esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 4 Consensus on \(Tso(4)^{n}\) with Reshaping
### Illustrative Examples
#### 4.2.3 Nine-Agent Ring Graph  
Now, we reconsider the case of \(N=9\) agents with the undirected ring graph in Figure 1b. Again, the initial conditions are as defined in Equation (44), and the simulation is conducted using the reshaping Case 1 and Case 2 protocols.  
Similarly to the five-agent base case converging to non-consensus equilibria in Figure 8a, the nine-agent Case 1 reshaping fails to destabilize the non-consensus equilibria, resulting in a steady-state value of the relative PRA \(\theta_{ijk}\) in Figure 9a. \(\theta_{ijk}\) converges to the non-consensus equilibria \(2\pi/N\) just as in the five-agent base case. This is a result of \(\theta_{ijk}=40^{\circ}<\arg\max_{\theta\in[0,180]}f_{1}(\theta)=50^{\circ}\). As described in Section 3.1, the reshaping algorithm fails to destabilize the non-consensus equilibria. Since stable non-consensus equilibria remain, the resulting configuration is only locally stable and not AGAS stable.  
Figure 8: Simulations on \(TSO(4)^{5}\) for (**a**) Base case protocol on a 5-agent ring graph and (**b**) Case 1 reshaping protocol with \(\arg\max_{\theta\in[0,180]}f(\theta)=50^{\circ}\) on a 5-agent ring graph. Initial conditions are chosen in the region of attraction of \(p=1\) non-consensus equilibria. Right plot demonstrates consensus, while left plot does not.  
Reshaping Case 2 successfully destabilizes the non-consensus equilibria. Figure 9b shows all \(\theta_{ijk}\) converging to zero, signifying that consensus has been achieved. This is expected since \(\theta_{ijk}=40^{\circ}>\arg\max_{\theta\in[0,180]}f_{2}(\theta)=35^{\circ}\). The results of the nine-agent ring graph simulation confirm the conclusions in [20]. Case 2 reshaping was designed to destabilize non-consensus equilibria and ensure the almost global stability of the consensus subspace under a communication topology of a fixed number of agents \(N\) on \(TSO(3)\), and this claim is justifiably extended to \(TSO(4)\).
## 5 Deep Learning Approach for Possible VLC System Enhancement  
In previous sections, we showed our proposed predistortion scheme with the coefficient approximation for nonlinear distortion compensation based on the performance results. We also explained that coefficient approximation could be considered the practical approach for actual implementation. However, we believe that, in Rician fading, the proposed methods did not fully address LED nonlinearity issues in the two aspects. One is the modeling of LED distortion. The LED modeling formula, including our LED modeling, can often be ill-posed and poorly represented in LED distortion patterns.  
In addition to the distortion modeling, each LED produced might not follow the same distortion characteristics as theoretical LED models, and our predistortion scheme based on the theoretical model may be ill-conditioned in the actual situation. Thus, instead of the LED modeling for correction, our focus must shift to the direct correction of LED distortion. For instance, our proposed approximation modeling schemes assume that the transmitter and receiver are stationary with the same coefficients. In the practical situation, the LED distortion coefficients can be different in each produced LED case, and a slight value-off can cause significant performance degradation. Hence, this VLC also has to correct LED distortion directly with the adaptation of these coefficient changes. To resolve the correction problem, we must consider using deep learning approaches, as the possible candidates.  
To find the best estimate of the model of the data and systems, deep learning algorithms, such as CNN and LSTM, gained recent popularity in various communication systems applications, including VLC research. This deep learning approach can also be appliable to solve this distortion problem. In this paper, we introduce one of the deep learning applications for possible VLC system enhancement, the so-called BLSTM.  
Figure 4: Nonlinear, predistortion, and linearized LED transfer function.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1  
Although STx2 is more toxic and disease-relevant, most studies over the last three decades focused on the retrograde trafficking of STx/STx1 B-subunit (STxB/STx1B) (the B subunits of STx and STx1  
Figure 1: Retrograde transport of Shiga toxin (STx), STx1, and STx2. (**A**) Direct early endosome-to-Golgi transport allows the toxins to evade degradation in late endosomes. (**B**) When early endosome-to-Golgi transport is inhibited, the toxins are routed to, and degraded in, late endosomes. ER, endoplasmic reticulum.  
are identical) [9; 11; 21; 22]. Indeed, STxB/STx1B was one of the first proteins shown to undergo direct early endosome-to-Golgi transport [23]. There is a fairly sophisticated understanding of the trafficking mechanisms of STxB/STx1B at the early endosome/Golgi interface, and STxB/STx1B was developed as a morphological and quantitative biochemical tool to study retrograde trafficking itself. Factors required for the early endosome-to-Golgi trafficking of STxB/STx1B are provided in Table 1 and described below.
## 3 Method
### Shadow Compensation
#### 3.3.2 Edge Fusion
The formula is as follows  
\[Conf(p_{x},p_{y})=\frac{\sum_{(x,y)\in Patch_{(p_{x},p_{y})}}Conf(x,y)}{N_{p}} \tag{19}\]  
where \(Patch_{(p_{x},p_{y})}\) represents the set of pixels within a \(9\times 9\) patch centered on pixel \((p_{x},p_{y})\) that do not require processing or have already been processed, and \(N_{p}\) denotes the number of pixels within this set. The update strategy for the data item \(D\) is to directly use the data item at the matching pixel location. The iterative process sequentially marks the pixels in the \(A_{p}\) region, producing the edge fusion result \(I_{Output2}\). The procedure of the edge fusion algorithm is provided in pseudocode Algorithm 1. ```
0: Hyperspectral image \(I_{Input}\), Area to be processed \(A_{p}\). 0: Edge fusion image \(I_{Output2}\). 1: Compute \(I_{Input}\) gradients \(Ux,Uy,Uxx,Uxy,Uyx,Uyy\). 2: Compute p-Laplacian Operator:
3: Compute \(Uzz\) and \(Unn\) using gradients, as shown in Equations (14) and (15). 4: Compute \(PLop\) from \(Uzz\) and \(Unn\), as shown in Equation (16). 5: Initialize Confidence (\(Conf\)) and Data (\(D\)) Terms:
6: Set \(Conf\) to 1 in \(A_{p}\) and 0 elsewhere, as shown in Equation (17). 7: Set \(D\) as the output of applying \(PLop\) to \(I_{Input}\). 8: Edge Fusion Process:
9:while\(A_{p}\) contains pixels to process do
10: Identify boundary pixels of \(A_{p}\) by applying a \(3\times 3\) Laplacian filter. 11:while Boundary pixels unprocessed do
12: Select the highest-priority pixel using \(Conf\) and \(D\), as shown in Equation (18). 13: Select the best-matching pixel within a \(9\times 9\) patch centered around the highest priority pixel. 14: Update \(A_{p}\), \(Conf\), \(D\), \(I_{Input}\). 15:endwhile
16:endwhile
17: Assign processed \(I_{Input}\) to \(I_{Output2}\). 18:Output:\(I_{Output2}\). ```  
**Algorithm 1** Edge fusion algorithm
# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 2 Materials and Methods  
The overall procedure for diagnosing the severity levels of six scalp and hair disorders is described in Figure 1. The scalp image open dataset was acquired from AI-Hub [23]. The open dataset consisted of six scalp and hair disorders (Figure 1a) and each disorder was divided into four severity levels (Table 2). The original dataset was augmented, resized, and normalized to fine-tune three deep learning models: ResNet152, EfficientNet-B6, and ViT-B/16 (Figure 1b). XAI techniques, such as Grad-CAM and attention rollout, were also applied to figure out the decision of the trained deep learning model and visualize the lesion region (Figure 1b). After the classification performance of three deep learning models, the ViT-B/16 model showed the best performance among tested models. Based on the ViT-B/16 model and attention rollout technique, 'Scalp checker' software was developed. When a microscopic scalp image was entered into the software, the software offered severity levels of six individual disorders and problem regions, visually (Figure 1c).  
Figure 1: Overall process for diagnosing the severity levels of six scalp and hair disorders. (**a**) Open dataset of scalp images was composed of six scalp and hair disorders. Each disorder was classified into four severity levels. (**b**) After image preprocessing, three deep learning models were fine-tuned on the scalp image dataset. By comparing classification performance, the ViT-B/16 model was selected among tested models. (**c**) Scalp checker software was developed based on the ViT-B/16 model combined with attention rollout technique. The severity level of each disorder and lesion area was shown in the software.
## 2 Results and Discussion  
### Materials Characteristics  
#### 2.1.1 SEM Studies  
The effect of the proportions of reacting TEVS and DVB components on the properties of the newly synthesized materials was investigated via scanning electron microscopy (SEM) and further correlated and developed by SAXS and nitrogen adsorption/desorption analysis. It was found that the copolymerization of divinylbenzene with triethoxyvinylsilane by the suspension polymerization method led to obtaining well-defined polymeric-inorganic microspheres. The SEM results presented in Figure 1 show low and high magnification images of the final microspheres.  
The particles of the studied inorganic-organic materials have spherical shapes with gradually changing surface smoothness and diameters ranging from 50 \(\upmu\)m to 200 \(\upmu\)m. Moreover, the applied polymerization conditions yielded about 80% microspheres in the range of 100-150 \(\upmu\)m. Depending on the molar ratio of components in the reaction mixture, differences in the morphology and porosity of the final microspheres were observed. When the ratio of organic to inorganic phases was 1:2, the spherical morphology of the material is disturbed with many deviations from the ideal spherical form, the material is heterogeneous, and several defects are observed (Figure 1a-c). The most regular shape, the highest homogeneity, and the lowest porosity were observed for the DVB:TEVS = 2:1 sample (Figure 1g-i). In this case, the surface of the spheres is the most uniform and smooth. The DVB:TEVS = 1:1 sample, shows intermediate morphological and pore prop  
Figure 1: SEM images of DVB:TEVS = 1:2 (**a–c**), DVB:TEVS = 1:1 microspheres (**d–f**) and DVB:TEVS = 2:1 microspheres (**g–i**) in different magnification.  
erties in comparison to the materials synthesized at proportions DVB:TEVS = 2:1 and DVB:TEVS = 1:2 (Figure 1d-f).  
The DVB:TEVS = 1:2 spheres are characterized by the most developed surface with the highest degree of roughness. The observed differences in the surface layers of the polymer spheres depending on the fraction of individual components indicate a clear pore-forming nature of the inorganic TEVS component and its role in the formation of an extensive surface on the created materials. The use of a silane coupling agent with a branched crosslinking nature may be responsible for the assembly of an increased porous network. The TEVS contains three ethoxy groups that occupy an area close to the Si atoms, thus limiting the formation of a polymer network in their vicinity and creating a porous system. In this case, the TEVS monomer is responsible for the observed hierarchical roughness and surface vinyl terminations [43; 44]. The DVB monomer and crosslinker with its simple unidirectional structure guide the materials into a more monolithic form [45; 46].  
Depending on the need to create materials for various applications (catalyst supports, adsorbents), strict control of the number of ingredients of different natures has a significant impact on the ultimate morphology and allows obtaining more or less heterogeneous and porous materialstructures.
## 5 Swarm Cluster Prediction
### Machine-Learning Model  
The dataset generated in the previous section is unlabeled, and UAVs are considered to be placed nearby. The optimum choice is to apply K-means clustering. The reason for the selection of K-means clustering is that the transmitted radiowave (\(E_{i}=E_{o}e^{-2\pi z/\lambda}\)) directly intersects with the receptor UAV. However, the diversity signal (multi-path) at the receiving UAV was ignored due to the near field. The K-means algorithm works with Euclidean distance between the UAVs, whereas the cosine and Manhattan algorithms do not use the direct distance approach. In order to satisfy Equation (1), \(d\) is considered as peer-to-peer distance between UAVs; therefore, only Euclidean distance could meet the requirement. According to Figure 6, the proposed mathematical model for swarm control has a two-dimensional (2D) assumption, and K-means with Euclidean distance can also fulfill this condition. However, the cosine, Manhattan, Minkowski and Chebyshev models can be used for higher dimensions. In fact, the distance and reallocation parameters are required for such applications.  
The cluster process of UAVs is divided into three steps. Firstly, the optimum numbers of clusters are found through within-cluster sum of squares (WCSS), as shown in Figure 13. According to the elbow plot, \(i=4\) for the given swarm datasets. Secondly, the Euclidean  
Figure 11: UAV acute swarm formation.  
Figure 12: UAV right swarm formation.  
distance of each UAV is depicted with reference to \(k_{i}\) centroids. In Figure 6, the coordinates of \(UAV_{1}\) and \(UAV_{2}\) are \((x_{1}(t),y_{1}(t))\) and \((x_{2}(t),y_{2}(t))\), respectively. The equation for euclidean distance can be expressed as below:  
\[d(UAV_{s1},UAV_{s2})=\sqrt{(x_{2}(t)-x_{1}(t))^{2}+(y_{2}(t)-y_{1}(t))^{2}} \tag{9}\]  
Thereafter, the centroids of UAV swarm can be found by  
\[arg\ min\ dist(k_{i},UAV_{x})^{2} \tag{10}\]  
\(k_{i}\) is the number of centroids (\(k_{i}\in 1\ldots 4\)) which have been found through WCSS. Thirdly, we assign each UAV to the closest centroid, once the UAVs gather around the centroid; then, we again find the centroid by taking the average of each cluster. Finally, we repeat the steps of averaging cluster centroids till the centroid does not change. Likewise, the given below expressed as:  
\[k_{i}=\frac{1}{|S_{i}|}\sum_{UAV_{i}\in S_{i}}UAV_{i} \tag{11}\]  
According to the Equation (11), the \(S_{i}\) is set of all UAVs assigned to \(k_{i}\) cluster. Finally, Figures 14-18 present the K-means cluster for all proposed types of UAV swarms formation. It has been observed from Figure 15 that one UAV has been marked as a cluster which can be justified the inherent property of outlier in K-means clustering.  
Figure 16: K-means isosceles-cluster swarm of UAVs.  
Figure 17: K-means equilateral-cluster swarm of UAVs.  
Figure 15: K-means acute-cluster swarm of UAVs.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 2 Methodology  
### Theoretical Modal Analysis  
The primary objective of theoretical modal analysis is to establish a connection between the physical parameters of a system, the modal parameters, and the frequency response function. The foundational equations governing the motion of a multi-degree-of-freedom system are outlined as follows:  
\[[\mathrm{M}]\big{\{}\ddot{\mathrm{x}}\big{\}}+[\mathrm{C}]\big{\{}\dot{\mathrm{x }}\big{\}}+[\mathrm{K}]\big{\{}\mathrm{x}\big{\}}=\{\mathrm{f}\} \tag{1}\]Based on the separation of variables method, decoupling by substituting the generalized expression \(\{x\}=[\Phi]\{q\}\) into Equation (1) yields:  
\[\{\ddot{q}\}+\begin{bmatrix}O&0\\ &2\xi,\omega_{n}&0\\ 0&O\end{bmatrix}\{\dot{q}\}+\begin{bmatrix}O&0\\ &\omega_{n}&0\\ 0&O\end{bmatrix}\{q\}=\ [\Phi][\Phi^{\top}]\{f\} \tag{2}\]  
where \([\Phi]\) is the modal matrix, \(\{q\}\) is the modal coordinates, \([M]\) is the mass matrix of the multi-degree-of-freedom system, \([C]\) is the system damping matrix, \([K]\) is the system stiffness matrix, and \(\{f\}\) is the external excitation matrix.  
According to Equation (2), the r-th-order-modal equation is given as:  
\[(K_{r}-\omega^{2}M_{r}\ +\ j\omega C_{r})q_{r}=\ F_{r} \tag{3}\]  
where \(F_{r}=\ \Phi_{r}^{T}\{f\}\), \(M_{r},K_{r},C_{r}\) are the modal mass, modal stiffness, and modal damping at the r-th order, respectively. The solution yields the deflection in this order of the mode:  
\[q_{r}=\frac{F_{r}}{K_{r}-\omega^{2}M_{r}+j\omega C_{r}} \tag{4}\]  
\[x_{l}(\omega)=\sum_{r=1}^{N}\varphi_{lr}q_{r} \tag{5}\]  
Assuming that the frictional excitation point is located at point p, the modal force is:  
\[F_{r}=\ \varphi_{pr}f_{r}(\omega) \tag{6}\]  
Substituting Equation (6) into Equation (4) yields the offset expression for the modal coordinates of this order:  
\[q_{r}=\frac{\varphi_{pr}f_{p}(\omega)}{K_{r}-\omega^{2}M_{r}+j\omega C_{r}} \tag{7}\]  
Substituting Equation (7) into (5) yields the response at point p:  
\[x_{l}(\omega)=\sum_{r=1}^{N}\frac{\varphi_{lr}\varphi_{pr}f_{p}(\omega)}{K_{ r}-\omega^{2}M_{r}+jC_{r}} \tag{8}\]  
In summary, the frequency response function between the response point l and the point p can be found as:  
\[H_{lp}(\omega)=\frac{x_{l}(\omega)}{f_{p}(\omega)}=\sum_{r=1}^{N}\frac{\varphi _{lp}\varphi_{pr}f_{p}(\omega)}{K_{r}-\omega^{2}M_{r}+j\omega C_{r}} \tag{9}\]  
The above equation can be transformed into:  
\[H_{lp}(\omega)=\sum_{r=1}^{N}\frac{1}{K_{er}(1-\omega^{-2}+2j\xi_{r}v)} \tag{10}\]  
where \(K_{er}=K_{r}/(\varphi_{lr}\varphi_{pr})\) is the equivalent stiffness, \(\omega=\omega/\omega_{r}\) represents the intrinsic frequency of the r-th-order mode, and \(\,\,\,\xi_{r}=C_{r}/(2M_{r}\omega_{r})\) is the damping ratio of the r-th-order mode.  
Substituting the equivalent mass \(M_{er}=M_{r}/(\varphi_{lr}\varphi_{pr})\) into Equation (10) yields:  
\[H_{lp}(\omega)=\sum_{r=1}^{N}\frac{1}{M_{er}(\omega_{r}^{\,2}-\omega^{2}+2j \xi_{r}\omega_{r}\omega)} \tag{11}\]
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
## 2 Data and Methods  
### Data Collections  
The data used in this study came from the Global Ionospheric Radio Observatory (GIRO), which provides public access to ionospheric observations collected at multiple locations and has real-time feeds available for most stations, offering precise and detailed information on ionospheric observations [24]. The spatial reconstruction range in this study was 110\({}^{\circ}\)-160\({}^{\circ}\)E longitude and 10\({}^{\circ}\)-45\({}^{\circ}\)S latitude, with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), collecting data from seven stations from 2013 to 2018. The seven stations' latitudes, longitudes, and distributions are shown in Figure 1.  
In the spatial reconstruction of the ionospheric foF2, to ensure the same sampling interval for the foF2 across different stations, a fixed sampling interval of 60 min was selected. The data collection periods for the seven stations at Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville involved 1584, 1570, 1583, 1560, 952, 1560, and 1466 data points, respectively, from 2013 to 2018. Next the following procedures were carried out:The raw GIRO dataset includes 24 h of data collected daily. Initially, we computed the monthly average of the data at the seven stations and stored them sequentially. Missing data between 2013 and 2018 at the seven stations were supplemented with foF2 data predicted by the International Reference Ionosphere (IRI) model to facilitate spatial reconstruction.
2. Given that the resolution of the data used for HAS modeling was \(5^{\circ}\times 5^{\circ}\), we approximated the latitude and longitude of each station to the closest whole number and stored the monthly average foF2 values at fixed times in corresponding grid points to facilitate interpolation.
## 5 Implementation
### Putting It All Together  
Finally, the previosly discussed parts of the SBT framework are integrated. Zephyr Zephyr (1997) is used for this integration. Because of its support for a wide variety of embedded devices from different vendors, Zephyr provides support for many different hardware layouts. The interface used to communicate data points to the protocol layer allows for easy integration of the SBT framework into Zephyr. This interfacing, allowing the use of Zephyr as well as use of other embedded programming languages, is a benefit of the SBT project. Every interaction with a hardware driver is abstracted so that a programmer does not have to implement it anew for novel hardware. Furthermore, the abstracted data points can be used in an architecture- and programming language-independent way. In general, the data points are defined for the application using the Google Protocol Buffer, since they are architecture and code agnostic. The communication protocols are implemented from scratch, using common drivers. Furthermore, they are set up with an abstraction layer so that the communication protocols can be set up to work with any given data point on any supported hardware. This is a contribution of SBT. Node-RED is used as an orchestration platform, with custom-made modules for data handling and storage. All components are sufficiently lightweight in terms of resource usage that they are feasible for IoT-environments.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.4 Model Comparison  
Finally, we needed a tool that allowed us to determine whether one model was statistically significantly better than another. We used the Diebold-Mariano test [25] (see also [26]), which compares whether the two forecasts \(\{a_{i}\}_{i=1}^{n}\) and \(\{b_{i}\}_{i=1}^{n}\) of a series of real numbers, \(\{x_{i}\}_{i=1}^{n}\), are significantly different. The null and alternative hypotheses of this test are  
Figure 4: Comparison between predicted and corrected curves.  
* \(H_{0}\). Both forecasts have the same accuracy, meaning that the observed differences are likely due to randomness;
* \(H_{1}\). One forecast has better accuracy than the other, meaning that the predictive difference between the models is unlikely to be due to randomness. To calculate the statistics, we obtain the series of the differences in losses. \[d_{i}=(x_{i}-a_{i})^{2}-(x_{i}-b_{i})^{2},\] (5) as well as their mean and autocovariance, which are, respectively, \[d=\frac{1}{n}\sum_{i=1}^{n}d_{i}\text{ and }\gamma_{k}=\frac{1}{n}\sum_{i=k+1}^{n}(d_ {i}-d)(d_{i-k}-d).\] (6) Finally, the value of the statistic is obtained from the expression \[\text{DM}\,=\frac{d}{\sqrt{\frac{1}{n}(\gamma_{0}+2\sum_{k=1}^{h-1}\gamma_{k} )}},\] (7) where it is generally adequate to use the value \(h=\sqrt[3]{n}+1\). Under the null hypothesis, the DM statistic distributes as an \(\mathcal{N}(0,1)\). In the case of rejecting the null hypothesis, it follows that the first model has better predictions if DM has a negative value; if DM has a positive value, then the second model is the one with the best performance.  
The problem we faced was that, as we have seen, the Diebold-Mariano test is designed to compare two time series of real numbers as predictions of another real number series, but the objects in our time series are curves. We addressed this issue by considering not the time series of curves but the sequence of errors (which are real numbers), meaning we substitute \(x_{i}-a_{i}\) and \(x_{i}-b_{i}\) in (5) with the prediction errors of the two curves' prediction methods. That is, instead of \(x_{i}-a_{i}\) and \(x_{i}-b_{i}\), we use \(d_{k}(x_{i},a_{i})\) and \(d_{k}(x_{i},b_{i})\), respectively. We have used the db.test function included in the forecast package in R 4.2.2 [27].