esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dual-Objective Optimisation Parameters
With the compromise solution for the hydraulic thermal parameters, we simulate the water, vapor, and heat transports. Figure 7 presents the simulated results of \(\theta\) (Figure 7a), \(T\) (Figure 7b), \(h\) (Figure 7c), and \(q\) (Figure 7d) at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm under different dual-objective functions. In contrast to single-objective optimization, overall the optimization results are improved over the original when adding another variable to any of the variables as a dual-objective. This improvement is especially noticeable when additional variables are incorporated based on \(T\), leading to enhanced overall simulation performance, especially in simulating water movement. In addition, we note that the results of the dual-objective optimization are generally improve when the  
Figure 6: Normalized range of optimal parameters values for the nine soil parameters. Grey, red, and blue line represent the Pareto optimal solutions, the extreme value solution of the objective function \(F_{1}\), and the extreme value solution of the objective function \(F_{2}\), respectively. The objective variables of the graphs are: (**a**) \(\theta+T\), (**b**) \(\theta+q\), (**c**) \(T+h\), and (**d**) \(h+q\). objective variable contains either \(\theta\) or \(h\), especially for the simulation of \(\theta\) and \(h\). Therefore, among all the dual-objective combinations, the simulation performance are the worst when \(T+q\) are used as the objective variables. In practice, using \(T+q\) for parameter inversion may lead to poor water and heat movement simulations. We also plot a 1:1 line to quantitatively analyze the simulation results (Figure 8). The 1-4 rows display the simulation results for \(\theta\), \(T\), \(h\), and \(q\) under various dual-objective  
Figure 7: Comparisons of simulated and observed values of (**a**) \(\theta\), (**b**) \(T\), (**c**) \(h\), and (**d**) \(q\) at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm under different dual-objective functions. The observed values are represented by solid black circles. The black, light blue, green, pink, dark blue, and red lines represent the simulated values when the objective functions are \(\theta+T\), \(\theta+h\), \(\theta+q\), \(T+h\), \(T+q\), and \(h+q\), respectively. functions, while the 1-6 columns correspond to different objective variables pairs: \(\theta+T\), \(\theta+h\), \(\theta+q\), \(T+h\), \(T+q\), and \(h+q\). Overall, compared to the single objective, the points under the double objective variables are more concentrated on both sides of the 1:1 line, which further indicates that the simulation performance is better under the dual-objective functions. We can clearly observe that when \(T+q\) are used as the objective variables (Figures 8e,k,q,w), the \(R^{2}\) of \(\theta\) and \(h\) are 0.98 and 0.96 respectively, which is lower than the simulation results under other dual-objective functions. The simulation of \(T\) and \(q\) performs poorly, with \(R^{2}\) below 0.7.
## 2 Materials and Methods
### Location Estimation Methods of Autonomous Driving Robot
#### 2.2.4 Heading Measurement Using IMU Sensor  
IMU sensors are used to estimate the attitude of aircrafts, ships, mobile robots, etc., by converging gyroscopes, acceleration sensors, and geomagnetic sensors. Although IMU sensors show high performance in estimating the position or angle of a mobile vehicle, they accumulate a large error when estimating the travel distance by integrating the acceleration. The IMU sensor used in this study is a small attitude and heading reference system device with a three-axis gyroscope, acceleration sensor, and a geomagnetic sensor (E2 BOX, Hanam, Korea). The acceleration, angular velocity, and magnetic force data measured by the IMU sensor are output in the form of a Euler angle by applying a Kalman filter, and correction algorithms such as the Robust Attitude and Robust Heading Algorithms are applied to minimize the error.
## 3 Results and Discussion
### Potential Study of Wolframite-Coated Cathode  
It should be pointed out that an enhanced power density, implying more efficient cathodic reactions, happened with the wolframite photocathode. Potential curves for four different types of photo/dark cathodes were obtained under actual operating conditions (Figure 6). Both anode and cathode potentials dropped gradually from the open circuit condition to short-circuit and a linear increase relationship was observed because of the domination of ohmic polarization. As far as the microbial anode was concerned, a similar potential of approximately \(-455\) mV was recorded under open circuit conditions, which was consistent with previously reported MFCs [19]. Moreover, significant potential was presented when the current densities were limited by the electron transfer process on the electrode surface (Figure 5a). However, the turning position positively shifted from 340 mA\(\cdot\)m\({}^{-2}\) to 620 mA\(\cdot\)m\({}^{-2}\) when using the wolframite photocathode. In addition, higher current densities and more negative potentials, indicating enhanced anodic bio-activity, occurred in the anode chamber. As shown in Figure 5b, the cathode potentials were more positive when a wolframite coated cathode was used rather than a bare graphite electrode in the MFC. The cell voltage of the MFC was calculated by \(E_{\text{cell}}=E_{\text{cathode}}-E_{\text{anode}}\), therefore, the highest voltage was observed in the wolframite photocathode MFC system. It is worth mentioning that the potential of the wolframite-coated cathode was further increased under light irradiation (Figure 6b). A higher potential, indicating a better ability for receiving electrons, was observed for the wolframite photocathode. The results unambiguously demonstrated that a solar-driven MFC was successfully built based on the synergistic effect of a wolframite photocathode and bioanode. Such cathodic potentials and reaction properties demonstrated that the natural wolframite was an efficient cathode material for electron accepting in MFCs.
## 3 Results and Analysis  
### Path-Tracking Deviation Pattern Identification Test  
#### 3.1.1 Test Preparation  
1. Evaluation Index  
In this study, the performance of the DBN model was carried out using the K-Fold cross-validation method, and the performance of each model was assessed by calculating Loss, MAE, Macro-F1 and Accuracy as in Equations (27)-(30).  
\[\text{Loss}:-\frac{1}{M}\sum_{i=1}^{M}\sum_{i=1}^{N}y_{i}\text{log}\big{(} \hat{y}_{ij}\big{)}, \tag{27}\]  
\[\text{MAE}:\frac{1}{M}\sum_{i=1}^{M}|y_{i}-\hat{y}_{i}|, \tag{28}\]  
\[Macro-F1:mean\left(\frac{2\cdot TP}{2\cdot TP+FP+FN}\right), \tag{29}\]  
\[Accuracy(\%):\frac{TP+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN}} \times 100, \tag{30}\]  
Figure 11: Response surface of the Equation (26): color from orange to blue indicate \(Decr_{test}\) from high to low.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{3}{c}{**Coefficient**} & \\ \cline{2-5}  & \(A\) & \(B\) & \(VAR_{test}\)/m\({}^{2}\) & \(Decr_{test}\)/m\({}^{2}\) \\ \hline
1 & 0.96 & 0.96 & 0.001125 & 0.000140 \\
2 & 0.96 & 0.64 & 0.001131 & 0.000134 \\
3 & 0.96 & 0.32 & 0.001143 & 0.000123 \\
4 & 0.64 & 0.96 & 0.001084 & 0.000181 \\
5 & 0.64 & 0.64 & 0.001090 & 0.000175 \\
6 & 0.64 & 0.32 & 0.001100 & 0.000165 \\
7 & 0.32 & 0.96 & 0.001041 & 0.000224 \\
8 & 0.32 & 0.64 & 0.001047 & 0.000218 \\
9 & 0.32 & 0.32 & 0.001057 & 0.000208 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Coefficient comparison test results.  
where \(M\) is the number of samples, \(N\) is the number of categories, \(y\) is the sample label, and \(\hat{y}\) is the prediction label.  
DBN performance is affected by a variety of hyperparameters, including the number of hidden layers, the number of hidden layer nodes, and the learning rate. Referring to DBN parameter setting [27], a DBN with a structure of 9-100-100-3 is set up based on engineering experience (trial-and-error method), and the parameters are shown in Table 10.  
Where the learning rate scale factor \(LS\) is multiplied by the learning rate \(LR\) to update the learning rate and prevent the model from overfitting due to too large a learning rate. The number of nodes in the input layer \(L_{input}\) is equal to the number of features in the dataset, and the number of nodes in the output layer \(L_{output}\) is equal to the total number of categories in the data labels. Other model hyperparameters, such as the number of nodes, batch size, learning rate, etc., need to be adjusted empirically.
### Research Objectives  
The main objective of this research is to develop a modernized model for predicting penetration depth using machine learning symbolic regression genetic programming in Python. The model can predict the penetration depth with greater accuracy on a wide range of 257 tests, compared with the NDRC and Li and Chen models. The total of 257 test datasets from [2; 8; 10; 13; 14; 15; 16; 19] is divided into three categories of narrow penetration depths, where \(X\)/\(d\) \(<\) 0.5; intermediate penetration depth, where 0.5 \(<X\)/\(d\) \(<\) 5.0; and deep penetration, where \(X\)/\(d\) \(>\) 5.0. \(X\) is the penetration depth and \(d\) is the diameter of the projectile. The significance of this research is that a reasonable number of 257 test datasets are trained and tested using machine learning symbolic regression with a genetic programming crossover, sub-tree transfiguration, hoist transfiguration, and point transfiguration to develop a model with better accuracy. The proposed model further compares with the NDRC and Li and Chen equations.
# Development of an Ontology-Based Solution to Reduce the Spread of Viruses
## 2 Presentation of the Study Environment  
According to the latest data from the World Health Organization, a large number of virus-specific symptoms have been encountered [11]. Often these viruses share many symptoms, such as fever, a change in facial features, and diarrhea. Where one of them appears, medical intervention must be carried out. The first symptoms may appear within 5 to 6 days after infection, but this period can extend to 14 days. The most common symptoms can be mentioned:  
* Fever: a person is considered to have a fever if his temperature is 38 degrees Celsius or more, and it may also be accompanied by flu symptoms such as headache, cough, sore throat, shortness of breath, and chest pain [12].
* Dry cough: coughing without sputum output. In rare cases, the cough may be accompanied by blood [13].
* Sore throat: a sore throat is often an early symptom, and due to the inhalation of respiratory viruses, they enter the nose and throat first and may multiply there early, which leads to throat pain and irritation [14].
* Pains and aches: these include muscles and joint pain.
* A persistent sense of \"nasal douche\": early nasal symptoms that often appear with loss of sense of smell (or decreased sense of smell) and loss of taste.  
The patient needs to have a medical file to monitor his health condition accurately (Table 1). This information is very important for the realization of the knowledge base, which will be approached in the next part of this work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Information** & **Description** & **Type** \\ \hline Identifier & Patient’s number is the same as the medical file number. & Alphanumeric \\ Name & Patient name & Alphabetical \\ Sex & Gender (male or female) & Alphabetical \\ Nationality & Patient’s nationality & Alphabetical \\ Date of birth & Patient’s date of birth & Alphanumeric \\ Marital status & Marital status of the patient (single/married) & Alphabetical \\ Profession & Patient’s profession & Alphabetical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of patient-file information.
## 4 Results
### Results of Update  
For each update step, an MCS has been performed, conducting 10,000 simulation runs for each. The results of the performance prediction updates are compared to the actual data and the prediction from planning in the first step. Figure 12 demonstrates that the prediction of the project duration improves significantly with the update steps. The actual project duration of 157 days was already barely in the prediction range of the second update after 25% of the project progress. By evaluating this result, it must be acknowledged that,  
Figure 10: Comparison of probability density functions of updated ringbuild duration input parameter and the histogram of the total actual recorded data for ringbuild duration [34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Update Step** & \multicolumn{2}{c}{**Distribution**} & **Parameters** \\ \hline \multirow{2}{*}{Planning phase} & A & Weibull & loc = 0.0; a = 15; \(\beta\) = 55 \\  & B & Weibull & loc = 0.0; a = 10; \(\beta\) = 25 \\ \hline \multirow{2}{*}{Update 1 (200 m)} & A & Beta & p = 4.083571, q = 5.214493, min = 0.065696, \\  & B & - & max = 80.314226 \\ \hline \multirow{2}{*}{Update 2 (500 m)} & A & Weibull & loc = 0.0; a = 4.086579; \(\beta\) = 42.181451 \\  & B & Log-Logistic & \(\alpha\) = 23.45602; \(\beta\) = 6.63146 \\ \hline \multirow{2}{*}{Update 3 (1000 m)} & A & Log-Logistic & \(\alpha\) = 38.517733: \(\beta\) = 4.850808 \\  & B & Gamma & \(\alpha\) = 8.584783; \(\beta\) = 2.448307; \(\gamma\) = 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Probability density functions for the update steps of the advance speed [mm/min] input parameter [34].  
in this illustrative study, only the core processes of advance and ringbuild were updated and that even better improvements in the performance prediction could be reached by updating further input parameters (see Section 3.2.2).  
In the next step, the mean value of the simulated duration of the updated ringbuild and advance processes is compared in Table 4 to highlight the impact of the prediction of the updated parameters. It can be observed that already after the first update, after 10% of the construction progress, the deviations in the predicted and the actual process durations could be reduced from 21% to 11% (advance) and from 18% to 13% (ringbuild). With the second update, the deviations are already below 10%.  
Figure 11: Comparison of probability density functions of updated advance speed input parameter and the histogram of the actual recorded advance speed for (**a**) section A and (**b**) section B [34].  
Figure 12: Comparison of the actual project duration to the results of an MCS with 10,000 simulation runs for the prediction made in planning with the updated predictions during construction [34].  
The improvement in the prognosis due to the update of the input parameters is overlaid by the effect of the integration of the actual construction process into the simulation. The effective impact of the update of the input parameters is therefore analyzed in the next step.
## Abbreviations  
\begin{tabular}{l l} DL & Deep learning \\ MVDR & Minimum variance unbiased response \\ GAN & Generative adversarial network \\ MS-GLMB & Multi-sensor generalized labeled multi-Bernoulli \\ RFS & Random finite set \\ T-F & Time–frequency \\ SNR & Signal-to-noise ratio \\ CSM & Complex spectral mapping \\ FoV & Field of view \\ GCRNN & Graph convolutional recurrent neural network \\ RDN & Residual dense network \\ SRP-PHAT & Steered response power phase transform \\ IPD & Inter-microphone phase difference \\ TV & Time-varying \\ GSC & Generalized side-lobe canceller \\ GSP & Graph signal processing \\ GCN & Graph convolutional network \\ CM & Contiguous memory \\ LFF & Local feature fusion \\ ReLU & Rectified linear unit \\ IN & Instance normalization \\ k-NN & k-nearest neighbors \\ LSTM & Long short-term memory \\ SI-SNR & Scale invariant signal-to-noise ratio \\ MSE & Mean square error \\ ISM & Image source method \\ RIR & Room impulse response \\ ESTOI & Enhanced short time objective intelligibility \\ PESQ & Perceptual evaluation of speech quality \\ SDR & Signal-to-distortion ratio \\ TI-MVDR & Time invariant minimum variance unbiased response \\ SCM & Signal covariance matrix \\ ISCM & Instantaneous signal covariance matrix \\ AIR & Aachen impulse response \\ ANOVA & Analysis-of-variance \\ \end{tabular}
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work  
CC algorithms are one of the key factors in determining network performance. After decades of development, existing CC algorithms are mainly divided into two categories, one based on the end-to-end mode [14] and the other based on the in-network control mode [15]. The CC algorithm based on the end-to-end mode treats the network as a black-box system. A series of adaptive algorithms are used to adjust the data transmission rate and window value of the sender. The CC algorithm based on the in-network control mode treats the network as a white-box system, which can obtain information such as the congestion situation in the network. The following is a brief introduction to both types of algorithms.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology  
The methodology of this research for the classification of sleep stages from EEG signals is based on a classic deep learning pipeline consisting of five steps: data acquisition and preprocessing, data splitting, class balancing, model training, and performance evaluation. Initially, EEG signals are extracted from PSG records and the corresponding sleep stage annotations are retrieved from the associated hypnogram (HYP) files. Subsequently, preprocessing and filtering are applied to the data to enhance the quality. The data are split into training, validation, and testing subsets to assess model training and evaluation. To handle imbalance issues among classes, this work introduces a random undersampling method, which is applied at the epoch level, specifically targeting the training and validation sets. Afterward, several neural network architectures, including a CNN, BiLSTM, and the attention mechanism in combination, are trained, validated, and tested in the subsets. Performance is measured based on several metrics, such as accuracy, the F1-score, and Cohen's kappa, among others. The procedural steps are outlined in the diagram in Figure 4. The following sections provide a detailed analysis of each step of the methodology, along with their theoretical foundations.
# Mobile Charging Scheduling Approach for Wireless
## 5 Experimental Results
### Experimental Setup  
The parameters adopted in the simulation are similar to those in [13]. We assume the sensors are randomly placed in a monitoring area \([0,1]\times[0,1]\), and the charging service station is located at (0,0). Initially, the MC is located at (0,0) and equipped with a full and rechargeable battery, of which the capacity is \(E_{\text{MC}}^{max}=100\) J. The initial remaining energy of the sensors is randomly distributed between 20 J and 40 J, and the capacity of the sensor is \(E_{\text{ST}}^{max}=50\) J. The moving speed and charging speed of MC are 0.1 m/s and 1 J/s, respectively. The energy consumed by MC moving one meter is 0.1 J, and the threshold of the number of failed sensors is 0.5.  
Some state-of-the-art approaches are selected as the baseline approaches to compare with the proposed approach, including the greedy algorithm, NJNP [9], TSCA [10], actor-critic reinforcement learning (ACRL) [13], and deep reinforcement learning for target coverage charging (DRL-TCC) [14]. The proposed approach and the baseline approaches are implemented with Python 3.9.7 and TensorFlow 2.7.0, and all experiments are conducted on a computer with 3.4 GHz Intel Core i7, 16 GB RAM, and NVIDIA GeForce 2080 Ti GPU. The time of all experimental training is between 12 and 36 h, depending on the complexity of the test. The test will be terminated early when any condition in (6) is not satisfied.  
The parameters adopted in training are summarized in Table 2. To clarify the neural network structure of different approaches based on the reinforcement learning framework, we summarize the input and output structures of neural networks in different approaches in Table 3. Furthermore, considering that the baseline approaches cannot adaptively adjust the charging upper threshold [13], we give the optimal fixed charging upper threshold of the baseline approaches, based on different numbers of sensors, described in Table 4. As for the important metrics, we use the average of 10 test results as the final result.
## 2 Materials and Methods
### Acoustic Measures and AVQI  
Because certain acoustic measures used in AVQI are only valid for voiced segments, the continuous speech samples needed to be edited. Detection and extraction of voiced segments from the first two sentences of the speech task were performed in the software Praat using the extraction Praat-script from [PERSON] et al. (2009) [5], an algorithm based on three criteria, proposed by [PERSON] and [PERSON] (2001) [6]. A segment was considered to be voiced if (a) sound energy exceeded 30% of the overall sound energy, (b) zero crossing rate was less than 1500 Hz, and (c) the normalized autocorrelation peak was above 0.3. Subsequently, the medial three seconds of the vowel /a/ were appended to the voiced segments, resulting in a concatenation sample with a correspondent single sound waveform. The six acoustic measures implemented in AVQI were obtained using the software Praat. First, harmonics-to-noise-ratio (i.e., HNR) was specified as the base-10-logarithm of the ratio between the periodic energy and the noise energy multiplied by 10. Secondly, shimmer local (i.e., SL) was the absolute mean difference between the amplitudes of successive periods divided by the average amplitude. Thirdly, the shimmer local dB (i.e., SLdB) was obtained as the base-10-logarithm of the differences between the amplitudes of successive periods multiplied by 20. Fourthly, the general spectral slope (i.e., Slope) was calculated as the difference between the energy in the 0-1 kHz range and the energy in the 1-10 kHz range of the long-term average spectrum. Fifthly, the spectral trendline inclination (i.e., Tilt) was measured as the difference between the energy in the 0-1 kHz range and the energy in the 1-10 kHz range of the trendline through the long-term average spectrum. Finally, the sixth and main acoustic measure implemented in AVQI, smoothed cepstral peak prominence (i.e., CPPS), was calculated as the distance between the first rahmonic's peak and the point with equal quefrency on the regression line through the smoothed cepstrum. To determine the CPPS in the software Praat on a Windows or Mac computer, the following steps were completed: First, click on the \"Analyze periodicity -\" menu, choose \"To PowerCepstrogram... \", and then complete the \"Sound: To PowerCepstrogram\" form as shown in Figure 1. Second, query the resulting PowerCepstrogram object by clicking on the \"Query -\" menu, choose the \"Get CPPS... \" option, and then finally complete the \"PowerCepstrogram: Get CPPS\" form as shown in Figure 2. The result of these steps is that the CPPS is shown as a value in dB in a \"Praat Info\" screen. Ultimately, AVQI (v.02.06) was calculated according to the following regression formula:  
\[\begin{array}{l}\text{AVQI}=[(3.295-(0.111\times\text{CPPS})-(0.073\times \text{HNR})-(0.213\times\text{SL})+(2.789\times\text{SLdB})-(0.032\times\text{ Slope})+(0.077\times\text{Tilt}))\times 2.208]+1.797\\ \end{array}\]