esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to improve the accuracy and reliability of an existing rutting performance prediction model, based on the long-term observation data of the RIOHTrack's full-scale pavement structure, the rutting performance prediction model in China's Specifications for Design of Highway Asphalt Pavement was evaluated, and the model correction method was proposed, which improves the model's reliability and makes it more suitable for rutting estimation in the region. The research found that the rutting model in China's Specifications for Design of Highway Asphalt Pavement has significant structural dependence. The model with the highest prediction accuracy and the smallest error is the semi-rigid base asphalt pavement structure with an asphalt concrete layer thickness of 12 cm; the prediction accuracy of other structures is not high. In order to improve the accuracy and reliability of the rutting prediction model, a new model is established by introducing local correction coefficients into the existing model. After local correction, the accuracy of the rutting prediction models for all structures has been greatly improved, and the determination coefficient \(R^{2}\) is greater than 0.87. Since the basic data has already reflected the characteristics of different pavement structures and materials, as well as the impact of local climate environment and traffic load conditions, the new model is more suitable for rutting prediction of various pavement structures in the region where the RIOHTrack is located.  
Keywords:rutting performance prediction model; long-term observation data; full-scale pavement structure; model accuracy evaluation; local correction coefficient; rutting prediction +
Footnote †: journal:  
Article Evaluation and Correction Method of Asphalt Pavement Rutting Performance Prediction Model Based on RIOHTrack Long-Term Observation Data
## 2 Experimental Equipment Construction and Thermal Deformation Measurement
### Governing Equations of Spindle Cooling Flow  
In this study, the finite volume method and fluid-solid coupling calculations were applied to simulate the flow field in the cooling channel, temperature distributions, and corresponding stress and strain of the spindle.  
To maintain the uniformity of flow field inside the spindle cooling channel, the flow velocity is low, and the fluid in the flow field is assumed to be an incompressible fluid. To simplify the model, Navier-Stokes equations and the energy equation of the three-dimensional incompressible fluid can be given as  
\[\begin{array}{c}\frac{\partial U_{j}}{\partial x_{j}}=0\,\ j=1,2,3\\ \frac{\partial(U_{j}U_{j})}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial P}{ \partial x_{j}}-g_{i}\beta(T-T_{\infty})+\frac{\partial}{\partial x_{j}}\left( v\frac{\partial U_{i}}{\partial x_{j}}-\overline{u_{i}u_{j}}\right),\ \text{and}\\ \frac{\partial(TU_{j})}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\left( \frac{v}{P_{T}}\frac{\partial T}{\partial x_{j}}-\overline{Tnu_{j}}\right) \end{array} \tag{11}\]  
where \(U_{j}\), \(T\), and \(P\) are the average velocity, average temperature, and average pressure, respectively. Using the Boussinesq assumption, the buoyancy effect is considered, and \(\beta\) is the thermal expansion coefficient. With the turbulent closure model, the turbulent stress \(-\overline{u_{i}u_{j}}\) and turbulent heat flux \(-\overline{T^{\prime}u_{j}}\) can be obtained by the following equation:  
\[-\overline{u_{i}u_{j}}=2v_{t}S_{ij}-\frac{2}{3}k\delta_{ij}-\overline{T^{ \prime}u_{j}}=\frac{v_{t}}{Pr_{t}}\left(\frac{\partial T}{\partial x_{j}} \right), \tag{12}\]  
where \(v_{t}\) and \(\mathrm{Pr}_{t}\) are the turbulent kinematic viscosity and turbulent Prandtl number, respectively. \(k\) is the kinetic turbulent energy, while \(k=(u_{x}^{2}+u_{y}^{2}+u_{z}^{2})/3\) and \(\delta_{ij}\) is Kronecker function. The turbulent closure problem is computed by the \(k\)-\(\varepsilon\) model. The turbulent stress and turbulent heat flux can be obtained by the kinetic turbulent energy equation and turbulent dissipation equation. The commercial software, ANSYS Fluent, was adopted in the simulations of the flow field, temperature field, and corresponding stress and strain in the computational domain.  
The environmental temperature is constant, and the coefficient of heat convection can be obtained from \(h=1.31(\Delta T)^{0.33}\), where \(\Delta T\) is the temperature difference, and L is the characteristic length [26].
## 2 System Model and Problem Formulation
### Problem Formulation  
Consider an MEC-assisted satellite network with multiple satellites and multiple users, where satellites have different resource capacities. Generally, the users prefer satellites with more resources, which ensures that the tasks can be completed within the maximum tolerable delay as much as possible. The ground users should offload the tasks to the satellites to complete the computation, and need to pay the corresponding computation cost to the satellites. Meanwhile, the satellites can obtain revenue by assisting the users with the task computation.  
According to [35], we introduce the service satisfaction index \(s_{n,m}\) to formulate the relationships between satellites and ground users, which expresses the satisfaction of satellite \(n\) with the service benefits of user \(m\). The service satisfaction of the satellite for different users' tasks depends on the service revenue and the computation cost, where the cost is given by the energy consumption associated with the tasks. Then, we can obtain the energy consumption cost as follows,  
\[s_{n,m}^{E}=1-e^{\theta_{e}E_{n}^{m}}, \tag{6}\]  
where \(\theta_{e}\) denotes the energy consumption factor and \(E_{n}^{m}\) denotes the total energy consumption to complete the tasks. Considering the revenue obtained from user \(m\), the revenue of the satellite is given by,  
\[s_{n,m}^{R}=1+e^{\theta_{r}R_{n}^{m}}, \tag{7}\]  
where \(\theta_{r}\) denotes the income coefficient and \(R_{n}^{m}\) denotes the income from completed tasks. Therefore, the overall service satisfaction of satellites can be described as,  
\[s_{n,m}=s_{n,m}^{E}+s_{n,m}^{R}, \tag{8}\]  
Based on the above equation, the problem of maximizing satisfaction for the satellites can be formulated as follows,\[\max_{x,R}\left(\sum_{i=1}^{M}\sum_{j=1}^{N}s_{i,j}\right)\] \[\text{s.t.}\ C1:T_{i}\leq T_{m},i\in M,\] \[\ C2:\sum_{i=1}^{n}F_{ij}\leq C_{j}^{max},i\in M,j\in N,\] \[\ C3:\sum_{i=1}^{n}D_{ij}\leq H_{j}^{max},i\in M,j\in N, \tag{9}\] \[\ C4:\sum_{i=1}^{n}x_{ij}\leq L_{j}^{max},i\in M,j\in N,\] \[\ C5:\sum_{j=1}^{M}x_{ij}\leq 1,j\in N,\] \[\ C6:x_{ij}\in\{0,1\},i\in M,j\in N.\]  
where constraint \(C1\) denotes the delay constraint of the tasks, constraint \(C2\) denotes the computational resource constraint of the satellites, constraint \(C3\) denotes the cache resource constraint of the satellites, constraint \(C4\) denotes the communication resource constraint of the satellites, constraint \(C5\) denotes that each task is served by at most one satellite, and constraint \(C6\) denotes the variable constraint.
## 2 Materials and Methods
### Data Processing  
* To qualitatively evaluate the reliability of the SHM system, postprocessing is applied on output data provided by: Two different sensor layouts are used: on the PNL1 panel, the fiber optic path considers both the two stringers perimeter (dotted curve in Figure 7a). For the PNL 2 panel only one stringer cap is sensorized and a sinusoidal (arbitrarily selected) path on the skin in the region between the two stringers is proposed (Figure 7b).
* Two different post-impact solicitations: unloaded condition for PNL1 and quasi-static three point bending for the PNL2.  
Sensor bonding procedure is another crucial point for a reliable measurement process. From previous work, of the author [34], the influence of different adhesives on fiber optic sensors are investigated. The results showed that at room temperature for low strain values (<0.5%) and quasi-static excitation, the sensor fixed by cyanoacrylate and two other epoxy resins with different viscosity, has a high linearity, with a linear correlation coefficient of 0.9996. In this current application, since the strain amplitude is expected to be lower than 0.1%, and static residual strain is considered, a M-Bond 200 modified acrylate is used. On the other hand, attention is mostly paid to provide a very thin bond line at the interface and enough glue around the fiber to improve the strain transfer [35; 36]. In addition, a second thicker layer of adhesive in combination to Kapton tape was used to seal and protect the outer surface of the fiber optic just during the impact to prevent accidental breakage or debonding. A double check of the bonding line integrity can be done by looking for unexpected strain values dropping to zero, from the output map provided by the interrogator.  
In addition, fiber optic sensor has a pig tail and a transition zone of protection loose tube being the first 20 cm of its length from the connector. When a temperature compensation is required, this transition zone, being a strain free section, is used to estimate the thermo-optic effect to be off-line subtracted to the mechanical effect. In this current activity, the measures are performed at constant RT, so no temperature compensation is applied.  
Figure 6: Scan pattern section view (upper) and scan setup (lower).
# Characterization and Crystallinity of Two Bioactive Sealers: Qualitative and Quantitative Analysis
###### Abstract  
Since the crystallinity of hydroxyapatite created by bioactive sealers could affect their solubility percentage, this study aimed to analyze the degree of crystallinity and mineral maturity of hydroxyapatite crystals formed by bioactive (Cerafil and Endosequence) root canal sealers. Set discs of each sealer were submerged, either in deionized water or phosphate buffer solution (PBS). After 30 days, the crystallinity indices, crystal size, and mineral maturity were determined. The data were statistically analyzed using ANOVA and Student's \(t\) tests with significance set at \(p<0.05\). After immersion in PBS, Endosequence had the most significant value of mineral maturity (\(1030/1110_{\text{Raman}}\)) and \(\text{PO}_{4}/\text{amide}\)\(\text{IFIF}\) ratio. However, the \(\text{CO}_{3}/\text{PO}_{4}\) ratio was reduced by both solutions, particularly by PBS. There was no significant difference between both bioactive sealers. Compared to Endosequence, Cerafil had the highest crystallinity indices for \(\text{Cl}_{\text{XRD}}\), \(\text{LF}_{\text{IFIF}}\), and \(\text{CI}_{\text{Raman}}\). AH-26 had a significantly greater crystal size (\(p<0.001\)). There was no significant difference in the crystal size of the Cerafil and Endosequence bioactive sealers (\(p>0.05\)). Overall, Cerafil and Endosequence successfully formed hydroxyapatite crystals when exposed to PBS, with high CIs obtained by Cerafil. The crystallinity and mineral maturity of Cerafil and Endosequence were comparable. On the other hand, AH-26 failed to produce hydroxyapatite.  
bioceramic root canal sealers; crystallinity; mineral maturity; bioactivity; characterization +
Footnote †: journal: Applied  
141285
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Content Popularity Prediction Based on Temporal Convolutional Networks  
In previous work, most studies have assumed by default that content popularity obeys a Zipf distribution [30; 31; 32]. In practice, due to the high-velocity mobility of vehicles and time-varying user demand, content popularity characteristics are difficult to capture in a timely manner and can only be predicted from historical information. Thus, this paper proposes a prediction method based on TCN. The TCN model consists of a content feature prediction module and a content popularity assessment module. After the content features are captured by the predictor module, the popularity assessment module will provide a weighted average of the past popularity data, thereby predicting the future popularity of the content, achieving a balance between long-term and short-term burst memory.
# Machine Learning Regression Model for Predicting Honey Harvestis
###### Abstract  
Honey yield from apiary sites varies significantly between years. This affects the beekeeper's ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarvores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, _Corymbia calophyllla_ (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services.  
remote sensing; weather; _Corymbia calophyllla_; honey; machine learning; prediction 2020, 10, 118; doi:10.3390/agriculture10040118
## 3 Results
### Niche Metrics and Diet of Anguilla anguilla
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**LP**} & \multicolumn{2}{c}{**IP**} & \multicolumn{2}{c}{**HP**} \\ \hline
**Food Sources** & **Taxa** & **Contribution** & **Taxa** & **Contribution** & **Taxa** & **Contribution** \\ \hline ECHINDERMS & & & & & & \\ Eleutherozoa (Asteroidea) & 1 & \(0.29\pm 0.10\) & - & - & - & - \\ Eueuchinoidea (Echinoidea) & 1 & \(4.40\pm 0.10\) & - & - & - & - \\ Ophinuidea & - & - & 1 & \(2.01\pm 0.10\) & - & - \\ ARTHROPODS & & & & & \\ Insecta & - & - & 1 & \(1.42\pm 0.10\) & 1 & \(1.62\pm 0.10\) \\ Malacostraca & & & & & \\ Amphipoda & 4 & \(4.74\pm 0.30\) & 4 & \(5.10\pm 0.40\) & 2 & \(4.13\pm 0.50\) \\ Decapoda & 4 & \(27.84\pm 4.60\) & 7 & \(26.13\pm 1.90\) & 2 & \(9.11\pm 1.90\) \\ Isopeda & 2 & \(1.15\pm 1.70\) & 4 & \(5.78\pm 0.30\) & - & - \\ NEMERTEANS & & & & & \\ Nemetea & - & - & 1 & \(1.08\pm 0.10\) & 1 & \(1.91\pm 0.10\) \\ BASAL RESOUCCES & & \(3.31\pm 0.35\) \({}^{\mathrm{a}}\) & & \(10.43\pm 1.54\) \({}^{\mathrm{ab}}\) & & \(17.87\pm 1.60\)\({}^{\mathrm{b}}\) \\ INVERTEBRATES & & \(61.93\pm 2.92\) & & \(58.93\pm 2.15\) & & \(69.96\pm 2.56\) \\ FISH & & \(34.76\pm 1.90\) \({}^{\mathrm{a}}\) & & \(30.65\pm 0.50\) \({}^{\mathrm{a}}\) & & \(16.18\pm 1.50\)\({}^{\mathrm{b}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 6: _Anguilla anguilla_ and _Diplodus annularis_ food webs in lakes with low (LP), intermediate (IP) and high (HP) eutrophication. Each node at the base of the food web represents a food source (in terms of class and respective families). Arrows point from each food item to its consumer. _Anguilla anguilla_ (black arrows) and _Diplodus annularis_ (grey arrows). The arrows’ thickness is proportional to the trophic interaction strength. TNW indicates trophic niche width. \(O\) indicates the niche overlap between \(D\). _annularis_ and _A. anguilla._ For details of metrics, please refer to the results section. The difference in resource use was associated with a difference in trophic niche width (Figures 2-6), which increased with eutrophication (TNW: 1.81, 2.06 and 2.32 in LP, IP and HP respectively), with significant differences between HP and LP (bootstrap comparison among populations, \(p\) <0.0001).
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Proposed Architecture
#### 3.1.1 Cloud Layer  
The cloud layer represents the core of the system and it is responsible of several global tasks. It acquires data from lower layers to analyze and update road conditions in a real-time way. This step allows ITS to improve the decision making process. The output is the global optimization of traffic distribution along available roads served by the system. Moreover, ITS is responsible to address emergencies that are reported from lower layers by arranging paths of rescue teams and allowing them to reach destinations in a short time.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 2 Control Design
### Dynamics of aHydraulic System  
The hydraulic robot manipulator is shown in Figure 1. This system consists of two hydraulic operated links, and one link is servo motor operated. The last link of the robot manipulator is used for simulation and experimental purposes. The dynamics of this third link is extracted from the signal compression method [25] which used an equivalent compressed signal to identify the system's linear parameters.  
The schematic diagram of the third link of the robot manipulator is shown in Figure 2, and its mathematical model is shown below.  
\[(J+\Delta J)\cdot\ddot{\theta}+(B+\Delta B+\varphi)\cdot\dot{\theta}+0.5\cdot M \cdot g\cdot L\cdot sin\theta+D=F\cdot L \tag{1}\]  
In the equation above, \(F\) is the force applied by the hydraulic cylinder, L is the length of the link, \(J\) and \(\Delta J\) are inertia terms and their uncertainty, whereas B and \(\Delta B\) are damper terms and its uncertainty. D is the external disturbance of the environment. \(\varphi\) is the viscosity term of the cylinder. The perturbation is defined below:  
\[\psi=\Delta J+\Delta B+D+\varphi \tag{2}\]  
Figure 1: Hydraulic robot manipulator.  
Figure 2: Third link schematic diagram.  
After solving further, the following equation is obtained:  
\[\ddot{\theta}=\frac{1}{\dot{f}}(u-B*\dot{\theta}+\psi) \tag{3}\]  
where \(\psi\) is the perturbation which is defined in Equation (2). The other parameters of Equation (1) were identified by the signal compression method. The values of the linear parameters are shown below in Table 1.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
###### Abstract  
With the popularization of Wi-Fi router devices, the application of device-free sensing has garnered significant attention due to its potential to make our lives more convenient. Wi-Fi signal-based through-the-wall human detection offers practical applications, such as emergency rescue and elderly monitoring. However, the accuracy of through-the-wall human detection is hindered by signal attenuation caused by wall materials and multiple propagation paths of interference. Therefore, through-the-wall human detection presents a substantial challenge. In this paper, we proposed a highly robust through-the-wall human detection method based on a commercial Wi-Fi device (TwSense). To mitigate interference from wall materials and other environmental factors, we employed the robust principal component analysis (OR-PCA) method to extract the target signal of Channel State Information (CSI). Subsequently, we segmented the action-induced Doppler shift feature image using the K-means clustering method. The features of the images were extracted using the Histogram of Oriented Gradients (HOG) algorithm. Finally, these features were fed into an SVM classifier (G-SVM) optimized by a grid search algorithm for action classification and recognition, thereby enhancing human detection accuracy. We evaluated the robustness of the entire system. The experimental results demonstrated that TwSense achieved the highest accuracy of 96%.  
Wi-Fi; through-the-wall; human detection; channel state information; device-free +
Footnote †: journal: Applied Sciences
## 4 Challenges and Limitations of Nanosensor Deployment
### Signal Processing and Data Interpretation
#### 4.1.2 High-Dimensional Data  
Nanosensors tend to generate high-dimensional data, which can be computationally intensive to process and manage, thereby demanding more robust data management and storage solutions. Machine learning (ML) models and artificial intelligence (AI) algorithms tailored to navigate through this high-dimensional space are pivotal to efficiently extract meaningful insights and predict potential structural deteriorations [88,89].  
Developing robust algorithms for real-time data analysis and decision-making is crucial for effective bridge maintenance. Additionally, the interpretation of sensor data should consider the context of the bridge's design, materials, and operating conditions [90,91].
## 2 Technologies in DTx
### Data Analysis  
Digital therapeutics require an accurate data analysis in both the development and service steps, as shown in Figure 2. Since DTx utilizes medical datasets, a high rate of sensitivity and specificity performance is required from the data analysis. For example, during the development stage of DTx, it is necessary to analyze the processed research data to generate algorithms for diagnosis, classification, and treatment recommendation. Moreover, from the perspective of service users who wish to use DTx, they expect accurate analysis results on their medical condition and optimally personalized treatments based on their data. Therefore, AI technology using various machine learning techniques is applied to each step.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 1 Introduction
Feature extraction refers to constructing some features via mapping the useful information contained in the original feature to a lower dimension feature space [24]. There are many ways to extract features such as data mining techniques, statistical feature extraction techniques, and neural network methods. Traditional feature extraction methods are employed to condition monitoring, including empirical mode decomposition [25], linear discriminant analysis (LDA) [26], and locality preserving projection (LPP) [27]. These feature extraction methods are proven to be effective in improving the accuracy of condition monitoring when data noise is high. For monitoring the 3D printer more effectively, it is necessary to extract features from raw data before ESN modeling. Correspondingly, a technique based on the echo state network is presented in this paper to monitor the transmission system of 3D printers. The rest of this paper is organized as follows. The proposed method is introduced in Section 2. Section 3 presents the details of the experiment using the proposed method for 3D printer monitoring. The results and discussion and the conclusion are reported in Sections 4 and 5, respectively.