esa-sceva/satcom-chunk-collection huggingface dataset:
# Attenuation of DC-Link Pulsation of a Four-Wire Inverter during Phase Unbalanced Current Operation
## 5 Conclusions  
This paper presented a novel algorithm for compensating an AC component of a DC link current in a grid-tied inverter operating with a renewable energy source and an energy storage. It was shown, that asymmetrical phase currents and resulting current flow in neutral wire cause the AC component to appear in the DC link current. Since applications with a local grid compensation are becoming more attractive for the grid operators, this current flow cannot be suppressed without preventing the local compensation. Since the AC component is undesirable for an electrochemical storage, authors propose a system of active compensation of the AC component in the DC link current. It is shown, that the proposed method can reduce AC oscillations by 97%, virtually eliminating the problem. The downsides of this solution include the increase in volume and the cost of the system implementing this algorithm, as well as requirement for fast signal processors, as the algorithm is based on multiple resonant compensators and demands rapid acquisition and rapid signal processing. However, the resulting system possesses desirable qualities for the grid operators. Further research in this area would include operation under transient states, island operation, and operation with heavily non-linear loads.  
Conceptualization, D.Z. and K.F.; methodology, D.Z.; software, D.Z. and K.F.; validation, D.Z. and K.F.; formal analysis, D.Z.; investigation, D.Z.; data curation, D.Z.; writing--original draft preparation, D.Z. and K.F.; writing--review and editing, D.Z. and K.F.; supervision, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Polish National Centre for Research and Development, grant number POIR.04.01.02-00-0007/17. The APC was funded by Lublin University of Technology.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interests.  
Figure 14: The capacitor bank current and voltage pulsation for cases without (before Time = 0.15 s) and with (after Time = 0.15 s) proposed compensation algorithm operation (experimental results, current measured at i_cap point in Figure 1).
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 3 Proposed Method
### SNN Classification  
Figure 2d depicts the final phase of classification. The classifier was constructed from an SCTN network comprising three layers. The input layer consisted of 121 neurons, representing the spatial mapping of each sub-channel. Each neuron in the input layer received 121 inputs from each of the five EEG sub-band maps, aggregating to 605 input synapses (\(5\times 121\)). Two fully connected hidden layers contained 64 and 32 SCTNs. The ReLU activation function was implemented for all neurons across all layers. The output layer contained three neurons which represented the three mental states: _focused_, _unfocused_, and _drowsy_[24].  
Figure 5: EEG feature mapping. (**a**) EEG topologic map consisting of \(11\times 11\) SCTNs for each sub band. (**b**) Weight distribution for the 14 synapses of each SCTN, according to an EEG electrode position map.  
Figure 6: EEG sub-band topography maps for FFT vs. SCTN. SCTN-based topographic maps (one for each sub-band) are compared to the spacial maps created using an FFT.  
The training process was based on an unsupervised STDP learning algorithm [45], where the weights of each SCTN were updated according to the following equation:  
\[\Delta W_{i,j}=\sum_{k\in S_{pre}}\sum_{l\in S_{post}}\begin{cases}+A\cdot e^{-|t_{ k}-t_{l}|/\tau_{s}},&\text{if }t_{k}\geq t_{l}\\ -A\cdot e^{-|t_{k}-t_{l}|/\tau_{s}},&\text{if }t_{k}<t_{l}\end{cases} \tag{5}\]  
where \(A\) represents the learning rate and \(\tau_{s}\) is the time constant used to control the synaptic potentiation and depression. The magnitude of the learning rate decreased exponentially with the absolute value of the timing difference. When multiple spikes were simultaneously fired, the required weight change was calculated by summing the individual changes derived from all possible spike pairs. Here, \(S_{pre}\) and \(S_{post}\) are sets of spikes representing the pre- and post-synaptic neurons, respectively, where \(t_{k}\) stands for the timing of spike \(k\) (from the \(S_{pre}\) set) and \(t_{l}\) represents the timing of spike \(l\) (from the \(S_{post}\) set).  
Each neuron in the final layer was assigned to a specific category and, by using unsupervised STDP training, automatically adjusted to detect the patterns tailored to their assigned categories. All of the weights in the classification SNN were initially randomly configured.  
The implementation of the proposed SNN-based architecture consisted of only 2776 spiking SCTNs. A total of 2072 (\(14\times 37\times 4\)) neurons were required to implement the feature extraction network (14 channels, each connected to 37 resonators composed of 4 neurons each). The feature mapping network consisted of 605 (\(5\times 11\times 11\)) neurons, while the classification network was composed of 99 (\(64+32+3\)) neurons.
# Nonlinear Random Forest Classification, a Copula-Based Approach
## 4 Numerical Results
### COVID-19 Dataset  
[PERSON] et al. (2020) [PERSON] et al. (2020), in a meta-analysis, merged 151 datasets of COVID-19 including patient symptoms and routine test results. Nineteen clinical variables were included as explanatory inputs. The variables included age, sex, serum levels of neutrophil (continuous and ordinal), serum levels of leukocytes (continuous and ordinal), serum levels of lymphocytes (continuous and ordinal), results of CT scans, results of chest X-rays, reported symptoms (diarrhea, fever, coughing, sore throat, nausea and fatigue), body temperature, and underlying risk factors (renal diseases and diabetes) [PERSON] et al. (2020). By applying machine learning methods, they reanalyzed these data and investigated correlation between explanatory variables and generated a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.  
In a COVID-19 patient case, an agglomerative approach test may help diagnosis of illness. We used our copula-based feature selection to identify the most effective attributes to make a discrimination between COVID-19 patients and influenza patients. We started with two attributes. The most relevant attributes were \"age\" and \"fatigue\". We then applied these two attributes to separate the COVID-19 and influenza patients and obtained evaluation values sensitivity, specificity and accuracy, respectively as 0.755, 0.864 and 0.836. Seeking the three most important classification attributes lead us to \"age\", \"fatigue\" and \"nausea/vomiting\" with sensitivity equaling 0.840, specificity equaling 0.886 and accuracy equaling 0.873. Table 2 summarizes the 10 most important features with their classification evaluation's scores. As understood from this table, there is a design-of-experiment approach that a physician may encounter. In fact, the required percentage of information determines the number and types of tests of patients. For example, if there is a required 85% accuracy of classification only, then it is enough to know \"age\", \"fatigue\" and \"nausea/vomiting\" of patients, while for 91.4% accuracy, we need to test the 15 most important attributes.
## 1 Introduction
Pre-reconstruction methods involve translating projections into  
Figure 1: Schematic representation of the structure of the microtomograph’s CCD detector, with the X-ray and visible-spectrum radiation areas marked. sinograms. The damaged area in the sinogram is represented by a darker strip running across all or part of the sinogram. The simplest methods are based on determining the positions of the stripes based on the sum of the pixel values in the image columns. The positions with a sum under a specific threshold are the location of the stripes which then are processed. Such a proposal for artifact reduction is presented in the work of [PERSON] (2011). This method is effective for very significant artifacts, for example, in the case of damaged pixels. More advanced methods are based on the processing of the obtained sinograms to visualize striations of variable intensity. Well-developed methods use frequency processing using Fourier transform or wavelet decomposition. Such a method is presented in the work of [PERSON] et al., which uses wavelet decomposition and frequency filtering followed by reconstruction to the original sinogram ([PERSON] et al., 2011). This method is more efficient; however, it significantly impacts the pixel values within the entire sinogram. The work of [PERSON] et al. presents a hybrid approach to reducing artifacts in the pre-reconstruction process. It involves using wavelet decomposition to identify the positions of artifact induction and then transforming the image within the raw sinogram at predefined positions ([PERSON] et al., 2018). The process of reducing artifacts after reconstruction involves transforming tomograms to polar coordinates, identifying artifacts, and removing them using various image processing techniques. In their paper, [PERSON] et al. presented such a method using local statistical evaluation of pixel values to identify stripes in a tomogram ([PERSON] et al., 2018). In Table 1, the authors present various papers on ring artifact reduction as well. They show on which data the cited methods were tested and present some relevant results of ring artifact reduction if they were presented in the paper.The authors of the paper present a method for pre-reconstruction artifact reduction that uses the identification of artifact induction sites using wavelet decomposition. They apply sinogram processing in two stages to the identified locations and within the entire sinogram. The authors of the current study have developed an original model for reducing ring artifacts using discrete wavelet decomposition supplemented by filtering using empirical mode decomposition (EMD). The authors have not encountered in the literature the use of wavelet decomposition combined with EMD in the filtering of medical tomographic images.
## 4 Results
### Power Consumption  
The power consumption is maximal in data transmission. At least 2A transient current should be supplied because of the pulse current in emission. If the current is below 2A, the system will restart due to the insufficient voltage. The average current of the system is less than 500 mA during data transmission. The maximum energy consumption is approximate 200 mW when the water-level sensor is operated, but it is only 25 mW in power saving mode.
## 1 Introduction
[PERSON] et al. (2008) established a triangular chlorophyll index (TCI) based on the green, red and red-edge bands to estimate the crop leaf chlorophyll content, which is a modified version of the triangular vegetation index (TVI) because it makes TVI more sensitive to chlorophyll effects. [PERSON] et al. (2013) proposed the establishment of a triangular greenness index (TGI)based on the red, green and blue bands. This index estimates the chlorophyll concentration in leaves and the canopy based on the area of the triangle. TGI is indicated to be sensitive to canopy chlorophyll content and relatively insensitive to LAI [15]. Peanut is a widely cultivated cash crop worldwide and is important in global agricultural production and trade [16]. Appropriate density and planting methods are the basis for improving the utilization rate of light energy. Therefore, it is one of the effective ways to increase peanut yield by selecting appropriate planting methods and density to increase peanut yield. In this study, peanut was used as research objects, allocated to seven different density treatments. Data for peanut leaves were determined by field experiments using single-photon avalanche diode (SPAD) and hyperspectral analysis. A novel method was used to systematically explore and identify sensitive bands, develop simpler spectral indicators and use hyperspectral sensing information to establish a peanut chlorophyll estimation model of greater accuracy than other models. Therefore, the aims of this study were as follows: (i) to explore the effect of planting density on chlorophyll content during the peanut growth period; (ii) to analyze the spectral characteristics of peanut canopies under different chlorophyll levels; and (iii) to identify the sensitive spectral bands of peanut chlorophyll and adjust the chlorophyll spectral estimation model according to different vegetation indices.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 2 Technology of Indoor Localization
### ZigBee Module  
In Figure 4, the ZigBee module shield shows a breakout board designed to interface with ZigBee communication modules like XBee. ZigBee is a low-power, short-range wireless communication protocol based on the IEEE 802.15.4 standard [54], commonly used  
Figure 3: HC-05 Bluetooth module.  
in applications such as home automation, healthcare monitoring, industrial automation, and Internet of Things (IoT) projects. This shield simplifies the process of connecting ZigBee modules to microcontrollers like Arduino or other development boards, making wireless communication integration more accessible and efficient [43; 55].  
The shield includes two sets of pin headers for mounting the ZigBee module, with dedicated Data In (DIN) and Data Out (DOUT) pins for transmitting and receiving data to and from the microcontroller. It also features configurable general purpose input/output (GPIO) pins (I/O1 to I/O7) for connecting sensors, actuators, or LEDs, a reset button (RST) for troubleshooting, and an ASSOCIATE LED for indicating the connection status. The shield is equipped with a voltage regulator to maintain a stable 3.3 V power supply, pulse width modulation (PWM) pins (PWM and PWM1) for controlling device intensity or speed, and terminal block connectors for alternative power and hardware connections [43]. This makes the shield suitable for various applications like home automation, healthcare monitoring, industrial automation, and IoT networks, providing reliable ZigBee-based wireless communication and network coverage through mesh networking [55].
## 3 Problem Definition  
Most routing protocols for UWSN data transmission propose linking to sink (destination) nodes by connecting multiple transitional sensor nodes. In existing protocols, there is a big chance of network delay, unbalanced energy consumption, node failure, and inefficient data gathering. An AUV that rotates in a predefined trajectory was introduced to overcome these problems. The best choice for the path of the AUV to follow when data gathering is elliptical because the proposed rotation covers the maximum network area and produces the maximum number of GNs that can help maintain the energy balancing and network life. This trajectory, however, has some flaws.  
1. One noteworthy challenge is to create a new trajectory for the following reasons: * The sensor's nodes consume high levels of energy when setting up connected dominant set (CDS) nodes at the initial stage. * Sorting the CDS to build an elliptical design has a high cost of computing in terms of energy and time.
2. Fixed-size elliptical shape used for various network area sizes.
3. Backtracking and network delay exist because the SPT is applied only to a specified network area.
## 7 Conclusions  
In this work, we have introduced several novel contributions to the field of record similarity and text mining:  
* _Fuzzy Record Similarity Metric (FRMS)_: We developed the FRMS, a robust metric for measuring approximate record similarity. The FRMS adheres to key mathematical principles, making it highly suitable for applications in text mining and cluster analysis. Our experiments demonstrate its superior performance compared to existing methods without the need for parameter tuning.
* _Optimal Q-gram filter_: We proposed an optimal Q-gram filtering method for token matching, ensuring the most efficient filtering based on shared token features. This filter serves as a foundational tool that is applicable to various similarity metrics.
* _Approximate Q-gram filter_: To enhance computational efficiency, we introduced an approximate Q-gram filter that operates in constant time. This approximation maintains high accuracy while significantly reducing processing time, as evidenced by our experimental results.
* _Filter efficiency and properties_: We analyzed the efficiency and key properties of the Q-gram filter, highlighting its effectiveness in relation to string lengths and similarity thresholds. Our analysis provides insights into optimizing filter performance for different applications.
* _Padding extension of the Q-gram filter_: We enhanced the Q-gram filter by incorporating padding techniques, which improve similarity measures by smoothing token boundaries and expanding the feature set. This extension leads to more accurate similarity assessments.  
Overall, our contributions provide advanced tools and methodologies for accurate and efficient record similarity measurement, with significant implications for text mining and data analysis applications.  
Conceptualization, O.R.; methodology, O.R.; software, O.R. and J.P.; experiments, O.R. and J.P.; investments, J.M. ([PERSON]); writing--original draft preparation, O.R. and J.M. ([PERSON]); writing--review and editing, O.R., J.M. ([PERSON]) and J.M. ([PERSON]); supervision, J.M. ([PERSON]) and J.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Links to the datasets used are given in the text of this paper.  
**Acknowledgments:** We would like to thank the software technology company Rozinet s.r.o. (www.rozinet.net) for their support, without which this article would not have been possible; they provided technical resources, data, and space for research. Further, we would like to thank [PERSON] (Charles University, Faculty of Mathematics and Physics) for their help with the technical mathematical formalism and corrections.  
**Conflicts of Interest:** Author [PERSON] was employed by the Rozinet s.r.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Model and Methods
### Power Shifting to Access Attractors or Windows  
#### 2.3.1 A Theoretical Example of Power Shifting  
The power-shift (PS) method requires a nonlinear system with an independent control parameter. Power shifting is a type of incremental perturbation of the control parameter with the result of either accessing known or discovering new attractors (or windows). The method employs specific timings to steer the dynamic trajectory from one known attractor to another that may even coexist.  
The power-shift method works in the laser with injected signal by changing the input signal \(Y\) at specific points on a known stable limit cycle. In Figure 3(a) there are 2 sets of limit cycles of known attractors superimposed on each other. The magenta \(x\) on base AVI (period four limit cycle in green) marks the position where the injected signal is down shifted from \(Y_{0}=\) 1.96 to \(Y_{PS}=\) 1.84. The system trajectory jumps from base AVI to resultant AV (period three limit cycle in blue). In Figure 3(b) for a different location on base AVI, again marked by a magenta \(x\), we show that the system trajectory moves off base AVI to resultant AII using the same down shift values from \(Y_{0}=\) 1.96 to \(Y_{PS}=\) 1.84. Note, AV and AII coexist at \(Y=\) 1.84.
## 2 Materials and Methods
### Finite Element Modeling  
Beams and columns were modeled as frame elements and slabs as area sections. The foundation was considered rigid, i.e., the interaction between the soil and structure was not considered, as [PERSON] et al. [25] concluded no significant contribution of soil-structure interaction even in the case of alluvial deposits for low-rise SMRF buildings. Each floor was assigned a rigid diaphragm. Lateral earthquake load was applied in both x-direction and y-direction, as per the IS: 1893-2016 guidelines [34], for seismic zone-V and medium to stiff soil conditions. The 3D rendered view of the model is shown in Figure 6. Plastic hinges were assigned to the frame elements to carry out nonlinear static analysis Hinges were assigned at 10% of the member length from each joint of beam and column. The properties were assigned as described in ASCE-41-17 [35]. For the infill model, link properties were assigned for inelastic behavior. Pushover analysis was performed to obtain the capacity curve of the structure. To capture the linked axial and biaxial bending behavior, P-M-M hinges were defined at both ends of columns and M3 hinges were defined for both ends of beams. Displacement controlled nonlinear analysis was performed considering only P-\(\Delta\) effects. The preloading of the structure was taken as dead load plus 25% of the live load.  
Nonlinear time history analysis is performed to estimate seismic fragility. Ground motions for time history analysis are selected to represent seven earthquake sequences as shown in Table 2. Since ground motion data from earthquake sequences in the study region are not available, data recorded from elsewhere are used. An example of ground motion of a seismic sequence is shown in Figure 7. Load cases are defined for earthquake sequences initializing from the preloading condition. Direct integration, nonlinear time history analysis was carried out considering P-\(\Delta\) and large displacements to estimate the peak structural demands. After the completion of each time history analysis, the constitutive models for the infills was calibrated depending on its uncracked, separation, or softening mechanism before applying subsequent ground motion time history.  
Figure 6: Finite element model of the building: (**a**) 3D model with infill; (**b**) 2D elevation of infill.