esa-sceva/satcom-chunk-collection huggingface dataset:
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
In Ecuador, these conditions may be underestimated, especially in populations with limited access to healthcare or an inadequate dietary intake of vitamin D-rich foods. Also, vitamin D modulates the immune system and reduces the risk of infections, including respiratory illnesses. With Ecuador's varied climates, including cold, high-altitude regions, vitamin D deficiency might contribute to higher rates of pneumonia or influenza rates [18]. And low vitamin D levels may provide insights into the increasing prevalence of such diseases in Ecuador [19]. Despite the geographical and cultural factors of Ecuador's equatorial position suggesting abundant sunlight, urbanization, sedentary lifestyles, and cultural practices, such as limited outdoor activities or extensive skin coverage, reduce the UVB exposure necessary for adequate vitamin D synthesis [20]. Likewise, vitamin D studies could impact vulnerable populations, such as indigenous populations, and individuals in rural areas might face higher risks due to dietary insufficiencies and limited access to fortified foods [21]. Understanding regional disparities is critical for public health interventions. Addressing vitamin D deficiency in Ecuador [22] requires public health strategies like fortification programs, dietary supplementation, and raising awareness about safe sun exposure. Further research on vitamin D levels among Ecuador's different populations is vital to tailor interventions effectively. Still, recent research [23; 24] indicates that inadequate vitamin D status in these locations might be a far more significant issue than expected [25]. This is because cutaneous production of vitamin D is dependent on both environmental and individual factors, and several aspects could impact a person's ability to synthesize vitamin D endogenously [26; 27]. Limited sun exposure due to cultural practices, such as wearing sun-protective clothing, spending extended time indoors, and the effects of air pollution significantly reduce UVB exposure, leading to widespread vitamin D deficiency [22; 28]. Populations with darker skin, which naturally reduces vitamin D synthesis, and the elderly, whose skin has lower production efficiency, are particularly vulnerable. Deficiency has been linked to increased risks of osteoporosis, cardiovascular diseases, and immune dysfunction [18]. Regular screening and monitoring are crucial to mitigating these risks, emphasizing the need for vitamin D assessment in regions traditionally assumed to be at low risk. The research of vitamin D tests carries high costs, and using predictive tools based on artificial intelligence or statistical learning models can help identify patterns that indicate vitamin D deficiency or risk factors using these clinical data. To address this issue, our work aims to create a web page dedicated to predicting vitamin D levels in Ecuador and other similar countries. This initiative is critically important for shedding light on the often-overlooked prevalence of vitamin D deficiency in these regions.
## 3 Results  
### Dataset  
In the experiments, an open dataset [40] is used to validate the effectiveness of the proposed MDCCSP method. It is a multi-modal dataset comprising NIRs and EEG data from 29 subjects, from which the EEG data are merely used in this study. Therefore, the EEG dataset is introduced in the following paragraph. The information (age and gender) of the subjects is represented in Table 1.  
The EEG signals in the dataset are acquired using a BrainAmp EEG amplifier, which is produced by a German company named Brain Products GmbH. During data acquisition, the electrodes are placed according to the 10-5 international standard, and 30-channel EEG signals are recorded with a sampling rate of 1000 Hz. Subjects who participated in the experiments are ensured healthy conditions. They are instructed to complete two tasks, which are baseline tasks and mental arithmetic (MA) tasks. In the baseline tasks, subjects are supposed to be relaxed without thinking, while in the MA tasks, subjects need to carry on MA following the instructions on the screen. The EEG signals from baseline tasks and from MA tasks are labeled as low engagement data and high engagement data, respectively. The detailed process of experiments is represented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Subject** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ \hline
**Age** & 28 & 25 & 26 & 23 & 27 & 34 & 28 & 29 & 27 & 32 & 31 & 29 & 33 & 27 & 25 \\
**Gender** & F & F & M & F & M & M & M & F & F & M & F & M & F & F \\ \hline
**Subject** & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 & 24 & 25 & 26 & 27 & 28 & 29 & \\ \hline
**Age** & 32 & 39 & 26 & 32 & 28 & 24 & 23 & 27 & 27 & 26 & 27 & 27 & 36 & 26 & \\
**Gender** & M & F & F & F & M & F & M & M & F & F & M & F & M & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of subjects in the dataset where “M” and “F” stands for “male” and “female”.  
Figure 2: EEG data acquisition process of the dataset and the segmented strategy of the original data.  
In both tasks, subjects first take a 60 s pre-test rest and then receive an instruction on the screen. The instruction is either a cross or a subtraction formula such as \"\(514-9\)\". After 2 s, the instruction disappears. A beep prompts the beginning of the task. In the baseline task, subjects only need to stare at the cross, while in the MA task, subjects need to repeatedly subtract the same value from the result of the last time in mind. For instance, \"\(514-9=505,505-9=496,496-9=487,\cdots\)\". Each task period lasts for 10 s, and each subject takes both tasks 20 times. There is a rest with a random length from 15 s to 17 s between every two tasks. During the whole experiment, subjects are required to keep still to avoid motion artifacts to the utmost extent.
### State of the Art and Objectives
The main goal of this paper is to present the analysis, design, and simulation of a two-level hierarchical control to supervise and regulate the interaction of the HESS and the main dc bus in the microgrid for ultrafast charging, as depicted in Figure 1. The low level or primary control is devoted to the regulation of the converters involved in the interaction between the HESS and the main dc bus, while the high level or secondary control supervises the operating modes and establishes the references for the primary control. The rest of this article is organized as follows. Section 2 presents an overview of the power distribution control, describing the interconnection of the HESS and main dc bus and explaining the corresponding operating modes and the role of the two layers of the hierarchical control. Section 3 shows the design of the primary control based on a cascade regulation strategy. Section 4 contains simulation results illustrating the regulated transition between the different modes that can emerge in the steady-state regime of one-day UFCS operation. Conclusions and research in progress are summarized in Section 5.
# A Study on Reactive Ultra-Fine Fly Ash and Sulfoaluminate Cement in Self-Leveling Mortar
## 3 Results
### Durability Properties
#### 3.3.4 Restrained Shrinkage Test  
The restrained shrinkage test measures the cracking time and the induced tensile stress characteristics of a specimen. We started this test 24 h after the mortar set to explore the performance of PUFA and SAC in replacing different amounts of Type I Portland cement in terms of restrained shrinkage cracking. The results in Table 10 show that as the PUFA volume percentage of total cementitious materials increases, cracking occurs after a longer period. The larger specific surface area of PUFA and its bonding properties effectively resist stress-induced cracking. In the early stages, the higher the PUFA volume percentage, the  
Figure 8: The permeability coefficients for (**a**) 0% and 10% SAC volume percentages of total cementitious materials and (**b**) 20% and 30% SAC volume percentages of total cementitious materials.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \begin{tabular}{c} **Abrasion** \\ **Depth** \\ \end{tabular} & **Group** & \begin{tabular}{c} **Abrasion** \\ **Depth** \\ \end{tabular} & **Group** & \
\begin{tabular}{c} **Abrasion** \\ **Depth** \\ \end{tabular} & **Group** &
\begin{tabular}{c} **Abrasion** \\ **Depth** \\ \end{tabular} \\ \hline S0F0 & 2.33 & S10F0 & 2.31 & S20F0 & 2.17 & S30F0 & 2.15 \\ \hline S0F5 & 2.22 & S10F5 & 2.12 & S20F5 & 2.04 & S30F5 & 1.94 \\ \hline S0F10 & 2.15 & S10F10 & 2.11 & S20F10 & 1.93 & S30F10 & 1.76 \\ \hline S0F15 & 2.07 & S10F15 & 1.95 & S20F15 & 1.90 & S30F15 & 1.73 \\ \hline \hline \end{tabular}  
* The unit of abrasion depth is mm.  
\end{table}
Table 9: Abrasion resistance test results.  
better the performance in shrinkage resistance compared with lower volume percentages. The compressive strength test results indicate that specimens with higher PUFA volume percentages demonstrate better 28-day strength than those with lower volume percentages, showing a positive trend in strength correlating with restrained shrinkage cracking. As the SAC volume percentage increases, the cracking time is effectively delayed. As mentioned earlier, SAC has early-strength properties, which suggests that SAC also helps delay cracking. Higher SAC volume percentages show poor performance in compressive strength, but they still contribute to delaying cracking behavior.
## 3 Results and Discussion
### Ionic Liquids  
Besides the aforementioned chemicals, FTIR spectra and the band assignments of ionic liquids, enzymes, and biomass-derived chemicals on poplar are presented in Table 4 and Figures S3-S5. The bands from 1-butyl-3-methylimidazolium chloride contaminant were observed at 3341, 1658, and 1604 cm\({}^{-1}\) representing the formation of quaternary amine salt formation with chlorine, C=C stretching, and C=N stretching, respectively. However, the band at 835 cm\({}^{-1}\) representing C-N stretching vibration was not clearly observed [50]. The bands from 1-benzyl-3-methylimidazolium chloride were 2961, 1574, 765, and 633 cm\({}^{-1}\) from C-H stretching, C-C stretching of ring vibration, and C-N/C-Cl in-plane bending, respectively [51]. Moreover, two bands at 1383 and 1176 cm\({}^{-1}\) were observed; however, further study is needed to identify them. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Contaminants** & **Wavenumber [cm\({}^{-1}\)]** & **Assignments** \\ \hline \multirow{4}{*}{1-Butyl-3-methylimidazolium chloride} & 3341 & Quaternary amine salt formation \\  & 1658 & C=C stretching \\  & 1604 & C=N stretching \\  & 2961 & C-H stretching \\ \multirow{2}{*}{1-Benzyl-3-methylimidazolium chloride} & 1574 & C-C stretching ring vibration \\  & 633 & C-N/C-Cl in-plane bending \\ \hline \hline \end{tabular}
\end{table} 
Table 4: FTIR band assignments of ionic liquids, enzymes, and biomass-derived chemicals on poplar [49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88  
The bands of choline chloride-urea, which is a well-known DES, were at 3435 and 3340 cm\({}^{-1}\), which ascribed to the stretching of -NH\({}_{2}\) (asymmetric and symmetric), 1669 cm\({}^{-1}\) for the bending vibration of -NH\({}_{2}\), 1597 cm\({}^{-1}\) for bending vibration of -OH possibly due to the existence of water, 1474 cm\({}^{-1}\) for CH\({}_{3}\) rocking, 1152 cm\({}^{-1}\) for asymmetric C-N stretching, 1062 cm\({}^{-1}\) for CH\({}_{2}\) rocking, 961 cm\({}^{-1}\) for asymmetric stretching of CCO from choline structure and 790 cm\({}^{-1}\) from C=O bonding [52, 53]. Three lignin-based DESs, choline chloride-\(p\)-hydroxybenzoic acid (PHA), choline chloride-4-hydroxybenzaldehyde (PB), and choline chloride-\(p\)-coumaric acid (PCA), were mixed with poplar sample to observe the possible contamination bands. The bands of choline chloride-PHA were observed at 3180 cm\({}^{-1}\) for O-H stretching, 1681 cm\({}^{-1}\) for C=O stretching, 1581 cm\({}^{-1}\) for the asymmetric stretch of COO vibrations, 1282 cm\({}^{-1}\) for C-O stretching vibration, 1082 cm\({}^{-1}\) for C-O stretching, 953 cm\({}^{-1}\) for C-N stretching, 861 cm\({}^{-1}\) for CH\({}_{2}\) rocking vibrations, 838 cm\({}^{-1}\) for aromatic C-H
# Simplified Model Predicts Binder Behavior in Sand Mold Printing
## 3 Results
### Penetration Comparison in the Simulation and Experiment  
We found that the penetration depth increased with decreasing porosity, as shown in the curves in Figures 3c and 4. In addition, the diffusion diameter also increased with increasing porosity, as observed in Figure 5b. More specifically, when porosity was low, the diffusion diameter values were relatively consistent; when porosity increased beyond 36.3%, the diffusion diameter values gradually became more dispersed. Moreover, we observed that the relationship between the diffusion diameter and binder volume followed a quadratic curve, as depicted in Figure 5b. Overall, these findings suggest that binder volume and porosity play critical roles in determining penetration depth and diffusion diameter, which can help enhance our understanding of the infiltration phenomenon.  
We determined the penetration of the binder in the powder bed using CT scans and compared the results with the simulation results. It can be seen that \(\mathrm{P_{inf}}\) is related to porosity and particle size. As the binder volume increases, \(\mathrm{P_{inf}}\) becomes more significant. However, if the porosity is too great, the binder will continue to infiltrate downward until resistance with the particle surfaces balances the force of penetration. Conversely, if the particle size range and porosity are small, the binder is more likely to be retained between the particles [40; 41; 42], decreasing the \(\mathrm{P_{inf}}\) of the binder. When the porosity exceeds a certain value, the permeation shape gradually becomes cylindrical, which is most obvious when porosity exceeds 36.3%, according to the simulation results in this paper. Figure 4 shows that the affected particles were displaced after being combined with the binder. Moreover, it presents a somewhat spherical penetration range.  
Moreover, at a porosity of 40.7%, the cross-sectional geometry is not clearly visible due to the difficulty in capturing displacement changes between particles under CT scans at higher porosities. As a result, the difference is not easily discernible from a side view. However, as depicted in Figure 4b,f, at lower porosities, the side view shows that the permeation geometry is between semicircular and cylindrical. The larger the porosity, the less resistance the particles exhibit to the binder. Gravity and inertia also have a more significant influence on the binder, resulting in a deeper penetration depth and vice versa. This trend aligns with the simulation result presented in Figure 3c and can be confirmed by the Washburn equation [43]. Ultimately, we present the penetration depth data results in Figure 5.
## 6 Discussion
### Importance of Our XAI Framework for IDSs  
In the current era of rapid information growth, the frequency of network attacks is escalating, as highlighted by recent research from the Center for Strategic & International Studies (CSIS) [91]. Despite advances in intrusion detection systems (IDSs) over the years, security analysts face the daunting challenge of analyzing potential attacks that occur more rapidly than they can be processed. Accurate explainable AI (XAI) intrusion detection systems represent a crucial solution to this issue.  
The framework presented in this paper builds upon previous signature-based research [54; 70; 82], employing a method to identify the most influential features applicable across various models. For each of the seven AI models evaluated in this study, the key features are determined using SHAP and further refined through frequency analysis to pinpoint the most recurrent features. This methodology is also used to ascertain the most critical features for each type of attack. Additionally, our framework offers comprehensible explanations of AI decision-making processes and the pertinent influential features.  
This approach is applied across three distinct datasets: NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021. Our research also includes a cost analysis of SHAP and LIME based on the number of samples utilized, offering insights into which AI models might be more cost-effective. This end-to-end XAI-based strategy extends beyond traditional multiclass classification for IDSs by highlighting the importance of influential features and providing tailored insights into specific types of attacks.
## 4 Proposed BiGR Approach  
The novel proposed BiGR approach is based on the hybrid layers stack of Bi-directional Gated Recurrent Units (GRUs) and Recurrent Neural Networks (RNN). The proposed approach is examined in this section.  
The Gated Recurrent Unit (GRU) [30] is a variation of Long Short-Term Memory (LSTM) [31], due to both being designed similarly and giving equally excellent results. The GRU solves the vanishing gradient issue, which a classical recurrent neural network has. The GRU uses gates to overcome the vanishing gradient. The reset gate and update gate are the two main blocks of GRU to save long-term information data. These two gate vectors decide which data is passed to the output. Long-term information keeping is the property of GRU. The typical structure of Bi-directional GRU (Bi-GRU) [32] is a sequence model based on two GRU models. The one GRU takes the input in the forward direction. The other GRU takes it in the backward direction. The Bi-directional GRU has only the input and forgets gates. The update gate and reset gate are calculated by Equations (3) and (4). The current and final memory content is calculated as expressed in Equations (5) and (6).  
\[z_{(t)}=\sigma(W^{(z)}x_{t}+U^{(z)}h_{(t-1)}), \tag{3}\]  
\[r_{(t)}=\sigma(W^{(r)}x_{t}+U^{(r)}h_{(t-1)}), \tag{4}\]  
\[h^{\prime}t=\tan h(Wx_{t}+r_{t}\odot Ult_{t-1}), \tag{5}\]  
\[h_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot h^{\prime}t, \tag{6}\]  
Recurrent Neural Networks (RNN) [33] are best known for sequence modeling tasks. The RNN used the backpropagation algorithm to calculate the gradients that are specific to sequence data. In RNN, the input data cycles through a loop that takes input and passes it to the middle layer present in the network. The RNN [34] activation functions, biases, and weights will standardize so that each layer has the same parameters. The RNN model is trained based on the errors calculated from its output layer to its input layer. The principle of the RNN model works on saving the individual layer output and feeding this back to the input layer to predict through the output layer. The RNN handles sequential data by memorizing previous inputs. Equations (7) and (8) expressed mathematical notations to calculate the RNN layer output. Where \(Y_{t}\) contains the m \(\times\) n neurons matrix layers  
Figure 7: The asteroseismology dataset features correlation analysis.  
outputs at time step \(t\), \(X_{t}\) contains the m \(\times\) n input matrix for all instances, \(W_{x}\) contains the weights for the inputs, \(W_{y}\) contains the weights for the outputs, and b is the neuron bias term.  
\[Y_{(t)}=\varnothing\Big{(}X_{(t)}\cdot W_{x}+Y_{(t-1)}\cdot W_{y}+\text{b}\Big{)} \tag{7}\]  
\[Y_{(t)}=\varnothing\Big{(}\Big{[}X_{(t)}\cdot Y_{(t-1)}\Big{]}\cdot\text{W}+ \text{b}\Big{)}\ with\ W=\begin{bmatrix}W_{x}\\ W_{y}\end{bmatrix} \tag{8}\]
## Appendix B  
The curvature radius \(\rho_{ij}\) and the center bracket's curvature radius, which are formed by the changes in each pull line during the driving process, are denoted as \(R\). This is the radius of the circle where the drive line is located. Given that \(\rho_{i}\varphi_{i}=l_{i},\rho_{ij}\varphi_{i}=l_{ij}\), we can calculate \(\rho_{ij}\) and \(l_{ij}\) according to Equations (A8) and (A9), respectively.  
\[\rho_{ij}=\rho_{i}+R_{i}\cos\ (\delta_{i})\] (A8)  
\[l_{ij}=l_{i}+R_{i}\varphi_{i}\cos\ \delta_{ij}\] (A9)  
The cables are evenly distributed in the cross-section of the base, with intervals of \(90^{\circ}\). Specifically, we have \(\delta_{i1}=\delta_{i};\delta_{i2}=\delta_{i}+\frac{\pi}{2};\delta_{i3}=\delta _{i}+\pi;\delta_{i4}=\delta_{i}+\frac{3\pi}{2}\). Consequently, the sum of cosine values of \(\sum_{i}\cos\delta_{i\overline{j}}=0\). Considering the relationship between the main arc length and the four chord lengths, we find that \(l_{i}=\frac{1}{4}\sum_{i}l_{ij}\). When the robot joint only undergoes a bending angle \(\delta\) while the rotation angle is 0, the transformation of the length of the first drive line can be expressed as Equation (A10).  
\[\
abla l_{l1}=l_{i}-l_{i1}=(\rho_{i}-\rho_{i1})\delta_{i}=\
abla\rho_{i_{1}} \delta_{i}=R_{i}\delta_{i}\] (A10)  
\(\varphi_{i},\theta_{i}\) and the variation of the four drive \(\
abla l_{ij}\) when the traction arm bends and twists at the same time. Substitute the rope drives 1 and 2 into \(l_{ij}=l_{i}+R_{i}\varphi_{i}\cos\delta_{ij}\) to obtain \(l_{i1}=l_{i}+R_{i}\varphi_{i}\cos\delta_{ij}\); \(l_{i2}=l_{i}-R_{i}\varphi_{i}\sin(\delta_{i})\). In the same way, substitute 1 and 3 to obtain \(l_{i1}=l_{i}+R_{i}\varphi_{i}\cos\delta_{ij}\); \(l_{i3}=l_{i}-R_{i}\varphi_{i}\cos(\delta_{i})\). By solving for \(\delta_{i}\) as shown in Equation (A11), we can determine its value.  
\[\delta_{i}=arc\ \tan\ \bigg{(}\frac{2(l_{i1}-l_{i2})}{(l_{i1}-l_{i3})}-1 \bigg{)},\delta_{i}\in\Big{(}-\frac{\pi}{2},\frac{\pi}{2}\Big{)},l_{i}\in R\] (A11)  
We can substitute the rope drives 2 and 4 into \(l_{ij}=l_{i}+R_{i}\varphi_{i}\text{cos }\delta_{ij}\) to obtain: \(l_{i2}=l_{i}-R_{i}\varphi_{i}\sin\delta_{ij}\); \(l_{i4}=l_{i}+R_{i}\varphi_{i}\sin\delta_{i}\). In the same way, substitute 1, 3 to obtain: \(l_{i1}=l_{i}+R_{i}\varphi_{i}\cos\delta_{ij}\); \(l_{i3}=l_{i}-R_{i}\varphi_{i}\cos(\delta_{i})\). By solving for \(\varphi_{i}\) as shown in Equation (A12), we can determine its value. And because \(\rho_{i}\varphi_{i}=l_{i}\), \(l_{i}=\frac{1}{4}\sum_{i}l_{ij}\), by solving for \(\rho_{i}\) as shown in Equation (A13), we can determine its value.  
\[\varphi_{i}=\frac{1}{2R_{i}}\sqrt{\left(l_{i4}-l_{i2}\right)^{2}+\left(l_{i3} -l_{i1}\right)^{2}}\] (A12)  
\[\rho_{i}=\frac{R_{i}\sum_{i}l_{ij}}{2\sqrt{\left(l_{i4}-l_{i2}\right)^{2}+ \left(l_{i3}-l_{i1}\right)^{2}}}\] (A13)