esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Creation of the Database  
Firstly, we proceed to the bibliographic search for publications with experimental results for uniaxial compressive tests on SFRC specimens (cylinders with dimensions of \(150\times 300\) mm\({}^{2}\), diameter \(\times\) height, and steel-fibers with hooked-ends of a single fold), thereby creating a database with a total of 197 tests. The distribution of the number of tests collected for each one of the responsesto be studied is summarized in Table 1. Figure 1 shows the distribution of the data of the principal factors included in the database. Note that all the papers contributing to the database assume that the compressive behavior of the material is isotropic. This is so because the filling of this type of specimen does not lead to any preferential orientation of the fibers, since the fresh material does not flow along the form, nor pass through obstacles like narrow parts or bars.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{N\({}^{\circ}\) Specimens with Fibers} & \multicolumn{2}{c}{N\({}^{\circ}\) Specimens without Fibers} \\ \hline Compressive strength & 167 & 30 \\ Elastic modulus & 83 & 17 \\ Strain under maximum load & 111 & 19 \\ Volumetric deformation work before the peak load & 90 & 14 \\ Volumetric deformation work after the peak load & 73 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Database for uniaxial compression tests in steel-fiber reinforced concrete (SFRC) specimens.  
Figure 1: Distribution of the data of the principal factors included in the database: (**a**) diameter of steel-fiber, \(d_{f}\); (**b**) fiber length, \(\ell_{f}\); (**c**) fiber aspect ratio, \(\lambda\); (**d**) fiber content, \(\phi_{f}\); (**e**) compressive strength of SFRC, \(f_{cf}\); and (**f**) maximum size of the coarse aggregate, \(d_{m}\).
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 1 Introduction
Multipath routing provides many benefits for MANETs and WSNs, including fault tolerance, load balancing, bandwidth aggregation, and reduced delay [4]. Therefore, multipath routing plays an important role in MANETs and WSNs. Several multipath routing protocols have been proposed to provide fault tolerance and load balancing for MANETs and WSNs [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Some of the multipath routing protocols have been proposed based on the AODV protocol, including on-demand multipath distance vector (AOMDV) [8], ad hoc on-demand distance vector multipath (AODVM) [9], and multipath AODV (MP-AODV) [10]. AODV discovers and establishes multiple link-disjoint paths, while AODVM and MP-AODV can set up multiple node-disjoint paths between a source and a destination in a wireless network. However, these multipath routing variants of AODV generate too many duplicates of the control messages in wireless networks. In addition, some of the multipath routing variants use a four-way route discovery process to discover and establish paths, which results in a long route discovery period. To solve the mentioned problems, this paper proposes a novel multipath routing variant of AODV, called node-disjoint multipath routing (NDMR). NDMR is an on-demand node-disjoint multipath routing technique that uses the request-reply mechanism to discover and establish two node-disjoint paths between a source and destination in a wireless network, such as a MANET or a WSN, whenever the source needs to communicate with a destination for which the source has no routing information in its route table. NDMR establishes the two node-disjoint paths by using a two-way or three-way handshake process. Once the two paths are established, the source sends two identical copies of each data frame to the destination through the paths, one frame on each path. By discarding duplicates of control messages and decreasing the number of steps in the route discovery process, NDMR significantly reduces the control overhead as well as the time needed to discover and establish routes compared with existing node-disjoint route discovery approaches, such as AODVM and MP-AODV. Additionally, with its traffic distribution strategy, NDMR can provide seamless communications with fault tolerance for MANETs and WSNs. The rest of this paper is organized as follows: Section 2 contains reports on several related studies. In Section 3, we describe NDMR. In Section 4, the performance of NDMR is analyzed, evaluated, and compared to that of existing approaches. Section 5 contains descriptions of various simulations and their results to evaluate and validate the performance analysis of NDMR. Finally, we provide our conclusions in Section 6.
## 2 Materials and Methods
### Extracting Features of Motion Data
#### 2.3.2 Extracting Features
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Feature** & **Feature ID** & **The Description of the Features** \\ \hline \multirow{5}{*}{Time-domain features} & 1–3 & The mean value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\  & 4–6 & The variance value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\  & 7–9 & The standard deviation value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\  & 10–12 & The skewness value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\  & 13–15 & The kurtosis value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\  & 16–18 & The quartile deviation value of the three vectors of \(\rm x\), \(\rm y\), \(\rm z\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The extracted features in this study. and independent component analysis (ICA) [27]. In this study, PCA was used to reduce the dimensionality of the features. PCA is used for dimensionality reduction to calculate the covariance matrix and its eigenvalues and arrange them according to the size of the eigenvalues [28; 29]. By setting the cumulative variance contribution rate threshold, the eigenvectors corresponding to a small number of eigenvalues are selected, and the projection matrix is formed. Finally, the original high-dimensional data can be projected to the low-dimensional space. This method has been widely used in motion recognition. [PERSON] et al. [22] used IMU to recognize 19 actions in daily life, and the dimension of the extracted feature vector was as high as 1170. They used PCA to reduce the dimension to 30 dimensions from 1170. The steps of dimensional reduction on PCA are as follows:  
* Let the original data be the n-dimensional data of m;
* Form the original data into matrix X with n-rows and m-columns;
* Zero-average each row of matrix X, that is, subtract the average value of this row;
* Calculate the covariance matrix;
* Calculate the eigenvalues of the covariance matrix and the corresponding eigenvector r.  
Sort the eigenvectors row-by-row, from top to bottom, according to the corresponding eigenvalues, into a matrix, and take the first k rows to form a matrix P. The matrix P is the data after dimension reduction to k dimension.
## 3 Materials and Methods
### Image-Based Transformation Techniques
#### 3.3.2 Sine Wave Transformation  
Sine Wave Transformation (SWT) involves generating a sine wave corresponding to the values in the one-dimensional behavior data [37]. With this technique, every data point is used to modulate a sine wave, where the amplitude, frequency, or phase may vary depending on the value. The sine wave generated is then represented as an image. This transformation emphasizes the periodic nature of the behavioral data and can capture recurring patterns or regular intervals in the student's activities. Sine Wave Transformation creates a continuous and smooth image that represents temporal data trends, allowing deep learning models to learn patterns related to periodic behavior changes. This method is especially useful in analyzing sequential data, where periodic learning behaviors may correlate with academic success or struggle.
# Determinants of Top Speed Sprinting: Minimum Requirements for Maximum Velocity
## 2 Materials and Methods  
The framework presented here enables the evaluation and prediction of kinematic and kinetic variables from top-speed sprinting by simply using the input parameters of running speed and leg length. During steady-speed running, the center of mass horizontal velocity during the flight phase is constant (discounting resistance due to air friction), and so the runner's forward _Speed_ (m/s) is determined by the time it takes the center of mass to traverse the contact length \(L_{c}\) (m) during the ground contact time \(t_{c}\) (s) [3,15]:  
\[Speed=\frac{L_{c}}{t_{c}} \tag{1}\]  
\(L_{c}\) is determined by leg length \(L_{0}\) (m) and the total excursion angle during contact \(\theta_{c}\) (deg):  
\[L_{c}=2\cdot L_{0}\cdot\sin\ \frac{\theta_{C}}{2} \tag{2}\]  
For faster running speeds, prior research has demonstrated that \(\theta_{c}\approx\) 60 deg for humans and other bipedal runners [12, 13, 14]. Based on Equation (2), it can be approximated that \(L_{c}\approx L_{0}\) (see Figure 6 in [5]). Equation (1) then shows that \(t_{c}\) is directly related to \(L_{0}\) and _Speed_:  
\[t_{c}=\frac{L_{0}}{Speed} \tag{3}\]  
Aerial time \(t_{a}\) (s) is the time interval from takeoff of one foot until touchdown of the contralateral foot. Prior research has demonstrated that \(t_{a}\) is approximately 0.12 \(\pm\) 0.02 s for runners across a broad range of top speeds (\(\sim\)6 to 11 m/s) [2, 3, 4, 5]. Step time \(t_{step}\) (s) is the time interval to complete one step, and is equal to the sum of \(t_{c}\) and \(t_{a}\):  
\[t_{step}=t_{c}+t_{a} \tag{4}\]  
Step Rate \(SR\) (steps/s) is the number of steps taken per second and is the inverse of \(t_{step}\):  
\[SR=\frac{1}{t_{step}} \tag{5}\]  
Step Length \(SL\) (m) is the distance traveled per step and can be calculated from _Speed_ and \(SR\):  
\[SL=\frac{Speed}{SR} \tag{6}\]  
During steady-speed running where the net vertical displacement of the body is zero, the time-averaged vertical ground reaction force must equal the body's weight. Thus, the ratio of stance-averaged vertical force to body weight (\(F_{Zavg}/mg\)) can be determined if \(t_{c}\) and \(t_{a}\) are known:  
\[\frac{F_{Zavg}}{mg}=\frac{t_{step}}{t_{c}}=\left(1+\frac{t_{a}}{t_{c}}\right) \tag{7}\]  
where \(m\) (kg) is body mass and \(g\) (9.8 m/s\({}^{2}\)) is gravitational acceleration [16,17]. Given that \(t_{c}\) is generally inversely related to top speed, and \(t_{a}\) remains relatively constant across a range of runners and top speeds, \(F_{Zavg}/mg\) generally increases with top speed [2, 3, 4,17]. Limb angular motion is also considered within the context of this framework. Figure 1a depicts a simplified example of harmonic oscillatory thigh motion that assumes symmetrical peak thigh flexion and extension values. Because this framework is based on equations of harmonic motion [18], thigh angular kinematics as a function of time are determined by the parameters of frequency and \(\theta_{total}\), which is the total thigh range of motion from peakextension through peak flexion (Figure 1b).
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System
### Single PNN Damage Detection Model  
The details of the single PNN classifier are illustrated in Figure 2 for damage detection. The model is mainly composed of three parts, data preprocessing, obtaining correlation fractal dimensions (CFD) and PNN damage detection decision.
# Peridynamics for Fracture Analysis of Reflective Cracks in Semi-Rigid Base Asphalt Pavement
## 5 Conclusions  
In this paper, the theory of peridynamics was first modified and verified by three typical examples, and then the method was applied to investigate the crack propagation process of semi-rigid asphalt pavement under load from the three impacts: different forms of load, different thicknesses of asphalt surface, and wheel friction. The main conclusions are as follows:  
1. A two-dimensional asphalt pavement delamination model was established by modifying the theory of PD. The simulation results of the proposed model-based PD theory are in good agreement with the analytical solution, experimental results, and finite element calculation results. The feasibility of the application of the model to asphalt pavement structures was verified. It provided a new analytical idea for simulating reflective crack propagation.
2. The propagation of original cracks in the base under different forms of load was compared. The results show that the influence of asymmetric loading on crack propagation is greater than that of symmetric loading.
3. The crack propagation features of asphalt surfaces with different thicknesses were summarized through PD simulation results. That is, the increase in the thickness of the asphalt overlay can delay the time of initial crack propagation, slow the crack propagation speed, and weaken the damage degree of the pavement structure. In addition, maintaining a good bond between the semi-rigid base and asphalt overlay can slow the propagation of reflective cracks.  
Figure 14: Crack propagation of asphalt pavement under the action of wheel friction is considered. (**a**) Vehicle load approaching the crack without friction; (**b**) vehicle load approaching the crack with friction; (**c**) vehicle load away from the crack with friction.  
4. The influence of friction between tires and roads on reflective crack propagation cannot be ignored. If the friction direction is consistent with the crack propagation direction, the crack propagation will be accelerated. In contrast, crack propagation will be inhibited. The most unfavorable load position is the asymmetrical load position when the vehicle is far from the crack.  
Conceptualization, Z.S. and L.X.; methodology, L.X.; software, Z.S.; validation, J.Y., L.X. and Z.S.; formal analysis, Z.S.; investigation, L.X.; resources, X.W.; data curation, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, L.X.; visualization, Z.S.; supervision, J.Y.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Science and Technology of Henan Transportation Department (Grant number 2019J1).  
Not applicable.  
The authors declare no conflict of interest.
# Ensemble Learning Traffic Model for Sofia: A Case Study
## 2 Preliminaries: Ensemble Learning Algorithms
### Bagging vs. Boosting vs. Stacking  
An alternative to the horizontal equity-type structure of Random Forest is relying on natural hierarchies to advance the system. The idea is to start with a simple base model trained on all available features and after accessing its performance, run it again but with biased data, shifted towards instances, for which it failed to give good predictions. This process is repeated over and over, like a karma cycle, until it is sufficiently refined, and the final result is given as a weighted sum of the predictions of all its versions (reincarnations). This process is known as 'boosting' and typically works with decision trees with specific structure in different algorithms, e.g.,'stumps' in AdaBoost, leaf-wise growing in Light GBM, symmetric ones in CatBoost. In particular, gradient boosting uses gradient descent for adjusting the weights and XGBost provides an improved version which runs much faster, handles missing values on its own and defies the risk of overfitting to a large extend.  
Finally, once we have trained a number of ensemble (or other) models, and ranked their performance according to a proper evaluation metric, we can obtain our predictions based on all of them, through a process named'stacking'. That is yet another weighted sum, this time on a higher level of hierarchy, which AutoGluon does automatically in WightedEnsemble_12. The idea is again that different approaches of decision making balance each other and thus a lot of their imperfections are being cancelled out. Our experiments confirm this expectation as the ensemble method wins in all scenarios, except for one particular case of very biased data which we might discard from the very beginning.
## 5 Test Experiments
### Length Measurement Experiment
#### 5.5.3 Experimental Results  
Based on the measured data and the proposed method, we obtained the coordinate measurement error of the laser tracker before and after transit offset and tilt compensation, as shown in Figure 16. In Figure 16, Number 1-4 were measured at four positions (position 1 to position 4, respectively) with the scale bar located at a horizontal angle of 0\({}^{\circ}\) relative to the HOME of the laser tracker; Number 5-8 were measured at four positions (position 1 to position 4, respectively) with the scale bar located at a horizontal angle of 90\({}^{\circ}\) relative to the HOME of the laser tracker; Number 9-12 were measured at four positions (position 1 to position 4, respectively) with the scale bar located at a horizontal angle of 180\({}^{\circ}\) relative to the HOME of the laser tracker; Number 13-16 were measured in four positions (position 1 to position 4, respectively) with the scale bar located at a horizontal angle of 270\({}^{\circ}\) relative to the HOME of the laser tracker. Serial 1-3 was the test number, and 1-16 were repeated three times.  
From the test results, we can see that the coordinate measurement error (\(E\)) could be reduced by compensating for the calibrated transit tilt and offset errors using the proposed calibration method, which illustrates the correctness of the calibration of the transit offset and tilt errors. When the measured points were 5 m from the laser tracker, the coordinate measurement errors could be decreased from 161 um to about 73 um after compensation. When the measured points were 10 m from the laser tracker, the coordinate measurement errors could be decreased from 143 um to about 108 um after compensation. Therefore, the experimental results show that the transit tilt and offset errors calibration method proposed in this paper are credible and reasonable.
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.6 Implementation  
Once the hybrid recommendation system determines the final node for service placement, the system proceeds with execution. If the selected Fog node has sufficient resources to accommodate the service, it is deployed immediately. Otherwise, if no suitable Fog node is found or all are overloaded, the request is escalated to the Cloud layer, ensuring service continuity. This step is illustrated with the pseudo-code Algorithm 5.  
```
1:Input: Final recommended node \(n_{f}\)
2:procedurePlaceExecution(\(n_{f}\))
3:if\(n_{f}\) is not NULL and satisfies resource constraints then
4: Deploy the service on node \(n_{f}\)
5:else
6: Forward the request to the Cloud layer
7:endif
8:endprocedure
```  
**Algorithm 5** Implementation  
To conclude, Figure 10 provides a global overview of the proposed solution, integrating both the initial placement process handled by the FOCN and the fallback mechanism based on the hybrid RS. It summarizes the entire workflow, from receiving a request to its final placement, whether locally, in another colony, or in the Cloud.  
The proposed solution consists of solving the SPP in FC, as it offers several advantages over existing solutions. We present these advantages below:  
* Use of Fog: our solution maximizes the use of Fog. Indeed, we aim to place the maximum number of services in the Fog layer instead of transferring them to the Cloud, since in the event of the unavailability of the current colony, instead of sending the request to the Cloud layer, the RS looks for a location in the other Fog colonies to increase the chance of placement in Fog.
* Dynamism: Our solution takes dynamism into account. If the RS detects the unavailability of the Fog node, it looks for another available node, then retransmits and reallocates the service to the most suitable node.
* Mobility: Our proposal takes mobility into account. Indeed, if the RS detects the mobility of the end user, then it looks for another node that is closer to this customer, then retransmits and re-routes the service to the most suitable node.
* Latency: Placing the service in the Fog layer reduces latency since it is closer to the user. What is more, in the event of the unavailability of a node in the current colony, the RS can search faster than moving from one colony to another.
* Response time: fog placement is faster than Cloud placement.  
* SLA: service placement considers service delay and prioritizes the service with the closest delay.
* QOS: SLA criteria, latency, etc., are QOS criteria that help satisfy the customer.
## 3 Results and Discussions  
### Color Properties  
The initial white color of PAISiN was changed to a red color immediately after \(\gamma\)-ray irradiation, as previously observed for 30 kV X-rays [17]. As examples, selected color images of PAISiN stored at room temperature (20 \({}^{\circ}\)C) for 0.25 h, 11.6 h and 70.6 h after irradiation are shown in Figure 2. It was confirmed that the pattern of its color change from clear white to nearly-pink red was easily detectable by the naked eye, which was preferable in a practical sense for on-site real-time radiation monitoring followed by flexible controls of occupational exposures.  
Figure 3 indicates the dose dependence of the 8-bit inverted intensities of the images shown in Figure 2 as a function of dose. It was seen that the radiation-induced coloration of PAISiN reverted to the initial white color in 3 days for this dose range (up to 20 Gy).  
It should be noted that the radiation-induced coloration of PAISiN is more sensitive and more visible than that of the Fricke gel dosimeter. While this dosimeter shows radiation-induced coloration through the oxidation reaction from ferrous (Fe\({}^{2+}\)) ions to ferric (Fe\({}^{3+}\)  
Figure 3: Dose responses of the 8-bit intensities of the inverted images of PAISiN at 0.25 h, 11.6 h and 70.6 h after \(\gamma\)-ray irradiation at room temperature (20 \({}^{\circ}\)C).  
Figure 2: Scanned images of PAISiN stored at 20 \({}^{\circ}\)C taken at 0.25 h (**upper**), 11.6 h (**middle**) and 70.6 h (**lower**) after \({}^{137}\)Cs \(\gamma\)-ray irradiation.  
ions, it needs tens of Gy to see a detectable color change [31]. Though gel-type Fricke dosimeters supplemented with xylenol orange, a Fe\({}^{3+}\) complexing agent, considerably increased its sensitivity [7; 8; 9; 10; 21; 22; 23; 34], accurate color recognition by the naked eye is still challenging for low doses below 10 Gy, partially because of its indigenous orange color. PAISiN can present a visible red color which is detectable for an exposure of a few Gy \(\gamma\)-rays; PAISiN is expected to be more sensitive to diagnostic X-rays because of the larger contribution of photoelectric effects, as presumed from its high value of the effective atomic number (see Table 1). In addition, the decolorization process of PAISiN irradiated with 5 Gy can be complete in 1 day at room temperature, which is judged to be more advantageous for application to routine monitoring of occupational exposure compared to leuco-dye-based radiochromic materials, which need several days to revert to their initial color state [12; 14; 16; 19].