esa-sceva/satcom-chunk-collection huggingface dataset:
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 2 Increasing the Surface Area of Graphene
### Buckling Behavior of Graphene  
The relationship between graphene's surface area and its buckling behavior can be connected through the adhesive interactions with the substrate, which play a crucial role in determining the material's structural stability. The study by [PERSON] et al. [11] showed that for graphene sheets supported on a substrate, the critical buckling stress could reach 4.39 N/m, and the critical buckling strain could be as high as 1.58%, which was about ten times greater than those for free-standing sheets [11]. This enhancement was primarily due to increased adhesion rather than the size of the graphene itself, as the critical buckling strain remained largely independent of its size for sheets longer than 20 nm. Additionally, the energy analysis indicated that the competition between adhesion energy and strain energy was crucial, suggesting that a larger surface area could improve adhesion and thus reduce the likelihood of buckling under compression. The buckling behavior was more directly correlated with the sheet's size, even though a larger surface area may improve adhesive interactions. It was subjected to compressive loads that tend to wrinkle, with the wavelength, amplitude, and direction of the wrinkles being adjustable by modifying the boundary conditions at the edges of its sheets [20]. Its modification to increase its surface area not only improves its potential for applications like energy storage [35] and catalysis [36] but also strengthens its mechanical resilience against buckling due to enhanced adhesion with the underlying substrate [38]. The schematic shown in Figure 5 illustrates the formation of graphene with an increased surface-to-volume ratio through buckling under compressive load. A compressive force is applied uniaxially on the graphene sheet. As the tensile load increases, the graphene sheet becomes unstable and buckles, forming a periodic, wrinkled structure. This buckling deformation significantly increases the surface area of the graphene compared to its initial flat state. The resulting morphology, including the amplitude and wavelength of the buckles, is influenced by factors such as the magnitude and direction of the tensile load, the number of graphene layers, the presence of defects, and the interaction with the substrate.
## 3 Proposed Identification Methods
### Proposed Hybrid Methods
#### 3.5.3 HPSO-QN Multi Local Search Method  
The third method, the HPSO-QN multi local search method (MLSM), improves upon the previous HPSO-QN SLSM by incorporating a new approach for balancing global and local search. The algorithm begins with the SPSO algorithm until a predefined stopping criterion is reached. Then, a specified percentage of the best particles is selected for the QN method, allowing the algorithm to focus on multiple promising solutions. The selected particles are determined by  
\[N_{QN}=N_{PSO}\times B \tag{21}\]  
where \(B\) represents the percentage of selected best particles and \(N_{PSO}\) denotes the total number of particles in the PSO algorithm. The QN method is then applied to each selected particle, and the \(gBest\) solution is replaced by the best solution found among the selected particles. This hybrid approach achieves a balance between global and local search by utilizing PSO to explore the search space and identify promising solutions, while QN fine tunes these solutions, increasing the likelihood of finding the global minimum. The pseudo-code of the HPSO-QN MLSM algorithm is presented in Algorithm 5.  
```
0:\(N_{PSO}\), \(N_{QN}\), \(k_{max}\), \(x_{min}\), \(x_{max}\), \(w_{min}\), \(w_{max}\), \(\varphi\), \(c_{1}\), \(c_{2}\) and \(F_{lh}\)
1: Set \(k=0\), randomly initialize particles' positions \(x_{i}(k)\) and velocities \(v_{i}(k)\)
2:repeat
3: set \(k=k+1\).
4:for each particle \(P_{k}(i),\ i=1,2,..N_{PSO}\)do
5: Evaluate \(Fcost(x_{k}(i))\) using Equation (8)
6: Update \(pBest_{k}(i)\) and \(gBest_{k}\) as shown in Algorithm 1
7: Calculate \(v_{k+1}(i)\) and \(x_{k+1}(i)\) using Equations (13) and (10)
8:endfor
9: Select \(N_{QN}\) particles for QN local search
10:for each particle \(Q(j),\ j=1,2,..N_{QN}\)do
11:\(x_{0}(j)=pBest_{k}(j)\)
12:\(QN(j)=fminunc(Fcost(x),x_{0}(j)\), 'Algorithm', 'quasi-newton')
13:if\(Fcost(QN(j))<Fcost(gBest_{k})\)then\(gBest_{k}=QN(j)\)
14:endif
15:endfor
16:until the termination criterion is met
```  
**Algorithm 5** HPSO-QN Multi Local Search Method.  
The proposed methods were implemented and evaluated on a computer system with the following specifications: 16 GB of RAM, a 64-bit operating system, and an Intel(R) Xeon(R) E-2124 CPU @ 3.30 GHz processor. The simulations were conducted using Matlab 2023a software [58].
## 4 Case Study Results and Discussion
### Relay Operating Time Calculation  
Using linear programming, the TMS value was calculated as a function of the fault location point.  
Substituting the TMS value and the obtained value of the relay constant in Equation (4), we obtain the operating time of each protection relay.  
Fault 2: OCR\({}_{2}\) with OCR\({}_{1}\):  
\[\begin{array}{c}\text{OCR1}\text{ }t_{\text{op1}}=a_{1}\text{(TMS}_{1})=2.27 \cdot 0.17=0.39\text{ s}\\ \text{OCR2}\text{ }t_{\text{op2}}=a_{2}\text{(TMS}_{2})=3.33\cdot 0.030=0.10\text{ s}\end{array} \tag{18}\]  
Fault 7: Recloser with OCR\({}_{2}\):  
\[\begin{array}{c}\text{OCR2}\text{ }t_{\text{op2}}=a_{2}\text{(TMS}_{2})=3.49 \cdot 0.038=0.13\text{ s}\\ \text{RECLOSER}\text{ }t_{\text{opRC}}=a_{\text{RC}}\text{(TMS}_{\text{RC}})=2.21 \cdot 0.11=0.24\text{ s}\end{array} \tag{19}\]Fault 21: OCR\({}_{3}\) with Recloser  
\[\begin{split}\text{OCR3}\;t_{\text{op3}}=a_{3}(\text{TMS}_{3})=4.57 \text{-}0.065=0.30\;\text{s}\\ \text{RECLOSER}\;t_{\text{opRC}}=a_{\text{RC}}(\text{TMS}_{\text{RC}})=3.2 3\text{-}0.025=0.1\;\text{s}\end{split} \tag{20}\]  
The values of constants \(a\) and TMS and relay operation times _top_ for the rest of the scenarios are shown in Appendix A Tables A1 and A2. Table A3, shows a comparison of the operation times between the coordination performed in a classical way and with the calculation of adjustments carried out by using LP. Implementing the results obtained in the simulation software
## 2 Study Area  
The case study presented in the current research is located on the administrative territory of Floresti village, Cluj County, Romania (Figure 1), presenting itself as a true model of torrential organism with a major dynamic in terms of surface erosion. The shape, dimensions, orientation, geological substrate and land use of the analysed torrential area allows the application of spatial analysis models in order to identify the best methods to reduce surface erosion, depth and its further development, by modelling dynamic scenarios and comparing them. The significant importance of the case study also stands out in terms of its spatial territorial location. The torrential area is located on the southwestern slope of Savadisla Valley in its upper third, having the potential impact of changing the boundary of neighbouring watersheds due to possible regressive development with a direct effect on the water scale that separates Savadisla Valley from Somesu Mic Valley.  
Regarding its shape, the classic form of the torrent with all its elements stands out: reception basin, drainage channel and dejection cone, elements affected by erosion processes generated mainly by the surface runoff that affects lateral slopes. The runoff in the collecting channel generates both a deep erosion, which leads to a deepening of the drainage channel, as well as a strong regressive erosion by affecting the receiving basin and its migration to the adjacent territory. The accentuated dynamic of the analysed torrential area and its impact in the territory that generate spatial vulnerability and major risk regarding the agricultural activities carried out in the area is ensured mainly by the land use that is absent. The whole surface is represented by clays, marls and limestone directly exposed to the process of erosion, transport and accumulation. These characteristics combined with the general orientation of the torrential area in the NE-SW direction, which contribute to a high degree of insolation and at the same time to a faster disintegration of clays and marls, are making them easier to erode in intervals of heavy rains, highly  
Figure 1: Geographical position of the study area.  
intensifying the risk on the slope on which the torrential area is developed, due to the downstream extension of the dejection cone and through changes brought to the landscape and the suitability of the land after the accumulation of eroded material with different characteristics in comparison with the rest of the area.  
All the aspects listed above highlight the impact that this type of erosion process has on the territory. The high predisposition to erosion due to land use and the high dynamics of secondary erosion processes installed on the torrential area demonstrates the need for the application of spatial analysis models to evaluate the current stage and to simulate its evolution over time based on hydraulic modelling scenarios. Thus, a real case study used to identify methodologies and methods for choosing the best intervention measures to reduce erosion, vulnerability and risk in the territory is elaborated.
## 3 Modeling and Validation
### Wind Turbine Modeling  
The research object of this paper is NREL 5 MW wind turbine [18]. Since the fluid-structure coupling involves both the fluid domain and the solid domain, it needs to be modeled separately. The specific method is as follows: the solid model of the whole 5 MW blade is first established in SOLIDWORKS, which is derived into IGS model, and then post-processed in STAR CCM+ and ABAQUS, respectively.  
Blade layup design is a prerequisite for accurate 3D finite element simulation of real wind turbine blades, and iterative adjustment requires a lot of time. Maximizing the restoration of the real blade lay-up while ensuring the stability and convergence of the finite element calculation is one of the difficulties of this paper's research. In this paper, we choose the literature [19] as the benchmark, which is a study of NREL 5 MW offshore floating wind turbine by SANDIA labs in 2013, designed according to the design criteria specified by IEC Class I onshore wind turbine standard with the stratification preprocessing software NuMAD. The wind turbine blade lay-up design was verified by FAST and ANSYS. However, NuMAD lay-up can only be imported into ANSYS for analysis. Therefore, this paper will use the composite layer manager module of ABAQUS for blade lay-up design.  
It can be seen from Figure 2 that the wind turbine blades are divided into several sections in the spreading direction and five areas (LE, LE_panel, Web, Cap, TE) in the cutting direction. A single blade is divided into nearly 400 small areas.  
Figure 1: Two-way coupling solution.  
The created IGS solid model is imported into ABAQUS. First of all, the solid model is converted into a shell model without the actual thickness by the shell extraction technique. Then, the blade surface and web are divided into different small areas by cutting the reference plane. Finally, according to the thickness of the composite plies extending from each section of the blade shown in Figure 2b, the details of delamination, fiber angle adjustment and thickness offset are adjusted sequentially in the CM module of ABAQUS to complete the whole finite element delamination design. The established composite laminated blade and the lamination of each section are shown in Figure 3.  
Figure 3: Schematic diagram of wind turbine blade layering model: (**a**) Cap layering model; (**b**) Web layering model; (**c**) LE_panel layering model; (**d**) LE layering model.  
Figure 2: Schematic diagram of blade layering partition of NREL 5 MW wind turbine: (**a**) Schematic diagram of wind turbine blade section; (**b**) Schematic diagram of spanwise zoning of single blade of wind turbine.
###### Abstract  
The rapid development of high-energy and high-power laser technology provides an important experimental means for the research of extreme physical state in the laboratory and for the design of large laser facilities for realizing inertial confinement fusion. However, when the incident laser field is very strong, the Kerr effect of materials affects the nominal performance of optical elements. In this work, the impact of Kerr effect on the focusing performance of an optical lens is studied by calculating and comparing the filed patterns of focal spots for three different incident laser beams together with three different levels of light intensities. The traditional transfer function of an optical lens is firstly modified according to the theory of nonlinear Kerr effect. We use the two-dimensional fast Fourier transform algorithm and angular spectrum algorithm to numerically calculate the field distributions of focal spots in the nominal focal plane of lens and its adjacent planes based on the Fresnel diffraction integral formula. The obtained results show that the Kerr effect affects the focusing characteristics of lens, especially for the incidence of high-order Gaussian beams, such as Hermite-Gaussian beams and Laguerre-Gaussian beams. At the same time, the focal length and refractive index of lens also change the field patterns of focal spots. The presented methodology is of great value in engineering applications where the practical problem with beam size up to 100 mm can be calculated using a common laptop computer. The work provides an efficient numerical technique for high-intensity incident laser beams focused by lens that takes Kerr effect into consideration, which has potential applications in high energy density physics and large laser facilities for inertial confinement fusion.  
high-intensity laser; Kerr effect; optical lens; focal spot; numerical analysis +
Footnote †: journal: Applied Physics Letters
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 3 Results
### Compared with Different Fiber-Reinforced Asphal Mixture  
Figure 8 shows the test results of the Marshall Stability, high-temperature stability, low-temperature stability and water sensitivity of asphalt mixture containing a different type of fiber. The optimal dosage of each fiber was added in the asphalt mixture. According to the test results above, the optimum dosage of basalt fiber is about 0.3%. The optimum dosage of polyester fiber and lignin fibers are 0.4% and 0.35%, which were recommended by the manufacturer. In contrast with the control group, after adding basalt fiber, polyester fiber and lignin fiber, the MS of asphalt mixture increases by 19.6%, 14.5% and 6.8%, respectively. Figure 8a also shows that the value of MS of asphalt mixture containing basalt fiber is higher than the other fibers. Figure 8b shows that the value of DS of basalt fiber-reinforced asphalt mixture and the polyester fiber-reinforced asphalt mixture are higher than lignin fiber-reinforced asphalt mixture. It can be seen from Table 5 that basalt fiber and polyester fiber have better mechanical properties than lignin fiber, which is beneficial for improving the rutting resistance and the shear capacity of asphalt mixture. Figure 8c shows the maximum bending strength of asphalt mixtures containing different kinds of fibers. As can be seen from Figure 8c, the maximum bending strength of basalt fiber-reinforced asphalt mixture is higher than that of lignin fiber-reinforced asphalt mixture and polyester fiber-reinforced asphalt mixture. The basalt fiber with higher tensile strength crosses the crack faces and slows down the delimitation growthof asphalt mixture. This may be the reason why the basalt fiber asphalt mixture has better anti-cracking properties at low temperatures. Figure 8d shows the value of TSR of the asphalt mixture containing three kinds of fiber and the control sample. As seen in Figure 8d, the value of the asphalt mixture containing basalt fiber is lower than the asphalt mixture containing polyester fiber. However, it is higher than the asphalt mixture with lignin fiber and the control sample.  
Figure 7: Tensile strength ratio (TSR) of asphalt mixture containing various dosages of basalt fiber.
## 4 Conclusions  
In conclusion, this paper has successfully addressed the critical challenge of detecting scratches on textured materials by developing an enhanced scratch defect detection system. Our approach combines optimized photometric stereo vision with FFT-Gabor filtering, enabling a more accurate and robust detection of defects. The optimized photometric stereo vision system captures images under various lighting conditions, which are then processed to generate a surface gradient image that mitigates the effects of high reflections and shadows. The FFT-Gabor filtering technique selectively enhances the visibility of defects while suppressing the inherent texture patterns, thereby improving the detectability of scratches. The experimental results demonstrate the effectiveness and superiority of our method, achieving a 100% success rate in scratch capture and a recognition detection rate of 98.43% \(\pm\) 1.0%, validating its applicability across a range of textured materials. However, it is acknowledged that the proposed system faces limitations, such as increased computational complexity due to the use of advanced imaging techniques and the potential variability in performance across different textures. Future work will focus on optimizing the algorithm to reduce computational demands, making the system suitable for real-time applications. Additionally, we aim to enhance the adaptability of the system to accommodate a wider variety of lighting conditions and texture variations, ensuring its robustness across diverse scenarios. These improvements will further solidify the practical utility of our method in industrial settings and contribute to advancements in automated quality control systems for textured materials. Conceptualization, Y.Y. and M.L. ; Methodology, Y.Y. and M.L. ; Validation, Y.Y., W.S. and K.Z. ; Formal analysis, M.L. ; Investigation, Y.Y., W.S. and K.Z. ; Resources, W.S. and M.L. ; Data curation, W.S. and K.Z. ; Writing--original draft, Y.Y. ; Writing--review & editing, M.L. ; Visualization, Y.Y. and W.S. ; Supervision, K.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Case 4: Design Automation  
The design automation case presents a proof-of-concept on how real usage data can be used to redesign crane components, specifically a rope sheave and bearing. This case was presented earlier in a project seminar presentation by consortium member [PERSON] [54] and in a related demo session. The case was implemented by a consortium member with usage data provided by the university.  
The application development started with an original design of a combination of a rope sheave and a bearing. Then a design-automation-enabled model was created into a form that can leverage usage data. Usage data collected from IImatar was fed to the automated model which created a new design that was optimized according to the usage. The information flow of the case is shown in Figure 10. The results showed a new design for rope sheave-bearing combination whose weight was reduced by 16% and dimensions reduced by 3-10%. The newly selected bearing was 20-30% cheaper.  
The case acts as a proof-of-concept of usage-data-driven design automation when implemented with only one crane, but the method can lead to significant benefits when applying to large fleets and more expensive parts. Being able to rely on actual usage data in dimensioning decisions can even enable deviation from standards when they are robust enough to ensure safety.
# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 2 Methodologies
### LSTM Network for In-Process TCF  
The LSTM network is well-suited for processing and predicting long time series, having been designed to remove the vanishing gradient problem [37]. An LSTM network is used in this paper, which estimates tool-wear values of the next drilling holes using several latest past tool-wear values, which are estimated by the ResNet-based model.  
The network is shown in Figure 6. It consists of an encoder, a decoder and a context tensor. The same LSTM units with the dimensions depicted in Figure 6 are used for both the encoder and decoder.  
The estimated \(n\) values of the TCM model are input to the encoder. Then, a context tensor is calculated by the encoder and a full connection layer, which is input to the decoder by a duplicated vector layer. The decoder estimates \(m\) sequential output values, which are the future tool-wear values.  
Figure 5: ResNet-based model.  
However, the input tool-wear values obtained using TCM may wave violently during the machining process, and the LSTM network may amplify the errors caused by the TCM model. The estimated values should not be directly output.  
A data correction method based on mean values is used to calculate the output from the original estimated values of the decoder. The tool-wear value regarding time \(t\) is calculated as follows for the estimated values (\(\widetilde{y}^{(1)}\), \(\widetilde{y}^{(2)}\),..., \(\widetilde{y}^{(m)}\)).  
\[y_{t}=\frac{1}{k}\sum_{k=1}^{m}\widetilde{y}^{(k)} \tag{7}\]  
Usually, the tool-wear values ascend gradually, which is different from what occurs with tool tipping. Thus, the final tool-wear value is obtained using Equation (8) to further reduce the impact of data fluctuations.  
\[w_{k}=\frac{y_{k}+y_{k-1}}{2} \tag{8}\]
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.4 \(\lambda\) Parameter Analysis  
We examine the relationship between the Lagrangian hyperparameter \(\lambda\) in Equation (16) and the accuracy of our proposed model. The detailed experimental results are presented in Figure 9b. Since the structure is dyadic in nature, we set \(\lambda_{x}=\lambda_{y}=\lambda\) for simplicity, resulting in a trend plot of the accuracy with respect to \(\lambda\) for our proposed model. From the graph, it is evident that as \(\lambda\) increases gradually, the model becomes more effective. The original model performs optimally with the dyadic structure when \(\lambda=0.025\).
## 1 Introduction
Section 5 is devoted to present the usefulness of In-network aggregation in the context of precision agriculture and the ability of PA-RPL to easily integrate such a mechanism compared to the standard RPL. The performance evaluation of PA-RPL and its comparison to the standard RPL through a case study, will be the object of Section 6. In this section, we evaluate the performance of PA-RPL in a realistic application that considers potato farm pest prevention. Section 7 serves to discuss the main advantages of the PA-RPL and its potentials. Finally, Section 8 concludes the paper.