esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
In this paper, the proposed methodology is structured around an ensemble learning framework, which is designed to enhance predictive performance by combining multiple machine learning models. The process begins with the acquisition of a dataset, specifically vehicle sensor data, stored in CSV format. These data are preprocessed to ensure consistency and quality, which includes merging multiple data frames, identifying missing values, and performing statistical analysis to understand the distributions and characteristics of the data. As shown in Figure 2. After that, the method moves on to the exploratory data analysis (EDA) phase, where different graphs like histograms and boxplots are made to help see the underlying patterns and find any numbers that do not fit the norm. This step is crucial as it directly informs the subsequent preprocessing strategies. Following the EDA, the pre-processed data undergoes further transformation, where numerical features are standardized using a \"standard scaler\" to ensure that they contribute equally to the model's performance [30]. The target variables, which include attributes like driving style and road surface conditions, are encoded using a \"label encoder\" to convert them into a format that can be processed by machine learning algorithms. The methodology's core is the application of ensemble learning via stacking. This involves training multiple base classifiers, in this case, a random forest classifier, a decision tree classifier, and a K-nearest neighbor classifier on the training data. Predictions from each classifier (Pred RF, Pred DT, and Pred KNN) serve as input features for the final meta-classifier. The meta-classifier, implemented via a soft voting mechanism in a \"voting classifier\", combines the predictions of the base classifiers to make a final prediction. This ensemble approach leverages the strengths of each individual classifier and often results in improved overall performance. The method includes an evaluation step that uses accuracy metrics, confusion matrices, and ROC curves to assess the strength of the individual classifiers and the ensemble model. We then utilize the best-performing model for the final predictions. Lastly, the methodology embraces the concept of explainable (AI) by employing techniques such as the tree interpreter to provide insights into the contribution of each feature to the predictive models. This is instrumental in understanding the decision-making process of the educational model, thereby enhancing the transparency and trustworthiness of the predictions. This all-around method includes data preprocessing, EDA, model training, evaluation, and explanation. Its goal is to provide a full framework for predictive modeling in-vehicle data analysis.
# Use of Evolutionary Computation to Improve Rock Slope Back Analysis
## 4 Optimization Strategy for Back Analysis  
Many geotechnical and other engineering problems can be formulated as optimization problems, for example stability-type problems. An optimization algorithm is a useful and powerful technique in both deterministic and probabilistic slope stability analysis.  
Generally, optimization algorithms can be divided into two basic classes: deterministic and heuristic/metaheuristic algorithms. The deterministic methods are based on a gradient function. However, the acquisition of gradient information can be very difficult or, in some cases, altogether impossible to obtain. The metaheuristic method is not restricted in this manner. The metaheuristic methods include algorithms such as genetic algorithms, particle swarm optimization, simulated annealing, Tabu search, and ant colony optimization. The metaheuristic methods are derivative free methods and can be applied to any optimization problem irrespective of the linearity or non-linearity of the objective function to be minimized and constraints.  
The application of optimization in slope stability problems was first investigated by [PERSON] in 1980 [26]. In this approach, [PERSON] combined an optimization search using the dynamic programming technique with [PERSON]'s method [27] to locate the critical slip surface and calculate the associated factor of safety. The method proposed by [PERSON] can be applied to slopes of any geometry, layering, pore pressure and external load distribution and could find a more critical slip surface with a lower factor of safety compared to preceding methods.  
Other studies [28; 29] proposed the genetic algorithm methodology for determining the critical slip surface for circular and multiple-wedge stability analysis, respectively. The results of these two studies demonstrated the effectiveness of the genetic algorithm approach.  
Figure 2 shows the optimization strategy for back analysis. The numerical model simulates the construction process and the geometry of geotechnical structures. Then, the optimization algorithm performs an iterative process computing solution for the geotechnical parameters to minimize the objective function. The objective function also known as the error function assesses the discrepancy between the computed and the monitored or measured values.
## 7 Simulation Results and Performance Analyses
### The Energy Consumption  
This section compares and analyzes the six protocols by the energy consumption. As displayed in Figure 11, when 300 network nodes are considered, the total energy consumption of the network increases as the number of rounds rises no matter which protocol is used. Nevertheless, the proposed EECRP has the best performance in energy consumption. For instance, in round 400, the total energy consumption of our proposed EECRP, FCMMFO, FBCPSO, EGRC, LEACH-ERE, and LEACH accounts for 26.5%, 28.6%, 33.4%, 36.2%, 51.2%, and 63.6% of the initial energy of the whole network, respectively. With respect to the situation where the network energy is exhausted, the EECRP has improved the energy efficiency by 46.5%, 8.2%, 18.8%, 26.7%, and 5.1% compared to the LEACH, FBCPSO, EGRC, LEACH-ERE, and FCMMFO, respectively. That is because the EECRP employs the BPNN to fuse the data and uses the optimal multi-hop paths for data transmission, thus minimizing the energy consumption.  
Moreover, Figure 12 illustrates the number of rounds when the energy of the whole network is completely consumed under the different number of network nodes, which verifies the influence of the different number of nodes on energy dissipation. With the decrease in the number of nodes, the distances between nodes increase, which consumes more energy for nodes to transmit data and thus shortens the network lifetime. However, the EECRP has the best performance among these protocols in all situations. For instance, when 250 nodes are considered in the network, the proposed EECRP protocol is 9.5%, 16.6%, 23.5%, 32.4%, and 67.1% more efficient than the FCMMFO, FBCPSO, EGRC, LEACH-ERE, and LEACH, respectively.  
Figure 11: The total energy consumption of the network versus the number of rounds.  
Figure 10: The number of rounds when FND (first node dead), HND (half of the nodes dead), and LND (last node dead) arise for different protocols.
## 4 Experimental results
### Network simulation  
The positions of the sensors are provided in [35], and the distribution of the sensors in the laboratory can be seen in Fig. 6. We analyzed the communication costs in different routing trees which were generated in the following way. The root node was always assumed to be the top right sensor node in Fig. 6 (node \(16\) in [35]). The routing trees were generated on the basis of the sensor positions and the radio range was varied from \(6\) meters (minimum threshold such that all sensor could find a parent) to \(50\) meters (all sensors in radio range of the root node). Starting from the root node, sensors were assigned to their parent in the routing tree using a shortest path metric, until all sensors were connected. An illustration of the routing tree obtained for a maximum communication range of \(10\mathrm{m}\) is reported in Fig. 6.
## 1 Introduction  
More than 3000 people die in road crashes every day worldwide, including approximately 1000 people in the United States. Such accidents cost $518 billion globally and $230.6 billion per year in the United States. In particular, many young people are affected by traffic accidents, as most traffic accidents are caused by the 15-29 age group (Association for Safe International Road Travel Data). Hence, substantial efforts have been made to address the main factors that cause traffic accidents, such as speeding, distracted driving, driver fatigue, road conditions, use of mobile phones, and poor weather conditions.  
Regarding bad road conditions due to the weather, previous studies have revealed that there is a strong correlation between road friction coefficient and traffic accident risk [1, 2]. The friction coefficient can be influenced by road conditions such as ice or wetness caused by snow or rainfall. Research has revealed that single-vehicle accidents are mostly caused by wet weather [3]. When a layer of water builds between the tire of a vehicle and the road surface, hydroplaning occurs. If all wheels experience hydroplaning simultaneously, the driver can no longer control the vehicle [4, 5]. It has been shown that hydroplaning occurs at vehicle speeds of 80 km/h on thick water films when the water depth exceeds 2.5 mm [6]. According to the Korean Transportation Safety Authority, the total number of fatalities in road traffic accidents in the country has decreased, but rain-related road deaths increased from 430 in 2013 to 463 in 2016. In particular, in Seoul, the traffic fatality rates on rainy days were the highest among all cities [7].  
Traffic accidents are unpredictable and can happen anywhere and at any time. However, drivers can at least be provided with useful information to help avoid accidents or reduce their probability. The forecasting of traffic accidents and the identification of related factors under various conditions are important for preventing accidents and reducing their frequency. For these reasons, traffic accident prediction models have been developed to reveal the significant factors that affect traffic accidents so that traffic safety can be improved by controlling and/or improving these factors [8].  
The rest of this paper is organized as follows. Next, we provide literature reviews on traffic accident forecasting and machine learning applications. The methodologies of the proposed random forest, artificial neural networks (ANN), and decision tree algorithms are introduced, and the data sets employed are described. The modeling results are presented as the estimated values (importance) of factors influencing accidents as well as interpretations of the model evaluations including the mean square error (MSE) and root mean square error (RMSE). Finally, a discussion is provided and conclusions are drawn.
## 10 Concluding Remarks  
As it has been aforementioned in Introduction, the main objective of this paper was to discuss the specifics of the quality measures of the generated mesh with respect to numerical calculations and fidelity of simulations. The obtained results lead to the following conclusions.  
1. In the situation similar to that considered in this paper, the low quality mesh may alter the results of numerical simulations by a factor greater than two. In the context of modeling transmission of signals in the synapse such a low-fidelity result makes the simulation useless.
2. In the contrary to opinions expressed in a few papers, the dihedral angle in the case of our simulations did not prove an important parameter determining the quality of the simulation in the considered case.  
The problem of generating the three-dimensional mesh itself is not trivial [30; 31]. If the geometry of the simulated object is relatively simple\(-\)in our example, a geosphere or a globe with regular internal structures (like the spherically-shaped domain of neurotransmitter supply)\(-\)then generation of a 3D mesh of the object does not cause radical problems. The tetrahedral mesh generator like TetGen can easily produce a mesh of a satisfying quality (Tables 2, 4, and Figure 1). On the other hand, if the geometry of the model is complex, then generation of the mesh is faced with essential difficulties. Even such dedicated software as TetGen is not always capable of dealing with the problem. The quality of the created mesh is significantly worse (see Table 7 as well as Figures 10-12). However, even moderately optimized meshes seem to produce satisfying results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time[s]** & **Mesh 1** & **Mesh 2** & **Mesh 3** \\ \hline
0.0022 & 1.67572458 & 0.93988859 & 0.91254579 \\
0.0023 & 2.52562556 & 1.25460856 & 1.25423054 \\
0.0024 & 1.42968640 & 0.76737063 & 0.77887697 \\
0.0025 & 1.01510141 & 0.71655252 & 0.71123427 \\
0.0026 & 0.80204156 & 0.59739848 & 0.60125321 \\
0.0027 & 0.36672566 & 0.33349662 & 0.32721117 \\ \hline \end{tabular}
\end{table}
Table 11: Release of neurotransmitter during exocytosis phase for three different meshes of the real bouton model [vesicles/time step]. Mesh 1—the worst mesh, Mesh 2—Mesh 1 improved with TetGen, Mesh 3—Mesh 2 improved with Stellar
## 2 Materials and Methods
### Photobiomodulation Application  
For all the included patients, the surface of the denture and palatal mucosa were sprayed with a methylene blue (MB) photosensitizer (ExSyn, Mumbai, India) at a concentration of 450 \(\mathrm{\SIUnitSymbolMicro mL}\) mixed in water. After 10 min of spraying, irradiation was carried out using a gallium-aluminum-arsenide (GaA1 As) diode laser. The laser was systematically moved across the entire surface of the denture to ensure comprehensive coverage. Both the anatomical structure of the palatal mucosa and the intaglio surface of the denture prosthesis were exposed to the laser. The characteristics of the applied laser, including the wavelength (660 nm (nm)), energy density (28 J/cm\({}^{2}\)), laser output (100 mW (mW)) power, and mode of transmission (continuous), were standardized. After treatment, the prosthesis was bathed in clean water and dried on absorbent paper. The PBM treatment was carried out 2 times a week with 72 h intervals for a period of 8 weeks. To optimize the therapeutic effect while minimizing potential damage to surrounding healthy tissues, the operator systematically directed the laser beam to different points on the anatomical structures during the irradiation process. Additionally, all patients were advised to scrub the dentures under running water with a soft toothbrush after meals and bedtime and soak the dentures overnight in clean water.
## 4 Conclusions  
This paper mainly compares the fixation performances of cephalomedullary nail in terms of the pull-out force when applying screw and helical blade types with respect to femur bone densities. It is substantially important to guide which types of fixation are applied during the operation, since it tends to induce the backout or migration of helical blade and screw depending on the weight and bone density under the fully tightened condition. To compare the pull-out forces between the normal person and an elderly person who has osteoporosis, two different densities of biomedical PUR foam blocks are utilized. The pre-installed screw or helical blade is pulled out while tracking the pull-out load until the final failure occurs. To make a physical comparison of the critical fixation force of screw and helical blade, we have suggested that stable fixation is guaranteed when the pull-out force, F\({}_{\text{pull-out}}\), is larger than F\({}_{\text{proj}}\). Based on these close examinations, the following conclusions can be drawn:  
1. The clamping force of the screw is about 177% higher than the helical blade in the normal foam density (0.35 g/cm\({}^{3}\)) and 198% in the low foam density (0.14 g/cm\({}^{3}\)).
2. Based on the previous experimental data [35], for a 100 kg person with normal bone density, both types of fixation such as screw and helical blade are available considering walking activity, since the pull-out force, F\({}_{\text{pull-out}}\), is greater than the projected force, F\({}_{\text{proj}}\). However, the helical blade type is evaluated as more favorable, since the screw-type tends to require predrilling for lateral cortex opening, inducing inevitable loss of cancellous bone.  
Figure 14: Comparison of projected force and pull-out force.  
* (3) For a 100 kg person with low bone density, the screw type is recommended to guarantee high clamping force against migration or loosening during walking activity.  
Conceptualization, J.Y. and Y.-C.P.; Data curation, D.-S.C., K.-Y.K., and Y.D.; Formal analysis, K.-Y.K. and Y.D.; Writing--original draft, S.-J.P. and J.Y.; Writing--review & editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
The author Prof. [PERSON] received research funding from the National Research Foundation of Korea (NRF-2020R1C1C1013166), and the author Prof. [PERSON] received research funding from the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2019R1A2C4070160).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 3 System Performance  
In this paper, two metrics are considered to evaluate the performance of FSO vertical links. First, the amount of harvested energy in the UAV is analyzed through the average EH parameter. Second, the received signal quality is studied in terms of the bit error rate (BER) parameter.  
In particular, we must remark that the harvested energy analysis detailed below is valid for any modulation scheme; nevertheless, our BER analysis will be particularized for a variant of the OOK scheme previously optimized for energy harvesting under a fixed peak power constraint [31]. An OOK scheme was selected because of its simplicity and low power consumption, especially interesting due to limited on-board UAVs battery lifetime. In such a scheme, named OOK-EH, a power \(P_{1}=P_{m}\) is emitted for the transmission of a logical '1', where \(P_{m}\) is the peak power of the laser; whilst a fraction of \(P_{m}\) is carried for transmitting a logical '0', i.e.,  
\[P_{0}=\zeta P_{m}, \tag{13}\]  
where \(0\leq\zeta<1\). In contrast to the classical OOK, where no light is emitted when a logical '0' is transmitted, our proposed scheme always carries a power level that will depend on the \(\zeta\) parameter. This added DC component increases the average optical power \(P_{av}\) and can be used to improve energy collection by adjusting the \(\zeta\) parameter appropriately. Furthermore, and in order to maximize the harvested energy, a power level \(P_{m}\) is also emitted when the transmitter is idle. However, any increase in the \(P_{0}\) level will reduce the dynamic range given by the peak average optical power ratio (PAOPR) and, consequently, its associated BER will be increased. The PAOPR is given by \(2/(1+\zeta)\). Consequently, there is an important trade-off between increasing harvested energy and degrading BER. For any particular set of channel conditions, this trade-off is achieved for an optimal \(\zeta\) that maximizes the harvested energy while keeping the BER performance below a predetermined target BER.  
In order to evaluate the performance of any vertical FSO link with our proposed OOK-EH technique under realistic channel conditions, analytical closed-form expressions for the average EH and BER are derived in this section, considering the aforementioned main phenomena degrading the quality of the received signal, i.e., noise, turbulence, path loss, geometric spread and pointing errors.
## 5 Dynamic Bandwidth Allocation
### Rate Stipulation for the Active Flows  
When active VM-to-VM flows are generated by the VDCs, the traffic rates should be stipulated for active VM-to-VM flows. The narrowest link can be anywhere in PN, and the traffic rates of VM-to-VM flows from the same source could be different because of various bottlenecks existing in the network. To ensure fair competition and avoid congestion among these flows, first of all, we pick up the narrowest link by counting active VM-to-VM flows; then, we further stipulate the sending and receiving rates for each flow. Based on the assumptions that there is a flow from VM-1 to VM-2 and that the network bottlenecks can only occur at the access link, the initial rates of VM-1 and VM-2 are 55 and 50, respectively, and the narrowest link where congestion occurs is the access link, as shown in Figure 6c. Therefore, by setting the bandwidth of VM-1 as 50, the excess bandwidth is added to VM-3, thus VM-3 gets 50. The narrowest link is determined according to the number of VM-to-VM flows belonging to the same VDC. If there are two active flows from VM-3 to VM-4 and to VM-5, respectively, then the narrowest link bandwidth for each VM-to-VM flow is 25.
## 5 Conclusions  
A machine learning-based fault diagnosis has been proposed for identifying delamination in fiber-reinforced laminated structures. Glass fiber-reinforced rectangular laminate composite plates with and without delamination were considered to develop a finite element model. The forced vibration responses of the delaminated and laminated composite structures were acquired using an in-house developed finite element program using MATLAB(r). The statistical information that was extracted from the forced vibration response was used to diagnose the damage in the laminated composite plates using machine learning algorithms. The greatest contributing features were identified from the extracted features using the attribute evaluator. The selected features were further classified using machine learning algorithms, such as decision tree, random forest, naive Bayes, and Bayes net algorithms for diagnosing the damage in the laminated structure. The decision tree algorithm was found to be a computationally effective model in diagnosing the delamination of the composite structure. It was observed that the proposed model produced 98.5% accuracy in diagnosing the damage in the fabricated composite structure. Hence, this research work motivates us to develop an online prognostic and health monitoring module for detecting early damage to prevent catastrophic failures of structures.  
Conceptualization, S.G.; Methodology, J.R.; Software, L.J. and S.D.; Investigation, L.J., J.R. and Y.X.; Resources, S.M.; Writing--review & editing, S.R. and Y.X.; Supervision, L.J. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vellore Institute of Technology (VIT) Chennai under VIT SEED GRANT and The APC was funded by VIT Chennai.  
Not applicable.  
The authors thank the Management of, School of Mechanical Engineering, Centre for Automation, VIT Chennai and Cranfield University, Cranfield, UK, for their support in publishing this work. Additionally, the authors would like to thank the research scholar [PERSON], who helped acquire the signals.  
The authors declare no conflict of interest.