esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 5 Empirical Modeling of the McKibben Muscle
### Dynamic Equation for Vertical Load System  
Most of the applications based on McKibben muscle involve direct load to the system, which is required for lifting and sustaining a solid body. Therefore, in this paper, the dynamic behavior of the vertical load system was considered within the mathematical formulation. When the static load is pulled up due to contracting force generated from the [PERSON] muscle, it causes friction between the thread and bladder of the McKibben muscle. As suggested in previous section, the dynamic friction model was affected with changes in static load and input excitation's frequency. The equationgenerated for the force dynamic of the vertical load system was properly described and shown in Equations (13)-(15). The implementation of these equations were illustrated as block diagram shown in Figure 4. Meanwhile, the description of model parameters and parameters optimized by PSO algorithm were shown in Table 2.  
\[F_{dyn}=F_{s}(P,\ \varepsilon)-Mg-F_{hys}\big{(}F,\dot{\varepsilon}\big{)} \tag{13}\]  
\[F_{dyn}=ML\ddot{\varepsilon} \tag{14}\]  
\[F_{dyn}=F_{net}(P,\ \varepsilon)-F_{hys}\big{(}F,\dot{\varepsilon}\big{)},\ \ \ \varepsilon>\frac{L-L_{o}}{L} \tag{15}\]  
\[F_{dyn}=F_{net}(P,\ \varepsilon)-F_{elastic}-F_{hys}\big{(}F,\dot{\varepsilon} \big{)},\ \ \ \varepsilon\leq\frac{L-L_{o}}{L} \tag{16}\]
## 5 Safety Assessment
### Structural Assessment  
The seismic assessment was carried out per Eurocode [25], since a literature review [26] and preliminary comparative analyses found it to be more conservative than the local Albanian Seismic Code KTP-N.2-89 [27]. The simulation of seismic actions acting on the structure was conducted by means of kinematic and nonlinear static (pushover) analysis. The performance of the structure at each one of the directions was assessed by means of the N2 method, a displacement-based approach proposed by [28], and implemented in various codes, such as Eurocode 8 [25]. This is a seismic assessment method that consists of estimating the demand of the maximum displacement (i.e., the target displacement, d') on the structure for the design earthquake corresponding to the site class and seismic risk characteristics of the site (i.e., Gjirokstra) and comparing it with the displacement capacity of the walls (d'u). The capacity of the structure was obtained through the pushover analyses, while the seismic demand was defined in Acceleration-Displacement Response Spectrum format according to Anne B of Eurocode 8 [25].  
In addition to the seismic loads, the seismic assessment considered wind and snow loads to validate the model, with the fundamental combination specified in Eurocode EN 1990 Basis of Structural Design [29] and the verifications were carried out for the Near Collapse (NC) requirements. Due to the cultural significance of the building, an importance factor \(\gamma_{\rm I}=1.4\) was applied to estimate the design ground acceleration \(\rm a_{g}=0.5842\) g for a reference peak ground acceleration \(\rm a_{gR}=0.416\) g, considering a probability of exceedance of 2% in 50 years and a life expectancy of 50 years. The evaluation was carried out for both Type 1 and Type 2 spectra. The site class was determined based on the wave velocities obtained from the  
Figure 18: Example of FEM analysis carried out to evaluate and design an engineering solution to stabilize unstable rock blocks. (Photo credit: GEA, aerial photo by [PERSON]).  
geological NDT testing campaign. The correlation proposed by [PERSON] [30] was used to obtain the average shear wave velocities for the different soil strata, resulting in category B for the conglomerate and category A for the flysch.
## 2 Materials and Methods
### Remote-Sensing Calculations  
We retrieved hyperspectral and LiDAR data of our plots from NEON (neonsciences.org accessed on 12 March 2018) for the year 2015. We used a Trimble Geo 7X handheld GPS (Trimble Inc., Sunnyvale, CA, USA) to find accurate plot centers and extracted data for all 16 areas. The image analysis software ENVI (Harris Geospatial Solutions Inc., Broomfield, CO, USA) was used to calculate various VIs for the plot areas, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), Atmospherically Resistant Vegetation Index (ARVI), photochemical reflectance index (PRI), normalized difference nitrogen index (NDNI), and Normalized Difference Lignin Index (NDLI). We examined the differences between the VIs' averages among the vegetation types. We then examined the VIs and the annual average R\({}_{\rm s}\) for each plot, the average growing season (May to September) R\({}_{\rm s}\) of each plot, the average R\({}_{\rm s}\) of each plot for each date sampled, and the average dormant season (October to April) R\({}_{\rm s}\) for correlation. We found the average canopy height of each of the sixteen plots from LiDAR data and then examined the relationship of these values to the overall annual average R\({}_{\rm s}\) for each plot, the average growing season R\({}_{\rm s}\), average dormant season R\({}_{\rm s}\), and the average R\({}_{\rm s}\) of each plot for each date sampled. We incorporated the VIs individually into our base model and observed any changes statistically to assess if they improved the predictability of R\({}_{\rm s}\).
# Machine Learning Sorting Method of Bauxite Based on SE-Enhanced Network
## 2 Design of Bauxite Separation Model
### Improvement of YOLOv4 Algorithm  
In the YOLOv4 network, nine anchor boxes can be preset to determine the length-width ratio of the detection target, and nine anchor boxes can be generated in each grid for detection to predict the bounding box of the target. In the detection of bauxite, the ore forms are different, and the anchor box default by YOLOv4 is not suitable for the detection of bauxite. Therefore, this paper uses the K-means clustering algorithm to cluster the bauxite size in the datasets and calculate the corresponding anchor box value.  
The SE attention mechanism module [20], which screens out the attention for channels by learning the correlation among channels, can be easily embedded into the network model and only adds a small amount of model overhead and complexity. In the bauxite detection task, with the deepening of the training network, the bauxite characteristics gradually weaken, which can easily cause missed detection. However, embedding the SE attention module in the YOLOv4 network can enhance the learning ability of the network, automatically learn the importance of different channel characteristics, and improve the accuracy of bauxite sorting. As shown in Figure 2, the SE module is embedded in the Inception module to form a new SE-Inception module. In this study, the SE attention module is embedded into the Resunit module to form a new SE-Resunit module, and the SE module is embedded behind the CSPn module to form a new SE-CSPn module. The specific structure is shown in Figure 3.  
Figure 1: YOLO v4 network structure.  
Figure 2: Original Inception module (**left**) and SE–Inception module (**right**).
# Speeding up Statistical Tolerance Analysis to Real Time
## 3 Research Question  
A comparatively high number of samples is required to obtain statistically reliable results when performing statistical tolerance simulations (such as tolerance analysis and tolerance synthesis) [20; 21; 22]. However, with an increasing sample size, not only does the accuracy of the obtained results increase, but also the computational effort [23; 24]. Recommendations range from 5000 [25] to 100,000 samples [24]. However, especially for more complex tolerance simulations, a size of 10,000 samples has become established among industrial experts and researchers [26]. The choice of the sampling procedure as well as the corresponding sample size have a significant influence on the reliability of a statistical tolerance simulation, and must therefore always be made with care. Hence, the key to speed up vector-chain-based statistical tolerance simulations is to accelerate the Monte Carlo simulation. The authors face the research question: Is it possible to realize an implementation of the Monte Carlo simulation in real time on a regular desktop computer with a sample size that satisfies the quality requirements in tolerance engineering?  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Floating-Point Format** & **Theoretical Performance** \\ \hline FP16 & 12.90 TFLOPS 1 \\ FP32 & 6.451 TFLOPS \\ FP64 & 201.6 GFLOPS \\ \hline \end{tabular} \({}^{1}\) FLOPS (floating-point operations per second); T = Tera; G = Giga.  
\end{table}
Table 1: Theoretical floating-point number performance of NVIDIA GEFORCE RTX 2060 [19].  
Figure 1: (a) Vectorization with NumPy Arrays. (b) Use of the GPU architecture with CuPy.  
In this paper, the definition of real time is based on response times of only a few milliseconds, which state-of-the-art automation solutions with a programmable logic controller or fieldbus-based production lines manage. In the following sections, statistical tolerance analyses and statistical tolerance syntheses are developed and executed with different programming approaches and varying sample sizes. Therefore, a non-trivial case study of an overconstraint door hinge assembly is used. We aim to quantify (i) how the computation times behave for different sample sizes with different implementations and (ii) whether, and if so how significantly, a tolerance simulation is sped up.
## 4 Experimental Results
### Results
#### 4.3.2 Classification Results  
We compare our proposed approach with different dropout methods and data augmentations using the best hyperparameters and the proposed strategy, as shown in Table 2. In this comparison, we employed a large model, PyramidNet-200, with 26.8 million parameters using the CIFAR100 dataset. Our approach, RSMDA(R), outperformed all of the aforementioned dropout methods and SOTA multi-image methods, except for CutMix, but it showed a competitive performance as compared to CutMix. It placed second showing error rates for top-1 and top-5 of 15.03 and 3.01, respectively. We compare the results based on the top-1 error % by following the trend as mentioned in Table 5 of the work [36].  
\begin{table}
\begin{tabular}{|l|c|c|c|c|c|} \hline
**Models** & **Baselines** & **RE** & **RSMDA (R)** & **RSMDA(C)** & **RSMDA(RC)** \\ \hline \multicolumn{6}{|c|}{**Fashion-MNIST**} \\ \hline  
We also compare our method with deeper and larger models, such as PyramidNet and ResNet110. The proposed approach showed competitive results on CutMix and superior results to the baseline for both of these models, as shown in Table 3.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Cliteria**} & \multirow{3}{*}{**FR**} & \multicolumn{8}{c}{**Blind**} \\ \cline{3-11}  & & & \multicolumn{4}{c}{**Depending on Difference Mean Opinion**} & \multicolumn{8}{c}{**Not Depending on DMOS**} \\ \cline{3-11}  & & & \multicolumn{4}{c}{**Scores (DMOS)**} & \multicolumn{4}{c}{**Not Depending on DMOS**} \\ \cline{3-11}  & **[PERSON]** & **[PERSON] [2]** & **[PERSON] [4]** & **[PERSON] [5]** & **[PERSON] [9]** & **[PERSON] [12]** & **IL\_NIQE** & **[PERSON] [16]** & **[PERSON] [13]** & **[PERSON] [14]** & **Proposed** \\ \hline PLCC & 0.899 & 0.873 & 0.640 & 0.901 & 0.937 & 0.973 & 0.896 & 0.871 & 0.877 & 0.887 & **0.925** \\ \hline SROCC & 0.882 & 0.830 & 0.395 & 0.899 & 0.914 & 0.965 & 0.876 & 0.873 & 0.866 & **0.892** & 0.887 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance evaluation of nine methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Distortion**} & \multirow{3}{*}{**Criteria**} & \multirow{3}{*}{**FR**} & \multicolumn{8}{c}{**Blind**} \\ \cline{3-11}  & & & \multicolumn{4}{c}{**Depending on DMOS**} & \multicolumn{8}{c}{**Not Depending on DMOS**} \\ \cline{3-11}  & & **[PERSON]** & **[PERSON] [2]** & **[PERSON] [4]** & **[PERSON] [5]** & **[PERSON] [9]** & **[PERSON] [12]** & **IL\_NIQE** & **[PERSON] [16]** & **[PERSON] [13]** & **[PERSON] [14]** & **Proposed** \\ \hline \multirow{3}{*}{JP2K} & PLCC & 0.865 & 0.838 & 0.905 & 0.907 & 0.934 & 0.988 & 0.854 & **0.919** & 0.901 & 0.848 & **0.939** \\ \cline{2-11}  & SROCC & 0.857 & 0.839 & 0.866 & 0.863 & 0.832 & 0.961 & 0.861 & **0.886** & 0.870 & 0.837 & **0.887** \\ \hline \multirow{3}{*}{JPEG} & PLCC & 0.485 & 0.214 & 0.729 & 0.695 & 0.744 & 0.916 & 0.533 & **0.722** & 0.456 & 0.626 & **0.673** \\ \cline{2-11}  & SROCC & 0.435 & 0.199 & 0.675 & 0.617 & 0.595 & 0.912 & 0.544 & **0.682** & 0.429 & **0.638** & 0.612 \\ \hline \multirow{3}{*}{WN} & PLCC & 0.937 & 0.928 & 0.904 & 0.917 & 0.962 & 0.988 & 0.927 & 0.858 & **0.950** & 0.925 & **0.943** \\ \cline{2-11}  & SROCC & 0.940 & 0.928 & 0.914 & 0.919 & 0.932 & 0.965 & 0.920 & **0.938** & 0.914 & **0.931** & 0.909 \\ \hline \multirow{3}{*}{Gblur} & PLCC & 0.920 & 0.948 & 0.617 & 0.917 & 0.971 & 0.974 & 0.904 & **0.923** & 0.919 & 0.899 & **0.976** \\ \cline{2-11}  & SROCC & 0.882 & 0.935 & 0.555 & 0.878 & 0.857 & 0.855 & 0.873 & 0.871 & **0.932** & 0.833 & **0.903** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of nine methods for each individual distortion type.
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 3 Graphene Perfect Absorbers without External Mirror
### 'All-Pass Filter-Based Absorber' Scheme  
Most of the THz PAs that have been proposed thus far are of the reflection type. As a result, the relevant modulators are also of the reflection type, meaning that only the reflected wave can be modulated. In 2019, [PERSON] et al. [88] proposed a GPA scheme employing an all-pass filter such that it functions as a highly efficient transmissive modulator in the THz range (Figure 8). All-pass filter generates a very rapid change from 0 to 360 degree in the vicinity of the resonance while maintaining a unity transmission efficiency, and in general, it is necessary to use at least two resonances (or resonators) [89; 90; 91; 92]. In ref. [88], the proposed absorber is composed of two coupled grating resonators with mirror inversion-symmetry as a pure all-pass filter, and the graphene layers are added to introduce loss (Figure 8a). They also theoretically analyzed the proposed scheme using the temporal coupled mode  
Figure 7: Perfect absorbers based on degenerate critical coupling. (**a**) Top: schematic of a GPA consisting of monolayer graphene placed on top of a photonic crystal slab. Bottom: absorption spectra as a function of slab thickness [80]. (**b**) Top: schematic of a PA consisting of Ge disks. Bottom: absorption spectra as a function of disk diameter [83]. Reproduced with permission from [80; 83], © 2023 AIP Publishing LLC; © 2023 WILEY-VCH.  
theory. The two resonators are coupled both indirectly, through the propagation channel with a phase retardation of \(\theta\), and directly, through evanescent coupling with a coupling coefficient of \(\mu\) (Figure 8b). The perfect absorption is obtained when \(\theta=\pi/2\), \(\mu=2/\tau\), and \(\tau_{L1}=\tau_{L2}=\tau/2\), which is completely different from degenerate critical coupling condition [80] because the suggested absorber scheme considers the coupling of two identical resonators. At the optimal structure, a considerably high modulation depth (\(\sim\)70 dB) is achieved via graphene chemical potential variation of \(\sim\)0.15 eV because the proposed transmissive modulator scheme is based on the low transmission state of the near perfect absorption (\(\sim\)99.8%) (Figure 8c,d). In ref. [45], there is another type of PA scheme with a similar inversion symmetry, in which a two-resonance, four-port model is required. Two opposite-direction propagation modes with nonzero in-plane momentum were considered. However, the design and fabrication of the structure will be rather complicated because the scheme works only for an oblique incident angle.
## 4 Experiments and Results
### Teacher Model Evaluation  
To identify the most effective teacher model for knowledge distillation, several architectures were evaluated. The performance metrics for each teacher model are summarized in Table 4.  
From Table 4, EfficientNet-B0 demonstrated the highest balance between accuracy, inference time, and model size among the evaluated teacher models. Although models like ResNet-152 achieved comparable accuracy, their significantly larger size and longer inference times make them less suitable for resource-constrained environments. EfficientNet-B0's superior performance and efficiency make it the optimal choice for our knowledge distillation framework.
###### Abstract  
As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the _embry_ tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study's validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.  
electric mobility; charging infrastructure; charging behavior; energy consumption; grid impact; public charging stations +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Premix, Sampling, and Analysis  
Each vitamin A product was added and mixed with either three vitamin premixes or three vitamin-trace mineral (VTM) premixes (Table 1). The amount added for each vitamin A product was determined such that including 0.30% vitamin premix or 1% VTM premix in the diet would provide the activity of vitamin A commonly used in pig diets (15,000 IU/kg for suckling pig, 12,000 IU/kg for weanling pig, and 8,000 IU/kg for finishing pig). Vitamin or VTM premixes were mixed at the designed levels with each vitamin A source, which were mixed using a paddle mixer for 5 min. The preparations of vitamin and VTM mineral premixes are described previously [9]. This process was repeated to yield six replicates per treatment. Vitamin premixes were stored in a controlled environment chamber setting at 25 \({}^{\circ}\)C and 60% relative humidity. The VTM premixes were stored in a storage room (approximately 22 \({}^{\circ}\)C). At sampling, each sample bag was pulled out at d 0, 90, 180, 270, and 360, and samples were sent immediately after collection to the Ministry of Agriculture and Rural Affairs Feed Efficacy and Safety Evaluation Center (Beijing, China) for vitamin A analysis using a slight modification of the AOAC official method (AOAC 2012.10) [10]. The stability of vitamin A in vitamin premixes and VTM premixes during storage was reported as the residual vitamin activity (% of initial) at each sampling point.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item 1**} & \multicolumn{4}{c}{**Vitamin Premixes**} & \multicolumn{4}{c}{**VTM Premixes**} \\ \cline{2-7}  & **Suckling** & **Weanling** & **Finishing** & **Suckling** & **Weanling** & **Finishing** \\ \hline Vitamin A, \(10^{4}\) IU/kg & 500 & 400 & 267 & 150 & 120 & 80 \\ Vitamin D\({}_{3}\), \(10^{4}\) IU/kg & 67 & 67 & 50 & 20 & 20 & 15 \\ Vitamin E, IU/kg & 50,000 & 33,333 & 25,000 & 10,000 & 3000 & 7000 \\ Vitamin K\({}_{3}\), mg/kg & 3333 & 1000 & 1333 & 1000 & 300 & 400 \\ Vitamin B\({}_{1}\), mg/kg & 167 & 133 & 67 & 50 & 40 & 20 \\ Vitamin B\({}_{2}\), mg/kg & 667 & 333 & 333 & 200 & 100 & 100 \\ Vitamin B\({}_{3}\), mg/kg & 23,333 & 15,000 & 10,000 & 7000 & 3000 & 4000 \\ Vitamin B\({}_{5}\), mg/kg & 13,333 & 11,667 & 10,000 & 4000 & 2000 & 4500 \\ Vitamin B\({}_{6}\), mg/kg & 1667 & 1000 & 667 & 500 & 300 & 200 \\ Folic acid, mg/kg & 5000 & 3333 & 3333 & 1500 & 1000 & 1000 \\ Biotin, mg/kg & 2333 & 2333 & 1000 & 700 & 500 & 300 \\ Vitamin B\({}_{12}\), mg/kg & 20 & 13 & 17 & 6 & 4 & 5 \\ Choline, mg/kg & - & - & - & 80,000 & 40,000 & 20,000 \\ Copper, mg/kg & - & - & - & 1500 & 1500 & 500 \\ Iodine, mg/kg & - & - & - & 12,000 & 10,000 & 4000 \\ Iron, mg/kg & - & - & - & 3500 & 3000 & 1500 \\ Manganese, mg/kg & - & - & - & 12,000 & 10,000 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition of vitamin and vitamin-trace mineral (VTM) premixes.
## 2 Material and Methods
### ANN Architecture Optimization  
The ML method, i.e., ANN, used in the ANN-PM and the Medlyn-PM, considers input variables, including Ta, P, SW, Ca, VPD, NIRv, and NDVI. Usually, in order to reduce over-fitting, the network model is repeatedly trained. Thus, we need to recognize the best ANN structure. In our study, the optimal ANN is determined in terms of mean square error (MSE) while minimizing the number of degrees of freedom based on the Akaike Information Criterion (AIC). AIC is a standard to measure the goodness of fit of the statistical model. AIC encourages the goodness of data fitting but tries to avoid over-fitting. Therefore, the priority model should be the one with the lowest AIC value. Cropland ET is estimated by combining the predictive output of ANN with the PM equation. The calculation formula of the AIC indicator is as follows [53]:  
\[\text{AIC}=\log(\text{MSE})+\frac{2\textsl{q}}{\textsl{n}} \tag{3}\]  
where MSE is mean square error, \(q\) is the total number of parameters in the network, and \(n\) is the number of observations in the training sample.