esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance
## 2 Materials and Methods
### Pavement Surface Texture Characterization  
The 3D evaluation parameters of rock surface roughness are various, including arithmetic mean deviation, maximum height, contour root mean square deviation, contour maximum peak height, contour maximum valley depth, contour support area, curved surface slope, etc., up to 17 evaluation parameters [33]. For the 3D geometric features characterization of a rough surface, it is often necessary to select several representative parameters for evaluation, which may result in a comparative one-sided evaluation, and not a comprehensive or accurate characterization of the rough surface.  
[PERSON] et al. proposed five typical 3D evaluation parameters to quantitatively characterize the rock joint features, including mean of elementary inclination angle over the whole surface (\(\theta_{s}\)), root-mean-square of the joint surface gradient (\(Z_{2s}\)), apparent anisotropy degree (\(K_{a}\)), surface roughness coefficient (\(R_{s}\)), and surface tortuosity coefficient (\(T_{s}\)) [34]. These parameters were not affected by the direction of the axis, which could accurately and stably reflect the geometric characteristics of the 3D rock joint surface. In addition, the sensitivity of the five parameters to the changes in surface geometry was different [35]. Therefore, the parameters had convenient applicability.  
This paper selected the five parameters to characterize pavement texture. SI was used to evaluation skid resistance, and its calculation method was according to previous research [20]. Test specimens were slabs with a geometric size of 300 mm (length) \(\times\) 300 mm (width) \(\times\) 50 mm (height) produced according to Chinese specification JTG E20-2011. There were three specimens for SMA pavement with the same mixture type and gradation. Due to the large amount of data acquired by the images, it had a significant impact on the calculation speed and efficiency. There was a significant difference in the aggregate size, so the calculated area was selected according to the mixture types. The analysis area of SMA-16 and SMA-13 was about 100 mm (length) \(\times\) 100 mm (width), while for SMA-10 and SMA-5, the analysis area was 55 mm(length) \(\times\) 55 mm(width). Each specimen was tested three times in different places.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 4 Results and Discussion
### Economic Effect--Quantified Analysis  
1. Reduced accident costs: the economic impact of drowsy driving is significant, with the U.S. National Highway Traffic Safety Administration (NHTSA) reporting nearly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual State**} \\ \cline{3-4}  & **Total = 500** & **Drowsy** & **Not Drowsy** \\  & & **18** & **482** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Category**  
100,000 road accidents and 1500 deaths annually attributed to drowsy driving. Although the exact role of drowsiness is challenging to quantify due to underreporting, studies by the AAA Foundation for Traffic Safety (AAAFTS) suggest that driver drowsiness may contribute to 21% of fatal crashes. Based on 39,508 fatal crashes in 2021, this implies that over 8300 deaths could be linked to drowsy driving annually. Financially, motor vehicle crashes in the United States result in an estimated USD 242 billion in economic costs each year (NHTSA). If 6% of all crashes are due to drowsiness, this equates to approximately UDS 14.5 billion annually. Preventing just 10% of these drowsy driving-related crashes through timely detection and intervention could save USD 1.45 billion per year.
2. Cost efficiency: the proposed drowsiness detection system achieves cost efficiency by leveraging existing technologies like cameras and computationally lightweight algorithms, avoiding high-end sensors or expensive deep learning frameworks. The absence of training requirements for the algorithms reduces implementation costs while ensuring real-time processing capability. In short, lower implementation costs due to reduced reliance on high-end sensors and training;
3. Numerical benefits: i. Accident reduction: if the system is implemented in 1% of vehicles in the U.S. (approximately 2.76 million vehicles based on 276 million registered vehicles), and assuming each system prevents one drowsy-driving-related crash per year, the potential reduction is 27,600 crashes annually; ii. Economic savings: at an average cost of USD 14,000 per crash (considering vehicle damage, medical costs, and productivity losses), this could save UDS 386.4 million per year; iii. Scalability: with widespread adoption (for instance, 10% of vehicles equipped), annual savings could exceed USD 3.86 billion.
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Limonene  
Limonene (1-methyl-4-prop-1-en-2-ylcyclohexene) is a monocyclic monoterpene hydrocarbon, a cyclohex-1-ene substituted by a methyl group at position 1 and a prop-1-en-2-yl group at position 4, respectively. Given that limonene has a carbon chiral center at position 4, it occurs as two optical isomers: D-limonene or [R-(+)-isomer] and L-limonene or [S-(+)-isomer]. Of the two, D-limonene (also called \"citrene\" or \"carvene\") is the most abundant in plants and it is typical of _Rutaceae_ such as orange, lemon, mandarin, etc. On the other hand, L-limonene is far less common and it can be found in essential oils obtained from _Pinus_ spp., _Illicium verum_, and _Mentha_ spp., while the D-L mixture (dipentene) is even less common [53; 105]. Table 3 reports average contents in D-limonene of some essential oils derived from trees [106].
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 2 Background Review and Related Works
### Related Works
The LoRa and wMBus wireless protocols are used to communicate with terminal devices, and NB-IoT and GSM cellular are used to connect them to the cloud [30], a suggested method for providing real-time data for smart cities based on IoT and Wireless Sensor Network. The current method relies solely on wireless technology, which is limited in range and consumes more power. They switched to LoRaWAN, which allowed them to cover a greater distance than Wi-Fi. [PERSON] et al. [31] proposed IOTA, a new distributed ledger system for the Internet of Things. It is based on a directed acyclic graph. IOTA circumvents the blockchain's present restrictions by providing a data communication protocol called masked authenticated messaging that enables safe data sharing between Internet of Things (IoT) devices. To make our study different, we implemented LoRaWAN architecture on wide areas in real-time at Tunghai University. We also compared the performance between the LoRa module and WiFi, such as in terms of energy use. For the LoRa's performance, we evaluated more details on receiver sensitivity data collection in the 915 kHz channel, receiver sensitivity data collection in the 868 kHz bands, the actual results of sensitivity receiving, and the average throughput.
## 5 Conclusions  
This study has presented a new CL-DLBIDC approach to detect and classify intent for natural language understanding. The CL-DLBIDC technique receives word-embedding as an input and uses learned meaningful features to determine the probable intention of the user query. In addition, the CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the DLMNN model for intent detection and classification. For the hyperparameter tuning process, the MFO algorithm was used in this study. The experimental analysis of the CL-DLBIDC algorithm took place under a set of simulations, and different aspects of the results have been scrutinized. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC method over other DL models, with a maximum accuracy of 99.51%. The enhanced performance of the proposed model is due to the hyperparameter tuning process using the MFO algorithm. In the future, hybrid DL models and the feature-selection process can improve the detection efficiency of the proposed model.  
Conceptualization, H.J.A.; Data curation, K.T.; Formal analysis, K.T.; Funding acquisition, M.A.H.; Investigation, H.A.; Methodology, H.J.A., H.A. and M.A.H.; Project administration, M.A.E. and M.A.H.; Resources, M.A.E. and A.Y.; Software, A.Y. and R.A.; Supervision, R.A.; Validation, O.A.; Visualization, O.A.; Writing--original draft, H.J.A.; Writing--review & editing, M.A.H. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R281), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Unm Al-Qura University for supporting this work with Grant Code: (22 UQU4331004 DSR01).  
This article does not contain any studies with human participants performed by any of the authors.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written with the contributions of all authors. All authors have given approval to the final version of the manuscript.  
Figure 9: \(F1_{score}\) analysis of the CL-DLBIDC approach with recent algorithms.
# Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro's Lagoon
## 2 Advancements in Maritime Technologies: Enhancing Navigational Safety and Communication
In conclusion, the theoretical framework presented in this paper identifies a significant gap in the existing solutions for real-time navigational support in maritime environments. Current technologies often have high costs and maintenance is complex, making them less accessible and practical for widespread adoption. Consequently, there is a critical need for innovative solutions that are affordable, user-friendly, and robust to enhance maritime safety, in both coastal and inland waters. The proposed system is designed to be affordable, easy to use and robust for navigational safety. It is intended to serve as a new standard in this field, exemplified by the unique challenges and opportunities presented by the Ria de Aveiro.
# Array Gain of a Linear Array in an Ocean Waveguide
## 4 Numerical Simulation Results and Discussion
### Array Gain When the Coherence Decreases Exponentially  
The coherent coefficient is \(\rho_{ik}=\rho^{|i-k|}\) (\(\rho\) is the coherence between the acoustic channels at the adjacent elements), which is the same as in ref. [6]. Here, two assumptions are made in the simulation: (1) the \(ATL\)s' fluctuation over the elements follows the standard normal distribution; (2) the ambient noise powers are equal and uncorrelated for all pairs of elements. The \(AG_{\text{U}}\) (colorful solid lines) and \(AG_{\text{W}}\) (colorful dash lines) as functions of the element number are plotted in Figure 1, where \(\rho\) is equal to 0.99, 0.95, 0.90 and 0.80, respectively. For comparison, the ideal value of \(AG\) (\(10\log_{10}M\)) is shown by the black solid line. An examination of Figure 1 shows that \(AG_{\text{U}}\) is always larger than \(AG_{\text{W}}\) for a given \(\rho\). Taking \(\rho=0.95\) as an example, the gain of a 200-element array is 15.47 dB for \(AG_{\text{U}}\), while the corresponding gain for \(AG_{\text{W}}\) is 14.78 dB for a reduction of 0.69 dB. However, due to the coherence degradation, both \(AG_{\text{U}}\) and \(AG_{\text{W}}\) are lower than the ideal value of AG.  
It should be noted that the red dash line (\(\rho=0.99\) for \(AG_{\text{W}}\)) intersects with the blue solid line (\(\rho=0.95\) for \(AG_{\text{U}}\)) when the element number is 10, as shown by the zoomed-in figure in the upper-left corner of Figure 1. Before the intersection point, \(AG_{\text{U}}\) is larger than or equal to \(AG_{\text{W}}\), even though the corresponding coherence for \(AG_{\text{U}}\) is less than that for \(AG_{\text{W}}\). This illustrates that \(AG\) for the weak coherence but without \(ATL\)'s fluctuation may be smaller than that with strong coherence but large fluctuation of \(ATL\). In other words, the propagation loss also affects AG, which should not be ignored in the ocean waveguide.
## 2 Geometric Modeling Approaches
### Geometric Modeling of a Rough-Surface Vehicle over Snow-Covered Soil
#### 2.2.2 Geometric Modeling of Snow-Covered Soil Surface  
For one-dimensional rough surfaces, the Monte Carlo method [22] is commonly used for simulation. In this study, the Monte Carlo method is applied to model both the snow  
Figure 4: The rough-surface vehicle model.  
Figure 5: The point sampling results of the vehicle.  
Figure 3: The CAD model of the rough-surface vehicle.  
layer surface and the underlying soil surface. The length of the randomly generated rough surface is defined as \(L\), which is discretized into \(n\) equal segments on average. The spacing between adjacent discrete points is \(\Delta x\). The \(n\)-th discrete point on the surface can be expressed as \(x_{n}=n\Delta x\). The expression for the simulated rough surface profile is given in Equation (1).  
\[f(x_{n})=\frac{1}{L}\sum_{j=-N/2}^{i=N/2}F(K_{j})exp(iK_{j}x_{n}) \tag{1}\]  
\(f(x_{n})\) represent the profile of the simulated rough surface. Its spatial Fourier transform is denoted as \(f(k_{j})\), which is defined in Equations (2) and (3).  
\[f(k_{j})=\frac{2\pi}{\sqrt{2\Delta K}}\sqrt{W(K_{j})}\cdot N(0,1)+iN(0,1),j>0 \tag{2}\]  
\[f(k_{j})=\frac{2\pi}{\sqrt{2\Delta K}}\sqrt{W(K_{j})}\cdot N(0,1),j=0 \tag{3}\]  
The roughness height in the spatial domain is denoted as \(f(x_{n})\), while \(f(k_{j})\) and the Fourier coefficient \(F(K_{j})\) represent its frequency domain counterparts, where \(k_{j}\) denotes the \(j\)-th frequency component and the power spectral density is represented by \(W(K_{j})\). The frequency component \(\Delta K\) is a randomly distributed value. The random variable \(N(0,\,1)\) follows a normal distribution with a mean of \(0\) and a variance of \(1\). To reduce spectral aliasing when \(j<0\), a conjugate symmetry condition is applied so that \(F(K_{j})=F(K_{-j})^{*}\). The power spectral density of the rough surface is given in Equation (4).  
\[W(K)=\sqrt{2\pi}\delta_{h}^{2}l^{2}(1+K^{2}l^{2})^{-\frac{3}{2}} \tag{4}\]
## 4 Experiment and analysis
### Simulation Result Analysis
#### 4.2.1 Performance of MSSN Algorithm under Different Mobile Similarity Thresholds
However, when the mobile similarity threshold continues to increase from 0.55, the average cost shows a certain upward trend, but the rate of increase is relatively slow. That's because when the mobile similarity threshold is around 0.55, we no longer consider the neighbor nodes that are not suitable as reliable relay nodes because of the low mobile similarity between them and the destination node, thus reducing the memory and processing resource overhead needed in computing. At the same time, we can get more reliable relay nodes, which increases the success rate and speed of destination node to get the message, and thus reduces the average network overhead. And when the mobile similarity threshold continues to increase on the basis of 0.55, as the threshold of most nodes is lower than this value, the range of neighbor nodes from which we choose reliable relay nodes decreases sharply, so that the number of reliable relay nodes we can obtain decreases and the overhead on average increases. Figure 6: Average end-to-end delay with various mobile similarity threshold.
## 6 Conclusions
\begin{tabular}{l l} \(A_{i}\) & \(i\)-th controller strategy \\ DOFs & Degrees of freedom \\ GA & Genetic algorithm \\ HIL & Hardware-in-the-loop simulation \\ MAST & Multi-axis simulation table \\ PSD & Power spectral density function \\ QvM & Quarter vehicle model \\ SEAT & Seat Effective Amplitude Transmissibility factor \\ TLVs & Daily threshold limit values \\
**Symbol Description** \\ \(a_{v}\) & Total frequency-weighted RMS acceleration \\ \(a_{w}\) & Frequency-weighted acceleration \\ \(W\) & Frequency-weighting filter \\ \(c_{b}\) & Damping coefficient of the driver's body \\ \(c_{s}\) & Damping coefficient of the vehicle suspension \\ \(c_{se}\) & Damping coefficient of the seat suspension \\ \(f\) & Optimization objective function \\ \(F_{a}\) & Actuator control force \\ \(F_{se}\) & Passive seat suspension force \\ \(g(1)\) & Seat stroke constraints \\ \(g(2)\) & Actuator capacity force constraints \\ \(\dot{\triangle}\) & Estimated dynamics of the MAST \\ \(J\) & Final optimization objective function \\ \(k_{b}\) & Stiffness of the driver's body \\ \(k_{s}\) & Stiffness of the vehicle suspension \\ \(k_{se}\) & Stiffness of the seat suspension \\ \(k_{t}\) & Stiffness of the tyre \\ \(m_{b}\) & Driver body mass \\ \(m_{s}\) & Sprung mass in the qvm \\ \(m_{se}\) & Total mass of the seat and driver \\ \(m_{uss}\) & Unsprung mass in the qvm \\ \(PG\) & Penalty function \\ V & Forward speed of the vehicle \\ \(x_{r}\) & Vertical road excitation to the qvm \\ \(x_{rel}\) & Relative displacement of the vehicle suspension \\ \(x_{s}\) & Vertical displacement of the sprung mass \\ \(x_{sc,max}\) & Maximum allowable seat stroke \\ \(x_{sc,min}\) & Minimum allowable seat stroke \\ \(x_{se}\) & Vertical displacement of the seat \\ \(x_{uss}\) & Vertical displacement of the unsprung mass \\ \end{tabular}
## 4 Experiments
### Result Analysis  
We have verified the effectiveness of an intelligent penetration test environment extended by social engineering factors and explored the performance of different agents in variable-scale network scenarios. The results of experiments A and B show that in the scenarios with social engineering, all three agents could complete the penetration test goal with fewer steps and reach the maximum reward value. DQL shows better performance among the three algorithms and stability. Through analysis of the process of the penetration test, the penetration tester agent carries out social engineering actions of  
Figure 14: The result of experiment C. (**a**) Variation of the average cumulative reward under the CyberBattleChain-50 test scenario. (**b**) Variation of the average cumulative reward under the CyberBattleChain-80 test scenario. (**c**) Variation of the average cumulative reward under the CyberBattleChain-100 test scenario.  
the people in the social network containing the key information of the host to obtain the connection credentials of the target host in another more effective way, and finally, achieve the goal of obtaining the flag with fewer action sequences. The penetration test simulation environment with extended social engineering factors reasonably models the behavior of non-traditional social engineering methods in the penetration test process. The SE-AIPT method we proposed has been proven to be universal and effective in improving the authenticity of simulated environment construction.  
Based on experiments A and B, in order to further verify the transferability of the DQL algorithm, the results of experiment C show that when the test network scale expands to a certain order of magnitude, social engineering actions would no longer effectively improve the performance of penetration tester agents. As the network scale increases, the number of actions required to finish the target also continues to surge, and even the penetration test target cannot be achieved within the specified number of steps (as shown in Figure 14c). The transferability of the DQL algorithm and the scale of the scene are closely related.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 1 Introduction
If the computational efficiency of the numerical scheme is too low, it will affect the scope of application, such as complex pipe network systems or timely warning of emergency events. Parallel computing is the primary strategy developed in recent years to improve computational efficiency, while the graphics processing unit (GPU) acceleration technology is the direction of rapid development [20; 21; 22; 23]. [PERSON] et al. [24] employed the GPU acceleration technology to establish a high-efficiency and high-precision hydrodynamic model of a rapid movement process of surface water flow for simulating rain and flood in a large-scale watershed resulting in a good acceleration effect. [PERSON] et al. [25] combined the GPU technology with the MOC of a one-dimensional open channel and indicated that the GPU technology could significantly improve the computational efficiency when the number of grids is large. Some scholars applied the GPU technique to solve the system of shallow water equations by taking the WENO scheme and achieved a significant acceleration effect [26; 27]. In this paper, the fifth-order WENO finite volume scheme is applied to a one-dimensional pipeline transient flow system, and a pipeline water hammer wave solution model is established based on GPU acceleration technology. This can provide technical support for the intelligent management of complex pipeline systems and rapid prediction of accident emergency dispatch.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 5 Simulation
### Simulation of Relationship between Area Size and Lifetime  
When the WSN system is deployed in different sizes, the energy consumption varies greatly. We chose a node density of 1 nodes/100 m\({}^{2}\) and then simulate the case where the side length is 200, 300, 400, and 500 m. The simulation results are shown in Figure 10.  
Figure 8: Simulation result, Relationship between node density and lifetime: (**a**) \(100\times 100\) m\({}^{2}\) (100 nodes), low density area; (**b**) \(100\times 100\) m\({}^{2}\) (200 nodes), middle density area; (**c**) \(100\times 100\) m\({}^{2}\) (300 nodes), high density area; (**d**) \(100\times 100\) m\({}^{2}\) (400 nodes), very high density area.  
Figure 9: Simulation result, Network lifetime comparison at different node density.  
At different sizes, the trend of the EEUC protocol is basically the same, that is, the node death is slow at the beginning, and then most of the nodes die quickly. When the area is large, the nodes at the beginning of LEACH and CH-leach will die quickly, and the remaining few nodes in the later stage can work for a long time. In our proposed solution, the system lifetime is longer than the other three options. For example, in the case of 400 m \(\times\) 400 m and 1600 nodes, most of the nodes use up energy in about 150 rounds under the EEUC protocol. The Power Line Connected scheme can fully balance the energy between nodes, and all nodes can work about 580 rounds. Figure 11 is a comparison of lifetime in different area sizes.  
Figure 11: Simulation result, relationship between area size and lifetime.  
Figure 10: Simulation result, network lifetime comparison at different area sizes: (**a**) small area size, (**b**) middle area size, (**c**) large area size, (**d**) very large area size.  
From the comparison results in the above figure, in general, as the size of the area increases, the lifetime of these four schemes decreases. On average, the lifetime of our proposed solution is 2.3, 2.8, and 1.7 times that of LEACH, EEUC, and CH-leach, respectively.