esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Materials
### Smart Grid Communication Challenges
#### 3.4.2 Transmission of Data Rate  
The smart grid's communication infrastructure is essential for various reasons, some of which are the collection and analysis of data and the distribution of instructions to the system's numerous nodes. On the other hand, the smart grid necessitates an abundance of real-time sensors as well as smart meters, both of which, when combined, generate a substantial quantity of data that has to be sent rapidly while maintaining its integrity. Inaddition, the foundation for mutual comprehension has to be created. Because of this, the smart grid requires a network connection that is both reliable and secure.
## 3 Results
### Comparative Performance Evaluation of G-S-M with Traditional Feature Selection Methods
Figure 6: AUC values of traditional feature selection methods, including XGB, SKB, IG, MRMR, CMIM, and FCBF, when coupled with different classifiers, including Adaboost, DT, LogitBoost, RF, SVM_opt, Stack_Logitboost_Kmeans, Stack_SVM_Kmeans, and XGBoost, compared with the AUC metrics of the EC-nomenclature-based G-S-M approach when coupled with RF, XGBoost, and DT classifiers and tested on the CRC-associated metagenomic dataset. Additionally, using the same CRC-associated enzyme abundance value dataset, we have tested the performance of the Recursive Cluster Elimination (RCE) algorithm with an SVM classifier as one of the wrapper methods. The SVM-RCE method resulted in 0.65 (\(\pm\)0.04) accuracy, 0.59 (\(\pm\)0.06) sensitivity, 0.70 (\(\pm\)0.05) specificity, and a 0.70 (\(\pm\)0.05) AUC value. As shown in Figure 6, the XGB and SKB feature selection methods have higher AUC values than the other tested methods. The top 10 most important enzymes selected by the XGB and SKB feature selection methods when coupled with the RF classifier are shown in Figure 7. One can infer from Figure 7 that glycine dehydrogenase (EC: 1.4.4.2) and glycine C-acetyltransferase activity (EC: 2.3.1.29) have been commonly identified by both XGB and SKB. In addition to that, the methylaspartate mutase activity (EC: 5.4.99.1) enzyme has been commonly selected by the EC-nomenclature-based G-S-M approach, XGB, and SKB (as shown in Figures 4 and 7). The citrate CoA-transferase (EC: 2.8.3.10) enzyme has been commonly detected by the EC-nomenclature-based G-S-M approach and SKB.
## 3 Results and Discussion  
### Dynamic Characterization of the System  
To determine the calibration values, the masses are attached to the load cell of the test bench. The masses can vibrate freely and therefore AM should be a real constant value over the whole frequency range which corresponds to the mass. Figure 5 shows the ideal values of the measured masses with dashed lines. The masses are given in Section 2.5. The values for \(AM_{meas}\) are derived from testing. They are marked blue for the low frequency and orange for the high frequency test bench. For each mass configuration studied, three repetitions were performed. The mass configuration and reputations were performed in a random order. Each test is evaluated at 200 different frequencies. All results are plotted in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(m_{sensor}\) & \(m_{1}\) & \(m_{2}\) & \(m_{3}\) & \(m_{4}\) \\ \hline low freq. test bench & 0.863 kg & 2.482 kg & 4.965 kg & 7.448 kg & 9.9316 kg \\ \hline high freq. test bench & 1.133 kg & 0.234 kg & 0.467 kg & 0.7011 kg & 0.9315 kg \\ \hline \end{tabular}
\end{table}
Table 1: Added masses \(m_{i}\) at low and high frequency test bench.  
Figure 4: (a) Compliant element A with two rubber buffers aligned; (b) compliant element B with one rubber buffer aligned; (c) size of rubber buffer.  
The deviation of the magnitude of the mass \(abs(AM)\) is mainly due to the additional mass \(m_{sensor}\), as it is to be extracted according to Ewins [26]. The phase diagram in Figure 5 shows the phase of AM. According to Equation (3), the AMs angle \(arg(AM)\) describes the phase difference between force and acceleration. Ideally, there should be no phase shift between force and acceleration. A phase shift differing from \(n\cdot\pi\) means that there is an imaginary part that is related to damping. A stiffness would lead to a phase shift of \(\pi\), resulting in a negative real part for AM. The test results show a phase that deviates from zero, which for the low frequency test bench increases from \(-0.2\) rad at 3 Hz to near 0 rad at 23 Hz. For the high frequency test bench it increases from around 0 up to 0.2 rad at 250 Hz and decreases back to near 0 rad at 500 Hz. The negative phase angle of the low frequency test bench indicates that the force is behind the acceleration signal in the time domain and this is equivalent to the force signal behind the displacement by more than \(\pi\). Instead, the positive phase angle at the high frequency test bench indicates that the acceleration is behind the force signal. Both deviations indicates that the phase shift is due to a delay in the measuring system.
# Laser-Induced Breakdown Spectroscopy for Determination of Spectral Fundamental Parameters
## 5 Experimental Determination of Spectral Parameters by LIBS
### Determination of Transition Probabilities
[PERSON] et al. [125] obtained in 2019 the atomic transition probability of six W I spectral lines using the 3D Boltzmann plot technique, a variation of classical Boltzmann plot which exploits the information from LIBS spectra taken at different delays to predict with a better accuracy the transition probabilities. All the spectral intensities were corrected using the El Sherbini method [54]. In 2020, [PERSON] et al. [126] measured the transition probability of the two spectral lines of Ag I at 827.35 and 768.77 nm on silver targets irradiated by a Nd:YAG laser at 532 nm, using the Boltzmann plot method. The authors corrected the line intensities for self-absorption, using the El Sherbini method [54]. They reported transition probabilities two orders of magnitude lower with respect to the values in the literature.
## 2 MRDP Design of HO-ARCs for IPDT Model
### IPDT Approximation of Non-Minimum Phase Process  
An even simpler identification method is presented here, based on the approximation of the non-minimum phase process by the IPDT model. This method was inspired by the fact that simple PI or PID controllers are sufficient to control the given process [8].  
A direct specification of parameters of HO-PID controller (7) would allow control of a slightly broader class of processes than when they are specified by HO-ARCs. However, although the MRDP method [25] can be used for the direct specification of the parameters of parallel HO-PIDs, it is more often combined with heuristic optimization carried out for the given process transfer function. In each case, however, an additional design of suitable anti-windup measures is required. In contrast to this, as we will show in this article, for the model-based design of HO-ARCs, it is enough to use an analytical MRDP method based on the IPDT process model, which is supplemented with simple restrictions for the design of filter (8). Therefore, the first task is to find a suitable IPDT model approximation for the measured open-loop step response of a given non-minimum phase process.  
The IPDT approximation of the non-minimum process can be derived by finding the time at which its step response characteristic crosses the initial (zero) value (see Figure 2). This value can be interpreted as the dead time of the IPDT model \(T_{dp}\). The slope of the characteristic curve at this intersection point can then be used to determine the IPDT parameter \(K_{sp}\). For example, the step response of the selected \(S(s)\) in Figure 2 can be used to obtain its approximation  
\[S(s)=\frac{Y(s)}{U(s)}=2\frac{-s+1}{(2s+1)^{2}};\ \ \text{IPDT:}\ T_{dp}=1.526,K_{sp}=0.37 \tag{12}\]  
For the above transfer function \(S(s)\) and the selected sensitivity value \(M_{s}=2\), the alternative tuning method [8] results in the following filtered PID controller (4):  
\[K_{P}=0.9497;\ T_{i}=4.8853;\ T_{D}=1.7785;\ T_{f}=1.8567;\ \beta=0.4050 \tag{13}\]  
Figure 2: Step response of \(S(s)=2(-s+1)/(2s+1)^{2}\) (black) and its IPDT approximation (9) with \(T_{dp}=1.526,K_{sp}=0.37\) (red).
## 3 Results and Discussions
### Formulation of Liposomes with Mulberry Extract and Their Characterization
#### 3.3.3 Determination of the Entrapment Efficiency (EE%) of the Mulberry Extract in Liposomes  
The entrapment efficiency of the mulberry extract in liposomes formulated by the lipid film hydration method was determined. The encapsulated efficiency revealed 88.25%of black mulberry included. Data from the literature shown different percentages of encapsulation depending on the extract/compound used. For example, [PERSON] ([PERSON]) et al., 2021 [24] obtained a percentage of inclusion of the extract of _Stellaria media_ (L.) [PERSON] of 92.09%, [PERSON] et al., 2020 [70] obtained a percentage of 77% for the extract of _Centella asiatica_, while [PERSON] et al., 2017 [71] included a norcantharimide derivative in liposomes at a percentage of 47.6%.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 6 Land-Based Systems Implementation-Premise: Ultrasonic Broken Rail Detector (UBRD)
### UBRD Hardware Configuration and Generated Signals  
Transmitters and receivers of the UBRD system are interleaved, as illustrated in Figure 15. Every receiver is able to acquire signals coming from both directions of the rail; to identify the exact orientation, the transmitted signals consist of different burst sequences depending on the transmitter. In fact, as it can be easily inferred from Figure 16, each transmitted sequence consists of a number of pulses spaced by a given _burst repetition interval_ (BRI) and is repeated at a specific _interrogation interval_ (II). This avoids the overlap at one receiver of burst trains coming from adjacent transmitters for extended periods. For such reason, receivers are also individually configured to recognize specific pulses as arriving from either the up or down direction depending on the settings of adjacent transmitters. Note also that each signal does not reach a receiver far from its transmitter because of the attenuation due to the propagation medium [50; 53].  
Figure 14: Signal processing steps accomplished in the passive reconstruction of the transfer function \(G_{AB}(\omega)\)[48].
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 2 The Methodology of the Work  
The experiments are based on different combinations of three cutting parameters, namely speed \(v_{c}\) (m\(\cdot\)min\({}^{-1}\)), feed \(f\) (mm) and depth of cut \(a_{p}\) (mm). For the sake of completeness, we have included a fourth, the influence of tool back angle \(\lambda\) (\({}^{\circ}\)), but in the following analysis, we include only the first three parameters. We consider these to be basic because they directly create the machined volume of the material, the structure and texture of the machined surface, as well as its instantaneous stress-strain state. There were 28 sessions in each combination. Parameter combinations are freely selected. To a large extent, they are still selected subjectively even in practice, since exact elaborated relationships for their discreet selection have not been derived yet. Hot-dip spraying has a characteristic structure that differs substantially from homogeneous materials. From a mechanical point of view, the quality of homogenization of the transition from the substrate to spraying depends, in particular, on the topographic parameters of the substrate surface. Based on the findings of the experiment, we will try to propose a method of selecting a combination of cutting parameters to control the desired state of the substrate surface. The quality of the transition homogenization determines the intensity of the adhesive stress and adhesive forces. The substrate is steel EN S235 JRG1, Stellite 6 is the spraying material, carbide WC-Co is included for comparison. Due to this different structure, they also exhibit other physical and chemical properties, in particular, stress-strain properties, which need to be identified, predicted and verified very accurately. Derived and used relations serve for analytical construction of deformation diagrams according to deformation stress \(\sigma_{dof}=\mathrm{f}\left(\Delta h\right)\) for studied materials. From a mechanical point of view (Section 3), the material used in the experiment is described in more detail, the experimental procedure is chosen from a technological point of view. Furthermore, the measurement results are presented, namely numerically, tabularly and graphically. The analyses and interpretation of measurement results are based on the comparison and verification of predicted analytical results (Sections 4-6).
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 2 Materials and Methods  
### Model Principle  
This study contemplates the amalgamation of the augmented dictionary transfer model and the transfer sparse coding (TSC) model, thereby enabling the transfer learning model to harness the strengths of both. It also takes into account the semi-supervised scenario, to discover the information pertaining to labeled sample categories and to establish a category dictionary augmented transfer learning model suitable for such a scenario. The implementation flow of the migration model is depicted in Figure 1.  
Initially, an incremental dictionary model is constructed. Following that, sparse features are extracted from this dictionary model for classification. This extraction process serves to pave the way for future transfer learning operations.  
Furthermore, a migration strategy is put forward. With the aim of minimizing the sum of distances, this strategy ensures strong connectivity between the source and target domains for the corresponding sparse coefficients of the labeled samples.  
Following that, an integration strategy is designed to migrate the sparse feature coefficients to different data domains. Subsequently, optimization is carried out for the fused objective function.  
The underlying principle of the method is illustrated in Figure 2, where the upper (light color) and lower (dark color) figures represent the original data space and the new space generated by the corresponding sparse coefficients, respectively. The different categories in the source domain (target domain) are depicted as triangles (pentagons) and circles (squares). Evidently, the aim of our model (SSDT) is to bring the labeled target domain samples closer to the source domain samples in the new space mapped by the category dictionary through distance constraints. Simultaneously, it introduces category means to mitigate the differences in conditional probability distributions between the original domain and the target domain.  
Figure 1: Implementation process of migration model.
# Graph Dilated Network with Rejection Mechanism
## 5 Graph Dilated Network with Rejection Mechanism
### Rejection Mechanism
#### 5.1.2 Discussion  
We notice that the graph neural network GG-NN [25] proposes a gate mechanism to aggregate neighbors, which is similar to the rejection mechanism proposed in our paper. The differences between these two mechanisms are summarized as follows. (1) The motivations of these two mechanisms are different. For GG-NN, the gate mechanism is proposed to dynamically aggregate neighbors' information based on the information of neighbors and the center node. For GraphDRej, the rejection mechanism is proposed to reject the information from distant nodes to address the over-smoothing problem. (2) The rejection mechanism is simpler and does not require much computation, whereas for GG-NN, the gate computation involves several multiplication and addition operations of matrices [25]. (3) The rejection mechanism is a more direct way that strictly limits the information from the distant nodes by the penalty parameters, whereas in GG-NN, according to Equation (6) in the original paper of GG-NN [25], not only the messages from the neighbors are rescaled, but also the message from the center node is rescaled. This means the gate mechanism in GG-NN is more like a weighted  
Figure 3: An example of the reject mechanism in a chain graph.  
average of the message from neighbors and the center node, and the messages from the center node may also be rejected. Experimental results (see Section 6.6) show GG-NN still suffers from the over-smoothing problem.  
To further analyze the connection between the rejection mechanism and the gate mechanism, we propose a gate-based version of GraphDRej (denoted as GraphDRej-Gate). The only difference between GraphDRej and GraphDRej-Gate is the computation of the penalty parameter. The penalty parameter \(c^{(k)}\) in GraphDRej is used for all the nodes in the same layer, whereas the penalty parameter \(c_{v}^{(k)}\) in GraphDRej-Gate is used for node \(v\) in the same layer.  
\[c_{v}^{(k)}=\textit{signoid}\big{(}W^{(k)}m_{v}^{(k)}+U^{(k)}h_{v}^{(k-1)} \big{)} \tag{9}\]  
where \(W^{(k)}\) and \(U^{(k)}\) are the transformation matrices. Compared with GG-NN, the rejection (gate) mechanism in GraphDRej-Gate is only applied to the neighbors' messages, which helps to address the over-smoothing problem (see Section 6.6). Note that although the gate mechanism is common in other research areas, how to apply the gate mechanism on GNNs to address the over-smoothing problem is still an open question. For example, GG-NN falls into the over-smoothing problem, while GraphDRej-Gate can solve this problem. Actually, the rejection mechanism is a more general idea and leads to a direction on addressing this problem, and the penalty parameter can be a simple learnable parameter or can be computed by a gate function.
## 1 Introduction
For instance, soils with higher silt and clay content generally exhibit higher water-holding capacity compared to sandy soils, which leads to higher SMC [25]. Additionally, soil depth has a crucial impact on SMC, as deeper layers often retain more moisture due to lower evaporation and higher water retention. [PERSON] et al. [26] demonstrated this in their study utilizing the water balance equation, showing that predictive model performance varied with depth, achieving lower RMSE values at 100 cm and 200 cm compared to shallow depths. In response to these developments, international frameworks such as the UN-Water 2023 Annual Report and the EU's \"Farm to Fork\" strategy have emphasized the significance of adaptive modern water resource management approaches that align with Sustainable Development Goal 6 (SDG6): Water and Sanitation for All [27]. The integration of IoT and ML in precision crop production contributes to these goals by allowing for better resource management, enabling data-driven decision making [28]. This research developed and validated soil texture-specific Gradient Boosting Regressor (GBR) models to predict SMC in two distinct soil textures, loam and silt loam. These soils differ in their moisture retention characteristics, where the loam soil exhibits moderate water-holding capacity due to its balanced sand, silt, and clay proportions, while silt loam retains more moisture because of its finer texture and higher silt content [29]. High-resolution meteorological data were collected using calibrated IoT-based sensors for model training. The key objectives of this research are to evaluate GBR model performance using multiple evaluation metrics; to identify optimal model hyperparameters by performing 5-fold cross-validation and grid search; and to identify the most influential predictors of SMC in each soil texture utilizing feature importance analysis. These will allow for better understanding of soil moisture dynamics in each soil texture, as well as better optimization of irrigation schedules and enhanced water resource use efficiency, aligning with SDG6 goals for sustainable water management.
## 4 Comparison with State-of-the-Art Works
Figure 38: CST simulated polar plots of the radiation patterns using the PAA thinning for implementation of the proposed EPP-BSA approach for 8-element ULAA at \(R=0.875\): (a) \(A_{c}=0\), (b) \(A_{c}=0.5\,\lambda\), (c) \(A_{c}=\lambda\), (d) \(A_{c}=2\,\lambda\), and changing from positive to negative. matrix with phase variations of \(\pm 135^{\circ}\) and \(\pm 45^{\circ}\) between its outputs. While the main drawbacks of the work are: (i) It did not offer continuous beam scanning; it just offered switched beams in limited and distinct directions. (ii) The beamwidth of the four main beams ranges from \(38.5^{\circ}\) to \(55.4^{\circ}\) in the H-plane and \(26^{\circ}\) to \(31.8^{\circ}\) in the E-plane, respectively. (iii) The proposed method suffers from significant HPBW broadening. (v) The feeding network design is complicated. In [21], the authors presented a switched beam antenna array. The main beam steering is performed using a 4 \(\times\) 4 Butler matrix phase-shifter. While the main drawbacks of the work are: (i) It did not offer continuous beam scanning; it just offered switched beams in limited and distinct directions. (ii) The feeding network design is simple. In [22], beam steering has been performed by utilizing a metasurface lens array that is fed by a phased array with fewer phase shifters. While the main drawbacks of the work are: (i) The design of the antenna array is complicated. (ii) It requires phase shifters. (iii) The integration of the lens array and the phased array is a cumbersome process and greatly affects the impedance matching of the array system. In [23], analog beam steering has been performed based on Huygens' metasurface pixel technique without using external phase shifters to the array elements. The required phase shifts between antenna elements were generated by integrating conventional probe-fed microstrip patch antenna arrays with anisotropic Huygens metasurface. In [24], the grid array antenna structure has been used to introduce switched beam steering. By switching the excitation of the elements at various locations, the array main beam can be steered, while it suffers from limited accuracy.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 3 Related Work
### Mathematical Expression Recognition  
OCR, i.e., the transcription of normal printed texts in documents and videos, has been extensively studied, e.g., [12; 13]. However, conventional OCR systems are not able to process mathematical expressions in documents or images due to the formulas' special structure, symbols and the position of the formulas' elements [14]. Therefore, as demonstrated in Figure 4, a specialized mathematical OCR system--called mathematical expression recognition (MER) in literature--is required to convert mathematical content into markup languages such as LaTeX [14].  
Figure 3: Example of a mathematical formula in LaTeX code.  
Figure 2: Challenges in reading and transcribing a mathematical formula.  
In recent years, these systems have evolved rapidly through deep learning. Usually, transformer-based approaches [2; 3; 4] have proven to outperform traditional statistical models [15; 16] and convolutional neural networks [5; 6; 17; 18; 19]. These neural networks are able to learn and recognize intricate patterns and features within images automatically, making them particularly well-suited for accurately extracting text with subscripts such as mathematical formulas from scanned documents or images [20].