esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Steps of Weather Disaster Emergency Response Capability Feature Extraction  
The extraction of efficient feature representation from the ability feature set using the constructed RNN autoencoder requires the steps of feature data processing, RNN autoencoder training, and feature extraction interpretation. Feature data processing solves the problems of missing data and inconsistent magnitudes; encoder training is the process of building a feature extraction model using the processed data; feature extraction interpretation uses a neural network interpretation method to theoretically analyze the feature extraction results and ensure that the feature extraction results are meaningful for capability evaluation and management.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable Only to External/Surface Defects
#### 3.1.3 Magnetic Particle Testing (MT)  
This method can be implemented to detect surface and near-surface internal flaws in steel bridge elements (Figure 5). Compared to the PT method, its application is faster and less costly, and requires less surface preparation. In this method, after cleaning the surface of the test component from oil, grease, or any other contaminations, a magnetic yoke is placed on, producing magnetic fields between the yoke probes (Figure 6). This creates magnetic flux lines which flow between the legs of the yoke. As such, any defect interrupting the magnetic flux lines produces a localized area of flux leakage, consequently changing the formation of iron particles that is spread on the suspect area. There are two methods for applying this technique: implementation with dry iron particles or wet iron particles. For dry MT, iron powder is applied to the surface of the test area while the magnetic force is applied by the yoke. Then, the surface is lightly blown using a bulb applicator, removing additional iron particles. In response, the particles are absorbed and clustered at flawed sites (magnetic leakage areas), creating a clear indication (Figure 7). For the wet MT application, the iron particles in a combination of water or oils are sprayed to the test areas. Other steps for the application of wet MT are the same as dry MT. Other works can be referred for more detailed procedures and practices [18,19].  
Figure 4: Steps for implementation of PT tests [13].  
Figure 5: Application of magnetic particle testing for steel bridges [10,20].
## 2 Materials and Methods
### CDSS for the Prediction of GDM  
To evaluate the impact of explanations on participants' advice-taking from a CDSS, we used our machine learning-based CDSS developed to predict the risk of GDM in women with overweight and obesity [22]. The CDSS consists of three different models  
Figure 2: Workflow Overview. The order of the explanations and patients/cases is randomized.  
that can predict GDM in theoretical, normal antenatalal visit and remote settings. In this study, we used the model for a normal antenatalal visit setting, due to the ease of use of the model in clinical practice. The model was developed based on a population from the Pregnancy Exercise and Nutrition Research Study (PEARS) dataset [23]. The majority of this population are white Irish women. Only descriptive features available in clinical routine were included for this model. After appropriate data preparation, synthetic minority oversampling technique and feature selection, the model was trained using SVM, a black-box machine learning method, in Python 3.8.8 using scikit-learn 0.24.2 (see [22] for more details). The model can predict GDM risk based on five features available at the first antenatal visit in Ireland: pregnant women's family history of diabetes mellitus, age, body weight, serum white cell count and gestational age at the first antenatal visit. At a threshold of 0.5, it classifies pregnant women into those at low/high risk of developing GDM.
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 4 The Comparisons of Simulated Data and Measured Results of The Prototype  
Figure 11 shows the prototype of the proposed antenna with an SMA connector [20] soldered to it, where the hand-soldered tin was not incorporated into the simulation. The antenna was measured by the Atenlab A3 compact-type OTA measurement system [21] and compared with its simulated data. Figure 12a-c show the measurement setup for radiation patterns in the x-y, x-z, and y-z planes. The proposed antenna was placed on a turntable and connected to the coaxial cable of the measurement system, which was not incorporated into the simulation. Figure 12a shows that the proposed antenna was connected directly to the coaxial cable of the measurement system, and the cable was parallel to the \(z\)-axis of the proposed antenna. Figure 12b,c show that the proposed antennas were connected to the coaxial cable of the measurement system via a right-angle SMA adapter [22], and the cable was perpendicular to the \(z\)-axis of the proposed antennas.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.1 Industrial Monitoring  
SPMIPv6 based IP-WSN applications for industrial monitoring can be related with a broad range of methods to increase productivity, energy efficiency, and safety of industrial operations in engineering facilities and manufacturing plants. Many industries currently use time-consuming and expensive manual monitoring to predict failures and to schedule maintenance or replacements in order to avoid costly manufacturing downtime. A SPMIPv6 based IP-WSN could be inexpensively installed and provide more frequent and more reliable data. The deployment of SPMIPv6 based IP-WSNs can reduce equipment downtime and eliminate manual equipment monitoring that is costly to be carried out. Additionally, data analysis functionality can be placed into the network, eliminating the need for manual data transfer and analysis.  
Figure 2: Operational Architecture of SPMIPv6.
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 3 Amine Neurotransmitter Detection via GFETs
### Serotonin  
Detecting SER accurately in biofluids is challenging due to its low concentration and interference from biological substances. The SER sensing mechanism involves oxidisation to the quinone imine in a multi-step reaction where two electrons and two protons are exchanged [231, 232]. However, this interaction can lead to byproducts and dimers hindering the electrode surface and decreasing the electroanalytical signal [233]. Modified electrodes with Au and Ppy nanoparticles have enhanced the detection capacity [26] and competitively adsorb SER at the electrode surface whilst reducing interferences from compounds. This interaction is facilitated by the SER indole ring, which interacts with the large carboxyl and carbonyl (C=O) functional groups on the overoxidised PPy nanostructures [234].  
In a recent study by [PERSON] et al. [163], a soft neural probe was developed for the site-selective functionalisation of aptamers on GFETs using electrografting, which enabled multiplexed SER monitoring. The CVD-grown graphene channel was functionalised with \(-\)COOH groups and coated with SU-8. Aptamers were covalently bonded to the graphene surface [143, 216], allowing for direct detection under physiological conditions [109]. The sensor achieved a remarkable sensitivity for SER, with a LOD at 10 pM; however, real-time physiological analyte detection remains a challenge. [PERSON] et al. [171] presented a portable SER antibody (SAb)-graphene micropatterned FET (GMFET) device for real-time SER monitoring. Sab was chemically immobilised with bis(2-aminoethylene)perylene-3,4,9,10-tetracarboxylicdiimide (PDA) linker onto CVD-grown monolayer graphene, enhancing conjugation efficiency [171]. GMFET exhibited ambipolar conductivity characteristics, with a real-time response of less than 10 s, achieving a response against 10 pM SER, which was a 30-fold improvement over bismuth vanadate (BiVO4)-based FETs [235]. The repeatability investigations over five replicates exhibited an error range of 2.5%, and the sensitivity was maintained for 4 days, with a slight performance degradation after day 5.  
[PERSON] et al. [172] explored molecularly imprinted polymer (MIP) template GFET with enhanced SER selectivity (a LOD of 0.05 fM) and reduced noise and resistance. The channel was functionalised via stabilising and managing micro-GFET arrays using artificial diazonium salts with maleimides linkers to bind SER stem-loop aptamers [236]. Real-time detection in the CVD-grown graphene channel revealed an increase in current following SER addition, correlating with a positive shift in the transfer curve [212]. The labile interaction between SER and the aptamer allowed for reversible binding [212] despite challenges related to fabrication homogeneity and current drift.
###### Abstract  
Consumption of ready-to-eat chopped fruits sold in the streets is a concern, as such activities are outside the regulation and protection in most developing countries. Ready-to-eat mangos are commonly sold as wedges in plastic cups at ambient temperature by mobile vendors in Ecuador, thus they are prone to contamination by bacteria, which poses a safety issue of concern. This work aimed to evaluate the effect of several antimicrobial cocktails consisting of previously designed specific peptide extract combinations from two probiotic bacteria _Lactobacillus plantarum_ UTNCys5-4 and _Lactococcus lactis_ subsp. _lactis_ UTNGI28, along with nisin, a commercial food additive, on mango wedges artificially inoculated with a logarithmic phase culture of a five-strain bacterial mixture (FSBM). Preliminary bacteriological analysis of mango wedges purchased from mobile vendors showed the presence of multiple antibiotic-resistant isolates such _E. coli_ spp., _Enterobacter_ spp., _Shigella_ spp., _Salmonella_ spp., along with yeasts and molds, indicating non-compliance with the food safety standards. The results revealed that two antimicrobial cocktails, T2 and T5, containing cell-free supernatant based (CFS) and precipitated peptides (PP) based cocktails from UTNCys5-4 and UTNGi28 strains applied at dose 1:3 (\(\%\)/\(\%\)), were the most efficient combinations that inhibited the colonization of total bacterial counts with 56.03\(\%\) and 55.61\(\%\) in mango wedges stored with refrigeration. The reduction of total _E. coli_ counts was 64.93\(\%\), while _Salmonella_ and _Shigella_ counts were reduced by 98.09\(\%\) and 97.93\(\%\), respectively, when mango wedges were treated with T5-cocktail. The commercial nisin inhibited total _Salmonella_ spp. counts by 40.13\(\%\), while _E. coli_ spp. and _Shigella_ spp. diminished by 28.20\(\%\) and 37.22\(\%\), respectively. Moreover, we showed that T5 but not T7 (nisin) damaged the target cell integrity, thereby eventually inhibiting their growth and reproduction. The selected antimicrobial cocktails exerted a bacteriolytic effect by killing the FSBM simultaneously in a fruit matrix and preventing their accumulation in mango wedges. Furthermore, there is a possibility of using peptide combinatorial treatments to combat drug-resistant bacteria in ready-to-eat fruits.  
peptides; lactic acid bacteria; mango; antimicrobial; bacteriolytic; membrane integrity +
Footnote †: journal: Applied Sci.  
Accepted: 16 December 2020  
Submitted: 4 March 2021
## 1 Introduction
The authors also optimized the pMRP via CST Microwave Studio [16], as well as physical modeling [17], and practically proved the feasibility of the probe to be mounted on the chamber wall for industrial plasma processing. [PERSON] et al. developed the first planar cutoff probe, which measures the cutoff frequency in a transmission microwave frequency spectrum (S\({}_{21}\)), analyzed the basic characteristics of the point-type probe, and optimized it via CST Microwave Studio [18]. They also fabricated the probe and firstly demonstrated its operation. Advanced types of the planar cutoff probe were subsequently developed by [PERSON] et al. : the ring-type planar cutoff probe (RCP) and bar-type planar cutoff probe (BCP) [19]. In [19], the authors simply compared the three designs in terms of the Q-factor of the cutoff peak using CST Microwave Studio and fabricated the BCP based on their simulation results. The BCP was then successfully applied to real-time plasma process monitoring. The basis of the planar cutoff probes is the cutoff probe, which, despite being an invasive type of probe, is considered as one of the most accurate diagnostic methods among microwave probes [20]. Therefore, it is believed that the three types of planar cutoff probes will be able to show high measurement accuracy compared to other planar microwave probes. Despite the promising potential and the fact that the RCP and BCP are more suitable for plasma process monitoring than the point-type probe [19], the basic characteristics of the ring- and bar-type probes have not been investigated yet. Hence, this paper characterizes the RCPand BCP considering various geometrical parameters, as well as a plasma parameter through the CST Microwave Studio. The main parameters include antenna size, antenna distance, dielectric thickness, and input electron density. This paper consists of three parts. Section 2 gives the simulation details, such as simulation method, geometry, boundary conditions, and materials. Section 3 presents the simulation results and analysis of each case. Finally, Section 4 summarizes the paper.
## 2 Theoretical Framework
### DC-DC Boost Converter and Operation Point  
DC-DC boost converter is used to regulate the electrical power output by adjusting the load seen by the SWT. This topology enables the turbine to operate at variable wind speeds while maintaining control over the electrical load, which is essential for both maximum power extraction and power limitation scenarios. The basic operation of the boost converter involves two switching states: when the switch is closed, energy is stored in the inductor; when the switch is opened, the inductor transfers its energy to the output capacitor and load, resulting in a higher output voltage than the input. The ideal steady-state relationship between input and output voltage is given by:  
\[V_{out}=\frac{\text{V}_{\text{in}}}{1-\text{D}} \tag{9}\]  
where V\({}_{\text{in}}\) is the input voltage (from the rectifier), \(V_{out}\) is the regulated output voltage, and D is the duty cycle of the switching signal. By adjusting D, the converter modifies the electrical conditions seen by the generator, effectively shifting the operating point of the turbine. A feedback control algorithm implemented on an Arduino(r) microcontroller continuously regulates this duty cycle based on real-time voltage and current measurements. This allows the system to either:  
* Follow the MPPT trajectory by imposing the optimal voltage corresponding to the best tip-speed ratio, or;
* Apply Power Curtailment, intentionally limiting output to match grid demands or storage constraints.  
This converter therefore acts as the central actuator in the turbine's energy management system, enabling flexible and dynamic integration of wind energy into microgrids or standalone setups. Its ability to respond rapidly to control references ensures stable operation across a wide range of wind conditions.
# Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM
## 3 Wavelet-GMDH Based Inductance Estimation Method
### Signal Denoising via Discrete Wavelet Transform
The SURE estimator as a threshold is given by [27]  
\[\text{SURE}(\tau)=T(\tau)+\frac{N-2N_{0}}{N}\sigma^{2} \tag{10}\]  
\[T(\tau)=\frac{1}{N}\|W_{\tau\tau}-W_{r}\|, \tag{11}\]  
where  
\(T(\tau)\) represents the SURE estimator as a threshold;  
\(N\) is the total number of wavelet coefficients;  
\(N_{0}\) is the number of coefficients that were replaced by zeros; and  
\(\sigma^{2}\) is the noise variance. However, the optimal choice of the threshold depends on the noise energy present. The universal threshold, as described in [28], is  
\[\tau=\sqrt{2 logN}\sigma, \tag{12}\]  
which uses the noise energy explicitly and selects a threshold proportional to \(\sigma\). The noise variance \(\sigma^{2}\) at each level \(j\) is estimated using the following robust estimator:  
\[\sigma_{j}^{-2}=\frac{MAD\big{(}|W_{r}|\big{)}}{0.6745}, \tag{13}\]  
where MAD represents the median absolute deviation from 0, and the factor 0.6745 is chosen for calibration with the Gaussian distribution. In the case of hard thresholding, the estimated coefficients will be [29; 30]  
\[W_{\tau\tau}=\left\{ \begin{array}{ll}W_{r}\;if\;|W_{r}|\geq\delta\\ 0,\;otherwise\end{array} \right.. \tag{14}\]  
In the case of soft thresholding, the estimated coefficients will be  
\[W_{\tau\tau}=\left\{ \begin{array}{ll}W_{r}-\delta\;if\;W_{r}\geq\delta\\ 0\;if\;|W_{r}|\leq\delta\\ W_{r}+\delta\;if\;W_{r}\leq-\delta\end{array} \right.. \tag{15}\]
## 3 Instrumental Analytical Techniques
### Gas Chromatography (GC)
#### 3.1.3 Gas Chromatography Coupled to Mass Spectrometry (GC-MS)  
GC-MS is a hyphenated instrumental technique that combines the separation attributes of GC with the sensitivity of MSD, making it suitable for analyzing low molecular mass and mid- to low-polarity compounds [103]. It is widely used for VOC identification because it has the advantage of rapid compound identification by comparing the analyte mass spectrum to those recorded on databases using specialized software that indicates the matching results, with high efficiency in recognition of molecule identity in the majority of the cases [104]. Nevertheless, this approach is not very frequently used for VOC quantification in complex samples because a calibration curve for each compound is necessary owing to the variation of the response factor from each analyte in the MSD [86].  
The sensitivity of GC-MS is mainly determined by a particular MSD device denominated as an analyzer, which separates and classifies the ions generated from components in groups according to their mass/charge ratio (m/z) [105]. From the distinct commercially available GC-MS analyzers, the most commonly used is the quadrupole, given its low cost and sufficient precision for compound identification. The employment of GC-MS has been reported in several studies such as the characterization of essential oils (some of them having activity on insects) [99; 106], metabolic profiling of plant VOCs emitted under different conditions [107; 108; 109], characterization of natural products with insect-repellency [110]. In addition, GC-MS has been also used for the investigation of plant VOCs related to some insects of interest such as pollinators [98; 111], phytophages [101; 112],parasitoids [37; 113], hosts [26] or vectors [67]. The study of VOCs emitted by insects has also required GC-MS analysis [27; 28; 30; 46; 114].  
When comparing the proportion of recent publications on VOCs active on insects that reference the use of GC coupled to single-quadrupole mass spectrometer (qMS) against other analyzers, for the latter, the number of citations is lower. Among these, the utilization of ion trap mass spectrometers (itMS) is referenced in studies addressing VOCs related to insect attraction in flowers [69] and whole plants [73], phytopathogenic vectors [115], and parasites [42]. In parallel, the magnetic sector analyzer has been reported for real-time monitoring of insect pheromone release [44] and time-of-flight (TOF) analyzers in the assessment of biosynthesis of VOCs important for pollinator attraction [21], in the characterization of volatile secretions from _Graphosoma lineatum_[116], or in the profiling of compounds present in _Tilia_ spp. nectar, which allowed to discard the assumption of the toxic character of nectar for bumblebees [117], among other studies.
## 5 Simulation Results and Analyses
### Comparison and Analysis of the Energy Consumption of the Network  
Figure 6 illustrates the total energy consumption versus the number of rounds for the six algorithms when 400 nodes were considered in the network, from which we can see that the total energy consumption rises with the increase in the network rounds regardless of which algorithm is used. However, the proposed ACOCR algorithm is the most efficient one in reducing the energy consumption. For example, in round 600, the total consumed energy of the ACOCR, EOCA, CUWSN, LEACH-ANT, DUCS, and LEACH accounts for 32.5%, 41.1%, 50.6%, 58.2%, 65.4%, and 83.8% of the initial energy of the network, respectively. As for the network energy that is completely consumed,  
Figure 5: The number of rounds when first node dead (FND), half of the nodes dead (HND), and last node dead (LND) arise for the six algorithms.  
the energy efficiency of the ACOCR is improved by 14.7%, 18.3%, 29.3%, 45.3%, and 65.2% compared to that of the EOCA, CUWSN, LEACH-ANT, DUCS, and LEACH, respectively. This is because the proposed ACOCR optimizes CHN selection and employs the optimal paths found by the improved ACO algorithm to transmit the data packets, thereby minimizing the energy consumption. The EOCA and the CUWSN outperform the LEACH, DUCS, and LEACH-ANT. However, both of them are inferior to the ACOCR, which is because neither of them optimizes the multi-hop paths for data transmission.  
Figure 7 demonstrates the number of rounds when the network energy is exhausted versus the different number of nodes in the network for the six algorithms, which validates the effect of the different number of network nodes on energy consumption. As the number of nodes increases, the number of rounds when the network energy is exhausted also increases. This is because more nodes in the network lead to a better balance of energy consumption. The proposed ACOCR outperforms the other five algorithms in all situations. For example, when there are 450 nodes in the network, the ACOCR algorithm is 10.1%, 15.6%, 19.2%, 43.4%, and 52.9% more efficient than the EOCA algorithm, the CUWSN algorithm, the LEACH-ANT algorithm, the DUCS algorithm, and the LEACH algorithm, respectively.  
Figure 6: The total energy consumption versus the number of rounds for the six algorithms.  
Figure 7: The number of rounds when energy is exhausted versus the number of nodes for the six algorithms.