esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control. fuzzy control system; fuzzy logic controller; intelligent control systems; power quality; reactive power; harmonics; voltage variations; frequency control; control device; power systems +
Footnote †: journal: _sciences_  
like genetic algorithms was demonstrated in several studies [33; 34]. In the last 15 years, there have been many papers focused on PQ improvement considering UPQCs, distributed generation, and other AI technologies in combination with fuzzy logic control. Fuzzy control systems are an important component of intelligent control systems. The most significant advantage of fuzzy logic that attracts power systems specialists is its ability to handle imprecise, incomplete, and vague data, as it broadens the set theory so that a set's element belongs to it based on a membership function value, as an alternative to conventional binary methods. Therefore, FCSs provide a way to deal with uncertainties through linguistic values and logical inferential rules, allowing specialists to perform dynamic modeling and controller descriptions using simple linguistic statements [35]. FLCs have been criticized due to the absence of a systematic design and a stability analysis method, plus the financial aspects regarding the replacement of traditional PID controllers [36]. Then again, in many cases, they are the first option when working with non-linear, time-delayed,
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 7 Chemical Cleaning in Submerged HF Membranes--Procedure, Effect on Membrane Performance  
Other than the physical cleaning means discussed in the previous section, chemical cleaning, which involves the use of acids, bases, oxidants, and surfactants, also aids in mitigating membrane fouling. Typically, physical cleaning is followed by chemical cleaning in membrane applications to effectively mitigate fouling [283]. Chemical cleaning was classified by [PERSON] et al. [283] into four categories: (i) clean-in-place (CIP), which involves directly adding chemicals to the submerged HF system; (ii) clean-out-off-place (COP), which involves cleaning the membrane in a separate tank with a higher concentration of chemicals; (iii) chemical washing (CW), which involves adding chemicals to the feed stream; and (iv) chemically enhanced backwashing (CEB), which involves combining chemical and physical cleaning means.  
For chemical cleaning, the key factors affecting efficiency in mitigating fouling are the type of chemical agents, cleaning duration and interval, concentration of chemicals, cleaning temperature, and flux [300; 301]. The type of chemical used depends mainly on the application, feed characteristics (e.g., pH, ionic strength, and temperature), and membrane materials (e.g., compatibility of the membrane with the chemicals) [302]. Sodium hypochlorite (NaOCI) and citric acid are the most common chemical cleaning agents provided by the main MBR suppliers [18], although they are reported to be ineffective for removing iron species [303], and less effective than the coupling of NaOCI and caustic soda for removing natural organic matter (NOM) [304].  
While the robust nature of most submerged HF membranes allows the use of relatively aggressive cleaning, some changes may occur. [PERSON] et al. [304] evaluated the effectiveness and changes in the membrane surface properties by acidic and alkali cleaning of PVDF HF membranes during the microfiltration of two feed waters. The results indicated that the feed-water quality played an important role in the cleaning efficiency; hence, experiments with the actual feed are necessary for the selection of cleaning procedures. In addition, chemical cleaning leads to changes in the surface properties of the membranes, which may lead to a gradual decrease in the recoverable flux.
## 3 Named Data Networking Architecture
### Table Management  
The NDN forwarding path contains three tables: FIB, PIT, and CS. The routing information base (RIB) is used to compute the FIB. All packet forwarding information is stored by FIB. The RIB is populated by NDN's routing protocol which is called the named data link state routing protocol (NLSR). Lastly, the core NDN protocol is implemented and evolved by the named data networking daemon (NFD) forwarder tool [19].  
As Figure 4 illustrates, the FIB is used to forward the interests to the potential sources. The FIB is updated by the FIB management protocol, which is operated by routing a forwarding application layer.  
Figure 4: Single interest/data packet forwarding mechanism.  
Figure 3: Named data networking forwarding (Adapted from [14]).
## 4 Performance Analysis
### Comparison of the Power Patterns for All Beamformers  
This section uses FDL, ODL, VDL, and NVL methodologies to compare the output power of LCAA, LCPAA, LRAA, and LCCAA beamformers with respect to the disparity angle (degree). The output power values are also examined for disparities of 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\).
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.2 Principal Component Analysis  
Principal component analysis (PCA) is an effective method for data dimensionality reduction, which converts a large number of correlated variables into a few combinations of unrelated variables, that is, separating the correlation between the original variables, and uses a few variables to express information of the overall data set [26; 27]. Since the amount of data increases with the number of bands and the correlation between adjacent bands is high, generating a large amount of redundant information, PCA greatly reduces the amount of data, thus shortening the computation time.  
Figure 1: Pictures of five kinds of corn seeds; (**a**) 2490; (**b**) 2780; (**c**) GD5; (**d**) XX8; (**e**) XY335.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Processes of c-di-GMP Signaling  
Cell differentiation and biofilm formation are the first processes found in c-di-GMP signaling. Subsequently, other bacterial phenotypes were described, such as the virulence of _V. cholerae_, bacterial survival, predatory behaviors, multicellular development, the transmission of obligate intracellular pathogens, antibiotic production in streptomycetes, the transition between motile and sessile lifestyles, heterocyst formation in cyanobacteria, lipid metabolism and transport in mycobacteria, and survival to nutritional stress [21; 22]. Cellular movement such as swimming, swarming, contracting, and gliding motility are mediated by c-diGMP in several Proteobacteria, Firmicutes, Spirochaetes, and Cyanobacteria [23].
Unfortunately, the reliability of acceleration data is severely compromised by measurement noise; this noise needs to be suppressed to the maximum extent possible, to be able to use the data for comfort assessment. [PERSON] et al. [15] presented a fifth-order Butterworth low-pass filter; it is based on the fully differential difference transconductance amplifier (FDDTA) building blocks. That is, it is fifth-order transfer function according to the Butterworth theory. The Butterworth low-pass filter is a suitable smoothing method for electrical signal processing [13; 14; 15]. [PERSON] et al. [16] proposed an in situ deflectometric measurement strategy for the robotic polishing of optical components where the accuracy is comparable to that of a coordinate measuring machine (CMM). In order to enhance the machining accuracy of curved contour on large thin-walled skin, [PERSON] et al. [17] developed a method of isometric-mapping-based adaptive machining where the measurement system is a laser-scanner-based on-machine measurement (OMM) system to obtain 3D real geometry deformed surface. There are several 3D measurement methods [18; 19] devoted to the assessment of manufacturing processes or the evolution of surface texture. Area scanning measurement can quickly obtain images, but a postprocessing step is required for photo distortion correction [6; 10], and a graphic processing step is required to obtain the contours [1; 2; 3; 4]. Moreover, the requirements of ambient light and a larger workpiece mean that the charge-coupled device (CCD) requires a higher resolution to maintain measurement accuracy; a high-resolution CCD is particularly expensive. The advantage of a line scanning system is that, when measuring a large workpiece, because of 1D linear image data, the sensor (camera) pixel requirements are much lower than those of an area scanning system [11; 12]. Moreover, there are no strict requirements for ambient light. However, in the workpiece platform control system, signal processing is more complex, and the workpiece moving speed needs to be adequate to obtain the correct superimposed image. In this study, we focused on measuring large workpieces of approximately 200 mm. To achieve high-speed detection, the following criteria should be met: easy installation of the workpiece, operation of the system should be simple, the system should be stable, measurement should be quick and precise, and the cost should be low. A general line scan method is applied for 2D measurements in plane, such as width, height, or length. The novel approach of this study is by integrating a rotational table and precise line scan device, to measure large circular workpiece. The micro-vibration of the transmission gears in the rotational table causes high-frequency vibration noise, which affects the measurement result; thus, this study used the Butterworth filter for low-pass filter processing.
## 2 Materials and Methods  
### Sample Preparation  
All samples were provided by Pirelli Tyre SpA (Milano, Italy). For the preparation of crosslinked rubbers, IR (_cis_-1,4-polyisoprene, \(\geq\)96% cis, \(M_{\rm w}=1.49\times 10^{6}\) g/mol, and \(M_{\rm n}=8.41\times 10^{5}\) g/mol, HC Neftekhimik Nizhnekamsk, Russia) or NR (natural rubber, Standard Indonesian Rubber SIR20) was mixed with 3 phr ZnO (A-ESSE S.p.A., Italy), 2 phr stearic acid (OLEON N.V., Belgium), 2 phr N-(1,3-dimethylbutyl)-N\({}^{\prime}\)-phenyl-p-phenylenediamine (6 PPD, Eastman Chemical Company, Kingsport, TN, USA) as an antioxidant, 3 phr N-tert-butyl-2-benzothiazole sulfenamide (TBBS, General Quimica S.A.U., Spain) as an accelerator, and 1, 2, or 3 phr sulfur (S, Zolfindustria Srl, Novara, Italy). The obtained blends were vulcanized into sheets of 1 mm thickness at the curing temperatures (\(T_{\rm vulc}\)) of 140, 150, and 170 \({}^{\circ}\)C. Samples vulcanized at the maximum degree of crosslinking were considered for NMR measurements. The codes IR_T\({}_{\rm vulc}\) and NR_T\({}_{\rm vulc}\)are used throughout the work to indicate the series of IR and NR samples vulcanized at \(T_{\rm{vulc}}\). When needed, sulfur contents in phr (x) are specified using the codes IR_\(T_{\rm{vulc}}\)_Sx and NR_\(T_{\rm{vulc}}\)_Sx.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method
### Local Balance Value  
In complex networks, during a propagation process, since nodes can both transmit and receive information, when nodes in a region are too closely connected to each other, the information will be propagated repeatedly in that region, which may lead to bias in the process of identifying critical nodes.  
For example, as shown in Figure 2, assuming the initial information value of each node is 1 and taking node 1 as an example, the information from its first- and second-order neighbors is aggregated. The information value of node 2 becomes 4 after aggregating its first-order neighbors. Similarly, the aggregated information values of the remaining first-order neighbors are computed. Finally, by aggregating the information of nodes 2, 3, 4, and 5 along with its own information, the result is 17. Similarly, the final information values of nodes 2, 3, 4, and 5 are calculated as 13. However, when node 1 is removed, information can still propagate between the remaining nodes, maintaining the overall connectivity of the network. If node 2 is removed, however, information cannot propagate from the main part of the network to node 7, and similar situations occur when nodes 3, 4, or 5 are removed. Therefore, from the perspective of overall network connectivity and information propagation, the contribution of node 1 to information diffusion is relatively low, while the role of node 2 becomes significantly more important.  
This indicates that the importance of a node depends on the number of its direct neighbors as well as the number of actual connected edges. The clustering coefficient in the network is used to describe the actual connection between the nodes. When the clustering coefficient is 1, it means that there are connecting edges between all the nodes in the region formed by the node and its neighboring nodes; so, the higher the clustering coefficient of a node, the more the information has been re-propagated, the higher the degree of a node, and the more nodes it can transmit information to. To balance the importance of the nodes and make them more representative, we comprehensively considered the clustering coefficient and degree value of each node in the network, and propose the concept of the local balance value \((LB_{v})\), as shown in Equation (6):  
\[LB_{v}(i)=\frac{d_{i}-1}{c_{i}+\sum_{j\in N(i)}c_{j}+1} \tag{6}\]  
where \(d_{i}\) is the degree of node \(i\), \(c_{i}\) is the clustering coefficient of node \(i\), and \(N(i)\) is the first-order neighbor set of node \(i\). This value can effectively address the issue of bias in assessing node importance.  
Through the calculation of Equation (6), \(LB_{v}=1\), \(LB_{v}(2,3,4,5)=9/8\), and \(LB_{v}(6,7,8,9)=0\). The results show that node 6, 7, 8, and 9 are in marginal positions and are, therefore, less important. \(LB_{v}(1)\) is lower than \(LB_{v}(2,3,4,5)\). Thus, the local balance value can accurately identify critical nodes within the restricted area.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Experiment I  
#### 4.1.1 Dataset Description  
The bearing dataset used in the first experiment is obtained from Case Western Reserve University (CWRU) [12]. The test bench of the CWRU dataset is shown in Figure 6. The bearing dataset was collected by an acceleration sensor at four different loads (0 HP, 1 HP, 2 HP, and 3 HP) corresponding to four different rotational speeds. The dataset is divided into four states: normal condition (N), inner race failure (IF), outer race failure (OF), and rolling body failure (RF). At the same time, corresponding to each type of fault, there are three different damage diameters on the bearing, 0.007 inch, 0.014 inch, and 0.021 inch, a total of 10 fault types. Specific bearing damage information is shown in Table 1.
## 1 Introduction
### Justification and Objectives
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Term of Solar Irradiance Forecasting**} \\ \hline  & [36] Best Practices Handbook for the Collection and Use of Solar Resource Data, selection of potential sites \\
**Monthly irradiance forecasting** & [37] Steps for solar resource assessment, selection of potential sites \\
[38] Solar resource assessment, selection of potential sites \\
[39] Monthly data, ANN models are used to estimate it in Saudi Arabia \\
[40] ANN models are used to estimate it in Saudi Arabia \\
[41] ANN models are used to estimate it in Uganda \\ \hline \hline \multicolumn{3}{c}{**Use of satellite-based data for solar resource assessment**} \\ \hline  & [42] Satellite data comparison with ground measurements in Morocco \\
[43] Satellite data comparison with ground measurements in North Africa \\
[44] Satellite data comparison with ground measurements in the Canary Islands \\
[45] Satellite data comparison with ground measurements for sites in Europe \\
[46] Satellite data validation through statistical methods in Algeria \\
[47] Satellite data validation through statistical methods in Spain \\
[48] Satellite data comparison with ground measurements for European and Mediterranean sites \\
[49; 50; 51] SARAH satellite database validation for several sites, especially \\  & for Africa \\
[52] PVGIS satellite database validation for both Europe and Africa \\
[53; 54] Variability of irradiance values in areas with variable landforms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)methodology is easily replicable to other locations, and it might be a useful tool to provide reliable solar irradiation data for future solar project assessments. The paper is organized as follows. First, a general overview of the employed methodology is given in Section 2. Then, the selected case study is defined, and the data collection process is explained, as well as the validation and quality control phase, in order to use the best possible set of satellite data. Once the definitive set of data is prepared, distribution -fitting tests are used and the frequency of the irradiation values for every hour in a typical day of each month is presented in Section 3. In Section 4, the discussion on the results obtained is undertaken followed by a discussion on the potential applications that this methodology has. Finally, conclusions are raised in Section 4.
## 4 Measurement Results of HR and HRV with Optical Homodyne and Heterodyne Technique
### Comparison of Results of Optical Homodyne and Heterodyne Technique
These results show that for measurement of a higher value of \(HR\), optical heterodyne measurement provides better accuracy compared to optical homodyne measurement. The delay \(\Delta t\) approaches to zero for the heterodyne measurement system. However, the delay \(\Delta t\) measured for homodyne detection is found to be less than 3 ms. The delay \(\Delta t\) leads to a maximum deviation of around 0.5 bpm from the ECG values. Thus, measurements recorded with the optical homodyne setup are comparable with both the optical heterodyne and ECG setup. The deviation values from the ECG are found to be less than 1%. Figure 37 shows the deviation of both techniques from the ECG values. Figure 38a,b shows the distribution of the deviations in bpm for the case of chest wall measurement (subject 7). The histogram is fitted with a Gaussian distribution, with a mean value of 0.04 bpm and a standard deviation of 1.1 bpm for heterodyne detection. The histogram is fitted with a Gaussian distribution mean value of 0.06 bpm and a standard deviation of 1.6 bpm for homodyne detection. The value of uncertainty calculated with the heterodyne detection method is \(\pm\)0.14 bpm, while for the homodyne detection method, the value of uncertainty is calculated as \(\pm\)0.25 bpm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **\(HR\) in bpm** & **\(HR\) in bpm** & **\(HR\) in bpm** & **Deviation in bpm** & **Deviation in bpm** \\
**Number** & **(ECG)** & **(Heterodyne)** & **(Homodyne)** & **(Heterodyne)** & **(Homodyne)** \\ \hline
1 & 108.02 & 108.2 & 108.3 & 0.18 & 0.28 \\
15 & 84.13 & 84.38 & 84.6 & 0.25 & 0.47 \\
12 & 80.54 & 80.86 & 80.97 & 0.32 & 0.43 \\
16 & 70.26 & 70.42 & 70.67 & 0.16 & 0.41 \\
5 & 59.37 & 59.29 & 59.25 & \(-\)0.08 & \(-\)0.12 \\
7 & 53.96 & 54 & 54 & 0.04 & 0.04 \\
19 & 41.92 & 41.96 & 42 & 0.04 & 0.08 \\
10 & 40.41 & 40.49 & 40.54 & 0.08 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deviation in \(HR\) for optical homodyne and heterodyne detection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject No.** & **Measurement Site** & \
\begin{tabular}{c} \(HR\) in bpm \\ **(Homodyne)** \\ \end{tabular} &
\begin{tabular}{c} \(HR\) in bpm \\ **(Heterodyne)** \\ \end{tabular} \\ \hline
5 & Coronary Artery & 59.29 & 59.25 \\
5 & Radial Artery & 60.12 & 60.04 \\ \hline
15 & Coronary Artery & 84.38 & 84.6 \\
15 & Radial Artery & 85.22 & 84.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Deviation in \(HR\) for optical homodyne and heterodyne detection. Figure 35: **(a)** Mapping of homodyne and heterodyne VCG trace for measurement of \(HR\) 54 bpm; **(b)** mapping of homodyne and heterodyne VCG trace for measurement of \(HR\) 84 bpm. Figure 36: **(a)** Mapping of optical heterodyne VCG trace with ECG for measurement of \(HR\) more than 70 bpm; **(b)** mapping of optical homodyne VCG trace with ECG for measurement of \(HR\) more than 70 bpm. Figure 37: Deviation of heterodyne and homodyne measurement from ECG.
## 5 Discussion
### Effectiveness of Imbalance-Handling Strategies  
Table 15 shows that none of the three imbalance-handling strategies--oversampling, class-specific augmentation, or their combination--deliver the expected gains in accuracy, micro-F1, or macro-F1; some metrics even decline slightly. According to the class counts reported in Table 1, the largest class contains about 2934 samples while the smallest has only 580, giving a max-to-min ratio of roughly 5.1:1, i.e., a moderate long-tailed distribution. Under this imbalance, simply applying oversampling or class-specific augmentation fails to raise macro-F1 for three main reasons. First, repeating minority-class samples by a factor of \(\approx\)3 encourages the model to memorize the training spectrograms rather than learn features that generalize, thereby suppressing recall on the validation set. Second, the strong cropping and color jitter designed for minority classes do not fully respect the time-frequency texture of birdsong spectrograms; they may disrupt key harmonic structures, so the added noise offsets any diversity gain. Third, long-tailed classes often coincide with recording-device or environmental differences, and simple duplication or generic augmentation cannot bridge this domain gap, leaving the decision boundary largely unchanged. In future work, we plan to explore few-shot learning techniques and advanced data synthesis methods to further improve classification accuracy under class-imbalanced conditions.