esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 3 Results and Discussion
### Comparison of Results with Existing Studies  
The overall evaluation of laboratory and simulation results reveal that children and baby brain tissue SAR 10g values (1.41, 1.20 W/kg) are higher than those induced in adult female and male (0.72, 1.16 W/kg). These results are in accordance with [11; 12]. The assessment of the SAR 10g in different tissues of the head has shown that the brain of the baby has the highest values, contrary to the whole-head SAR. The maximum of the ratios of brain overhead SAR 10g for baby is 0.69 while the values are 0.38, 0.16, 0.22 for child, female and male, respectively, at 1800 MHz. This result supports the findings of [10], wherein  
Figure 11: Box graph for SAR 10g, (a) 900 MHz and (b) 1800 MHz.  
decreased head size yielded an increase in EM energy absorbed in the brain. Results show that, similar to [14, 16, 18, 23], frequency, tissue type, the morphological specifications, and age/gender associated differences in electrical properties of tissues affect the induced SAR values.  
In addition, a comparison based on parameters such as frequency, age, tissue, and key results based on SAR was made and the results are given in Table 6.
# Diabetic Retinopathy Improved Detection Using Deep Learning
## 3 Fundus Oculi Proposed Classification  
A methodology capable of processing retinal fundus images for early detection of diabetic retinopathy and its degree, if present, is proposed. For this purpose, a deep learning model with transfer learning is used to analyze different images that are previously prepared, obtaining an inference of how likely is the existence of diabetic retinopathy among 5 different classes defined as No NPDR, Slight NPRD, Medium NPDR, Severe NDPR, and Proliferative DR.
## 2 Materials and Methods
### Data Processing and Model Development  
#### 2.4.1 Spectral Preprocessing  
Various environmental factors, instrument conditions, noise, and scattering due to disparities in size, shape, and surface color of distinct rapeseed plant seed types can impact the quality of spectral data acquisition. To enhance the differences between spectral samples and eliminate noise, spectral preprocessing techniques are utilized. The Unscrambler X 10.4 software was employed to compare the efficacy of diverse preprocessing methods, including Mean Centering (MC), First Derivative (FD), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky-Golay smoothing filter (SG), applied to the near-infrared spectra of rapeseed plant seed samples. The results informed the selection of the optimal preprocessing method.
# A Decision Tree for Rockburst Conditions Prediction
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Number of Data** & **Input Variables** & **Algorithm** & **Results/Description** \\ \hline [PERSON]. [PERSON] et al. [20] & 132 & D(m), \(\sigma_{v}\), \(\sigma_{v}\), \(\sigma_{\theta}\), \(\sigma_{\theta}\) / \(\sigma_{c}\), & \multirow{2}{*}{SVM} & The results indicated that SVM was feasible for rockburst conditions by indicating an average accuracy of 80\% \\ \hline [PERSON] et al. [25] & 46 & \(\sigma_{v}\), \(\sigma_{v}\), \(\sigma_{\theta}\), EEI & RF, ANN and SVM & \multirow{2}{*}{
\begin{tabular}{c} The results showed that the RF rockburst prediction model \\ outperformed those of SVM and ANN, based on the misjudment ratios \\ \end{tabular} } \\ \hline [PERSON] et al. [26] & 174 & \(\sigma_{v}\), \(\sigma_{v}\), \(\sigma_{\theta}\), \(\sigma_{\theta}\)/ \(\sigma_{v}\), & \multirow{2}{*}{FIS, ANFIS} & The performance of ANFIS, based on the metrics adopted, was better in predicting rockburst conditions \\ \hline [PERSON] et al. [27] & 164 & \(\sigma_{\theta}\)/ \(\sigma_{v}\), \(\sigma_{c}\)/ \(\sigma_{v}\), EEI & cloud modelling, MLR, ANN & The cloud model adopted performed better than MLR, but was superior generalization ability of the ANN in rockburst prediction \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of previous studies on ML predictions on rockburst conditions with input variables and results. \begin{table}
\begin{tabular}{c c} \hline
**Rockburst** & **Failure Characteristics** \\
**Condition/Intensity** & \\ \hline None & No sound of rockburst and rockburst activities. \\ \hline Light & The surrounding rock is deformed, cracked, or rib spalled, there is a weak sound, and no ejection phenomenon. \\ \hline Moderate & The surrounding rock is deformed and fractured, and there is a considerable number of rock chip ejections, loose and sudden destruction, accompanied by crisp cracking, and often presented in the local cavern of surrounding rock. \\ \hline Strong & The surrounding rock is busted severely, and suddenly thrown out or ejected into the tunnel, accompanied by a strong burst and roaring sound, air spray, and storm phenomena, with continuity that rapidly expands to the deep, surrounding rock. \\ \hline \end{tabular}
\end{table} 
Table 1: Standard classification of rockburst intensities. While these methods have demonstrated satisfactory results in predicting rockburst conditions, they are not without limitations. For instance, ANN may suffer a slow learn
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation  
We use GENSEN [30] tool to perform the simulation of proposed scheme. The nodes are placed randomly in the area of \(100\) m \(\times\) 100 m area.  
The proposed scheme compared the acceptance rate of flows with the previous scheme in [30]. In the previous scheme the IntraSend scheduling schedule the flows in the IntraSend time slots and not utilized the empty time slots of IntraRecv time slots. Similarly, the IntraRecv scheduling not utilizing the empty time slots of IntraSend Scheduling. While in the proposed scheme the clusters that have only outgoing flows then the IntraSend scheduling can utilize the empty IntraRecv time slots. Similarly, if the cluster has only incoming flows then the IntraRecv scheduling can utilize the empty IntraSend time slots. We analyzed the performance of compared schemes based on six metrics, i.e., the impact of a deadline, the impact of flows, the impact of clusters, the impact of intra-cluster slots, the utilization of IntraSend time slots and utilization of IntraRecv time slots.
## 2 Materials and Methods
### Data Preprocessing and Temporal Data Modeling  
Preprocessing the data involved various filtering, transformation, binarization, and joining techniques to create a data table for the study. We then applied further filtering to exclude patients with no attributes. The final dataset has a total of 830,125 unique patient records, with 135,045 glaucoma patients (labeled as a glaucoma dataset) and 695,080 non-glaucoma patients (marked as a non-glaucoma dataset). These data were organized into temporal order using timestamp information. Each patient record should have at least two variables in a 15-year-wide longitudinal dataset as an inclusion criterion. We subsampled a balanced dataset containing 33,611 unique patient records (16,805 glaucoma patients and 16,806 non-glaucoma patients) as input data for early prediction analysis. All 16,805 glaucoma patients were first diagnosed with the disease during a yearlong period from 2014 to 2015. For early predictive analysis, the input glaucoma dataset contains records up to one year before the diagnosis of glaucoma. There were 32 attributes in the final dataset from demographics, systemic diseases, and medications for analysis (Appendix A). Finally, one-hot encoding converted the categorical variables (race, gender) of \(n\) possible values into \(n\) binary variables, as shown in Figure 1.  
Figure 1: Overview of data pre-processing and the input data vector for predictive machine learning analysis.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 5 Computational Burden Analysis of the Complete Receiver  
In order to check the compatibility with the real-time implementation, an analysis of the computational load of the complete OSNMA-ready receiver is presented hereafter.
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 4 Conclusions  
In this paper, an on-board flickering pixel dynamic suppression method based on multi-feature fusion is proposed. This method is embedded in the IRPT detection process, and the results of IRPT detection and tracking are used to extract the visual and motion features of the flickering pixel. A small amount of extra computing resource was consumed. Therefore, our proposed method can achieve real-time uninterrupted blind pixel detection without interrupting IRPT detection. Furthermore, the confidence of the flickering pixel evaluation strategy and the selection mechanism of flickering pixel are introduced to fuse the above features, which can achieve frame-accurate dynamical flickering pixel suppression. Experimental results have shown that our proposed method is superior to others in blind pixel detection and flickering pixel suppression. The proposed method can be an addition to most other IRPT detection processes, and it has significant meaning that the ability to suppress blind pixels guarantees near-real-time IRPT detection.  
All the authors contributed to this study. Conceptualization, L.J. and P.R.; Investigation, L.J. and X.C.; Methodology, P.R. and X.C.; Software, L.J. and X.C.; Data curation, L.J.; Funding acquisition, P.R.; Project administration, X.C. and S.Q.; Supervision, S.Q. Writing--Original draft preparation, L.J.; Writing--review and editing, P.R. and S.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 62175251.  
Not applicable.  
The authors declare no conflict of interest.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 2 Materials and Methods
### Brightness and Contrast Enhancement
#### 2.2.3 Feature Extraction  
In our problem, features are extracted to classify hemorrhages and non-hemorrhages. Shape and geometric features are extracted using connected component analysis [34]. These features are effective to separate hemorrhages from blood vessels. Eleven shape features (area, major axis length, minor axis length, eccentricity, orientation, convex area, filled area, the equivalent diameter of the circle, solidity, perimeter, and mean intensity) and 20 texture features (autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, entropy, homogeneity, maximum probability, sum of squares, sum average, sum variance, sum entropy, difference variance, difference entropy, measurement of correlation 1, information measurement of correlation 2, inverse difference normalized, and inverse difference moment normalized) are extracted.  
Furthermore, CIE LAB and HSV color spaces features are used in this research. The CIE LAB isolates the color information in light (L*) and colors (a* and b*) channels while HSV retains the information as hue, saturation, and value [35; 36]. Apart from the features mentioned, four hand-crafted features are used. The SWAT can segment the hemorrhages completely; however, some other structures such as blood vessels and dark shades cannot be restricted within the windows. The reason is that the window stops when the vertices \(V\) of the window go beyond the search region. Hence, the open and closed contour of the object is taken as a feature. Similarly, the blood or liquid at vitreous humour spreads in each direction that yields a shape of hemorrhage with regular and fewer corners as compared to other segmented objects. Therefore, the distance of the spatial locations and the object's corner from its center is taken as a feature. Similarly, the sum of squares of gray levels at the object's contour from its mean is used as a feature. The macula of the retinal also has the same intensity profile as hemorrhage, but the edges of the macula are blurrier than hemorrhages. To distinguish hemorrhage from the macula, one feature using Laplacian of Gaussian is extracted. Overall, forty-one (41) features are extracted for classification.
## 1 Introduction
In the same manner, several papers deal with the improvement in corrosion resistance of the 3000- and 6000-series alloys [6; 8; 9]; in contrast, very few papers focussed their attention on the AA8079 alloy, which shows several remarkable properties, making it suitable to be used in the numerous applications mentioned before [10; 11]. As well as this, the scientific literature presents many papers dealing with the formation and characterisation of so-called \"Cr-free\" rinse conversion-layers of aluminium alloys (containing CrIIII [12; 13], while very few address their attention to the no-rinse \"true-Cr-free\" process [14]. Although the idea of using no-rinse \"true-Cr-free\" processes has been patented since 1988 [15], costs and product performances have delayed their industrial use until now. This work aimed to study the protective properties of a \"true\" chromium-free, no-rinse surface-treatment of the AA8079 to be used as a replacement for rinse chromium III-based processes, taking into account also the natural-resource consumption, and comparing the process costs. It will be highlighted that the no-rinse, \"true\" chromium-free conversion-coating industrial process exhibits similar, if not better, protective properties  
Figure 1: Bottle capsules, flexible and semi-rigid packaging produced using AA8079. when compared to the rinse CrIII-based one, allowing for the reduction in the environmental impact and production costs.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### Influence of the Satellite PIFCB
#### 3.2.1 Influence of the GPS PIFCB  
GPS satellites have significant time-varying characteristics of phase hardware bias [14; 42]. This section describes the four GPS static PPP models for positioning. Figure 2 shows the static positioning error curves of the stations KOUR and WIND on DOY127. It is observed that the TF-F models perform slightly worse in the convergence process, compared to the other triple-frequency PPP models. After convergence, the positioning error curves of the positioning error curves of the three PPP models, except for the TF-F model, largely overlap. For the PIFCB values throughout the day, the PIFCB estimated by the TF-C model was output, and Figure 3 shows the PIFCB values observed by the KOUR station on DOY121-127, where each color represents a GPS triple-frequency satellite. It is observed that the PIFCB values can reach the decimeter level [43]. Additionally, there are clear time-varying features within each day.  
A boxplot of the distribution of positioning accuracy and convergence time for the 60 selected stations for different static PPP models within DOY121-127 is shown in Figure 4. In the boxplot, the upper and lower edge line distributions represent the 99% and 0% quantiles, and the upper and lower end lines of the rectangular box represent the 75% and 25% quantiles, respectively. The inner lines of the rectangular box represent the 50% quantile. The median positioning accuracy and convergence times, representing the positioning performance of this model, are presented in Table 4. From Figure 4, ignoring the GPS PIFCB has a greater impact on the positioning accuracy, and the TF-F and TF-FP models take longer to converge. The TF-FP model has a long convergence time, probably due to the number of stations used by the IGMAS product and the fact that the stations are not exactly the same as in the experiments. The reason for the long convergence time of the TF-F model is that the GPS PIFCB cannot be neglected and also proves that the TF-F model is not applicable to the GPS PPP. As shown in Table 4, the convergence accuracies of DF and TF-C are similar: better than 1.0 cm in the E and U directions and better than 1.5 cm in the U direction. The convergence accuracy of the TF-FP model is slightly worse than that of the TF-C model and better than that of the TF-F model. The convergence time of the TF-FP model is significantly worse than that of the DF and TF-C models.  
The PPP convergence accuracy of the DF and TF-C models is largely consistent. This demonstrates that a reliable convergence accuracy can be obtained when the TF-C model is applied to GPS PPP as demonstrated by the PPP results of the TF-FP model. The average convergence time is 20.39 min for the DF model and 24.38 min for the TF-C model, which is consistent with previous studies [43]. This phenomenon prolongs the convergence  
Figure 1: Geographical distribution of the 60 selected MGEX stations.
## 1 Introduction
In this context, the main objective of this work was to carry out an analysis of the impact of the production of several PV installations connected to the grid in the south of Spain on some parameters that characterise the quality of the electrical signal in the grid, using data recorded by their inverters over a period of three years. The aim of this work was to determine the variation ranges of the voltage and frequency of the grid signal, comparing these values recorded by the inverters when the PV installations are in operation and injecting electricity into the grid, with those values measured when there is no production in the plants, thus analysing the probable correlation between the possible variations of these parameters with the injection of current into the grid by the PV installations. Based on the results and data available, it was also possible to estimate the maximum power of PV installations that could be connected to these distribution lines without its impact causing the voltage of the grid to exceed the limits indicated by the current regulation. The study carried out in this work was applied to several PV installations, with the aim of comparing the hosting capacity of different distribution lines to which these installations inject their production, both in urban and rural areas. It was observed by some authors that this effect is more important in weak residential and rural grids with high series resistance [36]. Furthermore, with the analysis of the results, this work highlights which inverter characteristics are most suitable for correctly measuring these grid parameters to carry out this type of study. The rest of the paper is organised as follows. Section 2 shows the characteristics of the PV installations in which the analysis of the data recorded by their inverters was carried out, describing the characteristics of the measured data. In Section 3, the results found are presented and discussed, and the main conclusions are shown in Section 4.
## 2 Materials and Methods
### RNA Sequencing Procedure and Data Analysis
#### 2.2.3 Read Alignment and Quantification of Gene Expression  
The preprocessed reads were aligned to the reference genome of _Lens culinaris_ (Lcu.2 RBY v.2.0) available in Phytoome v.13. STAR software [35] was employed for this purpose, encompassing read alignment, splice junction identification and merging of overlapping reads (the threshold for multiple alignments allowed for a read was set to 10). Gene expression levels were quantified using FeatureCounts [36], which tallied the number of paired-end reads mapped to gene-level meta-feature. The multi-mapping and multi-overlapping reads were excluded. This resulted in a count matrix, which served as the basis for subsequent analyses.
# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 3 Results and Discussion
### Comparison with SVM and KNN
#### 3.3.2 Model Evaluation  
To evaluate the performance of the proposed 3D-DL model, several statistical tests have been analyzed, including overall accuracy, F1-score, precision, and Recall through the following equations;  
\[OA=\frac{1}{M}\sum_{k=1}^{M}{\it True^{p}}_{k} \tag{5}\]  
\[Precision=\frac{1}{M}\sum_{k=1}^{M}{\frac{{\it True^{p}}_{k}}{{\it True^{p}}_{k }+{\it False^{p}}_{k}}} \tag{6}\]  
\[Recall=\frac{1}{M}\sum_{k=1}^{M}{\frac{{\it True^{p}}_{k}}{{\it True^{p}}_{k }+{\it False^{N}}_{k}}} \tag{7}\]  
\[F1-Score=\frac{2\times(Recall\times Precision)}{(Recall+Precision)} \tag{8}\]  
The values used in the above equations are computed through the confusion matrix. Where \(True^{p}\) and \(False^{p}\) represents the true and false positive, \(False^{N}\) represents the false-negative and \(M\) represents the number of samples. The statistical tests are presented in Table 2, where one can easily observe the statistical significance of our proposed model than the traditional classifier. In addition to that, Table 3 represents the overall accuracy (OA) of the traditional and state-of-the-art DL models and showed the DL model outperformed statistical classifier by computing the overall accuracy of 94% respectively. Where DL-PCA represents the accuracy achieved by \(pca\) based reduction of minced meat and DL-Isos  
Figure 8: The spectral behaviour of principal component analysis (PCA) based band reduction and proposed (_Isos-bestic_) band reduction method.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Dataset and Code Availability:** The experimental dataset and running demo will be provided upon reasonable request.