esa-sceva/satcom-chunk-collection huggingface dataset:
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 5 Results and Discussion
### Comparative Analysis  
The following section compares the proposed eight-element antenna performance with other Sub-6 GHz eight-element antennas in the literature. Compared to the presented antenna, the bandwidth of the antenna in [19] is wider. However, the overall size of the antenna is very large, with a lower isolation. The antenna proposed in [20] provides a very low isolation than the proposed antenna even while having a comparatively low profile. The overall antenna size of the antennas presented in [24; 31] is large with a low isolation value even though the bandwidth of the antenna is satisfactory. References [20; 31] discuss the channel capacity of the corresponding antennas, but it is evident that the CC of the proposed antenna is better than both of them. All these above-mentioned antennas are non-flexible in nature. Recently, a flexible eight-element antenna has been proposed in [2] for the specific band. However, the antenna has a lower bandwidth and lower isolation values  
Figure 25: Channel capacity of the proposed eight-element antenna.  
than the proposed eight-element antenna. Table 2 indicates that the proposed antenna outperforms the existing eight-element antenna in the specific band in terms of flexibility, satisfactory bandwidth, improved isolation, good MIMO diversity metrics, and enhanced channel capacity values.
## 1 Introduction
One of the objectives of this study is to investigate their adequacy for Multi-Degree-Of-Freedom (MDOF) systems. If the stiffness of the auxiliary components of the bracing system is included in the analysis model, it is common practice to estimate its value based only on the brace extender cross section and length, on the assumption that this approach will render a suitable approximation to the system properties [8]. The main reason for this simplification is the absence of accurate data regarding the stiffness of other auxiliary elements (such as gusset plates, cleats, bolts, clevises, frontal plates, etc.) included in the set. Moreover, the stiffness component of the damper element itself is generally unavailable in catalogs and manufacturer data, implying that this variable can be neglected by the designer. A second objective of this study is to examine the validity of this assumption, and to determine to which extent the stiffness of secondary elements--other than the brace extender and including the damper stiffness component--should be included in analysis models. In order to achieve the study targets, this study comprises two parts. First, available experimental data are used to assess the range of influence of auxiliary elements on the overall flexibility of the added damping system. Second, a parametric study is carried out in which two frames equipped with different viscous damping systems are modeled with and without consideration of the brace elements and auxiliary system stiffness. The frame is subjected to sets of accelerograms under different intensities, and the peak values for relevant variables at different storeys are compared. The study shows that the errors incurred by considering only the brace extender stiffness (without inclusion of other auxiliary elements) are not negligible. In fact, for some variables and storeys, they are of the same order of magnitude than the errors incurred by totally neglecting the stiffness. Thus, an accurate result can only be obtained if the flexibility of all auxiliary elements (including the damper itself) are considered. Given the scarcity of experimental data, these results should be considered as preliminary. However, they suggest the need for future experimental characterization of complete damper units, including all mounting elements.
## 3 LiCSAR Products: Current State and Future Trend  
The basic InSAR products generated by LiCSAR are original and spatially-filtered wrapped, as well as unwrapped, interferograms and original coherence maps, MLI images for each epoch, and complementary specialised frame images, including incidence angle map files needed for motion vector extraction [49], height values from the DEM used in processing, preprocessed GACOS products, and metadata information (e.g., perpendicular baseline list, date and acquisition time of the primary epoch image). The provided products are georeferenced to the WGS-84 geographic coordinate system. After the processing, results are shared in the form of GeoTIFF files and preview bitmap rasters (in PNG format, downsampled to 30% of the original GeoTIFF's dimensions). Some products of special interest (e.g., co-seismic interferograms) are converted into Google Earth KMZ files. In Section 3.1, we elaborate on the contents and coverage of the products.  
The LiCSAR products are publicly available through the COMET LiCS products web portal (currently at [[https://comet.nerc.ac.uk/COMET-LiCS-portal](https://comet.nerc.ac.uk/COMET-LiCS-portal)]([https://comet.nerc.ac.uk/COMET-LiCS-portal](https://comet.nerc.ac.uk/COMET-LiCS-portal))) in the structure described in Section 3.2. Selected InSAR products are or will be available within the European Plate Observing System (EPOS) ([[http://www.ics-c.epos-eu.org](http://www.ics-c.epos-eu.org)]([http://www.ics-c.epos-eu.org](http://www.ics-c.epos-eu.org))), and the CEDA Archive ([[https://catalogue.ceda.ac.uk](https://catalogue.ceda.ac.uk)]([https://catalogue.ceda.ac.uk](https://catalogue.ceda.ac.uk))), as well as other platforms (e.g., Google Earth Engine).
## 3 Materials and Methods
### Random Forest Classification  
Random forest (RF) is an ensemble machine learning method that was introduced by [42]. It is a combination of tree predictors, where each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest [42]. This approach aims to improve the predictive performance and robustness of individual decision trees by aggregating their outputs. Random forest is particularly suitable for large datasets and has shown rapid development due to its effectiveness in handling complex data [43].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Male** & **Female** & **Total** \\ \hline Number of patients & 179 (64.90\%) & 97 (35.10\%) & 276 (100.00\%) \\ Age, mean + standard & 64.49 \(\pm\) 12.40 & 71.22 \(\pm\) 10.47 & 66.85 \(\pm\) 12.17 \\ deviation (SD) & 58.87 \(\pm\) 12.78 & 65.62 \(\pm\) 10.94 & 61.24 \(\pm\) 12.56 \\ Age at first attack + SD & 68.97 \(\pm\) 21.09 & 58.56 \(\pm\) 19.07 & 65.31 \(\pm\) 20.97 \\ \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics and GFR levels of patients.  
The working principle of random forest involves the construction of multiple decision trees during the training phase. Each tree is built using a random subset of the training data and a random subset of the features. This randomness introduces diversity among the trees, which helps in reducing overfitting and capturing different aspects of the data. During the prediction phase, the individual trees \"vote\" on the outcome, and the most popular class label is chosen as the final prediction [44].  
Although the RF model uses entropy and Gini Index formulas to determine the best discriminative feature selection, it predominantly uses the Gini Index formula. The Gini Index was developed by [45], and its formula is given in Equation (3).  
\[Gini\ Index\ =\ 1\ -\ \sum_{i\ =\ 1}^{n}{(P_{i})^{2}} \tag{3}\]  
Table 3 shows the hyperparameter values of the RF model used in this study.
## Materials and Methods
### Spectra Data Extraction  
The calibrated hyperspectral image was segmented by using a simple threshold value as the average value of the background and tomato pixels to remove the effect of the background and to visualize only the tomato pixels. A 3 \(\times\) 4 median filter was applied to each of the tomato images separately for removing the dead pixels, which is caused by a camera detector. Furthermore, the region-of-interest (ROI) step was performed on the segmented image to extract the spectral signature and statistical information. An ROI was manually selected of each tomato image. The pixel spectra were averaged for each band within the entire ROI of each tomato separately before further analysis. In total, 95 average spectra (wavelength range = 948-2494 nm) representing the 95 scanned tomatoes were calculated and saved. Because of low signal-to-noise ratio performance due to inefficiencies of the lighting system at certain wavelength regions, e.g., low light output in the >1600 nm, the spectra were limited to the range 1000-1550 nm for further analysis of the relationship between the results of the reference measurement (MC, pH, and SSC) and the HSI data using multivariate analytical methods in combination with preprocessing techniques. The image correction and ROI selection steps were incorporated using MATLAB software (Version 8, The Mathworks Inc., Natick, MA, USA).
## 3 Results  
To understand the proximity of the measurements obtained with the Kinect(r) sensor and by manual methods, the calculation of the Pearson correlation coefficients was performed. Figure 11 shows the results of this correlation test.  
Examining the pairs of values obtained, between the Kinect(r) and the corresponding ones by the manual method, they were found to range from 0.83 to 0.93, for HW and ASC, respectively, having an average of 0.90.  
The two algorithms were executed with the parameters of Tables 2 and 3. The results of the coefficients and exponents for both algorithms are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficients** & **GA** & **CSA** \\ \hline \(a_{1}\) & 0.057 & 0.198 \\ \(a_{2}\) & 0.209 & 0.198 \\ \(a_{3}\) & 0.220 & 0.023 \\ \(a_{4}\) & 0.009 & 0.189 \\ \(a_{5}\) & 0.598 & 0.422 \\ \(a_{6}\) & \(6.4\times 10^{-4}\) & 0.035 \\ \(a_{7}\) & 0.026 & 0.0419 \\ \(a_{8}\) & 4 & 4 \\ \(a_{9}\) & 3 & 2 \\ \(a_{10}\) & 4 & 5 \\ \(a_{11}\) & 1 & 5 \\ \(a_{12}\) & 2 & 2 \\ \(a_{13}\) & 4 & 4 \\ \(a_{14}\) & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the coefficients and exponents obtained for both algorithms.  
Figure 11: Correlation matrix of the biometric parameters.  
This implies that the model obtained by the genetic algorithm is described by Equation (3), while the CSA is represented by Equation (4).  
\[0.057(HWK)^{4}+0.209(RHK)^{3}+0.22(BLK)^{4}+0.009(BDLK)\\ +0.548(BTLK)^{2}+6.4\times^{-4}(GSCK)^{4}+0.026(ASCK)^{4} \tag{3}\]  
\[0.198(HWK)^{4}+0.198(RHK)^{2}+0.023(BLK)^{5}+0.189(BDLK)^{5}\\ +0.422(BTLK)^{2}+0.035(GSCK)^{4}+0.0419(ASCK)^{3} \tag{4}\]  
With the previous equations, the statistical errors (Table 5) were obtained.  
A cross-validation stage was required since the methods are based on random values. Therefore, 10 runs were carried out, and the average statistical values of these runs are shown in Table 6.  
The convergence curves obtained for both algorithms are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimator** & **GA** & **CSA** \\ \hline RMSE (\%) & 8.83 & 9.68 \\ \(R^{2}\) (\%) & 72.46 & 66.59 \\ MBE (\%) & 1.43 & 0.95 \\ MAPE (\%) & 10.83 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-validation with 10 runs for the GA and CSA.  
Figure 12: Function costs for both algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimator** & **GA** & **CSA** \\ \hline RMSE (\%) & 7.68 & 7.55 \\ \(R^{2}\) (\%) & 79.23 & 79.98 \\ MBE (\%) & 0.33 & 2.26 \\ MAPE (\%) & 9.45 & 9.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical errors of the models obtained by the GA and CSA.  
Finally, the comparison of the error between the real values and the estimated ones is given in Figure 13.  
The results are discussed in depth in Section 4.
## 1 Introduction
[31] calculated vegetation greenness and canopy moisture indices at 15 m spatial resolution and validated using their equivalent-values at a spatial resolution of 30 m. Similarly, [PERSON]. 2014 [13] performed four pansharpening methods and two VIs, Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) to analyze the effect. Both researchers were limited to the same sensor fusion (with large SRR); they used Landsat PAN and MS bands to derive 15 m FVI and evaluated their result briefly without considering multi-sensor fusion with different SRF, SNR. Some past studies have used image fusion methods for VI mappings, like Landsat imagery to spatially downscale 250 m resolution MODIS VI data [32; 33]. It has become more significantfor researchers to have higher resolution imageries to calculate several vegetation indices since the spectral resolution of the PAN band spans 0.503-0.676 \(\upmu\)m with clearly no overlap with the blue, and NIR spectral bands in Landsat-8 OLI satellite platform (operating since February 2013) [31]. The daily surface reflectance products (MOD09 GA) at a spatial resolution of 500 m, captured from the MODIS sensor, were used along with the Landsat-8 OLI data to generate a 30-m resolution synthetic image [34]. A new index defined by [PERSON] et al. [1] proposed high-resolution NDVI = (Fused NIR-Fused R)/(Fused NIR + Fused R) which was a good solution in introducing the spatial resolution in computation of the NDVI and showed less confusion in the vegetation detection tested in IKONAS imagery. Similarly, the vegetation changes in the Zayandeh-rud river basin in the period 2001 to 2016 have been investigated based on the combination of the 15 different vegetation indices [9]. It can be seen that many image fusion algorithms and satellite images are used for VI mapping in different application areas. Most of them were focused on making a high-resolution FVI map using same-sensor fusion or multi-sensor fusion using Landsat and MODIS imagery, but not focused on many satellite images (like GF1, GF2, GF4, Landsat-8 OLI, MODIS etc.) and could not explain what happens to the quality when SRR is lower than 1:10 (SRR between GF2 to GF4 is 1:12, IKONAS to SPOT 4 is 1:20), also missing the sensitivity analysis of VI with image fusion and different factors affecting FVI quality. To address this research gap, our main objective was to study the effect of image fusion for the quality of VI mapping. To meet the main objective, we were focused on 1) analyzing the sensitivity of VI with image fusion 2) selecting fusion method with least impact on FVI quality 3) finding out factors of FVI quality and 4) evaluating how SRR and FVI are inter-related. We made a strategy on image fusion and quality assessment for different satellite images; GF1, GF2, GF4, Landsat-8 OLI and MODIS using their PAN and MS bands in different SRR (1:6 to 1:15) using both same-sensor and multi-sensor fusion. Five popular image fusion methods and six VIs were used with subjective and objective quality indices to analyze the result properly.
# Impact of Exposure to Commonly Used Carbamide Peroxide on Dental Pulp Stem Cells
###### Abstract  
**Background**: This study investigated the contact between adult dental pulp stem cells (DPSCs) and carbamide peroxide (CP), a bleaching agent that is a popular choice for at-home whitening products, using an in vitro model. **Objectives**: The aim of this study was to evaluate the impact of exposure to different concentrations and timings of a commonly used peroxide-based home tooth-whitening product on DPSCs. **Materials and methods**: Human DPSCs obtained from impacted third molars were cultured and exposed to various concentrations of carbamide peroxide (0.1%, 0.5%, and 1%). The effects of CP on DPSC proliferation and apoptosis were investigated by MTT assay and flow cytometry. Migration was investigated by micrographs of wound healing. An enzyme-linked immunosorbent assay (IL-6 and IL-8) was used to investigate the CP-stimulated cytokine production of DPSCs. Each experiment was performed three times with independent batches of DPSCs. Statistical analysis of the collected data was performed using one-way and two-way ANOVAs with the significance threshold set at \(p<0.05\). [PERSON]'s post hoc multiple comparison test was used to identify differences between groups. **Results**: Cell viability and adherence were lower in the CP-exposed cells compared to the non-stimulated cells, probably due to increased cell death (**\(p\leq 0.01\), ***\(p\leq 0.0001\)). CP-stimulated DPSCs exhibited a dose-dependent release of IL-6 and IL-8 (***\(p\leq 0.0001\)). CP did not affect wound healing at any concentration tested. **Conclusions**: Human DPSCs were able to sense CP. Consequently, CP contributed significantly to cell apoptosis and local inflammatory responses through cytokine release.  
DPSCs; carbamide peroxide; apoptosis; inflammatory reaction
###### Abstract  
Non-invasive diagnostics and finding biomarkers of disease in humans have been a very active research area. Some of the analytical technologies used for finding biomarkers of human disease are finding their use in livestock. Non-invasive sample collection from diseased cattle using breath and headspace of fecal samples have been reported. In this work, we explore the use of volatile organic compounds (VOCs) emitted from bovine nasal secretions and serum for finding biomarkers for bovine respiratory disease (BRD). One hundred nasal swabs and 100 serum samples (\(n=50\) for both'sick' and 'healthy') were collected at the time of treatment for suspected BRD. Solid-phase microextraction (SPME) was used to collect headspace samples that were analyzed using gas chromatography-mass spectrometry (GC-MS). It was possible to separate sick cattle using non-invasive analyses of nasal swabs and also serum samples by analyzing and comparing volatiles emitted from each group of samples. Four volatile compounds were found to be statistically significantly different between'sick' and 'normal' cattle nasal swabs samples. Five volatile compounds were found to be significantly different between'sick' and 'normal' cattle serum samples, with phenol being the common marker. Future studies are warranted to improve the extraction efficiency targeting VOCs preliminarily identified in this study. These findings bring us closer to the long-term goal of real-time, animal-side detection and separation of sick cattle.  
biomarkers; volatile organic compounds; phenol; cattle; bovine respiratory disease; non-invasive sample collection; solid-phase microextraction; gas chromatography-mass spectrometry +
Footnote †: journal: International Journal of Experimental and Experimental Methods
## 4 Exploring Vocabulary-Mismatched Dysarthric Speech Recognition  
### Motivation  
The previous section examined model training strategies aimed at improving target speech recognition in a speaker-independent setting. The findings show that incorporating in-domain datasets, whether from canonical or dysarthric speakers, significantly enhances recognition accuracy, indicating the feasibility of accurate unseen speakers' speech recognition if we have prior knowledge of the words they are likely to use.  
However, a critical limitation of current dysarthric datasets is their restricted vocabulary, which falls far short of covering the daily vocabulary needs of users. The challenge becomes even more pronounced in extreme cases, such as when dysarthric recordings of very few words are available, or when the target speakers use a language that lacks corresponding dysarthric or even in-domain canonical speech data. In such scenarios, the model has to be trained with vocabulary that has minimal or no overlap with the target vocabulary, a situation known as a vocabulary-mismatched scenario.  
In this section, we design experiments to simulate the vocabulary-mismatched settings to investigate how the training vocabulary impacts model performance after conventional training. For simplicity, we only use in-domain dysarthric data (**D_indm**) for training. We examine two types of scenarios: the very-low-resource setting and the cross-lingual setting. In the very-low-resource setting, we use data of limited vocabulary from each in-domain source speaker. As the speech content among speakers within the same dataset is typically similar, having fewer words in the training data increases the mismatch between the training and test data. For the cross-lingual setting, we train the model with dysarthric speech of languages different from the target dysarthric dataset.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
###### Abstract  
In this paper, we present a study of bit error rate (BER) for orthogonal frequency division multiplexing/offset quadrature phase shift keying (OFDM/OQPSK) and OFDM/offset quadrature amplitude modulation (OQAM) systems with an isotropic orthogonal transfer algorithm (IOTA) filter. The novel noise suppression method based on an IOTA filter is proposed to reduce the error of channel estimation caused by additive white Gaussian noise (AWGN). The OFDM/OQPSK and OFDM/OQAM systems do not insert the guard interval (GI) and pilots in the signal frames, thus they possess transmission efficiency. An analysis was carried out for convolutional coded OFDM/OQPSK and OFDM/OQAM systems in Rayleigh fading channels with generator polynomials and constraint lengths. Compared with conventional OFDM/QPSK and OFDM/QAM systems with the insertion of comb-type pilots, the proposed IOTA filter-based channel estimation method can provide significant energy per bit to time-varying noise power spectral density ratio gains over time and frequency-selective propagation Rayleigh fading channels in OFDM/OQPSK and OFDM/OQAM systems.  
Keywords:channel estimation; orthogonal frequency division multiplexing (OFDM); isotropic orthogonal transform algorithm (IOTA); offset quadrature phase shift keying (OQPSK); offset quadrature amplitude modulation (OQAM); additive white Gaussian noise (AWGN) +
Footnote †: journal: Applied