esa-sceva/satcom-chunk-collection huggingface dataset:
# Force Performance Analysis and Numerical Simulation of Assembled Ribbed-Slab Abutments
## 4 Parametric FE Analysis of Assembled Ribbed-Abutments
### Analysis of the Effect of Stiffening Rib Thickness  
The stiffening rib of the flange is key to ensuring the local stability of the ARBA and acts as a role transfer. To investigate the effect of the flange-stiffening rib thickness on the structure, we set the flange-stiffening rib thicknesses of 50 mm, 40 mm, and 30 mm with no analysis of the stiffening rib for the four working conditions, and the rest of the structural conditions remaining unchanged. The corresponding load-deflection curves are presented in Figure 15. The load-displacement curves of the flange models with different  
Figure 14: Load Location Schematic.  
Figure 13: Load–deflection curve of the mid-span section.  
stiffening rib thicknesses exhibited the same trend. The ultimate displacements of the flange model with 50 mm stiffening ribs increased by 10.8, 13.6, and 21.5%, respectively, compared with those of the flange models with 40 mm, 30 mm, and no stiffening ribs, whereas the corresponding ultimate load capacities increased by 2.7, 2.9, and 7.4%, respectively.  
Stiffening ribs are strip-shaped stiffeners designed to ensure the local stability of the flange and transmit concentrated forces. The results in Figure 15 show that, increasing the thickness of the stiffeners during the construction process of bearing vertical loads can improve the stability of the structure, have a significant impact on the ultimate displacement of the structure, and slightly increase the ultimate bearing capacity of the structure, but with a small increase in amplitude.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 2 Synthesis  
Various approaches are available for each of the three synthesis methods, namely solution-blending, melt-blending and in-situ polymerization. An important task in the preparation of polymer/nanoclay composites is to achieve a uniform dispersion of nanoclay in the polymer matrix. The solution-blending technique often yields favorable dispersion of clay layers in the polymer matrix, in comparison with melt blending, due to its low viscosity and high agitation power. On the other hand,  
\begin{table}
\begin{tabular}{c c c} \hline
**Clay Layer Type** & **Clay Group** & **Clay Species** \\ \hline
1:1 & \
\begin{tabular}{c} Rectorite, Kaolinite, \\ Halloysite, Chysotile \\ \end{tabular} &
\begin{tabular}{c} Lizardite, Berthierine, Cronstedtite, Kellyite, \\ Fraipontite, Brindleyite, Dickite, Nacrite \\ \end{tabular} \\ \hline
2:1 & \
\begin{tabular}{c} Smectite, Vermiculite, \\ Pyrophylite talc, Mica, \\ Brittle Mica \\ \end{tabular} &
\begin{tabular}{c} Montmorillonite, Laponite, Sepiolite, Hectorite, \\ Bentonite, Vermiculite, Pyrophyllite, Talc, \\ Muscovite, Paragonite, Clintonite, Bityite \\ \end{tabular} \\ \hline
2:1:1 & Chlorite & Amesite, Cookeite \\ \hline \end{tabular}
\end{table}
Table 1: Classification of clay minerals.  
Figure 1: Crystal structures of clay minerals: (**a**) Type 1:1; (**b**) Type 2:1 [49].  
melt blending is considered industrially viable and ecofriendly, with high economic potential [50]. The in-situ polymerization method is a widely-applied synthesis technique, which provides uniform dispersion and is easy to modify by changing the polymerization conditions [38]. Recently, a number of novel synthesis approaches have been proposed to design polymer/nanoclay composites with unique properties. In this work, different advanced synthesis approaches are reviewed and relationships between the synthesis strategy, the structure formation, physical and mechanical properties and characteristics of the polymer/nanoclay composites are discussed.
# Information and Communication Technology Solutions for the Circular Economy
###### Abstract  
The concept of circular economy (CE) is becoming progressively popular with academia, industry, and policymakers, as a potential path towards a more sustainable economic system. Information and communication technology (ICT) systems have influenced every aspect of modern life and the CE is no exception. Cutting-edge technologies, such as big data, cloud computing, cyber-physical systems, internet of things, virtual and augmented reality, and blockchain, can play an integral role in the embracing of CE concepts and the rollout of CE programs by governments, organizations, and society as a whole. The current paper conducts an extensive academic literature review on prominent ICT solutions paving the way towards a CE. For the categorization of the solutions, a novel two-fold approach is introduced, focusing on both the technological aspect of the solutions (e.g., communications, computing, data analysis, etc.), and the main CE concept(s) employed (i.e., reduce, reuse, recycle and restore) that each solution is the most relevant to. The role of each solution in the transition to CE is highlighted. Results suggest that ICT solutions related to data collection and data analysis, and in particular to the internet of things, blockchain, digital platforms, artificial intelligence algorithms, and software tools, are amongst the most popular solutions proposed by academic researchers. Results also suggest that greater emphasis is placed on the \"reduce\" component of the CE, although ICT solutions for the other \"R\" components, as well as holistic ICT-based solutions, do exist as well. Specific important challenges impeding the adoption of ICT solutions for the CE are also identified and reviewed, with consumer and business attitude, economic costs, possible environmental impacts, lack of education around the CE, and lack of familiarization with modern technologies being found among the most prominent ones.  
ICT; circular economy; IoT; big data; blockchain; artificial intelligence +
Footnote †: journal: _Sustainability_
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
## 3 Application Examples of the Topology Optimization Method
### Sensitive Analysis and Size Optimization  
Topology optimization is the design method in the structural conceptual design stage, so the reestablishment of the three-dimensional model has large subjective factors in the dimensions of each part. We perform design sensitivity analysis and size optimization on the final model of the primary mirror to approach the optimal thicknesses of each part. From Figure 3, it can be found that the compliance function \(C_{A}\) under the axial gravity is the main impact factor, so only RMS under axial gravity (\(RMS_{A}\)) is selected as the objective function in sensitivity analysis and size optimization.  
Initially, eight dimensional variables (DVs) are selected, and the initial specific design parameters of the mirror are shown in Table 3. To reduce the number of DVs and computation, sensitivities of the \(RMS_{A}\) with respect to the unit change of dimensional parameters of the mirror are analyzed with FEM and the results are shown in Figure 7. According to the results of the sensitivity analysis shown in Figure 7, five DVs are selected for further optimization, namely, DV1, DV2, DV4, DV5, and DV6. Table 4 summarizes the lower bound, upper bound, initial values, and optimum values of the DVs for primary mirror size optimization. For further lightweight, the dimension parameters of DV3 and DV8, which have low sensitivities, are reduced to 4 and 3 mm, respectively, and we delete the cylindrical structure for that DV7 which has the lowest sensitivity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable** & **Term** & **Value** \\ \hline DV1 & Front plate thickness & 5 mm \\ DV2 & Outer ring thickness & 5 mm \\ DV3 & Inner ring thickness & 5 mm \\ DV4 & Rib1 thickness & 15 mm \\ DV5 & Rib2 thickness & 8 mm \\ DV6 & Rib3 thickness & 6 mm \\ DV7 & Cylindrical structure radius & 162 mm \\ DV8 & Auxiliary rib thickness & 4 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: DVs of the \(\Phi\)650 mm SiC mirror.  
Figure 6: (a) Initial design based on topology optimization; (b) Modified rib design considering quilting effects.
###### Abstract  
In this study, a numerical methodology is developed to investigate the tip-vortex cavitation of NACA16-020 wings and their flow noise. The numerical method consists of a sequential one-way coupled application of Eulerian and Lagrangian approaches. First, the Eulerian method based on Reynolds-averaged Navier-Stokes equation is applied to predict the single-phase flow field around the wing, with particular emphasis on capturing high-resolution tip-vortex flow structures. Subsequently, the tip-vortex flow field is regenerated by applying the Scully vortex model. Secondly, the Lagrangian approach is applied to predict the tip-vortex cavitation inception and noise of the wing. The initial nuclei are distributed upstream of the wing. The subsequent time-varying size and position of each nucleus are traced by solving spherically symmetric bubble dynamics equations for the nuclei in combination with the flow field predicted from the Eulerian approach. The acoustic pressure at the observer position is computed by modelling each bubble as a point source. The numerical results of the acoustic pressure spectrum are best matched to the measured results when the nuclei number density of freshwater is used. Finally, the current numerical method is applied to the flows of various cavitation numbers. The results reveal that the cavitation inception determined by the predicted acoustic pressure spectrum well matched the experimental result.  
w -tip vortex cavitation; cavitation noise; bubble dynamics; vortex model; nuclei distribution 1
## 3 Full-Scale Experiments
### Data Acquisition  
The sampling interval of the small weather station was set as the shortest sampling interval, i.e., 5 min. The sampling interval of the CO\({}_{2}\) detection recorder and the temperature and humidity recorder were set to 1 min. The sampling interval of the miniature wind pressure gauge was set to 10 s.  
The arrangement of measurement points and instruments was as follows: the height of the outdoor temperature, wind speed, wind direction, and CO\({}_{2}\) concentration measurement points from the ground was approximately 20 m, and the horizontal distance to the test room was within 5 m. For the indoor CO\({}_{2}\) concentration measurement points, according to the provisions of the Indoor Air Quality Standard [32], 1-3 points should be set for rooms less than 50 m\({}^{2}\). To prevent the calculation error of the ventilation rate caused by the uneven distribution of indoor CO\({}_{2}\), the five CO\({}_{2}\) concentration measurement points were arranged in a quincunx shape. The arrangement of the temperature and humidity measurement points was the same as that of the CO\({}_{2}\) concentration measurement points. Based on relevant research [33], the wind pressure measurement points were symmetrically arranged, indoors and outdoors, at a distance of 30 mm from the window. All indoor measurement points were arranged at a height of 1.2 m above the ground, which is consistent with the breathing height of humans. As the warm air blocker can affect the uniformity of the indoor temperature and CO\({}_{2}\) distribution, it was placed on the south side of the room, near the interior wall. The warm air blower blows along the surface of the interior wall, thus reducing interference with the indoor air flow. Figure 3 shows the layout of the measurement points of wind-driven cross-ventilation (CV) and temperature-driven single-sided ventilation (SV).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instrument** & \
\begin{tabular}{c} **Parameter** \\ **Category** \\ \end{tabular} &
\begin{tabular}{c} **Measuring** \\ **Range** \\ \end{tabular} & **Resolution** & **Precision** & **Model** & **Supplier** \\ \hline \multirow{4}{*}{Small weather station} & Wind speed & 1–68 m/s & 0.1 m/s & \(\pm\)5\% (1.5\) m/s) & \multirow{4}{*}{Vantage pro2} & \multirow{4}{*}{DAVIS} \\  & Wind direction & 0\({}^{\circ}\)–360\({}^{\circ}\) & 1\({}^{\circ}\) & \(\pm\)3\({}^{\circ}\) & \\  & Air temperature & \(-\)40–65 \({}^{\circ}\)C & 0.1 \({}^{\circ}\)C & \(\pm\)0.5 \({}^{\circ}\)C & & \\  & Relative humidity & 0–100\% & 1\% & \(\pm\)3\% & & \\ \cline{1-1}  & & & & &
# Regional Geomagnetic Field Modeling Based on Associated Legendre Polynomials
## 3 Data and Modeling Results
### Regional Geomagnetic Anomaly Field Model Construction
Step 2: Determine the interquartile range \(R_{i}q=Q_{3}-Q_{1}\). Step 3: Detect predicted outliers: if \(Q_{1}-1.5R_{i}q\leq D_{i}\leq Q_{3}+1.5R_{i}q\), then \(M_{i}\) is not an outlier; otherwise, \(M_{i}\) is an outlier. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Index** & **N = 40** & **N = 50** & **N = 60** & **N = 70** & **N = 80** & **N = 90** \\ \hline Number of outliers & 7 & 10 & 18 & 28 & 59 & 116 \\ \hline Original RMSE (nT) & 174.31 & 1253.71 & 562.51 & \(2.53\times 10^{6}\) & \(1.88\times 10^{8}\) & \(2.79\times 10^{11}\) \\ \hline Cleaned RMSE (nT) & 5.42 & 4.64 & 3.91 & 3.58 & 3.42 & 3.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of outliers on model accuracy at different truncation levels. Step 4: Use the model coefficient matrix A to predict the geomagnetic data for the surrounding point coordinates, in order to obtain the surrounding point data values \(\{l_{1},l_{2},\ldots,l_{N}\}\). Step 5: Calculate the surrounding point average for each outlier: \(F_{i}=1/N(l_{1}+l_{2}+\ldots+l_{N})\). The obtained result is the result of the re-prediction of the predicted outliers.
## 2 Automatic Identification of Analysis Methods
### Identification Method  
Acceleration response data are employed to construct the bolt state identification method in this research. The acceleration response data are decomposed by VMD into each order of IMF (Equation (1)), and subsequently transformed by HHT. The obtained HHT data still contain scattered information, making limited feature recognition calculations problematic for machine learning. Following the HHT transformation (Equation (8)), the multi-value matrix is diagonalized using SVD decomposition to obtain the diagonal matrix (Equation (9)), which aids in SVM learning. Simultaneously, the permutation entropy is computed based on the original acceleration response data. Both types of feature data are combined to form a new feature vector matrix, which is then used to import feature values into SVM learning. The SVM parameters are determined using PSO, and the trained SVM can judge the status of the bolt preload.  
\[u_{k}(t)=H(\omega,t)=\text{Re}\Bigg{\{}\sum_{j=1}^{n}a_{j}(t)e^{i\int\omega_{j} (t)dt}\Bigg{\}} \tag{8}\]  
\[H(\omega,t)=U\Phi V^{T} \tag{9}\]  
\(U_{k}(t)\) is the IMF of each order after VMD decomposition. \(U\) and \(V\) are the decomposition vectors used by the SVD dimensionality reduction decomposition, with \(U^{T}U=I\), \(V^{T}V=I\), and \(\Phi=\text{diag}\) [\(\sigma_{1}\),..., \(\sigma_{p}/0\)]. The values of each order of the diagonal part of \(\Phi\) are used to form the feature vector. To improve the recognition accuracy, the value of PE is also added, and the feature vector is comprehensively formed. Finally, the bolt state identification flow chart is formed, as shown in Figure 3.
# Optimizing Retention Bunkers in Copper Mines with Numerical Methods and Gradient Descent
## 1 Introduction
This allows for the most efficient use of the operating point of the receiving bins. This article deals with the characteristics of retention bunkers and the problem of abrasive wear of bin elements. Key factors that could improve the efficiency of the receiving bins were identified, and an experiment was designed based on them, whose results were based on the material flow in a built 3D DEM model of the bin. The model was validated under real working conditions in an underground copper ore mine. The impact of all independent factors on the value of dependent factors in the experiment was determined. Optimization of the analyzed receiving bin's operation was conducted using a constrained gradient descent method [32], aiming to minimize the particle speed on the surface of the bin and the total force exerted on the bin, while maintaining required efficiency. The proposed optimization method allowed for considering the impact of all independent factors in the full possible design range, and in a clear, mathematically formalized way, determined the optimal operating point of the device. Thanks to the research results, guidelines for designing future retention bunker receiving bins were formulated. The innovation of the research results lay in the optimization of the bunker receiving bin's design using real structural loads, which had not been clearly defined until now. Based on simulation, it was proven that design changes to these unique structures allow for improvements in the bin's working conditions and potentially reduce damage.
## 4 Analysis
### Assessment of Robustness of SD Model Outputs  
The outputs of the SD model are tested for robustness by screening the variables to identify and select the most sensitive parameters for each target variable in the model. Monte Carlo simulations were performed (one for each target variable) to establish the confidence intervals (CI) for outputs responding to sensitive variables. The variation coefficient \(VC_{i,t}\) of the target variables was calculated for ten years based on the following equation:  
\[VC_{i,t}=\left(\frac{OM95_{i,t}-Om95_{i,t}}{O_{i}^{m}}\right)\times 100 \tag{6}\]  
where \(VC_{i,t}\) is relative variation of target variable \(I\) with respect to the mean using 95% CI; \(OM95_{i,t}\) and \(Om95_{i,t}\) are max. & min. values of the \(ith\) target variable at time \(t\), using the 95% CI; and \(O_{i}^{m}\) is mean value of target variable \(i\). There are 3 categories of response: low where \(VCi\) is less than 50%, moderate where variation coefficient is between 50-100% and high where variation coefficient is higher than 100%  
First, for each of the three responsive parameters (i.e., perception, number of accident and probability valve closed) a univariate analysis is performed to assess the sensitivity of the following target variables: deficient training, inefficient emergency plan, not comply with instruction, no check rules, deficient maintenance, wrong risk assessment, not obey standard and improper safety organization. A uniform distribution was applied. In this way, it is possible to characterize which variables are more sensitive to organizational response. The analysis shows that the influence of the parameter 'Perception' is high for 'Inefficient timely control' and 'Not comply with instructions' target variables, with values above the 100% sensitivity. 'Improper safety organisation', 'Inefficient emergency plan', 'No check rules' and 'Not obey standards' presents a sensitivity between 50% and 100%, Finally, 'adopt unsuitable equipment' is insensitive to the variation of the 'Perception' parameter, with a sensitivity value of about 1%. The 'Max number of accidents' parameter has little influence on the HOE variables, with values in the range between 0.1% and 23%. Finally, the variable 'Probability Valve Closed' has the lowest impact, in the range from 0.1 to 11. In general, it can be observed that the variable 'Perception' is by far the most impacting factor and the target variables of 'Inefficient timely control' and 'Not comply with instruction' are much more sensitive to variation of the reference parameters than the other variables. As multivariate analysis is used to assess the robustness of the model and to define the ranges of variations of the target variable. Results are reported in Table 8 and sensitivity of some variables are shown in Figure 28. See Appendix F Table 6 for list of parameters:
# Recycling Aged Asphalt Using Hard Asphalt Binder for Hot-Mixing Recycled Asphalt Mixture
## 3 Results and Discussion
### Performance of Hot-Mix Recycled Asphalt Mixture Containing Hard Asphalt  
Three kinds of asphalt mixtures were prepared to reveal the difference in performance between the conventional and the hot-mix recycled asphalt mixture (RHMA). The RHMA was designed based on the principle of the high-modulus mixture using the hard asphalt as a binder, containing 50% and 100% RAP, denoted by EME-20@50% and EME-20@100%, respectively. Figure (a)a indicates that EME-20@100% and EME-20@50% had similar dynamic stability (DS). Compared with the ordinary asphalt mixture containing hard asphalt binder, the high-temperature performance was lower.  
As shown in Figure (b)b, the AC-20 specimens had the highest bending strain compared to others, indicating the conventional AC-20 could bear a higher deformation than EME-20 specimens. Furthermore, there was no significant difference between EME-20@50% and EME-20@100%, which means that they have a similar deformation resistance. Figure (c)c shows the bending strength of tested samples; the results indicate that AC-20 has the lowest bending strength. In summary, the specimens designed using the concept of high-modulus asphalt mixture were stiffer than the conventional one, which it led to a greater bending strength and lower bending strain. To compare the low-temperature performance of samples, fracture energy was used, which can reveal the performance differences in terms of energy; the results are shown in Figure (d)d. Figure (d)d shows that EME-20@50% had a similar fracture energy to AC-20, indicating that the incorporation of hard asphalt and 50% RAP had no negative effect on the low-temperature cracking resistance of the asphalt mixture. Moreover, when the RAP content was increased to 100%, the fracture energy of EME-20@100% increased by 23% compared to that of AC-20 due to the fine gradation and high asphalt binder content.  
Figure 9: Rheological properties of recycled asphalt binder: (**a**) creep stiffness; (**b**) creep rate; (**c**) rutting factor; (**d**) fatigue factor.  
Moreover, Figure 10e suggests that EME-20@100% had the highest fatigue life under different stress ratios. The fatigue lives of EME-20@100% and EME-20@50% were both higher than that of AC-20, but the slope of the fatigue curve was greater, indicating that they were more sensitive to stress. This paper used a stress-controlled mode to carry out the fatigue experiment. The experimental results obtained under strain-controlled mode may be different, which should be further studied. In summary, fine gradation and a high asphalt binder content improved low-temperature crack resistance and fatigue life while maintaining a good high-temperature performance of RHMA. Therefore, the high modulus design concept can be adopted to increase RAP content in recycling.  
Figure 10: Performance of hot-mix recycled asphalt mixture containing hard asphalt: (**a**) High-temperature rutting resistance; (**b**) Maximum bending strain; (**c**) Bending strength; (**d**) Fracture energy; (**e**) Fatigue performance.