esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis for Microscopic Crowd Simulation
## 1 Introduction
### State-of-the-Art of Sensitivity Analysis for Crowd Simulations  
Pedestrian simulation experiences similar challenges as other (engineering) disciplines, in which methods for uncertainty quantification are already state-of-the-art: uncertainties in the model input parameters due to a lack of knowledge or limited measurement accuracy. Modelers are interested in the impact of the uncertainty in the input parameters on the simulation outputs, as well as the sensitivities that are associated with each input parameter.  
The interpretation of the term \"sensitivity analysis among crowd modelers differs from the one common in the uncertainty quantification community, a phenomenon that is not restricted to our application [24]. In the latter, the results of sensitivity analysis typically lie in the input parameter space. The analysis assigns an index to each input parameter and ranks them according to their impact on the simulation outcome. In pedestrian dynamics, sensitivity analysis usually refers to studying the impact of the uncertainties in input parameters on the simulation results [32; 33]. That meansthe results lie in the space of the quantity of interest. This kind of study is usually named forward propagation or uncertainty analysis in uncertainty quantification literature. In this work, we use the terminology common to the uncertainty quantification literature.  
We are only aware of a few publications that are related to crowd simulation where sensitivity indices are obtained: [PERSON] et al. use a one-at-a-time method to calculate sensitivity indices for eight input parameters of a Random Forest model for pre-evacuation times while observing the total evacuation time as quantity of interest [34].  
A common approach for crowd simulation is to use force-based models. They are not only applied in pedestrian dynamics, but also in traffic simulation. [PERSON] et al. calculate Sobol' total indices for six parameters of a social force model for car-following [35]. In addition, they identify non-influential parameters by a factor fixing setting and obtain a reduced model version. Systematic sensitivity analysis is generally more common in the adjacent traffic simulation community [36].  
To summarize, global sensitivity analysis methods have barely been used for crowd simulations. We argue that they would be beneficial for our community, because they provide a systematic approach to rank input parameters by their importance. In addition, they can serve as a first step for both calibration and for model analysis. Furthermore, common indices, such as Sobol' indices simplify the comparison of sensitivity studies. Finally, many of the methods are specifically suited for non-linear models, which is the case in pedestrian dynamics.
## 4 The Proposed Measuring Method  
There are six to eight rib plates connecting the main bore and the wire container, as shown in Figure 2b, according to the welding wire capacity. Generally, more ribs cause more variety in light and shadow, generating more measuring complexity; therefore, in this paper, a spool with eight rib plates was assumed. The areas near the rib plates are different to other areas in brightness and contrast, and the drive bore is located at one of the rib plates which actually indicates the spool's angular position. Thus, locating the drive bore is the most direct way to measure the spool's angular position. However, due to the low diversity of color and brightness of the captured spool image, it was found to be unreliable to locate the drive bore through circle-detecting methods like the Hough transform.  
As mentioned above, template matching presents good performance in similar applications, but normal area-based template matching is computationally time-consuming and less robust to image rotation. In this case, the drive rod bore may be in any angular position, resulting in different brightness and shadow shape; multiple templates were therefore necessary to improve the accuracy, as discussed in Section 5. Moreover, due to the limited illumination and monotone color of the spool, it was found that matching credibility was not high enough.  
To solve these problems, an FPDD strategy was proposed to initially locate the potential matching zone, and then the corresponding template was selected to conduct accurate template matching. The workflow of the proposed spool-angular-position-measuring method is shown in Figure 5, and the method is detailed in the following.  
Figure 4: Automatic spool positioning and loading process: (**a**) spool angular position measurement and adjustment; (**b**) spool loading and clamping.  
Figure 5: Workflow of the proposed spool-angular-position-measuring method.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 3 System Performance Analysis
### Energy Efficiency of SPNC Protocol  
In this section, we investigate the energy efficiency of the two-way satellite-terrestrial network. Consider the remaining \(\sqrt{1-\rho y_{2}}\) is used for RF energy harvesting during the second time slot in PNC and SPNC protocols. We assume user source node can harvest power with additional energy storage equipment. The harvested power \(E_{S_{2},EH}\) is written as:  
\[E_{S_{2},EH}=\left\{\begin{array}{ll}\eta(1-\beta)T(1-\rho)P_{R}|g|^{2},& \text{PNC protocol \& case 1 in SND protocol}\\ \eta(1-\beta)TP_{R}|g|^{2},&\text{case 2 in SND protocol}\end{array}\right. \tag{32}\]  
The system energy consumption is defined as:  
\[E_{sum}=(P_{1}P_{2})\beta T+P_{R}(1-\beta)T-E_{S_{2},EH} \tag{33}\]According to the energy efficiency \(\Theta_{EE}\) definition in [28], we can express the energy efficiency for a two-way satellite-terrestrial network as follows:  
\[\Theta_{EE}=\frac{E\big{[}Th_{spmc}\big{]}W}{(P_{1}+P_{2})\beta T+P_{R}(1-\beta) T-E_{S_{2},EH}} \tag{34}\]  
Substituting Equation (30) into (34), we can obtain the exact expressions of energy efficiency for the SPNC protocol. Moreover, we can obtain the numerical results with the help of Mathematica.
## 4 Numerical Analysis of the Stress-Strain State of the Cycloid Disc
In order to compare the numerical and the experimental results, the strain values were given as a function of time for the positions where the strain gauges were glued in the experimental testing. The positions of the strain gauges are shown in Figure 6--position of the glued strain gauges. The strain gauges were positioned identically to the ones shown in Figure 6. This was achieved by placing virtual sensors on the nodes of the corresp \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Node Number** & **MT Number** & **Node Number** & **MT Number** \\ \hline [PERSON] (1377) & MT1 & [PERSON] (1846) & MT6 \\ \hline [PERSON] (1378) & MT2 & [PERSON] (1847) & MT7 \\ \hline [PERSON] (1379) & MT3 & [PERSON] (1848) & MT8 \\ \hline [PERSON] (1380) & MT4 & [PERSON] (1849) & MT9 \\ \hline [PERSON] (1381) & MT5 & [PERSON] (1850) & MT10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative presentation of the strain gauges and the corresponding finite element nodes. Figure 9: Illustration of the limitations of the pusher. Figure 10 shows the numerical results for all three cases of meshing under the load of 150% of \(F_{\rm{nmax}}\) (Table 2). Figure 10: Von Mises strains in the time domain for the external load of 150% \(F_{\rm{Nmax}}\) for: **(a)** triple–tooth meshing, **(b)** double–tooth meshing, and **(c)** single–tooth meshing. The cycloid disc was made of steel 30 CrMoV9, which for the given thickness, according to the standard EN 10250-3:2000 [37], has a minimum yield stress of \(R_{\rm e}\) = 700 MPa and a minimum tensile strength of \(R_{\rm m}\) = 900 MPa. The other mechanical characteristics of the material needed for the numerical analysis were found in the software library: modulus of elasticity \(E\) = 2.07 \(\times\) 10\({}^{5}\) MPa and Poisson's ratio \(\mu\) = 0.3. The lowest stress value on the cycloid disc occurs in the case of the triple-tooth meshing of the cycloid disc and the ring gear (\(\sigma\) = 338 MPa). About 7% higher stresses occur at the double-tooth meshing compared to the triple-tooth meshing, while about 224% higher stresses occur at the single-tooth meshing compared to the triple-tooth meshing. At the single-tooth meshing and the load of 150% of \(F_{\rm Nmax}\), the stresses that occur are close to the yield stress value, but this is only for very short time intervals. According to the literature [23], the stress that occurs in the contact of the cycloid disc and the output rollers can have the most unfavorable effect on the operation of the cycloid speed reducer.
## 3 Results and Discussion
### Proximate Analysis of Lafun Samples and Raw Cassava Roots
Thus, the increase in the ash content recorded after fermentation of the fortified cassava could be the result of an incomplete utilisation of vitamin A. The lipid content of the raw bitter cassava is similar to that of its lafun products (0.5%) and statistically different from the rest of the cassava varieties (0.4% to 0.6%). The lipid contents of raw fortified cassava and sweet cassava (1.4% and 0.7%, respectively) were significantly different from those of their lafun products, ranging from 0.8% to 2.2% and 0.2% to 1.1%, respectively. In cases where there was a reduction in lipid content after fermentation, this could be advantageous in decreasing the chance of rancidity in hydrated lafun [22]. [PERSON] et al. [23] fermented pearl millet, like the former author, but recorded an increase in lipid content after fermentation. The opposing observation (increase or decrease in lipid content after fermentation) in both studies could suggest that the proximate composition of products of a fermented plant is influenced by the type of variety of that plant used. Samples with an increased lipid level might require additional storage controls to extend their shelf life. There was a significant increase (\(p\) < 0.05) in the protein content of lafun (2.5% to 3.1%) from fortified cassava relative to the raw material (1.8%). Previous studies have proven that fermentation can increase protein content in food [21; 24; 25]. However, [PERSON] and [PERSON] [26] reported results opposing the above (reduction in protein level after fermentation), following experiments with both bitter and sweet cassava lafun products. The reduction in protein content after the fermentation of bitter and sweet cassava varieties used could be an indication that the varieties have more sulphur amino acid content that might have been metabolised by the LAB starters [27]. Since lafun is never consumed alone, its lack of protein is compensated for by consuming it with soup and meat that are high in protein. It is interesting to note that the highest values in the ash (4.4%), protein (3.1%), and fibre (7.4%) contents were in fortified samples fermented with _L. mesenteroides_. There is limited data available regarding the role of _L. mesenteroides_ as a starter in the improvement of the proximate values of fermented cassava products or food in general. [PERSON] et al. [28] reported the ability of _L. mesenteroides_ to reduce cyanide in fermented cassava flour. There are also some reports on its excellent role in probiotic and antimicrobial activities and its impact on the texture and flavour quality of some other fermented foods, such as card, kimchi, and fermented cabbage [29; 30; 31].
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles
### Robust Estimates for Regression Problems
#### 2.2.3 S-Estimator  
The S-estimate of regression is defined as the estimator that minimizes the robust scale M-estimate,  
\[\hat{\mathbf{x}}_{S}=\arg\min_{\mathbf{x}}s_{M}(\mathbf{r}(\mathbf{x})), \tag{20}\]  
with \(s_{M}(\mathbf{r}(\mathbf{x}))\) the M-estimate of scale, which satisfies  
\[\frac{1}{n}\sum_{i=1}^{n}\rho\left(\frac{r_{i}(\mathbf{x})}{s_{M}(\mathbf{r}( \mathbf{x}))}\right)=b, \tag{21}\]and, thus,  
\[\hat{\mathbf{x}}_{S}=\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\rho\left(\frac{r_{i}( \mathbf{x})}{\hat{s}}\right)\quad,\quad\hat{s}=s_{M}(\mathbf{r}(\hat{\mathbf{x} }_{S}))\;. \tag{22}\]  
Again, this is solved by an IRLS approach. A typical choice for the \(\rho\)-function is the bisquare scale with \(\rho(x)=\min\{1-(1-x^{2})^{3},1\}\) and \(b=0.5\). In this case, \(W(x)=\min\{3-3x^{2}+x^{4},1/x^{2}\}\), where it is clear that larger values of \(x\) have smaller weights. S-estimator is characterized by a high breakdown point, while attaining a low efficiency at the normal distribution.
## 7 Factors Affecting Loss for Electrical Steel
### Impact of Harmonics  
Harmonics play a pivotal role in shaping the magnetic properties of electrical steel, exerting a notable influence on its behaviour and efficiency. When harmonics are introduced into electrical systems, they initiate a cascade of effects that ripple through a material. These effects are closely linked, creating a web of interconnected alterations in the steel's magnetic characteristics. At the heart of this phenomenon lies the increased energy losses incurred due to non-sinusoidal currents [66]. These losses are intricately tied to the emergence of eddy currents within the material. As harmonics distort the regular current flow, eddy currents circulate within the steel, generating localized heat and, consequently, dissipating energy. Additionally, the rapid and irregular changes in the magnetic field, induced by harmonics, lead to heightened hysteresis losses. The steel's magnetic domains struggle to align with the swiftly changing field, inducing friction-like losses as they realign. Furthermore, the material's magnetic permeability undergoes shifts under the influence of harmonics, altering its capacity to channel magnetic flux effectively [67]. This can disrupt the functioning of devices reliant on stable magnetic properties. Notably, harmonics can even trigger the premature saturation of the material's core, curbing its ability to handle expected magnetic field strengths. This effect could lead to compromised performance and potential overheating. Overall, the intricate shift between harmonics and electrical steel's magnetic properties underscores the need for careful consideration and mitigation strategies in design and operation. By addressing these linked alterations, the efficiency and reliability of electrical systems can be effectively enhanced in the presence of harmonics [68].
## 3 Numerical Simulation  
### Theoretical Model  
The Navier-Stokes equation of incompressible viscous fluid under the Arbitrary Lagrange-Euler (ALE) description is used and expressed as follows:  
\[\frac{\partial u_{j}}{\partial x_{j}}=0 \tag{1}\]  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho u_{i}u_{j}}{\partial x _{j}}=-\frac{\partial\rho}{\partial x_{i}}+\frac{\partial}{\partial x_{ij}} \Bigg{[}\mu+\mu_{r}\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\Bigg{)}\Bigg{]} \tag{2}\]  
where \(u_{i}\) and \(u_{j}\) are velocity components and \(\rho\) is fluid density.  
To solve these equations, a turbulence model was introduced. Because the SST k-\(\omega\) model proposed by [PERSON] is a combined turbulence model with k-\(\epsilon\) and k-\(\omega\), the SST k-\(\omega\) turbulence model adopts the k-\(\omega\) turbulence model in the near-wall area, and the k-\(\epsilon\) turbulence model in the expected free shear layer in the external wake region. [PERSON] et al. (2013) compared the k-\(\epsilon\), k-\(\omega\) and k-\(\omega\) SST methods and found that the SST k-\(\omega\) model was more suitable for studying the turbulent flow state around the cylinder. Therefore, the SST k-\(\omega\) model is used to simulate the cylindrical winding flow condition, which is presented as follows:  
\[\frac{\partial\rho k}{\partial t}+\frac{\partial\rho u_{i}k}{\partial x_{i}}= \frac{\partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{k}u_{r})\frac{\partial k }{\partial x_{j}}\Bigg{]}+\tau_{ij}\frac{\partial u_{i}}{\partial x_{i}}- \beta^{*}\rho k\omega \tag{3}\]  
\[\frac{\partial\rho\omega}{\partial t}+\frac{\partial\rho u_{i}\omega}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{m}\mu_{r} )\frac{\partial\omega}{\partial x_{j}}\Bigg{]}+\frac{\gamma}{v_{r}}\tau_{ij} \frac{\partial u_{i}}{\partial x_{j}}-\beta\omega^{2}+2\rho(1-F_{1})\frac{ \sigma_{\omega 2}\partial k\partial\omega}{\omega\partial x_{j}\partial x_{j}} \tag{4}\]  
where \(F_{1}\) is the mixed function; depending on the meshing of the model, it can be calculated as follows:  
\[F_{1}=\tanh(\gamma_{1}^{4}) \tag{5}\]  
\[\gamma_{1}=Min[Max(\frac{\sqrt{k}}{\beta^{*}\omega y},\frac{500\upsilon}{y^{ 2}\omega}),\frac{4\rho\sigma_{\omega 2}k}{CD_{k\omega}y^{2}}] \tag{6}\]  
\[CD_{k\omega}=Max(2\rho\sigma_{\omega 2}\frac{1}{\omega}\frac{\partial k}{ \partial x_{j}}\frac{\partial\omega}{\partial x_{j}},10^{-10}) \tag{7}\]  
where \(\sigma_{\omega 1}=2.00\), \(\sigma_{\omega 2}=1.186\) and \(\beta^{*}=0.09\).
## Data and Method
### Research Methods  
#### 2.2.1 Empirical Orthogonal Function (EOF) Analysis Method  
Empirical Orthogonal Function (EOF) analysis is also known as eigenvector analysis. EOF is a commonly used analysis method to study the spatial and temporal characteristics of meteorological variables [22]. The application of this technique greatly reduces the dimensionality of an original dataset, while preserving the majority of the variations in the original data as much as possible [23]. And the first few orthogonal models can represent the vast majority of the total variances of the original dataset [24]. The principle is to decompose the meteorological variable field with time and space into a linear combination of spatial pattern (V) and time coefficients (T) that are uncorrelated, which can be expressed in the following matrix form:  
\[X_{m\times n}=V_{m\times p}T_{p\times n} \tag{1}\]  
This method uses a small number of spatially distributed modes to describe the original variable field, and the basic information of the original variable field can be captured. It provides convenience for studying climate variables. The original variable field is given in matrix form:  
\[X=\begin{pmatrix}x_{11}&\ldots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{m1}&\ldots&x_{mn}\end{pmatrix}i=1,2,...,m;\quad j=1,2,...,n \tag{2}\]  
where \(m\) is the spatial point and \(n\) is the length of time series, i.e., the number of samples; \(x_{ij}\) represents the \(j\)-th observed value on the \(i\)-th spatial point.
## 2 Materials and Methods
### Characterizations  
Fourier transform-infrared (FTIR) spectroscopy analysis was investigated using a FTIR-4100 (JASCO, Tokyo, Japan) spectrometer with a solid pellet of KBr as a reference. JEOL FT-NMR (\({}^{1}\)H at 500 MHz and \({}^{13}\)C at 125 MHz) spectrophotometer was used to investigate the NMR data of the synthesized H3T-4-FOP SOM in deuterated chloroform (CDCl\({}_{3}\)) as a reference solvent. The chemical shift (8) values were measured in ppm referred to internal standard of tetramethyl silane (TMS). The mass spectra (MS) were determined by XEVO TQ-S spectrometry. The UV-Vis absorption and photoluminescence (PL) spectra were characterized by V-670 (JASCO, Japan) spectrophotometer and FP-6500 (JASCO, Japan) spectrofluorometer, respectively. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were measured at a scan rate of 10 \({}^{\circ}\)C/min under nitrogen gas atmosphere using with a thermal analyzer (TA) instrument Q-50. The cyclic voltammogram (CV) was elucidated with an electrochemical system (WPG 100 Potentiostat/Galvanostat, WonATech) comprising of H3T-4-FOP modified glassy carbon as working electrode, a reference saturated calomel electrode (SCE) and a platinum wire as a counter electrode in 0.1 M tetrabutylammonium hexafluorophosphate (TBAPF\({}_{6}\)) in acetonitrile as the supporting electrolyte. The surface morphology of the fabricated thin film was characterized by atomic force microscopy (AFM) in tapping mode using AFM Nanoscope IV Digital Instrument, Santa Barbara, USA. For the evaluation of photovoltaic parameters, the typical J-V curves of the fabricated BHJ-OSCs were measured under one sun, i.e., AM 1.5G (100 mW/cm\({}^{2}\)) which was maintained by using 1000 W metal halide lamp (Phillips, Amsterdam, The Netherlands) and one sun intensity was calibrated with a Si photo detector obtained from NREL (Golden, CO, USA).
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 3 Proposed Method
### Training Objectives  
Given the estimates \(\hat{\mathbf{s}}\) and \(\hat{\mathbf{n}}\) of the clean speech \(\mathbf{s}\) and noise \(\mathbf{n}\), we optimized the neural network parameters of the encoder and decoders by minimizing the mean squared error defined as follows:  
\[\mathcal{L}_{D_{s}}(\theta_{E},\theta_{D_{s}}) =\frac{1}{K}\sum_{k=1}^{K}\|\mathbf{s}_{k}-\hat{\mathbf{s}}_{k}\| ^{2}, \tag{6}\] \[\mathcal{L}_{D_{n}}(\theta_{E},\theta_{D_{n}}) =\frac{1}{K}\sum_{k=1}^{K}\|\mathbf{n}_{k}-\hat{\mathbf{n}}_{k}\| ^{2},\]  
where \(\|\cdot\|\) indicates the \(l_{2}\)-norm, \(K\) is the number of mini-batch size, and \(\hat{\mathbf{s}}_{k}\) (\(\hat{\mathbf{n}}_{k}\)) is the estimate of the \(k\)-th speech (noise) sample \(\mathbf{s}_{k}\) (\(\mathbf{n}_{k}\)) in the mini-batch. Similarly, we also train the encoder and the disentanglers by using the following objective functions:  
\[\mathcal{L}_{DE_{n}}(\theta_{E},\theta_{DE_{n}}) =\frac{1}{K}\sum_{k=1}^{K}\|\mathbf{n}_{k}-\hat{\mathbf{n}}_{k}\| ^{2}, \tag{7}\] \[\mathcal{L}_{DE_{s}}(\theta_{E},\theta_{DE_{s}}) =\frac{1}{K}\sum_{k=1}^{K}\|\mathbf{s}_{k}-\hat{\mathbf{s}}_{k}\| ^{2},\]where \(\tilde{\mathbf{n}}_{k}\) and \(\tilde{\mathbf{s}}_{k}\) are obtained through Equation (4). To obtain disentangled features, we minimize \(\mathcal{L}_{DE_{n}}\) and \(\mathcal{L}_{DE_{k}}\) defined in Equation (7) with respect to \(\theta_{DE_{n}}\) and \(\theta_{DE_{n}}\), while maximizing them with respect to \(\theta_{E}\) simultaneously. Combining Equations (6) and (7), the total loss of the proposed network was formulated as  
\[\begin{split}\mathcal{L}_{T}(&\theta_{E},\theta_{ D_{s}},\theta_{D_{n}},\theta_{DE_{n}},\theta_{DE_{n}})\\ &=[\mathcal{L}_{D_{s}}(\theta_{E},\theta_{D_{s}})-\lambda_{1} \mathcal{L}_{DE_{n}}(\theta_{E},\theta_{DE_{n}})]+\alpha[\mathcal{L}_{D_{n}}( \theta_{E},\theta_{D_{n}})-\lambda_{2}\mathcal{L}_{DE_{s}}(\theta_{E},\theta_ {DE_{s}})],\end{split} \tag{8}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are positive hyper-parameters which control the amount of gradient reversal in the back-propagation step, and \(\alpha\) denotes the weight controlling the contribution of the noise estimate.  
In recent studies [35; 36; 37; 38; 39; 40; 41], GRL has only been used for domain predictions under narrowly restricted settings (with only two possible domains, e.g., the source and the target) or for classifications of channels, speakers, and noise types. The proposed model distinguishes itself from the past approaches by using two GRLs to disentangle the speech and noise latent features in a regression manner.