esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Evaluations Utilizing Open Source Systems
### Results And Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Original Class**} & \multirow{2}{*}{**Threshold**} & \multicolumn{4}{c}{**Extracted Classes**} \\ \cline{2-2} \cline{5-7}  & \#Methods & & \multicolumn{1}{c}{**CBCS**} & \multicolumn{1}{c}{\#Methods**} & \multicolumn{1}{c}{**CBCS**} & \multicolumn{1}{c}{**Avg. CBCS**} \\ \hline GenericServlet & 12 & 0.113 & 0.193 & 8 & 0.204 & 0.208 & 0.206 \\ \hline ServletResponseWrapper & 17 & 0.042 & 0.125 & 7 & 0.095 & \\  & & & & 7 & 0.094 & \\  & & & & 3 & 0.111 & 0.100 \\ \hline ServletRequestWrapper & 31 & 0.114 & 0.2 & 25 & 0.157 & \\  & & & & 4 & 0.183 & 0.204 \\  & & & & 2 & 0.273 & \\ \hline HttpServletRequestWrapper & 25 & 0.143 & 0.222 & 18 & 0.154 & \\  & & & & 7 & 0.2 & 0.177 \\ \hline NoBodyResponse & 32 & 0.031 & 0.154 & 7 & 0.053 & \\  & & & & 18 & 0.053 & \\  & & & & 3 & 0.067 & \\  & & & & 4 & 0.056 & \\ \hline  & & & & 41 & 0.109 & \\  & & & & 47 & 0.11 & \\  & & & & 18 & 0.113 & \\  & & & & 3 & 0.167 & 0.132 \\  & & & & 6 & 0.116 & \\  & & & & 6 & 0.14 & \\  & & & & 2 & 0.167 & \\ \hline ObjectReader & 14 & 0.33 & 0.5 & 4 & 0.5 & \\  & & & & 3 & 1 & \\  & & & & & 4 & 0.75 & \\ \hline  & & & & 6 & 0.24 & \\  & & & & 7 & 0.214 & \\  & & & & 7 & 0.214 & \\  & & & & 7 & 0.214 & \\  & & & & & 7 & 0.214 & \\  & & & & & 5 & 0.267 & 0.250 \\  & & & & & 8 & 0.262 & \\  & & & & 4 & 0.278 & \\  & & & & 3 & 0.278 & \\ \hline  & & & & 10 & 0.168 & \\ ChannelData & 24 & 0.063 & 0.286 & 5 & 0.2 & \\  & & & & & 4 & 0.167 & \\  & & & & 5 & 0.204 & \\ \hline  & & & & 5 & 0.15 & \\  & & & & 12 & 0.12 & \\ MemberImpl & 39 & 0.045 & 0.333 & 15 & 0.114 & 0.168 \\  & & & & & 5 & 0.125 & \\  & & & & 2 & 0.333 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Extracted classes suggested by our approach for the considered classes of the Tomcat system. the extracted classes are greater the number of the original classes. The results shown in Table 6 are statistically significant because the \(p\)-values are less than 0.05. Based on these results, we can reject the null hypothesis \(\mathbf{H}_{0}\) and accept the alternative \(\mathbf{H}_{1}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(t\) & \(df\) & \(p\)-Value & \multicolumn{3}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{**Mean of the Differences**} \\ \cline{4-7}  & \(-3.1439\) & 9 & 0.005927 & \(-\infty\) & \(-0.05520043\) & \(-0.1324\) \\ Ant & \(-3.3383\) & 9 & 0.004342 & \(-\infty\) & \(-0.08088784\) & \(-0.1794\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The paired samples \(t\)-test for the Tomcat and Ant classes.
# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique  
### Overview of the Tsunami Prediction System  
Here, we define that tsunami prediction information includes tsunami arrival time, the height, inundation area, and its depth distribution. There are many methods for real-time tsunami prediction, but there are many issues to make use of them for regional tsunami disasters and prevention. Network systems above the ocean floor have recently been implemented, and the technology of real-time tsunami prediction has evolved with the use of their data [14; 15; 16].  
It is essential for tsunami prediction that there is a close correlation between the average of the absolute pressures read by DONET and the calculated heights of the first tsunami waves in coastal areas [14]. Our system basically adopts a model-based prediction like [PERSON] et al. (2014) [14]. DONET observatories record tsunamis and possible tsunami scenarios are then derived using the above correlation. At this stage, there are differences to select an appropriate fault model using statistical techniques. Our system predicts tsunami for each point along the coastal area because we consider that it is difficult to predict for each point using one common fault model due to heterogeneity on the slip distribution and other origins of tsunami-like seafloor landslides as described in Sections 2.2 and 2.3.  
In addition, the accuracy of the tsunami prediction depends on users' motivation and their policy. Users may hope to avoid underestimation rather than to extract close tsunami height. We prepared some options for how to predict them according to the users' preferences as described in Section 2.4.  
Real observed data include many types of noise. To install it as the system, it is significant to understand noise characteristics for each ocean floor network system. We described how to reduce the effects of noise in Section 2.5.
# A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
## 4 Experimental Validation
### Data Sets  
According to the latest time series database UCRArchive2018, in order to make the experimental results more credible, 100 data sets were obtained on the basis of removing null values in the data and show in Table 2. Each data set is divided into a training set and a testing set and a detailed documentation of the data. The datasets contain classes ranging from 2 to 60 and have the lengths of time series varying from 15 to 2844. In addition, the types of the data sets are also diverse, including image, sensor, motion, ECG, etc. [27]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Type** & **Name** & **Train** & **Test** & **Class** & **Length** \\ \hline
1 & Device & ACSF1 & 100 & 100 & 10 & 1460 \\
2 & Image & Adiac & 390 & 391 & 37 & 176 \\
3 & Image & ArrowHead & 36 & 175 & 3 & 251 \\
4 & Spectro & Beef & 30 & 30 & 5 & 470 \\
5 & Image & BeetleFly & 20 & 20 & 2 & 512 \\
6 & Image & BirdChicken & 20 & 20 & 2 & 512 \\
7 & Simulated & BME & 30 & 150 & 3 & 128 \\
8 & Sensor & Car & 60 & 60 & 4 & 577 \\
9 & Simulated & CBF & 30 & 900 & 3 & 128 \\
10 & Traffic & Chinatown & 20 & 343 & 2 & 24 \\
11 & Sensor & CinECGTorso & 40 & 1380 & 4 & 1639 \\
12 & Spectro & Coffee & 28 & 28 & 2 & 286 \\
13 & Device & Computers & 250 & 250 & 2 & 720 \\
14 & Motion & CricketC & 390 & 390 & 12 & 300 \\
15 & Motion & CricketY & 390 & 390 & 12 & 300 \\
16 & Motion & CricketZ & 390 & 390 & 12 & 300 \\
17 & Image & DiatomSizeReduction & 16 & 306 & 4 & 345 \\
18 & Image & DistalPhalancOutlineAgeGroup & 400 & 139 & 3 & 80 \\
19 & Image & DistalPhalancOutlineCorrect & 600 & 276 & 2 & 80 \\
20 & Image & DistalPhalancTW & 400 & 139 & 6 & 80 \\
21 & Sensor & Earthquakes & 322 & 139 & 2 & 512 \\
22 & ECG & ECG200 & 100 & 100 & 2 & 96 \\
23 & ECG & ECGFiveDays & 23 & 861 & 2 & 136 \\
24 & EOG & EOGHorizontalSignal & 362 & 362 & 12 & 1250 \\
25 & EOG & EOGVerticalSignal & 362 & 362 & 12 & 1250 \\
26 & Spectro & EthanolLevel & 504 & 500 & 4 & 1751 \\
27 & Image & FaceAll & 560 & 1690 & 14 & 131 \\
28 & Image & FaceFour & 24 & 88 & 4 & 350 \\
29 & Image & FacesUCR & 200 & 2050 & 14 & 131 \\
30 & Image & FiftyWords & 450 & 455 & 50 & 270 \\
31 & Image & Fish & 175 & 175 & 7 & 463 \\
32 & Sensor & FordA & 3601 & 1320 & 2 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 100 different types of time series datasets.
## 3 Materials and Methods
### Data Set
#### 3.1.2 Event Selection  
For EM shower, three-dimensional (3D) clusters close to generated electrons and positrons (\(\Delta R<0.2\)) are considered, where angular distance is calculated as \(\Delta R=\sqrt{(\eta_{cluster}-\eta_{genPart})^{2}+(\phi_{cluster}-\phi_{genPart })^{2})}\), where \(\eta\) is the pseudorapidity, the spatial coordinate describing the angle of a particle relative to the beam axis, while \(\phi\) is an angle that describes the rotation of a particle's trajectory in the plane perpendicular to the beamline. The requirement for the cluster's pt is that it has to be higher than 10 GeV. If there are more 3D clusters close to the generated particle, the clusters whose pt is between 80% and 120% of the pt of the belonging generated particle are selected, and the one with the highest pt value is picked. For PU, 3D clusters with pt higher than 5 GeV, that are not close to generated photons, electrons, and positrons (\(\Delta R>0.2\)) are chosen. For QCD jets, 3D clusters close (\(\Delta R<0.4\)) to genjets with \(pt>\) 30 GeV are selected. Genjets are jets produced from generator-level Monte Carlo particles, and they contain information about fractions from different types of generated particles (charged hadrons). Using the above description, 54,000 samples are generated: 18,000 EM samples, 18,000 PU samples, and 18,000 QCD jet samples. In this study, only low-level features are used: \(pt\), position (\(x\),\(y\),\(z\)), layer number, pseudorapidity (\(\eta\)), azimuthal angle (\(\phi\)), and _particle ID_ given by the generator.  
The generated events were split into training (68%), validation (17%), and testing (15%) data. This enabled us to use a validation set for hyperparameter optimization while ensuring that no overfitting occurred (through loss function analysis).
## 4 Analysis  
### Correlation Analysis  
An exploratory correlation analysis was performed to examine the relationships between the independent and dependent variables. Table 2 shows the correlation matrix of all variables. The correlations for Mexico and Ethiopia were calculated separately for each of the past two decades (the 1990s and the 2000s). Four environmental variables, i.e., MeanYield, MeanFlow, ConsecLowYield and ConsecLowFlow, were calculated based on the Lpjml_Gswp3 and Lpjml_Princeton datasets.  
Figure 2: Population distribution in Mexico (**upper panel**) and Ethiopia (**lower panel**) in 2000.  
In both Mexico and Ethiopia, the two control variables (Built-up and Pop) had strong positive correlations with the dependent variable (PopReDist). In Mexico, the correlation coefficient of the Built-up was 0.605 for the 1990s and 0.609 for the 2000s; these values were higher than the correlation coefficient of Pop, which was 0.512 for the 1990s and 0.454 for the 2000s. Possible reasons for the greater influence of Built-up on population redistribution in Mexico are higher migration from rural areas to urban areas or higher natural population growth rate in urban areas. In Ethiopia, the correlation coefficient of population distribution was 1.0 for 1990 and 0.977 for 2000, which indicates that population redistribution in Ethiopia is largely determined by natural population growth, and migration plays a relatively limited role.  
Regarding the environmental variables, in Mexico, with both sets of modeling data and in both decades, MeanYield was significantly and positively correlated with population redistribution; it was the only environmental variable to exhibit a significant correlation. In contrast, in Ethiopia, MeanYield and MeanFlow were positively correlated with population redistribution, whereas ConsecLowYield and ConsecLowFlow were negatively correlated with population redistribution. The correlation results were consistent across datasets and across decades, although not all correlations were significant in all cases. For Ethiopia, we must carefully demonstrate the correlation between environmental change factors and population redistribution.
## 4 Classifier Algorithm
### Random Forest  
The final discriminant result of random forest is the voting result of all decision trees. However, this voting method does not consider the difference between a strong classifier and a weak classifier. Once the number of decision trees with wrong results is greater than the number of decision trees with correct classification results, the recognition result will become worse. Therefore, we do not consider voting in the output, but a probability value is assigned to each category in the form of probability, which is the basic probability assignment in evidence theory. The recognition results of random forest subclassifiers placed in different positions are synthesized, and the category with the highest fusion probability is selected as the final category, which can greatly improve the recognition rate. The processing flow is shown in Figure 2.  
The calculation process is as follows:  
```
Input: training dataset, testing dataset Output: classification result (label out)
1. Training \(J\) random forest classifiers
2. For \(i=1\) to \(J\) Calculate each probability of every classifier \(p_{ij}\): \(p_{ij}=\frac{N_{ij}}{\textit{nfree}}\) End
3. Use Dempster-Shaferevidence theory to calculate each result probability of every classifier \(Pi\)
4. \(P_{\text{max}}=\text{max}(P_{i})\)
5. Label out = find (\(P_{i}==P_{\text{max}}\))
6. End
```  
**Algorithm:** Random Forest Classifier
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Coordinate Systems
#### 2.2.2 Local Vertical-Local Horizontal  
The Local Vertical-Local Horizontal (LVLH) reference frame is centered at the CoM of the spacecraft, its associated coordinate system has origin at the spacecraft CoM and is denoted by the basis vectors \(\{\mathbf{\Delta\hat{x}},\mathbf{\Delta\hat{y}},\mathbf{\Delta\hat{z}}\}\), where \(\mathbf{\Delta\hat{x}}\in\mathbb{R}^{3}\) is the unit vector that points from the CoM of the Earth towards the origin of the coordinate system, the unit vector \(\mathbf{\Delta\hat{z}}\in\mathbb{R}^{3}\) is aligned with the orbit angular momentum vector and the unit vector \(\mathbf{\Delta\hat{y}}\in\mathbb{R}^{3}\) competes a right-handed Cartesian coordinate system, see Figure 2. These unit vectors are defined as  
\[\mathbf{\Delta\hat{x}} \triangleq\frac{\mathbf{r}}{\|\mathbf{r}\|}, \tag{1}\] \[\mathbf{\Delta\hat{y}} \triangleq\Delta\hat{z}^{\times}\mathbf{\Delta\hat{x}},\] (2) \[\mathbf{\Delta\hat{z}} \triangleq\frac{r^{\times}\hat{\mathbf{r}}}{\|r^{\times}\hat{\mathbf{r}} \|}, \tag{3}\]  
where the vectors \(\mathbf{r}\triangleq[x~{}y~{}z]^{T}\in\mathbb{R}^{3}\) and \(\hat{\mathbf{r}}\in\mathbb{R}^{3}\) are the spacecraft ECI position and its time derivative, respectively. The skew symmetric matrix \(p^{\times}\in\mathbb{R}^{3\times 3}\) for a vector \(\mathbf{p}\triangleq[p_{1}~{}p_{2}~{}p_{3}]^{T}\in\mathbb{R}^{3}\) is defined as  
\[p^{\times}\triangleq\begin{bmatrix}0&-p_{3}&p_{2}\\ p_{3}&0&-p_{1}\\ -p_{2}&p_{1}&0\end{bmatrix}. \tag{4}\]
## 3 Experimental Results and Analyses
### Rotation of Abutment  
Figure 24 shows the relationship between inclination and displacement of the abutment. In Figure 24, the inclinations basically linear increase with the increases of displacement load in both positive and negative directions. When the displacement load is relatively small, the inclinations are nearly the same. After the displacement load reaches \(\pm\)8 mm, the significant difference of inclinations in the positive and negative directions begins to generate. Under the displacement of \(\pm\)16 mm, the inclination is 0.75\({}^{\circ}\) in the positive direction, and 0.51\({}^{\circ}\) in the negative direction. The former is 1.47 times as much as the latter, which means the rotation of abutment when bridge expands is larger than that when bridge contracts. The mainly reason is caused by the earth pressure of backfill. When the abutment moves to positive direction, the earth pressure provide a resistant force at the low part of abutment. A moment generated by resistant force (earth pressure resultant force) and MTS push force makes the abutment rotate easily, as shown in Figure 25a. However, when the abutment moves to negative direction, the moment generated by the earth pressure is against the moment generated by MTS pull force, which will prevent abutment to rotate difficultly as shown in Figure 25b.  
Figure 24: Relationship between inclination and displacement of the abutment.  
Figure 23: Distribution of horizontal deformation of specimen under negative displacement loads. (**a**) Under displacement loads of \(-\)2 mm\(\sim\)\(-\)10 mm. (**b**) Under displacement loads of \(-\)10 mm\(\sim\)\(-\)16 mm.
# Design and Testing of a Wearable System for Monitoring Car Drivers
## 2 Materials and Methods  
### Motivations and Requirements  
The primary motivation of this work is to enhance road safety by monitoring and analyzing driver behavior, particularly focusing on aggressive driving. Aggressive driving significantly increases the risk of road accidents, endangering both the driver and other road users. Emotions such as stress, anger, and joy can trigger aggressive driving behaviors, making it essential to monitor these physiological and psychological states in real time.  
A monitoring system must meet several significant requirements including the following:  
* Portability and Discreteness: The device should be compact, unobtrusive, and easily deployable without requiring permanent installation in the vehicle frames.
* Data Accuracy and Reliability: It should accurately collect and analyze physiological data to reliably detect aggressive driving behaviors.
* Safety and Compliance: The device must adhere to safety protocols and regulatory standards for wearable technology and data privacy.
* Real-time Processing: The system should process data in real time to provide timely alerts or interventions.
* Cost-effectiveness: The overall system should be cost-effective to make it accessible for widespread adoption.
## 4 Improved Control Method  
To address the aforementioned problems and to improve the system performance, in this study, we designed a composite controller that includes three parts based on the structure of triple-step control:  
\[u(t)=u_{c}^{*}(t)+u_{o}(t)+u_{f}(t) \tag{27}\]  
where \(u_{c}^{*}(t)\) is the modified PI feedback controller, which is used to suppress the vibration; \(u_{o}(t)\) is the compound disturbance compensation controller, which is used to compensate for the nonlinear friction and residual disturbance; and \(u_{f}(t)\) is the feedforward controller, which is used to enhance the system dynamic performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Values** \\ \hline \(J_{m},J_{b}\) & Motor rotor and platform base equivalent moment of inertia & 0.0015, 0.002 \\ \hline \(J_{l}\) & Load equivalent moment of inertia & 0.001/0.01 \\ \(B_{m},B_{b},B_{l}\) & Motor rotor, platform base, and load equivalent damping coefficient & 0.01, 0.01, 0.01 \\ \(K_{s}\) & Stiffness coefficient of the flex spline in Harmonic drive & 10 [kg m\({}^{2}\)/s\({}^{2}\)] \\ \(B_{s}\) & Internal damping coefficient of the flex spline in Harmonic drive & 0.01 [kg m\({}^{2}\)/s] \\ \(N\) & Ratio reduction in Harmonic drive & 100 \\ \hline \(K_{a}\) & Coefficient from the control voltage to the electromagnetic torque & 1 \\ \(K_{s}\) & Coefficient from the electromagnetic torque & [kg m\({}^{2}\)/(s\({}^{2}\)V)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical analysis parameters.  
Figure 3: Numerical analysis results. (**a**) Light load with \(J_{l}=0.001\); (**b**) Heavy load with \(J_{l}=0.01\).
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 6 Performance Evaluation and Simulation Environment  
Demonstrating the functionality and benefits of LoRaCog, simulations are conducted to evaluate the protocol against the rejected data packets compared to LoRaWAN. This section will describe the method used in the simulation, the comparison and the results.  
The communication parameters between EDs and GWs are the same for LoRaCog as in LoRaWAN (such as SF and sensitivity), with the only difference for LoRaCog being a wider frequency range to include the licensed spectrum.  
The main driver for the LoRaCog protocol is to ensure spectrum efficiency by utilizing the available unused frequencies (licensed or unlicensed) and reduce the congestion on unlicensed bands. The congestion will normally result in high collision or rejected packets when EDs are trying to communicate in non-cooperative manners.  
The rejected packet rate is a function of the channel's sensitivity between the EDs and GWs. This means that any sent packet with RSSI below the sensitivity level will not be detected by the GW and considered as rejected. The simulation is based on the known and used [PERSON]'s model [PERSON]'s model [PERSON] (1980) for radio frequency communication systems in urban areas to calculate the path loss and link budget. This model considers radio propagation in different areas as well as the impact of city buildings and environments on diffraction, reflection and scattering [PERSON] and [PERSON] (1980). Ref. [PERSON] and [PERSON] (1980) relied on Okumura-Hata to estimate path loss in urban areas for UHF (Ultra High Frequency) and VHF (Very High Frequency) land mobile radio services. The objective is to calculate the received power at the GW from each ED:  
\[P_{RX}=P_{TX}+G_{TX}-L_{TX}-L_{FS}-L_{M}+G_{RX}-L_{RX} \tag{1}\]  
where  
\(P_{RX}\): Received Signal Power (dBm);  
\(P_{TX}\): Transmitter Signal Power (dBm);  
\(G_{TX}\): Transmitter Antenna Gain (dBi);  
\(L_{TX}\): Transmitter Losses (dBm);  
\(L_{FS}\): Free Space Loss or Path Loss (dBm);  
\(L_{M}\): Miscellaneous Losses (dBm);  
\(G_{RX}\): Receiver Antenna Gain (dBi);  
\(L_{RX}\): Receiver Losses (connectors) (dBm).
## 3 Results and Discussion  
### Characterization of the BADCy Resin Microparticles  
The FT-IR spectra curves of the BADCy monomer and BADCy resin nanoparticles are shown in Figure 3, from which the molecular structure characteristics of the BADCy monomer and BADCy resin nanoparticle were ascertained. Compared with the BADCy monomer, the most apparent differences are the new peaks at 1568 and 1369 cm\({}^{-1}\). These two new peaks are due to the triazine ring structure formed by the monomer under the conditions of catalysts and heating [33]. Besides this, the -OCN functional group of the BADCy resin nanoparticles at 2270 cm\({}^{-1}\) is reduced compared with the monomer because the chemistry mechanism of the reaction is a trimerization of three -OCN groups into a triazine ring. According to the result of the FT-IR spectra, the -OCN functional group peak at 2270 cm\({}^{-1}\) does not completely disappear. It can be seen that the prepared BADCy resin nanoparticles still have an active -OCN functional group and can continue to react. Figure 4 shows a dynamic DSC curve of the BADCy resin nanoparticles and the matrix, indicating that the nanoparticles are still exotherms during the dynamic heating process, but with less heat than the matrix. It can also be concluded that the prepared BADCy resin nanoparticles have reactivity.  
The structure and size of the prepared BADCy resin nanoparticles were characterized by SEM (a) and TEM (b) as shown in Figure 5. After the reaction, the mixed solution was successively subjected to centrifugation, washing, and vacuum drying to obtain a pale yellow solid powder. The powders were subjected to SEM observation. From the SEM images of the BADCy resin nanoparticles, a large quantity of aggregates (micron-sized) was observed. A single aggregation particle ranged in size from 0.2 to 0.3 \(\upmu\)m. The powder was then dispersed in an ethanol solvent via ultrasonic dispersion. A drop of stable dispersion was dropped on a copper grid, dried, and observed using the transmission electron microscope. It can be seen that the BADCy resin nanoparticles ranged in size from 40 to 60 nm and there was no aggregation.  
From the above results, when the reaction solution was subjected to post-treatment, especially the drying process, the nanoparticles were aggregated and nano-sized particles became micro-sized. During the dying process, the nano-sized particles tended to aggregate as the solvent evaporated until a stable size was reached.  
Figure 4: Differential scanning calorimetry (DSC) curves of the BADCy prepolymer matrix and BADCy resin nanoparticles.  
Figure 3: FT-IR of the BADCy monomer and BADCy resin nanoparticle.