esa-sceva/satcom-chunk-collection huggingface dataset:
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 4 Case Study
### Overview of the Proposed Fault Diagnosis Algorithm  
To improve the calculation speed of the sparse coefficient solution and make the sparse representation more suitable for high-dimension industrial process data, the proposed fault classification algorithm was developed based on DBNs and sparse representation. As shown in the flowchart in Figure 6, the training data was first preprocessed by normalization, then the features were extracted from various classes with the DBNs drawing on the idea of dimension-reduction.  
Then, various features were trained with FDSR limitation to obtain the redundant subdictionaries, which were cascaded to construct the discriminative dictionary. The parameters of FDSR chosen by cross-validation are \(\lambda_{1}=0.005\), \(\lambda_{2}=0.05\), \(\lambda=0.001\) with \(1000\) iterations, \(125\) iterations for each  
Figure 6: The deep belief network-Fischer discriminative sparse representation (DBN-FDSR) algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Disturb** & **Type** \\ \hline
6 & **A feed loss** & Step \\
7 & C head pressure loss & Step \\
8 & **A/B/C composition changes** & Random \\
9 & D feed temperature changes & Random \\
10 & C feed temperature changes & Random \\
11 & Reactor cooling water inlet temperature changes & Random \\
12 & **Separator cooling water inlet temperature changes** & Slow drift \\
13 & **Reactor dynamic constants changes** & Slow drift \\
14 & **Reactor valve** & Sticking \\
15 & Separator valve & Sticking \\
16 & Unknown & Unknown \\
17 & Unknown & Unknown \\
18 & **Unknown** & Unknown \\
19 & Unknown & Unknown \\
20 & Unknown & Unknown \\
21 & Stable valve in stream 4 & Constant \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._case. We also did experiments on eight cases which are the faults in bold in Table 3 (including the normal case, fault 1, fault 6, fault 8, fault 12, fault 13, fault 14, and fault 18). The subdictionary is initialized by PCA and acquired 22 \(\times\) 960 matrix, and the sparse level is set to 21. In addition, the Fisher discrimination criterion was applied to dictionary learning to ensure the discrimination of both sparse coefficients and subdictionaries. First, the FDSR restricted the larger among-class scatter and smaller within-class scatter. Then, we created each class-specific subdictionary to best represent the training samples from the associated class, but this subdictionary poorly represented the samples from the other classes. Both ensure the discrimination of the sparse coefficients and the reconstruction error. Additionally, the sparse representation coefficients of the extracted features were resolved using the iterative projection algorithm. Once the coefficients and dictionaries were acquired, the reconstruction error between samples of class-specific and other classes could be calculated. Furthermore, the fault type corresponded to the dictionary with the smallest error. The DBN-FDSR method was applied to the TE process to validate its performance.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Comparison with State-of-the-Art Methods
#### 5.3.3 The Throughput of the Network
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**WSN\#**} & \multicolumn{3}{c}{**ABC [9]**} & \multicolumn{3}{c}{**CEEC [15]**} & \multicolumn{3}{c}{**SEED [11]**} & \multicolumn{3}{c}{**MOCHs**} \\ \cline{2-13}  & **FND** & **HND** & **LND** & **FND** & **HND** & **LND** & **FND** & **HND** & **LND** & **FND** & **HND** & **LND** \\ \hline
1 & 31,494 & 42,207 & 49,193 & 48,364 & 80,649 & 101,703 & 650,466 & 898,763 & 926,996 & 220,614 & 360,155 & 888,213 \\
2 & 33,945 & 44,467 & 52,345 & 48,945 & 79,702 & 99,044 & 658,340 & 899,517 & 919,863 & 227,300 & 363,950 & 897,446 \\
3 & 32,281 & 39,644 & 48,860 & 47,055 & 77,852 & 97,350 & 647,950 & 882,923 & 907,599 & 228,851 & 369,314 & 903,309 \\
4 & 31,257 & 40,024 & 48,750 & 50,787 & 81,261 & 102,020 & 623,863 & 866,588 & 903,315 & 237,280 & 364,807 & 894,681 \\
5 & 32,959 & 39,911 & 48,487 & 46,953 & 77,7664 & 98,608 & 616,818 & 848,482 & 877,849 & 243,195 & 363,623 & 894,313 \\
**Average** & 32,314.4 & 41,250.6 & 49,527 & 48,420.8 & 79,425.6 & 99,745 & 639,487.4 & 879,254.6 & 907,120.4 & 231,448 & 364,369.8 & 895,592.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Total number of data packets received at the BS over simulations of five WSNs (with 300 nodes).
## 3 Methods
### Evaluation of Accuracy  
The accuracy of different GNSS equipment positionings was evaluated by calculating the RMSE between the reference trajectory location (LOCREF) and the GNSS location (LOCGNSS) concurrently with GPS time at 1 sec intervals, Equation (1).  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(LOC_{REFi}-LOC_{G\text{ GNSS}i}\right)^{2}}{n}} \tag{1}\]  
A single factor Analysis of Variance (ANOVA) was carried out to test whether the forest conditions had a significant effect on the results. The significance threshold was set at 0.05.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 2 Background
### Model Predictive Control  
The EWMS design presented in this work is based on model predictive control (MPC). Notably, MPC has been used in the literature to improve the operation of systems based on optimizing future system behavior in regard to future control actions [17]. For this controller design, a discrete-time model of the system is required to predict future behavior. The future control actions \(\{u(k),u(k+1),\ldots,u(k+N_{u}-1)\}\) are then determined by optimizing an objective function with constraints for the manipulated and controlled variables. Then, only the first action \(u(k)\) of the optimal future control is applied during the current time step. In the next time step, a new optimal sequence is obtained (rolling horizon). In general, the MPC optimization problem solved at each instant is given by:  
\[\min_{u(k),\ldots,u(k+N_{u}-1)} \sum_{j=1}^{N_{y}}F_{y}(\hat{y}(k+j),r(k+j))+\lambda\sum_{j=1}^{N_{ u}}F_{u}(u(k+j-1))\] (2) s.t. \[\quad g(k+j)=f(\hat{y}(k+j-1)\ldots,u(k+j-1),\ldots,\,,\,,\quad j =1,\ldots,N_{y} \tag{3}\]  
where \(\hat{y}(k+j)\) is the \(j\)-step-ahead prediction for the controlled variable, which is given by nonlinear prediction model \(f\). Additionally, \(F_{y}\) is the cost associated with the predicted output, \(r(k+j)\) is the reference value for the controlled variable, \(F_{u}\) is the cost associated with the control action, and \(\lambda\) is the weighting factor. Finally, \(N_{y}\) and \(N_{u}\) are the prediction and control horizons, respectively.
## 4 Conclusions  
In this study, an analysis method for TSNAs in the liquid and aerosol of EC was developed and validated using LC-MS/MS. The liquid phase samples were analyzed after diluting the EC solution under two variable conditions: (1) dilution factor and (2) dilution solution. Dilution of liquid phase samples using two different solvents, AA (100 mmol) and ACN, was assessed in terms of RR. This represents a balance between diluting the sample enough so it is compatible with the analytical system, but not diluting it so much that the detection limits are compromised. The EC solution that was diluted 50-fold with AA showed stable RRs (83.1 \(\pm\) 4.65 for NNN, 88.0 \(\pm\) 5.16 for NNK, 81.5 \(\pm\) 3.63 for NAT, and 88.8 \(\pm\) 4.45% for NAB). The gaseous phase samples were analyzed by extracting the filter captured EC aerosol with the solvent (AA) through the MCT approach. The sampling method of the EC aerosol sample using two different sampling filters (CFP and QW) was also assessed in terms of RR. The QW filter captured EC vapor efficiently and it showed stable RR of 84.3 \(\pm\) 13.7 for NNN, 73.3 \(\pm\) 10.9 for NNK, 75.3 \(\pm\) 16.3 for NAT, and 93.8 \(\pm\) 12.3% for NAB. Also, the QW filter has the advantage of a comparatively low dilution factor and consumption of a small amount of extraction solvent (AA). Under the estimated analysis conditions, the MDL values of NNN, NNK, NAT, and NAT were 4.40, 4.47, 3.71, and 3.28 ng mL\({}^{-1}\), respectively. In tests of six commercial ECs, we detected none of the four TSNAs in E-solution and aerosol. It is well known that a portion of the TSNAs is related to the content of nitrogen oxides. Based on the theoretical relationship of TSNAs and nitrogen oxide, the six EC solutions have very small amounts of TSNAs that are undetectable. A follow-up study should be performed to quantify TSNAs according to the nicotine content to confirm the generation or transfer characteristics of TSNAs.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/12/2699/s1](http://www.mdpi.com/2076-3417/8/12/2699/s1)]([http://www.mdpi.com/2076-3417/8/12/2699/s1](http://www.mdpi.com/2076-3417/8/12/2699/s1)), this manuscript accompanies supplementary information.  
[PERSON]-S.L., [PERSON]-H.K., [PERSON], and [PERSON] contributed toward the preparation of the manuscript text, figures, and tables. [PERSON]-[PERSON] and [PERSON]-H.J. conducted all major experiments.  
This research received funding as described in Acknowledgments.  
This study was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, & Future Planning (No. 2016R1E1A1A01940995). This research was supported by a grant (14182 MFDS977) from the Ministry of Food and Drug Safety in 2017.  
The authors declare no conflict of interests in financial/non-financial or any kinds of interests.  
The datasets generated from this study are included in this published article and in Supplementary information.
## 3 Dual-Fusion Active Contour Model
### Dual-Fusion Active Contour Model  
To use less fitting energy at the target contours than at other locations, we use an edge indicator function [32; 33] to indicate target contours. The function can be expressed as follows:  
\[g\triangleq\frac{1}{1+\left|\
abla G_{\sigma}\cdot I\right|^{2}}, \tag{16}\]  
Then, we define the dual-fusion intensity fitting energy functional as follows:  
\[E^{\text{DIFF}}(\phi)=g\big{[}\omega_{1}E_{\text{org}}+(1-\omega_{1})E_{\text{sal }}\big{]}, \tag{17}\]  
where \(\omega_{1}\) is a weight coefficient (\(0\leq\omega_{1}\leq 1\)), and \(E_{\text{org}}\) and \(E_{\text{sal}}\) are the original images' fitting energy functional and the saliency images' fitting energy functional, respectively.  
Finally, the dual-fusion intensity fitting energy functional \(E^{\text{DIFF}}(\phi)\) can be obtained by combining Equations (14)-(17):  
\[\begin{split} E^{\text{DIFF}}(\phi,I_{1},I_{2})&=g \big{[}\omega_{1}E_{\text{org}}+(1-\omega_{1})E_{\text{sal}}\big{]}=\\ & g\big{[}\omega_{1}\frac{1}{2}\int_{\Omega}\left|I(x)-I^{LFI}(x) \right|^{2}dx+(1-\omega_{1})\big{(}\lambda_{1}\int_{\Omega}(S-I_{1}(x))^{2}H_{ \varepsilon}(\phi(x))dx+\\ &\lambda_{2}\int_{\Omega}(S-I_{2}(x))^{2}(1-H_{\varepsilon}( \phi(x)))dx\big{)}\big{]}\end{split} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LBF** & **LGIF** & **LIF** & **RMPCM** \\ \hline \multicolumn{5}{c}{\(1239\times 731\) pixels} \\ Iterations & 200 & 200 & 200 & 200 \\ Time (s) & 93.2969 & 55.5938 & 38.5469 & 63.3052 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Iterations and CPU time (in seconds).  
Figure 1: (**a**–**d**) shows the segmentation results of the LBF, LGIF, LIF, and RMPCM model, respectively.  
Then, we minimize \(E^{\text{DFIF}}(\phi,I_{1},I_{2})\) with respect to \(\phi\) to obtain the corresponding gradient descent flow [29; 30; 31]:  
\[\frac{\partial\phi}{\partial t}=g\delta_{\varepsilon}(\phi)[\omega_{1}e_{1}+(1- \omega_{1})e_{2}], \tag{19}\]  
where:  
\[\left\{\begin{array}{c}e_{1}=(I-m_{1}H_{\varepsilon}(\phi(x))-m_{2}(1-H_{ \varepsilon}(\phi(x))))(m_{1}-m_{2})\\ e_{2}=-\lambda_{1}(S-I_{1}(x))^{2}+\lambda_{2}(S-I_{2}(x))^{2}\end{array} \right., \tag{20}\]  
where \(I\) and \(S\) are the original images and the saliency images, respectively. \(I_{1}(x)\) represents the integrated local and global intensities, and \(m_{1}\) and \(m_{2}\) are averages of the image intensities in a Gaussian window inside and outside the contour.
## 2 Equivalence Model of IBDG
### PQ Control Strategy of IBDGs  
Most IBDGs in the existing distribution network are controlled by a constant power (PQ) control strategy [25]. Under normal operation, the control system directs the universal voltage phasor of the grid connection point to the d-axis (assuming that the d-axis phase is ahead of the q-axis phase). The power outer loop control link of the PQ control strategy can be expressed as [26; 27].  
\[\left\{\begin{array}{l}P_{\text{out}}=P_{\text{ref}}=\frac{3}{2}U_{\text{ prec,d}}I_{\text{d,ref}}\\ Q_{\text{out}}=Q_{\text{ref}}=\frac{3}{2}U_{\text{prec,d}}I_{\text{q,ref}}\\ \end{array}\right. \tag{2}\]  
where, \(P_{\text{out}}\) and \(Q_{\text{out}}\) are the active and reactive power provided by the IBDG and \(P_{\text{ref}}\) and \(Q_{\text{ref}}\) are the given active and reactive reference power. To maximize the use of clean energy, the IBDG is usually constrained to output only active power, i.e., such that \(Q_{\text{out}}=Q_{\text{ref}}=0\); \(U_{\text{prec,d}}\) is the d-axis component of the universal voltage phasor of the grid connection point; and \(I_{\text{d,ref}}\) and \(I_{\text{q,ref}}\) are the current reference values of the d-axis and q-axis in the current inner loop control link, representing the active and reactive currents, respectively.  
Figure 1: Typical structure of IBDG (**a**) under fundamental frequency signals, and (**b**) under characteristic frequency signals.
## 6 Discussion  
In this study, the design and fabrication of an inexpensive GLS system are proposed. The GLS is assembled with acrylic, nylamid, Polylactic Acid Thermoplastic, and a 2 mm thick steel sheet, as illustrated in Figures 2-5. As shown in Figure 9, RSSI signals are affected by reflections, absorption, and degradation. The studies in [22; 23] proposed statistical filters and other signal processing methods, such as particle filters in [24]. However, to estimate the position, multiple successive iterations are necessary.  
As noticed in Table 1 and Figures 9-11, large errors are detected when the UAV is outside a 5 m radius to the GLS. Therefore, GPS is suggested even with its lack of accuracy for navigation at these relatively large distances. Inside the radius, the error is continuously reduced when the UAV approaches the GLS. The proposed MLE algorithm, in combination with the filtered RSSI data and the trilateration algorithm, achieves average errors of less than 0.04 m when the UAV is less than 0.5 m from the GLS.  
Table 2 also shows a comparative analysis with similar GLS systems and RSSI-based UAV target position estimation. Refs. [17; 25] show good position estimation results with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UAV Position Coordinates** & **Estimated Position** & **Error** \\ \hline \((-4.0,0.1,0.5)\) & \(\mathrm{P_{1}}\) (m) & \\ \((-3.39,-0.24,0.27)\) & (0.61, 0.34, 0.23) \\ \hline \((-2.0,0.1,0.5)\) & \(\mathrm{P_{2}}\) (m) & \\ \((-1.80,-0.13,0.25)\) & (0.20, 0.23, 0.25) \\ \hline \((-0.5,0.1,0.5)\) & \(\mathrm{P_{3}}\) (m) & \\ \((-0.69,0.06,0.36)\) & (0.19, 0.04, 0.14) \\ \hline \((1.5,0.1,0.5)\) & \(\mathrm{P_{4}}\) (m) & \\ \((1.33,-0.16,0.33)\) & (0.17, 0.26, 0.17) \\ \hline \((3.0,0.1,0.5)\) & \(\mathrm{P_{5}}\) (m) & \\ \((3.40,0.42,0.86)\) & (0.40, 0.32, 0.36) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MLE target estimation results.  
Figure 11: MLE-based target position estimation test.  
errors of 0.78 and 0.70 m, respectively. However, these systems do not consider a GLS design. An accurate system with an error of only 0.001 m was achieved by [11]. The main disadvantage of this system is that complex actuated mechanisms, as well as infrared filters and expensive vision systems, are used. The presented approach finds a good balance between GLS design and the target position estimation accuracy, achieving average errors of less than 0.04 m.  
There is no standard test or set of testing conditions for evaluation of accuracy of UAV docking procedures. This is due to the fact that there are such a large variety of UAV types and also many different docking mechanisms and processes. That being said, it is clear that for small UAVs with rotor spans less than 4 meters, an accuracy two orders of magnitude less than the rotor span would be very beneficial for most docking processes.
## 5 Discussion
Computational resources impact model training and evaluation, as limitations may restrict the use of advanced models or extensive datasets. This study [56] explores the use of deep learning models for classifying EEG signals from brain-computer interface applications. The authors use a dataset similar to the one that we are using and apply convolutional neural networks (CNNs) for feature extraction and classification. The deep learning approach demonstrates high classification accuracy and robustness in noisy environments. This study [57] could provide valuable insights into using advanced neural networks for EEG classification and help benchmark performance. This paper focuses on using wavelet transform for feature extraction from EEG signals, followed by classification using support vector machines (SVMs). The dataset used is comparable to our standard EEG datasets. The combination of wavelet transform with SVM shows effective results in distinguishing different mental states. This study highlights the utility of classical feature extraction methods in EEG signal processing. On the other hand, this research [58] compares Independent Component Analysis (ICA) and Principal Component Analysis (PCA) for denoising and classifying EEG signals. The study uses similar preprocessing and feature extraction techniques. The comparison provides insights into the effectiveness of ICA versus PCA in different scenarios. This paper could be useful for understanding the trade-offs between these techniques in EEG signal processing. All the three studies above use EEG signal datasets, similar to our proposed work. They explore a range of methods including deep learning, wavelet transforms, and ICA/PCA, which are relevant to our feature extraction and classification approaches. Each study offers different perspectives and methods that could complement our research, from advanced neural networks to classical signal processing techniques.
## 3 Results and Discussion
### Pilot Testing  
A more extensive pilot testing was conducted in a real-life application context, aimed at confirming the performance achieved by the ElGo system during the preliminary test and collecting feedback from the participants.  
The first scenario was at the gym of a high school. A group of 24 students without disabilities aged between 16 and 18, both male and female, was involved. In addition, the two boys with motor-skill disabilities already involved in the previous test were included, making a total of 26 participating players. Two teachers were also included in the sample. The setup of this test is shown in Figure 7a,b. The player with motor-skill impairments stands behind the goal sitting on their wheelchair so that the goal will also protect them from being hit by the ball. Then he controls the dummy moving left and right along the goal line, trying to save kicks performed by the other players. The test lasted approximately 3 h, with a pause of half an hour, and the users with disabilities played with ElGo both during a football match and a set of penalty kicks. In terms of goal-saving performance, the test reported results in line with the experimental data described above. In addition to the positive results on the technical side, we got some very positive ones on the user experience. Indeed, the tests confirmed the ElGo system's usability.  
Another pilot testing session was led during a sporting event in the central square of a town. The setup of this test is shown in Figure 8a,b. This test was open to anyone who wanted to try the system. On this occasion, we had roughly one hundred users, including people with different degrees of disabilities curious to try ElGo. The test lasted more than 4 h of continuous play, but considering the context, no performance statistics were collected.  
During a total of around ten hours of testing, there were no faults in the electro-mechanical components nor in the electronic boards and sensors, demonstrating the high robustness of the system. Our tests showed also very good behavior of the safety system, facing only three faults, all due to the wrong positioning of the goal infrastructure.  
Figure 7: Pictures of the first pilot testing session: (**a**) user and ElGo system and (**b**) construction of dynamic action.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 3 Results and Discussion
### Desorption of Metals Ions  
An effective adsorbent for removing metal ions should not only show a good adsorption capacity but also allow for desorption of metal ions, which is desirable from economic and ecological point of view. For this reason, we investigated the desorption of Cu(II), Pb(II) and Cd(II) ions from loaded monoliths. Several studies have shown that HCl, HNO\({}_{3}\) and H\({}_{2}\)SO\({}_{4}\) are effective in desorbing of heavy metals from loaded adsorbents [49]. In our study 0.1 M HCl was initially used for desorption but such a stripping agent caused a rapid destruction of the hybrid monoliths. Therefore, distilled water was used for further experiments. The desorption process was carried out for 10 h. Unfortunately, also under these conditions, only few of the monoliths were not disintegrated into small pieces. After this time, the observed level of desorption was in the range of 26-69%. To verify the correctness of the experiment, after the desorption process, the remaining monolith was separated from the solution and washed with distilled water. Next, the microwave oven digestion was employed for the determination of metal ions remained in monoliths. The sample after mineralization was diluted with distilled water to a specific volume and the concentration of metals was measured. Then, the amounts of copper and cadmium in the adsorption and desorption process were compared. The results are shown in Table 6. Mass balance was calculated using data denoted in columns three and six (Table 6), according to the equation provided in column seven.  
A satisfactory agreement between mass of metal adsorbed and desorbed was observed for each considered metal. The mass balance of Cd(II) and Cu(II) determined as the difference between the initial amount of metal in the aqueous phases and after adsorption agreed with the contents of these species in the aqueous phase after desorption and adsorbed in gelatin hybrid with an error of 8.2-13.7%. In light of the above, it can be stated that hybrid monoliths are promising biopolymer-based novel adsorbents. The results showed that in the case of hybrid monoliths that are stable in aqueous solutions prepared with a desired amount of the siloxane, the desorption of metal ions can reach 70%.