that the parameter \(\beta\) enables qualitative control of the ratio between normal and shear stress in the interface of the optimization result.  
Figure 10: Optimization results of the bracket case for different values of the parameter \(\beta\) (**a**) \(\beta=0\) (iteration 245). (**b**) \(\beta=0.25\) (iteration 217). (**c**) \(\beta=0.5\) (iteration 198). Material 2 (blue) in the optimization with \(\beta=0\) consists of one component, whereas it consists of two components in the other two optimizations.