esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 1 Introduction
### Our Contribution  
First, we define the 2E-EVRP-TW problem by means of a three-index node-based mixed-integer linear programming (MILP) model. Any general-purpose MILP solver, such as CPLEX or Gurobi, may be used to solve this model. However, due to the multi-tier structure of the distribution network, the limited driving range of electric vehicles, and the time window constraints, the 2E-EVRP-TW problem is rather complex. In fact, our computational experiments show that CPLEX is only able to solve small-sized problems to optimality. Therefore, we also developed a variable neighborhood search (VNS) approach to solve the problem. In addition, we developed an initial solution generation method based on [PERSON]'s savings algorithm [PERSON] and [PERSON] (1986), considering 2E-EVRP-TW assumptions and characteristics. The VNS approach makes use of this heuristic to obtain an initial solution.  
VNS provides a powerful search performance by systematically changing neighborhood structures (shaking) to avoid getting stuck in local optima and by intensifying the search in the vicinity of the incumbent solution by applying local search. In addition to the classical shaking and local search operators, we also utilize large neighborhood search (LNS) operators known as \"destroy and repair\", resp. \"removal and insertion\", to enhance the performance of VNS. In this context, note that, since (1) the problem dimension of the first echelon is much smaller than that of the second echelon and (2) the first echelon does not include any constraint, it can easily be solved using a savings heuristic. Therefore, whenever the solution for the second echelon changes, the first echelon tours are generated again by utilizing our [PERSON] and Wright savings heuristic.  
Finally, a last contribution concerns the generation of new problem-specific benchmark sets. This was necessary due to the lack of an available benchmark set for the 2E-EVRP-TW problem.
# Improving the Energy Conversion Efficiency for Hydrokinetic Turbines Using MPPT Controller
## 3 Findings and Interpretation of Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Water Velocity** & **Experimental Power** & **Power Output According to** & **Approximation** \\
**(m/s)** & **Output (W)** & **Simulation (W)** & **Error (\%)** \\ \hline \(1.33\) & 110 & 105 & 4.54 \\ \(1\) & 60 & 63 & 5 \\ \(0.83\) & 30 & 42 & 40 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison between experimental and simulation results. Figure 8: Rectifier DC power output. Figure 9 shows all the three voltages: the rectifier output voltage (blue); the DC boost converter output voltage (green); and the buck converter output voltage, Vbuck (red). The buck converter output fed a resistive load that was simulated considering the duty cycle was set to 40%, as can be seen in the block diagram from Figure 2.  
As shown in Figure 9, the buck converter maintained the voltage in a narrow range and operated as expected; thus, it was demonstrated that its role is essential in stabilizing the Vdc voltage, which has significant variations after the boost converter. The limitations of the algorithm are related to using a pure resistive load instead of a battery model, which would better simulate a realistic system. If the considered load cannot use the whole power provided by the turbine, then there may be losses in the operation of the system. This can be overcome by using a battery system to store the power which is not needed at a certain time. Therefore, future research directions may focus on improving the algorithm by considering a battery between the buck converter and load. The simulated algorithm can be integrated in a hardware device provided with storage (battery + BMS), which will maximize the extracted power. This kind of device can be suitable for all types of low-power applications (phone charging, night lighting, signaling, data communication, etc. ).
## 3 Results and Discussion
Comparison of Physiological Activities of Unfermented and Fermented Methanolic Extracts of CF Leaves and Unhulled Seeds  
On the basis of the results presented in the preceding section, we used only the methanolic extracts of CF leaves and unhulled seeds for our subsequent fermentation experiments. Figure 2 illustrates the physiological activities of the unfermented and fermented methanolic extracts of CF leaves and unhulled seeds. Compared with the DPPH scavenging activity assay, ABTS is more suitable because this assay can be used for measuring the antioxidative capacity of hydrophilic and hydrophobic compounds [34]. Thus, an ABTS scavenging activity assay was conducted. Figure 2A presents that the _A. oryzae_-refmented methanolic CF extract was more effective compared with the unfermented CF extract (methanolic extract). The IC\({}_{50}\) values of the unfermented leaf extract, fermented leaf extract, unfermented unhulled seed extract, and fermented unhulled seed extract were 293.6, 22.9, 362.7, and 63.5 mg/L, respectively. The ABTS scavenging activities of the fermented leaf extract and seed extract were higher than those of the unfermented leaf extract and unfermented unhulled seed extract by 12.8 and 5.7 times, respectively. The IC\({}_{50}\) value (22.9 mg/L) of the fermented leaf extract for scavenging ABTS was superior to that (100 mg/L) of the ethanolic extract of CF hull and that (2350 mg/L) of the CF product fermented by _L. plantarum_[30; 35]. The improvement in the ABTS scavenging activity of the _A. oryzae_-refmented CF leaf extract was more pronounced compared with that of the seed extract. Figure 2: _Cont._  
Figure 2B presents the antityrosinase activity of the unfermented and fermented extracts of CF leaves and unhulled seeds. After fermentation, the antityrosinase activity of the extracts was enhanced. The IC\({}_{50}\) values of the unfermented and fermented leaf extracts and unfermented and fermented unhulled seed extracts were 226.5, 14.1, 282.8, and 29.6 mg/L, respectively. The antityrosinase activities of the fermented leaf and unhulled seed extracts were 16.1 and 9.6 times higher than those of the corresponding unfermented extracts, and the effect of fermentation was significant on the CF leaf extract. The antityrosinase activities of extracts after fermentation were considerably higher than those of the CF hull extract after high pressure extraction [30]. Thus, the fermented extracts were more active than the unfermented extracts. CF leaves are typically discarded as waste; therefore, the CF leaf methanolic extract was selected for fermentation in subsequent experiments.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 6 Application for Regional Railway Line  
As an initial test of track fault detection using machine learning, we created clusters of simple features from the car-body vibration data for a regional railway line. To measure the vibration, we used an on-board sensing device comprising an accelerometer, a gyroscope, and a GNSS receiver as shown in Figure 12.  
Figure 11: Detection results.  
Figure 12: Measurement of car-body vibration in a regional railway line.  
The training data were measured in January, February, May, June, July, and November 2016 for sections of the line where repairs for longitudinal level irregularities were performed in October 2016. Clusters of abnormal track data were created based on the following conditions:  
* Longitudinal level irregularity: vertical acceleration \(\geq 1.0\,\mathrm{m/s^{2}}\)
* Alignment irregularity: lateral acceleration \(\geq 1.1\,\mathrm{m/s^{2}}\) and roll rate \(\leq 0.05\,\mathrm{rad/s}\)
* Cross level irregularity: roll rate \(\geq 0.05\,\mathrm{rad/s}\)  
Figure 13 shows the feature space of car-body vibration in a regional railway line. The normal cluster was constructed from car-body vibration data measured in November 2016, immediately after longitudinal level irregularities were repaired. The algorithm detects the location and type of track fault for every 10 m of track. Using the diagnosis algorithm, we diagnosed the track condition in May 2018.  
Figure 14 shows the measured RMS values for the car-body vibrations and the results obtained by the detection algorithm (colour map in the upper part of each figure). Locations where the RMS values for vertical acceleration, lateral acceleration, and roll rate are large are successfully diagnosed as longitudinal level, alignment, and cross level irregularities, respectively.  
As shown, multiple track faults were detected in location from 25.8 km to 25.9 km. The results indicated that combined faults can be detected by the developed algorithm. Therefore, we can conclude that the proposed method is effective for identifying the location and type of track fault in a regional railway line.  
Figure 13: Feature space of car-body vibration in a regional railway line.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
###### Abstract  
Fire is an abnormal event that can cause significant damage to lives and property. Deep learning approach has made large progress in vision-based fire detection. However, there is still the problem of false detections due to the objects which have similar fire-like visual properties such as colors or textures. In the previous video-based approach, Faster Region-based Convolutional Neural Network (R-CNN) is used to detect the suspected regions of fire (SRoFs), and long short-term memory (LSTM) accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then, majority voting of the short-term decisions is taken to make the decision reliable in a long-term period. To ensure that the final fire decision is more robust, however, this paper proposes to use a Bayesian network to fuse various types of information. Because there are so many types of Bayesian network according to the situations or domains where the fire detection is needed, we construct a simple Bayesian network as an example which combines environmental information (e.g., humidity) with visual information including the results of location recognition and smoke detection, and long-term video-based majority voting. Our experiments show that the Bayesian network successfully improves the fire detection accuracy when compared against the previous video-based method and the state of art performance has been achieved with a public dataset. The proposed method also reduces the latency for perfect fire decisions, as compared with the previous video-based method.  
deep learning; fire detection; Faster R-CNN; spatiotemporal feature; LSTM; majority voting; dynamic fire behavior; Bayesian network +
Footnote †: journal: Applied  
1
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 2 Related Work
The aim was to maximize energy efficiency while maintaining the QoS. To obtain a greater throughput in D2D communication, the joint scheduling problem on mode selection, radio resource allocation, and power coordination was investigated in [24]. The aim was to improve the traffic offloading capacity of cellular-assisted D2D communication by incorporating D2D relays. Relay-aided and \"local route\" (i.e., devices communicate utilizing the BS as a relay station) D2D were evaluated, and the suggested model increased the capacity of relay-enabled cellular-assisted D2D communication. The simulation results indicated that the suggested technique improves the traffic offloading capacity of D2D-capable UEs in LTE networks.
# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
## 6 Results and Discussion
### Hinselmann
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{l}{**Class Balancing Technique**} & \multicolumn{1}{l}{\(AUC\)} & \multicolumn{1}{l}{\(\sigma(AUC)\)} & \multicolumn{1}{l}{\(\overline{MCC}\)} & \multicolumn{1}{l}{\(\sigma(MCC)\)} \\ \hline \multirow{6}{*}{Logistic regression} & Random oversampling & 0.79 & 0.02 & 0.55 & 0.04 \\  & SMOTE & 0.78 & 0.05 & 0.49 & 0.06 \\  & ADASYN & 0.73 & 0.04 & 0.42 & 0.03 \\  & SMOTEN & 0.79 & 0.03 & 0.50 & 0.06 \\  & SMOTETOMEK & 0.78 & 0.11 & 0.49 & 0.09 \\ \hline \multirow{6}{*}{KNN classifier} & Random oversampling & 0.98 & 0.02 & 0.96 & 0.02 \\  & SMOTE & 0.97 & 0.04 & 0.92 & 0.05 \\  & ADASYN & 0.90 & 0.09 & 0.80 & 0.04 \\  & SMOTEEN & 0.97 & 0.02 & 0.94 & 0.02 \\  & SMOTETOMEK & 0.97 & 0.03 & 0.92 & 0.02 \\ \hline \multirow{6}{*}{SVM classifier} & Random oversampling & 0.75 & 0.13 & 0.49 & 0.15 \\  & SMOTE & 0.86 & 0.19 & 0.64 & 0.21 \\  & ADASYN & 0.76 & 0.15 & 0.52 & 0.17 \\  & SMOTEN & 0.80 & 0.13 & 0.47 & 0.12 \\  & SMOTETOMEK & 0.79 & 0.15 & 0.49 & 0.16 \\ \hline \multirow{6}{*}{MLP Classifier} & Random oversampling & 0.97 & 0.08 & 0.95 & 0.09 \\  & SMOTE & 0.94 & 0.07 & 0.93 & 0.06 \\ \cline{1-1}  & ADASYN & 0.94 & 0.02 & 0.94 & 0.02 \\ \cline{1-1}  & SMOTETON & 0.94 & 0.05 & 0.93 & 0.06 \\ \cline{1-1}  & SMOTETOMEK & 0.94 & 0.03 & 0.92 & 0.04 \\ \hline \multirow{6}{*}{Naive Bayes classifier} & Random oversampling & 0.61 & 0.02 & 0.58 & 0.02 \\ \cline{1-1}  & SMOTE & 0.78 & 0.02 & 0.70 & 0.02 \\ \cline{1-1}  & ADASYN & 0.76 & 0.01 & 0.68 & 0.01 \\ \cline{1-1}  & SMOTEN & 0.82 & 0.02 & 0.76 & 0.02 \\ \cline{1-1}  & SMOTETOMEK & 0.80 & 0.01 & 0.72 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results achieved with Hinselmann output. The hyper-parameters used to construct the above-presented classifiers are presented in Table 10.
# Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration
## 4 An Application Example
Then, based on Equations (37) and (39), we calculate the values of \(d_{R}\), \(d_{T}\) and \(d_{R}/2d_{T}\), respectively, as follows:  
\(UC_{R}=\left(3\gamma_{0}^{*}\sqrt{\left(\frac{\delta_{0}^{*}}{\gamma_{0}}-E_{ 0}(0.01)\right)^{2}+\frac{\left(n-1\right)R}{\gamma_{0.005\gamma_{n-1}}^{2}} }\right)^{-1}\)  
\(=\)\(\left(3\times 0.31\sqrt{\left(\frac{0.31}{0.26}-0.3485\right)^{2}+\frac{35 \times 0.4}{16.03}}\right)^{-1}\)  
\(=\)\(1.136\)  
\(UC_{M}=\left(3\sqrt{\left(\delta_{0}^{*}\right)^{2}+\frac{\left(n-1\right)R}{ \gamma_{0.5n-1}^{2}}}\gamma_{0}^{*2}\right)^{-1}\)  
\(=\left(3\sqrt{\left(0.31\right)^{2}+\frac{35\times 0.4}{34.34}\left(0.26\right)^{2}} \right)^{-1}\)  
\(=\)\(0.748\)  
Figure 3 presents a diagram of membership function \(\eta(x)\) with \(UC_{M}=0.748\), \(VL=1.000\) and \(UC_{R}=1.136\). \(d_{R}=UC_{R}-VL=1.136-1.0=0.136\)  
\(d_{T}=UC_{R}-UC_{M}=\)\(1.136-0.748=0.388\)  
\(\frac{d_{R}}{2d_{T}}=\frac{0.136}{2\times 0.388}=0.175\)  
Step 7: Decision and discussion  
Let \(\phi_{1}=0.2\) and \(\phi_{2}=0.4\). According to the fuzzy test rules, if \(d_{R}/2d_{T}<\phi_{1}=0.20\), then reject \(H_{0}\) and conclude \(C_{PIM}<1.0\). In fact, when \(C_{PIM}^{*}=0.95\), the value of \(UC_{PIM}\) with \(\alpha=0.05\) is 1.053. If the result of the statistical inference is \(UC_{PIM}\geq 1.0\), it is obvious that the fuzzy evaluation model proposed by this study seems to be more reasonable than the statistical inference.
# How Much Does the Dynamic F0 Curve Affect the Expression of Emotion in Utterances?
###### Abstract  
The modulation of vocal elements, such as pitch, loudness, and duration, plays a crucial role in conveying both linguistic information and the speaker's emotional state. While acoustic features like fundamental frequency (F0) variability have been widely studied in emotional speech analysis, accurately classifying emotion remains challenging due to the complex and dynamic nature of vocal expressions. Traditional analytical methods often oversimplify these dynamics, potentially overlooking intricate patterns indicative of specific emotions. This study examines the influences of emotion and temporal variation on dynamic F0 contours in the analytical framework, utilizing a dataset valuable for its diverse emotional expressions. However, the analysis is constrained by the limited variety of sentences employed, which may affect the generalizability of the findings to broader linguistic contexts. We utilized the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), focusing on eight distinct emotional states performed by 24 professional actors. Sonorant segments were extracted, and F0 measurements were converted into semitones relative to a 100 Hz baseline to standardize pitch variations. By employing Generalized Additive Mixed Models (GAMMs), we modeled non-linear trajectories of F0 contours over time, accounting for fixed effects (emotions) and random effects (individual speaker variability). Our analysis revealed that incorporating emotion-specific, non-linear time effects and individual speaker differences significantly improved the model's explanatory power, ultimately explaining up to 66.5% of the variance in the F0. The inclusion of random smooths for time within speakers captured individual temporal modulation patterns, providing a more accurate representation of emotional speech dynamics. The results demonstrate that dynamic modeling of F0 contours using GAMMs enhances the accuracy of emotion classification in speech. This approach captures the nuanced pitch patterns associated with different emotions and accounts for individual variability among speakers. The findings contribute to a deeper understanding of the vocal expression of emotions and offer valuable insights for advancing speech emotion recognition systems.  
emotional speech recognition; fundamental frequency (F0); pitch contours; generalized additive mixed models (GAMMs); non-linear dynamics; speech processing +
Footnote †: journal: Applied sciences
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 5 Experiments
### Results  
The performance of the forecasting models was assessed based on the test set (the last 951 samples) of each series. For the sake of simplicity, the proposed method is henceforth referred to as HetDS(\(m\),\(k\)), standing for a heterogeneous ensemble dynamic selection with parameters \(m\) (best-selected models) and \(k\) (size of the region of competence). Table 3 presents the results achieved by the models described in Section 2, ARIMA, DBN, ELM, GB, MLP, RF, and SVR, and the variations of the proposed models. The values presented are the average of 10 independent simulations, and the best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Station** & **Coordinates** & **Altitude** & **Mean** & **STD** & **CV** \\ \hline Florianopolis & \(-27.0253\); & \(48.620096\) & \(4.87\) & \(1230.7\) & \(1101.2\) & \(0.895\) \\ Fortaleza & \(-3.815701\); & \(29.55\) & \(1223.9\) & \(883.18\) & \(0.722\) \\ Salvador & \(-13.551500\); & \(47.56\) & \(1348.2\) & \(1122.6\) & \(0.833\) \\ São Paulo & \(-23.496294\); & \(785.64\) & \(1355.4\) & \(1204.6\) & \(0.889\) \\ \hline \end{tabular}
\end{table} 
Table 2: Location of the solar irradiation stations and descriptive statistics regarding the mean and deviation of solar irradiation.
## 4 Depth Estimation Algorithm
### Processing Steps
This leads to the multi-view stereo problem, because every point of interest is projected by several micro-lenses. Correspondence is searched for a given \(CT\{P_{ri}(x,y)\}\) of a reference  
Figure 5: Micro-lenses hexagonal arrangement in the MLA. Figure 6: Two adjacent micro-lenses in an MLA with center points and a reference point \(P_{r}(x,y)\). micro-image \(I_{ri}(c_{xi},c_{yi})\) in target micro-images \(I_{ti}\). To save computation costs, search is restricted by matching only target pixels that lie on the epipolar lines within the neighborhood \(N(P_{r}(x,y))\) micro-images. Since the micro-lenses in the MLA are rectified and their respective planes are parallel, the epipolar line \(e_{p}\) of a stereo pair is parallel to the \(b\), as shown in Figure 7. A virtual point \(P_{v}\) is projected on two adjacent micro-images planes \(I_{1}\) and \(I_{2}\), at P and P', with centers \(C_{1}\) and \(C_{2}\) respectively. For each micro-image pair, stereo-matching is performed, the respective error is then subsequently compared. It results in a best-match for the given \(P_{ri}(x,y)\). The modified algorithm uses Hamming distance as the cost function to determine the error of similarity during the process of finding the correspondence. Along with the computation of best-match, respective disparity is computed. Finally, in _depth-evaluation_, virtual depth is estimated by using corresponding disparity and baseline distance of the best-match measurement.
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### WiFi  
The IEEE suite of standards for wireless LANs, IEEE 802.11, is the most commonly deployed wireless standard within homes. As such, the devices and integrated circuits (ICs) are relatively cheap, making it an attractive solution. Amendments to the standard incorporate mesh networking capability, which is used in HANs. The data rate of WiFi ranges from 11 Mbps to 54 Mbps. It operates in the 2.4 GHz band, and it has a range of 30-46 m.
## 4 CFD Modelling
### Turbulence Modelling  
The turbulence modelling at the Reynolds-Averaged Navier-Stokes (RANS) approach requires the modeling and correlation of the Reynolds stresses to the mean velocity gradients. The Boussinesq hypothesis is used in the \(k-\varepsilon\) model using the following relation:  
\[-\rho\overset{\rightarrow}{u_{t}^{\prime}u_{j}^{\prime}}=\mu_{t}\left(\frac{ \partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right)- \frac{2}{3}\left(\rho k+\mu_{t}\frac{\partial u_{k}}{\partial x_{k}}\right) \delta_{ij} \tag{4}\]  
where \(\delta_{ij}\) is the Kronecker's delta, \(k\) is the turbulence kinetic energy, and \(\mu_{t}\) the turbulent viscosity.  
The \(k-\varepsilon\) turbulence closure scheme was appropriately parameterized for atmospheric flows. It assumes \(\mu_{t}\) is an isotropic scalar quantity and it is computed as a function of the model transport equations for the turbulence kinetic energy, \(k\) and its dissipation rate, \(\varepsilon\). The two additional transport equations are given below [15]:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{i }}=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho\varepsilon-Y_{ m}+S_{k} \tag{5}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac {\mu_{t}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{ j}}\right]+C_{1\varepsilon}\frac{\varepsilon}{k}(G_{k}+C_{3\varepsilon}G_{b})-C_{2 \varepsilon}\rho\frac{\varepsilon^{2}}{k}+S_{\varepsilon} \tag{6}\]  
In Equations (5) and (6), \(G_{k}\) represents the generation of turbulence kinetic energy \(k\) due to the mean velocity gradients, \(G_{b}\) is the generation rate of \(k\) due to buoyancy, \(Y_{M}\) is the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, and \(S_{k}\) and \(S_{\varepsilon}\) are source terms.  
The turbulent (or eddy) viscosity, \(\mu_{t}\) is computed by:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{7}\]  
where \(C_{\mu}\) is a constant. The constants of the standard \(k-\varepsilon\) model are modified according to the analysis presented in detail in [PERSON] et al. [16] as follows:  
\[C_{\mu}=0.033,\ C_{1\varepsilon}=1.12,\ C_{2\varepsilon}=1.83,\ \sigma_{k}=1.0,\ \sigma_{ \varepsilon}=1.3 \tag{8}\]