esa-sceva/satcom-chunk-collection huggingface dataset:
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 2 Materials and Methods
### Methods
#### 2.1.3 Cross-Modal Emotion Distillation  
In order to transfer the emotion information contained in the teacher model's deep emotion features, especially the information corresponding to the soundtrack, we introduced cross-modal emotion distillation to our framework.  
Between the response-level and feature-level, we adopted the latter to directly imitate the hidden representation of the teacher's features. Following the protocol of knowledge distillation [32; 46; 47; 48; 49], we used Kullback-Leibler divergence [50] to minimize the distance between the deep emotion features produced by the teacher and the student models. The procedure of cross-modal emotion distillation is illustrated in Figure 5. The blue parts indicate the procedure of the calculation of the Kullback-Leibler divergence. The teacher's deep emotion features and the student's firstly go through a softmax (for the student's is log softmax) function to normalize their distribution, as defined in Equations (2) and (3), respectively. Then. the Kullback-Leibler divergence \(L_{KLdiv}(p^{S}(\tau)||p^{T}(\tau))\) is calculated as Equation (4), which is regarded as the KL divergence loss in the total loss function.  
The calculation of the Kullback-Leibler divergence can be formulated as:  
\[p^{S}_{m}(\tau)=log(\frac{\exp(S_{m}/\tau)}{\sum_{i=1}^{M}\exp (S_{i}/\tau)}) \tag{2}\] \[p^{T}_{m}(\tau)=\frac{\exp(T_{m}/\tau)}{\sum_{i=1}^{M}\exp(T_{ i}/\tau)}\] (3) \[L_{KLdiv}(p^{S}(\tau)||p^{T}(\tau))=\sum_{m=1}^{M}p^{S}_{m}( \tau)\log(\frac{p^{S}_{m}(\tau)}{p^{T}_{m}(\tau)}) \tag{4}\]  
where \(S\) is the vector of thhe student's deep emotion features, and \(T\) represents the teacher's. \(\tau\) is a hyper-parameter; in our work, we set \(\tau=2\) according to the results of the comparative experiment. \(p^{S}_{m}(\tau)\) is the vector of the student's emotion features after log softmax with the hyper-parameter \(\tau\) at the point \(m\). Finally, \(p^{T}_{m}(\tau)\) is the teacher's vector after softmax with the hyper-parameter \(\tau\) at the point \(m\). Equation (4) gives the formulation of Kullback-Leibler divergence.  
Figure 4: The structure of the student speech emotion recognition model based on 4-layer Bi-LSTM.  
Thus, as Equation (5) shows, the total loss function of the student model can be separated in two parts: the KL divergence between two deep emotion features and the cross entropy between the student-predicted label and the ground truth, respectively.  
\[Loss=\alpha\tau^{2}\cdot L_{KLdiv}(p^{S}(\tau)||p^{T}(\tau))+(1-\alpha)\cdot L_{ CrossEntropy}(Q_{S},y_{\text{true}}) \tag{5}\]  
where \(\alpha\) is a hyper-parameter which represents the proportion of the \(KL\) divergence loss in the loss function. We set \(\alpha=0.6\) according to the results of the comparative experiment. \(Q_{S}\) is the predicted label of the student model, and \(y_{true}\) is the ground truth.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 7 Implementing HWLNets  
Most studies that have been conducted on HWLNets were focused on solving problems within two different network systems, LiFi and WiFi. To enable total integration, we must look at this hybrid network as one entity with both WiFi and LiFi working in parallel to avoid vertical handover, which is a root to major complications and restrictions in terms of network performance. In order to lift this, we must integrate the two technologies across network layers. This section provides a brief overview of LiFi-WiFi integration in multiple network layers.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 5 Simulation Results  
### Simulation Results of MLE Discriminator  
To test the validity of the proposed MLE discriminator, the convergence curves of phase and frequency are shown in Figure 7a. The simulation parameters are listed in Table 2. The true frequency and phase error are 7 Hz and 1 rad, respectively, expressed in coordinates as (7, 1). It can be seen that the iteration process ends after eight iterations, which means the gradient matrix G reaches the threshold. The frequency and phase converge to (6.9, 1.1) finally. The LM algorithm shows high efficiency and accuracy in this process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier-to-noise ratio \(C/N_{0}\) & 28 dB-Hz \\ Sampling rate \(f_{s}\) & 5.714 MHz \\ Integration time \(T\) & 1 ms \\ Observation point number \(N\) & 20 \\ Iteration number threshold \(M\) & 20 \\ Gradient threshold \(\Gamma\) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 6: Block diagram of the ML-KF loop. ML-KF: maximum likelihood and Kalman filter.  
Figure 7: Convergence performance of the LM algorithm. (**a**) Frequency and phase convergence curves of a single example; (**b**) Root-mean-square error (RMSE) of frequency and phase with respect to the iteration number.  
Figure 7b shows the root-mean-square error (RMSE) of the frequency and phase with respect to the number of iteration. The initial frequency residual is random from \(-\)10 to 10 Hz and the initial phase residual is random from \(-\)1 to 1 rad. In total, 50,000 MC simulations are made to calculate the RMSE. It can be seen that the accuracy of the frequency and phase have the same trend because they are estimated simultaneously. When the iteration number exceeds six, the RMSE of the frequency and phase is almost unchanged. So considering both the computation burden and tracking accuracy, the number of iteration is set to six in the following simulations.
## 3 Materials and Methods
### Model Training with Cross-Validation  
As our goal was to estimate the power plant emissions when no reported generation data exist, it is essential to validate that our models generalize well to unseen plants. To do this, we used cross-validation, a resampling procedure in which the data are divided into a set of folds, and models are iteratively trained and tested on different subsets of these folds. We built a regular \(1^{\circ}\times 1^{\circ}\) grid in latitude-longitude space and placed plants in the same grid cell into the same fold. Each plant was assigned to a single fold, with all of its images belonging to that same fold. By rotating the assignment of these folds to training/tuning/testing, we were able to train and evaluate four different models. For each one, two of the four folds of plants were used for training, one for tuning/validation, and one for testing. The details of model training are specified by hyperparameters such as patch size and learning rate. We independently tuned the hyperparameters of both XGBoost and CNN models to optimize for validation mean average precision (mAP) and root mean squared error (RMSE) for classification and regression, respectively. We selected mAP due to the class imbalance; plants were \"on\" in about \(\sim\)80% of the training images.  
Since the generation models are trained on the outputs of other ML models--the sounding models--we must take extra care to prevent the use of information from model training that would not be available at the prediction time, i.e., data leakage. Therefore, the following cross-validation approach was adopted for the training and evaluation of the generation models. Each of the four generation model instances was trained with features based on soundings from the validation fold and evaluated with features based  
Figure 5: A sample of model predictions for Belchatów in Poland, Europe’s largest fossil-fuel burning power plant, for the years 2019–2022. Included are predictions from the NDT (blue line) and FGD (orange line) generation models to compare to the reported electricity generation (green line). The rolling average 30-day capacity factor never fell below 45% capacity from 2019 to 2022 and was maintained above 60% for the entirety of 2022. The NDT model performs over two times better than the FGD model for this power plant, a representative characteristic of the NDT task given its clearer signal.  
on the sounding predictions from the testing fold. This way, both sets of sounding predictions come from the same sounding model and were not used for its training. While the sounding models might be overfitted to their validation fold due to early stopping and hyperparameter tuning, the test fold was not used for any model optimization or selection. Thus, measuring the performance on the test fold provides a reasonable representation of predictions on unseen plants.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 4 Experiments
### Experimental Parameters  
#### 4.3.1 Number of Iterations  
Although an increase in the number of iterations will improve the experimental results, the computational cost should be considered during the actual operation.  
To determine the appropriate iteration parameters, we employ the proposed method on the Rain 12 dataset to obtain the average objective evaluation indexes and the execution times under different numbers of iterations. The experimental results are shown in Figure 3, which shows a line chart of the average PSNR, SSIM values, and running time of test code (in seconds) as the number of iterations increases. The abscissa represents the number of iterations, and the ordinate indicates the changes in the curve of the PSNR values, SSIM values, and running time of test code. The results of 24 iterations are recorded during this  
Figure 2: Ten test images in our experiments.  
experiment, and the average time of each iteration is approximately 50 s. To better display the experimental data and compare their differences, we reduce the obtained average PSNR, SSIM values, and the running time, and only mark certain indicators (the SSIM is the original value, the PSNR value is reduced by 100-fold, and the running time is reduced by 1000-fold). As can be seen from Figure 3, when the number of iterations is 19, the average values of the PSNR and SSIM reach the maximum values, which are only 0.001 dB and 0.0007 higher than the average values for 14 iterations, whereas the time consumed increases by nearly 240 s. Therefore, considering the computational cost, we finally choose T = 14 iterations.
## 6 Verification with Modified Noises
### Comparison of Results between Jury Test and SVM  
The expert listeners were asked whether they could hear or not, or if they had a neutral opinion (not sure). The results are shown in Table 10. To summarize the results, the results of the jury test showed more than 95% classification accuracy for three types of noise. The classification accuracy was over 90% when classified using the pre-learned hyperplane through SVM. The results of the jury test and kernel SVM showed that the lower the dB of the noise source produced in general, the greater the error. This means that the certain level of refrigerant noise was masked by other noises. To plot the classification performance of the kernel SVM, two arbitrary axes were selected to represent the distribution and classification of the signal (Figure 8). This is a two-dimensional representation of 1272 dimensions (the number of characteristic vectors) by selecting two arbitrary axes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Kernel SVM Classification (\%)** & **Jury Test (\%)** \\ \hline Slug flow & 92\% & 98\% \\ Churn flow & 94\% & 95\% \\ Annular & 90\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of classification by kernel SVM and jury test.  
Figure 8: Kernel SVM classification for modified noises (2-D plot): (**a**) Slug flow, (**b**) Churn flow, (**c**) Annular flow.  
The accuracy of the jury test was higher than that of the kernel SVM (Table 10). This is because the learning data and test data are different. If the data sets are similar, the accuracy is increased, as shown in Table 6 in the previous section. The performance of the kernel SVM may be somewhat reduced because the test data changed as the noise was modified. However, it shows a high accuracy of more than 90%.
###### Abstract  
In this article, a high gain and compact 4 \(\times\) 4 circularly polarized microstrip patch antenna array is reported for the data transmission of the next-generation small satellite. The radiating element of the circularly polarized antenna array is realized by the conventional model of the patch with truncated corners. A compact two-stage sequential rotational phase feeding is adopted that broadens the operating bandwidth of the 4 \(\times\) 4 array. A small stub is embedded in the sequential rotational feed, which results in better performance in terms of the S-parameters and sequential phases at the output ports than sequential rotational feed without open stub. A prototype of the array is fabricated and measured. Fulfilling the application requirements of the next-generation small satellites, the array has the left-handed circularly polarized gain of more than 12 dBic with the axial ratio level below 1.5 dB in the \(\pm\)10\({}^{\circ}\) angular space with respect to the broadside direction for the whole bandwidth from 8.05 GHz to 8.25 GHz. Moreover, the left-handed circularly polarized gain varies from 15 to 15.5 dBic in the desired band. The radiation patterns are measured; both the co- and X-pol are validated.  
broadband antenna; circular polarization; sequential rotational phase feed; impedance bandwidth; axial ratio +
Footnote †: journal: Applied Sciences
## 5 Proposed NSGA-II Algorithm with Adaptive Local Search
### Constraints Handling  
Using the layout construction method mentioned in Section 5.1, the obtained layouts can satisfy all the constraints in Section 4, except for Equation (13) which ensures all departments are placed inside the floor space. To ensure the departments are arranged in the available region of each floor, we introduce a penalty term. When any department is not fully located in the floor area (the right edge of the department is beyond the right edge of the floor space), a penalty term will be added to the two objective functions. For each department, the penalty term is calculated by using the following Equation (21)  
\[penalty\_x_{i}=max\{0,x_{i}+0.5l_{i}-wr_{2}\} \tag{21}\]  
Figure 2: An example of a construction layout.  
Equation (21) is used to ensure that all departments are located in the floor area. Therefore, the above two objective functions in Section 4 should also be modified accordingly by adding the penalty term as follows:  
\[\text{min}F_{1}=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}c_{ij}\cdot d_{ij}+P\sum_{i=1}^ {n}penalty\_x_{i} \tag{22}\]  
\[\text{max}F_{2}=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}r_{ij}\cdot b_{ij}-P\sum_{i=1}^ {n}penalty\_x_{i} \tag{23}\]  
We calculate the nondomination rank and crowding-distance of each solution based on the objective values calculated by Equations (22) and (23) which are used in NSGA-II as selection and replacement operators.
However, both methods generally necessitate the presence of external disturbance signals and are unable to leverage steady-state operational measurement data for estimation. In response to this, ref. [33] proposes a novel data-driven method using ambient measurements, which seems to be a method that can estimate the virtual inertia constants of CIGs simultaneously without disturbing the system's normal operating condition. Refs. [34; 35] applied a multi-input multi-output autoregressive moving average model to determine the system inertia co-efficient in the presence of small-signal disturbances in the environment. Nevertheless, the assessment and analysis of the equivalent virtual inertia of the wind farm are not encompassed within this study. Meanwhile, the above methods share a common limitation in that the evaluation accuracy depends significantly on the selection of the model order and the identified models lack explicit analytical expressions, thereby restricting their applicability in detailed inertia response analysis. In conclusion, contemporary methodologies for assessing wind farm inertia encounter several obstacles, including restricted access to control structures and parameters due to commercial confidentiality (the control information of wind turbines is limited), as well as limited applicability to inertia response analysis. To address these issues, this paper introduces a symbolic regression approach for inertia assessment. Initially, modules pertinent to inertia response are classified and associated with extensive operational data, employing sparse dynamic modeling to develop a nonlinear feature library that captures frequency-active power variations. Following this, a sparse relaxation optimization algorithm is utilized to regress the control parameters, enabling the calculation of the wind farm's inertia through the equivalent virtual inertia expression applicable to doubly fed wind farms. The efficacy of this method is subsequently validated through simulations conducted on the IEEE three-machine nine-bus system. The organization of this paper is delineated as follows: Section 2 introduces the mathematical framework governing DFIG, alongside two prevalent inertia control methodologies, and the limited control information of wind turbines is defined; it subsequently formulates the analytical expression for the equivalent virtual inertia of DFIG, considering various control strategies. Section 3 constructs an inertia response model of DFIG with limited control information utilizing sparse dynamic modeling. Section 4 employs the sparse relaxed regularization (SR3) algorithm for the identification of model parameters, which are subsequently utilized to evaluate the inertia of the wind farm. Section 5 presents a comparative analysis of the identified equivalent virtual inertia against simulation outcomes across diverse conditions. Finally, Section 6 encapsulates the benefits of the pro-posed methodology and delineates prospective avenues for future research.
## 3 Results
### Water Productivity  
An average seasonal water productivity of 17.0 kg ha\({}^{-1}\) mm\({}^{-1}\) was obtained across all experimental sites (Figure 10). The seasonal WP value varied from 15.5 kg ha\({}^{-1}\) mm\({}^{-1}\) at site C, field under furrow irrigation, in the 2021 season to 18.9 kg ha\({}^{-1}\) mm\({}^{-1}\) at site B, field under border irrigation, in the 2019 season.  
The WP values per each harvest cycle for sites B, C, and D in the 2019 season are given in Table 3. In general, the WP values were greater at cuts 2-4 than later cuts. For instance, the third cutting had the greatest WP at the site B (27.1 kg ha\({}^{-1}\) mm\({}^{-1}\)) while the greatest WP was occurred in the second cutting at site C (24.4 kg ha\({}^{-1}\) mm\({}^{-1}\)). These values are near to the maximum values reported for alfalfa WP by earlier studies, 10-34 kg ha\({}^{-1}\) mm\({}^{-1}\)[11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].  
Figure 8: Mean annual dry matter yields at the different experimental sites and crop seasons. [PERSON]’s least significant difference (LSD) test was conducted between the mean yield values which the LSD value (as 2.9 Mg ha\({}^{-1}\) yr\({}^{-1}\)) is demonstrated a vertical bar. There is significant yield difference (\(p\) < 0.05) between the sites that have a mean DM yield difference greater than 2.9 Mg ha\({}^{-1}\) yr\({}^{-1}\).  
Figure 10: Water productivity (WP) of fully-irrigated alfalfa at the different experimental sites and crop seasons. [PERSON]’s least significant difference (LSD) test was conducted between the WP values which the LSD value (as 1.92 kg ha\({}^{-1}\) mm\({}^{-1}\)) is demonstrated as the vertical bar. There is significant WP difference (\(p<0.05\)) between the sites that have a WP value difference greater than 1.92 kg ha\({}^{-1}\) mm\({}^{-1}\).  
Figure 9: 30 min soil water potential (kPa) measured at multiple depths of 15–120 cm in plots under farmer irrigation practice at site C from March 2019 to September 2019 (**a**) and at site D, under summer deficit irrigation strategy, from March 2019 to November 2021 (**b**). Red arrows show the cutting dates over the periods. Recommended soil water potential for alfalfa crop at site C should be within the range of 90–120 kPa. This range at site D would be 50–70 kPa. Site D occasionally experienced water stress around cutting events.  
These data show that alfalfa WP in the region is very high during the harvest cycles of April-June and declines considerably during the harvest cycles of July through the late fall (the second half of crop season). The findings revealed that the average WP of the second half of alfalfa crop season may be reduced by nearly 40% (site B) to more than 90% (site D) in compared with the average WP of the first half of crop season. Site D has a sandy loam soil texture which exhibited moderate water stress around the harvest cycles even during the first half of crop season (Figure 9b). However, this water stress continued and was even more severe during the late summer period.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 3 Fractal Reflectarray Antennas  
Recently, several fractal antenna elements have been proposed for the design of reflectarray unit cells, which have a reduced size and quite good phase coverage [33]. Reflectarray antennas are flat reflectors made up of an array of microstrip elements illuminated by a feed antenna. Currently, the design of reflectarray antennas faces several challenges, such as the need to increase bandwidth, reduce losses, and miniaturize unit cells [34,35]. Tackling some of the above challenges, some fractal-based reflectarray configurations have been proposed in the literature. The miniaturization capabilities of fractal patches make them attractive for designing reflectarrays and phased-array antennas. Small array radiators offer many  
Figure 5: Various fractal antenna elements.  
Figure 6: Various fractal array arrangements.  
advantages, including a reduction in mutual coupling and the ability to work with smaller array grids, which is useful to increase the bandwidth of reflectarray antennas [36; 37] or to ensure good scanning performance over a wider frequency range.