esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Defect Detection Technique Using Multi-Channel Eddy Current System
### Calibration Data  
#### 2.4.1 Calibration Specim  
This study aimed to measure the defects occurring in the rail head using a 16 Ch eddy current sensor. However, since the 16 Ch eddy current sensor was manufactured by hand, a difference occurred in the amplitude measured by the sensor when passing through the same defect. In order to minimize the error between the sensors, a calibration test piece, as shown in Figure 4, was prepared. In order to ensure that all sensors passed through the same defect, the calibration test piece was manufactured with a defect that had a length of 1 mm and a depth of 3 mm over the entire head.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c}{**Phase (\({}^{\circ}\))**} & \multirow{2}{*}{**Diff.**} \\ \cline{3-4} \cline{6-7}  & & & & & & \\
**(kHz)** & **No.** & **2** & **4** & **6** & **8** & **10** \\ \hline \multirow{4}{*  
As shown in Figure 2 because the sensors are not on a straight line arranged in a \(4\times 4\) as shown in Figure 5a. Therefore, when scanning the test piece in Figure 4, it was possible to obtain the results shown in Figure 5a. Since the actual defect lay in a straight line, a position correction was required, and a calibration value that minimized the amplitude difference between sensors was required.  
* Calibration value measurement method (position and amplitude) 1. The defect location of each sensor is checked. 2. The sensor number that found the defect first is called \"A1\". 3. The distance difference from each sensor to \"A1\" is called the position calibration value. 4. The amplitude of each sensor is checked. 5. The sensor number with the maximum amplitude is called \"B1\". 6. The value is multiplied so that the amplitude of each sensor becomes \"B1\", and this is called the amplitude calibration value.  
Figure 5b shows the result of applying the calibration value, and it was confirmed that all had the same amplitude at the same location.
## 4 Experiment
### Homography Estimation Experiment
#### 4.3.2 Metrics  
In this experiment, the mean matching accuracy (MMA) across all image pairs is adopted as the evaluation metric. A predicted correspondence is considered correct if its reprojection error is below a specified threshold, ranging from 1 to 10 pixels; otherwise, it is regarded as incorrect.  
This paper employs the corner accuracy metric to evaluate the performance of the algorithm. The metric is based on the reprojection error of four corner points in an image,and the proportion of images with an average reprojection error below 1, 3, or 5 pixels is computed. Specifically, the proposed algorithm is first applied for feature matching, and the predicted homography matrix between two matched images is computed using functions from OpenCV. Using the predicted homography matrix \(\widehat{H}\) and the ground truth homography matrix \(H\), the four corner points of the first image are projected onto the second image, and the average reprojection error \(\varepsilon\) is calculated. Finally, the proportion of images with reprojection errors below a predefined threshold is computed to assess the algorithm's matching accuracy.
## 2 Materials and Methods
### Spring Constant and Actuation Voltage Analysis  
As shown in Figure 7, the electrode sits on the signal of the CPW. MEMS switches have a spring constant \(k\), which may be calculated as follows.  
\[k_{1}\ =\ -\ \frac{P}{\frac{2}{EI}\int_{\frac{l}{2}}^{x}\frac{\rho}{48}(l^{3}-6l^{2 }a+9 la^{2}-4a^{3})da} \tag{9}\]  
\[k_{2}\ =\ -\ \frac{P}{2\int_{\frac{l}{2}}^{x}\frac{\rho}{2S}(l-a)da} \tag{10}\]  
\[k\ =\ k_{1}+k_{2} \tag{11}\]  
\[I\ =\ \frac{wt^{3}}{12} \tag{12}\]  
\[P\ =\ 2\rho\bigg{(}x-\frac{l}{2}\bigg{)} \tag{13}\]  
\[S\ =\ \ \sigma(1-v)tw \tag{14}\]  
where the spring constant \(k_{1}\) is the spring constant caused by the stiffness of the beam, \(k_{2}\) is the spring constant caused by biaxial residual stress of the beam, \(\rho\) is the uniformly distributed load of the beam, \(S\) is the tensile force of biaxial residual stress, \(\sigma\) is biaxial residual stress, \(v\) is the Possion ratio, \(E\) is the Young's modulus, \(l\) is the length of the beam, \(w\) is the width of the beam, and \(t\) is the thickness of the beam.  
The actuation voltage of the MEMS switch can be calculated as follows:  
\[V_{p}\ =\ \sqrt{\frac{8k}{27\epsilon_{0}\Big{(}2\Big{(}x-\frac{l}{2}\Big{)} \Big{)}w}g_{0}^{3}} \tag{15}\]  
Figure 8 depicts the relationship between the spring constant and the electrode coordinates. From Figure 8, we can observe that, as the area of the electrode decreases, the spring constant decreases. For the rectangle beam with Au (\(E\) = 78 Gpa, \(v\) = 0.44), the length of the beam is 370 \(\upmu\)m, the width of the beam is 160 \(\upmu\)m, the thickness of the beam is 1 \(\upmu\)m, \(g_{0}\) (the gap between beam and CPW) is 2 \(\upmu\)m and \(\sigma\ =\ 10\) Mpa.  
Figure 6: The relationship between the parameters of \(g\), \(Cr\) and \(\lambda\).  
Figure 9 shows the actuation voltage \(V_{p}\) is changed with the change of \(x\) and \(g_{0}\). When \(g_{0}=2\)\(\mu\)m, \(x\geq 235\)\(\mu\)m, \(V_{p}\leq 15\) V. When \(g_{0}=1.5\)\(\mu\)m, \(x\geq 235\)\(\mu\)m, \(V_{p}\geq 10\) V. When \(g_{0}=1\)\(\mu\)m, \(x\geq 235\)\(\mu\)m, \(V_{p}\leq 6.5\) V.  
Figure 8: The relationship between \(x\) and \(k_{1}\), \(k\).  
Figure 7: Electrode topology of the MEMS switch.
# Antioxidant and Antibacterial Activity of Extracts from Selected Plant Material
## 3 Results and Discussion
### Antioxidant Activity
In turn, a positive correlation was also observed between antioxidant activity determined by DPPH and FRAP methodologies and the TPC content in extracts from lemon balm cultivated in Mexico [41]. This was influenced by the concentration of phenolic compounds, especially phenolic acids, mainly derived from hydroxycinnamic acids, such as rosmarinic acid. This information is in accordance with our data and is presented in [39]. Rosmarinic acid was confirmed by HPLC as one of the main components present in the profile of phenolic compounds of _Melissa officinalis_ extracts. Both rosmarinic acid and caffe acid are responsible for most of the biological activities of this plant, particularly its antioxidant and antibacterial activities [65]. In our study, they accounted for 91% of the phenolic compounds identified in lemon balm extract. In turn, ellagic acid identified in _G. urhamum_ extract is responsible for various biological properties of this plant, including antioxidant, antimicrobial and anticancer activity [66,67]. Both this acid and quercetin and its derivatives identified in red raspberry (rutin, hyperoside) could also determine the biological properties of the extract obtained from this plant material, including its antibacterial and antioxidant activity [24,68]. These components accounted for about 67% of the total phenolic compounds identified in the red raspberry extract. Flavonoids, i.e., quercetin and kaempfer derivatives (94%), were also predominant among the phenolic components identified in the studied extract from _C. incanus_. They may contribute significantly to the biological activity assessed in this study. However, the polyphenols with the highest content in the tested material did not always determine their antioxidant activity. This would require additional research.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network
### Estimation of Antenna Azimuth and Movement Vector  
The azimuth of an antenna refers to the horizontal orientation angle at which it directs its signal [10]. In this study, the azimuth is measured from the east (positive X-axis), defined as \(0^{\circ}\), aligning with the reference system used throughout the analysis. This convention ensures consistency when operating in both rectangular and polar coordinate systems.  
To estimate the antenna azimuths, a method based on intra-handover zone analysis is applied, following the approach described in [13]. A handover refers to the process by which a mobile device transitions from one cell sector to another to maintain connectivity during movement. Specifically, intra-handover zones are regions where two adjacent sectors of the same base station overlap, leading to simultaneous reception of signals from both antennas. In these areas, user equipment (UE) records concurrent signal strength measurements from both sectors, which enables the identification of angular transitions. This makes it possible to estimate the coverage boundary and azimuth orientation of each antenna based on observed signal variations.  
A key element in this estimation process is the incorporation of the mobile terminal's movement vector. This vector, calculated from consecutive GPS positions, represents the  
Figure 10: Selected routes for data collection and base stations in the suburban area. Each color corresponds to the serving base station associated with the measured data along that route segment.  
direction and magnitude of the user's displacement. By projecting the signal strength gradient onto this movement vector, the directional derivative can be evaluated, highlighting the rate of change in the signal power as the terminal advances. This approach allows more accurate detection of sector boundaries and azimuth directions, particularly when signal transitions align with the user's path.  
Figure 11 shows a representative model of a three-sector base station, each covering an angular width of \(\Phi=120^{\circ}\), with an overlap of \(\rho=20^{\circ}\) between adjacent sectors. Using measurement data, these overlapping zones can be identified and delimited, enabling the estimation of the effective angular coverage of each sector.  
On the other hand, inter-handover events occur when a mobile terminal transitions between sectors belonging to different base stations (eNBs). These transitions are identified during data processing by detecting changes in both the cell ID and eNB ID, allowing the segmentation of coverage boundaries between adjacent base stations.
## 2 Materials and Methods
### Hyperspectral Image Calibration and Region of Interest (ROI) Extraction  
To minimize the signal noises due to the disturbance of instrument structure and detector sensitivity, the raw hyperspectral reflectance images were normalized into relative hyperspectral reflectance images using white reference and dark reference images, referring to the following formula [18]:  
\[R=\frac{R_{0}-R_{D}}{R_{W}-R_{D}} \tag{1}\]  
where \(R\) is the relative reflectance image, \(R_{0}\) is the raw reflectance image, and \(R_{W}\) and \(R_{D}\) are the white and dark reference images, respectively. Before image collection, the white reference images were acquired by collecting the hyperspectral image of a uniform and stable white calibration plate, and the dark reference image was collected when the camera lens was shut off.  
To promote the signal-to-noise ratio (S/N), data at the beginning and end of the spectral bands were removed; that is, only the remaining region from 950 nm to 1700 nm (213 bands) was used. To remove the background and edge of the Petri dish, three sub-images (284 \(\times\) 284 pixels) of the powder area in the three Petri dishes were cut off manually from each relative reflectance image. Then, each sub-image was divided equally into nine regions of interest (ROIs). The spectra of all of the pixels in each ROI were averaged to a spectrum. Hence, 513 spectra in total (27 spectra per image \(\times\) 19 images) were used for the following analysis. Among these, 243 spectra (27 spectra per image \(\times\) nine images (eight mixtures and pure flour)) were separated by a ratio of 2:1 into a calibration set (162 spectra) and a prediction set (81 spectra) for the development and validation of the corresponding PLSR models, respectively. A calibration set was also used to perform five-fold cross-validation.
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 3 Model Design
At the same time, the effects of the two prediction models are compared and evaluated by using the Mean Square Error (MSE) and R-square (R\({}^{2}\)). The data used by the LSTM here is the microseism data of the whole working face, without clustering analysis. On the one hand, it is to compare the effect of clustering analysis on improving the accuracy of machine learning prediction model, and on the other hand, it also meets the requirement of method for large sample size. The proposed framework consists of the ST-DBSCAN and SVR algorithms, so its time complexity depends on the time complexity of the two algorithms. When the ST-DBSCAN runs, it needs to traverse all the points in the dataset and calculate the number of density-reachable points of each point, so its time complexity is \(O\left(N^{2}\right)\), where N is the number of the training samples. Meanwhile, the time complexity of SVR is \(O\left(N^{3}\right)\)[31]. Only the item with the fastest growth rate is considered, so the time complexity of proposed framework is \(O\left(N^{3}\right)\).
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
###### Abstract  
The swift advancement of fifth-generation (5G) wireless technology brings forth a range of enhancements to address the increasing demand for data, the proliferation of smart devices, and the growth of the Internet of Things (IoT). This highly interconnected communication environment necessitates using multiple-input multiple-output (MIMO) systems to achieve adequate channel capacity. In this article, a 2-port MIMO system using two flipped parallel 1 \(\times\) 2 arrays and a 2-port MIMO system using two opposite 1 \(\times\) 4 arrays designed and fabricated antennas for 5G wireless communication in the sub-6 GHz band, are presented, overcoming the limitations of previous designs in gain, radiation efficiency and MIMO performance. The designed and fabricated single-element antenna features a circular microstrip patch design based on ROGER 5880 (RT5880) substrate, which has a thickness of 1.57 mm, a permittivity of 2.2, and a tangential loss of 0.0009. The 2-port MIMO of two 1 \(\times\) 2 arrays and the 2-port MIMO of two 1 \(\times\) 4 arrays have overall dimensions of 132 \(\times\) 66 \(\times\) 1.57 mm\({}^{3}\) and 140 \(\times\) 132 \(\times\) 1.57 mm\({}^{3}\), respectively. The MIMO of two 1 \(\times\) 2 arrays and MIMO of two 1 \(\times\) 4 arrays encompass maximum gains of 8.3 dBi and 10.9 dBi, respectively, with maximum radiation efficiency reaching 95% and 97.46%. High MIMO performance outcomes are observed for both the MIMO of two 1 \(\times\) 2 arrays and the MIMO of two 1 \(\times\) 4 arrays, with the channel capacity loss (CCL) \(<\) 0.4 bit/s/Hz and \(<\)0.3 bit/s/Hz, respectively, an envelope correlation coefficient (ECC) \(<\) 0.006 and \(<\)0.003, respectively, directivity gain (DG) about 10 dB, and a total active reflection coefficient (TARC) under \(-\)10 dB, ensuring impedance matching and effective mutual coupling among neighboring parameters, which confirms their effectiveness for 5G applications. The three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework version 19.5 for 5G systems, demonstrating operational effectiveness in 5G applications.  
5G communication applications; sub-6 GHz; MIMO; RT5880; high gain; MIMO application framework +
Footnote †: journal: _etlecom_  
**Keywords:** 5G communication applications; sub-6 GHz; MIMO; RT5880; high gain; MIMO application framework
## 4 Results
### Experimental Results
#### 4.3.1 Virtual Resistance Effect on the Transference  
In Section 4.2.1, the theoretical effectiveness of the virtual resistance algorithm to smooth the transference between grid-(dis)connected modes was demonstrated by simulations. For the experimental validation, only the \(R_{\text{\tiny{P}}}\) value of 1 \(\Omega\) has been used when the reconnection occurs, as detailed in Section 4.2.1. Figure 16 shows the experimental results for two scenarios, grid-connected to grid-disconnected and vice versa. A load of 1.81 \(\Omega\) has been considered.  
Scenario 1--Transition from grid-connected to grid-disconnected at maximum power without local loads. Phase \(u\) is delibering 45 kW while the other two phases are with null \(PQ\) requests. Then, a non-intentional disconnection to start operating in grid-disconnected is done. Figure 16a  
Figure 16: Scenario 1 (**a**) and 2 (**b**). Microgrid transitions between operation modes.  
Figure 15: Scenario 4 (three-phase to neutral short-circuit).  
shows the proper behaviour of the voltage, creating a short-duration dip that meets the ITI curve. After the transition, the delivered current goes to zero and the voltage of the microgrid is maintained.  
Scenario 2--Transition from grid-disconnected to grid-connected with load. A reconnection procedure is intentionally done with a high current local load. Figure 16b allows for observing that, after the reconnection, the \(u_{C_{u^{\prime}m^{\prime}}}\) voltage suffers a dip during two cycles, but the current \(i_{L2}\) is extinguished just after the reconnection is finished. The experimental results are close to the simulation shown in Figure 10b, validating the exposed method.  
In both cases, the PCC voltage suffers an alteration, the transition from grid-disconnected to the grid-connected mode being more critical. However, in both cases, the voltage alterations are resolved by control in less than 40 ms, meeting the ITI curve requirements. This trade-off permits a safe system connection even in weak grids while complying the regulation requirements.
###### Abstract  
This paper describes the development of an optimized corn yield prediction model under extreme weather conditions for the Midwestern United States (US). We tested six different artificial intelligence (AI) models using satellite images and meteorological data for the dominant growth period. To examine the effects of extreme weather events, we defined the drought and heatwave by considering the characteristics of corn growth and selected the cases for sensitivity tests from a historical database. In particular, we conducted an optimization for the hyperparameters of the deep neural network (DNN) model to ensure the best configuration for accuracy improvement. The result for drought cases showed that our DNN model was approximately 51-98% more accurate than the other five AI models in terms of root mean square error (RMSE). For the heatwave cases, our DNN model showed approximately 30-77% better accuracy in terms of RMSE. The correlation coefficient was 0.954 for drought cases and 0.887-0.914 for heatwave cases. Moreover, the accuracy of our DNN model was very stable, despite the increases in the duration of the heatwave. It indicates that the optimized DNN model can provide robust predictions for corn yield under conditions of extreme weather and can be extended to other prediction models for various crops in future work.  
c 2020, 10, 3785; doi:10.3390/app10113785
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation
#### 4.1.7 Extraction of Multiple Cyanotoxins  
Workflows to extract multiple toxin classes typically attempt either to group compatible species together or to accept compromises in recovery arising from the impossibility of trying to recover diverse species simultaneously. As described above, procedures for the extraction of MCs and NODs have typically settled on 75-90% methanolic water as an extraction solvent. In studies such as [64; 65; 20; 66], extraction of both MCs and NODs is described and the solvent mixtures used were uniformly in that range, showing the ease of extracting similar classes of toxins in tandem. However, studies attempting to extract those toxins as well as the more hydrophilic species, such as STXs or CYNs, as in [67; 68; 69; 70], typically reduced the percentage of organic solvent to ~50% in order to improve solubility of the more hydrophilic constituents of the methods. An alternative approach was followed in [71], where two separate extraction procedures were devised to recover collectively STX, ANA-a, and CYN with 25% acetonitrile in water, while MCs and NOD were extracted with 75% acetonitrile in water from a split sample. The latter approach attempts to reduce the impact of chemical incompatibility, but at a cost of doubling extraction and analysis requirements. Once the samples are prepared based on the extraction and clean-up techniques described above, the many analytical methods discussed in Section 4.2 may be used for detection and quantification.
## 4 On-Site Monitoring Results Analysis  
### Monitoring Principles and Calculation Equation  
This project was mainly implemented using quasi-distributed fiber Bragg grating sensing technology (FBG). The quasi-distributed fiber grating sensing technology (FBG) was mainly used to integrate the fiber grating strain gauge into a number of different types of fiber grating sensors and install them on the objects to be measured, thereby generating the measured values of various deformation parameters of the objects. The principle of the detection system is shown in Figure 22. When a broadband light was transmitted through the FBG sensor, the incident light at a specific wavelength was reflected back. When an axial strain change was imposed on FBG, the center wavelength shifted through the Bragg condition.  
The calculation equation was:  
\[D=K_{p}\times[(P-P_{o})-(P_{T}-P_{\mathrm{TO}})] \tag{1}\]  
where \(K_{p}\)\((\mathrm{mm}/\mathrm{nm})\)\(=\) 46.902, 45.009; \(P\) (nm) is the measuring wavelength; \(P_{o}(\mathrm{nm})\) is the initial wavelength; \(P_{T}(\mathrm{nm})\) is the temperature-compensated wavelength; and \(P_{TO}(\mathrm{nm})\) is the temperature-compensated initial wavelength.  
Figure 20: Line protection. (**a**) Line damage; (**b**) wire hose protection; (**c**) galvanized steel pipe protection with reflective strip.