esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
(2023) [15] integrated supervised and knowledge-based methods for Marathi WSD, improving the overall accuracy and performance. This paper advances the theoretical foundations of word sense disambiguation (WSD) and hybrid transformer-based large language models (LLMs) for low-resource languages, particularly Sesotho sa Leboa. This research offers useful, real-world applications in low-resource NLP, notably for Sesotho sa Leboa and other Bantu languages, in addition to theoretical advances:  
* It broadens the theoretical knowledge of WSD in low-resource, morphologically rich languages, particularly for Sesotho sa Leboa, by achieving up to 85% accuracy in resolving WSD using BiGRU and transformer-based hybrid models. * It proposes a novel hybrid model to optimize WSD for Sesotho sa Leboa, integrating deep learning (BiLSTM, BiGRU, and transformers) with classical machine learning (SVMs and decision trees), achieving a peak accuracy of 85% (BiGRU + DeBERTa). * It enhances the theoretical foundation for WSD by incorporating linguistic information into deep learning models, improving polysemous word disambiguation with models like Hybrid BiGRU + BERT (79%) and Hybrid BiGRU + RoBERTa (70%). * It develops domain-specific and hierarchical graph representations to simulate semantic relationships between Sesotho sa Leboa words, with hierarchical graph models achieving a 79% accuracy and domain-specific graph models achieving a 75% accuracy
* It creates a sense-annotated dataset for WSD, contributing to training and evaluation benchmarks for NLP models in Sesotho sa Leboa. * It delivers a high-performing hybrid transformer-based WSD model, significantly improving the accuracy (up to 85%), recall, and F1-score in resolving polysemous word ambiguity. * It bridges the data scarcity gap in computational linguistics for Sesotho sa Leboa by integrating corpus-based, transformer-based, and deep learning approaches, outperforming classical techniques (e.g., decision trees at 80%) in WSD tasks.
## 3 Material and Methods
### Background and Challenges
#### 3.1.3 Challenges  
The unsupervised learning scenario incurs some fundamental challenges for outlier detection:  
**Challenge 1** (Outliers in the Training Data).: _Training data can contain unknown outliers that influence the prediction models._  
When outliers are defined relative to a model trained on past data, in the ideal situation the training dataset should not have any outlier subsequences. If we can guarantee that there are no outliers in the training data, we effectively have a semi-supervised scenario [30]. If the model learns, based on \"dirty\" training data, to predict outliers, the observed values will not deviate from the expected and we cannot identify them. This phenomenon is known as \"masking\" in the statistics literature [30]. A strategy to alleviate the problem is to remove the apparently most outlierish objects (e.g., those exhibiting the highest outlier scores) from the training data or limit the expressiveness of the model.  
**Challenge 2** (Outliers in the Context Window).: _The context window used for predicting the outlier windows will occasionally contain outliers._  
Even if Challenge 1 is somehow addressed, outliers can still be present in the context window and detail the predictions. Therefore we want the model to be robust against the influence of outliers in the context window. This relates to robustness in statistical methods [31]. A strategy that can alleviate this is to ensure that the model uses several context features in the predictions.  
**Challenge 3** (Lack of User Preference Data).: _We seek to detect outliers interesting to users, but in an unsupervised setting, there is no structured data on user preferences._  
Users find interesting what to them is unexpected, so our model should be complex enough to predict what the user predicts, and nothing else (due to Challenge 1). Without supervision in the form of labels, we cannot know if a model finds interesting outliers or not and we cannot compare the quality of two models. Instead of labels, we need alternative ways of evaluating and getting feedback from users.
# Steering Pull Model and Its Sensitivity Analysis
## 4 Steering Pull Model
### Estimation of Steering Pull Distance  
A steering pull distance is determined by the turning radius. The turning radius of a wheel is (Equation (20))  
\[r=\frac{MV^{2}\cos\theta_{BA}}{Mg\sin\theta_{BA}-F_{yf}} \tag{20}\]  
and the steering pull distance is (Equation (21))  
\[d_{W}=\sqrt{L^{2}+r^{2}}-r=\sqrt{L^{2}+\left(\frac{MV^{2}\cos\theta_{BA}}{Mg \sin\theta_{BA}-F_{yf}}\right)^{2}}-\frac{MV^{2}\cos\theta_{BA}}{Mg\sin\theta _{BA}-F_{yf}} \tag{21}\]  
where \(d_{W}\) = steering pull distance of a wheel, \(L\) = moving distance.  
Equation (21) represents the steering pull distance of the wheel, and therefore, the steering pull distance of the vehicle can be derived by employing the turning geometry of the bicycle model. The steering pull distance of the vehicle is (Equation (22))  
\[d_{V}=\frac{c}{b+c}\left(\sqrt{L^{2}+\left(\frac{MV^{2}\cos\theta_{BA}}{Mg\sin \theta_{BA}-F_{yf}}\right)^{2}}-\frac{MV^{2}\cos\theta_{BA}}{Mg\sin\theta_{BA} -F_{yf}}\right) \tag{22}\]  
where \(d_{V}\) = steering pull distance of vehicle.  
By integrating all models, a complete steering pull model can be derived. The block diagram of the developed steering pull model on SIMULINK environment is described in Figure 8. The steering pull model consists of four parts--1. load transfer with vehicle dimension parameters, 2. wheel alignment with tire characteristics and suspension design parameters, 3. steering system, and 4. steering pull estimation based on experimental conditions.  
Figure 8: Developed steering pull model.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 2 Building Structures' Geometry Verification  
Verification of buildings' structure means inspection (comparison to the design) the position, orientation (including verticality), dimensions, in case of planar surfaces (e.g., of walls) the flatness of a given structural element, and the relationship to other structures in terms of geometry. In the following, we describe the developed approach to enable verification of wall structures using point clouds and BIM models.  
To ensure the highest possible level of automation, information about the planned geometry of buildings' structural elements must be presented in digital form. This requirement is fulfilled using a BIM model, where data is stored in the graphic data of the information model. One of the key requirements of the entire BIM process is interoperability. The solution in this field is the use of a common data environment for sharing all the data related to the BIM process. In terms of geometry generation, it is also very important to use a \"readable\" data exchange format, which simplifies the procedure. Several file formatsensure appropriate storage of the model's data, such as CAD formats, CIS/2, CityGML, etc. All of them are focusing on data for specific use. One of the most widespread data exchange formats worldwide is Industry Foundation Classes (IFC). IFC is an ASCII text file format that aims to provide open and neutral access to the storage and exchange of BIM models between different software. The format is developed by a non-profit organization buildingSMART, and it is defined by the standard _ISO 16739-1:2018 Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries--Part 1: Data schema._  
The proposed and presented approach uses the data exchange format IFC and as-build data in form of a point cloud. The procedure can be divided into four main steps: identification and derivation of the geometry of building structures from the BIM model, segmentation of the point cloud (segmentation of the planes of walls' surfaces), quantification of deviations, and creation of deviation maps (Figure 1). Each step is described in more detail in the following sections.
# _remote sensing_
## 3. HORUS Multiangle and Multispectral Observation Module
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of** & **HFOV** & **VFOV** & **Optical Axis** & **Filter’s Central** \\
**Camera** & **(degrees)** & **(degrees)** & **(degrees)** & **Wavelength (nm)** \\ \hline \hline a & 33.4 & 33.4 & +50.1 & 1. a1: 443 (blue) \\  & & & & 2. a2: 555 (green) \\  & & & & 3. a3: 670 (red) \\  & & & & 4. a4: 865 (NIR) \\  & & & & 1. b1: 443 (blue) \\  & & & & 2. b2: 555 (green) \\  & & & & 3. b3: 670 (red) \\  & & & & 4. b4: 865 (NIR) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Main features of the HORUS payload module. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Focal length & 18.7 mm \\ F\# & 1.4 \\ Effective aperture diameter & 13.7 mm \\ HFOV (along-track) & 33.4 degrees \\ VFOV (cross-track) & 33.4 degrees \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main features of the selected optics for HORUS payload. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Pixel size & 5.5 μm (H) \(\times\) 5.5 μm (V) \\ Resolution & 4 MP, 2048 \(\times\) 2048 px \\ Maximum frame rate & 90 fps \\ Shutter type & Global \\ Power consumption (typical) & 3.2 W \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main features of the selected CMOS sensor for HORUS payload. \begin{table}
\begin{tabular}{c c} \hline \hline
**Spectral Band** & **Central Wavelength (nm)** & **Spectral Bandwidth** \\ \hline Blue & 443 & 30 \\ Green & 555 & 20 \\ Dark red & 670 & 20 \\ NIR & 865 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main features of the selected COTS filters for HORUS payload.
## 4 DICATION: Proposed Methodology
### Projection Model Design  
The projection function \(\text{Proj}_{\theta}^{\text{DNN}}\) is a DNN with an input dimension \(|\text{Ext}(M,K_{\text{ext}})|\) and an output dimension \(|b|\) (watermark size). In this work, we employ a two-layer, fully-connected network:  
\[\text{Proj}_{\theta}^{\text{DNN}}(x)=\sigma(W_{2}\cdot\text{ReLU}(W_{1}\cdot x +b_{1})+b_{2}) \tag{22}\]where \(\sigma\) is the sigmoid function for binary watermark output, \(W_{1},W_{2}\) are weight matrices, and \(b_{1},b_{2}\) are bias vectors.  
Architecture Details:  
* Hidden layer: A total of 256 neurons with ReLU activation, and a dropout rate of 0.3;
* Learning rates: A total of \(10^{-3}\) for \(\text{Proj}_{\theta}\) and \(10^{-4}\) for \(M^{\text{wat}}\);
* Alternative architectures: ResNet-style or CNN-based architectures can be used for applications requiring higher capacity.  
Joint training strategy: The projection model and watermarked model are trained jointly through alternating optimization:  
\[\text{Update }\theta:\min_{\theta}E_{\theta}=\lambda(\delta( \text{Proj}_{\theta}(\text{Ext}(M^{\text{wat}},K_{\text{ext}})),b)+\delta( \text{Proj}_{\theta}(\text{Ext}(M,K_{\text{ext}})),b_{r})) \tag{23}\] \[\text{Update }w^{\text{wat}}:\min_{w^{\text{wat}}}E_{w^{\text{wat}}} =E_{0}(X_{\text{Train}},Y_{\text{Train}})+\lambda\delta(\text{Proj}_{\theta}( \text{Ext}(M^{\text{wat}},K_{\text{ext}})),b) \tag{24}\]  
This alternating optimization ensures that both the projection function learns to discriminate correctly between watermarked and non-watermarked activation maps, while the watermarked model maintains its primary task performance and embeds the watermark effectively.
## Theory
### Calculation of Modal Participant Coefficients
#### 2.2.2 Solving Equations Method (SEM)  
According to the basic idea that the displacement can be obtained by superposing the displacements of each order mode, the modal participant coefficient can be acquired by solving the displacement equations.  
First, \(Q\) (\(Q\) \(\geq\) number of modes involved in the equation set) points on the surface of the vibrating structure is selected. The way to choose these points is subject to the principle of randomly and uniformly selecting points to avoid the correlation between the equations. The \(p\) order modal displacement can be represented as \(\phi_{p}\), where \(p\) is from 1 to \(P\), and the actual displacement of the vibrating structure can be represented as \(\Phi\), and the location of the measurement points can be represented as \(x_{q}\), where \(q\) is from 1 to \(Q\)(\(Q\)\(\geq\)\(P\)), and \(p\) order modal participant coefficients will be expressed as \(W_{p}\). According to the principle of modal superposition, the displacement of a cylindrical shell can be expressed as the weighted superposition of modal displacements and then we can obtain some equations. The \(P\) modes involved in the equations all contribute largely to the actual vibration displacement of the cylindrical shell. If the actual vibration displacement and modal displacement are known, the equations can be solved and the modal participant coefficients can be obtained, as shown in Equation (3).  
\[\left[\begin{array}{c}W_{1}\\ W_{2}\\ \vdots\\ W_{p}\end{array}\right]=\left[\begin{array}{cccc}\phi_{1}(x_{1})&\phi_{2}(x _{1})&\cdots&\phi_{P}(x_{1})\\ \phi_{1}(x_{2})&\phi_{2}(x_{2})&\cdots&\phi_{P}(x_{2})\\ \vdots&\vdots&\ddots&\vdots\\ \phi_{1}\Big{(}x_{Q}\Big{)}&\phi_{2}\Big{(}x_{Q}\Big{)}&\cdots&\phi_{P}\Big{(} x_{Q}\Big{)}\end{array}\right]^{-1}\left[\begin{array}{c}\Phi(x_{1})\\ \Phi(x_{2})\\ \vdots\\ \Phi\Big{(}x_{Q}\Big{)}\end{array}\right] \tag{3}\]  
The number of columns of the above participant coefficient matrix depends on the number of modes involved in the equation. If only a few modes have a large contribution to the actual displacement, the number of measuring points required in this method will be small. Then, the participant coefficient of these several modes can be easily solved. In the latter part of this article, this characteristic is used when solving the modal participant coefficients, which could effectively reduce the number of measuring points.
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 2 Mathematical Model and Control Method  
### Mathematical Model of the Three-Phase LCL-Type Grid-Connected Inverter  
The main circuit topology of the three-phase grid-connected inverter with the LCL filter is shown in Figure 1, wherein \(L_{1}\) is the inductance on the inverter side; \(L_{2}\) is the inductance on the grid side; \(C\) is a filter capacitor. The inverter supplies power to the power grid through the LCL filter. \(U_{\text{dc}}\) is the DC bus voltage, \(u_{\text{a}}\), \(u_{\text{b}}\), and \(u_{\text{c}}\) are the midpoint voltages of each bridge arm of the inverter, \(i_{\text{la}}\), \(i_{\text{lb}}\), and \(i_{\text{lc}}\) are the three-phase inductor currents on the inverter side, \(i_{\text{2a}}\), \(i_{\text{2b}}\), and \(i_{\text{2c}}\) are the three-phase inductor currents on the grid side, \(i_{\text{ca}}\), \(i_{\text{Gb}}\), and \(i_{\text{cc}}\) are the three-phase currents of the filter capacitor, \(u_{\text{Ca}}\), \(u_{\text{Gb}}\), and \(u_{\text{Cc}}\) are the three-phase voltages of the filter capacitor, \(u_{\text{ga}}\), \(u_{\text{gb}}\), and \(u_{\text{gc}}\) are the three-phase grid voltages, \(N\) is the neutral point of the LCL filter, \(N^{\prime}\) is the neutral point of the grid, and \(Q_{1}\)-\(Q_{6}\) represent the six IGBT switch tubes of the inverter.  
Assume that the three-phase grid voltage is balanced. According to Figure 1, select the inverter side inductor currents \(i_{\text{la}}\), \(i_{\text{lb}}\), \(i_{\text{lc}}\), the grid side inductor currents \(i_{\text{2a}}\), \(i_{\text{2b}}\), \(i_{\text{2c}}\), and the filter capacitor voltages \(u_{\text{Ca}}\), \(u_{\text{Cb}}\), \(u_{\text{Cc}}\) as state variables; the state equation in the three-phase static coordinate system can be obtained, as shown in Equation (1).  
\[\left\{\begin{array}{ll}L_{1}\frac{\text{d}i_{\text{jk}}}{\text{d}t}&=u_{k}- u_{\text{Ck}}\\ L_{2}\frac{\text{d}i_{\text{2b}}}{\text{d}t}&=u_{\text{Ck}}-u_{\text{gb}}\\ C\frac{\text{d}i_{\text{UC}}}{\text{d}t}&=i_{1k}-i_{2k}\end{array}\right.\,k=a,b,c \tag{1}\]
## 3 Results and Discussion
### S Technique  
The S technique is a simpler method, and it is easy to apply. When the S technique is employed for the estimation of surface diffusivity (\(D_{s,sim}\)), the obtained simulated curve is referred as a simplified concentration decay curve (SCD curve) using Equations (13)-(16), as shown in the Mathematical Methods  
Figure 3: Procedures for rigorous diffusion technique.  
Therefore, Steps 3 and 4 mentioned in Figure 3 (R technique) are not required. The procedures for the S technique are illustrated in Figure 4. In this technique (S technique), Steps 1 and 2 (Figure 4) are the same as those in the R technique (Figure 3). In Step 3 (Figure 4), only one simulated theoretical concentration decay curve (STCD) is required. In the S technique, it is assumed that the main diffusion resistance is intra-particle resistance. For example, when Bi = 40, it is assumed that the intra-particle resistance is much greater than that of the fluid-to-solid film resistance in the system. In this case, (Bi = 40), the time ratio (T/t) is equal to 1.63 \(\times\) 10\({}^{-4}\) min\({}^{-1}\) by matching the ECD and STCD curves. The value of DS was determined to be 2.72 \(\times\) 10\({}^{-8}\) cm\({}^{2}\)/s, as shown in Figure 4 (Step 4). This value is slightly smaller (about 9.33%) than that of the value obtained using the R technique (\(D_{s,rig}\) = 3.0 \(\times\) 10\({}^{-8}\) cm\({}^{2}\)/s). The S technique only considers intra-particle resistance (without fluid-to-solid film resistance). The S technique is useful for cases when the intra-particle resistance is much greater than the fluid-to-solid film resistance. However, if the fluid-to-solid film resistance cannot be ignored or is greater than the intra-particle resistance, the S technique may not be valid. The three cases of various Bi numbers (Bi = 40, 10, and 4) estimated in this case are shown in Table 1. The value of the Bi number indicates the ratios of intra-particle resistance and fluid-to-solid film resistance. For example, Bi  
Figure 4: Procedures for simplified diffusion technique.  
Figure 5: \(Cont.\)
## 3 Results and Discussion
### Data Focus  
Table 4 shows the particle size distribution in the sediments coming from the San Giuliano reservoir. The percentages given allow us to identify a predominance of the sandy matrix in the samples coming mainly from the downstream areas of the reservoir; on the contrary, upstream samples exhibit a prominent proportion of clayey-silt particles. Table 5 shows the percentage distribution of the different matrices in the sediments sampled at the various stations at the Camastra reservoir. The data analysis highlights what has already been observed for the materials taken from the San Giuliano reservoir. The samples collected in the downstream areas display a prevailing sandy content; those from the upstream areas of the reservoir, on the other hand, are mainly characterized by a silt-clay component. It is also noted that the sediments are, in some places, of a coarser matrix, both within the same reservoir and in comparison, with the classification of materials sampled at the San Giuliano reservoir. \begin{table}
\begin{tabular}{c c c c} \hline
**Samples** & **Sand > 63 \(\upmu\)m (\%)** & **63 \(\upmu\)m \% Silt > 2 \(\upmu\)m (\%)** & **Clay < 2 \(\upmu\)m (\%)** \\ \hline SG\_S1 & 61.21 & 32.40 & 6.39 \\ SG\_S2 & 95.39 & 4.42 & 0.19 \\ SG\_S3 & 89.97 & 4.91 & 5.12 \\ SG\_S4 & 75.90 & 17.47 & 6.63 \\ SG\_S5 & 34.39 & 32.60 & 33.01 \\ SG\_S6 & 31.35 & 39.59 & 29.06 \\ SG\_S7 & 48.02 & 38.42 & 13.56 \\ SG\_S8 & 15.51 & 47.56 & 36.93 \\ SG\_S9 & 23.76 & 49.48 & 26.76 \\ SG\_S10 & 25.85 & 58.25 & 15.90 \\ SG\_S11 & 4.28 & 61.90 & 33.82 \\ SG\_S12 & 59.27 & 32.19 & 8.54 \\ \hline \end{tabular} Note: See Figure 2 for the sample’s references.  
\end{table} 
Table 4: Grain size distribution of the San Giuliano sediments. \begin{table}
\begin{tabular}{c c c c c} \hline
**Samples** & **2 cm > Gravel > 2 mm (\%)** & **2 mm > Sand > 63 \(\upmu\)m (\%)** & **63 \(\upmu\)m \% Silt > 2 \(\upmu\)m (\%)** & **Clay < 2 \(\upmu\)m** \\ \hline SG\_S1 & 0 & 29.3 & 70.7 & 0 \\ SG\_S2 & 0 & 14.8 & 85.2 & 0 \\ SG\_S3 & 0 & 1.8 & 40.1 & 58.2 \\ SG\_S4 & 0 & 0.4 & 37.8 & 61.8 \\ SG\_S5 & 34.2 & 44.0 & 10.6 & 11.2 \\ SG\_S6 & 38.1 & 34.1 & 12.2 & 15.6 \\ SG\_S7 & 23.4 & 48.4 & 14.9 & 13.3 \\ SG\_S8 & 35.0 & 26.6 & 18.3 & 20.1 \\ SG\_S9 & 0 & 6.8 & 70.5 & 22.7 \\ SG\_S10 & 0 & 29.0 & 66.4 & 4.6 \\ SG\_S11 & 0 & 30.2 & 69.2 & 0.5 \\ SG\_S12 & 0 & 48.1 & 51.9 & 0 \\ \hline \end{tabular} Note: See Figure 2 for the sample’s references.  
\end{table} 
Table 5: Grain size distribution of the Camastra sediments. Tables 6 and 7 show the values of major heavy metals for the San Giuliano and Camastra reservoirs, respectively, explaining the regulatory limits set out in column A of Table 1, Annex V, Part IV, Title V of Legislative Decree 152/06, some of which are also graphically represented in Figure 5 below. As it is evident, the parameters investigated are more significant in number; nevertheless, we have presented only those with more significant findings. The remaining heavy metals investigated were not graphically displayed because the values do not show high variability compared to the mean value.
## 3 Principle and Implementation of Markov Chain Random Carrier Frequency Modulation
### Random Parameter Optimization Based on Particle Swarm Optimization Algorithm  
Based on the above analysis, it can be seen that the random gain \(R\) and the transition probability \(P\) have significant effects on the modulation effect after the carrier frequency randomization, and these two parameters need to be constrained to the range of values in the design of the system parameters. In order to improve the computational speed, particle swarm optimization (PSO) algorithm is chosen to quickly find the optimal \(R\) and \(P\) parameters. Based on the optimal random parameters, three-state Markov chain random carrier frequency modulation is introduced to improve the random carrier frequency distribution, so as to meet the optimal sideband harmonic components and vibro-acoustic response suppression effects.  
As a stochastic search algorithm, the core driving factor of the PSO algorithm is to share and update the global historical optimal solution \(G_{best}\) with the individual historical optimal solution \(P_{best}\), so as to realize the optimal solution of the individual extremum and the particle swarm globally, and the specific process is shown in Figure 7.  
Figure 6: The random carrier frequency results with three-state Markov chain.  
The results of the random parameter optimization based on the PSO algorithm are shown in Figure 8. During the iteration process, the maximum number of iterative steps is set to 60, and the error value of the objective function stabilizes within 40 iterative steps, indicating that the suppression effect of sideband current harmonics reaches the optimization. At this time, the value of the transition probability \(P\) is 0.68. Figure 8b shows that the peak value of sideband harmonics decreases with the increase in stochastic gain \(R\) and reaches the optimal effect at \(R\) = 2000. Figure 8c shows that the suppression of sideband harmonics response tends to be saturated when the value of \(R\) is larger than 2000 Hz. According to the above random parameter optimization analysis, setting the transition probability \(P\) to 0.68 and the random gain \(R\) to 2000 can optimize the suppression effect of sideband current harmonics.  
Figure 7: Flowchart of particle swarm optimization.