esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Ion-Responsive Molecularly Imprinted Polymers  
Currently, ion-responsive molecularly imprinted polymers are rarely reported. As mentioned above, the PNIPAm-based hydrogels undergo volume changes in response to the ionic strength, leading to ion-responsive binding properties. Intriguingly, [PERSON] _et al._ reported a molecular-recognition microcapsule for ion-responsive controlled release, which may bring us some inspirations for the preparation of ion-responsive MIPs [115]. The reported ion-responsive microcapsule is composed of a core-shell porous membrane and linear grafted poly (_N_-isopropylacrylamide-_co_-benzo-18-crown[6]-acrylamide) (PNIPAm-_co_-PBCAm) chains in the pores acting as molecular-recognition gates. Benzo-18-crown[6] acrylamide (BCAm) is a crown ether receptor, which allows ionic molecular recognition. When specific guest molecule such as Ba\({}^{2+}\) is captured by the crown ether host, the grafted polymer swells and closes the pores. To the contrary, when Ba\({}^{2+}\) is removed from the crown ether host, the grafted polymer shrinks and then the pores open. The changes lead to a release of solute from the prepared microcapsules significantly sensitive to the existence of Ba\({}^{2+}\) ions. Significantly, the prepared poly (NIPAm-_co_-BCAm)-grafted microcapsules showed us an excellent approach to design ion-responsive MIPs by a surface grafting of MIPs with similar functional polymers. Taking advantage of the ion-imprinting strategy, [PERSON] _et al._ developed proton-imprinted nanoparticles using NIPAm and AAC as comonomer by pseudo-precipitation polymerization (Figure 10). The p\(K_{\mathrm{a}}\) variation range was up to 2.2 which is significantly larger than that of the nonimprinted NPs. More importantly, the apparent p\(K_{\mathrm{a}}\) value of the proton-imprinted particles reversibly shifted up and down without significant changes in the p\(K_{\mathrm{a}}\) variation range during repeated shrinking (heating, 75 \({}^{\circ}\)C) and swelling (cooling, 30 \({}^{\circ}\)C) cycles. The principles employed in the preparation of proton-imprinted nanoparticles can be used to other stimuli-responsive materials and will be an integral step in the creation of active proton transporters that enable effective ion transport, gas separation, and catalytic reactions [89].  
Figure 9: Outline of the biomolecular imprinting scheme used to create superatamer thrombin-responsive hydrogels. Reprinted with permission from Reference 86. Copyright 2013, ACS.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Frequency Robustness  
In the experiments, to check the robustness of proposed DECC at SNR, we set up a control group and three experimental groups to analyze the robustness of DECC to signals with different SNRs. The control group is shown in Section 4.2, i.e., 80% of the overall mixed SNR dataset is used for training and 20% is used for testing. Experimental group 1 was trained with the same mixed SNR dataset and tested with only the same number of SNR10 datasets as the control group. The training set of experimental group 2 is the same as above, and the test set uses the same number of SNR20 datasets. The training set of experimental group 3 is the same as above, and the test set uses the same number of SNR50 datasets.  
Figure 8: Ablation experiment for the DECC network: (**a**) control group 1 and 2 and (**b**) control group (‘full version’).  
Figure 7: Visualized distance error: (**a**) the urban canyon scenario and (**b**) the indoor scenario.  
As can be seen from Figures 9 and 10, when only the three datasets--SNR10, SNR20, and SNR50--are used to test the model, the results--0.27, 0.26, and 0.26, respectively--are similar to those of the control group, indicating that our model can achieve relatively stable results under different signal-to-noise ratios.
###### Abstract
The proposed model allows for this in a manner that exceeds the capabilities of conventional methodologies. This technique is paramount for evaluating lecturers' emotional states because it enables the identification of potential stressors, anxiety, or emotional distress that might inhibit the efficacy of their teaching. Upon detecting these emotional indicators, SER technology can instigate suitable interventions, ranging from offering counseling to providing lecturers with appropriate support services, thereby ensuring that they reach their optimal teaching capacities [7]. Furthermore, the integration of SER into intelligent tutoring systems paves the way for a more nuanced personalization of instructional content, tone, and feedback by fostering responsive and effective teaching-learning environments that are underpinned by a deeper understanding of emotional cues. In this study, Mel-frequency cepstral coefficients (MFCCs); chroma; the Mel spectrogram; the zero-crossing rate (ZCR); the spectral contrast, centroid, bandwidth, and roll-off; and the root-mean square (RMS) were obtained from raw audio data, after which noise was added and the audio data were shifted and time-stretched. Then, an ensemble model comprising a combination of transformer, convolutional neural network (CNN), and long short-term memory (LSTM) architectures was used to classify emotions from the static features in the data. This study makes several significant contributions to the field:  
1. The creation and use of the Saudi Higher-Education Instructor Emotions (SHEIE) dataset: This dataset is a distinct resource for SER research because it includes meticulous annotations of emotions and specifically focuses on Saudi Arabian instructors, making it unique and useful in terms of SER in education. 2. A comprehensive series of experiments: This study explored the effectiveness of various data augmentation (DA) and feature extraction techniques for speech emotion classification. The evaluation steps were conducted using five benchmark datasets, namely the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, the Berlin Database of Emotional Speech (EMO-DB) dataset, the Surrey Audio-Visual Expressed Emotion (SAVEE) dataset, the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, and SHEIE. 3. The proposal of a new model for classifying emotions from speech: This model uses a transformer architecture that incorporates multi-head self-attention. Furthermore, the favorable attributes of CNN and LSTM networks were combined to extract the spectral features and determine the temporal dynamics. The rest of the paper is organized as follows: Section 2 discusses the pertinent literature; Section 3 outlines the methodology adopted for this study; Section 4 details the experimental outcomes; Section 5 discusses the research findings; and Section 6 concludes the paper and outlines future research avenues.
# Safety Risk Assessment in Urban Public Space Using Structural Equation Modelling
## 3 Materials and Methods
### Introduction of Evaluation Index System
#### 3.2.2 Steps in Constructing an Index System  
The construction of the urban public space safety risk evaluation index system can be divided into five steps, as shown in Figure 2. First, the data on public safety emergencies in recent years are analyzed. Then the risk factors affecting urban public safety are summarized. Finally, a preliminary risk evaluation index system is established.  
After completing the five steps, the index system is divided into three layers: the target layer, criterion layer and index layer [28]. The target layer is urban public space safety, which is the main study object. The criterion layer includes five factors: conscious behavior, social governance, construction equipment, urban environment, and road traffic. At the index level, 21 specific factors are selected by analyzing the factors at the criterion layer. See Table 2 for details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Layer** & **Criterion Layer** & **Index Layer** & **References** \\ \hline \multirow{6}{*}{Conscious behavior} & & Q1 Education level & [22] \\  & & Q2 Epidemic prevention awareness & [31] \\  & & Q3 Fire awareness & [34] \\  & & Q4 Self-protection awareness & [32] \\ \hline \multirow{6}{*}{Social governance} & & Q5 Criminal act & [22; 26] \\  & & Q6 Public safety & [22] \\ \cline{1-1}  & & Q7 Dangerous goods safety check & [25; 26] \\ \cline{1-1}  & & Q8 Population density & [21; 27] \\ \cline{1-1} \cline{2-5}  & & Q9 Seismic resistance of public buildings & [28] \\ \cline{1-1}  & & Q10 Safety of public buildings & [21; 35] \\ \cline{1-1} Urban public space safety & Construction equipment & Q11 Fire-fighting equipment in public places & [34] \\ \cline{1-1}  & & Q12 Anti-riot equipment in public places & [26] \\ \cline{1-1}  & & Q13 Early warning system in public places & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Urban public space safety evaluation index system.  
Figure 2: Steps of constructing index system.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 4 Experimental Design
### Data Description  
In order to make the experiment more representative, four typical months of a year were selected for each wind farm, namely the real-time wind farm power output data, wind tower measurements and numerical weather forecast. The time interval between two adjacent data points is 15 min. The unit for the wind power data is MW. The three wind farms are located in Suizhou (Wind Farm #1, installed capacity is 110 MW), Huanggang (Wind Farm #2, installed capacity is 220 MW), and Lichuan (Wind Farm #4, installed capacity is 126.3 MW). These wind farms are located in Hubei Province, which has different altitudes and topographical features.
## 3 Results and Discussion
### Structural Parameters and Transmission Spectrum
the resonant dips for the core mode and cladding modes increases continuously. It is seen that the transmission power of the core mode (at the wavelength of \(\sim\)1.547 \(\upmu\)m) increases by 18 dB (from \(-\)2 dB to \(-\)20 dB). When the grating length increases, the transmission spectrum does not shift, and the number of modes propagating in the grating is not affected. Therefore, when the resonant dip intensity is weakened with the decrease in the tilt angle, it can be appropriately compensated for by increasing the grating length. In Figure 5, which compares the transmission spectrum changes of the TMFBG, the grating refractive index modulation depth is set to 0.0001, 0.0002, 0.0003, 0.0004, and 0.0005, respectively. The wavelength positions of all modes are maintained. However, as the modulation depth increases from 0.0001 to 0.0005, the transmission depth gradually increases. When the modulation depth is 0.0001, the maximum transmission power is approximately \(-\)2 dB. When it increases to 0.0005, the transmission depth increases significantly, with a maximum power of approximately \(-\)30 dB. Therefore, it is concluded that an increase in the refractive index modulation depth will also change the transmission depth of the core mode and the cladding mode, but it will not change their wavelength positions. Figure 4: Transmission spectrum of TMFBG with grating lengths of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm, respectively. Figure 5: Transmission spectrum of TMFBG with grating refractive index modulation depths of 0.0001, 0.0002, 0.0003, 0.0004, and 0.0005, respectively.
## 3 Result and Discussion
### Quantitative Analysis of Methanol in Gasoline  
To quantitatively analyze the methanol content in methanol gasoline, two regression methods of PLSR and LS-SVM were applied to build the quantitatively analysis model. There are a total of 1762 variables in the ATR-FTIR spectral data. Firstly, PLSR and LS-SVM were established to predict methanol content based on the full spectral variables. Furthermore, the calculated results are shown in Table 1, and it can be seen that all the R\({}^{2}_{\text{p}}\) values were higher than 0.96 for PLSR or LS-SVM prediction models, which means that it is feasible to quantitatively analyze the methanol content in methanol gasoline using ATR-FTIR spectral data. In addition, it can be found that there is no significant difference between PLSR and the LS-SVM model for methanol prediction when the performances of PLSR and LS-SVM were compared. When the robustness of models was considered, it can be found that the value of RMSEC is larger than that of RMSEP (1.658 \(>\) 0.405 and 1.661 \(>\) 0.395), which means that  
Figure 3: Classification result of partial least squares discriminant analysis (PLS-DA) with 8 latent variables (LVs). these two regression models of PLSR and LS-SVM have a slight overfitting. This may be caused by the reasons of too many variables and an unbalanced distribution of samples in the calibration model [32]. In general, the regression model for predicting methanol content in methanol gasoline is acceptable and accurate. Although the prediction model achieved high accuracy, there are too many useless and uninformative input variables that will result in the decline in model accuracy and robustness. To eliminate these unrelated variables and improve the performance of the prediction model, two variables selection method of CARS and UVE were conducted to identity the optimal variables [33]. Then the PLSR and LS-SVM regression models were developed and the corresponding calculated results were presented in Table 1. It can be found that the number of variables was reduced to only 0.027% (48/1762) of the total variables for CARS. Based on these selected variables, the simpler PLSR and LS-SVM models were built. As shown in Table 1, the prediction performance of CARS-PLSR and CARS-LS-SVM model was improved slightly in comparison with PLSR and LS-SVM model based on the full variables. When the UVE was considered, it can be found that UVE-PLSR and UVE-LS-SVM have the R\({}^{2}\)p value of 0.972 and 0.964, respectively. As shown in Figure 4, scatter plots of the actual values versus the predicted values in prediction set are well distributed along the diagonal line by UVE-PLS model, indicating a good performance of the predictive capability. Additionally, the stability of the prediction model has become more stable after performing UVE, and the ABS value decreased from 1.253 to 0.847. Therefore, UVE is a more suitable variable selection method than CARS in improving the performance of predicting the methanol content in gasoline by ATR-FT-IR spectroscopy.
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 6 Results
### Force Dynamic Characteristics  
Figure 5 shows the force dynamic characteristics of the proposed empirical modeling at various static loads and input pressures. Experimental contacting force data (\(F_{exp}\)) was obtained through  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Variable** & \multicolumn{1}{c}{**Description**} & **Variable** & **Description** \\ \hline \(P\) & Input pressure & \(\gamma_{f}\) & Rate dependency \\  & & & correction function \\ \(\varepsilon\), \(\dot{\varepsilon}\), \(\ddot{\varepsilon}\) & Contracting length, velocity and & \(F_{dyn}\) & Dynamic force \\ \(\alpha_{o}\) & Initial braid angle & \(F_{s}\) & Static force \\ \(M\) & Mass of the weight & \(F_{hys}\) & Hysteretic force \\ \(\bar{g}\) & Gravitational acceleration & \(F_{cr}\) & Coulomb friction \\ \(\bar{D}_{o}\) & Initial diameter & \(F_{cr}\) & Viscous friction \\ \(L_{o}\), \(L\) & Initial length and length & \(F_{net}\) & Net force \\ \(K\) & Coefficient of elasticity (PSO) & \(F_{elastic}\) & Elastic force \\ \(c_{1}\),\(c_{2}\),\(c_{3}\),\(c_{4}\),\(c_{5}\),\(c_{6}\) & Constant coefficients (PSO) & \(N\) & Number of population \\ \(c_{cv}\), \(c_{9}\) & Coulomb and viscous friction coefficients & \(G\) & Number of generation \\ \(q\) & Shape irregularity correction function & \(v_{1}\), \(v_{2}\) & Velocity constants \\ \(\mu\) & Slenderness correction function & \(P_{best}\) & Local optimum \\ \(\gamma_{F}\) & Load dependency correction function & \(G_{best}\) & Global optimum \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters.  
Figure 4: Block diagram of empirical modeling.  
actual testing of McKibben muscles using the vertical load system. While a static contracting force (\(F_{s}\)) illustrates the graphical solution from the empirical modeling. This static force remains unchanged, which indicates the actual force characteristic of the McKibben muscle with zero load implementation. Net contracting force (\(F_{net}\)) and dynamic contracting force (\(F_{dyn}\)) show the dynamic behavior of the vertical load system. Where the static contracting force was affected by the static load and frictional effects of the contacting surface in between rubber tube and braided mesh sleeve, thus yielding to the hysteresis phenomena during cyclic contraction and expansion.
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 4 Methods
### Harmony Search  
At the beginning of the algorithm the memory harmony is initialized [26]. It is set of randomly generated and evaluated solution models. The size of the memory is constant. In each iteration a new solution is created using the following steps:  
* For each node pair in set \(\mathcal{N}\) choose solution which satisfies the demand.
* With probability \(p_{a}\), a solution is chosen randomly from memory. There is also probability \(p_{m}\) that the chosen solution will be modified using _change path operator_.
* Otherwise, a solution is created using _change realization operator_.  
After creation, the solution is evaluated and if it is better than any of the solutions stored in the memory, the worst solution is replaced by the newly created solution. Due to two ways of generating solutions, the algorithm has the option to explore and exploit solution space. Furthermore, it is certain that the best solution generated will be the final solution because of the way of storing solutions in memory. The pseudo-code of the algorithm is presented in Algorithm 2.
# Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling
## 3 Simulation Validation
### Model Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Global 7 Layers**} \\ \hline
**Trajectory** & **MAE [mm]** & **MSE [mm2]** & **RMSE [mm]** & **MAXE [mm]** & **R\({}^{2}\) [-]** \\ \hline Circle in Y-Z & \(1.07\times 10^{0}\) & \(1.17\times 10^{0}\) & \(1.08\times 10^{0}\) & \(1.32\times 10^{0}\) & 99.967\% \\ Spiral X-Y & \(7.46\times 10^{-1}\) & \(6.42\times 10^{-1}\) & \(8.01\times 10^{-1}\) & \(1.33\times 10^{0}\) & 99.984\% \\ Rectangle X-Y & \(7.96\times 10^{-1}\) & \(7.14\times 10^{-1}\) & \(8.45\times 10^{-1}\) & \(1.27\times 10^{0}\) & 99.993\% \\ S-shape in X-Y & \(6.86\times 10^{-1}\) & \(5.41\times 10^{-1}\) & \(7.36\times 10^{-1}\) & \(1.25\times 10^{0}\) & 99.993\% \\ \hline \hline  & \multicolumn{4}{c}{**Sequential From TCP**} \\ \hline
**Trajectory** & **MAE [mm]** & **MSE [mm2]** & **RMSE [mm]** & **MAXE [mm]** & **R\({}^{2}\) [-]** \\ \hline Circle in Y-Z & \(6.54\times 10^{-1}\) & \(4.78\times 10^{-1}\) & \(6.92\times 10^{-1}\) & \(9.49\times 10^{-1}\) & 99.987\% \\ Spiral X-Y & \(6.39\times 10^{-1}\) & \(4.69\times 10^{-1}\) & \(6.85\times 10^{-1}\) & \(1.19\times 10^{0}\) & 99.989\% \\ Rectangle X-Y & \(5.13\times 10^{-1}\) & \(3.26\times 10^{-1}\) & \(5.71\times 10^{-1}\) & \(8.66\times 10^{-1}\) & 99.997\% \\ S-shape in X-Y & \(5.02\times 10^{-1}\) & \(2.89\times 10^{-1}\) & \(5.38\times 10^{-1}\) & \(8.52\times 10^{-1}\) & 99.996\% \\ \hline \hline  & \multicolumn{4}{c}{**Sequential From Base**} \\ \hline
**Trajectory** & **MAE [mm]** & **MSE [mm2]** & **RMSE [mm]** & **MAXE [mm]** & **R\({}^{2}\) [-]** \\ \hline Circle in Y-Z & \(4.24\times 10^{-1}\) & \(2.40\times 10^{-1}\) & \(4.90\times 10^{-1}\) & \(8.77\times 10^{-1}\) & 99.993\% \\ Spiral X-Y & \(4.48\times 10^{-1}\) & \(2.47\times 10^{-1}\) & \(4.97\times 10^{-1}\) & \(8.66\times 10^{-1}\) & 99.994\% \\ Rectangle X-Y & \(5.03\times 10^{-1}\) & \(3.21\times 10^{-1}\) & \(5.67\times 10^{-1}\) & \(1.15\times 10^{0}\) & 99.997\% \\ S-shape in X-Y & \(4.27\times 10^{-1}\) & \(2.04\times 10^{-1}\) & \(4.51\times 10^{-1}\) & \(6.34\times 10^{-1}\) & 99.997\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Global IK–Sequential IK trajectory error results in the virtual environment: MAE, MSE, MAXE. Finally, the MSE and RMSE are compared to literature applications [22; 23; 24; 27]. The results of the MSE are closed to 1.0 \(\times\) 10\({}^{-4}\) rad\({}^{2}\), while the proposed sequential methodology has shown a reduction of the mean squared error, that is equal to 1.73 \(\times\) 10\({}^{-6}\) rad\({}^{2}\), starting from the robot Base, and 1.95 \(\times\) 10\({}^{-6}\) rad\({}^{2}\), starting from the robot TCP, respectively. In the same way, the obtained results of the RMSE are promising since the maximum is lower than 1.60 \(\times\) 10\({}^{-2}\) rad.
## 3 Method Used  
In this study, four main techniques, namely ANFIS, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and SVM were used. Out of these methods, the GA and PSO were used to optimize the parameters of the ANFIS to develop hybrid AI models (PSOANFIS and GAANFIS), whereas the SVM was used as a single benchmark model for comparison. A brief description of these techniques is given in the following sections.