esa-sceva/satcom-chunk-collection huggingface dataset:
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML
#### 2.2.1 TinyML as a Novel Facilitator in IoT Big Data Management
In the IoT Big Data landscape, as delineated in Table 1, key challenges include managing the vast volume of data from numerous devices, necessitating advanced storage and processing systems. Rapid data generation requires real-time analysis and response, highlighting the importance of data velocity. The variety of data, both structured and unstructured, from diverse sources, complicates integration and analysis. Ensuring data veracity, or accuracy and trustworthiness, is increasingly challenging. Integrating various data sources while maintaining integrity is vital. Security and privacy concerns are paramount due to heightened interconnectivity, necessitating robust protocols. Lastly, minimizing latency to avoid obsolete insights is crucial in IoT Big Data management. Table 2 highlights how TinyML addresses key challenges in Big Data and IoT. It offers solutions to data overload by facilitating on-device data filtering and summarization, significantly reducing the amount of data that needs to be transmitted to central systems. This approach is pivotal for real-time processing needs, where localized TinyML models enable instant data analysis, ensuring timely insights without the dependency on external servers. Such capability is crucial in scenarios with limited or no connectivity, maintaining device functionality. Additionally, TinyML greatly enhances energy efficiency by optimizing models for specific tasks, thereby conserving resources and extending battery life. This technology also bolsters security and privacy; by processing data locally, it minimizes the risks associated with data transmission and ensures that sensitive information remains within the user's control. Furthermore, TinyML contributes to the longevity of devices by reducing the strain on their components through local processing, potentially extending their operational lifespan. These enhancements demonstrate TinyML's significant role in improving the efficiency, security, and sustainability of IoT systems.
## 3 Two-Step Parametrization Method  
### Key Idea  
The two-step parametrization method's key idea is to subdivide the given nested state-space model (6) and (7) into two submodels. The nested model is separable in the following way:  
\[\mathbf{x} =\mathbf{x}_{\text{th}}, \mathbf{f}(\mathbf{x},\mathbf{u},\theta) =\mathbf{f}_{\text{th}}(\mathbf{x},\mathbf{u},\theta_{\text{th}}) \tag{9}\] \[\mathbf{y} =\begin{bmatrix}\mathbf{y}_{\text{th}}\\ \mathbf{y}_{\text{el}}\end{bmatrix}=\begin{bmatrix}p_{\text{ca,em}}\\ p_{\text{ca,em}}\\ \dfrac{p_{\text{an,em}}}{U}\end{bmatrix}, \mathbf{g}(\mathbf{x},\mathbf{u},\theta) =\begin{bmatrix}\mathbf{g}_{\text{th}}(\mathbf{x},\mathbf{u},\theta_{ \text{th}})\\ \dfrac{\mathbf{g}_{\text{el}}(\mathbf{x},\mathbf{u},\theta)}{g_{\text{el}}( \mathbf{x},\mathbf{u},\theta)}\end{bmatrix} \tag{10}\]  
The model's parameter vector is divisible as well:  
\[\theta=\begin{bmatrix}\theta_{\text{th}}\\ \theta_{\text{el}}\end{bmatrix}=\begin{bmatrix}\begin{bmatrix}V_{\text{ca,sm}},V_{\text{ca,cm}},V_{\text{ca,em}},\dots\\ \dots V_{\text{an,sm}},V_{\text{an,cm}},V_{\text{an,em}},\dots\\ \dots k_{\text{perm}},k_{\text{cond}},k_{\text{evap}},\dots\\ \dots k_{\text{ca,sm,out}},k_{\text{ca,em,in}},k_{\text{ca,em,out}},\dots\\ \dots k_{\text{an,em,out}},k_{\text{an,em,in}},k_{\text{an,em,out}},\dots\\ \dots k_{\text{an,leak}},\tau_{\text{m}}\end{bmatrix}^{\text{T}}\\ \hline\begin{bmatrix}\epsilon_{2},R_{\text{c}},E_{\text{ca,act}},E_{\text{an, act}},\dots\\ \dots K_{\text{ca}},K_{\text{an}},CD_{\text{Ca}},CD_{\text{an}}]^{\text{T}} \end{bmatrix}\end{bmatrix} \tag{11}\]  
On the one hand, the subdivision yields the thermodynamic submodel  
\[\dot{\mathbf{x}} =\mathbf{f}(\mathbf{x},\mathbf{u},\theta_{\text{th}}), \tag{12}\] \[\mathbf{y}_{\text{th}} =\mathbf{g}_{\text{th}}(\mathbf{x},\mathbf{u},\theta_{\text{th}}), \tag{13}\]  
and on the other hand, it results in the electrochemical submodel  
\[y_{\text{el}}=g_{\text{el}}(\mathbf{x},\mathbf{u},\theta). \tag{14}\]  
The thermodynamic submodel (12) and (13) is only dependent on the thermodynamic parameter vector \(\theta_{\text{th}}\), which also holds for the system function \(\mathbf{f}\) according to Equation (9). Therefore the electrochemical parameter vector \(\theta_{\text{el}}\) does not affect the states \(\mathbf{x}\) and the thermodynamic outputs \(\mathbf{y}_{\text{th}}\), but only the electrochemical output \(y_{\text{el}}\). Thus the electrochemical submodel (14) utilizes the full parameter vector \(\theta=[\theta_{\text{th}},\theta_{\text{el}}]^{\text{T}}\). Note that the thermodynamic submodel consists of ODEs, and the electrochemical one does not. The two-step parametrization method exploits the described properties.
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis
### EMD Fault Data Decomposition  
By using EMD for decomposition, the preprocessed sample data was decomposed into seven IMF components and one residual component \(\mathbf{r}\), of which IMF1, IMF2, and IMF3 were the high-frequency signals, IMF4 and IMF5 were the mid-frequency signals, and IMF6 and IMF7 were the low-frequency signals, forming multidimensional vector data. Compared to the original data, the decomposed IMF components became smoother. The experimental simulation is shown in Figure 5 (\(\mathbf{r}\) is included in the IMF components).
## 4 Numerical Modelling
### Model Parameters  
The fundamental parameters of the Modified Cam-Clay (MCC) soil model include \(M\), \(e_{0}\), \(e_{1}\), \(\lambda\), \(K\), and \(\
u\). These parameters can be estimated based on theoretical analysis and empirical data [55, 56, 57]. In the \(p\)-\(q\) coordinate system, the slope of the Critical State Line (_CSL_), denoted as \(M\), is given by \(M=6\sin\!\varphi/(3-\sin\!\varphi)\). In the \(e\)-_hnp_ coordinate system, the slope of the Normal Consolidation Line (_NCL_) is represented by \(\lambda=\text{C}c/2.3\), and in the same coordi  
Figure 9: Schematic of the numerical model. 1—backfill; 2—immersed tunnel; 3—SCP; 4—gravel cushion; 5—incident wave.  
nate system, the slope of the rebound line is denoted as Kr, which falls within the range of 0.2 to 0.5 times \(\lambda\). The relationship between the soil void ratio and the over-consolidation ratio (\(OCR\)) at a specific location is expressed as \(e_{1}=e_{0}+\lambda\text{ln}p_{0}+\text{Krln}(OCR)\). During the consolidation process of the seabed, the sand compaction pile forms drainage channels that reduce excess pore water pressure. The permeability coefficient \(k_{\mathbf{s}}\) in the soil is related to the change in soil void ratio \(e\) during deformation. The equation for \(k_{\text{s}}\) is \(k_{\text{s}}=C_{f}\,e^{3}/(1+e)\), where \(C_{f}=k_{0}(1+e_{0})/e_{0}{}^{3}\) is an empirical coefficient [58]. Here, \(e_{0}\) and \(k_{0}\) represent the initial void ratio and permeability, respectively. This process increases the seabed density and deformation. On the other hand, the hardening behavior of SCP under seismic loads is simulated using the Cap model (_DP_) and the Porous Elastic (_Proelastic_) material model. The immersed tunnel and the gravel cushion layer are considered impermeable and experience minimal deformation. Therefore, linear elasticity modeling is applied to simulate their behavior. Specific parameters for these components can be found in Table 2.
## 3 Experimental Setups for Indoor Positioning
### Setup for Experiment 1  
Figure 10 shows the first experimental setup and indoor distances between each receiver and repeater. The repeater configurations are kept as provided in Table 3. Although Repeater 2 has a third amplifier, its attenuator is set to 15 dB. Therefore, its gain is 7 dB higher from Repeaters 2 and 3. The outdoor GPS antenna directions are set such that the beam of an antenna does not overlap in azimuth with other GPS antennas in the setup. The directional GPS antennas in Repeaters 1, 2, and 3 are, respectively, towards the geographic east, south, and west. In this experiment, the receiver is located at the intersection of two perpendicular corridors to provide a line-of-sight condition.  
Figure 10: Distance between the receiver and each repeater, directional GPS antenna azimuth (Az) and elevation (El), and the attenuator value in each repeater in Experiment 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Repeater Number (Ri)** & **Measured Group Delay (\(\tau_{i}\))** &
\begin{tabular}{c} **Position** \\ **(Latitude, Longitude)** \\ \end{tabular} \\ \hline R1 & 48.5 ns & 40.89072, 29.37955 \\ R2 & 103 ns * & 40.89048, 29.37941 \\ R3 & 86 ns & 40.89044, 29.37966 \\ \hline \hline \end{tabular} The latitudes and longitudes are given in degrees. * Repeater 2 has a third LHA-13 LN+ amplifier.  
\end{table}
Table 3: Repeater configurations.  
The real values of the indoor distances \(d_{1}\), \(d_{2}\), and \(d_{3}\) are determined with a physical measurement using a laser pointer for a later comparison with the estimated values. The distance measurements are performed with respect to the known coordinates of the building.
## 3 Methodology
### Evaluation Metrics  
The performance of our model was evaluated using the numbers of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions. The evaluation metrics were accuracy, precision, recall or sensitivity, and F1-score, which provided a comprehensive assessment of both the model's ability to correctly classify different postures and its overall performance.  
Accuracy gives the proportion of the total number of predictions that were correct. Precision or the positive predictive value, is the fraction of positive values out of the total predicted positive instances. In other words, precision is the proportion of positive values that were correctly identified. Sensitivity, recall, or the TP rate is the fraction of positive values out of the total actual positive instances, i.e., the proportion of actual positive cases that are correctly identified). The F1 score is the harmonic mean of precision and sensitivity; it gives importance to both factors.
## 4 Discussion
### Clinical Implications  
The findings of this review align with the existing literature, suggesting that orthodontic treatment does not typically exacerbate TMDs symptoms. However, the presence of pre-existing TMDs significantly influences the likelihood of symptom progression following treatment. [PERSON] et al. conducted a 20-year cohort study, reporting that patients without TMDs symptoms prior to orthodontic intervention were less likely to develop new symptoms post-treatment. In contrast, those with pre-existing symptoms exhibited variable outcomes, which depended on the severity and type of TMDs [35]. Similarly, research indicates that while mild TMDs symptoms may remain stable during orthodontic treatment,moderate to severe TMDs pose a higher risk for symptom aggravation, particularly when occlusal adjustments are substantial or treatment biomechanics increase joint loading [35].  
These findings underscore the critical importance of comprehensive pre-treatment assessments for TMDs in orthodontic patients. A diagnostic approach integrating detailed patient history, clinical examination, and advanced imaging, such as MRI or CBCT when indicated, is essential to identify the type and severity of any existing TMDs [74]. Identifying these factors allows clinicians to individualize treatment plans, including adjustments to appliance types, force application, or treatment sequencing, to minimize TMJ stress and improve long-term outcomes. Furthermore, the potential influence of orthodontic interventions on TMJ health highlights the importance of addressing not only physical but also psychological and behavioral factors in patient care.
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.4 Multi-Scale GPP Products  
Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products were used in this paper to compare the accuracy when validating field GPP at the footprint scale.  
A 30 m GPP product with a temporal resolution of 8-day in the study area was also used in this paper to compare the accuracy when validating at field scale and at footprint scale. This GPP product was generated by using a downscaled model based on MODIS and Landsat data [43]. Specifically, fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) with a spatial resolution of 30 m and a temporal resolution of 8 days were firstly generated from a downscaled method, and then 30 m GPP were obtained by using a light use efficiency model [43].
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 4 Results and Discussion  
In this section, we study the early diagnosis of dementia, according to results of data mining techniques such as multilayer perceptron, random forest, bagging, SVM, logistic regression, Bayesian network, and Naive Bayes that were explained above. We then compare them to discern which is more accurate in the diagnosis of dementia.  
As stated earlier, in Section 3.2, we used data from the Gangbuk-Gu center for dementia. Since classification of data includes two classes--namely Phase 1, consisting of 14,000 data including the normal class (9799 data) and cognitive decline class (4201 data), and Phase 2, consisting of 1236 data including the MCI class (663 data) and dementia class (573 data)--we used 10-fold cross-validation.  
In cross-validation, data is divided into two segments in order to statistically compare and assess learning algorithms. One data segment (the training set) trains a model, while the other (the validation set) validates it. Usually, these sets are required to be staggered over successive rounds, so that each data point can be validated against the next. K-fold cross-validation is the standard cross-validation form, providing the basis modified in special cases or repeated rounds of cross-validation [28]. For this reason, we show the accuracy of these criteria: precision, recall and F-measure to diagnose dementia. Each of these criteria has been obtained from Equations (4)-(6).  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
\[recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F-measure=2\times\frac{precision\times recall}{precision+recall} \tag{6}\]  
According to Equations (4)-(6), _TP_ (True Positives) is equivalent to the number of samples that correctly have been identified as positive. Likewise, _FP_ (False Positives) is equivalent to the number ofsamples that have been wrongly identified as positive, and \(FN\) (False Negative) is equivalent to the number of samples that have been wrongly identified as negative [29].
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 2 Analysis of Fractal Surface Contact Behavior  
The key factor determining the contact behavior of a fractal surface is the interaction of the asperities on the contact surface. The asperities cause the real contact area to be smaller than the nominal contact area, and lead to greater contact pressure. As the contact pressure increases, the asperities will undergo elastic, elastoplastic and perfect plastic deformation. The W-M fractal function is utilized for characterizing the profile of a rough surface, and it can clearly describe the asperity morphology and the process of contact deformation.
# A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
###### Abstract
[29] employed Classification and Regression Trees (CART) to analyze highly educated people's satisfaction with airlines' services. Compared to these studies, our work not only finds various relationships between factors influencing airline customer propensities but also includes the comparison of the performance of different machine learning and deep learning approaches. Moreover, to the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. Specifically, we make the following contributions in this paper:  
* First, we collected data from users who have used airplanes at least once within the last five years. The users responded to the 50 questions related to the physical and social environments of the airlines, brand experience and loyalty, and customer satisfaction. We then performed preprocessing to obtain the final dataset that was used as a training dataset in our predictive models. The preprocessing procedure contains the following steps: (1) consolidating the dataset, (2) cleaning invalid data, and (3) feature selection. * Second, we evaluated the performance of various machine learning and deep learning models for predicting customer churn risk and satisfaction from airline customer data. For this study, we selected well-known machine learning models, such as kNN and decision tree, ensemble learning models, such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and deep learning models, such as Convolutional Neural Networks (CNN) and CNN Long Short-Term Memory Networks (CNN-LSTM). The experiment results revealed that deep learning models are more accurate at predicting customer churn risk and satisfaction. * Third, we demonstrate that considering the social servicescap in addition to the physical servicescap can significantly increase the model accuracy in predicting the customer churn risk and satisfaction. For this, we consider human services, which constitute the social servicescap, including the viewpoints of the cabin crew and passengers using aircraft. The experimental results demonstrated that the proposedmethod increased the accuracy of models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively. The remaining of the paper is organized as follows: Section 2 reviews the literature related to the airline customer propensities. Section 3 describes the materials and methods used in this study. Section 4 presents the experimental results. Section 5 discusses our findings and concludes the paper.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis
#### 4.5.2 Grayscale Image Dimension Comparison  
The original grayscale image was constructed as a 5 \(\times\) 5 matrix based on 23 DNS features. To explore whether image dimension affects model expressiveness and accuracy, we compared three configurations:  
* 5 \(\times\) 5 (23 features + 2 zero-pads)
* 5 \(\times\) 5 (23 features + 2 the most important features-pads)
* 6 \(\times\) 6 (23 features + 13 zero-pads)
* 8 \(\times\) 8 (23 features + 41 zero-pads)  
All configurations maintained the same feature order and padding strategy. The results are shown in Table 5.  
Larger matrix configurations do not yield substantial performance improvements; instead, they introduce excessive padding, which may act as noise and reduce spatial coherence. These results reaffirm that the 5 \(\times\) 5 configuration offers a compact and effective structure for representing the current 23-dimensional feature set. While both zero-padding and high-contribution feature replication achieve comparable performance, the latter may inadvertently increase the model's reliance on a few dominant features, potentially impairing its generalization ability across more diverse or unse  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Arrangement Type** & **Accuracy** & **F1-Score** \\ \hline Fixed Order (Baseline) & **99.92\%** & **99.91**\% \\ Random Order & 97.61\% & 96.85\% \\ Correlation-Based & 99.84\% & 99.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy under different feature arrangement strategies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Dimension** & **Accuracy** & **F1-Score** \\ \hline
5 \(\times\) 5 (zero-pads) & **99.92**\% & **99.91**\% \\
5 \(\times\) 5 (the most important features-pads) & 99.93\% & 99.91\% \\
6 \(\times\) 6 & 99.71\% & 99.55\% \\
8 \(\times\) 8 & 99.32\% & 99.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance under different grayscale image sizes.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Simplified Channel Models for Code Design
#### 3.3.2 Single Interferer Model
(**a**) QPSK-modulated constellation after phase compensation at the receiver side. We highlight symbol \(S_{1}=(1,0)\). If the symbol is affected by QPSK-modulated interference with the same unit power, no noise, and random phase, the received symbol will lay on the red unit circle centred in \((1,0)\). (**b**) QPSK-modulated constellation after phase compensation at the receiver side and one QPSK-modulated interferer with the same unit power, no noise, and \(\Delta\varphi=10^{\circ}\) relative phase shift. Red crosses represent the four possible received symbols if the considered transmitted symbol was \(S_{1}=(1,0)\). Figure 3: The \(i\)th codeword symbol (azure) affected by a single interferer (red), with a relative epoch of \(\Delta e\). Both interferer symbols \(k-1\) and \(k\) impact the codeword symbol \(i\). In Figure 5, the probability density function (PDF) of the bit LLRs assuming a symbol-synchronous QPSK interferer either uniform at random phase or with a fixed relative phase offset plus AWGN noise with \(E_{s}/N_{0}=6\) dB is shown. For the plot, we assume that either symbol \(S_{0}\) or \(S_{1}\) has been transmitted, i.e., Bit level 1 is zero. The curve with \(\Delta\varphi=0\) shows the PDF of the bit LLRs for the case of the aligned phase. The channel LLRs are no-longer Gaussian, and thus quantized density evolution, which passes the entire LLR though the decoding algorithm, rather than a Gaussian model, will be used to calculate the code thresholds.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 3 Results
### Current Signal  
The three-phase stator current waveform of the motor with inter-turn short circuit fault is shown in Figure 7. It can be seen in this diagram that the amplitude of the current in phase A, the phase with short circuit fault, is obviously higher than that in the other two phases.  
Figure 5: LabVIEW program for fault detection.  
Figure 6: Improved wavelet packet algorithm.  
Then, the signal of fault phase was extracted separately, and the improved wavelet packet algorithm was used to decompose it into four wavelet packet nodes: (2, 0), (2, 1), (2, 2) and (2, 3). The results of reconstructing these wavelet packet nodes are shown in Figure 8.  
To observe the frequency distribution, the reconstructed nodes were transformed by FFT and compared with the results of normal motor signals. As shown in Figure 9, the changes of the frequency at (2, 0) and (2, 1) are obvious.  
Figure 8: Reconstruction signals of the current of the fault motor.  
Figure 7: Three-phase current waveform of the fault motor.  
Figure 9: \(Cont\).  
It can be seen from (2, 0) that the amplitude of fundamental frequency, in other words, \(f_{s}\) = 50 Hz, increases. From (2, 1) it can be seen that the amplitude at the third harmonic frequency, in other words, \(3f_{s}\) = 150 Hz, of the normal motor signal is very small, but the third harmonic component of the fault motor signal increases and even reaches about six times the normal motor signal.  
The experimental results are in agreement with the theory that the current signal energy increases and the third harmonic component increases in the fault PMSM.