esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Three Directional Displacement Field of the Shaft  
Upon a 0.5 m MSD of the horsehead roadway's roof, Figure 7 illustrates the computed three-dimensional displacement field of the shaft. It is pertinent to note that the four Z-axis cross-sections, ordered from left to right with respect to the roadway's roof, are situated at distances of approximately 100 m (representing the model's top surface), 66 m, 33 m, and 0 m (corresponding to the location of the roadway's roof), consistent with the configurations described subsequently. Analysis reveals minimal variance in shaft displacement across different elevations, with the Z-direction displacement nearing 8 cm (refer to Figure 7a). In the X and Y directions, the shaft's MSD is observed at the junction with the horsehead roadway, registering modest values of roughly 1.5 cm and 0.5 cm, respectively (see Figure 7b,c). Synthesizing insights from the three-dimensional displacement field indicates that the shaft undergoes overall cohesive deformation, exhibiting no notable anomalies. Any potential fracturing or separation is most plausible at the interface between the shaft and the horsehead roadway.  
When the MSD of the horsehead roadway's roof reaches 1.0 m, Figure 8 presents the associated three-dimensional displacement field of the shaft. A pronounced discrepancy is observed in the shaft's displacement at varying elevations in the Z-direction, with the peak displacement in this direction approximating 25 cm--an increment of 17 cm from the 0.5 m subsidence scenario. In the X and Y directions, the shaft's MSD is once again noted at its junction with the horsehead roadway, registering modest values of approximately 4 cm and 0.5 cm, respectively. Analysis of the three-dimensional displacement field suggests a deviation from the previously observed collective deformation pattern of the shaft, with significant tensile fracturing in the Z-direction occurring within the upper 33 m of the horsehead roadway.  
Upon further elevation of the horsehead roadway's roof subsidence displacement to 1.5 m, the resulting three-dimensional displacement field of the shaft is depicted in Figure 9. The displacement across various elevations of the shaft shows negligible variance, with Z-direction displacement nearing 20 cm. Displacements in the X and Y directions are minimal, with the exception of the area where the shaft intersects the horsehead roadway. The substantial settling of the rock stratum at the roadway's roof has induced a general subsidence of the shaft within the model's confines. Owing to the structural symmetry between the horsehead roadway and the shaft, the shaft has not exhibited significant lateral deviation, as confirmed by the research of [PERSON] et al. [39].  
Figure 8: _Cont_.  
Figure 7: Three directional displacement fields of the shaft (cm) (\(d_{\max}\) = 0.5 m).  
Figure 8: Three directional displacement fields of the shaft (cm) (\(d_{\max}=1.0\) m).  
Figure 9: Three directional displacement fields of the shaft (cm) (\(d_{\max}=1.5\) m).
## 1 Introduction
Additionally, suitable algorithms need to be developed to solve this dynamic optimization problem. Corresponding to these issues, the main contributions of this paper are as follows:  
* A joint push-pull communication mode is proposed to obtain real-time traffic conditions and charging infrastructure service status information. Moreover, the paper discusses the selection of relay vehicles based on link stability, vehicle distance, and service satisfaction in multi-hop communication routing. * A dynamic optimization problem is formulated based on the mode of real-time communication to solve the dynamic path optimization with charging selection. This problem considers several factors, such as travel time, queuing time, charging time, and consumption of charge, while also including both CSs and ESs as energy providers. * A novel approach for addressing dynamic optimization problems in transportation networks is proposed. The proposed method involves transforming the original problem into a series of single optimization problems and simplifying the road network through preprocessing. After that, a dynamic real-time A* (DRT-A*) algorithm is proposed to efficiently solve the path-optimization problem. Furthermore, a dynamic real-time charging selection (DRT-CS) algorithm based on dynamic path optimization to solve the charging selection problem is proposed. **Paper organization:** The remainder of this paper is organized as follows. In Section 2, related work is reviewed. Section 3 describes proposed dynamic path optimization with a charging selection system. The transmission of real-time traffic conditions and charging infrastructures service status information is described in Section 4. Section 5 formulates AEV travel and charging costs. Section 6 describes dynamic path optimization with a charging selection model, and Section 7 proposes the simplified model and algorithm. Section 8 shows the performance evaluations. Section 9 concludes the paper.
## 2 Related Work
### Clustering Methods  
Clustering is known as an unsupervised ML technique where data are classified into clusters of data that have similar characteristics that are different than the characteristics of the data in the other clusters [57]. Various clustering algorithms were applied to educational datasets to cluster students based on their performance in order to give educational organizations better insights in understanding their students and their different learning styles to find the best strategies for their students' success [58]. For example, in [59] [PERSON] et al. employed the k-mean clustering method to classify their student performance to improve it. Their study was carried on using data for 306 students from different universities. The collected data consist of demographic features such as gender, origin, GPA, grade of certain courses, and course attendance. They found that these input features formed three different clusters; smart, normal, and low. [PERSON] et al. [60] employed the latent class analysis (LCA) method as a clustering method for educational data to extract common features from online behavior data of 612 courses tracked from the Learning management system and database of a South Korean University. Their work identified four different clusters of how Blended Learning is adopted and implemented, which gives the educational organization better visualization of the data and helps in providing strategic plans. These groups are immature which consist of 50% of the courses, collaboration (24.3%), discussion (18%) and sharing (7.2%). [PERSON] et al. [61] proposed a methodology based on two clustering algorithms; Simple K-means and Markov Clustering (MCL) for the purpose of improving the content quality of Learning Management Systems (LMS) by analyzing their log data files. The former algorithm is used to cluster the courses and the latter for clustering the students' activity, giving the instructors better insights into both students and courses.
## 3 Experiment and Results
### Experimental Setup
Wind speed and wind power series from Site 1 to Site 4 in the second season were collected at 10 min intervals to establish four combined models based on different optimization algorithms. Experiment III aimed to verify the applicability of the combined model based on five sub-models and optimized by MODA. Wind speed and wind power time series data collected at 10 min intervals in the third and fourth seasons were employed to verify the prediction performance of the combined model for day-ahead forecasting. Table 2 shows the forecasting precision and stability of the models during point forecasting.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
###### Abstract
Limited flight time is due to various factors including sensor accuracy, harsh atmospheric conditions, fixed-wing size and battery endurance. UAVs have several vulnerabilities that increase the rate of being exposed to malicious attacks. Several studies are devoted to assess UAVs vulnerabilities, attacks, threats and have suggested viable solutions to overcome these challenges, such as using high quality devices including batteries, wing, geometry, manufacturing materials and motors etc. Some studies have reported optimization algorithms to find shortest route for UAVs to reach their intended destination [4,5].
# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 3 Results
### R Modeling Using the Machine Learning Methods  
The estimated accuracies in the prediction of the daily \(R\) by the data-driven methods used in this study are shown in Table 4. Similar to the GPP forecasting, the GMDH model in estimating the daily \(R\) also gives the lowest model efficiency in the testing period, while the ANFIS model provides the best performance based on the \(R^{2}\), NSE, RMSE and MAE metrics. In addition, the overall performance ranks of the data-driven models used in the present study in the testing period can be concluded as follows: ANFIS, SVM, ELM, ANN, GRNN and GMDH (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Validation**} & \multicolumn{4}{c}{**Testing**} \\ \cline{2-13}  & \(R^{2}\) & **NSE** & **RMSE** & **MAE** & \(R^{2}\) & **NSE** & **RMSE** & **MAE** & \(R^{2}\) & **NSE** & **RMSE** & **MAE** \\ \hline ANN & 0.9373 & 0.9366 & 0.4250 & 0.2656 & 0.9421 & 0.9210 & 0.5000 & 0.3209 & 0.9489 & 0.9303 & 0.4848 & 0.3377 \\ GRNN & 0.9637 & 0.9636 & 0.3223 & 0.1895 & 0.9360 & 0.9128 & 0.5254 & 0.3416 & 0.9413 & 0.9293 & 0.4880 & 0.3423 \\ ELM & 0.9297 & 0.9297 & 0.4477 & 0.2874 & 0.9411 & 0.9280 & 0.4775 & 0.3061 & 0.9422 & 0.9310 & 0.4821 & 0.3289 \\ ANFIS & 0.9391 & 0.9391 & 0.4167 & 0.2619 & 0.9418 & 0.9241 & 0.4902 & 0.3084 & 0.9498 & 0.9393 & 0.4522 & 0.3199 \\ SVM & 0.9414 & 0.9413 & 0.4089 & 0.2334 & 0.9425 & 0.9257 & 0.4850 & 0.3102 & 0.9471 & 0.9347 & 0.4692 & 0.3385 \\ GMDH & 0.9277 & 0.9277 & 0.4541 & 0.2958 & 0.9348 & 0.9203 & 0.5023 & 0.3343 & 0.9307 & 0.9099 & 0.5511 & 0.3969 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of data-driven models for ecosystem respiration (\(R\), g-C-m\({}^{-2}\)-Day\({}^{-1}\)) for the training, validation and testing periods.  
Figure 4: Comparisons of annual total carbon and water fluxes measured by eddy covariance technique and predicted by data-driven models in the whole period. (**a**) GPP; (**b**) \(R\); (**c**) NEE; and (**d**) ET.  
As shown in Figure 5, the regression fit line between observed and ANFIS predicted for daily \(R\) in the testing period (slope = 0.85) is closer to the exact fit line (1:1 line). However, the fit line of the GMDH model (slope = 0.79) tends to deviate from the ideal fit line more than those for the other models. Moreover, as illustrated in Figure 3b, the observed values of daily \(R\) well match the predicted ones using the ANN model during the whole period, whereas the peaks during the validation and testing periods are again significantly underestimated. In addition, according to the annual estimates by the applied models, the total \(R\) in 2009 is under-estimated by 4% to 6% (Figure 4b).
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 4 Evaluation Setup of ID-Based Authentication
### Leakage Model in Authentication
#### 4.2.2 Profiling Model: XOR Model  
As a profiling model, we used the XOR model that was proposed in [5]. In [5], the advantage of the XOR model in correctly profiling the leakage of AES-comp implementation was shown. The XOR model leads to successful key recovery with reduced power traces compared to the HD model. In the HD model, it is assumed that the amount of bit-flipped information, i.e., the Hamming distance, is proportional to the physical information, e.g., the power consumption and the amount of EM radiation. Since the Hamming distance does not distinguish between bits, the HD model for the 8-bit intermediate value classifies the leakage into nine classes from 0 to 8. Meanwhile, in the XOR model, it is assumed that the bit reversed position affects the amount of side-channel leakage. Specifically, the XOR model classifies the side-channel leakage for 8-bit intermediate values into 256 classes ranging from 0 to 255.  
The side-channel authentication is classified into a profiling phase and an authentication phase. In the profiling phase, the properties of each device are investigated in pre-processing. Specifically, the amount of EM radiation for an XORed value that changes with each product, called a model value \(\mathbf{A}\), is derived using the side-channel information whose intermediate value is known. When authenticating using the XOR model for 16-byte AES, the XOR model is classified into 256 \(\times\) 16 classes. Therefore, the model value \(\mathbf{A}\) is expressed as  
\[\mathbf{A}=\left(\begin{array}{cccccccc}a_{1,0}&a_{1,1}&a_{1,2}&\ldots&a_{t, r}&\ldots&a_{16,254}&a_{16,255}\end{array}\right)\]  
where \(a_{t,r}\) is the amount of EM radiation when the XORed value of the \(t\)-th byte is \(r\).  
In the authentication phase, the correlation coefficient is calculated using the model value \(\mathbf{A}\) derived in the profiling phase. Based on the intermediate value derived from the challenge and a secret key, the amount of EM radiation is estimated using the model value \(\mathbf{A}\). The process is exactly the same as that using HD model, except that the Hamming distance model is replaced with the profiling model \(\mathbf{A}\). After that, the correlation coefficient is calculated between the acquired EM radiation and the estimated EM radiation. The leakage profiles for 256 classes for 16 s-boxes are obtained by solving the system of equations with the profiling measurement. Note that the authentication scheme using the XOR model was first discussed by us in [10].
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results  
### Setup  
We conduct experiments on three widely used benchmark datasets: NSL-KDD, CSE-CIC-IDS2017, and CSE-CIC-IDS2018. All datasets are used in their processed form, i.e., as tabular features extracted from raw network traffic, rather than raw packets. This ensures compatibility with feature-based learning models and aligns with common practice in prior NIDS studies. Details of these datasets and how to preprocess them are presented in the following sections.
## 3 Results  
### EEI Petrophysic Optimum \(\chi\) Angle  
The inversion for porosity, water saturation and the volume of shale in the field was conducted using the near and far seismic and Well one log data as input. The AI and GI at Well one sites are, firstly, calculated by solving a two term AVO equation [15] for the given Well one near and far seismic traces. The optimum \(\chi\) angle for each porosity, water saturation and volume of shale are obtained using EEI formulation, Equation (1), for \(\chi\) angle scanned from \(-90\) to \(+90\) degrees. The correlation coefficient between the logarithmic of EEI, which is the estimated petrophysical property, and the corresponding petrophysical log are calculated for every simulated \(\chi\) angle value and cross-plotted. Figure 6 shows the correlation coefficient and \(\chi\) angle cross plot for the three petrophysical parameters under investigation.  
The cross-plot in Figure 6 shows a low correlation coefficient for all porosity, water saturation and the volume of shale. For a \(\chi\) angle equal to \(-57^{\circ}\), the porosity and volume of shale show the strongest positive correlation with ln (EEI). Differently, the water saturation gives the highest correlation with ln (EEI) for the \(\chi\) angle equal to \(-54^{\circ}\). Although small in value, the three-correlation coefficient and the \(\chi\) angle cross plots show a maximum/minimum peak which implies that each volume of shale, porosity and water saturation from Well one has a unique relationship with the EEI of the study field. The \(\chi\) angle \(-57^{\circ}\) is a predictor for porosity and the volume of shale, whereas \(-54^{\circ}\) is a predictor for water saturation in the study field. The low correlation coefficients may indicate that predicting porosity, water saturation and volume of shale using EEI is less reliable for the study field.
## 3 Experiment and Result  
For the installation of the power-generating bike lane, parallel piezoelectric devices were placed along a straight line in the middle of the synthetic rubber floor. The design pavement was a section of 50 cm \(\times\) 50 cm with the 0.5 W LED lamp installed on the edge of each side of the pavement, as shown in Figure (a)a. Under the cover of each section, eight piezoelectric plates were installed as shown in Figure (b)b. Thus, a total of 24 piezoelectric plates and six LED lamps (total power consumption of 3 W) were installed every 1.5 m along the track. Under this configuration, when a bicycle entered a certain area, that area and the ones ahead were illuminated; as the bicycle moved forward, the subsequent areas were illuminated, whereas the lamps located in the previous area were turned off. This helped indicate the bike's position on the lane and also helped improve visibility under insufficient lighting conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Trial Number**} & **Voltage When the Spring** & **Voltage When the Spring** \\  & **Was Not Installed (V)** & **Was Installed (V)** \\ \hline
1 & 5.12 & 15.30 \\ \hline
2 & 4.87 & 16.71 \\ \hline
3 & 4.87 & 15.66 \\ \hline
4 & 5.57 & 13.67 \\ \hline
5 & 6.14 & 15.98 \\ \hline
6 & 5.14 & 15.14 \\ \hline
7 & 4.98 & 14.65 \\ \hline
8 & 5.88 & 14.47 \\ \hline
9 & 5.00 & 16.11 \\ \hline
10 & 4.31 & 17.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Voltage measurements with and without the spring installed on the power-generating floor.  
Figure 11: _Cont._  
The amount of energy generated was measured for riders of different weights and driving speeds, as shown in Tables 5 and 6, respectively. The experimental results showed that, when the piezoelectric devices were subjected to a pressure owing to the increase in weight, the electric power also increased; this was because the increased weight led to an increase in the force applied on the spring, causing the piezoelectric device to oscillate to a greater extent. In contrast, higher speeds resulted in lower load intensities, thereby reducing the amount of energy that could be harvested. The trial was a simulated experiment in an area where the main lighting was inaccessible, thus limiting the driving speed obtained by surveying its use in that area. The rider weight was the average weight based on the surveys of the actual users in the study area, divided into three periods. It was found that the operating weight and speed range were able to harvest energy from cycling for use in insufficiently illuminated areas for natural navigation lights. As shown in the experimental results, each weight and velocity condition obtained different electrical power. Moreover, the brightness of the lamps installed was deemed satisfactory according to the zoning regulations, as demonstrated in Figure 11c.  
Figure 11: An overview of the power-generating bike lane demonstration section. (**a**) The overview of the 1.5 m pavement section. (**b**) An actual installation of piezoelectric plates. (**c**) Brightness of the lamps installed according to zoning.
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 4 Results and Discussion  
The trained model is evaluated on a test dataset. The accuracy of the model, as well as the memory it takes and its processing speed, is also tested on resource-constrained IoT devices. The confusion matrix for the test dataset is shown in Table 4. For non-snoring predictions, the model correctly classified 96.8% of non-snoring instances as non-snoring and incorrectly classified 1.1% of non-snoring instances as snoring. It also classified 2.1% of non-snoring instances as uncertain, indicating that the model could not confidently classify the instance as either snoring or non-snoring. Similarly, the model correctly classified 96.4% of snoring instances as snoring and incorrectly classified 0.5% of snoring instances as non-snoring. It also classified 3.1% of snoring instances as uncertain. F1 scores are measures of the accuracy of the model, and the F1 scores for 'non-Snoring' and'snoring' voices were 0.98, which indicates that the model achieved high accuracy in classifying both classes in new data. The graph in Figure 8 shows the model performance on the test dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Non-Snoring** & **Snoring** & **Uncertain** \\ \hline Non-Snoring & 96.8\% & 1.1\% & 2.1\% \\ Snoring & 0.5\% & 96.4\% & 3.1\% \\ F1 Scores & 0.98 & 0.98 & \\ \hline \end{tabular}
\end{table}
Table 4: Confusion matrix (test dataset).  
Figure 7: Model accuracy during training for snoring (94.3%) and non-snoring (98.8%).  
The main objective of this research is to find a suitable model architecture that can be deployed in the resource-constrained environment of the target wearable device. This requires analyzing the hardware requirements of the target device and accordingly designing and selecting the best TinyML model to achieve maximum performance and accuracy. Analysis of input data, signal processing, and neural network structures was conducted to build efficient model architecture per the computing power and memory requirements of the device. The following architecture and configurations were used to deploy a TinyML model:  
* Dataset category: voice events
* Target device: Cortex M4
* Time per inference: 100 ms
* Target RAM: 340 KB
* Target ROM: 1024 KB  
After a thorough investigation, the best model for the device was selected, and the model was quantized to be deployed in the target device. The following Table 5 provides a comparison of the converted TensorFlow Lite model (float32) and the quantized model (int8) in terms of latency, memory, and accuracy.