esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
### Limitations and Future Research Directions  
First, although a pilot test was conducted to verify the framework's effectiveness, further experimentation across various school levels is necessary to validate its general applicability. Future research should conduct experiments across different educational levels to empirically examine the general effectiveness and long-term impact of the framework on learners' academic performance and overall educational outcomes. Such studies would help in understanding practical differences in outcomes and identifying potential areas for further refinement of the framework.  
Second, the current study's validation relied primarily on theoretical foundations and expert assessments, without delving into personalized educational approaches that consider individual learner characteristics, prior knowledge, and learning preferences. Future studies should develop customized learning strategies that cater to individual learner differences and propose AI educational methodologies based on these strategies. In particular, educational models that provide personalized feedback in each stage according to the learner's level are necessary, aligning with the principles of adaptive learning [42].  
Third, this research concentrated on computer vision education at the elementary and secondary levels. However, computer vision is also highly relevant in higher education and vocational training. Subsequent research should develop computer vision education frameworks targeting university students or vocational trainees and explore educational methods suitable for their specific characteristics and needs.  
Lastly, while the proposed framework emphasizes technical aspects, the utilization of computer vision technology entails ethical and social considerations. Concerns such as privacy in facial recognition and liability in autonomous systems point to the necessity of incorporating ethical education into the curriculum. Future research should integrate ethical perspectives into computer vision education, enabling learners to critically reflect on the societal impacts of technology, as recommended by Ref. [50].  
Conceptualization, I.-S.J. and S.-J.K.; methodology, S.J.K. and S.-J.K.; validation, I.-S.J., SJ.K., and S.-J.K.; formal analysis, I.-S.J.; investigation, I.-S.J.; resources, S.J.K. and S.-J.K.; data curation, I.-S.J.; writing--original draft preparation, I.-S.J.; writing--review and editing, S.J.K. and S.-J.K.; visualization, I.-S.J.; supervision, S.-J.K.; project administration, S.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Korea National University of Education.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 3 Methodology
### Definition of Proposed Model  
In this subsection, the model architecture proposed for this research is defined. First is the definition of the model, which is made up of layers of densely connected neural networks. The model has a sequential architecture with seven layers one input, five hidden, and one output.  
The initial layer, the input layer, has 78 neurons, which encapsulates the 78 attributes of the dataset used. The hidden layers segment is defined by densely connected layers, which can be understood as those in which each neuron receives an input from all the neurons instantiated in the previous layer [28].  
A rectified linear activation function (ReLU) is handled in each layer. ReLU evaluates if the input is greater than 0. If so, the output is the same as the input; otherwise, it is equal to 0. The decision to use ReLU as the main activation function of the model was a consequence of its computational simplicity and its being an evaluation function of the maximum between the input value and 0 [29].  
In this model, a layer called Dropout was used. The function of this layer is to ignore a rate of nodes in a random fashion. This in part represents an effect in which neurons can modify the way in which they correct the errors of other neurons, which entails complex co-adaptations that do not generalize unseen information, as raised by [PERSON] et al. [30].  
The model output layer is instantiated with a Softmax activation function. The choice of this function is associated with the main task of the model, which is classification in a multiclass context. This function will return a probability of the maximum value in an array, which is interpretable as the most probabilistically accurate label for the sample being evaluated [31].  
The loss function used in the model is categorical cross-entropy [32], also known as Softmax loss, and the instantiated optimizer is Adam [33]. A representative image of the defined model is in Figure 2.
## 3 Results
### Width vs. Inclination of the Upper Central Incisors (Convergent/Divergent)  
#### 3.2.1 L1 level  
Angulation of the upper central incisors did not correlate with canal width when analysing the total group or by sex. The absence of a correlation was statistically significant. However, a high correlation was found at the L1 level in both women and men aged 13 to 20 years. This correlation was negative in the group of women (r = \(-0.617\)), which means that the interapical distance increases with increasing canal width, while in the case of men (r = 0.640), this correlation was positive, i.e., the wider the canal, the more convergent  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gender/Age** & **Mean Width (SD)** & **Mean Length (SD)** & **r(X,Y)** & **r\({}^{2}\)** & **t** & **_p_** \\ \hline F 13–20 & 3.58 (\(\pm 0.97\)) & 3.46 (\(\pm 0.56\)) & 0.753 & 0.57 & 3.03 & 0.02 \\ \hline M13–20 & 3.66 (\(\pm 0.98\)) & 3.54 (\(\pm 1.66\)) & 0.629 & 0.4 & 1.81 & 0.13 \\ \hline F 21–29 & 4.29 (\(\pm 0.87\)) & 4.02 (\(\pm 1.02\)) & 0.815 & 0.66 & 3.72 & 0.01 \\ \hline M 21–29 & 3.86 (\(\pm 1.43\)) & 4.05 (\(\pm 1.33\)) & 0.945 & 0.89 & 7.08 & 0 \\ \hline F 30–39 & 4.19 (\(\pm 1.33\)) & 3.88 (\(\pm 0.97\)) & 0.361 & 0.13 & 0.95 & 0.38 \\ \hline M 30–39 & 4.14 (\(\pm 1.13\)) & 3.73 (\(\pm 1.15\)) & 0.876 & 0.77 & 4.45 & 0 \\ \hline F 40–49 & 4.66 (\(\pm 0.64\)) & 4.16 (\(\pm 0.52\)) & 0.479 & 0.23 & 1.44 & 0.2 \\ \hline M 40–49 & 4.22 (\(\pm 1.12\)) & 4.39 (\(\pm 1.13\)) & 0.623 & 0.39 & 2.11 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Width (Cl-Cl) vs. length (Ant—post dimension of the canal) of incisal canal—L2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gender/Age** & **Mean Width (SD)** & **Mean Length (SD)** & **R(X,Y)** & **r\({}^{2}\)** & **t** & **_p_** \\ \hline F 13–20 & 3.93 (\(\pm 1.13\)) & 3.59 (\(\pm 0.95\)) & 0.72 & 0.52 & 2.75 & 0.03 \\ \hline M13–20 & 3.49 (\(\pm 1.2\)) & 3.53 (\(\pm 1.58\)) & 0.729 & 0.53 & 2.38 & 0.06 \\ \hline F 21–29 & 3.68 (\(\pm 0.8\)) & 3.77 (\(\pm 1.25\)) & 0.936 & 0.88 & 7.03 & 0 \\ \hline M 21–29 & 3.28 (\(\pm 0.9\)) & 2.94 (\(\pm 0.8\)) & 0.128 & 0.02 & 0.32 & 0.76 \\ \hline F 30–39 & 4.06 (\(\pm 1.19\)) & 3.43 (\(\pm 1.09\)) & 0.678 & 0.46 & 2.26 & 0.06 \\ \hline M 30–39 & 4 (\(\pm 1.59\)) & 3.24 (\(\pm 1.63\)) & 0.921 & 0.85 & 5.78 & 0 \\ \hline F 40–49 & 4.46 (\(\pm 1.02\)) & 4.2 (\(\pm 1.17\)) & 0.423 & 0.18 & 1.23 & 0.26 \\ \hline M 40–49 & 4.49 (\(\pm 0.97\)) & 3.83 (\(\pm 0.93\)) & 0.71 & 0.5 & 2.67 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Width (Cl-Cl) vs. length (Ant-post dimension of the canal) of incisal canal—L3.  
the roots. A high positive correlation was present in the group of men up to 30 years of age. Patients aged 30 to 39 years showed strong negative correlations. A strong negative correlation was found in women up to 40 years of age. Between the ages of 21 and 40 years, the correlation was also negative but less strong. Correlations in the age groups did not show statistical significance (Table 4).
## 2 Related Work
### G&E Model  
The proposed G&E model focuses on reflecting an attacker's purpose of attack and the consequences of a successful attack, rather than on specific attack methods. It defines the attacker's goal to the victim and the target's damage effect from the attack. The goal or effect to be reflected in the model is identified by a unique number. Each G&E model has a unique name and an explanation. Additionally, it has various attributes to model the details. Figure 1 shows the conceptual diagram of G&E model.  
Referring to various cyber attack cases, 11 G&E models are identified and applied to the proposed model. The identified G&E models are social engineering, reconnaissance, privilege escalation, forgery, denial of service, command and control, exfiltration, destroy device, spreading, resource consumption, and unknown. Table 1 shows explanation and attributes.  
As shown in Table 1, the G&E model has individual attributes and can be expressed in detail through the settings. In reconnaissance with G&E 2, six attribute values exist. Scan Range sets the range for scanning, and Target Device specifies the target to find through the scan. Start Time sets the time to start the scan and Inter-arrival Time means the interval to perform the scan. Packet Size sets the size of the packet to be transmitted during the scan, and Duration means the duration of the scan.  
Figure 1: Conceptual diagram of G&E model. G&E has a unique number, a name, an explanation, and attributes.  
For privilege escalation with G&E 3, three attribute values exist. CVE List is a list of CVE IDs used to obtain privileges. Gain Privilege Probability specifies the probability of obtaining a privilege if the CVE List is not specified. Privilege Type sets whether the privilege type is user or administrator.  
The G&E model can be mapped to the tactic concept of ATT&CK, and various attack patterns of CAPEC can be applied. New attacks will continue to emerge, and the G&E model can be extended to reflect this.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 3 Proposed Strong Multipath Detection Using C/N\({}_{0}\) Information  
In this section, we introduce a new concept for detecting satellites affected by strong multipath, especially in the case of NLOS signal reception. As demonstrated in Section 2.3, we identified two important facts regarding the relationship between the pseudo-range error and C/N\({}_{0}\) in the case of receiving NLOS signals, which are as follows:  
1. The C/N\({}_{0}\) of satellites affected by strong NLOS signals can easily reach 40 dB-Hz and above. They also fluctuate significantly from below 30 dB-Hz to over 40 dB-Hz. Furthermore, the large pseudo-range errors of these satellites continue for a long time even though large fluctuations can be observed.
2. The C/N\({}_{0}\) values of satellites that clearly receive LOS signals do not decrease below 30 dB-Hz, and relatively high C/N\({}_{0}\) values of more than 40 dB-Hz are maintained. Moreover, they sometimes fluctuate owing to the reception of the reflected signal. However, these pseudo-range errors can be reduced using correlator-based multipath mitigation because they receive LOS signals. The pseudo-range errors are mostly less than 5 m.  
Based on these two facts, it is very difficult to detect NLOS signals by setting the minimum threshold for the C/N\({}_{0}\) of satellites because high C/N\({}_{0}\) values of more than 40 dB-Hz can easily be reached. Therefore, we focused on determining whether the C/N\({}_{0}\) value decreases below the threshold. When the value of the satellite C/N\({}_{0}\) is less than the threshold, the satellite is not used for positioning for a certain period. Figure 7 depicts our new method for detecting satellites with large pseudo-range errors using C/N\({}_{0}\) information. It illustrates the temporal C/N\({}_{0}\) for GPS 03 in the dense urban areas depicted in Section 2.1. Two important parameters needed to be set for this method: the C/N\({}_{0}\) threshold and the period in which the satellite was not used for positioning. As an example, in Figure 7, we set 30 dB-Hz and 1 min as the threshold and period, respectively. Those values are just examples. As indicated in the figure, even if the value of C/N\({}_{0}\) recovered over 30 dB-Hz after 462,460 s, the satellite was not used for positioning for at least 1 min. After 462,520 s, the satellite could be used for positioning but it returned to being unusable for positioning after only 24 s. In reality, GPS 03 could not be used for positioning from 462,520 to 462,544 s because the pseudo-range error was continuously large over 60 m.  
Figure 7: New concept for detecting multipath-contaminated satellites.
## 4 CD, GFD, and Nutritional Deficiencies  
### At Diagnosis  
The most common nutritional inadequacies in diagnosed CD patients are deficiencies in iron, calcium, zinc, vitamin B12, vitamin D, and folate. Indeed, the proximal small bowel is an important site for absorption of nutrients; in untreated CD, there is a loss of brush border proteins and enzymes needed in the absorption of micronutrients [26].  
Various studies showed a prevalence of folate deficiency in newly diagnosed CD patients ranging from 18% to 90%, depending on the methods of measurement of folates [27, 28]. In particular, a negative association was found between erythrocyte folate concentrations and the grade of villous atrophy in adults with symptomatic CD [29]. Low folate concentrations may be attributable to vitamin B12-deficiency since folate requires vitamin B12 for its activation. Additionally, iron deficiencies are also found in one-third of the CD patients at diagnosis [30]. Calcium deficiency is one of the common nutritional deficiencies in newly diagnosed CD patients leading to potential subsequent metabolic bone diseases. A study enrolling 86 consecutive newly diagnosed CD patients found that 34% of patients had a normal bone mineral density, 40% osteopenia, and 26% osteoporosis [31]. Several studies showed that zinc deficiency might also occur in patients with CD because of the damaged intestinal epithelial cell membrane, such as a cumulative loss of insoluble zinc complexes with fat and phosphate, exudation of zinc protein complexes into the intestinal lumen, and massive loss of intestinal secretions or impaired zinc absorption [32]. In children newly CD-diagnosed, vitamin D, zinc, and iron deficiencies are frequently observed at the time of diagnosis [33]. The reduced growth rate in CD children may be related, in part, to zinc deficiency [34]. In CD adults, [PERSON] and colleagues also reported an association between zinc, iron, copper, folate, and vitamin B-12 deficiencies and the severity of villous atrophy: the more pronounced is the lesion, the lower are the plasmatic levels [35]. Although B-vitamins are absorbed in the proximal small bowel, which is the most prominent site affected in CD patients [36], recent studies did not report vitamin B2 and B6 deficiencies in untreated CD-patients [27, 37]. On the other hand, vitamin B12 deficiency is found in 8-41% of CD patients at diagnosis [36, 38], as well as vitamins A, D, E, and K [36]. These deficiencies may be related to small intestinal bacterial overgrowth, which often occurs due to the small intestinal injury [39].  
Thus, newly diagnosed celiac patients often present several micronutrient deficiencies, suggesting that specific nutritional recommendations should be provided to these patients in terms of the quality of the nutrients.
# Electrochemical Deposition of Copper on Epitaxial Graphene
## Materials and Methods
### Samples and Processes  
An Autolab type III potentiostat (Autolab, EcoChemie, Utrecht, Netherlands) was used for the electrochemical measurements. An Ag/AgCl electrode in 3 M KCl and a platinum wire were employed as reference and counter electrodes, respectively, for all measurements.  
The samples of nominally monolayer epitaxial graphene (EG) on SiC (substrate area \(7\times 7\) mm\({}^{2}\) and thickness 0.4 mm) were obtained from Graphene AB and produced by the high temperature thermal decomposition of the Si-face (0001) 4H-SiC substrate (\(7\times 7\) mm\({}^{2}\)) in an argon atmosphere using an inductively heated graphite container with a well-controlled temperature profile [81]. Most of the substrate surface is coated with monolayer graphene after graphenization, while the fraction of bilayer graphene islands is negligibly small, which is evidenced by optical reflectance mapping [82]. The samples of epitaxial graphene were used as a working electrode in the open electrochemical cell obtained from Redoxme AB [83]. The cell consisted of a cup of 300 \(\mu\)L with a 2 mm diameter hole and a Vitron o-ring on the bottom. The EG sample was fixed under the hole with the o-ring using screws on the lid. A dry contact for the EG was formed by an aluminum adhesive. The mounted EG sample was kept inside the cell during all wet measurements and procedures in order to avoid sample drift.  
The electrolyte solution was prepared from ultrapure 0.1 mol\(\cdot\)L\({}^{-1}\) HCl\({}_{0}\), 0.1 mM Cu\({}^{2+}\) (the purity of copper sulfate, CuSO\({}_{4}\), was higher than 99%), and Milli-Q water. The non-complexing character of the perchlorate ions (ClO\({}^{4-}\)), with respect to metal cations in aqueous solutions [84; 85; 86], suggests that the cyclic voltammetry (CV) measurements will enable us to gain insights into Cu-involved oxidation-reduction reactions, rather than reactions involving more complicated chemical complexes. To explore the Cu redox behavior and kinetics at the working electrode, we exploited the quite concentrated aqueous solution of Cu salt to ensure an intense electrochemical signal. The scan rate was 20 mV/s. The electrochemical reactions are expected to occur at the nominal electrode area of 3.1 mm\({}^{2}\). To elucidate the nature of the Cu kinetics, current-time transients were recorded during the early stages of Cu electrodeposition on Gr/SiC.
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 2 Reduced-Order Modeling
### Unsteady Aerodynamic and Aeroelasticity Modeling  
In this work, we employ the proposed KAGRN framework to model unsteady aerodynamics and aeroelasticity. Specifically, the input and output vectors at time step \(t\) are defined as follows:  
\[x_{t} =\left[(h/b)_{t}\quad\alpha_{t}\right] \tag{9}\] \[y_{t} =\left[(C_{l})_{t}\quad(C_{m})_{t}\right]\]  
where \(h\) denotes the plunging displacement, \(b\) represents the semi-chord length, \(\alpha\) is the pitching angle, and \(C_{l}\) and \(C_{m}\) correspond to the lift and pitching moment coefficients, respectively. Unlike conventional nonlinear reduced-order modeling approaches, such as the Nonlinear AutoRegressive models with eXogenous input (NARX) framework [19; 22; 23], our proposed method explicitly introduces hidden state representations to capture the intrinsic time-delay characteristics associated with unsteady aerodynamic responses. This approach not only improves the accuracy of the model, but also significantly enhances its generalizability across various aerodynamic conditions, particularly when handling nonlinearities and complex transient phenomena.  
Figure 3 illustrates the flowchart for training and application procedures of the proposed reduced-order model (ROM). In this study, the ROM is trained using stochastic motion data as inputs, while the corresponding aerodynamic coefficients, obtained from computational fluid dynamics (CFD) simulations, serve as the outputs. A detailed description of the training procedure and model parameters is provided in Section 4.1. After successful training, the ROM can be applied for the following purposes:  
1. Aerodynamic prediction: Given the prescribed structural motions as input, the trained ROM predicts the corresponding nonlinear and unsteady aerodynamic coefficients.
2. Aeroelastic simulation: Given the nonlinear aerodynamic ROM and the structural equations of motion, a loosely coupled aeroelastic simulation strategy is employed. Specifically, at each time step, the trained ROM predicts the aerodynamic coefficients, which are then used to update the structural response through the structural equations of motion before advancing to the next time step.
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.3. LRSD-Based Methods (IPI, IMNNLWEC)  
The LRSD-based methods, IPI and IMNNLWEC, demonstrate strong performance in partially complex and simple backgrounds. IPI achieves the second-highest BSF and SCRG values in Seq.4 and Seq.5, along with relatively high AUC values in Seq.1 and Seq.2, with its superior false alarm suppression capability particularly evidenced by competitive D-statistic values in Seq.4-Seq.6 (2.70 \(\times\) 10\({}^{-5}\), 6.93 \(\times\) 10\({}^{-6}\), and 7.14 \(\times\) 10\({}^{-6}\), respectively). However, in specific scenarios--such as Seq.3 and Seq.6 for IPI, and Seq.1 to Seq.3 for IMNNLWEC--these methods experience near-complete detection failures, with AUC values approaching 0.5 and ROC curves aligning closely with the lower axis. Notably, while maintaining decent background suppression in simpler environments (D-statistic < 8.51 \(\times\) 10\({}^{-6}\) in Seq.5-6), their dramatic performance degradation under cross-scenario validation (e.g., D-statistic surges to 3.79 \(\times\) 10\({}^{-4}\) in Seq.2 for IPI) fundamentally  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sequence** & **Metrics** & **DNGM** & **TLCM** & **SSTLCM** & **FKBW** & **TVF** & **IPI** & **IMNNLWEC** & **UIUNet** & **Proposed** \\ \hline \multirow{4}{*}{Seq.1} & AUC & 0.9734 & 0.8334 & 0.9871 & 0.5377 & 0.9937 & 0.9906 & 0.4996 & 0.6048 & **0.9998** \\  & SCRG & 2.64 & 7.34 & 6.87 & 0.31 & 21.36 & 27.59 & 0.00 & 7.27 & 33.38 \\  & BSF & 4.59 & 3.27 & 4.70 & 1.61 & 7.54 & 238 & 3.44 & 11.91 & 21.39 \\  & D-statistic & \(7.48\times 10^{-5}\) & \(1.65\times 10^{-4}\) & \(5.33\times 10^{-5}\) & \(5.67\times 10^{-4}\) & \(4.61\times 10^{-4}\) & \(9.03\times 10^{-4}\) & \(4.40\times 10^{-4}\) & \(2.56\times 10^{-3}\) & \(2.52\times 10^{-3}\) \\ \hline \multirow{4}{*}{Seq.2} & AUC & 0.8868 & 0.7517 & 0.9984 & 0.6772 & 0.9650 & 0.8998 & 0.4997 & 0.8498 & **0.9999** \\  & SCRG & 1.58 & 1.38 & 11.80 & 2.87 & 30.86 & **90.27** & 0.00 & 24.13 & 57.26 \\  & BSF & 1.479 & 9.67 & 6.04 & 3.32 & 5.09 & 26.25 & **58.35** & 23.60 & 34.88 \\  & D-statistic & \(3.92\times 10^{-5}\) & \(9.93\times 10^{-5}\) & \(5.17\times 10^{-5}\) & \(3.83\times 10^{-5}\) & \(2.20\times 10^{-4}\) & \(3.97\times 10^{-4}\) & \(2.57\times 10^{-4}\) & \(3.88\times 10^{-3}\) & \(2.96\times 10^{-5}\) \\ \hline \multirow{4}{*}{Seq.3} & AUC & 0.6057 & 0.5229 & 0.9617 & 0.5165 & 0.9729 & 0.5400 & 0.5000 & 0.6845 & **0.9991** \\  & SCRG & 0.31 & 0.20 & 4.41 & 0.44 & 10.70 & 19.92 & 0.00 & 35.59 & **80.34** \\  & BSF & 4.14 & 6.54 & 12.21 & 4.61 & 7.89 & **31.21** & 4.66 & 23.68 & 29.56 \\  & D-reflects insufficient robustness. These results highlight the lack of robustness of these methods across diverse scenarios.