esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Preprocessing and Exploration of Independent Variables  
With 167 variables from the augmented data, we evaluated multicollinearity using the Variance Inflation Factor (VIF) and searched for significant variables. VIF is a measure used to avoid collisions and duplications between variables and to increase model reliability by identifying correlations between the predictors in regression. Variables with a VIF of less than 10 were considered as significant variables [28; 29; 30]. Among the total of 167 variables, 11 significant variables were derived by repeatedly removing the variable with the highest VIF. The VIFs of the significant independent variables in the gasoline order prediction model are presented in Table 1.  
Furthermore, the optimal time points for the impact of each independent variable on gasoline orders are needed. Table 2 describes the overall variables in the gasoline order prediction model. The independent variables related to gasoline orders for a particular month \(t\) are as follows: the cooling degree day \((t-12)\), Dubai crude oil prices \((t-3)\), Federal Funds Rate (FFR) \((t-2)\), US Producer Price Index (PPI) fluctuation rate \((t-2)\), News Sentiment Index (NSI) \((t-1)\), US Dollar Index (USDX) \((t-1)\), international gasoline (95 RON) prices \((t-1)\), Geopolitical Risk Index (GPR) \((t-1)\), gasoline inventory at the gas station \((t-1)\), Standard & Poor's 500 stock (S&P 500) \((t)\), and fuel tax \((t)\).  
Figure 3: Flowchart of data augmentation in training sets: The training sets were grouped into \(K\) clusters of 4 to 28 datasets, and the data in each cluster were augmented into 85 datasets. The training sets were augmented into 935 sets.  
Figure 2: Elbow method was used to determine optimal number of clusters in K-means algorithm. \(K\) was optimized using elbow method. \(K\) was set to 11 in this study.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Secure Data Dissemination Scheme to Mobile Sinks
#### 5.3.3. Sink Mobility Management  
Periodically, the sink checks the distance to the agent's cell. If it recognizes that it no longer reaches that cell within the sensor transmission range, the sink has to compute new cell ID and selects a coordinator in that cell as a new agent. Then, the sink re-sends a query to the source to establish a new data dissemination route by using the same mechanism described in the phase 2. By re-sending queries only when the sink moves out of transmission range, SCODE_plus_ significantly reduces communication overhead compared with other approaches. Hence, collision and energy consumption are reduced. Also, the number of loss data packet is decreased.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
###### Abstract  
Flexible electronic devices, such as the typical thin-film transistors, are widely adopted in the area of sensors, displays, wearable equipment, and such large-area applications, for their features of bending and stretching; additionally, in some applications of lower-resolution data converters recently, where a trend appears that implementing more parts of system with flexible devices to realize the fully flexible system. Nevertheless, relatively fewer works on the computation parts with flexible electronic devices are reported, due to their poor carrier mobility, which blocks the way to realize the fully flexible systems with uniform manufacturing process. In this paper, a novel circuit architecture for image processing accelerator using Oxide Thin-film transistor (TFT), which could realize real-time image pre-processing and classification in the analog domain, is proposed, where the performance and fault-tolerance of image signal processing is exploited. All of the computation is done in the analog signal domain and no clock signal is needed. Therefore, certain weaknesses of flexible electronic devices, such as low carrier mobility, could be remedied dramatically. In this paper, Simulations based on Oxide TFT device model have demonstrated that the flexible computing parts could perform \(5\times 5\) Gaussian convolution operation at a speed of 3.3 MOPS/s with the energy efficiency of 1.83 TOPS/J, and realize image classification at a speed of 10 k fps, with the energy efficiency of 5.25 GOPS/J, which means that the potential applications to realize real-time computing parts of complex algorithms with flexible electronic devices, as well as the future fully flexible systems containing sensors, data converters, energy suppliers, and real-time signal processing modules, all with flexible devices.  
flexible electronics; thin-film transistors; image signal processing; machine learning; analog-to-information processing; physical computing
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 5 Analysis
### Application of Machine Learning to LiDAR Data  
The primary objective of this section is to explore various parameter configurations to develop the most optimal and robust model for analyzing the LiDAR sensors' measurements for tie and ballast differentiation. Three essential parameters have been identified that significantly impact the model's performance: the algorithm, the input for training the ML models, and the window size. As previously mentioned, the two algorithms under consideration are Logistic Regression and Decision Tree, and the three inputs are illustrated in Figure 11. These six models will be evaluated across different window sizes based on their accuracy. Here, \"accuracy\" is assessed by Equation (4).  
Figure 11: A schematic of six distinct models, developed and investigated to determine the best model for processing LiDAR data to differentiate between ties and ballasts.  
\[Accuracy=\frac{Number\ of\ data\ points\ that\ are\ correctly\ classified\ by\ the\ model}{Number\ of\ data\ points\ in\ the\ test\ data\ set} \tag{4}\]  
This section is divided into three subsections, each dedicated to analyzing one input. For each input, both algorithms (Logistic Regression and Decision Tree) are considered across a wide range of window sizes.
# NMR Evaluation of Shale Oil Mobility: Combined Pyrolysis and CO\({}_{2}\) [PERSON]
## 4 Conclusions  
In this study, 2D NMR technology was utilized to conduct pyrolysis-NMR joint testing and CO\({}_{2}\) huff-n-puff-NMR joint testing experiments for the investigation of shale oil mobility. The pyrolysis-NMR joint testing allowed for the identification and localization of shale sample components on the \(T_{1}\)-\(T_{2}\) spectrum, as well as the development of quantitative description methods and recognition criteria for these components. The CO\({}_{2}\) huff-n-puff-NMR joint testing revealed that the initial cycle resulted in the most significant signal change. This indicates that oil from larger pores was preferentially extracted, leading to a subsequent decline in recovery rate across subsequent cycles. The average recovery rate of the three samples was 27.76%, indicating that the mobility of shale oil in the study area is relatively good, suggesting good development potential. Finally, based on the experimental results, a fluid component recognition template for the study area was established, which mainly consists of the following six parts: movable oil, adsorbed oil, asphaltene, clay-bound water, structural water, and kerogen. The practical significance of this study is primarily evident in the optimization of the CO\({}_{2}\) huff-n-puff pressure settings and calculation method. In this study, a more rational and accurate method is provided for evaluating the mobility of shale oil, which is of significant importance for the development of shale oil reservoirs.  
Conceptualization, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and F.S.; methodology, Y.Y. and J.L.; validation, K.L. and P.C.; formal analysis, P.C.; investigation, Y.Y. and F.S.; resources, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); data curation, P.C.; writing--original draft preparation, [PERSON] ([PERSON]) and P.C.; writing--review and editing, Y.Y., J.L. and P.C.; visualization, Y.Y. and K.L.; supervision, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, and the State Energy Center for Shale Oil Research and Development.  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in the published article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 8: The new shale oil component template established in this study.
## 4 Experiments
### Results and Analysis
#### 4.2.4 Effect of the Number of Layers of GCN  
The model GCNTRec proposed in this paper finally uses a two-layer GCN structure. This parameter selection is based on the experimental comparative analysis of the number of layers of the graph neural network. On the dataset Steam-200k, the influence of the number of layers of the graph convolutional neural network on the experimental results is shown in Figure 8.  
Obviously, when we use a layer of a graph convolutional neural network for feature extraction, the experiment produced poor results. Using a two-layer graph convolutional neural network is significantly better than a single-layer neural network. When the number of layers of the GCN is 3, the value of the metrics does not change significantly, but we can still see a slight decrease. Similarly, for a four-layer graph convolutional neural network, accuracy and recall are almost unchanged, but precision has a significant drop.  
Figure 7: Effect of the number of heads in a transformer encoder layer on the experiment with the Book-Crossing dataset.
## 3 Evaluation Methods of Grout-Injection Material
### Grout-Injection Materials Property Change Evaluation Regime
#### 3.2.2 Evaluation Method for Property Change Due to Chemical Exposure  
The specimens from Figure 5 in Section 3.1 are used for this testing. The mass of the specimens before testing are measured and then placed in containers filled with mixed chemical solutions (0.1% HCI, 0.2% H\({}_{2}\)SO\({}_{4}\), 0.2% HNO\({}_{3}\), 3% NaOH, and 3% NaCl). After 72 h of exposure in each of the containers, the specimens are removed from the container and dried in desiccators until they reach a constant mass. The mass of the specimens is calculated up to two decimal places in grams (or the percentage of the remaining material in the container). Refer to the below Figure 8 for an illustration of test method.  
Figure 8: The test method apparatus: (**a**) exposure to the chemical solution and (**b**) the mass measurement after exposure.  
Figure 7: The test method outline and apparatus.  
The waterproofing property change with respect to a mass loss due to corrosion or a chemical reaction with the mixed solutions is calculated into a ratio to provide a measurement index for a relative comparison. The mass loss ratio of the grout-injection material is measured with the following equation based on the results of the chemical exposure resistance test results.  
\[\{w/(m\times m_{lt})\}\times 100\%(!)=C_{l} \tag{2}\]  
where \(m\) is the mass of the grout-injection material before testing (g); \(m_{lt}\) is the mass loss threshold percentage (the mass lost should not be more than 15% as in accordance to KS F 4935 standard); \(w\) is the average lost mass of the injection material during the chemical solution exposure (g); and \(C_{l}\) is the mass loss ratio relative to the standard mass loss threshold (%).
# A Microscopic Traffic Flow Model Characterization for Weather Conditions
## 6 Conclusions  
The weather has a significant impact on traffic behavior. Snow, ice, and rain reduce road friction, which leads to higher accident rates. In this paper, a microscopic traffic flow model was proposed that characterizes traffic behavior based on the road surface conditions. These conditions were determined using the weather severity index. The performance of this model was evaluated for different weather severity indices and compared with the ID model over a circular road of length 800 m. Results were presented that show during adverse weather, the changes in density with the proposed model are smaller than with the ID model. With the ID model, the traffic queue does not dissipate with a larger exponent \(\delta\), which is unrealistic. Conversely, with the proposed model, the queue dissipates quickly with a larger weather severity index (adverse weather), while it dissipates slowly with a smaller index (normal weather), as expected. Moreover, vehicle movement with the ID model is based on a constant \(\delta\), which leads to unrealistic traffic behavior. Conversely, vehicle motion with the proposed model is based on the weather severity index, which results in a smooth flow that is realistic. The variations in flow are smaller than with the ID model. Further, the ID model velocity exhibits oscillatory behavior that increases with \(\delta\); thus, the model can be unstable. Conversely, the oscillations with the proposed model decrease with an increase in the weather severity index, as expected.  
The results obtained suggest that the proposed model can be employed in adaptive cruise control (ACC) systems and connected and automated vehicles (CAVs) for effective traffic control. It can also be incorporated into advanced driver assistance systems (ADASs) to improve traffic safety by providing information about road surface conditions during adverse weather. Existing studies based on data obtained in the field are limited by the variability in weather conditions and the type and amount of data obtained. Thus, future research can incorporate weather parameters, such as radiation, temperature, visibility, and wind obtained in real-time from roadside units.  
Conceptualization, F.A. and Z.H.K.; methodology, F.A.; software, F.A.; validation, F.A., Z.H.K., K.S.K. and T.A.G.; formal analysis, F.A., Z.H.K., K.S.K. and T.A.G.; investigation, F.A., Z.H.K., K.S.K. and T.A.G.; writing--original draft, F.A.; writing--review and editing, F.A., Z.H.K., K.S.K. and T.A.G.; visualization, F.A., Z.H.K., K.S.K. and T.A.G.; funding acquisition, Z.H.K., K.S.K. and T.A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 2 Materials and Methods  
### Description of the Study Area  
In this study, the Mikurahana quarry site, owned and maintained by Toseki material Co., Ltd., in Hachirogata town, Akita prefecture, Japan, was selected as the target location for this research. The Mikurahana quarry can produce roughly 2000 tons of rock daily, the primary rock type being rhyolitic andesite. Figure 2 is a map that illustrates how the Mikurahana quarry is situated concerning its surroundings. As seen from the picture, there is a town, and roads that are classified as national highways, close to the quarry site. Based on this information, it was determined that the location was an appropriate target site for this research, because the inhabitants of the village live in an environment that is vulnerable to ground vibrations induced by the blasting that takes place at the Mikurahana quarry. Using the collected data on the ground vibration caused by blasting, a blasting prediction model for the quarry was developed.  
The blasting design parameters from the Mikurahana quarry are as follows. The diameter of vertical and horizontal holes is 65 mm, the space between vertical holes is 2-2.4 m, the length of the vertical hole is 9-9.5 m, the bench height is about 10 m, and the inclination of the vertical hole is 80\({}^{\circ}\)-85\({}^{\circ}\). Explosives such as ANFO, water-containing explosives, and electric detonators are the most common type of explosives used in the quarry. All the blasting design parameters of the quarry site are depicted in Figure 3.  
Figure 2: Demonstrates (**a**) the Mikurahana quarry site, which is located in Hachirogata town, Akita prefecture, and (**b**) a map of Akita prefecture.
## 6 Conclusions  
SE, well known in informational theory for its capability to characterize complex time series, has been applied to reservoir-triggered seismicity at the Song Tranh 2 reservoir in Vietnam. Our findings suggest a clear link between the time variability of the water level and the time variation in the SE of the interevent times of earthquakes. Although the potential of water reservoirs to trigger earthquakes in tectonic areas is well known, no entropic analysis of reservoir-triggered seismicity has been performed to date. Furthermore, the application of SE has furnished a novel way to interpret the occurrence of reservoir-triggered seismicity in terms of increased instability or disorder in the seismic system due to variation in water level, leading to an increase in seismic activity. Further studies will be necessary, focused on the analysis not only of several other cases of reservoir-triggered seismicity but also of other seismological variables (like epicenter distance or magnitude) in order to get a more complete description of the entropic properties of this type of seismicity and illuminate water-related causative mechanisms that are not exhaustively clarified yet. Thus, the present study suggests a new perspective for investigation of reservoir-triggered seismicity.  
Conceptualization, L.T.; methodology, L.T.; software, M.L.; formal analysis, L.T. and A.T.T.; investigation, L.T., A.T.T., D.T.C. and L.M.N.; resources, L.T. and A.T.T.; data curation, A.T.T., D.T.C. and L.M.N.; writing--original draft preparation, L.T.; writing--review and editing, L.T. and A.T.T.; visualization, L.T. and A.T.T.; project administration, L.T. and A.T.T.; funding acquisition, L.T. and A.T.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.05-2019.328 and by CNR-VAST Italy-Vietnam common project 2020-2023, grant number QITIT01.02/20-21.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Identification of Digital Signal Modulation Based on InceptionResNetV2
### Preprocessing  
The analog signal at the transmitting end is converted into a baseband signal through three processes of sampling, quantization and encoding, and then reaches the receiving end through a frequency band transmission. Because the signal carries noise during transmission, the signal obtained at the receiving end is a noisy signal, which is written as follows:  
\[s(t)=\text{Re}\left(\sum_{m}A_{m}e^{i\theta_{m}}g[t-mT_{s}]e^{i(2\pi f_{s}+ \theta_{i})}+n(t)\right)0<t<T=NT_{s} \tag{1}\]  
\(A_{m}\) is the amplitude value of the \(m\)-th symbol, \(T_{s}\) is the symbol interval, \(e^{i\theta_{m}}\) is a constellation point, \(f_{c}\) is the carrier frequency of the signal, \(\theta_{i}\) is the carrier phase difference and \(g(t)\) is a rectangular pulse function. \(N\) is the number of symbols in the observation time \(T\), and \(n(t)\) is noise.  
In digital communication, the constellation diagram is the most direct way to observe the amplitude and phase of a signal. It is obtained by orthogonally demodulating the received signal. After receiving the signal \(s(t)\), the receiver divides the signal into two channels and multiplies them with two carriers with a phase difference of \(\pi\)/2, and then filters the high-frequency components through a low-pass filter. Finally, two relatively independent components are obtained: the in-phase (I) component and quadrature (Q) component. The two components are orthogonal to each other and are irrelevant. These two components are usually expressed in complex form as \(a+jb\), corresponding to a point on the complex plane, which is called a constellation point. Moreover, each type of amplitude-phase modulation signal has a corresponding point set used to represent such a modulation method, and these point sets form a constellation diagram representing the signal. The modulus indicates the amplitude change of the signal, and the phase indicates the phase change of the signal. Finally, the constellation map is sent to the InceptionResNetV2 network to complete the feature extraction operation.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 5 Performance Evaluation and Results
### Results  
We tested the authentication performance using sEMG-based biometrics for each of the 10 subjects as the client. Subjects other than the client were considered as impostors for the test. For a dataset obtained in Section 5.1, we randomly selected 10 instances from the client for training. Other instances of the dataset were used for testing. The data was normalized using min-max normalization. The process was repeated 10 times for each subject. Python and the scikit-learn library [41] were used for the experiment.  
Tables 1 and 2 show the test results for unlock patterns 1 and 2, respectively. The mean and standard deviation of the results obtained from the 10 repeated tests on a subject are reported in the tables. It can be seen that both OCSVM and LOF achieve good performance in terms of the \(F_{1}\) score, indicating that they are good at finding attempts from impostors. On the other hand, LOF has significantly better performance than OCSVM in terms of HTER, which is due to the high FRR obtained by OCSVM. Thus, LOF is a better classifier than OCSVM for our task. We also observe that the results obtained from the two patterns are similar. The performance evaluation demonstrates that using LOF with time domain features extracted from sEMG signals for user identification is a viable solution for enhancing the security of pattern unlock.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**OCSVM**} & \multicolumn{2}{c}{**LOF**} \\ \hline
**Subject** & \(\mathbf{F_{1}}\) & **HTER** & \(\mathbf{F_{1}}\) & **HTER** \\ \hline
**1** & 0.9760 \(\pm\) 0.0054 & 0.1911 \(\pm\) 0.0505 & 0.9755 \(\pm\) 0.0060 & 0.1200 \(\pm\) 0.0338 \\ \hline
**2** & 0.9746 \(\pm\) 0.0066 & 0.2350 \(\pm\) 0.0626 & 0.9882 \(\pm\) 0.0045 & 0.1031 \(\pm\) 0.0383 \\ \hline
**3** & 0.9786 \(\pm\) 0.0075 & 0.1931 \(\pm\) 0.0740 & 0.9812 \(\pm\) 0.0070 & 0.1144 \(\pm\) 0.0720 \\ \hline
**4** & 0.9786 \(\pm\) 0.0042 & 0.1975 \(\pm\) 0.0399 & 0.9789 \(\pm\) 0.0067 & 0.1281 \(\pm\) 0.0363 \\ \hline
**5** & 0.9764 \(\pm\) 0.0071 & 0.1706 \(\pm\) 0.0496 & 0.9751 \(\pm\) 0.0072 & 0.1003 \(\pm\) 0.0388 \\ \hline
**6** & 0.9602 \(\pm\) 0.0104 & 0.2042 \(\pm\) 0.0478 & 0.9617 \(\pm\) 0.0109 & 0.1094 \(\pm\) 0.0256 \\ \hline
**7** & 0.9831 \(\pm\) 0.0064 & 0.1550 \(\pm\) 0.0599 & 0.9912 \(\pm\) 0.0046 & 0.0733 \(\pm\) 0.0442 \\ \hline
**8** & 0.9759 \(\pm\) 0.0052 & 0.2225 \(\pm\) 0.0492 & 0.9920 \(\pm\) 0.0049 & 0.0725 \(\pm\) 0.0045 \\ \hline
**9** & 0.9812 \(\pm\) 0.0058 & 0.1725 \(\pm\) 0.0546 & 0.9920 \(\pm\) 0.0047 & 0.0725 \(\pm\) 0.0432 \\ \hline
**10** & 0.9807 \(\pm\) 0.0054 & 0.1775 \(\pm\) 0.0506 & 0.9844 \(\pm\) 0.0033 & 0.0844 \(\pm\) 0.0254 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Authentication test results of 10 subjects (Pattern 1).