esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 3 The Proposed Algorithm
### Feature Extraction  
After the raw data are preprocessed, the next step is to extract relevant features that aid in the classification process. The goal of feature extraction is to represent the raw sensor data in a form that captures the underlying patterns of different events (e.g., speed bumps, potholes, sudden brakings) in a compact and meaningful way.  
To extract features, we divide each preprocessed time window \(W_{k}\) into statistical and frequency domain features. Statistical features provide information on the central tendency and dispersion of the data, while frequency domain features capture the spectral characteristics of the signal.  
Statistical Features: We compute basic statistical descriptors, such as  
\[f_{1}=\text{mean}(W_{k}),\quad f_{2}=\text{variance}(W_{k}),\quad f_{3}=\text {skewness}(W_{k}),\quad f_{4}=\text{kurtosis}(W_{k}) \tag{3}\]  
Frequency Domain Features: We also derive frequency domain features from time-series data using methods such as fast Fourier transform (FFT), which helps identify dominant frequencies and spectral energy. The relevant frequency-domain features include the following:  
\[f_{m+1}=\text{dominant frequency}(W_{k}),\quad f_{m+2}=\text{ spectral entropy}(W_{k}),\quad f_{m+3}=\text{power spectral density}(W_{k}) \tag{4}\]These features are crucial in distinguishing between different types of events, such as speed bumps, potholes, and sudden braking events. After extracting these features from each time window, we assemble them into a feature vector that is used as input to the classification model.  
Algorithm 2 illustrates the steps involved in the feature extraction process. This ensures that after preprocessing, relevant statistical and frequency domain features are extracted from the sensor data. These features are then used as input for the classification model, providing essential information to distinguish between different types of events, such as speed bumps, potholes, and sudden brakings.  
```
1:Input: Preprocessed time windows \(W_{k}\) from raw sensor data
2:Output: Feature vectors \(F_{k}\) for each time window \(W_{k}\)
3: For each time window \(W_{k}\) (for \(k=1,2,\ldots,K\)):
4:1. Compute statistical features:
5:\(f_{1}=\) mean \((W_{k})\)
6:\(f_{2}=\) variance \((W_{k})\)
7:\(f_{3}=\) skewness \((W_{k})\)
8:\(f_{4}=\) kurtosis \((W_{k})\)
9:2. Compute frequency domain features using FFT:
10:\(\text{FFT}(W_{k})=\) Fourier transform \((W_{k})\)
11:\(f_{m+1}=\) dominant frequency \((W_{k})\)
12:\(f_{m+2}=\) spectral entropy \((W_{k})\)
13:\(f_{m+3}=\) power spectral density \((W_{k})\)
14:3. Construct feature vector:
15:\(F_{k}=\{f_{1},f_{2},f_{3},f_{4},\ldots,f_{m+3}\}\)
16: Return feature vectors \(F_{k}\) for all time windows
```  
**Algorithm 2**Feature Extraction
# Research on Clock Synchronization of Data Acquisition Based on NoC
## 6 Conclusions  
In this paper, based on the proposed NoC data acquisition system, the White Rabbit clock synchronization protocol is applied to the clock synchronization of an NoC router, and it is improved to meet the high-precision synchronization requirements for TI-ADC's sampling clock. The traditional White Rabbit protocol is based on Ethernet to transmit the clock and clock message. For the synchronization of multiple clock domains on the chip in this paper, the improved White Rabbit protocol no longer uses the Sync-E in the original White Rabbit protocol, and the clock message transmission format of PTP protocol is also simplified. The improved White Rabbit clock synchronization protocol is adopted to synchronize the router's clock. Firstly, the improved PTP protocol is used to determine and correct the offset of the master and slave clocks. Then, DDMTD is used to determine the phases of the master and slave clocks. Finally, the phase-shift function of the PLL is used to correct the phase of the slave clock. A Modelsim simulation shows that the average absolute error in the TI-ADC's sampling clock after clock synchronization can be kept within 20 ps, achieving a higher sampling rate.  
The architecture scale validated in this paper comprises only nine routing nodes. Subsequently, the data acquisition system's scale can be expanded and validated to improve system performance. Furthermore, the router's clock can be used as the sampling clock for the ADCs. When outputting, consideration should be given to the path delay from the FPGA chip to the ADC chip's clock pins to enhance clock synchronization accuracy and eliminate associated errors.  
Conceptualization, C.X. and C.M.; methodology, J.L. and C.M.; software, J.L. and C.M.; validation, J.L.; investigation, J.L.; resources, J.L. and C.X.; data curation, J.L. and C.M.; writing--original draft preparation, C.M.; writing--review and editing, C.M.; visualization, J.L. and  
Figure 16: Average absolute error of clock sync. The maximum average absolute error is 22 ps and the minimum average error is 7 ps. In most cases, the error can be kept within 20 ps.  
C.M.; supervision, C.X.; project administration, C.X.; funding acquisition, C.M., C.X., and J.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (62161008 and 61861012).  
Not applicable.  
The data that support the findings of this study will be available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest and the funders had no role in the design of the study.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 4 Solar Potential Assessment Using GISs  
### Overview  
Solar potential can essentially be categorized into three different types [30]. First, physical potential is the total amount of solar energy reaching the target surface, which can be referred to as the total solar radiation on the surface or rooftop. Second, geographic potential is the spatial availability of the surface or building rooftop where solar energy can be obtained, which can be referred to as the available area  
Figure 6: Floating PV analysis for reservoirs in Korea using a GIS: (**a**) Map of average water depth for each reservoir; (**b**) Solar radiance map considering terrain ([PERSON] et al. [55]).  
for solar PV installation. Third, technical potential is the total amount of electricity considering the technical characteristics of the solar PV system (e.g., module efficiency, inverter capacity, and system design), which can be referred to as electricity generation. A GIS can be used for physical potential and geographic potential.  
In this study, 39 published articles on GIS-based solar potential assessment were reviewed. Table 3 summarizes the classification and number of articles according to the role of the GIS, solar type, or scale. GISs were utilized for various purposes in solar potential assessment, such as DB and visualization, rooftop extraction, radiation modeling, shading analysis, and spatial analysis tools. Regarding solar type, most studies were focused on PV or both PV and CSP, while few dealt with CSP. In terms of analysis scale, most of the solar potential assessment studies were conducted on national or regional scales, while few were performed on global, continental, or object (building) scales.  
This section consists of five sub-sections corresponding to the roles of GISs in research as DB and visualization, rooftop extraction, radiation modeling, shading analysis, and spatial analysis tools.
## 2 Standard Long-Term HSR Demand-Forecasting Methodology in Korea  
This section introduces the standard long-term HSR demand-forecasting method in Korea. A preliminary feasibility study is a system in which government agencies review the feasibility of a project in advance. A preliminary feasibility study must be conducted for projects costing USD 35 million (KRW 50 billion) or more. The guideline for a preliminary feasibility study presents a long-term demand-forecasting methodology. The guideline provides a conventional four-step method of trip generation, trip distribution, mode split, and trip assignment for long-term demand forecasting, as shown in Figure 2[5]. The O/D trip and network data included in the Korea Transport Database (KTDB) are used in this process. Since the KTDB includes O/D data by mode, the first three steps (trip generation, trip distribution, and mode split) in the conventional four-step method are omitted when predicting long-term demand. Therefore, the accuracy of the long-term demand forecasting varies according to the trip assignment algorithm. An optimal strategy algorithm without capacity constraints is applied for long-term railway demand forecasting using macrosimulations, such as Emme or TransCAD. To simulate the current trip pattern of a trip assignment algorithm, a calibration process based on a trial-and-error approach is iteratively performed to correct the error between the volume estimated by the algorithm and the observed volume. After the calibration process, the long-term demand is predicted by applying the calibration results to the future network.  
The calibration reference value in railway trip assignment is the error rate between the estimated and observed boarding/alighting volumes at major stations, calculated as Equation (1) [5]. The guideline suggests that the error rate must be under 30% for stations but does not provide specific calibration methods. Accordingly, the analyst repeats the calibration process, such as by adjusting the network parameter, to satisfy the 30% critical error rate; however, there are limits to improving this accuracy due to the temporal inefficiency and limitations of the all-or-nothing-based optimal strategy algorithm:  
\[\text{Error rate }\epsilon(\%)=100\times\frac{f^{est}-f^{obs}}{f^{obs}} \tag{1}\]  
where \(f^{est}\) is the estimated number of passengers at each station using the trip assignment model, and \(f^{obs}\) is the observed number of passengers at each station.
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 2 Methodology
### Target Field Data Generation  
An open-source CFD tool, OpenFOAM [31], was employed to generate a target dataset. For mesh generation, a domain with a channel shape of \(192\times 192\) was converted into the 3D 192 mm \(\times\) 192 mm \(\times\) 1 mm domain of the voxel file that was obtained through the method explained in Section 2.1. The voxel file was finally converted into an STL file and the mesh was created by cutting out the basic domain generated by blockMesh to match the shape of the channel using an STL file, without the refinement process.  
In our problem setup, when gravity is neglected, a fluid with kinematic viscosity \(\
u\) of \(1\times 10^{-4}\) m\({}^{2}\)/s enters the channel from the left side as a uniform flow with an x-component velocity \(u_{\text{inlet}}\) of \(1\times 10^{-3}\) m/s. The fluid then flows through the 2D channel, where the velocity at the boundaries and the pressure gradient in the normal direction are zero. Finally, the fluid exits through the right side of the channel, where the gauge pressure \(p\) is equal to \(0\)\(Pa\). Based on these boundary conditions, the continuity and momentum equations are expressed as follows:  
\[\
abla\cdot V=0 \tag{1}\]  
\[(V\cdot\
abla)V=-\
abla P+\
u\
abla^{2}V \tag{2}\]  
\[P=\frac{p}{\rho_{0}}, \tag{3}\]  
where \(\
abla\), \(V\), \(P\), and \(\rho_{0}\) denote the _del_ operator, velocity vector, kinematic pressure, and constant mass density of the fluid, respectively. Our models were set to the laminar because our inlet velocity was low enough. To generate the target data, the SIMPLE algorithm [32] was used for the CFD solution. During post-processing, the corner vortex region near the wall was calculated as the region of interest (RoI) to compare the accuracy at the region. A sample set of fields in the target data is shown in Figure 5, where the channel region is highlighted in black to enhance the distinction between the wall and fluid regions.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 2 Liquid-Based Antennas
### Liquid Solutions and Other Combinations
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline
**Ref.** & \begin{tabular}{l} **Antenna** \\ **Type** \\ \end{tabular} & \begin{tabular}{l} **Pla-Nar** \\ **mm3**(\(\lambda_{0}\)) \\ \end{tabular} & \begin{tabular}{l} **Demensions** \\ **mm3**(\(\lambda_{0}\)) \\ \end{tabular} & \begin{tabular}{l} **Reconfig.** \\ **Type** \\ \end{tabular} & \begin{tabular}{l} **Liquid Type** \\ **Tuning Type** \\ \end{tabular} & \begin{tabular}{l} **Tuning Type** \\ **Range** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{l} **Frequency** \\ **Equation (1)** \\ \end{tabular} & \
\begin{tabular}{l} **TR** \\ **Equation (3)** \\ \end{tabular} &
\begin{tabular}{l} **TX** \\ **Efficiency** \\ \end{tabular} \\ \hline
[49] & cone & no &
\begin{tabular}{l} 100 \(\times\) 100 \(\times\) \\ 41 (2\(\lambda_{0}\)) \\ \end{tabular} & frequency & TPC & contin. \({}^{a}\) & 1.6-5.0 & 103\% & 3.125 & N/A \\ \hline
[50] & spiral \({}^{b}\) & no & \
\begin{tabular}{l} 100 \(\times\) 100 \(\times\) \\ 41 (0.93\(\lambda_{0}\)) \\ \end{tabular} & frequency &
\begin{tabular}{l} Choline \\ L-alanine \\ \end{tabular} & contin. \({}^{a}\) & a few bands & - & - & N/A \\  & & & & & & & between 1.37 & & & \\ \hline
[75] & mono- pole & no &
\begin{tabular}{l} 30 \(\times\) 28.6 \(\times\) \\ 28.6 (0.43\(\lambda\)) \\ \end{tabular} & frequency & BSTO in oil & contin. & 2.75–4.22 & 42\% & 1.54 & N/A \\ \hline
[76] & Yagi- Uda & no &
\begin{tabular}{l} 950 \(\times\) 100 \(\times\) \\ 80 (0.56\(\lambda_{0}\)) \\ \end{tabular} & frequency & pure water \& & & contin. & 0.069–0.177 & 88\% & 2.57 & 40–74\% \\ \hline
[78] & dual-tube & no &
\begin{tabular}{l} 39.5 \(\times\) 9.5 \(\times\) \\ 9.5 (0.37\(\lambda_{0}\)) \\ \end{tabular} & frequency & pure water \& & & contin. & 1.57–2.83 & 57\% & 1.8 & 52–84\% \\ \hline
[80] & mono- pole & no & \
\begin{tabular}{l} 947 \\ acetate \\ \end{tabular} &
\begin{tabular}{l} glass \& ethyl \\ acetate \\ \end{tabular} & discrete (N = 2) & 3.75–5.37 & - & - & - & \textgreater{}80\% \\ \hline \end{tabular}  
\({}^{a}\) not explicitly demonstrated in the paper. \({}^{b}\) the data shown here is for the case without the metasurface lid.  
\end{table} 
Table 4: Liquid Variations, Liquid Solutions and Other Combinations.
## 4 Experimental Results and Evaluation
### Experiment C
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **[PERSON] et al. [37]** & **[PERSON] et al. [38]** & **[PERSON] and [PERSON] [39]** & **[PERSON] and** & **[PERSON] [40]** & **[PERSON] and** **[PERSON] [41]** \\ \cline{2-10}
**Test Case** & \multicolumn{2}{c}{**Spatial**} & \multicolumn{2}{c}{**Description**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**Tabu Search**} \\ \cline{2-10}  & **Representation** & **Layersing Algorithm** & \multicolumn{2}{c}{**Uelouristics**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**Tabu Search**} \\ \cline{2-10}  & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** \\ \hline LN01 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN02 & 35.2 & 11.5 & \(-\)52.2 & 0.3 & 0.0 & 0.0 & 10.3 & 0.6 & \(-\)25.0 & \(-\)6.8 \\ LN03 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN04 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN05 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN06 & 58.3 & 3.9 & 16.7 & 3.0 & 74.0 & 11.0 & 37.5 & 1.2 & 59.2 & 1.1 \\ LN07 & 100.0 & 3.5 & 100.0 & 1.0 & 100.0 & 7.6 & 100.0 & 1.7 & 0.0 & 0.0 \\ LN08 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN09 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN10 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN11 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN12 & 0.0 & 0.0 & 100.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN13 & 100.0 & 1.8 & 100.0 & 4.0 & 100.0 & 9.6 & 0.0 & 0.0 & 100.0 & 1.5 \\ LN14 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ LN15 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparative results of the relative improvement indicator for each approach.
## 3 Method
### Scheduling Policy Optimization Algorithm Based on HEGNN-PPO
#### 3.4.1 Scheduling Decision  
For each actionable operation \(a_{t}=\big{(}O_{ij},M_{k}\big{)}\), the operation embedding, machine embedding, and global state are concatenated and input into an MLP to calculate the priority index:  
\[P(a_{t},s_{t})=\text{MLP}_{\omega}\Big{(}h_{final}\Big{)} \tag{21}\]  
where \(h_{final}=\alpha_{ij}^{\prime}\parallel\beta_{k}^{\prime}\parallel h_{global}\), \(\alpha_{ij}^{\prime}\) is the updated operation node feature, \(\beta_{k}^{\prime}\) is the updated machine node feature, and \(h_{global}\) is the global feature.  
The probability of selecting each action is calculated using the softmax function:  
\[\pi_{\omega}(a_{t}|s_{t})=\frac{\exp(P(a_{t},s_{t}))}{\sum_{a_{t}^{\prime}\in A _{t}}\exp(P(a_{t}^{\prime},s_{t}))}\ \forall a_{t}\in A_{t} \tag{22}\]  
During the training phase, actions are sampled according to the policy \(\pi_{\omega}\) to facilitate exploration; during the testing phase, the action with the highest probability (greedy strategy) is selected as the final decision [43].
## 2 Results and Discussion
### Non-Targeted Analysis of Two Coffee Groups
#### 2.2.2 Prediction Model  
The RF algorithm was also used to build a prediction model that aimed at predicting the coffee type (regular or decaf) of 10 selected samples. This model was built using the original 16 samples and 630 features data set investigated in the present work. Once again, all sample triplicates were considered for the implementation of the model (\(n=48\)). The complete data set was divided into a six-sample subset (\(n=18\)) used to train the model and a 10-sample subset (\(n=30\)) used to validate the model.  
Three pairs of coffee, namely (R-R and D-R), (R-DE and D-DE), and (R-L and D-L) were selected to train the model. These samples were carefully selected to account for the two types of coffee beans: Arabica and Arabica Robusta, and the three different decaffeination processes: \(\mathrm{LiCO_{2}/water}\), \(\mathrm{DCM/water}\) and \(\mathrm{scCO_{2}}\) (Table 3). The misclassification error of the training set was equal to 0. The validation set consisted of five pairs of coffee, namely (R-F and D-F), (R-VO and D-VO), (R-VI and D-VI), (R-S and D-S) and (R-I and D-I). Its  
Figure 6: (**a**) The confusion matrix of the validation set (\(n=19\)) obtained using the random forest classification algorithm. (**b**) The chemical classes of the top 20 discriminatory features. In red: the compounds that have the highest peak areas in decaffeinated coffee samples. In green: the compounds that have the highest peak areas in regular coffee samples. (**c**) PCA score plot using 630 features. (**d**) PCA score plot using only the top 20 discriminatory features.  
misclassification error was also equal to 0. The prediction model was able to accurately predict the type of all the coffee samples in the validation set. The ROC curve of the validation set is presented in the Supplementary Materials Section (Figure S3).  
The workflow built with this prediction model can be further applied to a wider range of decaffeinated coffees subjected to different decaffeination methods. In fact, using water-based or CO\({}_{2}\)-based decaffeination is considered to be healthier for coffee drinkers compared to solvent (DCM)-based methods. In this regard and for future applications, this model can help distinguish between the different decaffeination methods, and hence contribute to ensuring food quality and detecting potential food fraud.
## Appendix A Numerical Method SMPIMC  
### Basic Idea of Path Integral Monte Carlo Methods  
Let us consider a multiple integral defining the average value of a function \(a(\mathbf{x})\) with a distribution function \(f(\mathbf{x})\) (sign-alternating in general case):  
\[\langle a\rangle_{f}=\frac{1}{Z}\int a(\mathbf{x})\,f(\mathbf{x})\,\mathrm{d} ^{n}\mathbf{x},\quad Z=\int f(\mathbf{x})\,\mathrm{d}^{n}\mathbf{x}, \tag{2}\]  
where \(Z\) is the normalization factor. Replacing the sign-alternating distribution function with the product of the normalized absolute value \(w(\mathbf{x})=|f(\mathbf{x})|/C\) and the weight function \(g(\mathbf{x})=\text{sign}f(\mathbf{x})\), one can give the probabilistic interpretation of (1):  
\[\langle a\rangle_{f}=\frac{\langle a\,g\rangle_{w}}{\langle g\rangle_{w}}, \quad\text{where}\quad\langle b\rangle_{w}=\int b(\mathbf{x})\,w(\mathbf{x})\, \mathrm{d}^{n}\mathbf{x}. \tag{3}\]  
Now, the function \(w(\mathbf{x})\), can be interpreted as the probability density for a random vector \(\mathbf{x}\). The basic idea of a Monte Carlo method is to estimate the integral via averaging the integrands over a random sample with the probability density \(w(\mathbf{x})\):  
\[\langle a\rangle_{f}\approx\frac{\sum_{i=1}^{N_{\text{cr}}}a(\mathbf{x}_{i}) \,g(\mathbf{x}_{i})}{\sum_{i=1}^{N_{\text{cr}}}g(\mathbf{x}_{i})}. \tag{4}\]  
If random vectors \(\mathbf{x}_{i}\) in the sample are not correlated, the statistical error is \(O(N^{-1/2})\), and can be estimated via \(3\sigma\)-rule according to the law of large numbers.  
The sample \(\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{N_{\text{mc}}}\}\) can be created via the Metropolis algorithm [42]. This algorithm consists of sequential steps divided into two sub-steps: the proposal and the acceptance. If the value of a random vector on the \(i\)-th step is \(\mathbf{x}_{i}\), and a new random value \(\mathbf{x}_{i}^{\prime}\) uniformly distributed in some \(n\)-dimensional cube is proposed, this new state has to be accepted with the probability:  
\[A(\mathbf{x}_{i}\rightarrow\mathbf{x}_{i}^{\prime})=\max\biggl{(}1,\frac{w( \mathbf{x}_{i+1})}{w(\mathbf{x}_{i})}\biggr{)}. \tag{5}\]  
In the case of acceptance, the state on the \(i+1\) step becomes \(\mathbf{x}_{i+1}=\mathbf{x}_{i}^{\prime}\), and in the case of rejection, the old value is saved: \(\mathbf{x}_{i+1}=\mathbf{x}_{i}\). Performing \(N_{\text{mc}}\) steps of the Metropolis algorithm gives us the required sample.