esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Experimental Setup
#### 3.7.3 Implementation Details  
We implement our frameworks using PyTorch v2.6 [64]. All models are trained using the Adam optimizer [65] with a learning rate of 0.001. Each dataset is split into training, validation, and test sets in the ratio 80:10:10. We select the optimal regularizationparameter \(\lambda_{1}\) from the set {0.01, 0.05, 0.25, 0.5, 1} based on validation performance. For local explainability, we set the neighborhood size to k = 10 during Phase 1 and k = 100 during Phase 2. All experiments are repeated five times, and the results are averaged across runs.  
As our black-box model, we use Factorization Machines [53], which are widely used for classification, regression, and recommendation tasks. We chose Factorization Machines for our experiments as they represent a middle ground--more complex than linear models but less complex than deep networks, making them ideal for demonstrating our explainability improvements. For the white-box explainer model, we use sparse logistic regression, which offers several advantages over alternative interpretable models. We chose sparse logistic regression over decision trees and rule-based systems for three key reasons: (1) _Differentiability:_ Unlike decision trees, logistic regression provides smooth gradients necessary for our Jensen-Shannon divergence-based optimization. (2) _Global coherence:_ While decision trees can provide local interpretability, logistic regression offers consistent global feature importance scores across the entire dataset. (3) _Sparsity:_ The L1 regularization in sparse logistic regression naturally identifies the most relevant features, which is particularly valuable for high-dimensional datasets like HELOC with 23 features.
# Derivation and Validation of Bandgap Equation Using Serepentine Resonator
## 3 Numerical Validation  
### Resonant Frequency  
To validate the equations for resonant frequency and bandgap of the serpentine resonator derived in Section 2, the theoretical values were compared with the results of a finite element method (FEM) analysis. The FEM analysis was conducted through the 3D Multiphysics software COMSOL with eigenfrequency analysis. The analysis model is a serpentine resonator made of acrylic fixed to a steel plate, which is the host structure. The material properties of the acrylic were \(E=3.2\) GPa, Poisson's ratio of 0.4, mass density of 1165 kg/m\({}^{3}\), and concentrated mass = 5.2 g with acrylic. Table 2 shows the list of dimensions used in the analysis model.  
Figure 4 shows the resonance frequencies and their mode shapes. Five resonance frequencies below 500 Hz are indicated in the figure. At the first and second resonance frequencies, the first bending mode appears. The two mode shapes differ only in the direction of vibration. At the fourth and fifth resonance frequencies, the second bending mode and torsional mode appear, respectively. At the third resonance frequency of 158 Hz, the longitudinal mode, which is the mode of interest in this study, appears. Comparing this frequency with the theoretical result of 151 Hz, it is observed that the theoretical and simulation values are similar and the cause of error is the simplification of the resonator model to reduce the simulation cost.  
Figure 5 shows an error map for the relative errors between simulation and theory with respect to \(l_{p}\)/\(b\) and \(l_{o}\)/\(b\) for different values of \(N\). The other design parameters, namely, thickness and \(b\), were set to 10 and 1 mm, respectively. \(N\), \(l_{p}\), and \(l_{o}\) were then modified to compare the third resonance frequency, which is the desired frequency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(l_{o}\) & \(l_{p}\) & \(b\) & \(t\) & \(L_{xm}\) & \(L_{ym}\) & \(L_{xh}\) & \(L_{yh}\) & \(T\) \\ \hline
8.75 mm & 2 mm & 1 mm & 10 mm & 8 mm & 8 mm & 25 mm & 25 mm & 1 mm \\ \hline \end{tabular}
\end{table}
Table 2: Dimensions of the analysis model.  
Figure 4: Mode shapes according to each resonance frequency.  
Figure 5 indicates that as \(l_{p}\)/\(b\) and \(l_{o}\)/\(b\) increase, the analysis result becomes stiffer than the theoretical result, and thus the error decreases. In the simulation, the total stiffness increases due to the geometric stiffness of the corner, which is not considered in the theoretical implementation. Additionally, the larger the joint, the less effective is the hypothesis of an ideal beam in theory. Hence, the simulation result becomes stiffer when the joints of the corners are larger. Similarly, when \(l_{p}\)/\(b\) is large, the simulation result becomes stiffer as \(N\) increases. Therefore, the parts indicated by the black dotted line are set as the design zone because the error of resonance frequency is small, and designing with parameter bounds in the design zone is considered reasonable.
## 3 Results
### Optimization Results of Model Parameters  
The number of trees and leaves of the random forest initial parameters were 500 and 5, respectively. Based on the above data, the RF model was optimized by GA. The minimum RMSE value of training data was used as the objective function to optimize the number of trees and leaves. This was done to improve the prediction accuracy of the model. The initial population of GA is set randomly, assuming that the population size is 50, the maximum number of iterations is 100, the crossover probability is 0.8, and the mutation probability is 0.1.  
Figure 11 shows the convergence curve of tree count and leaf count based on the GA optimized RF model. The X-axis is the number of iterations and the Y-axis is the fitness value of the objective function RMSE. GA maintains a high convergence rate and falls into the optimal solution after about 40 iterations of optimization. Based on the optimal solution of objective function, the optimal number of trees and leaves in random forest is 398 and 1.  
The RF model optimized by GA is used to predict the oxygen concentration in the return airway corner of the working face. The number of trees and the number of leaves corresponding to random forest parameters after optimization of objective function \(f=\min(RMSE)\) are 398 and 1. The input indexes are selected after importance analysis and screening. Eighty percent of the data are selected as model training samples, and 20% of the data are selected as model testing and verification. The comparison between the oxygen concentration predicted by GA-RF model and the actual data is shown in Figure 12.  
Figure 11: Minimum RMSE fitness fusion characteristic curve based on GA optimized RF parameters.  
Figure 10: Importance analysis of input indicators.
## 2 EM Techniques Applied to CH  
Tangible Cultural Heritage (CH) includes movable and immovable artifacts, buried and underwater objects, archaeological and natural sites, industrial heritage, and cave paintings [3; 85]. This paper focuses specifically on indoor-stored artifacts and collections, excluding large archaeological/natural sites as well as buried or underwater objects. Throughout this paper, the term \"cultural heritage\" (CH) refers to tangible CH unless otherwise specified.  
A thorough understanding of an artifact's material composition is essential for effective intervention. Given the wide variety of methods, techniques, and artifact characteristics (such as material, shape, age, and condition), it is crucial to clarify:  
* Why the analysis is needed,
* What type of data is required and why,
* The artifact's material and historical context.  
The scientific literature classifies diagnostic and conservation techniques in three primary ways:  
* According to CH's dimensional scale [86],
* Based on the type of application--diagnosis or conservation [87],
* According to the underlying physical principles and methods [88; 89; 90].  
In this paper, we used the first approach for the introduction, as it is the most intuitive for beginners. For the presentation of specific techniques, however, the latter two classifications are more suitable, as they help clarify which methods are most appropriate for particular conservation challenges.  
Despite their technical differences, all electromagnetic (EM) techniques discussed in this review are fundamentally based on spectroscopy.  
Spectroscopy, a field that dates back to [PERSON]--who first used the term \"spectrum\" [91]--is introduced here in its basic form to help readers understand the physical phenomena behind EM techniques without delving into its historical development. According to [92], EM waves or charged particles interact with matter through the following processes:  
* Emission
* Absorption
* Fluorescence  
These responses occur as a result of atomic transitions: atoms absorb energy (absorption) or release it (emission) when they decay to a more stable state [88].  
Fluorescence spectroscopy involves the emission of visible light following the absorption of non-visible radiation (e.g., ultraviolet). The interaction depends on the emitted energy and the nature of the material.  
In the following sections, we will detail the key electromagnetic (EM) techniques used in Cultural Heritage (CH) diagnosis, explaining their underlying principles and the types of data they provide. This discussion aligns with the existing literature and emphasizes the role of these techniques in revealing material composition, as well as artists' methods and intentions.  
Table 4 summarizes the EM techniques covered in this review. References [88; 89; 90; 93; 94; 95; 96; 97] serve as the core sources for the fundamental principles of EM techniques applied to CH. Additional references will be cited throughout the paper as needed.
## 3 Results and Discussion
### Prediction of Tensile Modulus and Comparison of Models' Performances  
The prediction of Tensile Modulus was performed for both different orientation groups and as one group without separation on the orientations (called \"All\" in Table 5). Results show that prediction is not yet possible in the XYZ, XZY, and Angle orientations due to various reasons. Since large data sets are a well-known requirement for the successful application of machine learning techniques, a small number of data points is one of the reasons why developed models for the XYZ, XZY, and Angle orientations do not work well. A limited number of investigated features could be considered as another reason for such model performance. Since many studies have earlier reported that material properties have a significant impact on mechanical properties [3; 13; 18; 23; 41], and in this study they are not considered, this may have an impact on the obtained results.  
However, the prediction of Tensile Modulus based only on part positioning and STL model properties in the ZYX orientation has promising results. A model based on the gradient boost regressor algorithm outperformed other models with a prediction accuracy of 0.808 out of 1 (80.8% out of 100%) for the ZYX orientation. Additionally, it can be seen that an increased number of data points (see All in Table 5, where the number of data points represents number of data points used to train the model) leads to the higher prediction accuracy of 0.88 or 88% for the developed model. This means that the description of the part placement (positioning) in the build chamber in combination with part  
Figure 5: Distribution of nominal stress for different orientations based on kernel distribution estimation. The straight solid (48 MPa for the XYZ orientation) and dashed lines (42 MPa for the ZYX orientation) correspond to the values from the EOS Balanced datasheet.  
Figure 6: Distribution of elongation at break for different orientations based on kernel distribution estimation. The straight solid (18% for the XYZ orientation) and dashed lines (4% for the ZYX orientation) correspond to the values from the EOS Balanced datasheet.  
orientation and STL model properties can be used to predict Tensile Modulus, especially when collected data is not divided in the groups corresponding to each orientation, but instead this information is used as an additional feature. This results show a similar trend to the one [PERSON] et al. (2018) have reported in their study. Thermal distribution in the build chamber has an impact on the resulting mechanical properties, and the presented results are in a good agreement with the results reported in ([PERSON] et al., 2018). Besides, more details on which parameters are used in this model are described in the section below.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 2 System Model  
A cooperative WBAN scenario is considered, consisting of three sensors mounted in/on the body for various physiological measurements, as shown in Figure 1, with links identified with respective PERs. Similar to the conventional one-way cooperative network, these sensor nodes are called the source \(S\), destination \(D\) and relay \(R\). The links between the nodes are considered symmetric and communication is assumed to be half-duplex. Furthermore, all the nodes lie within each other's transmission range. The main focus is on Tier-1 communication, spanning the area within or around the body up to the radius of about 2 m [23]. The communication architecture, considered as the core of WBAN, is specified by four different channel models, namely, \(CM1-CM4\), based on various in-body and on-body transmission links of WBAN [24].  
Two-way relay cooperation occurs between \(S\) and \(D\), in which both nodes exchange their data using \(R\). This is different from a conventional one-way cooperative network in which only \(S\) sends the data to \(D\) through \(R\), as shown in Figure 1a. In the first phase of two-way relay communication, \(S\) transmits the data to \(D\), which is also overheard by \(R\). In the second phase, \(D\) transmits its data to \(R\). In the third phase, \(R\) broadcasts the exclusive-OR (XOR)-ed data of both \(S\) and \(D\). A simple XOR operation is performed at both nodes to get the relevant data. The packet size from both nodesshould be of equal length for two-way exchange; otherwise, there would be a need for bit padding. The variable packet size could also be tackled by XOR-ing the bits up to smaller packet length and transmitting the others as it is to avoid overhead. The two-way relay scenario is explained in Figure 1b. The transmission of a packet from \(S\) to \(D\) and back to \(S\) in the case of one-way cooperation requires 4 slots, whereas two-way relay requires only three slots [25]. Thus, providing cooperative diversity while reducing the number of slots.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 2 Related Work
### Battery Modeling Using Neural Networks  
With the demand of EVs and the growth of NNs, researchers have attempted to utilize NNs to model EV batteries. [PERSON] et al. [19] used NNs to acquire accurate SoC estimation via optimization schemes to tune the parameters of NN models. [PERSON] et al. [16] used multi-channel profiles to estimate SoH, comparing three types of networks: forward neural networks (FNNs), CNNs, and LSTM. They used the NASA battery dataset with eight Li-ion batteries running three different operational profiles, i.e., charging, discharging, and rest period, at room temperature. [PERSON] et al. [20] found that a 1D CNN was suitable for producing accurate SoC estimates for EV batteries. [PERSON] et al. [14] suggested using gate recurrent unit (GRU) networks to predict SoC. They collected data with battery testing equipment to deploy their models and compared RNN, LSTM and GRU, obtaining marginally smaller errors with the latter. [PERSON] et al. [21] used bidirectional LSTM as a speed predictor in conjunction with an energy management system to ensure accurate prediction. [PERSON] et al. [22] used a transformer architecture for battery SoC estimation, and they also introduced a self-supervised learning framework for SoC labeling.  
Table 1 summarizes the related works in terms of dataset type, NN architecture type, and the use of xAI. Existing works have demonstrated the feasibility of using NNs to estimate SoC. However, each of these works uses different datasets and a different set of NN architectures, making it difficult to draw conclusions or compare performances. In addition, these works use datasets acquired in experimental settings under laboratory conditions. _Our goal_ is to benchmark NN architectures that excel in time-series applications in the context of SoC estimation using field data that are not simulated or obtained within a laboratory controlled environment.
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 2 Algorithms
### Efficiency Test of Travel Time Calculation  
[PERSON]'s algorithm has high efficiency because it only needs to search once to complete the travel time calculation from one source to all other nodes.  
The computation amount of travel time is affected by the number of nodes and adjacency radius size. The spatial resolution depends on node density, whereas the angular resolution of a ray path is determined by the adjacency radius. Namely, both the calculation accuracy and the amount of computation increase with increasing node density or adjacency radius. Therefore, we need to achieve a balance between accuracy and computation consumption.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Adjacency Radius (Cell Number)** & **Maximum Relative Error** \\ \hline
4 & 1.46\% \\
5 & 1.15\% \\
6 & 0.74\% \\
7 & 0.50\% \\
8 & 0.40\% \\
9 & 0.34\% \\
10 & 0.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum relative error of straight-path travel time for various adjacency radii.  
Figure 2: Diagram of linear path correction between two blocks. The AE ray only deflects at the interface between blocks. Black polylines indicate the ray path calculated with the shortest path algorithm as the adjacent radius is 4 cells; red lines show the corrected linear path.  
Our algorithm includes the calculation of the shortest path and the correction of ray path and travel time, which is difficult to estimate the time complexity. Thus, we evaluate the efficiency of the algorithm by using a laptop computer (CPU: Intel (R) Xeon (R) W-11,855M, 6 cores, basic frequency of 3.20 GHz) to calculate the travel time from one node (where one detector is located) to all other nodes. The adjacency radius is equal to 7 cells in all calculations. The time consumption and memory usage under different amounts of nodes are listed in Table 2. Memory usage is nearly proportional to the number of nodes. Figure 3 shows the approximate linear relationship between the time consumption \(t\) and \(n\log_{2}n\), where \(n\) is the number of nodes.  
Therefore, the time and space complexity of our algorithm is \(O(n\log_{2}n)\) and \(O(n)\), respectively.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 4 Factors Influencing the Production of Microbial Biopolymers
### Intrinsic Factors  
(i) Selection of microorganisms: The selection of microbial strains plays a vital role in the fabrication of biopolymers. There is only a limited number of commercially available strains allowed by the Food Drug Administration (FDA) for use in the fabrication process. The effectiveness, yield, and functional, mechanical, and rheological properties are directly reliant upon the selected microbial strain and its metabolic pathways [10].  
(ii) Medium composition: One of the most significant factors for effective microbial fermentation is the medium composition [243]. An adequate supply of carbon and nitrogen in the culture medium is the most significant factor for the production of microbial biopolymers. Generally, microbes use polysaccharides such as glucose, sucrose, and fructose as carbon sources, as well as amino acids and ammonium salts as nitrogen sources, depending upon the type of biopolymer. Appropriate carbon-to-nitrogen ratios are desirable for the cultivation of microbial biopolymers [244]. Moreover, microbes require nutrients, minerals, and trace elements for the synthesis of microbial biopolymers (Table 4). The right concentrations of nutrients and trace elements like beef extract, yeast extract, sulfur, iron, and phosphorus are obligatory for the microbial metabolic pathway. Additionally, the medium must be free of any elements that can interfere with microbial action. Any imbalance leads to creating a disturbance in the metabolic pathway, which in turn influences the production of biopolymers [7; 10; 245; 246].  
(iii) pH, temperature, and oxygen supply: The yield and characteristics of microbial biopolymers can be reformed with the supply of oxygen. Several factors, including the microorganism, there are several pHs as well as temperature levels that are ideal for fabrication. Microbial growth rates and the fabrication of biopolymers could be impaired by deviations from the ideal circumstances.  
Figure 2: Factors influencing the fabrication of microbial biopolymers.
## 3 Analysis of Torque
### Effect of the Current Value on the Magnetic Field  
In Section 3.2, we explored the effect of gap shape on the magnetic circuit. In the following section, we explore the effect of current on the magnetic field. The coil current was continuously changed from 0 A to 3.5 A. The magnetic flux distribution clouds for the planar and semi-circular drives when the current was 3.0 A are shown in Figure 9.  
Figure 8: Effect of the arc size on the magnetic field.  
Figure 9 shows that the magnetic flux density of the MRF working gap has an increasing trend in the radial direction. Disregarding the effect of heat generation, when the radial working gap lengths are all 30 mm, the equivalent distances \(R\) for the semi-circular gap and the planar gap are 47.12 mm and 30 mm, respectively. The magnetic flux density caused by different currents is shown in Figure 10.  
The equivalent distance \(R\) is defined by equating the length of the curved disc-type MR drive to the length corresponding to the straight disc-type MR drive. As the coil of the disc-type drive is located on the outside of the working gap, the magnetic field generated by the coil decays in the direction away from the coil. From Figure 10, we can observe that the magnetic flux density increases as the current increases, but it changes more slowly with the radius of the working gap, and the data show an overall wavy growth trend. The magnetic flux density at the transition of the arc is lower, the maximum value appears at the circular angle \(\alpha=45^{\circ}\), and the maximum fluctuation rate is 28%. The data on the magnetic flux density \(B\) in the working gap are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Magnetic Flux Density \(B\)/(Tesla)**} \\ \cline{2-5}
**Current \(I\)/(A)** & \multicolumn{2}{c}{**Multi-Arc**} & \multicolumn{2}{c}{**Planar**} \\ \cline{2-5}  & **Min** & **Max** & **Min** & **Max** \\ \hline
1.0 & 0.209 & 0.456 & 0.340 & 0.382 \\
2.0 & 0.251 & 0.721 & 0.465 & 0.553 \\
3.0 & 0.297 & 0.863 & 0.501 & 0.606 \\
3.5 & 0.313 & 0.920 & 0.513 & 0.628 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The range of variation in the magnetic flux density for the two working gaps.  
Figure 10: Curve of the magnetic field distribution in the MRF working gap. (**a**) Curved disc-type drive; (**b**) planar disc-type drive.  
Figure 9: Cloud diagram of the magnetic flux distribution. (**a**) Curved disc-type drive; (**b**) planar disc-type drive.  
As can be noticed from Table 3, when the current exceeds 3 A, the magnetic flux density of the MRF increases slowly as the current increases. The MRF approached magnetic saturation after a current of 3.0 A, so the maximum current was set to 3.0 A. By comparison, it can be observed that the magnetic flux density within the semi-circular gap has a wider range of variation when the working radius is the same, and the tunability of the torque is better than that of the planar disc-type drive.