esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Heterogeneous Ensemble Learning by SQGA
### Weight Optimization by SQGA
If the crossover probability is too large, the probability of a genetic mode being destroyed is greater, which makes the individual structure with high fitness be destroyed quickly; if the crossover probability is too small, the search process is slow, so as to stagnate. For the mutation probability, if the value is too small, it is not easy to produce a new structure; if the value is too large, it will become a pure random search algorithm. The adaptive algorithm can solve this problem well, and the crossover probability and mutation probability can be adjusted automatically with the fitness. For the individuals whose fitness is higher than the average fitness of the population, the lower crossing probability and mutation probability should be adopted to protect the individual into the next generation, while the individual with fitness lower than the average value should be selected with a higher cross probability and mutation probability to make the individual eliminated. In the adaptive strategy, the quantum rotation angles \(\Delta\theta\), the crossover probability \(P_{c}\) and the mutation probability \(P_{m}\) are calculated as follows:  
\[\Delta\theta=\theta_{\min}+\frac{f_{\max}-f_{x}}{f_{\max}-f_{\min}}\left( \theta_{\max}-\theta_{\min}\right); \tag{13}\]  
\[P_{c}= \begin{cases}\frac{k_{1}(f_{\max}-f)}{f_{\max}-f_{avg}}&f\geq f_{avg}\\ k_{2}&f<f_{avg}\end{cases} ; \tag{14}\]  
\[P_{m}= \begin{cases}\frac{k_{3}(f_{\max}-f^{\prime})}{f_{\max}-f_{avg}}&f^{ \prime}\geq f_{avg}\\ k_{4}&f^{\prime}<f_{avg}\end{cases} . \tag{15}\]  
where: \(f_{\max}\) is the maximum fitness of the population; \(f_{avg}\) is the maximum fitness of the population; is the average fitness of the population; \(f\) is the larger fitness of the two individuals to be crossed; \(f^{\prime}\) is the fitness of the mutant individual; \(f_{x}\) is the current individual fitness; \(\theta_{\max}\) is the maximum value of \(\Delta\theta\) interval; \(\theta_{\min}\) is the minimum value of \(\Delta\theta\) interval; and \(k_{1}\), \(k_{2}\), \(k_{3}\) and \(k_{4}\) are constants. Compared with QGA, the SQGA's crossover and mutation probabilities are generated adaptively by Formulas (14) and (15) to perform quantum crossover mutation operation. The algorithm can dynamically adjust the key parameters of the algorithm, improve the search efficiency and find the optimal solution conveniently, which is suitable for determining the weight of decision-making level.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## Appendix F Experiments
### Variational Inference vs. MCMC  
We conducted experiments using both Gibbs sampling (MCMC) and mean field approximation (variational inference) for the Bayesian PCA simulation [28] under varying noise levels, assuming that the true noise was known. In our experiments, \"satisfactory estimation\" was defined as the point when the fourth smallest precision (i.e., the inverse of variance) was at least 100 times smaller than the fifth--indicating that the four true signal dimensions (with variances [5; 4; 3; 2]) had been correctly identified. For computational tractability, we capped VI at 200,000 iterations (approximately 200 s) and MCMC sampling at 20,000 iterations (about 20 min), with a burn-in period of 200 iterations and thinning set to 10.  
Figure 15 illustrates the runtime for VI and MCMC to identify the correct components. Our key findings are as follows:  
1. When the noise level was close to the signal, neither MCMC or VI found the true dimension in the limited iterations (and probably never would have), because the data were heavily polluted.
2. As the noise level decreased toward zero, the number of iterations (and runtime) required for satisfactory estimation increased dramatically; VI began to fail around a noise level of \(1\times 10^{-4}\) and MCMC sampling around \(1\times 10^{-3}\), within the set time constraints.
3. Across the 10 noise levels (about \(3\times 10^{-3}\) to \(2\times 10^{-1}\)) where both successfully identified the correct dimensionality, VI was consistently completed much faster than MCMC sampling. VI was 85.57 \(\pm\) 50.24 times faster on average, in the range of 32.46 to 189.12.  
These results indicate that both MCMC sampling and VI become slower as the noise decreases due to strong dependencies in the posterior. We hypothesize this is because both MCMC and VI suffer from the dependency introduced by low noise, which is a known long-standing issue with ongoing research methods, e.g., structured VI [38] or blocked/collapsed Gibbs samplers [40]. However, both MCMC and VI work well provided that there are sufficient iterations. This behavior suggests that the dependency induced by very low noise levels creates an optimization challenge rather than a fundamental modeling issue.  
In summary, (1) VI is significantly faster than MCMC, (2) both methods slow down as the noise level decreases, and (3) both fail to recover the correct components when the noise is excessively high.
# An Uncertainty-Driven Proactive Self-Healing Model for
## 2 Preliminaries and Scenario Description
### Uncertainty-Based Estimation of Overloading  
Our model uses two parameters to detect if a node tends to become overloaded or not. The first parameter is \(\tau\), which shows the trend of \(\mu\) through the considered time intervals. The second parameter is \(P_{\delta}\), which depicts that the status of the local queue and the number of tasks on it exceeds a pre-defined threshold \(\theta\). The realizations of \(\tau\) and \(P_{\delta}\) are used as inputs to define the antecedent part of our Mamdani Fuzzy System and set a value for the consequent part, i.e., the _Overload Indicator_ (\(OI\)) bounded in the unity interval. We define a fuzzy knowledge base adopted every time a node receives a request for new processing in the form of a task. We propose a set of Fuzzy Rules (FRs) formulated as follows: \"If the probability of the number of tasks exceeding \(\theta\) is high and the trend of service rate is low, the \(OI\) for that node should be high\". The proposed FRs have the following structure:  
**IF \(P_{\delta}\)** is \(A_{1}\)**AND \(\tau\)** is \(A_{2}\)**THEN**OI is \(B\),  
where \(A_{1}\), \(A_{2}\) and \(B\) are the membership functions of the corresponding fuzzy sets. We characterize the value of \(A_{1}\) and \(B\) by the fuzzy sets _Extreme Low Low Low_, _Medium_, _High_ and _Extreme High_ and the value of \(A_{2}\) through the fuzzy sets _High Negative_, _Low Negative_, _Neutral_, _Low Positive_, and _High Positive_. In our Mamdani Fuzzy System, we adopt triangular membership functions for the definition of fuzzy sets for variables \(P_{\delta}\), \(\tau\) and \(OI\) and involvethe centroid defuzzification method for the calculation of the final crisp output value, i.e., the value of \(OI\). We have to notice that FRs and membership functions for the proposed Mamdani Fuzzy System are defined by the experts. We consider a knowledge base with 25 rules, i.e., a rule for every combination of the two inputs.
## 3 Results and Discussion
### Surface Structural Deformation
#### 3.2.2 Structural Deformation Caused by PV and PVH Complexes
When a vacancy exists, P atom can move towards to that site, reducing the force caused by compressive stress and is easier to form. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Position of P**} & \multirow{2}{*}{**Position of V**} & \multicolumn{1}{c}{\multirow{2}{*}{**Position of H**}} & \multicolumn{3}{c}{**Bond Length (Å)**} & \multicolumn{3}{c}{**Change in Bond Length (\%)**} \\ \cline{6
###### Abstract  
In this study, we used images obtained by Unmanned Aerial Vehicles (UAV) and an instance segmentation model based on deep learning (Mask R-CNN) to evaluate the ability to detect and delineate canopies in high density orange plantations. The main objective of the work was to evaluate the improvement acquired by the segmentation model when integrating the Canopy Height Model (CHM) as a fourth band to the images. Two models were evaluated, one with RGB images and the other with RGB + CHM images, and the results indicated that the model with combined images presents better results (overall accuracy from 90.42% to 97.01%). In addition to the comparison, this work suggests a more efficient ground truth mapping method and proposes a methodology for mosaicking the results by Mask R-CNN on remotely sensed images.  
precision agriculture; instance segmentation; tree detection; tree delineation; UAV-based images; Mask R-CNN Article
## 6 Experimental Results
### Comparison Models  
In order to validate the effectiveness of our model, we compared the CDAENet model with four state-of-the-art methods, including SVM [13], DT [14], CNN [15], and SCAE.  
SVM [13]: This is the first and most used ML-based multipath signal recognition method. SVM is one of the most important traditional ML algorithms, with large advantages in prediction accuracy. The detection and removal of NLOS satellites using the methods in this article can achieve up to 87% classification accuracy.  
DT [14]: This is one of the most classical ML methods and has also been used to attempt to detect MP and NLOS. In this article, we use the CART-type DT method.  
CNN [15]: This is a DL method that uses CNN for feature extraction of data. The model can recognize both MP and NLOS in static and dynamic environments.  
CSAE: Basic CAE was used to achieve data reduction and deep feature extraction, and LSTM was introduced into the model for learning the features of time series.
## 3 UEP Using Spatial Modulation
### UEP Using Spatial Modulation
\(G_{1}\) bits are used to choose the index of the activated transmit antennas and \(G_{2}\) bits are carried via the constellation diagram of M-QAM modulation then transmitted over the active antenna. At the receiver side, the \(G_{1}\) received bits are recovered based on the SM antenna detection mapping table and the modulated uncritical received bits \(G_{2}\) are recovered by using ML decoder and M-QAM demodulation. Then the two groups (critical and uncritical) bits are used to decode the M-SPIHT decoder to recover the transmitted image at the receiver side. M-SPIHT decoder will set the received bits as \(\widetilde{G}=\left[\widetilde{G}_{1},\widetilde{G}_{2}\right]\) to reconstruct the received image as shown in Figure 5.  
Figure 4: System model. Figure 3: M- SPIHT progressive source encoder. UEP-SM is dividing the encoded SPHHT bitstream into two group of bits and transmitting each group of bits over one of SM transmitting antenna carrying information unit. The MSE of reconstructed image in the UEP-SM will be reduced, as the high significant bits with high MSE contribution in the reconstructed image is transmitted over a low BER SM transmitting carrying information unit and transmitting the low significant bits with low MSE contribution in reconstructed image transmitting over the high BER SM carrying information unit. In other words, despite the ABER of the SM and UEP-SM being equal, the effect of this error is unequal on the MSE of the reconstructed image. The MSE of the reconstructed transmitted image using conventional SM and proposed UEP-SM can be written as  
Figure 2:0  
Figure 2:1  
The proposed UEP-SM can be applied for any of space modulation techniques (SMTs) not only SM. SM-UEP can also be used in the concept of control/user (C/U) plane splitting in the underwater wireless communication sensor network [22; 23] or next 5G mobile network [24], by transmitting the control signal over the low ABER index symbols and transmitting the user data plane over high ABER data symbols.
## 3 Results
### Kinetics of Photocatalytic Oxidation
#### 3.2.1 Influence of pH of the Working Solution  
The initial rate values were calculated according to the methodology described above and were plotted as a function to the initial concentration of 2,4-DCF expressed in terms of organic carbon (Figure 5a) for the four pH values of the working solution. To determine the kinetic parameters, the linearized form of the Langmuir-Hinshelwood model was used (Figure 5b). Figure 5b shows that for all pH values in the chosen concentration range (18.5-112.5 mg OC/L), there is a linear dependence characterized by regression coefficients with values close to unity. Table 4 shows the values of the kinetic parameters obtained for the three pH values of the working solution at a hydrogen peroxide/chlorophenol molar ratio (N) of 1.  
From the data presented in Table 4 it is observed that the photocatalytic oxidation of 2,4-DCF takes place at a faster rate with the increasing of acidity of the working solution with a maximum corresponding to pH 3. The rate constant of the photocatalytic oxidation process (k) is about twice as high at pH 3 comparative with pH 7, and the adsorption constant (K) is about 1.4 times higher at pH 3 compared to that corresponding to pH 7. These results are consistent with the results obtained in the adsorption study in the absence of UV radiation (dark adsorption experiments), which confirms that at low pH values of the working solution the adsorption capacity of the catalyst is higher. Figure 6 shows that complete mineralization of 2,4-dichlorophenol at an initial concentration of 34.6 mg/L is reached after about 4 h if the working solution has a pH of 3 and after 6 h if the working solution has a pH of 7. If the working solution has a concentration of 112.5 mg/L the time required for complete mineralization of the organic substrate exceeds 9 h in the case of the solution with pH 3 and 17 h in the case of solution with pH 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline pH & k (mg/L - min) & K (L/mg) \\ \hline
2 & 0.804 & 0.039 \\
3 & 0.872 & 0.041 \\
5 & 0.748 & 0.029 \\
7 & 0.393 & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kinetic parameters derived from Langmuir–Hinshelwood model for photocatalytic oxidation of 2,4-DCF from aqueous solutions with different pH values; hydrogen peroxide/chlorophenol molar ratio (N) of 1.  
Figure 5: Photocatalytic oxidation kinetics of 2,4-DCF as a function of pH of the working solution: (**a**) non-linear fitting of Langmuir–Hinshelwood kinetics; (**b**) linear fitting of Langmuir–Hinshelwood kinetics; hydrogen peroxide/chlorophenol molar ratio (N) of 1.
## 2 Materials and Methods
### Empirical Orthogonal Function (EOF) Ellipse  
EOF analysis, short for Empirical Orthogonal Function analysis, is a statistical technique commonly used to decompose and analyze multivariate data. Its main idea is to transform the multi-variable data into a set of linearly independent spatial modes, ranked by their variance contributions, thus obtaining the primary spatial and temporal features of the dataset. To compare the radar and model velocities, an ellipse was generated using EOF analysis. This involved creating a data matrix with the u-component velocity and the v-component velocity arranged in the first and second columns, respectively. The velocity matrix was then decomposed into the first and second modes using EOF analysis. The ellipse was constructed by setting the major axis to the first mode of eigenvalues, which reflects the largest standard deviation of total velocity, and the minor axis to the second mode of eigenvalues. The ellipse's orientation was subsequently calculated as \(\theta=\arctan\bigl{(}\frac{v2}{v1}\bigr{)}\), where \(v1\) and \(v2\) refer to the first and second eigenvector modes, respectively.
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 3 Simulation and Verification of Real World Signal
### Simulation
We can see that the observation deviation range of the KFAT method is about 100-1000 m. In the initial tracking stage, the deviation is large, between 800-1000 m. After 300 s, that is, after 15 transmission cycles, the minimum observed deviation is around 100 m and the maximum is around 900 m. The deviation range of the Kalman filtering result is reduced to within 700 m, and as the tracking continues, the deviation range is gradually reduced to within 400 m.  
Figure 19b shows the deviation comparison of the DCNN-KFAT method before and after filtering. We can see that the observation deviation is stable within 200 m, and there are slight fluctuations in the tracking process. Figure 19c shows the comparison of the observation bias of the two methods. The deviation of the KFAT method fluctuates sharply. Between 100 and 1000 m, the observation bias of the DCNN-KFAT method is stable at about 200 m. Obviously, the stability of the DCNN-KFAT method is better. Through analyzing the data, it is found that the observation  
Figure 19: The deviations of KFAT and DCNN-KFAT before and after filtering are listed separately, and the deviation of DCNN-KFAT is significantly smaller than that of KFAT, with an improvement of 500–700 m.  
deviation is mainly caused by the estimation deviation of the echo arrival time and the azimuth angle. The deviation caused by the azimuth angle increases with the increase of the distance. Compared with the KFAT method, the DCNN-KFAT method greatly reduces the observation bias and improves the accuracy of the original data. Figure 19d shows the comparison of the deviations of the two methods after filtering. The deviation of the KFAT method after filtering fluctuates in a larger range, gradually narrowing from 100-1000 m to 150-700 m. The deviation of the DCNN-KFAT method after filtering remains stable at about 200 m.  
It can be seen from the above simulation test results that the DCNN-KFAT method proposed in this paper is not only more accurate than the KFAT method in terms of target determination and target data association, but also greatly reduces the observation bias and improves the tracking accuracy.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 4 Experimental Verification
### Experimental Results  
#### 4.3.1 Steady-State Performance of the System  
Figure 13 displays the experimental frequency, output power, and transmission efficiency curves for different total transmission distances, when \(R_{L}=10\)\(\Omega\). As depicted in Figure 13a, the measured switching frequency aligns with the calculated results in Figure 13c, maintaining the system's transmission efficiency at around 89% and a steady output power of approximately 48 W. Once the total transmission distance \(d\) exceeds 32 cm, the system operates in the PT-broken region, substantially reducing the transmission efficiency. Hence, it is essential to avoid operating in the PT-broken region. The experimental waveforms are depicted in Figure 14.  
The inverter's output voltage and current, as shown in Figure 14, persistently maintain the same phase, affirming the method's ability to retain a permanent negative resistance.  
Figure 12: Measurement results of the coupling coefficient between adjacent coils and the total transmission distance.  
Figure 13: Transmission performance curves concerning transmission distance: (**a**) operating frequency; (**b**) efficiency; (**c**) output power.
# Practical Approach to Designing and Implementing a Recommendation System for Healthy Challenges
## 2 Materials and Methods
### Recommendation Algorithm Design  
After establishing the value for the different features or dimensions of each challenge according to the proposed model and assessing the user parameters, the recommendation algorithm can be defined. In this task, the model was implemented using Python, and it was supported on Google Colab. The recommendation model proposed tries to mimic the rationale for the selection of a challenge for a healthy person by a human expert in terms of screening criteria. Going into detail, once the user has access to the system, there is a set of steps undertaken to receive the final recommendation that goes as follows:  
1. Data gathering from the user. The user is requested to answer some general questions about bio-physical and sociodemographic definition of the users and a set of well-known questionnaires (already discussed in previous sections). With the obtained responses, a detailed profile is generated that addresses all relevant dimensions of the user. This includes the physical dimension (based on PF, BP, and GH scores), psychological dimension (MH, V, and SF), fruit and vegetable consumption dimension (FC, GVC, and LVC), added sugars, whole grains, and dairy consumption dimension (ASC, WGC, and DC), and sleep dimension (SSS, IS, and HS). The overall score for the food dimensions is calculated as the arithmetic mean, while the other dimensions use the geometric mean. The geometric mean is applied instead of the arithmetic ones to prevent high values from hiding the relevance of low values in close features. The practical deployment of the model confirmed the positive practical impact of this approach. Additionally, the previously mentioned AE index is calculated by taking the arithmetic mean of the PF and MH components. This index reflects the user's physical and mental capacity to undertake the challenges. Table 3 shows, as an example, the complete profile of an individual entered the RS, which is hereinafter referred to as tester A. This information is also depicted in Figure 1, where on the vertical axis, a dimensionless grading scale from 0 to 10 is observed; and on the horizontal axis, a list with the name of the 15 dimensions is assessed. The reader is presented with the comprehensive profile of an actual individual integrated within the system. This profile meticulously displays the achieved scores across the assessed dimensions. Within this model, higher scores indicate more favorable outcomes. It is noteworthy that the subject's lowest score is attributed to their consumption of whole grains. This indicates that this particular user has a deficiency in the consumption of whole grains, hence the low score obtained in this dimension. This applies to the other values as well: a low value indicates a poor level. This way, a low level of sugar consumption would imply that a lot of sugar is ingested.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the SPME Arrow Procedure
#### 3.1.2 Selection of Sample Volume  
For the SPME Arrow extraction procedure, different sample volumes (i.e., 1, 3, 5 and 10 mL) were tested in 20 mL vials. Under conditions of exhaustive extraction which are applicable to small sample volumes, large fiber coating volumes and/or very large sample/coating partitioning coefficients, the amount of analytes extracted is proportional to the quantity and number of the adsorbed analytes, which are proportional to sample volume, resulting in higher analyte extraction with higher sample volume until a certain point. However, if the sample volume exceeds that point, fiber saturation can be observed. In this case, an increase in sample volume can have negative effects for quantitation, as the region of direct proportionality of analyte concentration and response is left. In this experiment, the extraction of the examined analytes, as well as the extraction of total volatiles, was enhanced by increasing the sample volume from 1 to 5 mL. However, a further increase from 5 to 10 mL had negative effect on the extraction [15]. Therefore, the optimum sample volume of milk for the SPME Arrow procedure was set as 5 mL. Figure 1 shows the effect of sample volume in the SPME Arrow extraction procedure.  
Figure 1: Evaluation of the sample volume for solid-phase microextraction (SPME) Arrow.