esa-sceva/satcom-chunk-collection huggingface dataset:
# Sewater Effect on Fatigue Behaviour of Notched Carbon/Epoxy Laminates
## 4 Conclusions  
This paper studied the effect of seawater immersion on the fatigue behaviour of notched carbon/epoxy laminates. The experimental programme was conducted using rectangular cross-section specimens with a central hole. Specimens were immersed in seawater for different immersion times (0, 30 and 60 days). Fatigue tests were performed under uniaxial cyclic loading, under the load-control mode, using a stress ratio equal to 0.1. After the tests, fracture surfaces were examined to identify the main failure mechanisms. The following conclusions can be drawn:  
* Water absorption occurred slowly, and the weight remained almost constant after 30 days of immersion, which suggests that saturation had been reached, either for natural or artificial seawater.
* Seawater immersion affected the fatigue strength. However, considering the scattered nature of the fatigue phenomenon, the roles of the immersion time (30 and 60 days) or the type of seawater (natural or artificial) were not relevant.
* The effect of seawater for long lives, close to 1 million cycles, can be neglected. On the contrary, as the lifetime decreases, the reduction of fatigue strength increases progressively. For fatigue lives of \(10^{4}\) cycles, the stress amplitude was 1.2 higher in dry laminates than in wet laminates.
* The failure mechanisms were similar for all conditions, evidencing the fracture of axially aligned fibres and longitudinal delamination between layers. Debonding between the fibres and matrix produced by normal stresses was also observed for layers aligned in the \(90^{\circ},45^{\circ}\) and \(-45^{\circ}\) directions.  
Figure 8: Fracture surfaces for: (**a**) dry sample (control sample), (**b**) sample immersed 30 days in natural seawater, (**c**) sample immersed 60 days in natural seawater, and (**d**) sample immersed 60 days in artificial seawater.  
**Author Contributions:** [PERSON] and P.N.B.R. conceived and designed the study; [PERSON], P.N.B.R. and A.M.A. performed the tests, and analysed the results; [PERSON], P.N.B.R., M.A.N., J.D.C. and A.M.A. participated in the research work. Original draft preparation P.N.B.R., A.M.A. and R.B.; writing--review and editing. [PERSON], P.N.B.R., M.A.N., J.D.C. and A.M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Not applicable.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This research is sponsored by national funds through FCT--Fundacao para a Ciencia e a Tecnologia, under the project UIDB/00285/2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments
### Recursive Feature Elimination  
RFE is a robust feature selection technique that iteratively fits a model and eliminates the least important features based on model coefficients or importance scores until the optimal subset of features is identified. This process enhances model accuracy and interpretability by reducing the feature space and mitigating the risk of overfitting. The RFE process involves two key steps:  
1. Filtering out incomplete cases: ensuring that only complete data are used for model training.
2. Defining a cross-validation strategy: a 10-fold cross-validation approach is implemented to balance bias and variance using the refined dataset, which includes 792 observations and 39 variables after handling missing data and feature selection.  
In the 10-fold cross-validation strategy, the dataset is randomly partitioned into 10 equal subsets. The model is trained using 9 of these folds (the training set), and the model's performance is evaluated on the remaining 1 subset (the validation set). This process is repeated 10 times, with each fold serving as the validation set once, ensuring more reliable performance estimates.  
The performance of the RFE process is evaluated using two metrics: accuracy and Cohen's Kappa.  
* Diagnosis model: the model's performance improved with the number of features, reaching optimal results with 19 features. It achieved an accuracy of 96.93% and a Kappa value of 0.9363. The most influential features include appendix diameter, appendix on US, length of stay, Diagnosis Presumptive, and peritonitis.
* Management model: the optimal performance was achieved with 31 features, resulting in an accuracy of 91.95% and a Kappa value of 0.8340. Key features for distinguishing between management strategies include length of stay, appendix diameter, peritonitis, CRP, and neutrophil percentage.
* Severity model: the model performed best with 11 features, attaining an accuracy of 93.60% and a Kappa value of 0.7416. The most critical features are length of stay, CRP, and WBC count, with neutrophil percentage and appendix diameter also playing significant roles.  
Following feature selection, we used 19 features for the Diagnosis model, 31 features for the Management model, and 11 features for the Severity model. These selections were made based on Recursive Feature Elimination (RFE) to optimize predictive performance.  
In summary, a larger number of features generally enhanced model accuracy and Kappa value for Diagnosis and Management, reflecting the complexity of these tasks. Conversely, the Severity model could be accurately predicted with fewer features, highlighting its more focused nature.
## 4 Evacuation Model Design
### Evacuation Transportation (Mode Choice)  
The evacuation timetable could be affected by the kind, quantity, and capacity of vehicles. These factors directly influence the road network capacity available for others to evacuate.  
As a result, it is critical to know the types and quantities of cars that people have used for this process and the capacity of the vehicle utilized in the evacuation. The vehicles used by the evacuees during the evacuation of Haifa, Israel, due to wildfire, were investigated by [PERSON] et al. [61] utilizing online questionnaires. In that research, it was discovered that most people (92%) used private automobiles to evacuate, including drivers and passengers, with a far lower number taking public transit or walking. Only 10% of those who escaped alone did so, and the evacuation groups were three people on average. The average vehicle count per family was 0.89, with bigger families owning more cars. The personal vehicles used in this study were less than the same cases from the United States, which could be because the ownership of cars is less in Israel than in the US.  
Hurricane evacuation statistics might provide insight into WUI fire evacuation. For example, several hurricane studies in the United States indicated substantial preferences for private automobiles over alternative means of evacuation [45]. Factors such as level of experience, destination, socio-demographic variables, and home characteristics were found to be influential on the use of public transport by research in the United States [62]. In addition, research on the number and kinds of used vehicles during an evacuation showed that American families use a high number of vehicles. On top of that, even trailers, recreational vehicles, boats, and livestock trailers were carried through the evacuation, posing high pressure on the traffic system [45]. These behaviors are crucial because bigger cars take up more space on the road; nevertheless, comparable research on WUI fires is also required.  
While studies indicate that individuals are more inclined to leave together, it is unclear if they would seek to leave together if they were initially stationed in various locations at the initiation of the evacuation process. The studies of evacuation without prior alerts [63] and wildfire evacuation [61] showed that the individuals that have to make short trips before the actual evacuation are more than the ones that do not. [PERSON] et al. discovered that more than half of the intermediate trips were to pick up or meet household members, particularly youngsters [63]. The evacuees in the Israel fire did an average of 1.1 side trips before the evacuation [61]. The side trips and chain trips were also modeled by researchers, showing that they will add to the overall time required for the evacuation [45]. It is also worth noting that even if family members refuse to evacuate, they may still travel, producing additional traffic on the road, sometimes known as \"background traffic\" [45].
# A Unified Nonlinear Elastic Model for Rock Material
## 5 Experimental Validation of the New Model
### The Range of \(E_{ni}\) Values for the Rock Material  
The scatter plots of the \(E_{ni}\) and \(m\) values are listed in Figure 12. All the curves in Figure 12a-d show a downwards trend, indicating that the \(E_{ni}\) values are negatively correlated with the \(m\) values, which follows the same rule of fit for a single curve shown in Figure 6. The \(E_{ni}\) values increase as the serial numbers of the different rocks increases, and the position of the curve is also higher. The linear relationship of all the curves was good when the \(m\) values were less than 1.2, and the curves gradually approach each other with decreasing rates when the \(m\) values were greater than 1.2, as shown in Figure 12a-d. The scatter points hardly overlap with one another, that is, the \(E_{ni}\) values have good independence and non-repeatability. The \(E_{ni}\) values depend on the basic properties of the rocks, such as the \(m\) values, and the basic attributes are natural. It is not feasible to obtain the \(E_{ni}\) values of all rocks by using the same \(m\) value, which is why the unified nonlinear elastic model contains both \(E_{ni}\) and \(m\) values.  
The range of \(E_{ni}\) values for the different rock types were determined and are listed in Table 3. The range of \(E_{ni}\) values of different rock types is slightly different, and dolomite and granite have larger ranges than limestone and sandstone. In general, the maximum \(E_{ni}\) values for all the rock types were less than 21 MPa.
# A Creep Model of Asphal Mixture Based on Variable Order Fractional Derivative
## 2 Fractional Viscoelastic Element  
### Constant Fractional Viscoelastic Element  
There are various definitions of fractional calculus, and the definition of fractional calculus based on Riemann-Liouville (R-L) is a widely used one. The R-L fractional integral of function \(f(t)\) with order \(\beta\) is defined by [15]:  
\[{}_{a}I_{t}^{\beta}f(t)=\frac{1}{\Gamma(\beta)}\int_{a}^{t}\frac{f(\tau)}{(t- \tau)^{1-\beta}}\mathrm{d}\tau,\ (t>a) \tag{1}\]  
where \(0<\beta<1\), and \(\Gamma(\beta)=\int_{0}^{\infty}t^{\beta-1}e^{-t}dt\) is the gamma function. The R-L fractional differential of function \(f(t)\) with order \(\beta\) is defined as the inverse operation of the fractional integral:  
\[{}_{a}D_{t}^{\beta}f(t)=\frac{\mathrm{d}^{\beta}f(t)}{\mathrm{d}t^{\beta}}= \frac{\mathrm{d}^{n}}{\mathrm{d}t^{n}}\big{[}a_{t}^{n-\beta}f(t)\big{]}=\frac{ 1}{\Gamma(n-\beta)}\frac{\mathrm{d}^{n}}{\mathrm{d}t^{n}}\big{\rightsquigarrow_{ a}^{t}}\frac{f(\tau)}{(t-\tau)^{1+\beta-n}}\mathrm{d}\tau,\ (t>a) \tag{2}\]  
where \(n\) is the minimum positive integer greater than \(\beta\). \(n=1\) if \(0<\beta<1\), and the R-L fractional differential of function \(f(t)\) with order \(\beta\) is then written as:  
\[{}_{a}D_{t}^{\beta}f(t)=\frac{\mathrm{d}^{\beta}f(t)}{\mathrm{d}t^{\beta}}= \frac{1}{\Gamma(1-\beta)}\frac{\mathrm{d}}{\mathrm{d}t}\big{\rightsquigarrow_{ a}^{t}}\frac{f(\tau)}{(t-\tau)^{\beta}}\mathrm{d}\tau,\ (t>a) \tag{3}\]  
In the classical viscoelastic constitutive modeling, the series and/or parallel combinations of springs and dashpots are usually used to describe viscoelasticity. In order to describe the viscoelastic mechanical behavior of materials more flexibly, [PERSON] established the relationship between the stress and the fractional derivative of the strain, which is modeled by the fractional viscoelastic element as shown in Figure 1. The constitutive relation of the fractional viscoelastic element reads:  
\[\sigma(t)=\xi\cdot{}_{0}D_{t}^{\beta}\varepsilon(t),\ (0<\beta<1) \tag{4}\]  
where \(\xi=Et^{\beta}\), representing the viscosity coefficient of the material, \(E\) is the material modulus, \(\tau\) is the characteristic time. It can be seen that when \(\beta{\rightarrow}0\), the fractional dashpot element reduces into a Hooke spring, and when \(\beta{\rightarrow}1\), the fractional dashpot element reduces into a Newtonian dashpot.  
Figure 1: Constant order fractional viscoelastic element.  
Let \(\sigma(t)=\sigma_{0}H(t)\) (\(H(t)\) is the Heaviside function), the creep equation for the fractional viscoelastic element can be obtained by integrating Equation (4):  
\[\varepsilon(t)=\frac{\sigma_{0}}{\xi}\frac{t^{\beta}}{\Gamma(1+\beta)},\ (0<\beta<1) \tag{5}\]
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results
### Case A  
In this case, all nodes are stationary. Figure 5a shows the averaged BER per gateway for the SCA and Figure 5b shows the average BER for the RL-CAA. The average error rate for SCA is a little over 10%, whereas it is less than 10% for each gateway with the RL-CAA. The average throughput per gateway provided by both algorithms is quite similar, as shown in Figure 6a,b.  
All the users have acceptable QoS so no dissatisfaction is observed (probability = 0). The SCA is a static algorithm whereas the RL-CAA is a dynamic and robust algorithm (i.e., it can promptly act on any change in the network parameters and can adjust accordingly to provide sufficient QoS), and it is suited for scenarios with changing traffic requirements. In case of a static scenario, there are no much benefits with the RL-CAA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Frame size & 2040 bits \\ Transmission power (fixed power case) & 3 dBm \\ Node Throughput & 971.4 Kbps \\ Total Nodes per Gateway & 3 \\ Zone Area & 25 m\({}^{2}\) \\ Total Region & 100 m\({}^{2}\) \\ Maximum distance between a node—gateway (before handover may occur) & 3.54 m \\ Total Simulation Time & 600 s \\ Simulation statistics averaging period & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# An Energy-Efficient Routing Algorithm
## 3 System Models  
Considering a static USN with \(n\) sensor nodes which are randomly deployed at different depths and used for environmental monitoring (as shown in Figure 1), each sensor node has a non-identical communication radius \(r_{i}\) based on their degree. Assume that two nodes can communicate with each other if their Euclidian distance \(d\) is smaller than \(r_{i}\). Additionally, each node has a unique node ID and knows the location information within its broadcast radius \(r_{i}\).  
Let \(\mathbf{x}=\left[x_{1},x_{2},\cdots x_{N}\right]^{T}\) denote the original signal, \(x_{i}\) is the reading of the \(i^{th}\) node. Since the sensed data \(\mathbf{x}\) are often spatially and/or temporally correlated, it is compressible in an orthogonal basis \(\Psi\) (e.g., fast Fourier transform (FFT) basis). Take as an example the original temperature on 3 September 2012 GMT, in the Southern California coast (data from [[http://ourocean.jpl.nasa.gov](http://ourocean.jpl.nasa.gov)]([http://ourocean.jpl.nasa.gov](http://ourocean.jpl.nasa.gov))). The original signal \(\mathbf{x}\) and its FFT transformation are shown in Figure 2.  
Figure 2 shows that the original signal \(\mathbf{x}\) is sparse in FFT basis. Based on CS, a small quantity of random measurements \(\mathbf{y}\) can be used to reconstruct the original signal \(\mathbf{x}\). Then, the signal model of the CS-CULM scheme can be expressed as \(\mathbf{y}=\Phi\mathbf{x}=\Phi\Psi\theta\).
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 2 Synthetical Modal Parameters Identification (SMPI) Method
### Parameter Matching Approach  
The Prony algorithm only extracts precisely natural frequencies, amplitudes, and phase angles of DLFO, while SSI accurately obtains natural frequencies and damping ratios. Due to noise interference and overestimation of order, algorithms often identify modes containing false information, i.e., false modes (or fictitious modes). In line with the features of SSI and Prony, it is needed to execute parameter matching to search for the exact amplitudes, phase angles, and damping ratios according to the similar frequencies from SSI and Prony. In respect of the previous investigation, there is no such method that integrates SSI with Prony to identify the full modal parameters of DLFO accurately.  
The steps for this technique are as follows:  
Step 1: Calculate the dimensions \(d_{s}\) and \(d_{p}\) of the frequency \(f_{s}\) acquired by SSI and the frequency \(f_{p}\) computed by Prony, respectively;  
Step 2: Determine whether the number of iterations \(si\) is less than or equal to \(d_{s}\). If not met the condition, the modal parameter result, i.e., the successful matched frequencies \(f_{mi}\), damped ratios \(\boldsymbol{\xi}_{mi}\), phase angles \(\boldsymbol{\theta}_{mi}\) and \(A_{mi}\) will be output; if the condition is met, jump to Step 3;  
Step 3: Calculate the absolute error \(\boldsymbol{\Delta}f\), the minimum absolute error \(\boldsymbol{\Delta}f_{min}\), and the relative error \(\boldsymbol{\delta}_{f}\) between \(f_{s}\) and \(f_{p}\);  
Step 4: Determine whether the number of iterations \(pi\) is less than or equal to \(d_{p}\). If the condition is not met, go to Step 2, carrying out the next iteration; if the condition is met, determine whether the parameter matching conditions are fulfilled in Step 5;  
Step 5: Determine whether \(f_{p(i)}\) meets the matching condition, that is, the number of successful matching \(j\)s is less than \(d_{s}\), and the relative error \(\delta_{f(pi)}\) is less than the allowable error value \(\boldsymbol{\varepsilon}_{a}\), and the absolute error \(\boldsymbol{\Delta}f_{pi}\) is just the minimum value \(\boldsymbol{\Delta}f_{min}\). If the condition is not met, go to Step 4 to carry out the next iteration; if the condition is met, go to Step 6 to carry out the parameter matching; and  
Step 6: Matching parameters, that is, \(f_{mi}=f_{s(si)}\), \(\boldsymbol{\xi}_{mi}=\boldsymbol{\xi}_{s(si)}\), \(\boldsymbol{\theta}_{mi}=\boldsymbol{\theta}_{p(pi)}\), \(A_{mi}=A_{p(pi)}\). After completing a round of pairing, return to Step 4 to carry out the next iteration.  
The flowchart of parameter matching of DLFO through the SMPI method describes the detailed process of the steps above as Figure 2.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 2 Algorithm Theory and Method  
### Method Flowchart  
In this paper, the modal features of monitored signals are extracted by EEMD and the trend fitting of the features is applied to real-time estimation of tool wear. The process of method is shown in Figure 1.  
Figure 1: The flowchart of the method.  
There are mainly three parts, i.e., signal acquisition and analysis, feature extraction of monitored signals, and tool wear estimation. The first part is signal acquisition and analysis, here the milling parameters and the processing conditions are known to collect process signals, and the monitored signals that reflect the inherent modal information are selected in the milling process and calculated the correlation coefficients with the tool wear. The second part is feature extraction of selected signals. Monitored signals are collected in the milling process, and the modal features of monitored signals are extracted by EEMD. The third part is tool wear estimation, i.e., the characteristics of monitored signals are analyzed based on feature extraction to estimate the tool wear. The modal information of the monitored signals with different inherent frequency is selected as the input variables for trend fitting, and method tests and contrasts are executed. In the end, an example for tool analysis is investigated in this research to verify the effectiveness of the method and the estimation results can be shown in real time.
## 3 Data Acquisition and Processing
### Multi-Source Point Cloud Fusion
It can be seen that the distance between the nearest point pairs was concentrated below 2.8 m, there were few unregistered point pairs, and the fusion effect of point cloud was better. Figure 9: Multi-source point cloud fusion diagram: (**a**) position-specific TLS point cloud; (**b**) position-specific UAV point cloud; (**c**) uniform coordinate system method for roughly fusing point clouds; (**d**) improved ICP algorithm to fuse point clouds.
## 4 Magnetic sensors for space missions. The implementation of magnetic COTS in spacecrafts
For example SMILE-Small Magnetometer In Low-mass Experiment consists in the development of a miniaturized fluxgate with FPGAs developed in the School of Electrical Engineering of the Royal Institute of Technology [74] and the Magnetometer Front-end ASIC (MFA), developed by the Space Research Institute (Austrian Academy of Sciences) in Austria [75]. Regarding the implementation of these VLSI techniques for COTS it can be mentioned the launch of the German cubesatellite Compass-1 on April 28\({}^{\text{th}}\) with the PSLV - Polar Satellite Launch Vehicle (PSLV) from the Satish Dhawan Space Centre in India. The satellite, devoted to the technology demonstration of a miniature GPS receiver, and a transceiver for fast RF communication, uses an ASIC integrated two-axis COTS magnetometer HMC6352 [76] by Honeywell, together with Sun sensors and magnetotorquers for the ACS. Nowadays the implementation of FPGAs and ASICs in the front-end of magnetometers implies an increase of the cost and the complexity of the development of the sensors in general and in particular for space applications. But due to the fact these circuits are being more demanded by the market, it can be said facing the future that the use of FPGAs and ASICs in the spacecrafts is likely to be the trend for the actual and future concept of satellites: the sensor-satellites and even the satellite-on-a chip. To summarize, so far the two trends of magnetic sensors coexist in the space panorama for different requirements: the combination of two magnetometers manufactured _ad hoc_ for geomagnetic field mapping and magnetic COTS sensors. Miniaturized magnetic sensors (miniaturized fluxgates, AMR sensors, etc) seem to have poorer stability and less resolution than the bulky fluxgates manufactured _ad hoc_ for in orbit geomagnetic purposes but in contrast, COTS miniaturized sensors offer a lighter, low power and reasonable good performance solution for many cases. Figure 3: The graph represents the evolution of AMR-based sensors of INTA. In the right hand side of the picture the techniques used in the evolution are listed. In the left hand side of the picture the time evolution of the sensors is shown, starting in 2004 with NANOSAT-01 and the actual goal for the MetNet Precursor magnetometer. The present evolution towards miniaturization can be considered to aim for two main objectives: on the one hand, a huge effort is being done in the implementation of FPGAs and ASICs in the front-end of the magnetic sensors and on the other hand, some qualification process are on the road for the adaptation of brand new technologies to space.
# Performance Model for Video Service in 5G Networks
## 3 Approaches  
### Interference Coordination Approach  
Large networks are partitioned into coordination clusters for scalability considerations. This causes an inter-cluster interference problem; i.e., within a cluster, the inter-cell interference has been minimized, but between clusters, inter-cluster interference still exists. Therefore, we present a two-level IC approach to improve the performance model, which reduces intra- and inter-cluster interference.