esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Validation and Performance of Hybrid HCGA Algorithm
### Mechanistic Analysis of Improved Hybrid Algorithm Performance  
Considering the benchmark function optimization results in Section 4.2, it is obvious that HCGA is superior compared to CA and GAs. The mechanistic analyses of improved hybrid algorithm performance are as follows:  
1. Compared with the traditional CA population space with only a single mutation operator, HCGA uses GAs as a cultural framework for the population space evolution model, and the rich genetic operators of GAs can increase the population ergodicity and global exploration ability of the algorithm.
2. The belief space is constructed using situational knowledge, normative knowledge and historical knowledge, and used to guide the evolution of the population space, which effectively records the experience formed during the evolution of the algorithm and improves the evolutionary efficiency. The use of historical knowledge can also prevent the algorithm from falling into local optima to a certain extent.
3. A knowledge-guided \(t\)-mutation operator is developed to make the mutation step change adaptively with the evolutionary process, so that the algorithm can transition adaptively between global exploration and local exploitation. It does not depend on evolutionary generations but on population diversity to generate the adaptive step size. It can generate larger mutation steps in the early evolutionary stage, which increases the global exploration ability of the algorithm, and at the same time it can also avoid rapid loss of population diversity and make efficient use of the search space. It generates smaller mutation steps in the late evolutionary stage to enhance the local exploitation ability of the algorithm, which makes the algorithm converge rapidly.  
The benchmark results obtained with mathematical functions and the above analysis demonstrate that HCGA is an efficient optimization algorithm with potential applications for complex optimization problems.
# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 3 Improved White Shark Optimisation Algorithm
### Chaos Elite Pool Strategy
#### 3.1.1 Sine Mapping for Population Initialization  
The randomness of chaotic variables helps maintain population diversity, and their ergodicity and unpredictability [17] effectively assist the algorithm in escaping local optima, thereby expanding the search capability globally. Consequently, many researchers have applied chaotic mapping to optimization problems. Chaotic mapping, a complex dynamic method found in nonlinear systems, generates disordered values between 0 and 1, replacing traditional pseudo-random number generation in algorithm optimization.  
In this study, chaotic variables were generated using Sine chaotic mapping, and these variables were introduced into the solution space of the problem to perturb individuals. As shown in Figure 2, which depicts population distributions under various chaotic mappings, Sine mapping exhibited more disorder and efficiency in the search space compared to Chebyshev mapping, which had uneven distribution, and Tent mapping, which tended to degenerate into periodic sequences [18]. This characteristic of Sine mapping significantly contributed to increasing population diversity. The mathematical formula for Sine chaotic mapping is presented below, where \(\alpha\) is an adjustable parameter within the range [0, 4], tailored to the chaotic effect requirements during the initialization phase of the improved WSO algorithm.  
\[x_{n+1}=\frac{\alpha}{4}\sin(\pi x_{n}),0<\alpha\leq 4 \tag{6}\]
## 3 System Model and Problem Formulation
### System Model
Therefore, we can express the mutual interference for the above-defined network as follows:  
\[I_{i}=\sum_{j=1}^{N_{D}}\zeta_{j,f}P_{j}h_{j,r}d_{j,e}^{-\alpha}+\sum_{k=1}^{ N_{S}}\zeta_{k,f}P_{k}h_{k,e}d_{k,e}^{-\alpha}+N_{0} \tag{4}\]  
\[I_{j}=P_{t}h_{i,r}d_{i,r}^{-\alpha}+\sum_{f^{\prime}=1,f^{\prime}}^{N_{D}} \zeta_{f^{\prime},f^{\prime}}P_{f}h_{f^{\prime}}d_{f^{\prime}}^{-\alpha}+\sum_ {k=1}^{N_{S}}\zeta_{k,f}P_{k}h_{k,r}d_{k,r}^{-\alpha}+N_{0} \tag{5}\]  
\[I_{k}=P_{t}h_{i,s}d_{i,s}^{-\alpha}+\sum_{j=1}^{N_{D}}\zeta_{j,f^{\prime}}P_{ j}h_{j,s}d_{j,s}^{-\alpha}+\sum_{k^{\prime}=1,k^{\prime},k^{\prime}\
eq k}^{N_{S}} \zeta_{k^{\prime},f^{\prime}}P_{k^{\prime}}h_{k^{\prime},s}d_{k^{\prime},s}^ {-\alpha}+N_{0} \tag{6}\]Here, \(I_{i}\), \(I_{j}\), and \(I_{k}\) are the total interference received by eNB, all \(j_{th}\) D2D pairs except the \(j_{th}\) transmitter, and all SUEs except the \(k_{th}\) with regard to the SCeNB, respectively. Binary variable \(\psi_{j}^{f}\in\{0,1\}\) indicates whether D2D users share the sub-channel \(f\) with cellular users. If a D2D user reuses the spectrum resource of \(i_{th}\) CUE, then \(\psi_{j}^{f}=1\); otherwise, \(\psi_{j}^{f}=0\). That is,  
\[\psi_{j}^{f}= \begin{cases}1,&\text{if sub-channel $f$ is shared $i_{th}$ CUE},\\ 0,&\text{otherwise}.\end{cases} \]  
Similarly,  
\[\psi_{k}^{f}= \begin{cases}1,&\text{if sub-channel $f$ is shared $i_{th}$ CUE},\\ 0,&\text{otherwise}.\end{cases} \]  
where \(\psi_{k}^{f}\in\{0,1\}\) shows whether small cell users share the sub-channel \(f\) with cellular users or not. Here, the impact analysis of various factors on interference in a D2D-enabled cellular network is limited to performance matrices of small cell and D2D pairs density, SINR, and distances.
###### Abstract  
This paper aims to study the preparation and viscoelastic properties of asphalt binder modified by tetrahydrofuran soluble fraction (THFS) extracted from direct coal liquefaction residue. The modified asphalt binders, which blended with SK-90 (control asphalt binder) and 4%, 6%, 8% and 10% THFS (by weight of SK-90), were fabricated. The preparation process for asphalt binder was optimized in terms of the orthogonal array test strategy and gray correlation analysis results. The properties of asphalt binder were measured by applying Penetration performance grade and Superpave performance grade specifications. In addition, the temperature step and frequency sweep test in Dynamic Shear Rheometer were conducted to predict the rheological behavior, temperature and frequency susceptibility of asphalt binder. The test results suggested the optimal preparation process, such as 150 \({}^{\circ}\)C shearing temperature, 45 min shearing time and 4000 rpm shearing rate. Subsequently, the addition of THFS was beneficial in increasing the high-temperature properties but decreased the low-temperature properties and resistance to fatigue. The content analysis of THFS showed the percentage of 4-6% achieved a balance in the high-and-low temperature properties of asphalt binder. The asphalt binder with higher THFS content exhibited higher resistance to rutting and less sensitivity to frequency and temperature.  
Keywords:direct coal liquefaction residue; tetrahydrofuran soluble fraction; THFS; preparation process; viscoelastic properties; temperature susceptibility; frequency susceptibility +
Footnote †: journal: Applied Sciences
# Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq
## Methodology
### Weibull Distribution  
Wind speed is not constant and is continuously changing. Hence, turbine design requires full data and knowledge concerning how often the wind blows strongly. This is considered an essential step to predict the energy production of wind turbines. In order to analyze the wind data, wind speed data are sorted into categories and then the relationship between the percentage of repetitions distribution and the wind speed is determined. The distribution appears as a curve and the peak of the curve represents the most common wind speed. Weibull distribution, which is the most common model in statistics and probability laws, can be used for many natural phenomena. The Weibull distribution includes more than one probability distribution function and a two-parameter function, which are the basis for wind energy applications and wind speed data analysis [4]. This function can be expressed by the following equation:  
\[f(v)=\frac{k}{c}\Big{(}\frac{v}{c}\Big{)}^{k-1}exp\bigg{(}-\Big{(}\frac{v}{c} \Big{)}^{k}\bigg{)}. \tag{5}\]  
where: \(f(v)\geq 0\), \(v\geq 0\); \(k>0\), \(c>0\).  
\(f(v)\): Function probability of wind speed \(v\).  
\(k\): The shape of the parameter, which indicates the fluctuation or stability of the wind speed \(v\), which is a dimensionless parameter.  
\(c\): Scale parameter (m/s), which is associated with the mean wind speed.  
There are different methods used to estimate the dimensionless scale and shape parameters of the Weibull distribution function, such as Energy Pattern Factor method, Probability Plot method, Modified Moment method, Maximum Likelihood method, Hazard Plot method and Mabchour's method (MMab). The proposed method by [PERSON] (1999) was selected in the assessment of wind energy potential \(k\) and \(c\) as [10]:  
\[k=1+(0.483\times(v-2))^{0.51} \tag{6}\]  
\[c=\frac{v}{\Gamma(1+1/k)} \tag{7}\]
## 3 Results
### Further Analysis of New Model
#### 3.2.1 Model Performance Under Different Conditions
In summary, the performance of New Model was superior to other existing \(C_{d}\) models, with higher accuracy and more robustness, but it works better over the vegetated surface than over the non-vegetated surface. Figure 11: The same as Figure 10, but for different land cover types. Figure 10: Validation accuracy of the \(R_{nl\text{-}Cd\text{-}NM}\) estimates at different moments during 9:30–14:30 hours. The red and blue dot dash lines represent the Bias and rRMSE, respectively.
## 2 Materials and Methods  
### Finite Element Modeling  
For the finite element tooth model, a normal occlusion adult tooth model (Model-i21D-400G, Nissin Dental Products(r), Kyoto, Japan) was used. Maximillary right teeth were three-dimensionally laser-scanned and created as a computer file. The arch form was modeled based on the Broad arch form of Ormco (Orange, CA, USA). The size of the bracket was modeled based on the Micro-arch (Tomy Co., Tokyo, Japan) stainless steel bracket [11]. A reproduction of the entire maxillary arch was made through a mirror image of the teeth on the right side of the maxilla. Finite element analysis calculated only one side and made it mirror-symmetric when imaging the result, making it possible to reduce the computation time. To make the extracted model, the maxillary first premolar was removed to complete the modeling of the tooth part. Each tooth is independent, having a contact point with an adjacent tooth through a contact point on the mesio-distal side [12]. Tooth inclination and angulation were arranged with reference to the angle as described previously. Brackets were positioned at the facial axis points of each crown [13; 14; 15]. The curve of Spee and the curve of Wilson were not established (Figure 1).  
A stainless-steel wire (0.019 \(\times\) 0.025 in) was used for the analysis. It was set to slide without friction between the arch wire and the bracket. No play was allowed between the arch wire and the bracket. The thickness of the periodontal ligament was modeled as 0.2 mm based on a study by [PERSON] and [PERSON] et al. [16; 17]. It was assumed to be an isotropic, homogeneous, linear elastomer. The alveolar bone was modeled so that the level was located 1 mm apical from the cementoanamel junction (CE) [18]. The ARH was made of stainless steel with a diameter of 0.9 mm. The retraction force was 300 g on one side. [PERSON]'s modulus and [PERSON]'s ratio (Table 1) were based on previous studies [19; 20; 21; 22].  
Figure 1: Finite element modeling of teeth, brackets, and archive.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Weighting Combination  
Above, we obtain the predictive values of textual and pictorial regions of an SCI. To derive the overall predictive value of this SCI, in this study, we propose an activity weighting strategy to fuse the predictive values of textual and pictorial regions and this strategy is based on the properties of human vision. In general, human vision has greater sensitivity to the high-frequency content (for example, edges and textures) than the background content with slight variation in an image. Thus, the degradation of the high-frequency content is easier to find by human vision than the background content. In this study, to quantify the high-frequency characteristic in an SCI, the activity measure of the gradient map of this SCI is adopted.  
The activity measure can describe the change degree of the image content [19]. Here, the activity measure map \(A(x,y)\) of an image \(f(x,y)\) is defined as  
\[A(x,y)=\gamma V_{1}(x,y)+(1-\gamma)V_{2}(x,y) \tag{35}\]  
where \(V_{1}(x,y)\) denotes the one-distance variation in diagonal orientations; \(V_{2}(x,y)\) represents the two-distance change in the horizontal and vertical orientations; and \(\gamma\) stands for a weighting coefficient to tune the combination of \(V_{1}(x,y)\) and \(V_{2}(x,y)\). In [19], the optimum performance of the activity measure can be achieved when \(\gamma\) ranges from 0.3 to 0.5. More detail about \(\gamma\) can be found in [19]. In this paper, \(\gamma\) is set to 0.4. \(V_{1}(x,y)\) and \(V_{2}(x,y)\) are defined as  
\[\begin{array}{c}V_{1}(x,y)=\left(f(x,y)-f(x-1,y-1)\right)^{2}+\left(f(x,y)-f (x+1,y+1)\right)^{2}\\ +\left(f(x,y)-f(x-1,y+1)\right)^{2}+\left(f(x,y)-f(x+1,y-1)\right)^{2}\end{array} \tag{36}\]\[V_{2}(x,y)=\left(f(x-1,y-1)-f(x+1,y+1)\right)^{2}+\left(f(x-1,y+1)-f(x+1,y-1) \right)^{2} \tag{37}\]  
In this paper, the Prewitt filter is used first to compute the gradient map of textual and pictorial regions via (3) and \(M^{1}(x,y)\) in (3) denotes this gradient map. Secondly, we compute the activity measure maps \(A_{T}^{G}(x,y)\) and \(A_{P}^{G}(x,y)\) of the gradient maps of textual and pictorial regions via (35), respectively. Finally, in this paper, the predictive value \(Q\) of a distorted SCI is defined as  
\[Q=\frac{1}{A_{T}^{M,G}+A_{P}^{M,G}}(A_{T}^{M,G}Q_{T}+A_{P}^{M,G}Q_{P}) \tag{38}\]  
where \(Q_{T}\) and \(Q_{P}\) denote, respectively, the predictive values of textual and pictorial regions calculated in (31) and (34); and \(A_{T}^{M,G}\) and \(A_{P}^{M,G}\) represent the mean activity measure values of the gradient maps of textual and pictorial regions in this SCI, respectively. \(A_{T}^{M,G}\) and \(A_{P}^{M,G}\) are defined as  
\[A_{T}^{M,G}=\frac{1}{N_{T}^{G}}\sum_{n=1}^{N_{T}}A_{T}^{G}(x,y) \tag{39}\]  
\[A_{P}^{M,G}=\frac{1}{N_{P}^{G}}\sum_{n=1}^{N_{P}}A_{P}^{G}(x,y) \tag{40}\]  
where \(N_{T}^{G}\) and \(N_{P}^{G}\) denote the numbers of pixels in the gradient maps of textual and pictorial regions, respectively.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### Finite Element Model of UHS  
1. Material parameters  
The top plate and bottom basin of UHS are made of ZG20 MnMo low alloy steel with an elastic modulus of 196 GPa and a Poisson's ratio of 0.3.
2. Mesh partition  
To ensure the calculational accuracy, the FE model of UHS was simplified to reduce the computational costs, and the simplified model is shown in Figure 5. The element type of the component of UHS adopts C3D10 in this model.  
3. Boundary conditions and loads  
The bottom of the bottom basin is completely fixed by being \"pinned\", and the upper surface of the top plate is coupled with the reference point \"RP\" to constrain its translational displacement in the \(x\)-axis and \(y\)-axis directions through \"coupling\". In order to ensure a good convergence of the model, torque loading is replaced by angular displacement loading, and the angular displacement is loaded by a step of 3\({}^{\circ}\) (0.05 rad) around the \(z\)-axis.  
Figure 5: Numerical model of UHS.  
Definition of interaction  
The normal behavior between the bottom basin and the top plate adopts the \"hard contact\" model, and the tangential behavior adopts the \"penalty friction\".
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 3 Development of the Technology to Improve the Recycled Fine Aggregate Quality
### Principle of Technology  
Technology is needed to resolve the quality decline, due to impurities, by processing the waste materials, implementing technology to remove mortar from the surface of the recycled fine aggregate, and producing recycled fine aggregate with a density of 2.3 g/cm\({}^{3}\) or more, an absorption rate of less than 4%, and organic impurity contents of less than 0.3%. The appearance and perspective drawing of the equipment for stripping impurities and recycled fine aggregates, and the entire production systems, are shown in Figures 3 and 4, respectively.  
Figure 3: Appearance and perspective drawing of stripping the removal equipment for the impurities of recycled fine aggregate.  
Figure 2: The principle of air-blower impurity sorting.  
These facilities are closed and can accommodate a multi-phase flow. The recycled fine aggregate is stripped by the Speed Adjusting Rotor Hammer, and the wind pressure and direction are varied, three times, to remove the impurities.  
To separate the aggregate and impurities, a knife-type air-speed supply device and an inhaling device are used to capture the impurities. The wind pressure used to separate the impurities is generated by a recycling fan, with a capacity of 120 m\({}^{3}\)/min and the structure between the upper rotation rotor and final discharge phase is enclosed to prevent the scattering of dust. In addition, by adjusting the wind velocity and absorption volume, the system can accommodate various impurities. Furthermore, the Speed Adjusting Rotor Hammer rotational impact device, accelerates the initially supplied aggregate, and the organic impurity is separated by the absorption device in the upper part, due to the wind speed of the rotational force. Mortar on the aggregate surface is removed through the stripping plate. The principle of mortar removal is shown schematically, in Figure 5.  
Figure 4: The production process of the technology development scheme.  
Figure 5: The principle of mortar removal.  
Experiments and Results of the Recycled Fine Aggregate Using Stripping Removal Equipment for the Impurities of Recycled Fine Aggregate  
The equipment developed in this study removed cement paste, aggregate particles, and the impurities attached to the surface of recycled aggregate. Removal of the cement paste increased the absolute dry density, decreased the absorption rate, and increased the percentage of the absolute volume of the particle shape. Additionally, through the removal of aggregate particles, the penetration rate of 0.08 mm was reduced and the content of organic and inorganic impurities was reduced through the removal of impurities.
## 7 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
ADAM Adaptive Moment Estimation  
ADAS Advanced Driving Assistance Systems  
BMDV German Federal Ministry of Digital and Transport  
BMWK German Federal Ministry for Economic Affairs and Climate Action  
BRDF Bidirectional Reflectance Distribution Function  
CPU Central Processing Unit  
DL Deep Learning  
GPU Graphics Processing Unit  
KNN k-Nearest Neighbor  
LiDAR Light Detection and Ranging  
LSTM Long Short-Term Memory  
MAC Multiply-Accumulate  
MAE Mean Absolute Error  
MEMS Micro-Electromechanical System  
ML Machine Learning  
MSA Multihead Self-Attention  
PIDL Physics-Informed Deep Learning  
PREC Precipitation Rate Estimation and Classification  
PSU Perception Sensor Unit  
ResNet Residual Network  
RGB Red Green Blue  
ROS Robot Operating System  
SA Self-Attention  
SGD Stochastic Gradient Descent  
SUT Sensors Under Test  
SVM Support Vector Machine  
SYNOP Surface Synoptic Observations  
ViT Vision Transformer  
ViViT Video Vision Transformer  
WMO World Meteorological Organization  
WSU Weather Sensor Unit \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{l}{**(a)** Convolution-LSTM.} \\ \hline
**Parameter** & **Range** & \\ \hline Image size & (32, 128) & \\ Frames & 200 & - \\ Kernel size & 3–7 & 2 \\ Num channels & 3–7 & \\ Hidden state size & 32–128 & \\ MLP dim & 32–128 & \\ \hline \hline \multicolumn{1}{l}{**(b)** 3D Residual Network (ResNet3D).} \\ \hline
**Parameter** & **Range** & **ResNet3D-18** \\ \hline Image size & (32, 128) & 224 \\ Frames & 200 & - \\ Blocks/Layer & 1-3 & 2 \\ Kernels size & 3–7 & (1, 3, 7) \\ Num channels & 1-64 & 64–512 \\ Bias & True, False & False \\ Batch size & 4–128 & - \\ MLP dim & 16–128 & 512 \\ \hline \hline \multicolumn{1}{l}{**\# Parameters:**} & 2.67 M max & 33.7 M \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Best performing model parameterization for the investigated transformer architectures. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{l}{**(d)** Video Vision Transformer (ViViT).} \\ \hline
**Parameter** & **Range** & **ViViT** \\ \hline Image size & (32, 128) & 224 \\ Frames & 200 & 32 \\ Image patch & (2–32, 4–128) & 16–16 \\ Frame patch & 2–20 & 2 \\ Dim & 8–128 & 768 \\ Spat depth & 1–3 & 1 \\ Temp depth & 1–3 & 4 \\ Heads & 8–128 & 12 \\ Head dim & 64 & 64 \\ MLP dim & 8–128 & 3072 \\ Pooling & \{cls, mean\} & \{cls, mean\} \\ \hline
**\# Parameters:** & 46.7 M max & 55.2 M \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._ \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{l}{(**a**) Conv-LSTM-PREC.} \\ \hline
**Parameter** & **Class** & **Regression** \\ \hline Image size & (32, 128) & (32, 128) \\ Frames & 200 & 200 \\ Frame patch & (32, 128) & (32, 128) \\ Frame patch & 2 & 2 \\ Dimension & 46 & 76 \\ Spat depth & 1 & 3 \\ Temp depth & 1 & 1 \\ Heads & 77 & 8 \\ Head dim & 64 & 64 \\ MLP dim & 95 & 29 \\ Pooling & cls token & cls token \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best performing model parameterization for the investigated baseline architectures.
# Vesicles and Reservoirs of Basic Lava Flows in the Laobeishan and Huoshaoshan Volcano, NE China
## 1 Introduction  
In recent years, many volcanic hydrocarbon reservoirs have been discovered in petro-liferous basins such as Songliao and Junggar in China, Neuquen in Argentina, and Bamer in India [1; 2; 3; 4]. Lava reservoirs account for approximately 60%. Lava flows rich in vesicles can also form good oil and gas reservoirs, and the top vesicles zone can even develop highly productive oil and gas reservoirs [5; 6; 7]. In lava reservoirs, vesicle development affects the reservoir performance of volcanic rocks and controls the reservoir quality [8; 9; 10]. Therefore, vesicles have received considerable attention, particularly focusing on their zonation [11; 12; 13], their origin [14; 15; 16; 17], main controlling factors [18; 19], filling [20] and shape [21; 22; 23; 24], with a range of results. However, to date, there has been relatively little research on lava reservoirs, including the relationship between vesicles and the facies belt, the assessment of the vertical zonation of vesicles, and the original connectivity of vesicles.  
To explore the vertical and lateral variations of vesicles in lava flows, the Quaternary Laobeishan and Huoshaoshan of Wudalianchi, which are similar to the alkaline basalts commonly found in fault basins, were selected as research examples. Research was conducted on different facies belts with vesicle shape, distribution, arrangement, quantity, size, and connectivity to fractures. This study is of theoretical importance in examining the degassing of lava flows and the mechanism of vesicle growth, which is of practical value in oil and gas exploration and provides a reference for hydrocarbon exploration in basic lava flow reservoirs.