esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
The research methodology of this study consists of data preprocessing, model implementation, validation methods, data augmentation, and multi-class classification metrics (Figure 1). Firstly, we preprocessed the Brazilian CKD dataset (i.e., binarization of attributes) and translated it to English.  
We implemented ensemble (Figure 1a) and non-ensemble (Figure 1b) models using the algorithms DT, RF, and multi-class AdaBoosted DTs. We also selected the DCS (OLA and LCA) and DES (KNORA-U, KNORA-E, and META-DES) methods. We used the default configuration with a pool of classifiers of 10 decision trees. We chose this configuration because decision tree-based algorithms usually present high performance in imbalanced datasets. We implemented the ensemble models based on the framework proposed by [PERSON] et al. (2019).  
We applied three ensemble and non-ensemble models validation methods: hold-out validation, multiple stratified CV, and nested CV. We used these methods to investigate whether they satisfactorily control overfitting caused due to the limited size of our dataset ([PERSON] et al., 2019). We applied the multiple stratified CV and nested CV with 10 folds and five repetitions. For the hold-out method, we split our dataset into 70% for training and 30% for testing. Thus, we conducted data augmentation only for the training set to ensure that the test set contained only real data. Our approach combines the data oversampling using: (1) manual augmentation, validated by an experienced nephrologist, and (2) automated augmentation (experimenting with the SMOTE, Borderline-SMOTE, and Borderline-SMOTE SVM).  
Hence, we applied the following multi-class classification metrics: precision, accuracy score, recall, weighted F-score (F1), macro F1, Matthew's correlation coefficient (MCC), Fowlkes-Mallows (FMI), ROC, and PRC. We used the python scikit-learn library ([PERSON] et al., 2012) to implement the models and to apply the validation methods and metrics. For dynamic selection techniques, we used the DESlib library ([PERSON] et al., 2019).  
Figure 1: **(a)** Research steps based on the framework proposed by [PERSON] et al. (2018): data preprocessing, model implementation, validation methods, data augmentation, and multi-class classification metrics. **(b)** Research steps based on simple approach: data preprocessing, model implementation, validation methods, data augmentation, and multi-class classification metrics.
# Double-Peaked Mid-Infrared Generation Based on Intracavity Difference Frequency Generation
## 2 Experimental Setup and Methods
An anti-reflection (AR) focusing lens is utilized to focus the beams into the OPO cavity. The OPO cavity is a linear structure consisting of concave mirrors M\({}_{1}\) and M\({}_{2}\). The crystal inside the OPO cavity is a MgO: periodically poled lithium niobate (PPLN) with a size of 40 mm \(\times\) 10 mm \(\times\) 1 mm, placed between M\({}_{1}\) and M\({}_{2}\). The fan structure of the nonlinear crystal optimizes the OPO. Moreover, the chirp of the fan-shaped nonlinear crystal can provide sufficient phase-matching bandwidth for the nonlinear frequency conversion process of the broadband pumping source. The lens collimates the beam generated by the OPO cavity. M\({}_{3}\) and M\({}_{4}\) separate the remaining pump beam and signal beam in the output laser of the OPO cavity. M\({}_{5}\) is a germanium (Ge) sheet, which further absorbs the near-infrared laser from the mid-infrared laser. M\({}_{6}\) is a dichroic mirror that separates the OPO idler beam from the DFG idler beam. Figure 2: (**a**) Experimental setup of the double-peaked pump source. (**b**) The spectrum of the ASE seed source. (**c**) The TFBG transmission spectrum. (**d**) The pump source spectrum after power amplification. The generated double-peaked mid-infrared laser is fitted with the difference frequency theory, assuming that the covariance is converted to a Gaussian beam [43]. \[\eta=\frac{P_{i}}{P_{p}}=\frac{4\omega_{i}^{2}d_{eff}{}^{2}k_{s}L}{\pi c^{3}\varepsilon _{0}n_{p}n_{s}n_{i}(k_{p}+k_{s})}P_{s}h(\mu,\xi)\sin c^{2}\big{(}\Delta kL/2 \big{)} \tag{5}\]  
In Equation (5), \(P\) is the beam power, \(L\) is the crystal length, and \(\omega\) denotes the angular frequency. The \(d_{eff}\) is the effective nonlinear coefficient, \(k\) is the finger wave vector, \(c\) is the speed of light in vacuum, \(\varepsilon_{0}\) is the free space permittivity, and \(n\) is the refractive index. The \(h(\mu,\xi)\) is the focusing parameter, a constant value. The spectral data, such as the double peaks' central wavelength, are eventually substituted to generate the simulated idler spectrum. As shown in Figure 4, the TFBG-modulated ASE fiber source can generate a double-peaked idler beam with the double-peaked spacing of 32 nm, and the FWHM of the primary peak is 36 nm through the intracavity DFG. Figure 4: Simulated double-peaked idler beam. Figure 3: Double-peaked pumping source intracavity DFG experimental schematic.
## 3 Results and Discussion
### Effect of the Operational pH Level  
The solution pH was a significant factor on the reduction of KMnO\({}_{4}\). In this study, we applied four different operation pH levels which used the NaOH to adjust the operational pH level. Figure 6 indicated the effect of different pH level on the reduction and immobilization of KMnO\({}_{4}\) in which the concentration of KMnO\({}_{4}\) was 20 mg/L and the BT-3 dosage was 100 g.  
The optimum pH\({}_{i}\) was about 6, at which KMnO\({}_{4}\) could be virtually reduced to MnO\({}_{2}\) in 10 min. [PERSON] [17] discussed the reduction of KMnO\({}_{4}\) in H\({}_{2}\)SO\({}_{4}\) solution at temperature of 70-95 \({}^{\circ}\)C for 30 min, the chemical reaction between potassium permanganate and sulfuric acid can be formulated as:  
\[\mathrm{MnO_{4}}^{-}+8\mathrm{H}^{+}+5\mathrm{e}^{-} \mathrm{\text{---}}\mathrm{Mn}^{2+}+4\mathrm{H_{2}O}\] \[2\mathrm{MnO_{4}}^{-}+3\mathrm{Mn}^{2+}+2\mathrm{H_{2}O} \mathrm{\text{---}}5\mathrm{MnO_{2}}+4\mathrm{H}^{+}\]  
It showed low pH of the reaction system would prohibit the formation of MnO\({}_{2}\). Therefore, a neutral or weak acid system medium is preferable for such reaction.  
Figure 5: Effect of dosage of BT-3 on the immobilization of KMnO\({}_{4}\) in binary system.  
Figure 6: Effect of pH on the reduction and immobilization of KMnO\({}_{4}\).
## 5 Developing the Models for Predicting UCS
### Bpnn  
BPNN is a typically and widely used neural network model for prediction problems in geotechnical engineering [80,81]. To develop an effective BPNN model for predicting UCS of rock samples, the numbers of hidden layers and neurons should be determined to prevent overfitting and reduce the computation time. Therefore, double hidden layers with different neurons were developed; the RMSE and R\({}^{2}\) were recorded as evaluation indicators to compare the performance of all models. Table 4 shows a summary of the obtained values of RMSE and R\({}^{2}\) in the training and testing phases. As can be seen in this table, model number 11, i.e., two hidden layers with 6 and 8 neurons, has shown the lowest value of RMSE in both the training and testing phases; the highest value of R\({}^{2}\) in the testing phase. Therefore, model number 11 was considered as the final BPNN model to predict UCS, as depicted in Figure 8.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Hidden Layers**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-7}  & **1** & **2** & **Training** & **Testing** & **Training** & **Testing** \\ \hline
1 & 2 & 2 & 24.7580 & 20.6986 & 0.7613 & 0.8326 \\
2 & 2 & 4 & 20.3518 & 17.7909 & 0.8387 & 0.8763 \\
3 & 2 & 6 & 21.6890 & 24.4836 & 0.8168 & 0.7658 \\
4 & 2 & 8 & 20.0367 & 17.5967 & 0.8437 & 0.8790 \\
5 & 2 & 10 & 25.3568 & 21.7866 & 0.7496 & 0.8145 \\
6 & 4 & 4 & 20.4635 & 18.0377 & 0.8369 & 0.8729 \\
7 & 4 & 6 & 23.7747 & 21.6982 & 0.7799 & 0.8160 \\
8 & 4 & 8 & 19.9445 & 17.2805 & 0.8602 & 0.8833 \\
9 & 4 & 10 & 20.0098 & 17.9689 & 0.8441 & 0.8738 \\
10 & 6 & 6 & 24.7290 & 20.3359 & 0.7619 & 0.8384 \\
**11** & **6** & **8** & **19.2109** & **17.1627** & **0.8563** & **0.8849** \\
12 & 6 & 10 & 22.4402 & 18.1689 & 0.8039 & 0.8710 \\
13 & 8 & 8 & 19.7370 & 17.5722 & 0.8633 & 0.8793 \\
14 & 8 & 10 & 22.5188 & 19.8386 & 0.8025 & 0.8462 \\
15 & 10 & 10 & 22.7941 & 19.1961 & 0.7941 & 0.8560 \\ \hline \hline \end{tabular} Note: Line in bold represents the better solution.  
\end{table}
Table 4: Performance evaluation of BPNN with various neurons of two hidden layers.  
Figure 8: Considered BPNN architecture.  
Figure 7: Considered DELM architecture.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 5 Conclusions and Future Work  
In this paper, we propose a novel approach for spatial localization of targets using binocular cameras and CNNs, which enables accurate identification of the object class and its position. To enhance the accuracy and robustness of target position determination, we introduce a method that integrates semantic information, edge information, and corner information to extract and describe feature points. Additionally, we present preliminary results from indoor positioning tests. The experiments demonstrate that our approach offers high accuracy and stability, with static positioning accuracy at the centimeter level and dynamic positioning accuracy at the decimeter level. Future attention should be given to three tasks: firstly, investigating target continuous tracking algorithms to mitigate computational complexity; secondly, exploring multi-target positioning methods across disparate cameras; and thirdly, exploring strategies to compensate for long-distance errors.  
Conceptualization, S.L., L.W. and B.Y.; methodology, S.L.; software, S.D.; validation, S.D.; formal analysis, S.L.; investigation, L.W.; resources, Z.Y.; data curation, Z.Y.; writing--original draft preparation, S.L.; writing--review and editing, S.L.; visualization, S.L.; supervision, S.L.; project administration, B.Y.; funding acquisition, B.Y. All authors have read and agreed to the published version of the manuscript.  
This study received partial support from the Key Research Development Program of State: (Project No. 2021 YFB3900803) and Hebei Province Technology Research and Development platform construction (Project No. 215676105H).  
Not applicable.  
The authors declare no conflict of interest.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 3 Materials and Methods
### Model Proposition  
In this work, we developed three classifiers, i.e., a custom CNN model, the CNN model proposed by Edge Impulse, and a custom LSTM model, that can diagnose two distinct respiratory conditions, that is, asthma and normal. We drew inspiration from the existing literature [31; 34], which demonstrated promising results in lung disease detection. Tables 4-6 illustrate the architectures of these three models.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Custom CNN Model** \\ \hline \hline \multicolumn{2}{c}{Input(shape=(975, ))} \\ \hline \multicolumn{2}{c}{Reshape((975 / 13, 13))} \\ \hline \multicolumn{2}{c}{Conv1D(32, kernel_size=3, activation=’relu’)} \\ \hline \multicolumn{2}{c}{MaxPooling1D(pool_size=2)} \\ \hline \multicolumn{2}{c}{BatchNorm Normalization()} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{Conv1D(64, kernel_size=3, activation=’relu’)} \\ \hline \multicolumn{2}{c}{MaxPooling1D(pool_size=2)} \\ \hline \multicolumn{2}{c}{BatchNorm Normalization()} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{Conv1D(128, kernel_size=3, activation=’relu’)} \\ \hline \multicolumn{2}{c}{MaxPooling1D(pool_size=2)} \\ \hline \multicolumn{2}{c}{BatchNorm Normalization()} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{Flatten()} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Our CNN model.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Our LSTM Model** \\ \hline \multicolumn{2}{c}{Input(shape=(975, ))} \\ \hline \multicolumn{2}{c}{Reshape((975 / 13, 13))} \\ \hline \multicolumn{2}{c}{LSTM(128)} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{LSTM(64)} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{LSTM(32)} \\ \hline \multicolumn{2}{c}{Dropout(20\%)} \\ \hline \multicolumn{2}{c}{Dense(512, activation=’relu’)} \\ \hline \multicolumn{2}{c}{Dropout(40)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Custom LSTM model.  
To tune the hyperparameters, a total of 396 combinations of epochs, learning rates, and mini-batch sizes were explored across all three models. Specifically, we tested epochs ranging from 10 to 300, with learning rates of \(10^{-3}\), \(10^{-4}\), \(6\times 10^{-3}\), and \(6\times 10^{-4}\), and mini-batch sizes of 32, 16, and 8. The Adam optimizer was consistently applied across all models to facilitate the optimization process. Table 7 summarizes the optimal hyperparameters, presenting the configurations that show the highest accuracy.
## 2 Materials and Methods
### Data Analysis  
The adsorption and desorption parameters of metolachlor in aerobic and anaerobic conditions for each soil type were calculated using the transformed Freundlich equation log \(C_{\rm s}\) = log \(K+1/n\) log \(C_{\rm e}\), where \(C_{\rm s}\) is the amount of metolachlor adsorbed to the soil (mg Kg\({}^{-1}\)), \(C_{\rm e}\) is the equilibrium concentration in the soil solution (mg L\({}^{-1}\)), and \(K\) and \(1/n\) are empirical constants that reflect the affinity of the soil for the herbicide and the degree of linearity between the amount adsorbed and the solution concentration, respectively. Regression analysis was performed on the adsorption and desorption isotherms to calculatethe \(K\) (intercept) and \(1/n\) (slope) values of metolachlor in aerobic and anaerobic soils. Hereafter, \(K_{ads}\) and \(1/n_{ads}\) will indicate the Freundlich parameters for adsorption, and \(K_{des}\) and \(1/n_{des}\) will refer to the desorption parameters.  
The degradation data of metolachlor in soil were fitted into the first-order kinetics model \(\mathrm{C_{t}}=\mathrm{C_{0}}\;e^{-kt}\), where \(\mathrm{C_{0}}\) is the initial concentration (mg \(\mathrm{Kg^{-1}}\) soil) of the herbicide in the soil, \(\mathrm{C_{t}}\) is the herbicide concentration (mg \(\mathrm{Kg^{-1}}\) soil) detected in the soil at time \(t\), and \(k\) is the first-order rate constant. The degradation rate constants were calculated by the linear regression of the natural logarithm of the percentage of herbicide remaining against the time. The aerobic and anaerobic degradation half-life (\(\mathrm{T_{1/2}}\)) for each soil type was calculated using the equation \(\mathrm{T_{1/2}}=ln2/k\). The statistical program SAS (Statistical Analysis Systems, version 9.3; SAS Institute Inc., Cary, NC, USA) was used to calculate the treatment means and standard errors (\(n=3\)). The differences between the treatments were evaluated using a one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) test at \(p<0.05\).
## 4 Experimental Setup and Results
### Description of the Case of Use  
In our case of use, the policy that dynamically manages the maximum bandwidth allocated to services of residential users is implemented and demonstrated. As an example, we design Flexible Internet Plans differentiated by a basic bandwidth (guaranteed bandwidth) plus an excess bandwidth (Table 1). In case the basic bandwidth is below 100 Mbps, the excess bandwidth is considered as 20% of the basic (Basic Internet Plan), while if the basic bandwidth is higher than 100 Mbps, a 30% of excess bandwidth is considered (Premium Internet Plan). These maximum rates are calculated independently for the upstream and downstream bandwidth although they are symmetric. It should be noted, that although the proposed Internet plans in our case consider a certain amount of the basic bandwidth (20% and 30%), the operators and ISPs could stipulate other levels or conditions for the excess bandwidth. Then, these Flexible Internet plans ensure a fixed basic bandwidth to residential users plus an extra bandwidth, so that while users do not require this extra bandwidth it will be free to use transparently among other users who have greater bandwidth needs, so there is greater control over the total available bandwidth to distribute it in real-time and efficiently between all users, thus making the most of all network resources.
# Comprehensive Energy Demand Response Optimization Dispatch Method Based on Carbon Trading
## 6 Case Analysis  
### Parameter Settings  
In order to verify the effectiveness of the comprehensive energy demand response scheduling method based on carbon trading proposed in this paper, we conducted a simulation verification based on the comprehensive energy system data provided in [27, 28]. The running conditions of the given scenario of the calculation example are as follows:  
1. The names and installed capacity of the power plants in the integrated energy system used in this paper are shown in Table 1:  
See Appendix A for other parameters.  
Among them, the minimum technical output of the coal-fired units is 50% of the rated capacity, and the ramp rate is the rated capacity of the unit that increases or decreases by 50% per hour. The minimum output of the gas turbine is 30% of the rated capacity, and the ramp rate is 100% of the rated capacity of the unit rising or falling every hour. The conversion efficiency of the electric boiler is 0.65.
2. The integrated energy system is connected to the external power grid and gas grid. The thermal system is not connected to the outside world. The predicted values of the electricity, gas, and thermal load of the system are shown in Figure 2--the forecasting curve of the electric thermal load of the integrated energy system.  
The predicted output data of wind turbines and photovoltaic units is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit Name** & **Unit Type** & **Capacity (MW)** \\ \hline W1 & Wind power station & 18 \\ W2 & Wind power station & 18 \\ P1 & Photovoltaic power station & 20 \\ P2 & Photovoltaic power station & 20 \\ C1 & Coal-fired power plant & 40 \\ G1 & Gas turbine power plant & 30 \\ GH1 & Gas heating plant & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power plant parameters.  
Figure 2: The forecasting curve of the electric thermal load of the integrated energy system.  
The basic cost of carbon emissions is set at 67 USD/t, the interval length is set at 80 t, and the carbon-trading price increase is set at 25% [28].  
In order to study the effect of the comprehensive energy demand response scheduling method based on carbon trading proposed in this paper on reducing the operating cost of the system, this paper sets up the following three comparison scenarios:  
Scenario 1: An integrated energy system economic dispatch model that does not consider carbon-trading mechanisms and demand response.  
Scenario 2: The economic dispatch model of an integrated energy system that takes the lowest operating cost as the goal and considers the carbon-trading mechanism and does not consider the demand response.  
Scenario 3: An integrated energy system economic dispatch model that takes into account carbon-trading mechanisms and demand response and aims at the lowest operating cost.  
This paper takes 24 h as the cycle and 1 h as the step to simulate, and analyze, the carbon emissions and the operating cost of the integrated energy system under different scenarios.
## 4 Experiments
### Compared Methods  
To verify the effectiveness and superiority of MDNet, several state-of-the-art change detection methods were selected as comparison methods, including pure convolution-based methods, attention-based methods, Transformer-based methods, hybrid CNN-Transformer methods, and semi-supervised methods. The brief descriptions are as follows:  
Figure 5: Example images from the LEVIR-CD, WHU-CD, and CLCD datasets. A and B represent the same location at different times. The labels represent the changed areas, where white represents the changed area and black represents the unchanged area.  
(i) FC-EF [49] is a change detection model that combines the U-Net architecture with the characteristics of fully convolutional networks. It adopts an early fusion strategy, splicing two bi-temporal images in the channel dimension to form a unified feature representation.  
(ii) SNUNet [50] realizes hierarchical extraction of multi-level features through a weight-sharing NestedUNet architecture, optimizes intermediate-layer feature representation by embedding a channel attention mechanism, and further strengthens the effective fusion of multi-semantic level features.  
(iii) BiT [30] is a Transformer-based network model, in which the encoder is designed to model the spatio-temporal context of compact pixel-level information, and then the decoder is used to optimize the original features.  
(iv) AMTNet [51] is a feature-interactive siamese network based on a CNN-Transformer architecture, which realizes efficient modeling of contextual information in bi-temporal images through an attention mechanism and Transformer modules.  
(v) WS-Net++ [52] is a semi-supervised change detection model that integrates CNN and wavelet transform. It analyzes the spatial and frequency domain features of images through wavelet transform to enhance boundary integrity, reduces the difference between unchanged and changed regions by means of semi-supervised methods, and reduces false detections while improving detection accuracy.
# Revealing Coupled Periodicities in Sunspot Time Series Using Bispectrum--An Inverse Problem
###### Abstract  
Sunspot daily time series have been available for almost two centuries providing vast and complicated information about the behavior of our star and especially the interaction of the motion of the planets and other possible interstellar phenomena and their effects on the surface of the Sun. The main result obtained from the sunspot time series analysis is the imprint of various periodicities, such as the planets' orbital periods and the planetary synodic periods on the sunspots signature. A detailed spectrum representation is achieved by means of a periodogram and a virtual extension of the time length segments with zeroed samples for longer representations. Furthermore, the dependence or coupling of these periodicities is explored by means of a bispectrum. We establish the exact interdependencies of the periodic phenomena on the sunspot time series. Specific couplings are explored that are proved to be the key issues for the coupled periodicities on the sunspot time series. In this work, contrary to what has been presented in the literature, all periodic phenomena are limited within the time period of an 11-year cycle as well as the periodicities of the orbits of the planets. The main findings are the observed strong coupling of the Mercury, Venus, and Mars periodicities, as well as synodic periodicities with all other periodicities that appear on the sunspot series. Simultaneously, the rotation of the Sun around itself (25.6 to 33.5 days) provides an extensive coupling of all recorded periodicities. Finally, there is strong evidence of the existence of a quadratic mechanism, which couples all the recorded periodicities, but in such a way that only frequency pairs that sum up to specific periods are coupled. The justification for this kind of coupling is left open to the scientific community.  
Sunspot series; planetary orbital periods; synodic periods; coupled periodicities; bispectral content; 11-year cycle; nonlinearities +
Footnote †: journal: Applied Sciences