esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### A Comparison of the Three Models Using Validation Accuracy  
Table 1 shows the CNN architecture, number of epochs, batch size, optimizer, dataset sizes, types of skin cancers/lesions, number of kernels, kernel size, padding, dropout rates, and activation functions. This thorough comparison highlights the differences and similarities that influence each model's performance and accuracy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Comparison Factor** & **Model 1** & **Model 2** & **Model 3** \\ \hline CNN model & VGG16 & VGG16 & VGG16 \\ \hline Number of epochs & 40 & 40 & 40 \\ \hline Batch size & 32 & 32 & 32 \\ \hline Optimizer & Adam & Adam & Adam \\ \hline Number of images in training dataset & 14,454 & 13,232 & 10,232 \\ \hline Number of images in validation dataset & 1671 & 820 & 820 \\ \hline Types of skin cancers/lesions & bcc/mel/NV & bcc/mel/NV & bcc/mel/NV \\ \hline Number of kernels & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of the 3 models during the training and validation steps using some factors.  
Table 2 summarizes the results of the three models. Model 1, trained on 14,454 images, achieved a validation accuracy of 86%. In contrast, Model 2 and Model 3, trained on smaller datasets of 13,232 images and 10,232 images, respectively, achieved higher validation accuracies of 94% and 93%. For the loss values of these models, Model 2 and Model 3 achieved better results compared to Model 1.  
Figures 4-6 represent a set of visual representations that help clarify the obtained results further. Each figure includes accuracy learning curves, loss learning curves, and the confusion matrix for each model.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Comparison Factor** & **Model 1** & **Model 2** & **Model 3** \\ \hline Kernel size & \(\mathbf{3\times 3}\) & \(\mathbf{3\times 3}\) & \(\mathbf{3\times 3}\) \\ \hline Padding & valid & valid & valid \\ \hline Dropout & 0.75 & 0.75 & 0.75 \\ \hline Activation function & Softmax & Softmax & Softmax \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: (a) The validation accuracy curve of Model 1; (b) the validation loss curve of Model 1; and (c) the confusion matrix of Model 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Comparison Metrics** & **Model 1** & **Model 2** & **Model 3** \\ \hline validation accuracy & 86\% & 94\% & 93\% \\ \hline loss & 38\% & 16\% & 17\% \\ \hline \end{tabular}
\end{table}
Table 2: A comparison of the 3 models’ performance.  
Figure 5: (**a**) The validation accuracy curve of Model 2; (**b**) the validation loss curve of Model 2; and (**c**) the confusion matrix of Model 2.  
Figure 6: (**a**) The validation accuracy curve of Model 3; (**b**) the validation loss curve of Model 3; and (**c**) the confusion matrix of Model 3.
# Robust Adaptive Synchronization of a Class of Uncertain Chaotic Systems with Unknown Time-Delay
## 1 Introduction
To illustrate the proposed approach in engineering applications, thishas been exploited for secure communication via multiple heterogeneous chaotic systems. In [28], an observer-based sliding mode control method is proposed to synchronize the time-delayed chaotic neural networks with unknown perturbation. The utilization of the proposed controller for the master and slave chaotic systems with non-identical structure as well as the slave system with unknown disturbance is the advantage of the proposed procedure. One of the items that can be considered for time-delayed uncertain systems is the uncertainty of the time delay existingin the system. This issue can pose serious challenges to the controller design process for a variety of purposes, including the synchronization process. On the other hand, the uncertainty of the amount of time delay in the system gives rise to an increase in the degree of complexity of the system model, which can be considered asenhancing the level of data security in the field of secure telecommunications. Accordingly, this paper deals with an adaptive control strategy for robust synchronization of uncertain chaotic systems with unknown fixed and variable time delay. Using [PERSON]'s direct method and the Lipschitz condition in chaotic systems, the update rules, and the estimation of the unknown parameters are determined to ensure the stability of the proposed controller. Finally, in order to evaluate the performance of the proposed control strategy, the synchronization of two Jerk chaotic systems with unknown fixed and variable time-delay coupled with uncertainty and disturbance are investigated and simulated. The simulation results exhibit the effectiveness of the proposed adaptive control method for robust synchronization due to uncertainty, external distortions, and unknown fixed and variable time-delays. Also, based on the results, the proposed control strategy has been effective in estimating uncertain parameters and unknown fixed and variable delays. This paper is organized as follows: in Section 2, the basic definitions used in this paper are presented. Adaptive controller design to robust synchronization of fractional order chaotic systems with unknown time-delay, uncertainty and disturbance is givenin Section 3. The synchronization of two Jerk chaotic systems with unknown fixed and variable time delay and uncertainty and disturbance is analyzed and simulated in Section 4, and finally, in Section 5, conclusions based on the stated theories are presented.
## 5 Methodology
### System Initialization  
In our system, we envision a setup with a total of \(n\) users, represented by the set \(U\), where each user \(i\) is assigned a unique identifier ranging from \(1\) to \(n\). The server begins by setting up the initial model parameter \(w_{0}\) and choosing a large prime number \(p\). We define \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\) as two distinct multiplicative cyclic groups that operate under multiplication and have the same prime order \(p\), with \(g_{1}\) and \(g_{2}\) serving as their respective generators.  
The server also picks a secret number \(\kappa\in\mathbb{Z}_{p}^{*}\), and calculates \(h_{1}\) and \(h_{2}\) as \(g_{1}^{\kappa}\) and \(g_{2}^{\kappa}\). Additionally, the server defines a bilinear mapping function \(e:\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{T}\) and selects two hash functions: \(H_{0}:\{0,1\}\rightarrow\mathbb{Z}_{p^{2}}^{*}\) and \(H_{1}:\{0,1\}\rightarrow\mathbb{G}_{1}\).  
After these preparations, the server makes public the system parameters, which include \(p,w_{0},H_{0},H_{1},\mathbb{G}_{1},\mathbb{G}_{2},e,h_{1},h_{2},g_{1}\), and \(g_{2}\). Furthermore, each user \(\mathcal{U}_{i}\) independently selects a private key \(sk_{i}\in\mathbb{Z}_{p}^{*}\), the fog node (FN) chooses its own private key \(sk_{FN}\in\mathbb{Z}_{p}^{*}\), and the server selects a private key \(sk_{CS}\in\mathbb{Z}_{p}^{*}\).
## 2 Review and Analysis
### Concentrating Solar Panels  
Let us consider concentrating solar panels (CSP) [22; 23]. Laying concentrators under the heat-absorbing elements of solar panels allows for increasing the temperature of the heat-carrying medium to 120-250 \({}^{\circ}\)C. Parabolocylindrical concentrators have the shape of a parabola elongated in a straight line, the focus of which is a tube with a heat-carrying medium heated to a temperature of 300-390 \({}^{\circ}\)C. They can be reflective and lenticular. Reflective concentrators are mirrors and surfaces of a certain shape (paraboloid) made of special materials [24]; they are made up to 50 m long and placed in rows at a distance of several meters when oriented along the north-south axis.  
Parabolic concentrators have the shape of a satellite plate (Figure 4) [24] and are controlled by two coordinates when tracking the sun. The focusing of solar energy is carried out on a small area, and the reflection of solar radiation falling on them by mirrors is about 92%. Photoelectric elements or a Stirling engine, the heating area of which was in the focus of the reflector, are fixed in the focus of the reflector on the bracket. Usually, hydrogen or helium is used as a heat-carrying medium in a Stirling engine.  
Figure 4: Diagram of a parabolic-cylindrical concentrator: 1—concentrator; 2—receiver; 3—solar energy.  
The concentration of solar radiation makes it possible to reduce the size of the heat-receiving element, but there is a need to cool the system. It is also necessary to use mechanisms for tracking them after the Sun and to constantly keep the concentrator clean. This significantly increases the cost of the received energy and complicates the operation of such plants. Therefore, a number of scientific studies were conducted to increase the efficiency of solar energy concentrating installations [11; 25; 26].
## 4 Results and Discussion
### Objective Evaluations  
1. Comparison between baseline and proposed methods: objective evaluations were first performed to compare the performance of CD, PESQ, STOI, and segSNR of the proposed and reference methods introduced above. Tables 1 and 2 summarize the objective assessment results of source esophageal speech ES-SRC, and enhanced speech obtained by JD-GMM, DNN, LSTM, BiLSTM, and Seq2 Seq-based methods. First, we can see that the cepstral vectors of ES-SRC are very different from those of laryngeal speech (target). Then, since the JD-GMM is a linear model and has a poor ability to model nonlinear relationships, its performance in converting ES to laryngeal voice is poorer than all other methods. Compared to the DNN model, the LSTM and BiLSTM models have better inter-frame characterization capability because the LSTM networks can take advantage of the relationship between long-distance frames. As indicated in Table 1, the BiLSTM model achieved better conversion performance than the GMM, DNN, and LSTM methods. This is because we adopt the BiLSTM model in our proposed Seq2 Seq since it is adequate to characterize the difference between ES and its laryngeal speech counterpart. Note that the Seq2 Seq-based method has a similar inter-frame characterization ability to the BiLSTM. However, our proposed method adopts the principle of the attention mechanism to accomplish an adaptive mapping between parallel sequences of esophageal and laryngeal speech. Compared to the BiLSTM model, our proposed method has improvements in CD, PESQ, STOI, and segSNR.
2. Comparison between different variants: in this experiment, we compared our proposed model based on a BiLSTM encoder-decoder network with three variants of this model: * (V1) Keeping the BiLSTM encoder but excluding the attention mechanism; * (V2) Replacing the BiLSTM encoder by an LSTM of 256 hidden units and keeping attention; * (V3) Using the LSTM network but excluding attention.  
Table 3 lists the evaluation results. The proposed method outperforms the three other variants, with a larger PESQ score and lower CD value. In addition, adding the attention mechanism seems to have little effect on encoder-decoder networks based on LSTM. This explains that the hidden state \(\vec{h}_{i}\) generated by an LSTM network only considers the information in \(X_{\leq i}\); consequently, the attention mechanism will be inefficient due to insufficient information in the source hidden states. Moreover, the proposed method exceeds its variant without attention in terms of PESQ and STOI, which indicates that the incorporation of the attention mechanism would improve the performance of the VC system.
## 3 Results
### Adsorption Isotherm
The Freundlich model is a multilayer adsorption model. The linear form of the Freundlich model is written in Equation (5). \[\ln q_{e}=\ln k_{f}+\frac{1}{n}\ln C_{e} \tag{5}\]  
where \(K_{f}\) is the Freundlich constant, expressing the capacity, \(C_{e}\) defines the adsorbate concentrations of analytes, \(q_{e}\) is the quantity of ions at equilibrium, and \(n\) is the Freundlich exponent. \(K_{f}\) and \(n\) were determined from plot \(\ln q_{e}\) vs. \(\ln C_{e}\) (Figure S3). The \(n\) value shows the nature of adsorption (\(1/n<1\): normal process), (\(1/n>1\): cooperative process) [38; 39; 40]. The Temkin isotherm depicts the interaction between the adsorbate and the adsorbent and causes a linear decrease in the adsorption energy with surface coverage of the adsorbent. Equation (6) shows the linear form of this model. \[q_{e}=\frac{RT}{B}\ln A_{T}+\frac{RT}{B}\ln C_{e} \tag{6}\]  
where \(A_{T}\) is the Temkin isotherm equilibrium binding constant, \(R\) of \(8.314\times 10^{3}\) J mol\({}^{-1}\) K\({}^{-1}\) is the universal gas constant, \(T\) is the absolute temperature, and \(B\) is the model constant and shows the heat of absorption [40; 41]. Both \(B\) and \(A_{T}\) can be extracted from the plot \(q_{e}\) vs. \(\ln C_{e}\) (Figure S4). The Halsey isotherm model expresses the multilayer adsorption of the analyte over the heterosporous adsorbent. Equation (7) shows the linear form for the model. \[\ln q_{e}=(\frac{1}{n_{H}})\ln K_{H}-(\frac{1}{n_{H}})\ln C_{e} \tag{7}\]  
The Halsey isotherm model constant and Halsey isotherm model exponent are represented as \(K_{H}\) and \(n_{H}\), respectively [38; 40; 41]. This model is plotted in Figure S5. The Elovich model is based on multilayer adsorption, assuming exponential increasing trend in the adsorption sites as adsorption takes place. It is given by the equation below:  
\[\ln\biggl{(}\frac{q_{e}}{C_{e}}\biggr{)}=\ln(K_{e}q_{m})-\frac{q_{e}}{q_{m}} \tag{8}\]  
\(K_{e}\) and \(q_{m}\) are the Elovich constants, representing the initial adsorption rate and maximum adsorption capacity, respectively [38; 40; 41]. Figure S6 shows the model. Among the five mentioned models, Pb(II) follows the Freundlich model to be adsorbed onto EgLiCl-mGO, but Langmuir is fitted with the adsorption of Cd(II) onto EgLiCl-mGO. Therefore, the adsorption of Pb(II) onto the surface of EgLiCl-mGO is multilayered and a different layer of Pb(II) can be adsorbed onto each other but the adsorption of Cd(II) onto the surface of EgLiCl-mGO is the monolayer and after the first layer of Cd(II) no more Cd(II) is adsorbed. Isotherm parameters of each model are summarized in Table 6. R\({}^{2}\) shows the best model for each of analyte. All the figures (Figures S1-S6) are plotted in the Electronic Supplementary Materials (ESMs).
## 5 Results And Discussion  
### Linear Trajectory  
Figure 15 illustrates the cable management results for the linear trajectory including buoy distance control mode, passive slack mode, and passive taut mode. The feeder appears to be quite reactive and smooth in winding/unwinding the cable depending on ROV motion and the distance between buoys. The buoy-ballast system exhibits highly dynamic transient behavior between points 2 and 4 due to the changes in velocity. When moving backward, the feeder also takes more time for buoy gap convergence back to the target distance (0.7 m).  
Figure 16 presents the paths followed by the ROV projected onto the horizontal plane as measured by the _Qualisys_ system for the three cable modes.  
If there were no external disturbances at all, the path would be rectilinear. The ROV shifts slightly to the left when the cable is slack. This deviation is slightly larger in the control mode and is observed during both its forward and backward motion. The deviation is significantly greater in passive taut mode. Furthermore, the distance covered by the ROV is shorter with the taut cable.  
Figure 17 shows the evolution of depth control of the ROV for the three modes. Only the control mode is efficient in helping to regulate the ROV depth. In addition, the vertical thrusters' work significantly increases more when the cable is not controlled than when it is controlled.  
Figure 16: Top view of actual forward–backward trajectories of the ROV using cable control, slack, or passive taut cable. Paths are superimposed at point 2 for easier comparison.  
Figure 15: Distance between buoys, feeder speed, and unwound length of cable in control mode, passive slack mode, and passive taut mode for forward–backward trajectory. ([[https://youtu.be/owekUkN_UtMforcontrolmode](https://youtu.be/owekUkN_UtMforcontrolmode)]([https://youtu.be/owekUkN_UtMforcontrolmode](https://youtu.be/owekUkN_UtMforcontrolmode)), [[https://youtu.be/1](https://youtu.be/1) RTT23-USDYforpassiveautmode]([https://youtu.be/1](https://youtu.be/1) RTT23-USDYforpassiveautmode), [[https://youtu.be/FGSiyNjzckforpassiveslackmode](https://youtu.be/FGSiyNjzckforpassiveslackmode)]([https://youtu.be/FGSiyNjzckforpassiveslackmode](https://youtu.be/FGSiyNjzckforpassiveslackmode)) (accessed on 1 December 2023)).
# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 3 Proposed Dual-AGV-Ganged Path-Planning Method
### Algorithm Process  
The pseudocode for the GA-based dual-AGV-ganged path-planning method is presented as Algorithm 1.  
```
Input: Start pose: \((X_{S},Y_{S},Q_{S})\), Goal pose: \((X_{G},Y_{G},Q_{G})\), \(R_{\text{Turm}}\), Map, iterations\({}_{\text{max}}\) Output: BestPath
1 Path control point encoding;
2 Population \(\Leftarrow\)GenerateInitialPopulation(Start pose, Goal pose, Map, \(R_{\text{Turm}}\));
3 whilenot satisfied termination conditiondo
4 for iterations = 1 to iterations\({}_{\text{max}}\)do
5 for each individual in Populationdo
6\(C_{\text{Start}}\Leftarrow\)Calculate\(\_\)cost(\(R_{\text{Turm}}\), \(Q_{S}\), Map, individual);
7\(C_{\text{Middle}}\Leftarrow\)Calculate\(\_\)cost(\(R_{\text{Turm}}\), Map, individual);
8\(C_{\text{Goal}}\Leftarrow\)Calculate\(\_\)cost(\(R_{\text{Turm}}\), \(Q_{G}\), Map, individual);
9\(\text{fitness}\Leftarrow\frac{1}{C_{\text{Start}}+C_{\text{Middle}}+C_{\text{Goal}}}\);
10 UpdateFitness(individual, fitness);
11
12 end for
13
14 elites \(\Leftarrow\) SelectElites(Population);
15 newPopulation \(\Leftarrow\) Crossover(Population);
16 newPopulation \(\Leftarrow\) Mutate(newPopulation);
17 Population \(\Leftarrow\) Combine(newPopulation, elites);
18
19 end for
20
21 end for
22 Path control point decoding;
23 BestPath \(\Leftarrow\) DecodeBestPath(Population);
24 return BestPath
```  
**Algorithm 1**Genetic Algorithm
## 4 Field Application
### Monitoring of Cutting Surrounding Rock Deformation  
Accurate measurement of the displacement of the cutting surrounding rocks was performed, and irrelevant data from abandoned monitoring points were excluded. The relationship between the number of monitoring days and displacement is illustrated in Figure 11.  
Figure 11: Approach amount of roadway roof and two sides in 81309 working face cutting: (**a**) approach amount of both sides of the roadway in the 81309 working face cutting: (**b**) changes in the roof and floor of the 81309 working face cutting.  
Figure 10: Layout diagram of roadway surface displacement measuring station.  
From the observation data in Figure 10, it is observed that with the passage of time, the displacement of the roadway roof, floor, and sides generally increased within the first 60 days and then gradually stabilized.  
Both the roof and the sides of the roadway exhibited maximum displacement at shallow depths. The maximum approach amount of the roof reached 96 mm. A comprehensive analysis of the monitoring results indicated that the displacement of the roadway sides and the roof was less than 100 mm, and the displacement at a depth of more than 2 m from the cutting surface was less than 60 mm. These values met the requirements of the design regulations. The on-site experimental results were consistent with the results obtained from numerical simulations.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 3 Proposed Methodology  
### Data Preprocessing  
#### 3.1.1 Data Cleaning  
This study is based on the emotional speech data from Emo-DB. The research was conducted on six emotions, excluding the disgust category. The number of voice data samples for each emotional state is summarized in Table 1.  
Voice data often contain various noises or irregular silences at the beginning and end that are unnecessary for the experiment. These segments can hinder accurate data analysis;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Emotion** & **Emo-DB** \\ \hline Anger & 127 \\ Boredom & 81 \\ Fear & 69 \\ Happiness & 71 \\ Sadness & 62 \\ Neutral & 79 \\ \hline Total & 489 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples per emotion in the Emo-DB database.  
hence, they must be removed through preprocessing. In this process, we used the 'trim' function from the librosa library [33]. This function effectively eliminates background noise and silent sections by detecting the significant start and end points in the voice data. This step is crucial as it extracts only the valid signal, excluding unnecessary parts, thereby increasing the accuracy of the analysis.
## 2 Experimental Methods
### Preparation of Specimens  
#### 2.2.1 Particle-Dispersed Resin Mixture Film  
The resin mixture films laden with PZT and SMA particles were fabricated using custom-made film-casting equipment. First, the particles were added to the epoxy resin. The particles were then evenly dispersed in the resin using a three-roll-mill machine (EXAKT 80E, Oklahoma City, OK, USA). The resin and particle mixture thus obtained was placed into a vacuum oven to dry volatiles in the resin. To fabricate the resin film, the mixture was poured into the resin reservoir of the film casting equipment. A Polyethylene Terephthalate (PET) film, which has been surface-treated so that it can be easily released, passed through the reservoir and subsequently through the rollers. The mixture coated PET film passes through a blade controlling the thickness of the mixture film. In this case, the specimen with a thickness of 20 \(\upmu\)m was very well casted on the PET film. Specimens in which particles were directly dispersed were also prepared for comparison with film-dispersed particles. In this case, \"directly\" means dispersed by hand. On the other hand, the particle-dispersed resin mixture film was casted isotropically on PET film and directly transcribed over the unidirectional prepreg. In addition, as the average size of the used particles is 10 \(\upmu\)m, a better dispersion of the particles may be expected with 20 \(\upmu\)m thick film. The viscosity of the epoxy resin used in film production is 12,000 cps and the density is 1.16 g/cm\({}^{3}\).
## Materials and Methods
### Statistical Data Elaboration  
Data obtained by GC-MS and GC\(\times\)GC-TOF-MS were processed by analysis of variance (one-way ANOVA). Least significant difference (LSD) post-hoc test was used to compare the mean values of concentrations at \(p<0.05\). Multivariate analysis of data was performed by PCA and forward stepwise linear discriminant analysis (SLDA). The original dataset which included 32 wines and 350 volatile aroma compounds (33 determined by GC-MS + 317 determined by GC\(\times\)GC-TOF-MS analysis; in the case of compounds determined by both techniques GC\(\times\)GC-TOF-MS data were used), was reduced based on Fisher ratios (\(F\)-ratios). Multivariate techniques were applied on the variables (mean-centered concentrations of volatile compounds) with the highest \(F\)-ratios. PCA was performed with 40 variables with the highest \(F\)-ratio, while SLDA and hierarchical clustering were performed with 60 variables with the highest \(F\)-ratio, in both cases with GC-MS and GC\(\times\)GC-TOF-MS data combined. Two additional SLDA models were built with the concentrations of terpen which were significantly different between wines, using GC-MS and GC\(\times\)GC-TOF-MS data separately. In SLDA, variables were selected based on Wilk's lambda, with \(F\) to enter = 1 and \(F\) to remove = 0.5. Cross-validation was applied to check the prediction capacity of the developed SLDA models. ANOVA, PCA, and SLDA were performed by Statistica v. 13.2 software (StatSoft Inc., Tulsa, OK, USA). Hierarchical clustering was conducted and a heatmap was generated by Ward algorithm and Euclidean distance analysis using MetaboAnalyst v. 4.0 ([[http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)]([http://www.metaboanalyst.ca](http://www.metaboanalyst.ca))), created at the University of Alberta, Canada [33].