esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Harmonic Suppression Algorithm for Asymmetric Dual Three-Phase PMSM
## 7 Conclusions  
Through the analysis of dead-time effects, this paper proposes a current harmonic suppression algorithm that combines the feedforward voltage compensation with a resonant controller implemented in the \(x\)-\(y\) subspace as the feedback current controller. The feedforward voltage compensation can eliminate the current harmonics between 73% and 78% of its original value based on the accurate phase current polarities, which are determined based on the relationship between the current vector angle and the polarities. An improved resonant controller adopted in the \(x\)-\(y\) subspace as the feedback current controller can suppress the current harmonics between 76% and 84% in the \(x\)-\(y\) subspace. The experimental results verify that not only the feedforward voltage compensation but also the improved resonant controller employed in the \(x\)-\(y\) subspace can suppress the current harmonics effectively. Meanwhile, employing both methods at the same time can eliminate the current harmonics between 84% and 86%, which can achieve even better results compared to the cases of adopting only one method.  
Conceptualization, [PERSON], Y.Z. and W.G.; methodology, [PERSON], Y.Z. and W.G.; software, [PERSON], Y.Z., and K.L.; validation, [PERSON], Y.Z., W.G., K.L., and J.G.; formal analysis, Z.W.; investigation, W.G. and K.L.; resources, [PERSON], Y.Z., W.G., K.L., and J.G.; data curation, [PERSON], Y.Z., W.G., K.L., and J.G.; writing--original draft preparation, [PERSON], Y.Z., W.G., K.L., and J.G.; writing--review and editing, [PERSON], Y.Z., W.G., K.L., and J.G.; visualization, Z.W., Y.Z., W.G., K.L., and J.G.; supervision, Z.W., project administration, Y.Z.; funding acquisition, Z.W., Y.Z., and K.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by National Key Research and Development Program of China (2016 YFB0100804).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Phase Current 20 A** & **Phase Current 35 A** \\ \hline Case 1 & 19.91\% & 17.98\% \\ Case 2 & 5.23\% & 4.11\% \\ Case 3 & 4.53\% & 3.25\% \\ Case 4 & 3.12\% & 2.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of THDs under 1000 rpm for four different cases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Phase Current 20 A** & **Phase Current 35 A** \\ \hline Case 1 & 23.62\% & 20.53\% \\ Case 2 & 5.82\% & 4.60\% \\ Case 3 & 4.91\% & 3.52\% \\ Case 4 & 3.68\% & 2.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of THDs under 500 rpm for four different cases.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.7 Support Vector Regression (SVR)  
SVR is a supervised learning algorithm derived from support vector machines (SVM), and is widely employed for regression tasks involving nonlinear and high-dimensional data. SVR aims to identify a function that deviates from the actual target values by no more than a specified margin (\(\epsilon\)) while maintaining model complexity as low as possible. It utilizes kernel functions--such as radial basis function (RBF), polynomial, or linear kernels--to transform the input space into a higher-dimensional feature space, allowing for the modeling of complex nonlinear relationships. In this study, key hyperparametersof the SVR model, including the penalty parameter (C), the kernel type, the \(\varepsilon\)-insensitive loss function width, and the kernel-specific parameters (e.g., \(\gamma\) for the RBF kernel), were optimized to enhance model accuracy. SVR offers advantages in terms of robustness to overfitting and strong generalization performance, particularly with appropriately tuned kernels. However, its computational cost can be high for large datasets, and model performance is highly sensitive to the choice of hyperparameters and kernel function [54].
### Proposal of This Paper and Its Significance  
This work centers on enhancing automatic modulation classification using a hybrid deep learning model integrating CNN and TCN to address the complexity of dynamic wireless signals. CNNs excel in capturing local features and signal patterns by applying convolutional filters across time-series data, making them effective at recognizing modulation types under various noise levels. The TCNs complement CNNs by modeling longer temporal dependencies without losing context, essential for accurately classifying signal modulations in rapidly fluctuating environments. Together, CNN-TCN architectures adaptively enhance classification reliability, even in low-SNR conditions.
## 1 Introduction
), providing both mechanical protection and a controlled release of active elements when used as an antibacterial coating, which is important also in medical implants [29; 30]. Additionally, silver can be used in conjunction with other elements, such as Ti [31], Cu [32], O [33], etc., to provide improved antibacterial effect. Different versions of magnetron sputtering, such as DC (Direct Current) sputtering [22], RF (Radio Frequency) sputtering [34] and HiPIMS [26] have been used to produce silver-containing coatings. It is known that the presence of O\({}_{2}\) plays a crucial role in the strong antibacterial activity of the TiO\({}_{2}\)+Ag+O\({}_{2}\) system by a synergetic contribution in both improving the photocatalytic activity of TiO\({}_{2}\) and further enhancing antibacterial efficiency by influencing the structural state of AgNPs and triggering Ag ion release [35]. As documented by numerous studies, O\({}_{2}\) prevents electron-hole recombination when it comes to UV/Visible light exposure of TiO\({}_{2}\) and contributes to reactive oxygen species formation, resulting in very high oxidative stress and bacterial cell membrane disruption [36; 37; 38]. Additionally, O\({}_{2}\) exposure can partially oxidize the AgNPs [39] and lead to a more stable dispersed state of AgNPs, preventing the formation of nanoclusters and triggering a strong antimicrobial effect by Interacting with bacterial cell walls, protein denaturation, blocking DNA replication, transcription processes alteration, etc. [40; 41; 42; 43; 44]. In our contribution, we focus on the deposition of Ag-containing coatings using a combination of HIPIMS and RF sputtering in a reactive gas environment. The addition of O\({}_{2}\) to the deposition process is evaluated as a solution in terms of the enhanced antibacterial activity and overall quality of the thin film. The targeted application refers to obtaining antibacterial coatings on thin, transparent polymer foils intended to be used as protective foils for mobile devices, with particular interest in the devices and screens used in medical environments, as described in our previous work [26; 27].
## 5 Conclusions  
In order to increase the jamming resistance of GPS sensors, we have designed a GPS SDR for a four-element CRPA array. This GPS SDR is capable of real-time beamsteering toward 12 GPS satellites with a sampling rate of 40 Msps for future GPS L5 signals. The on-the-fly phase calibration of this paper provides a simple way to align phases between multiple antenna elements of a stationary GPS CRPA. The computational requirement of our CRPA processing has not been satisfied by previous GPS SDRs in the literature. We propose an optimal GPS SDR architecture using CPU and GPU and maximize the usage of their parallel computation capabilities including thread-level parallelism and hardware parallelism. General-purpose GPU technology is relatively new and actively evolving. Our GPU kernel is specifically optimized for the recent development of a new GPU architecture with compute capability 2.x and a new CUDA 4.0 Toolkit. Since design and optimization strategies using this new generation GPU are not widely available in the GPS sensors community, this paper tries to provide sufficient details for future GPS SDR developments for other applications.  
Although this paper focuses on the design of a GPU kernel, a modern multi-core CPU is powerful enough to share a large portion of the computational load. Modern processors are evolving towards increasing the number of cores rather than the speed of processing clocks. As a result, modern processors demonstrate very high instruction throughputs, but users need to parallelize their computations in order to properly utilize the parallel resources. Multi-threading on CPU and SIMD operations by each thread, as implemented in our SDR, are essential to fully appreciate the CPU resources.  
The GPS CRPA technology is more mature in the military market, but the technology is mostly confidential and unreleased for civil applications. However, GPS RFI is not just a military concern and some civil applications, such as GPS-based aviation, require certain level of resistance to GPS RFI. This paper demonstrates that GPS CRPA technology can be implemented using cost-efficient COTS hardware and processors, which envisions wide applications of this technology.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 2 Damage Identification Method Based on Additional Virtual Mass and Damage Sparsity
### Damage Identification Method Based on Sparsity
#### 2.2.1 Basic Theory and Regression Model
\begin{gathered}\big{[}\widetilde{\omega}^{T}(\mu_{0}),R^{T}\big{]} ^{T}=\big{(}\mu_{D}^{T}Q\mu_{D}\big{)}^{-1}\mu_{D}^{T}Q\omega_{D}\\ \text{K}_{h}(\mu_{i}-\mu_{0})=\frac{1}{h}\varepsilon^{\frac{|\mu_ {i}-\mu_{0}|^{2}}{(2h^{2})}}\\ \mathbf{Q}=diag(\text{K}_{h}(\mu_{i}-\mu_{0}))\\ \mu_{D}=[\mathbf{I}_{\text{N}\times 1},\ \mu_{D0}]\\ \mu_{D0}=[(\mu_{1}-\mu_{0})^{T},\ \ldots,\ (\mu_{i}-\mu_{0})^{T},\ \ldots,\ \big{(}\mu_{N}-\mu_{0})^{T}]^{T}\\ \mathbf{\omega_{D}}=\big{[}\mathbf{\omega_{1}^{T}},\ \ldots,\ \mathbf{\omega_{i}^{T}},\ \ldots,\ \mathbf{\omega_{N}^{T}}\big{]}^{T}\end{gathered}  \tag{12}\]  
\(\text{K}_{h}\) is the Gaussian kernel function, and the \(h\) is the bandwidth which represents the influence range [31]. \(\mathbf{Q}\) is the weight matrix which is consist of \(\text{K}_{h}(\mathbf{\mu}_{i}-\mu_{0})\) as the diagonal element.
## 5 Discussion
### Evaluation of SPPs against Ground Rainfall Observations
[81] evaluated an ensemble multi-satellite precipitation data set that optimally merges four mainstream SPPs (3B42 RT, 3B42V7, CMORPH,and PERSIANN-CDR) using the dynamic Bayesian model averaging scheme. They found that the ensemble satellite data set outperforms IMERG and GSMaP-MVK in almost all metrics in the summers of 2014 and 2015 at daily and 0.25\({}^{\circ}\) scales over the Tibetan Plateau, as well as performs remarkably in reproducing the probability density function for daily rainfall amount and estimating moderate and heavy rainfall [81]. [PERSON] et al. [82] developed the Multi-Source Weighted-Ensemble Precipitation global data set by merging two global gauge-based precipitation data (GPCC and CPC unified), three SPPs (CMORPH, GSMaP-MVK, and 3B42V7), and two atmospheric model reanalysis (ERA-interim and JRA-55) as a function of timescale and location. Unlike the four gauge-adjusted data sets (WFDEI-CRU, GPCP-1 DD, 3B42V7, and CPC Unified) that use independent precipitation data from 125 FLUXNET tower stations around the globe, the ensemble precipitation shows the highest daily CC among the five data sets over 60% of stations, and the merged data generally present improved hydrological capabilities than other data sets [82]. These merging techniques could be used in the Chindwin River basin in the future.
## 1 Introduction
(2) The measurement efficiency is low, and the high-speed and full-information measurement cannot be achieved. (3) Challenges arise, due to the effect of the radius of the probe ball, the cosine error, serious fluctuations or even the loss of measured data. With the development of technology, non-contact measurement technology has become a new hotspot in blade measurement research. [PERSON] et al. [13] presented a method for non-contact measurement and evaluation of the aeroengine blade. The coordinates are collected by scanning the blade profile using the laser sensor, the analysis and evaluation of the blade parameters are realized by the comparison of measurement model and design model of the blade. [PERSON] et al. [14] studied a mathematical model of inclination error compensation, where the coordinate acquisition accuracy of non-contact measurement is improved based on the proposed error-compensation model. The non-contact optical measuring technology improves the measurement efficiency significantly, but it still has some problems in optical adaptability and measurement accuracy due to the limitation of the measurement principle. Aiming at the problems in the accuracy and adaptability of blade twist measurements, a high-precision and form-free measurement method of blade twist based on evaluations of twist angular position and twist angle is proposed, and the theoretical model, the measurement principle and the key technologies of this method are discussed in detail. The high-precision acquisition of coordinates and the extraction of chord angles without a theoretical model of the measured blade are key issues and difficulties of this study. The method proposed in this study significantly improves the accuracy and adaptability of blade twist measurement, and provides a new technical solution for the measurement and evaluation of the twist of aeroengine blade.
## 2 Method
### Trajectory Replanning Using Gradient-Based Optimization
#### 2.2.4 Reference Trajectory Cost Function  
The reference trajectory cost function is designed to align the replanned trajectory with the pre-optimized reference trajectory. This cost term is defined as follows:  
\[\mathbb{J}_{r}=\frac{1}{2}\sum_{i=1}^{n}\|\xi_{i}-\xi_{i}^{ref}\|_{M_{r}}^{2} \tag{28}\]  
where \(\xi_{i}^{ref}\) is the expected waypoint along the reference trajectory, and \(M_{r}\) is a weight metric that penalizes deviations from the reference trajectory.
## 3 Results and Discussion
### Microstructure Observations  
To observe the shape of the binder in the preform, an ECLIPSE LV 150N optical microscope was used. Unlike the smooth surface of the neat fabric preform (Figure 9a), the original size of the powder binders lightly attached to the fiber surface was observed on the binder-treated preform without heat treatment during the preforming process (Figure 9b). In contrast, the preform that underwent heat treatment during the preforming process exhibited binders pressed in the in-plane direction and that adhered to the fiber in a flat shape (Figure 9c). The binder processed with a temperature above the glass transition temperature was flattened while being applied by a vacuum bag of 1 atm, and some of these binders permeated between the fibers and had a flat shape in the fiber in-plane direction to reduce the out-of-plane directional space where the fluid can flow. In the two types of binder-treated fabric preform (Figure 9b,c), the impregnation of the test fluid was disturbed by the binder occupying space to pass, resulting in a decrease in the permeability of the preform, especially in the heat-treated preform (Figure 9c), where the binders having a flat shape in the fiber in-plane direction led to a significant decrease (>97%) in the out-of-plane directional permeability.  
Figure 8: Change in the permeability in the out-of-plane direction according to the binder treatment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Permeability–K\({}_{3}\) (m\({}^{2}\))**} \\ \cline{2-4}
**Fiber Volume Fraction** & \multirow{2}{*}{**Neat Fabric**} & \multicolumn{2}{c}{**Binder**} \\ \cline{3-4}
**(\%)** & & **Non-Thermal Treated** & **Thermal Treated** \\ \hline
**50** & \(1.12\times 10^{-12}\) & \(2.36\times 10^{-13}\) & \(2.38\times 10^{-14}\) \\ \hline
**55** & \(9.13\times 10^{-13}\) & \(1.88\times 10^{-13}\) & \(2.09\times 10^{-14}\) \\ \hline
**60** & \(6.87\times 10^{-13}\) & \(1.53\times 10^{-13}\) & \(1.85\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Permeability of the fabric preform in the out-of-plane direction.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 3 Methodology
### Two Metrics  
The weighting hyperparameters of the combined robust loss are closely related to the training sample. For example, complex datasets require more active learning (such as large \(\alpha\)) and less passive learning (such as small \(\beta\)) to achieve good performance [17]. Furthermore, the weighting parameters are correlated with the noise rate in the trained samples, because noisy samples require more passive learning. Therefore, two metrics are designed to reflect the sample complexity and noise level.  
In the field of digital image processing, the complexity of an image is reflected by its information entropy \(E(.)\) which is defined as Equation (2). Furthermore, Equation (3) defines the standard deviation of the output layer of the classifier \(S(.)\). This indirectly reflects the complexity of the sample, because a more complex image is more difficult for the classifier, resulting in a greater difference in the predicted probability of each category in the output layer and a lower value of \(S(.)\).  
\[E(p_{i})=-\sum_{i=1}^{C}\left(p_{i}logp_{i}\right) \tag{2}\]  
\[S(p_{i})=\sqrt{\frac{1}{C}\sum_{i=1}^{C}\left(\hat{p_{i}}-p_{i}\right)^{2}} \tag{3}\]  
where \(C\) represents the total number of labels, \(p_{i}\) represents the predicted probability of each class from the output layer of the classifier, and \(\hat{p_{i}}\) means the average value of \(p_{i}\).  
It is important to note that the value of \(E(.)\) increases and the value of \(S(.)\) decreases as the image complexity increases. Therefore, as shown in Equation (4), the first metric is defined as the ratio of entropy to standard deviation (abbreviated as RES). The higher the RES, the higher the complexity of the sample.  
\[\text{RES}=\frac{E(p_{i})}{S(p_{i})} \tag{4}\]In addition, the probability difference \(\delta\)[42] is selected as the second metric, which can be used to determine the noise level of the sample. It is defined as follows:  
\[\delta=p_{y}-p_{n} \tag{5}\]  
where \(p_{y}\) denotes the probability of being predicted as a correct category by the classifier, \(p_{n}\) denotes the maximum probability of being predicted as other incorrect categories, and \(\delta\epsilon[-1,\ 1]\). The smaller the \(\delta\) value of a sample when \(\delta<0\), the more likely it is to be a noisy sample.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 1 Introduction
However, it is worth pointing out that this hierarchical or grouping-based algorithm enhances the ability of the algorithm to search different regions of the feasible search space. For solving high-dimensional multi-objective optimization and uncertainty simultaneously, the set-based method of the conversion optimization problem, that reflects convergence and imprecision, may be more effective. However, it should be noted that the previous SetGA utilized an internal crossover operator during the evolution process, implying a lack of information exchange among the set-based individuals. To overcome this issue, we introduce the set-based concept to the basic GSO [39] and similarly transformed the original stochastic MaOP into set-based deterministic bi-objective functions using the metrics of hyper-volume and average imprecision. We introduce the concepts of global producers and local producers to enhance information exchange among set-based individuals during the evolutionary process. The scrounger follows these producers, the Pareto solution, and the search coefficient of Rangers updates along with the number of iterations to maintain the diversity of the set solution. The contributions of this paper can be summarized as follows:  
1. A set-based group search optimizer (SetGSO), introducing the set-based concept into the basic GSO, is proposed to transform the stochastic many-objective optimal power flow (MaOPF) into a set-based deterministic bi-objective function, utilizing metrics of hyper-volume and average imprecision. This transformation effectively addresses the issue of decreased selection pressure on Pareto optimal solutions that arises as the number of objectives increases. 2. The proposed SetGSO, operating under the evolutionary strategy of the fundamental GSO, delineates the underlying stochastic variables using the average imprecision metric, devoid of repeated sampling or probabilistic information. Subsequently, the set-based evolutionary strategy is embedded into the GSO, updating the set-based individuals and appointing the global and local producers using the interval credility strategy, which enhances the exchange of evolutionary information among the set-based individuals. 3. In terms of performance comparison, the simulation results based on three distinct node systems incorporating varying proportions of renewable energy sources indicate that the proposed algorithm yields greater computational time savings compared to the sampling method. Furthermore, in comparison to the existing set-based algorithm, SetGA, SetGSO attains superior Pareto frontier solutions. The rest of the paper is arranged as follows. Section 2 introduces an optimal power flow problem model. Section 3 presents the stochastic many-objective optimization algorithm, SetGSO. In Section 4, simulation studies on a modified three difference bus system are discussed and Section 5 concludes the paper.
## 3 Comparison among Different AC Signal Analysis Methods
### Memory Usage
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Required Memory Cases** \\ \hline FFT & \(N\) or \(2N\), plus additional 14–50 cases \\ \hline Goertzel & \(7N_{f}\) \\ \hline \multirow{2}{*}{Cross-Correlation} & \(N_{f}\)-(N+2) + 3, plus either 1 or \(NN_{f}\) \\  & SIMD: (\(2N\)+2)\(N_{f}\) + 3 + optional \(2N+3N\) \\ \hline \multirow{2}{*}{Sine-fit (linear)} & \(6 NN_{f}+8\)\(N_{f}^{2}+2\)\(N_{f}\) \\  & \(\bullet\) Cofactor/Adjoint, determinant, transpose require up to \\  & \(4N_{f}^{2}+8\) cases \\ \hline \multirow{2}{*}{Sine-fit (non-linear)} & \(6 NN_{f}+2\)\(N+8\)\(N_{f}^{2}+2\)\(N_{f}+2\) \\  & \(\bullet\) SIMD requires \(N\) for intermediate calculations \\  & \(\bullet\) Cofactor/Adjoint, determinant, transpose require up to \\  & \(4N_{f}^{2}+8\) cases \\ \hline DTFT & \(4N_{f}\), plus either 2 or \(2 NN_{f}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between the Memory Allocation Excluding Signal Length Required for the AC signal analysis. When these results are visualized, it is shown that Goertzel, then DTFT is advantageous in most of the cases, as compared to FFT as shown in Figure 3a. Although, on the other hand, FFT requires less memory than sine fitting (linear/non-linear) and cross-correlation, the asymptotic memory usage of Sine-fit linear, non-linear, and cross-correlation is similar. Therefore only one example (Sine-fit) was visualized in Figure 3b in comparison to FFT. In this graph, the FFT has a slight advantage in the light purple area and a landslide advantage in the turquoise area.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 3 Decolorization Methods  
In this paper, decolorization was conducted using different methods:  
* basic conversion from RGB to YUV format using ITU Recommendations 601, 709 and 2020;
* basic conversion from RGB to LAB color space;
* CDF method from [PERSON] et al. [9];
* global decolorization method from [PERSON] and Dodgson (GD) [21];
* decolorization method based on deep learning (DL) from [PERSON] et al. [30].