esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model Results  
### Hourly Prediction  
#### 5.1.1 Model Performance  
Accident prediction models were established for Nantung and Soochow, respectively, and then SVM, XGBoost, and Random Forest models were compared. Parameters were tuned to find the optimal parameters of the model, and the classification threshold was adjusted to examine the trade-off between accuracy and sensitivity. Parameters were tuned through a grid search with three-fold cross-validation with the criteria of the Gini impurity. How a thoroughly a clustering algorithm was divided is determined using a feature called Gini impurity. In essence, it assists us in selecting the ideal splitter so that we may create a clean decision tree. Gini impurities have values between 0 and 0.5. The easiest solution for model parameters tweaking is grid search. We essentially create a continuous grid within the feature subset region. Furthermore, utilizing cross-validation to calculate certain quality metrics, we attempt each possible permutation of the variables in this matrix.  
Hyperparameter-tuning is completed for each single model independently. For XGBoost, the learning rate was 0.3, the number of estimators was 100, and max depth was 6. The optimal parameters of the random forest were 250 trees in the forest, the maximum depth with the value of 10, and 6 features for extraction when split. The SVM model used RBF kernel, penalty coefficient was 100, and \(\gamma\) is equal to 0.01.  
The model effects are shown in Table 2:  
The ROC curve obtained by the random forest for hourly accident prediction for both Nantung and Soochow direction is shown in Figures 2 and 3, respectively. The diagonal dashed line is the reference line (random points). Area above the reference line represents good classification that is better than random and vice versa.  
It can be seen from the table that the effect of SVM is poor. The sensitivity of the random forest is better than XGBoost. Since this paper focuses on the correct prediction  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Directions** & **Model** & **Threshold** & **Accuracy** & **Sensitivity** & **AUC** \\ \hline \multirow{3}{*}{Nantung} & SVM & NA & 0.947 & 0.011 & 0.504 \\ \cline{2-6}  & XGBoost & 0.01 & 0.656 & 0.734 & 0.717 \\ \cline{2-6}  & Random Forest & 0.03 & 0.706 & 0.766 & 0.735 \\ \hline \multirow{3}{*}{Soochow} & SVM & NA & 0.953 & 0 & 0.500 \\ \cline{2-6}  & XGBoost & 0.008 & 0.801 & 0.762 & 0.782 \\ \cline{2-6}  & Random Forest & 0.04 & 0.778 & 0.774 & 0.775 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance of hourly prediction.  
Figure 3: ROC in Soochow direction.  
Figure 2: ROC in Nantung direction.  
of accidents, random forest is a more suitable model. Figures 4 and 5 displayed below are the feature importance plots representing the importance of different features on the dependent variable, which is crash incidence in our study.
## 2 Overview of Microreactors
The research questions (RQ) are as follows:  
**RQ1:**: _What is the energy production capacity of microreactors and how can this capacity be optimized?_
**RQ2:**: _What are the main technical challenges encountered during the installation and operation of microreactors?_
**RQ3:**: _How do fuel limitations affect the widespread adoption of microreactors?_
**RQ4:**: _How does the economic feasibility of microreactors compare to large-scale nuclear power plants?_
**RQ5:**: _What are the environmental impacts of microreactors and how can these impacts be minimized?_
**RQ6:**: _What are the safety standards for microreactors and what challenges are encountered in the implementation of these standards?_
**RQ7:**: _How do licensing processes affect the widespread adoption of microreactors and how can these processes be improved?_
**RQ8:**: _What role can blockchain technology play in the licensing and regulatory processes of microreactors?_
**RQ9:**: _How does the modular structure of microreactors affect maintenance and repair processes?_
**RQ10:**: _What are the industrial applications of microreactors and how can these applications be developed?_
**RQ11:**: _What advantages and challenges do microreactors bring in remote and hard-to-reach areas?_  
**RQ12:**: _How do the portability and deployment time of microreactors impact energy supply during emergencies?_
**RQ13:**: _How can the capital and operational costs of microreactors be optimized?_
**RQ14:**: _What are the refielling processes of microreactors and how can these processes be improved?_
**RQ15:**: _How can the energy efficiency of microreactors be increased?_
**RQ16:**: _What are the safety protocols and emergency response plans for microreactors?_
**RQ17:**: _How can the integration of microreactors into the electrical grid be ensured?_
**RQ18:**: _How can the availability and reliability of microreactors in energy production be increased?_
**RQ19:**: _What are the proliferation risks of microreactors and how can these risks be managed?_
**RQ20:**: _What are the future research and development areas for microreactors and what innovations are expected in these areas?_  
These research questions aim to explore the potential and challenges of microreactors in depth. Each question provides essential insights to better understand the role of microreactors in the energy sector and ensure the effective use of this technology. The search strategies employed are detailed in Table 1. Keywords and phrases such as \"all: Microreactor\", \"Nuclear fuel\", \"HALEU\", \"Nuclear energy\", \"Small Modular Reactor (SMR)\", and \"blockchain\" were utilized to encompass similar studies. As the initial search approach did not yield the desired outcomes, the method was broadened to cover all years, starting with the most recent publications.
## 4 Simulation Results and Analysis
### Analysis of Detection Results for Single-Tone and Multi-Tone Interference  
The detection probability is defined as the ratio of the number of times interference frequencies are correctly detected to the total number of Monte Carlo simulation runs, expressed as  
\[P_{d}=a/5000 \tag{5}\]  
where \(a\) represents the number of times interference frequencies are correctly detected.  
A higher detection probability indicates better algorithm performance, whereas a lower detection probability suggests poorer performance.  
As illustrated in Figure 5, the detection performance of the CME, FCME, and the proposed algorithm is nearly identical. For single-tone interference, when JNR \(>-18\) dB, the detection probability can approximately reach 1. For multi-tone interference, when JNR \(>-13\) dB, the detection probability can approximately reach 1. Under the same JNR conditions, the detection probability of single-tone interference is higher than that of multi-tone interference. For the same detection probability, single-tone interference outperforms multi-tone interference by approximately 5 dB. The increase in the number of interference frequencies causes the interference signal energy to become more dispersed, thus requiring a higher JNR to achieve the same detection probability.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 4 Numerical Analysis  
### FE Model and Calibration  
As changing the soil parameters is time consuming and sometimes challenging, an FE model was built using the software ABAQUS, and then the soil parameters and influences were considered with further cases. In order to compare the numerical and tested results, the model dimensions were the same as those of the sand box used in the laboratory test. Due to symmetry, only half a sand box was built in the model (Figure 22). The soil and concrete plate were modelled using a linear reduced integral solid element C3D8R with a meshing size of 0.01 m.  
The soil parameters were varied for the different cases. The concrete had a P-wave velocity of 4000 m/s, S-wave velocity of 2236 m/s, Poisson's ratio of 0.2 and density of 2400 kg/m\({}^{3}\).  
Rayleigh damping was employed. The damping matrix \(\mathbf{C}\) is defined as a linear superposition of the mass matrix \(\mathbf{M}\) and stiffness matrix \(\mathbf{K}\) with coefficients \(\alpha\) and \(\beta\). The two coefficients can only be defined by the damping ratio of soils and analysis frequencies. In the model, the constant damping ratio of the soil was 0.3; therefore, the coefficients could finally be calculated as being \(\alpha=1\times 10^{-6}\) and \(\beta=5\times 10^{-4}\).  
In order to prevent vibration wave reflection on the boundaries, infinite elements were applied to the three sides of the 3D dynamic model and fixed boundaries were applied at the bottom.  
A half-sine pulse with a peak value of 600 N and time duration of 0.4 ms (Figure 23) was applied at the plate centre to model the impact load.  
As shown in Figure 22, the detection point was at the plate centre, and the impact location was first located 2 cm below this. When analysing the void size and influence areas, the locations of the impact and detection points were moved along the vertical and horizontal axes.  
Figure 23: (**a**) Time history and (**b**) Fourier spectrum of the applied pulse force.  
Figure 22: Finite element (FE) model.  
The vibration response of the test case D3 was used for the FE model calibration. A comparison of the time history and Fourier spectrum is illustrated in Figure 24. It can be concluded that the test and FE results were generally well matched, particularly in the time domain. Due to the energy attenuation at high frequencies when using the hand hammer and the second peak amplitude of the half-sine signal (Figure 23), the difference was found to be between 4000 and 6000 Hz.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results
### Push Notification in Case of Abnormalities  
The PlaIMoS platform has an alarm system (push notification) that is executed at the time of an anomaly. For the system to detect that an abnormal event has occurred, the healthcare provider must set the minimum and maximum values of the physiological variable to be monitored. If the measurement received from any of the sensors is outside the range, a push notification will be transmitted to the healthcare provider's smartphone. A total of two alarms were executed (on queue) during the study in order to validate push notification functionality:  
1. Temperature of 30 \({}^{\circ}\)C. (a cold object was placed over the temperature sensor)
2. The patient let himself fall to represent a faint  
Results show that the smartphone received the temperature alarm 7 s after the PlaIMoS remote node sent a measurement to the Web service and the fall alarm was received 6 s after the simulated incident. The time difference is due, in large part, to the time it takes the Apple, Google and Microsoft servers to receive the requests from the PlaIMoS platform and for them to forward the notification to the smartphone.
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 2 Materials and Methods
### Data
#### 2.1.1 County-Level Modeling Data  
For county-level yearly analysis, temperature and humidity were calculated as yearly averages of daily mean values for each county. Precipitation was represented as the sum of daily precipitation measurements within each county for a given year. Socioeconomic factors were incorporated directly as their respective yearly measurements for each county. The variables used in these models and their descriptions are presented in Table 1.  
To address the varying scales and distributions of the selected variables, appropriate normalization techniques were applied, and the resulting distributions are presented in Figure 1. Temperature and humidity underwent z-score normalization due to their approximately normal distributions, while precipitation and all socioeconomic indicators were subjected to log1p normalization to address their right-skewed distributions. This normalization approach facilitates more effective model fitting and coefficient interpretation by reducing the impact of outliers and bringing variables to comparable scales.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### New Reflectance-Based Stitching Algorithm
#### 2.2.2 Exhaustive Enumeration Reflectance Algorithm  
To simplify and accelerate the optimization process, a hypothesis is made on Phong's model. In this model, the best contrast is obtained if the \(\theta\) angle between the viewing and the lighting positions is equal to 90\({}^{\circ}\). Moreover, the best contrast is obtained if the lighting angle is at a grazing angle to the surface and this second hypothesis is assumed for the optimization process. Those simplifications are incorporated by setting the viewing angle at the surface normal to the surface (\(\theta\) = 0\({}^{\circ}\)) and the lighting source at a grazing angle (\(\theta\) = 90\({}^{\circ}\)).  
Therefore, the contrast optimization of the reflectance map focuses on one variable considering the previous assumptions: the lighting in-plane angle (i.e., rotation around Z-axis). The optimization consists in maximizing the contrast of the reflectance map. The autocovariance can be used for this purpose and the contrast level is computed as the maximum value of the autocovariance matrix (Equation (1)). This represents the objective function in this optimization problem.  
\[y=f(\theta,\,\phi)=\max_{i,j}\bigl{(}x_{i,j}\bigr{)}\,\,for\,\,X=A(\theta,\, \phi)^{\mathrm{T}}*A(\theta,\,\phi)\,\,\,for\,\,i,\,j\in N \tag{1}\]  
where \(A(\theta,\,\phi)\) is the reflectance map computed from a topography for given lighting angles \(\theta\) and \(\phi\) (the variables in the contrast optimization process) and \(A(\theta,\,\phi)^{\mathrm{T}}\) is the transposed matrix of \(A(\theta,\,\phi)\).  
A simple approach is to compute the objective function for multiple angles, for example, the \(\phi\) angles from 0\({}^{\circ}\) to 359\({}^{\circ}\). Figure 4 sums up the optimization process of the reflectance in-plane lighting angle for an indented surface. The best lighting angle is determined when the value of the maximum of the autocovariance matrix is maximized, i.e., for a \(\phi\) angle of 274\({}^{\circ}\).  
Figure 4: Optimization curve of the reflectance lighting angle \(\phi\) obtained with the exhaustive enumeration reflectance algorithm for an indented surface, with the resulting reflectance maps (\(\theta_{light}\) = 90\({}^{\circ}\)).  
This variant of the rising sun algorithm is a simple method, but is lacking in speed. In the case of the lighting angle optimization, which contains only one variable, the exhaustive enumeration method is sufficient. Yet, it is possible to obtain a more efficient algorithm, which is necessary for a more intensive use, such as in the stitching procedure.
## 5 Experiments and Analyses
### Video Delivery Experiment
#### 5.1.1 The Request Allocation  
In the first part of the first experiment, we sent a video-code delivery request 100 times once per 15 s from a user node, _i.e._, a total of 100 jobs was submitted. Each delivered a 10 MB video program, and each experiment lasted 1500 s. The default parameters are listed in Table 11. We recorded which resource node was allocated to execute which job by Resource Broker. Table 12 lists the experimental results, in which 78 (= 40 + 38)% of requests were allocated to nodes 1 and 4 because the two nodes were the ones with the highest performance among the six resource nodes. Resource Broker allocated fewer jobs to nodes 3, 5 and 6 due to their relatively low performance. When two or more resource nodes are of the same score, the one with the smallest ID will be chosen. That is why even though nodes 2 and 5 and nodes 3 and 6 themselves had the same scores initially, node 2 (node 3) was chosen more frequently than node 5 (node 6).  
Table 13 lists the turnaround time of a request on the Random and the SPA, excluding the preemptive SPA portion (we call it the SPA with no preemption, SPA-NP for short). With the SPA-NP and Random, the average turnaround times were respectively 151.93 s and 174.70 s. Due to poor stability of wireless networks, the SPA-NP's maximum turnaround time is 1.75 (= 260.8/151.93) times its own average turnaround time. The Random's maximum turnaround time is 1.99 (= 347.19/174.70) times its own average turnaround time, implying stability is a very important issue for wireless transmission.  
Figure 10 shows the average loads of the CPUs when the two algorithms are employed. It is clear that the loads of the SPA-NP are lower than those of the Random, indicating that balancing the loads of resource nodes can effectively reduce CPU burden. The peaks of the line with the Random appear every 15 s which is the frequency of submitting requests, _i.e._, every time a request is submitted, due to job initialization which is 125 s, the average CPU load of the six resource nodes is higher than those sometime later but before the start of the next request. On the other hand, the SPA-NP algorithm's line is relatively smooth. The reason is stated above.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Algorithm** & **Max turnaround time (s)** & **Min turnaround time (s)** & **Avg. turnaround time (s)** & **SD** \\ \hline SPA-NP & 260.80 & 106.49 & 151.93 & 38.76 \\ Random & 347.19 & 150.87 & 174.70 & 45.33 \\ \hline \end{tabular}
\end{table}
Table 13: Turnaround time of a job/request.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Node** & **1** & **2** & **3** & **4** & **5** & **6** & **Total** \\ \hline Number of jobs allocated & 40 & 15 & 5 & 38 & 1 & 1 & 100 \\ Percentage of the allocation & 40\% & 15\% & 5\% & 38\% & 1\% & 1\% & 100\% \\ \hline \end{tabular}
\end{table}
Table 12: Numbers of jobs allocated to resource nodes.
## 1 Introduction
### The 2D Flow and Acoustic Numerical Setup  
The 2D flow computational domain is modelled using ANSYS Workbench. The inlet and outlet boundaries are extended \(8.5D\) and \(20.5D\) from the centerline of the cylinder diameter (\(D=0.019\) m) upstream and downstream, respectively. The top and bottom boundaries are located at \(10.5D\) in both directions from the cylinder center. The entire domain is then discretized using an unstructured mesh with triangular cells. However, near to the cylinder wall, a structured mesh with rectangular elements is used to capture the boundary layer (BL). The boundary conditions are assigned as follows: velocity inlet, \(\rm U_{in}=69.2\) m/s and pressure outlet, \(\rm P_{gauge}=0\) Pa to prevent any backflow into the domain. Both the flow density, \(\rho=1.225\) kg/m\({}^{3}\) and viscosity, \(\mu=1.7894\times 10^{-5}\) kg/m.s are kept as the default values for the air. As a result, the calculated \(\rm Re=(U_{in}D\rho)/\mu=9.0\times 10^{4}\) and Mach number, \(\rm M=U_{in}/c_{0}=0.2\), where the speed of sound in the air, c The pressure coefficient, C\({}_{\rm P}\), distribution around the upper surface of the cylinder is monitored and validated with the analytical solution of an inviscid flow and the experimental results of [PERSON] et al. [31] at \(\rm{Re}=1.4\times 10^{5}\). The C\({}_{\rm P}\) values show good agreement with the experimental and the numerical results, particularly at the front surface of the cylinder, as shown in Figure 1. However, the present 2D LES result over-predicts the measured C\({}_{\rm P}\) in the back of the cylinder compared to the 2D Shear Stress Transport (SST) and 3D LES data [22]. Overall, the flow quantities are reasonably reproduced using the 2D CFD simulation with less computational cost compared to the 3D simulation [22; 32; 33; 34; 35; 36; 37; 38; 39].  
Indeed, the present 2D LES simulation requires extremely less computational efforts compared with that of the 3D LES simulation performed by [PERSON] et al. [22]. For instance, the 2D simulation requires approximately 10 h CPU time (single-core) to complete \(28\times 10^{3}\) time steps (the time step size, \(\Delta\)t = \(5.0\times 10^{-6}\) s). However, the 3D simulation required 35 days to execute \(50\times 10^{3}\) time steps (\(\Delta\)t = 3.4 \(\times\) 10\({}^{-6}\) s) using 16 parallel multi-core CPU [22]. This huge difference in computational cost is expected to increase when performing the same analysis on a complex LG structure.
## 4 Experimental Study in Long Delay Regime
### Numerical Analysis of the Experiment  
In our experiment, the coupling strength is estimated to be only \(-40\) dB. Consequently, photon number increase and photon lifetime enhancement do not contribute much to linewidth reduction. On the other hand, the mutual phase-locking mechanism is believed to be responsible for such pronounced linewidth reduction in a mutually coupled system with weak coupling and long delay. Figure 15 estimates the linewidth narrowing performance of lasers coupled in the long delay regime by taking mutual phase-locking mechanism into consideration. The delay time is set as 50 ns which corresponds to the 10-m-long coupling fiber adopted in Figure 14b. The intrinsic linewidth is suppressed from 2.2 MHz in  
Figure 13: (**a**) Linewidth narrowing phenomenon measured by the local oscillator (LO)-heterodyne method; (**b**) linewidth narrowing phenomenon measured by delayed self-heterodyne method.  
free-running state to 103.5 Hz upon mutual injection locking. The simulation matches the improvement of coherence indicated by self-heterodyne signals shown in Figure 13b and the intrinsic linewidth achieved in Figure 14b.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm
### _Subproblem 2: Local Accuracy_  
When \(h\), \(\mathbf{q}_{m}\), \(\mathbf{f}\), \(\mathbf{t}\), \(\mathbf{p}\), \(\mathbf{b}\) are determined, the \(\eta\) optimization problem can be expressed as the P5 problem  
\[\begin{array}{rl}\text{P5}:&\min\limits_{\eta}\frac{\lambda_{1}\log_{2}\left( \frac{1}{\eta}\right)+\lambda_{2}}{(1-\eta)}\\ &\text{s.t.~{}\text{C1}:&\text{$I_{g}\left(t_{i}+I_{l}\frac{C_{l}D_{i}}{J_{i}} \right)\leq T,\forall i\in\mathcal{N}_{m}$}\\ &\text{C2}:&\text{$0\leq\eta\leq 1$}\end{array} \tag{20}\]where \(\lambda_{1}=a\sum\limits_{i\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\kappa C_{i}D_{i}f _{i}^{2}\), \(\lambda_{2}=a\sum\limits_{\begin{subarray}{c}i\in\mathcal{N}_{m}\\ i\in\mathcal{N}_{m}\end{subarray}}^{|\mathcal{N}_{m}|}t_{i}p_{i}\) are constants. The constraint C1 is equivalent to \(t_{i}\leq v(\eta_{i})\), where \(v(\eta)=\frac{1-\eta}{a}T+\frac{\zeta_{i}D_{i}\log(\eta)v}{f_{i}}\), because \(v(\eta)^{\prime\prime}\leq 0\), so \(v(\eta)\) is a convex function, and the authors of [10] provided specific proof. Therefore, constraint C1 can be converted to \(\eta^{\min}\leq\eta\leq\eta^{\max}\), where \(\eta^{\max}=\min\limits_{i\in\mathcal{N}_{m}}\eta^{\max}_{i}\), \(\eta^{\min}=\max\limits_{i\in\mathcal{N}_{m}}\eta^{\min}_{i}\). So, the P5 problem is transformed into the P6 form.  
\[\begin{array}{ll}\text{P6}:&\min\limits_{\eta}\frac{\lambda_{1}\log\left( \frac{1}{\eta}\right)+\lambda_{2}}{(1-\eta)}\\ &\text{s.t.\,C1}:\eta^{\min}\ \leq\ \eta\ \leq\ \eta^{\max}\end{array} \tag{21}\]  
Obviously, the P6 problem is a fraction form at this time, so it is difficult to solve it directly. Based on the work of [10, 28], the P6 problem is equivalent to finding the root of the nonlinear function \(H(\zeta)\).  
\[H(\zeta)=\min\limits_{\eta^{\min}\leq\eta\leq\eta^{\max}}\lambda_{1}\log(1/ \eta)+\lambda_{2}-\zeta(1-\eta) \tag{22}\]  
We can therefore use the Dinkelbach Method to solve the above question. The details are shown in Algorithm 2.  
```
Input: Initial \(\zeta=\zeta^{(0)}>0\), \(\eta^{\min}\), \(\eta^{\max}\), iteration number \(r_{2}=0\), and set the accuracy \(e\) Output:\(\eta^{*}\)
1:repeat
2: Computing the optimal \(\eta^{*}=\lambda_{1}/\zeta^{(n)}\)
3:if\(\eta^{*}<\eta^{\min}\)
4:\(\eta^{*}=\eta^{\min}\)
5:elseif\(\eta^{*}>\eta^{\max}\)
6:\(\eta^{*}=\eta^{\max}\)
7:endif
8: Update \(\zeta^{(n+1)}=(\lambda_{1}\log_{2}(1/\eta^{*})+\lambda_{2})/(1-\eta^{*})\)
9: Set \(r_{2}=r_{2}+1\)
10:until\(\Big{|}H\Big{(}\zeta^{(n+1)}\Big{)}\Big{|}/\Big{|}H\Big{(}\zeta^{(n)}\Big{)} \Big{|}<e\)
```  
**Algorithm 2** Local Accuracy Optimization Algorithm