esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical Analysis of the Monitoring Data
#### 3.3.1 Pearson's Correlation Analysis
Among 30 correlations calculated for parameters measured in the piezometers located in the area of the facilities adjacent to the landfill (Table 6), only one pair of variables has revealed a very strong relation (\(r\) = 0.878, measured between BOD and COD in piezometer P-14A). In piezometer P-10A, moderate correlation was observed between BOD and COD (\(r\) = 0.462), and COD and TOC (\(r\) = 0.468). Correlations between other selected indicators were found to be weak or very weak for this piezometer. With the exception of the correlation between BOD and EC (\(r\) = 0.494), a similar tendency was observed for parameters analyzed in piezometer P-17. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & **BOD** & **COD** & **TOC** & **pH** & **EC** \\ \hline \multirow{4}{*}{**BOD**} & \multirow{4}{*}{1} & **PIEZOMETER 10A** & & \\  & & 0.462 \({}^{*}\) & 0.311 \({}^{**}\) & \(-\)0.089 & 0.517 \({}^{*}\) \\
**COD** & & & 1 & 0.468 \({}^{*}\) & 0.298 \({}^{**}\) & 0.321 \({}^{**}\) \\
**TOC** & & & 1 & 0.140 & 0.369 \({}^{**}\) \\
**pH** & & & & 1 & \(-\)0.380 \\
**EC** & & & & 1 & **\\ \hline \hline \end{tabular} \({}^{*}\) correlation significant at \(p\) < 0.01 (two-tailed), \({}^{**}\) correlation significant at \(p\) < 0.05 (two-tailed).  
\end{table} 
Table 6: Pearson’s correlation analysis between selected pollution indicators based on monitoring data from piezometers located in the area of the adjacent facilities. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & **BOD** & **COD** & **TOC** & **pH** & **EC** \\ \hline \multirow{4}{*}{**BOD**} & \multirow{4}{*}{1} & **PIEZOMETER 11A** & & \\  & & 0.599 \({}^{*}\) & \(-\)0.517 \({}^{*}\) & 0.102 & \(-\)0.410 \({}^{*}\) \\
**COD** & & & 1 & \(-\)0.080 & 0.303 \({}^{**}\) & \(-\)0.005 \\
**TOC** & & & 1 & 0.022 & 0.424 \({}^{*}\) \\ pH & & & & 1 & \(-\)0.201 \\
**EC** & & & & 1 & \\ \hline \hline \end{tabular} \({}^{*}\) correlation significant at \(p\) < 0.01 (two-tailed), \({}^{**}\) correlation significant at \(p\) < 0.05 (two-tailed).  
\end{table} 
Table 5: Pearson’s correlation analysis between selected pollution indicators based on monitoring data from piezometers located within the composite area.
# Groundwater Mixing Process Identification in Deep Mines Based on Hydrogeochemical Property Analysis
## 3 Methodologies
### Simulation of the Evolution of Mixing Groundwater  
If the groundwater derived from different aquifers mixes together, a chemical reaction should occuer between groundwater with different ion concentrations and water-rock interaction between mixing groundwater and aquifer rocks. Therefore, geochemical program of PHREEQC Interactive was applied to revise the mixing ratio of different groundwater sources based on the results of piper diagram analysis, and to simulate the hydrogeochemical evolution of mixing groundwater. The calculation procedure is described as follows.  
First, establish potential reaction phases in the PHREEQC model. Through previous geological investigation and groundwater evolution in the study area, the potential reaction phases existing in Carboniferous Taiyuan limestone karst-fissure aquifer systems can be determined, including anhydrite, calcite, dolomite, gypsum, halite, sulfur, and CO\({}_{2}\) (g). The possible reactions, which result in the  
Figure 4: Graphical method by piper diagram, where A and B are parent solutions and C is the mixture.  
change of the conventional groundwater chemical ions concentration in groundwater, are listed in Table 1.  
The hydrogeochemical model used in PHREEQC follows the principle of mass balance. In the model, Cl\({}^{-}\) was considered as a conservative ion, by adjusting the initial mixing ratio (getting from piper diagram calculation) to keep the simulation value of Cl\({}^{-}\) concentration equaled to the practical value. This will allow the concentration of other ions to change slightly. According hydrogeological conditions of this study area, partial pressure of carbon dioxide (logP\({}_{\text{CO}_{2}}\)) was set to \(-1.25\) atm. The average value of the geothermal gradient in the study area is 2.4 Celsius per 100 m. According to the average depth of the sampling points, the groundwater temperature of the Carboniferous Taiyuan limestone karst-fissure aquifer system is 36.29 \({}^{\circ}\)C, whereas the groundwater temperature of the Ordovician limestone karst-fissure aquifer system is 39.03 \({}^{\circ}\)C.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 4 Additional Research
### Energy and Capacity Adequacy  
Attainment of a zero-carbon grid requires careful planning. Power plants and transmission facilities must meet all the demand at all locations and at all times. In general, energy adequacy planners were traditionally of concern mainly for grids dominated by hydroelectricity but will become ever more important for VRE-dominated grids. The planners commonly explore the metrics for the loss of load probability (LOLP) or the loss of load expectation (LOLE) [80]. Moreover, even if power-plant numbers are adequate, transmission capacity may be lacking and if so, traditional planners built more peak-load plants. However, other options become more relevant as VRE proportions increase. These include contributions from behind-the-meter storage in forms of electrical and other energy within the same and neighboring regions. Assessment of capacity adequacy relies on the capture of rare risks, for example, when a high load demand triggers simultaneous generation/transmission failure [81].
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 3 Methodology
### Self-Built Dataset
#### 3.1.2 Data Preprocessing  
* Abnormal data recognition and correction Data preprocessing is first performed to resolve data outliers before clustering and prediction is performed [28]. Missing data: Lagrange interpolation is used to handle missing values. Data duplication: Remove duplicate datasets by similarity. Data mutation: 3 criteria.
* Data normalization Normalized values for the clustering process can reduce the adverse effect on the clustering effect in the data domain due to the weight of the distance occupied between different attributes of the initial values. Normalization can eliminate the influence of the size of the load data volume on the distance in the clustering analysis, and thus, the information on the load pattern is highlighted [29].  
Data normalization: Uniform metrics on data. \(Z\) Score normalization is shown in Equation (10). After normalization, the load data are normalized to the interval \([-1,1]\).  
\[Z_{X}=\frac{x_{i}-\overline{X}}{\sigma} \tag{10}\]  
Data preprocessing: The equation for normalizing continuous data is given in Equation (11). Using One Hot encoding for discrete sequences, each state has unique register bits; only one bit is 1, and the rest are 0.  
\[MinMax_{x}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}} \tag{11}\]
## 2 Materials and Methods  
### Datasets  
In our study, multiple datasets were used, and in order to address the differences in spatial resolution across these datasets, a unified resampling method was employed, which adjusted all the data to the same spatial resolution, ensuring the consistency of the analysis results. Specifically, this study employed a bilinear interpolation method, which is capable of preserving the spatial characteristics of the original data to the greatest extent, maintaining the spatial continuity and features of the original data to some degree. This method avoids the jagged effects that could arise from simple nearest-neighbor interpolation, while also minimizing the potential errors that might occur due to resolution differences. In the analysis of NDVI and meteorological factors, this study standardized the data to the resolution of the ERA5 dataset, and necessary projection transformations were applied to all the datasets, ensuring that they were all in the same coordinate system, WGS84.
# Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
## 3 Results
### Comparison of CNN Models and ResNet50 Model for Sensitivity and Specificity  
The models developed on the original dataset with no augmentation achieved the best in specificity, which shows that it is less likely for patients who are healthy wrongly. The augmented datasets, on the other hand, performed a bit more in sensitivity, showing an improvement in the detection of cases with pneumonia. The reduced dataset shows a performance that is lower in sensitivity and specificity, which shows that a smaller dataset may not generalize greatly, especially with augmentation (Table 6).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline CNN original dataset & 0.79 & 0.76 & 0.73 & 0.74 & 0.85 \\ with no augmentation & 0.79 & 0.76 & 0.73 & 0.74 & 0.82 \\ \hline CNN reduced dataset & & & & & \\ with no augmentation & 0.75 & 0.72 & 0.67 & 0.68 & 0.78 \\ \hline CNN reduced dataset & & & & & \\ with augmentation & 0.77 & 0.74 & 0.71 & 0.72 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of CNN models.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline ResNet-5- Original data & 0.74 & 0.73 & 0.63 & 0.63 & 0.75 \\ with augmentation & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of ResNet-50 model.  
The results are trustworthy because they were validated and tested on the test dataset. The results are good and self-contained. However, it may have been influenced by over-fitting and the result can be extended, as using more different parameters to prevent over-fitting could lead to an improved outcome.
## 1 Introduction
### Contributions  
This paper presents the following.  
1. State-of-the-art in passive RF wake-up radio technology, architecture, extensive survey, and classification of the research efforts and recent breakthroughs made in the field.
2. The structure of the passive RF harvesting circuit, offering a critical review of previous works in the RF energy transfer and harvesting techniques.
3. Description of the communication protocols as well as an extensive survey of the existing MAC protocols used for the passive wake-up radios.
4. Outlining the potential Internet of Things applications, research opportunities, and challenges in the passive wake-up radio technology.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 5 In-Laboratory Tests and Calibrations  
The capability of NGeneApp of acting as an interference detector was first assessed with in-laboratory tests, aimed at determining the detection sensitivity of the App to two basic kinds of interference: wideband noise and CW. This test campaign also served to calibrate the spectral detection masks and GoF reference correlations. A picture of the experiment setup is shown in Figure 3.
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 3 Proposed Methodology
### Data Pre-Processing  
Data pre-processing is a vital phase in every machine learning project, requiring the processing and cleansing of raw data before they can be incorporated into a model. In  
Figure 4: Sample images from Kaggle dataset (0-500x0-400 pixels).  
Figure 5: Sample images from Health Informatics dataset (0-60x0-50 pixels).  
this study, the initial stage of data pre-processing involved the visual representation of the Kaggle dataset, which contains images of individuals with open or closed eyes, as well as yawning or non-yawning faces. The focus was focused on the eye region, as the dataset includes photos properly labeled for open and closed eyes, avoiding the necessity for facial detection in those circumstances. However, for yawning detection, the separation of facial identification and the mouth region was necessary, as the mouth is the key characteristic for identifying yawns. This was accomplished using the Haar cascade classifier, a machine learning-based system that classifies objects in photos by assessing intensity variations between neighboring rectangles.  
The Haar cascade classifier was applied to recognize yawning faces, and the separated mouth regions were scaled to 145 \(\times\) 145 pixels. Two different data frames were produced for the training and testing datasets: one for eye images and another for mouth images, categorized as yawning or not yawning. The training dataset comprised 1520 photos (1234 eye images and 286 mouth images), whereas the testing dataset included 262 images (218 eye images and 44 mouth images). These data frames were then compared to measure their correctness.  
In order to evaluate the working of these models with unobserved data, we used an additional dataset that was obtained from the field of health informatics [5]. Since this dataset only contains pictures of the eye part and the mouth part, there was no need for us to perform any pre-processing with this dataset. Due to incongruities within the names of the dataset compared to those utilized for data preparation, an agent test from this dataset was extracted and subsequently re-labeled to adjust to this study's prerequisites. This step was basic as the names in this dataset varied from the first data preparation names. During the information pre-processing arrangement, yawning activity was utilized to identify faces and extricate mouth locales from the pictures utilizing the Haar cascade classifier. Furthermore, the images were uniformly resized to a resolution of 145 by 145 pixels, and corresponding labels were assigned to each image. This phase was critical in preparing the data for the machine learning model and guaranteeing that it would be accurate and efficient in recognising yawning. Specifically, this stage ensured that the models would be able to identify yawning.
## 3 Results  
The analysis focused on four key evaluation criteria: solution availability, convergence time, accuracy, and the precision of ZTD estimation. The following subsections present the results for each of these aspects.
## 3 Methodology
### Pythagorean Fuzzy Rough Approximation Construction
: _Let \(\vartheta\) and \(\theta\) be two Pythagorean fuzzy closeness measures, for \(\forall\alpha,\beta\in[0,1]\), \(0\leq\alpha+\beta\leq 1\), then_  
_(1) \(\vartheta^{\beta}_{\alpha}=\vartheta_{\alpha}\cap\vartheta^{\beta}\);_  
_(2) if \(0\leq\alpha_{1}\leq\alpha_{2}\leq 1\) and \(0\leq\beta_{2}\leq\beta_{1}\leq 1\), then \(\vartheta^{\beta_{2}}_{\alpha_{2}}\subseteq\vartheta^{\beta_{1}}_{\alpha_{1}}\);_  
_(3) if \(\vartheta\subseteq\theta\), then \(\vartheta^{\beta}_{\alpha}\subseteq\vartheta^{\beta}_{\alpha}\);_  
_(4) \(\left(\vartheta\cap\theta\right)_{\alpha}=\vartheta_{\alpha}\cap\theta_{ \alpha}\), \(\left(\vartheta\cap\theta\right)^{\beta}=\vartheta^{\beta}\cap\theta^{\beta}\), \(\left(\vartheta\cap\theta\right)^{\beta}_{\alpha}=\vartheta^{\beta}_{\alpha} \cap\theta^{\beta}_{\alpha}\);_  
_(5) \(\left(\vartheta\cup\theta\right)_{\alpha}=\vartheta_{\alpha}\cup\theta_{ \alpha}\), \(\left(\vartheta\cup\theta\right)^{\beta}=\vartheta^{\beta}\cup\theta^{\beta}\), \(\left(\vartheta\cup\theta\right)^{\beta}_{\alpha}\triangleq\vartheta^{\beta}_ {\alpha}\cup\theta^{\beta}_{\alpha}\)._  
The proof of Property 3 is shown in Appendix A. **Definition 8**. : _Let \(PFDS=(U,AT=C\cup D,V,f)\) be a Pythagorean fuzzy decision system and \((L,\geq)\) be a complete bounded lattice, where \(L=\left\{(x,y)\in[0,1]\times[0,1]|0\leq x^{2}+y^{2}\leq 1\right\}\). For any \(\alpha,\beta\in[0,1]\), \(\alpha\) and \(\beta\) satisfy \(0\leq\alpha+\beta\leq 1\), and the \((\alpha,\beta)\)-level cut set \(X^{\beta}_{\alpha}\) under the Pythagorean fuzzy concept is defined as_  
\[X^{\beta}_{\alpha}=\left\{x\in X|f(x,c)\geq(\alpha,\beta),c\in D\right\} \tag{8}\]  
_where \((\alpha,\beta)\) is the constant PFN consisting of the thresholds \(\alpha\) and \(\beta\)._  
To illustrate the calculation process of the \((\alpha,\beta)\)-level cut set \(X^{\beta}_{\alpha}\) under the Pythagorean fuzzy concept, we take an example. Let the thresholds \((\alpha,\beta)=(0.7,0.3)\), and all the objectsunder the decision attribute \(c_{d}\) are \(x_{1}\), \(x_{2}\), \(x_{3}\) and \(x_{4}\). The objects that satisfy the condition \(f(x,c_{6})\geq(\alpha,\beta)\) are \(x_{1}\), \(x_{3}\) and \(x_{4}\). Therefore, \(X_{\alpha}^{\beta}=\{x_{1},x_{3},x_{4}\}\). **Definition 9**. : _Let \((U,\vartheta)\) be a non-empty Pythagorean fuzzy approximate universe and \(\vartheta_{\alpha}^{\beta}(x)\) be the similar class under the Pythagorean fuzzy \((\alpha,\beta)\)-level cut set, where \(\vartheta\) is the Pythagorean fuzzy closeness measure on the universe \(U\). For \(\forall\overline{\alpha},\overline{\beta}\in[0,1]\), \(\overline{\alpha}\) and \(\overline{\beta}\) satisfy \(0\leq\overline{\beta}<\overline{\alpha}\leq 1\).
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results  
Our comprehensive evaluation of GAF-Net covers key aspects of person re-Identification using two distinct datasets, namely iLIDS-VID and MARS. This study focuses on the integration of skeleton gait data into appearance features, exploring both feature-level and score-level fusion techniques. We evaluate our approach on these datasets to demonstrate its effectiveness in different scenarios, comparing it with existing state-of-the-art methods. Additionally, we investigate the influence of fusion factors \(\lambda\) and \(\alpha\) on various deep learning models, providing insights into optimal settings for the enhancement of Re-ID accuracy.  
We begin by presenting our results obtained on the iLIDS-VID dataset, followed by an analysis of performance on the more complex MARS dataset. This dual-dataset approach allows us to assess GAF-Net's robustness and adaptability across varying conditions.
## 3 Results and Discussion
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compounds** & **Ret Time** & **Type** & \multicolumn{2}{c}{**FB-lc**} & \multicolumn{2}{c}{**FB-sp**} & \multicolumn{2}{c}{**FB-lsp**} \\ \hline  & & & **Area** & **Area \%** & **Area** & **Area \%** & **Area** & **Area \%** \\ \hline acetic acid, ethyl ester & 3.179 & BV & NF & NF & 3,714,514 & 2.94 & 42,368,725 & 7.472 \\ butylraldehyde, 2-methyl- & 3.631 & BB & 3,166,922 & 2.372 & 3,165,840 & 2.50 & 3,259,781 & 0.574 \\
2-amino-1,3-propanediol & 4.085 & BV & NF & NF & 3,512,197 & 2.78 & 24,311,405 & 4.287 \\ n-propyl acetate & 4.282 & PV & 2,752,332 & 2.062 & 3,065,189 & 2.42 & 112,465,982 & 19.83 \\
1-butanol, 3-methyl-, formate & 4.676 & BV & 2,205,104 & 1.652 & 2,365,918 & 1.87 & 8,500,143 & 1.499 \\ propanoic acid & 4.975 & PV & 1,216,092 & 0.911 & 1,305,077 & 1.03 & 9,172,563 & 1.617 \\ propanoic acid, propyl ester & 5.857 & PV & 724,850 & 0.543 & 832,916 & 0.66 & 5,365,518 & 0.946 \\
2-oxopentaned acid & 5.996 & BV & 1,244,074 & 0.932 & 2,245,910 & 1.78 & NF & NF \\ lactic acid & 7.841 & BV & 566,468 & 0.424 & 9,635,316 & 7.62 & 2,134,730 & 0.376 \\ alpha-pinene & 8.138 & VV & 480,350 & 0.359 & 526,813 & 0.42 & 12,589,365 & 2.220 \\ camphene & 8.414 & VB & 449,670 & 0.336 & 563,489 & 0.45 & 6,925,660 & 1.221 \\ \(\alpha\)-phellandrene & 9.404 & PV & NF & NF & 448,923 & 0.00 & 13,479,075 & 2.377 \\ alpha-terpinene & 9.633 & BV & 841,209 & 0.630 & 1,935,872 & 0.36 & 9,501,040 & 1.676 \\ p-cimene & 9.790 & VB & 28,89,327 & 2.164 & 2,889,327 & 0.00 & 18,702,987 & 3.298 \\ D-limonene & 9.879 & BV & NF & NF & 125,683 & 1.53 & 10,850,613 & 1.914 \\ eucalypt & 9.950 & VB & 82,174,283 & 61.569 & 45,227,359 & 2.29 & 8,296,759 & 1.463 \\ gamma-terpinene & 10.522 & PV & 887,853 & 0.665 & 889,853 & 0.10 & 2,486,838 & 0.438 \\ terpinolene & 11.298 & VV & 2,302,629 & 1.725 & 2,302,629 & 35.77 & 159,646,370 & 28.158 \\ fenchone & 11.300 & VV & NF & NF & 0.70 & 29,477,277 & 5.199 \\ linalol & 11.554 & BB & 604,976 & 0.453 & 20,304,384 & 1.82 & 36,435,120 & 6.426 \\ camphor & 12.844 & BB & 12,301,015 & 9.216 & 12,303,015 & 9.73 & 8,640,472 & 1.523 \\ endo-borneol & 13.341 & PV & 3,391,528 & 2.541 & 4,485,728 & 16.06 & 28,204,267 & 4.975 \\ terpinen-4-ol & 13.556 & VB & 15,049,231 & 11.275 & 3,263,980 & 9.73 & 14,150,705 & 2.496 \\ \(\alpha\)-terpine & 13.885 & VB & 218,307 & 0.163 & 1,318,397 & 3.55 & NF & NF \\ \hline \hline \end{tabular} FB-sp: beverage fermented by _Bifidae Determin were 0632_. FB-lcsp: beverage fermented by a mix of both strains. FB-lc: beverage fermented by _Latcholiculus acidophilus_ ATCC 43121. NF: not found. BV: baseline and valley points (start at a baseline point, end at a dropline from a valley point). VB: Valleyproline and baseline points. PV: penetration point and valley point. BB: start at a baseline point, end at a dropline baseline point. V: start and dropline at a valley point.  
\end{table} 
Table 3: Aromatic compounds identified from fermented beverage extracts of by GC-MS head space.
# Effects of Vertical Glottal Duct Length on Intraglottal Pressures in the Convergent Glottis
## 4 Discussion
This study is for static glottal geometries rather than dynamic modeling, and thus we ignored mass changes, stiffness changes, three-dimensional morphology changes, and muscle contraction changes that accompany changes of the glottal duct length. From a contour point of view, this study did not take into consideration covariation of vertical glottal duct length with changes in the anterior-posterior length of the vocal fold (held constant in this study at 1.2 cm). The increase of the vertical glottal duct length also may have resulted in medial bulging of the vocal fold or altered the inlet and outlet radii of the vocal fold geometry, which were held constant in this study. This study also assumed that the pressure profile along the anterior-posterior length of the vocal fold is constant, which is not consistent with the finding that the intraglottal pressure varies somewhat along the anterior-posterior direction due to three-dimensional flow effects [43; 47]. It is also noted that contraction of the vocalis and cricothyroid muscles for changing the glottal duct length may vary the ant-post stiffness of the vocal fold, which is an important factor in determining the PTP and cannot be studied with static models. The convergent glottal shape occurs during glottal opening, when the vocal folds are separating. The results of this study may give some indication of the intraglottal pressure magnitudes and effects during the glottal opening phase that drive the vocal folds to separate, assuming the quasi-steady assumption is maintained. The sequence of Figure 2(a-iv,c-ii,d-vi) is the sequence for a vertical glottal duct length of 0.308 cm, a diameter increase of 0.01 cm to 0.04 cm to 0.16 cm, and a convergent glottal angle of \(-5^{\circ}\) to \(-10^{\circ}\) to \(-20^{\circ}\). That is, as the glottis opens, here it is assumed that as the vocal folds separate, the convergent glottal angle increases. The mentioned figures show that the pressures in the glottis are the greatest for the first configuration, less for the second, and least for the third. This decreasing pressure to move the vocal folds outward would be countered by the increasing recoil forces to bring the vocal folds back to the equilibrium position, eventually creating a maximum vocal fold excursion before the closing phase of the glottis begins. These results are consistent with pressure exchanges during a cycle of phonation shown in Figures 11-15 in [PERSON] (2017) [48] for a three-mass model whose intraglottal driving pressures were obtained empirically from physical model M5.