esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Whistler Replica: Canvas  
On the Wistler replica paintings, we mainly focus on the canvas structure, to determine if a differentiation can be made between the different textile fibres and painting techniques.  
With respect to the results described in Section 3.1, it could be concluded that IR-A reflectography from the front side will not penetrate the canvas. Therefore, we opted to position the illumination source from the back. In this way, a registration is made of the transmitted energy only. It was found that the 940 nm IR LED illumination source of 800 W delivered the most promising results, which are presented in Figure 14. The hemp and nettle fibres of canvas A have a thicker and less structured appearance, which makes them clearly visible through the upper paint layers. This is in large contrast with the fine structure of the flax and juniper fibres of canvas B. It should be marked that the information is only visible for the center of the canvas and an access to the back side of the canvas is mandatory.  
Referring to Section 3.1, it is concluded that IR-B reflectography provides less accurate results when thicker layers of oil pigment should be penetrated. However, due to active thermography within the IR-C' spectrum, similar results could be received after PCT analysis. This is shown in Figure 15, where PCT results are provided after flash excitation on both Whistler paintings. Comparing the fifth principal component, a large distinction can be made between both canvas types, which is comparable with the IR-A transmission results. The first four principal component bins show no significant difference. The large benefit is that inspection from the front side is possible. The drawback can be that more time is needed and minimal heating of the painting  
Figure 13: Active thermography results.  
occurs. However, these results can conclude that canvas fiber analysis can be performed with an IR-B+C' device over the full canvas and no access to the back side is mandatory.  
Within Figure 16, a comparison is made of both canvases; both in IR-A and IR-B+C' spectra, the reflectography results with an illumination from the front side are shown. Within these results, some small distinctions can be made in local reflectivity of both paintings. However, in our opinion, this is not enough evidence for the difference in painting technique. It could only give an indication for further investigation.  
Figure 14: IR-A transmission results of Whistler paintings with an 800W IR 940 nm LED illumination source from the backside. A difference occurs with respect to the canvas type.  
Figure 15: Active thermography results (IR-C’) of the Whistler paintings after PCT (bin 5) where difference in canvas structure is detectable.
## 2 Proposed Methodology for Generating Synthetic Fermentation Data of Shindari
### Generative Adversarial Networks for Generating Synthetic Fermentation Data for Shindari  
#### 2.4.1 Overview  
Generative Adversarial Networks were first introduced by [PERSON] et al. [27] in the year 2014 as a machine learning framework where two neural network competes against each other in a zero-sum game. GAN is considered as one of the cutting edge frameworks that can be effectively applied in various fields, producing incredible results. The two networks in the GAN model are known as the generator network \((G)\) and discriminator  
Figure 4: Architecture of Stacking Ensemble.  
network \((D)\). As shown in Figure 5, random input noise variable \(Z_{p}(p)\) is first passed through the generator network that retains the data distribution \(D_{pd}\) over data \(x\). The discriminator is fed with the samples generated from the generator and the real data. The discriminator then attempts to evaluate and correctly distinguish between the sample from the generator and the real data. The goal of the GAN architecture is that the generator continuously tries to fool the discriminator and at one point it becomes successful in generating realistic samples that cannot be distinguished from the real data. So, the generator tries to minimize \(log(1-D(G(p)))\) and the minmax game function can be defined as in Equation (3):  
\[\min_{G}\max_{D}F(D,G)=\mathbb{E}_{x\sim D_{pd}(x)}[logD(x)]+\mathbb{E}_{p\sim Z _{p}(p)}[log(1-D(G(p)))]. \tag{3}\]  
In this paper, we propose a combination resulting in an improved version of the well-known GAN model to generate synthetic tabular data related to the fermentation of Shindari. The architectures involved in the proposed methodology are as follows:  
1. Improved version of Tabular GAN (TGAN) with skip connections [28];
2. Enhancing TGAN-skip with WGAN-GP (Wasserstein GAN with gradient penalty architecture.  
Therefore, we combine these architectures to form TGAN-skip-WGAN-GP for generating synthetic data. We first preprocess the data by removing unwanted data and perform data transformation to make sense of the continuous and categorical values. After the transformation, we pass the data through our GAN model (TGAN-skip-WGAN-GP). The output data from the GAN model goes through a reverse transformation and finally generates the synthetic data, which is then evaluated.
## 3 Methodologies
### Machine Learning Models
#### 3.5.4 Random Forest  
The random forest (RF) algorithm is a machine learning approach that utilizes bagging to create multiple independent training sets and constructs numerous classification and regression trees (CARTs) for prediction. The final output can be determined by majority voting or averaging the predicted scores [36; 37]. The core principle of the RF is that combining multiple classifiers produces more accurate predictions than relying on a single classifier. Using the bagging technique, n samples (approximately two-thirds of the total dataset) were randomly drawn with replacement to form an independent training set. A CART tree can then be constructed for each training set, where m factors are randomly selected at each internal node for branching without pruning, resulting in n independent random decision trees. The outcome is determined by the class with the highest number of votes or by averaging the predictions of these trees. The data not included in each sampling (approximately one-third of the total dataset) are referred to as out-of-bag (OOB) data. The OOB data were used to estimate the internal error, calculate the OOB error for each tree and determine the overall OOB error of the RF model. The generalization error bound of the random forest model is defined as follows:  
\[P^{*}\leq\overline{\rho}(1-S^{2})S^{2}, \tag{14}\]  
where \(P^{*}\) represents the generalization error; \(\overline{\rho}\) is the average correlation between CART trees; and \(S^{2}\) denotes the average strength of the trees.
A greater challenge arises in adapting methods like the Structural Similarity Index Measure (SSIM), which relies on local pixel structures defined as fixed-size rectangles used to partition the image into sub-regions. Since graphs lack a regular structure, a different approach is necessary. This paper's approaches aim to overcome the traditional methods' limitations, as mentioned above, and to provide a robust framework for evaluating the fidelity of reconstructed images in this emerging domain of graph-based ECT. Specifically, we propose to adopt some existing image quality and similarity assessment methods designed for raster images to the graph-based representation of 3D ECT images. The remainder of this paper is structured as follows. First, Section 2 provides a comprehensive overview of the foundations of Electrical Capacitance Tomography (ECT), including image reconstruction techniques and traditional methods for assessing ECTimage quality. Then follows Section 3, which summarizes our experimental data and introduces our novel ECT image Quality Assessment methods. Section 4 presents and analyzes the experimental results demonstrating the performance of our proposed metrics in both High- and Low-quality reconstructed images. Section 5 offers a detailed discussion of the experimental findings. Finally, Section 6 concludes the paper with a summary of our key contributions and future research directions. The overall structure and logic of the paper can be seen in Figure 1:
# Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities
## 3 Design Procedure of Robust Sliding Mode Control  
Based on variable structure systems (VSS) control theory, sliding mode control (SMC) maintains sliding mode system states by continuously changing the controller structure in response to fluctuations in the state variables. By using high-frequency switching control, SMC alters the system dynamics [12]. There are two parts to the controller design. The sliding surface is chosen in the first segment according to the sequence of the system under consideration.  
Figure 2: Block Diagram of System.  
The designed sliding surface can be expressed as:  
\[s(t)=\left(\frac{d}{d_{t}}+\lambda\right)^{n}\ \int_{0}^{t}\xi_{\xi}(t)\ d_{t} \tag{15}\]  
where \(s(t)\) is known as the selected manifold as the sliding surface and \(e_{\xi_{t}}\) is the measured tracked output with the new state as a state variable. It is vital to pay emphasis to the control rule that forces the control factors to their set point while choosing the sliding surface. The control law can be formulated mathematically as:  
\[u=u_{eq}+u_{dis} \tag{16}\]  
while  
\[u_{dis}=-ksign(s) \tag{17}\]  
where \(u_{eq}\) represents the equivalent controller, \(sign(s)\) is a sign function that creates a discontinuous control input resulting from a limited switching around the sliding surface and \(k_{1}\) is a constant. The trajectory of the controlled system moves along the manifold during the application of SMC as a result of the actions of several structures and follows the switch condition known as the chattering phenomenon displayed in Figure 3. It has been suggested that switching functions [36], denoted by \(x\), that may be a scalar or a vector, define the system structure during SMC. In the phase plane, the switching surface can be seen as a line with the symbol \(s(x)=0\).  
A strategy that is frequently used to evaluate the stability of nonlinear systems based on ODE theory is the use of a Lyapunov function. The Lyapunov function should be determined in order to verify the system's stability according to ODE class theory, it must be negative definite. This requirement is adequate to ensure the nonlinear system's asymptotic stability. However, creating a suitable Lyapunov function for ODEs can be challenging. It has been noted that the sliding motion in the context of sliding mode control (SMC) happens close to the sliding surface, much like the frequency switching phenomenon. In SMC, the system's nonlinear behavior may attempt to move away from the sliding surface, but the controller will compel the system to follow the line until it reaches the origin, referred to as the boundary layer. SMC simulations have demonstrated satisfactory convergence for pitch and yaw angles. However, the SMC chattering pattern for these systems is unsuitable as it can pose a risk to the actuators. Figure 4 shows the rapid and sudden changes in voltage patterns that require highly  
Figure 3: Chattering phenomena.  
The TRMS output requires smooth convergence with a regular voltage pattern that can be optimized using an optimization method.
## 2 Literature Works
### Comparative Summary and Innovation of This Study  
Table 1 summarizes key studies and their contributions. As shown, existing approaches lack full integration of four critical components: (1) real-time UI adaptation via multi-agent reinforcement learning, (2) behavioral clustering through unsupervised methods like KMeans++, (3) automated UI generation using GANs, and (4) model-driven development (MDD) for scalable and maintainable implementation.  
The proposed AI-MDD-UX framework is the first to unify behavioral clustering, generative AI, reinforcement learning and MDD to enable real-time, context-aware adaptive UI/UX. Unlike traditional GAN-based models that focus mainly on visual generation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Related Works** & **Adaptation Type** & **Technique Used** & **Key Limitation** \\ \hline
[7] & Static & Rule-based AUI & Limited runtime flexibility \\
[8] & Static & Hybrid Adaptive/Adaptable UI & Lacks deep personalization \\
[9] & Static & Model-based UI & Limited AI integration \\
[10] & Dynamic & Reinforcement Learning (RL) & No MDD integration \\
[11] & Dynamic & Rule-based and Pseudo-ID & Limited to activity logs \\
[12] & Dynamic & TADAP (ML and Feedback) & UX not fully automated \\
[13] & Dynamic & DSLs for MDD & No AI or learning ability \\
[14] & Dynamic & GANs & Manual refinement required \\ Proposed & Static and Dynamic & GANs + RL + KMeans++ + MDD & Addresses limitations of exiting studies \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative summary among existing works.  
through reused design components, our approach integrates 4-level MDD pipeline (CIM, PIM, PSM, Code) to ensure scalability and maintainability. AI-MDD-UX enhances the user experience by integrating both direct and indirect feedback mechanisms for personalization. It combines static rule-based logic with dynamic adaptation using KMeans++ for unsupervised user clustering, GANs for generating diverse UI variations without manual effort, and multi-agent reinforcement learning for continuous interface optimization.
# Some Dynamic Aspects of a Sextic Galactic Potential in a Rotating Reference Frame
## 3 Equilibria  
The equilibria of the system in Equation (3) can be obtained by setting the left-hand sides (the derivatives) in the Hamiltonian equations equal to zero to obtain  
\[p_{x} =-\Omega y,\qquad p_{y}=\Omega x, \tag{16a}\] \[\Omega p_{y} =x\Big{[}1+ax^{4}+2 cx^{2}y^{2}+dy^{4}\Big{]},\] (16b) \[\Omega p_{x} =-y\Big{[}1+cx^{4}+2 dx^{2}y^{2}+by^{4}\Big{]}. \tag{16c}\]  
Substituting the two expressions in Equation (16a) into the Equations (16b) and (16c) gives  
\[x[ax^{4}+2 cx^{2}y^{2}+dy^{4}+1-\Omega^{2}] =0, \tag{17a}\] \[y[by^{4}+2 dx^{2}y^{2}+cx^{4}+1-\Omega^{2}] =0. \tag{17b}\]  
Rather than referring to the equilibrium positions as \((x,y,p_{x},p_{y})\), we indicate them as \((x,y)\), since \(p_{x},p_{y}\) can be directly evaluated by Equation (16a). By solving Equations (17a) and (17b), the equilibria are obtained in the next theorem:  
Theorem 3.1: _The Hamiltonian system in Equation (3), corresponding to the Hamiltonian in Equation (1), has the following as equilibrium positions:_  
1. _If_ \(a,b,c,d,1-\Omega^{2}>0\)_, or if_ \(a,b,c,d,1-\Omega^{2}<0\)_, then_ \(\mathcal{E}_{0}=(0,0)\) _is the only equilibrium point;_
2. _If_ \(b(\Omega^{2}-1)>0\)_, then there is an equilibrium point_ \(\mathcal{E}_{1,2}=\left(0,\pm\sqrt[4]{\frac{\Omega^{2}-1}{b}}\right)\)_;_
3. _If_ \(a(\Omega^{2}-1)>0\)_, then there is an equilibrium point_ \(\mathcal{E}_{3,4}=\left(\pm\sqrt[4]{\frac{\Omega^{2}-1}{a}},0\right)\)_;_
4. _If_ \(b=a\
eq c=d\) _such that_ \((a+3c)(\Omega^{2}-1)>0\)_, then there is an equilibrium point_ \(\mathcal{E}_{5,6}=\left(\pm\sqrt[4]{\frac{\Omega^{2}-1}{a+3c}},\pm\sqrt[4]{ \frac{\Omega^{2}-1}{a+3c}}\right)\)_._  
It should be mentioned that there are some limiting cases in which either \(a\), \(b\), or \(a+3c\) approaches zero, taking into account the equilibrium position existence condition. For example, if \(b(\Omega^{2}-1)>0\), then \(\mathcal{E}_{1}\) blows up to infinity through the positive \(Y\) axis, while \(\mathcal{E}_{2}\) blows up to infinity through the negative \(Y\) axis. The same applies to the equilibrium positions \(\mathcal{E}_{3,4}\) via the positive and negative \(X\) axes. If the conditions in item 4 are satisfied, then equilibrium positions \(\mathcal{E}_{5,6}\) blow up to infinity through the line \(y=x\) if the coordinates of the equilibrium point have the same sign or through the line \(y=-x\) if the coordinates have a distinct sign. Moreover, if \(a=b=c=d=0\), then the analysis of the dynamics reduces to that of a linear system which is readily understood. Dynamic analysis is more subtle if the constants are not zero simultaneously, as the nonlinear terms alter the behavior of the system.
# Automated Dataset-Creation and Evaluation Pipeline for NER in Russian Literary Heritage
## 2 Related Work
### Domain-Specific NER Dataset Creation  
The creation of domain-specific NER datasets is emphasized in recent research as a means of improving tool accuracy and performance. The study [15] examined datasets from English literature, highlighting the effects of annotation guidelines. The authors of [16] developed FiNER-ORD for the finance domain, while the authors of [17] presented a German legal dataset with 54,000 annotated items. The Bulgarian Event Corpus (BEC), introduced by [18], is a multi-domain dataset for social sciences and humanities. It showed encouraging outcomes for tasks like event detection, nested NER, and NEL. The paper [19] introduces a 19 th-20 th-century Serbian literary corpus and a publicly available NER model achieving 91% F1 score across seven entity types. These studies collectively highlight how important domain-specific NER datasets are to enhancing the accuracy and applicability of NER techniques for specific applications.  
The study [20] highlights that NER systems sometimes misidentify female names as chemical entities or fail to recognize the names of minority groups. To address such issues, the authors proposed an automated process for dataset creation and testing and refinement of NER systems. This approach is based on contextual analysis, under the assumption that entities within the same context should exhibit consistent meanings. Additionally, large language models (LLMs) can assist in dataset expansion, as shown in [21]. This hybrid approach combines manual and automated annotation, reducing the cost of dataset creation without compromising quality.
## 1 Introduction  
Software-defined networking (SDN) has gained a lot of attention in recent years, because it addresses the lack of programmability in existing networking architectures and enables easier and faster network innovation. SDN clearly separates the data plane from the control plane and facilitates software implementations of complex networking applications on top. There is the hope for less specific and cheaper hardware that can be controlled by software applications through standardized interfaces. Additionally, there is the expectation for more flexibility by dynamically adding new features to the network in the form of networking applications. This concept is known from mobile phone operating systems, such as Apple's iOS and Google's Android, where \"apps\" can dynamically be added to the system.  
In this paper, we introduce the notion of SDN using the definition from the Open Networking Foundation (ONF). OpenFlow is just an option for a control protocol in SDN, but it is the predominant one. As OpenFlow currently evolves, several versions of its specification exist; newer releases are more powerful, as their feature sets support IPv6, Multiprotocol Label Switching (MPLS), rerouting, metering, policing and more scalable control. However, most existing applications are based on version 1.0, whose feature set is rather limited. We will highlight how SDN contributes to network innovation by various networking applications that were implemented and analyzed in recent years. These applications fall in the areas of network management and traffic engineering, security, network virtualization and routing optimization. Several surveys of SDN already exist [1; 2; 3]; this contribution differs from them through an analysis of architectural design choices for OpenFlow-based SDN networks. We discuss performance issues of OpenFlow-based SDN and refer to studies regarding these aspects.  
Section 2 explains the concept of SDN, and Section 3 gives an overview of the OpenFlow protocol, which is currently the major protocol to control network elements in SDN. Section 4 gives a survey of networking applications and groups them by topic areas. Section 5 discusses architectural design choices for SDN. Performance issues of OpenFlow-based SDN are discussed in Section 6, and Section 7 summarizes OpenFlow-based SDN. Finally, Section 8 concludes this work.
## 4 Design Procedures and Simulation Results
### Design of Synchronverter Topology  
The main parameters to be computed to implement a synchronverter are the moment of inertia \(J\) and the damping factor \(D_{p}\). The parameter \(D_{p}\) can be calculated using Equation (20) from [29].  
\[D_{p}=-\frac{\Delta T}{\Delta\omega}=-\frac{\Delta P}{\omega_{g}\Delta\omega} \tag{20}\]  
Once \(D_{p}\) was calculated, the moment inertia \(J\) was computed using the desired time constant for the system, \(\tau_{f}\):  
\[\tau_{f}=\frac{J}{D_{p}} \tag{21}\]  
In this case, \(D_{p}\) was calculated to be 14.072 assuming \(\Delta P\) of 100% (10 kW) for 0.5% change in the angular frequency (1.885 rad/s). Then for a time-constant of 0.01 s, the \(J\) value was calculated to be 0.140. The inertia constant from the synchronverter is:  
\[H=\frac{J\omega_{g}^{2}}{2P_{rated}}=1\,s \tag{22}\]  
The frequency and ROCOF of the system after a step-increase of 2 kW on the load, with and without the synchronverter, are presented in Figure 12a,b, respectively. The dip in frequency and the ROCOF of the system was reduced with the addition of the synchronverter, as expected. The additional inertia from the synchronverter increased the settling time for the frequency compared to when there was no synchronverter in the system. As shown in Figure 12c, the synchronverter increases its active power output in response to the frequency event much like the behavior of a SG.  
Figure 11: Schematic diagram of the virtual inertia simulation benchmark.  
Figure 12: Simulation results from a synchronverter: (**a**) system frequency after a step-increase of 2 kW load; (**b**) ROCOF after a step-increase of 2 kW load; (**c**) increase in inverter power in response to system frequency decrease [29].