esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 3 Materials and Methods
### Feature Extraction
#### 3.3.3 National Research Council Lexicons  
NRC lexicons are a list of word association lexicons that are primarily used to analyze sentiments and emotions in texts. We utilized the NRC lexicons to incorporate emotion-specific features into our analysis. By employing features that convey the intensity of specific emotions expressed in text, we sought to enhance the differentiation between suicidal and non-suicidal texts. In this paper, two NRC lexicons were utilized: the Emotion Intensity Lexicon (EIL) [35] and the Valence, Arousal, and Dominance lexicon (VAD) [36].  
The EIL consists of 9829 words with associated intensity scores for eight fundamental emotions, including anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. These emotion categories are based on the Wheel of Emotions psychological theory [37]. For a given term and emotion, the score can range from 0 to 1. If the score is 1 or very close to 1, then the term conveys the highest expression of the said emotion. Conversely, if the score is 0 or close to it, then the term conveys the lowest expression.  
The VAD lexicon consists of 23,342 words and their associated scores related to the valence, arousal, and dominance dimensions that describe emotions. Similar to EIL, for a given term and a dimension, the score can range from 0 to 1, with 0 representing the lowest and 1 representing the highest expression of a particular dimension. Valence refers to a degree to which the emotion is negative or positive, e.g., a high valence indicates positive emotions. Arousal refers to the intensity of the emotion. Dominance represents the level of control the person feels. It ranges from being submissive to dominant. For example, fear and anger are both negative, but anger is a more dominant emotion compared to fear.  
Two features are generated for each psycholinguistic category from both lexicons: average lexicon value _avgLexVal_ and lexicon ratio _lexRatio_. For example, for a fear category  
Figure 1: Example of the TF-IDF matrix.  
from EIL, there are two features called _fear_inten_avgLexVal_ and _fear_inten_lexRation_. Every term from a post is compared to lexicon words from a particular emotion category. _AvgLexVal_ is calculated as the average score of all matching words. For example, if a post contains the words \"torture\", \"kill\", and \"suicide\", their associated scores from the fear category (_score\({}_{lotture}\)_ = 0.984, _score\({}_{kill}\)_ = 0.962, and _score\({}_{suicide}\)_ = 0.879) will be averaged, and the resulting _fear_inten_avgLexVal_ will be 0.942. _LexRatio_ is calculated as the number of matching words divided by the total number of words in the post. For example, the _valence_vad_lexRation_ score of 0.15 signifies that 15% of the words in the post matched with words from the valence category. To generate NRC features, we used the Emotion Dynamics (version 1) Python package [38]. As the outcome of this step, we extracted 22 NRC features.
## 3 Materials and Methods
### Training Dataset for IDS
#### 3.2.4 Feature Selection and Feature Extraction  
In this work, we utilized chi-square [40] for feature selection (FS) and Principal Component Analysis (PCA) for feature extraction (FE) [41] in order to reduce the features. The features selected by chi-square are shown in Table 1. PCA transforms original features into new, uncorrelated features (principal components) that capture the variance in data. The first few principal components usually capture most of the variance, allowing for effective dimensionality reduction while preserving the data's essential structure. In this work, we tested PCA for FE using 10, 15, and 25 components.
## 1 Introduction  
The fourth state of matter, plasma, is a partially ionized gas containing a high density of electrons and various reactive radicals and non-radicals. Recently, to enable the use of plasma in biomedical applications, various types of low-temperature atmospheric-pressure plasmas have been studied [1; 2; 3; 4; 5; 6]. Dielectric barrier discharge is most often used to generate plasma because of its high electron density and insulation ability [4].  
Cancer is among the leading causes of mortality worldwide and the treatment of patients with metastatic or relapsing cancer represents a main therapeutic challenge. New approaches to cancer therapy may involve the use of plasma. Biomedical applications of plasma have been demonstrated in a variety of experiments [5; 6; 7; 8; 9; 10; 11]. Recently, studies have revealed the ability of atmospheric-pressure plasma to selectively eliminate cancer cells, indicating the potential of nonthermal atmospheric-pressure plasma as an anticancer agent [1; 3; 7; 8; 9; 10]. Nonthermal atmospheric-pressure plasma appears to be safe in the human body and normal cells but shows anticancer activity [1; 3; 7; 8; 9; 10]. In contrast, plasma treatment may induce the death of normal primary prostate epithelial cells via DNA damage-mediated necrosis [11]. The selective and non-selective actions of plasma may occur through its dose-dependent differential effects on cancer and normal cells or cell type-dependent cytotoxic effects. Therefore, additional studies are needed to validate the potential therapeutic use of nonthermal atmospheric-pressure plasma, particularly against cancer. We and many groups have reported various characteristics of atmospheric-pressure plasma, such as induction of apoptosis in cancer cells via DNA damage, mitochondrial collapse, and aberrations in the cellular membrane [1; 2; 3; 5; 9; 12; 13; 14; 15; 16; 17; 18; 19]. Additionally, recent studies revealed that reactive oxygen species (ROS) and reactive nitrogen species (RNS) generated by atmospheric-pressure plasma play crucial roles in inducing cancer cell death [1; 3; 10; 15; 16; 19].  
However, atmospheric-pressure plasma has two main limitations in biomedical applications. One is the limited penetration of plasma into tissues, the other is storage difficulties. To overcome these limitations, we employed plasma-activated medium (PAM) generated with nonthermal atmospheric-pressure plasma [3; 8; 20; 21; 22; 23; 24; 25]. We evaluated the anticancer efficacy and storage feasibility of the PAM.
## 4 Experiments and Results
### Experimental Setup  
#### 4.1.1 Experimental Site  
The room where the experiments were conducted was an office of size 10.5 \(\times\) 10.5 m\({}^{2}\). There was an open space of the size about 3 \(\times\) 5 m\({}^{2}\) in the middle surrounded by tables and chairs. The Room Impulse Response (RIR) was measured according to [39] as shown in Figure 3. During the measurements, a four-channel microphone array was positioned in the center of the room 1 m above the ground. At the same height, a loudspeaker was installed. The loudspeakers were placed at positions on a circle with a 1 m radius around the microphone array. An initial position of the loudspeaker was marked as 0\({}^{\circ}\), from which the measurements were conducted in a counter-clockwise direction at 10\({}^{\circ}\) intervals. The Reverberation Time (RT60) of this room was about 1.52 s.
## 3 Results
### Quantity of Total Organic Carbon in Relation to Soil Depth  
A repeated measures ANOVA revealed that the TOC differs by soil depth (\(p\)! 0.001, df = 5) (Table 3). A general pattern of decreasing TOC with increasing depth is observed from the surface to the deepest layers, with a slight disturbance at 30-50 cm depth (for Cernisois) and 30-60 cm depth (for Hidrisois) in arale lands (Figure 4). The largest difference in TOC was identified between soil depths of 0-10 cm and all the other investigated depths (\(p\)! 0.001), with the TOC quantity being three times higher at the 0-10 cm interval than at 50-60 cm. A repeated measures ANOVA assessing the impact of the land use type and soil depth revealed that both factors and the interaction between them have statistically significant impacts on the TOC (\(p\)! 0.001, df = 2.8). A statistically significant interaction (\(p\) = 0.02, df = 5.8) was also highlighted between the sample depth, land use type, and soil classes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Land Use**} & \multirow{2}{*}{**Depth**} & \multicolumn{2}{c}{**Min *} & \multicolumn{1}{c}{**AM**} & \multicolumn{1}{c}{**SD**} & \multicolumn{1}{c}{**Mdn**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**CV**} \\  & & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) & (\(\times\)10\({}^{3}\) mg/kg) \\ \hline \multirow{8}{*}{Grasslands} & 0–10 & 19.33 & 44.15 & 15.30 & 42.50 & 70.00 & 35\% \\  & 10–20 & 0.58 & 28.69 & 14.55 & 31.00 & 60.00 & 51\% \\  & 20–30 & 0.31 & 20.56 & 13.35 & 21.17 & 43.00 & 65\% \\ \cline{1-1}  & 30–40 & 0.20 & 16.94 & 12.97 & 19.43 & 42.33 & 77\% \\ \cline{1-1}  & 40–50 & 0.27 & 14.76 & 11.45 & 16.67 & 39.95 & 78\% \\ \cline{1-1}  & 50–60 & 0.17 & 12.15 & 9.28 & 13.04 & 33.89 & 76\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of TOC content in relation to soil depth and land use.  
Figure 3: Variation in TOC with depth according to soil class (data are represented as AM \(\pm\) SE).
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 3 WAMS Operation and Dynamic Fault Detection
### Limitation of PMU Placement in Developing Countries  
In recent studies on WAMS, the PMU and PDC roles have been explored extensively vis a vis the cost-effective means of their deployment [5; 13; 14; 21; 25; 48]. The main reason WAMS has not been deployed in energy infrastructures in less developed countries is the initial cost [14]. In these economies, power systems are significantly plagued with issues captured in wide-area monitoring systems that SCADA systems cannot detect [26]. Undoubtedly, authors [14; 24; 25] that know about the infrastructure mechanisms in WAMS are beginning to identify cost-effective means by which the communication infrastructure can accommodate the deployment of PMUs and PDCs on the grid to maximize the potential of WAMS. [PERSON] et al. provided one example that illustrates the tendency for PMUs to be deployed and produce lower efficiency compared to others with the same communication jacket (CJ). The node-node interconnection achieved with PMUs is unveiled as the characteristic vehicle for transmitting the data collected on the grid to the control centers [1; 21; 25]. Though wireless, the potency remains subject to the sophistication and technicality of the PMU deployment [1].  
[PERSON] and [PERSON] et al. supported this position by analyzing different models for deploying PMUs on the grid using the same communication infrastructure [14; 42]. In practice, [PERSON]'s team identified the N-1 contingency when a single-line or single-PMU outage occurs. They operated the WAMS architecture as one designed to maximize the communication infrastructure [42]. They showed that different algorithms are to be used to determine the efficiency of both PMU-PDC complexes in a WAMS [42]. Duringthe WAMS application processes, [PERSON]'s team suggested and analyzed several algorithms to determine the node-node iteration and provide a viable N-1 contingency for the effective operation of WAMS. Regarding the non-dominated sorting genetic algorithm, binary particle swarm optimization, and binary imperialistic competition algorithm, they found these inefficient in determining the safest and most efficient single path for PMU placement [42]. After analyzing different studies and their various means of placing PMUs, [PERSON] et al. identified the need to consider the communication infrastructure required by those designs and make the entire WAMS design and deployment both efficient and cost-effective [42]. In accomplishing these tasks, [PERSON] et al. used the [PERSON] single-source shortest-path algorithm to determine the effective method of PMU-PDC deployment at a considerable communication infrastructure cost for WAMS to be advantageous in practice [1].
# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 1 Introduction  
Traditional maintenance strategies have been based on corrective or preventative means, but as industries have evolved, condition-based maintenance (CBM) strategies have taken precedence [1]. CBM is a planned maintenance strategy based on real-time condition monitoring data that is typically collected from smart sensors attached to various parts of a machine [1, 2]. Because it allows engineers to identify and solve problems before they occur, CBM is an ideal approach for maintenance of critical machinery [3].  
However, a common issue encountered by organizations seeking to employ CBM as their primary maintenance strategy is the problem of missing data [1, 2]. Missing sensor data can arise from nonexistent attributes, temporarily unavailable data, and data loss or damage [4]. The presence of missing values in condition monitoring data can negatively impact the decision-making capabilities of a system [2, 4, 5, 6].  
In this paper, we present a comprehensive framework for automatically assessing whether missing data is MCAR, MAR, or MNAR. We can also automatically assess the percentage of missing data in a given sample of data. Once the type and percentage of missing data is determined, our system adaptively imputes the missing values using a variety of statistical and ML-based techniques. To evaluate our system, we present a simulated dataset containing information that could realistically be collected by an advanced manufacturing facility. We will test the efficacy of our system at identifying the three types ofmissing data under different conditions. The performance of the imputation process will be evaluated using common performance metrics. Once we have examined the performance of the imputation system on our simulated dataset, we will then explore the impact that data imputation has on automated fault diagnosis by evaluating the performance of an ML classifier at diagnosing faults in our simulated machine following imputation.  
The rationale behind our methodology lies in several areas. First, traditional imputation methods such as mean imputation or listwise deletion can lead to biased estimates and reduced statistical power. These methods often don't account for the underlying mechanisms causing the missing data. Our adaptive approach enables the dynamic selection of the most appropriate imputation technique based on the identified type of missing data, ensuring more accurate and reliable imputations. The adaptive nature of our framework also ensures that it can be tailored to different datasets, enhancing its generalizability to various industrial settings. This capability is vital for CBM systems, where data characteristics can vary across different machines and operational environments.
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 2 Configuration of the Antenna Element  
The proposed slotted antenna element configuration is depicted in Figure 2. Comprising two components, namely, the circular polarizer and the slotted ridge waveguide, the element functions as follows. A single-ridge waveguide with a straight slot positioned along the centerline of the broad wall serves as a feed waveguide. A metallic cylinder placed near the slot disturbs the electromagnetic field distribution within the waveguide. The slotted ridge waveguide emits linearly polarized waves through the straight slot on the waveguide. The circular polarizer, configured as an open-ended quad-ridge waveguide, is mounted on the slot at a \(45^{\circ}\) angle.  
Figure 1: The relationship curve between the element aperture spacing and maximum scan angle.
## 5 Performance Evaluation
### Classical Approach
We consider that one cluster has eight member nodes which need to send one packet of data to the corresponding CH. Therefore, each member node's transmission energy per packet can be calculated from Equation (8): 43.2 \(\upmu\)J/packet (distance to the CH, \(d_{to~{}CH}\) = 20 m). In intra-cluster communications, the CH receives a total of eight packets from its member nodes, which consumes 3200 \(\upmu\)J, as calculated from Equation (6). The total energy consumption of intra-cluster communications is shown in Table 7.  
Table 7 clearly shows that as the number of clusters increases, energy consumption decreases, just as energy savings increase in the network. The next step for energy consumption is from the CH to the BS, (inter-cluster energy consumption). From Equation (7), CH energy consumption can be calculated. The total inter-cluster energy consumption is shown in Table 8.  
We note that the transmission power level should be at a maximum value for inter-cluster communications. From Table 8, we can see that very low inter-cluster transmission energy is required if the number of clusters increases. Therefore, in both intra- and inter-cluster communications, energy savings is at the highest level when the cluster size increases.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## Other Functional Foods
### Polyphenols
#### 5.3.2 Green Tea  
Tea (from _Camellia sinensis_) is one of the most widely consumed drinks worldwide. Green tea contains numerous polyphenols (collectively, 30% dry weight) including low molecular weight phenols (such catechins), which account for approximately 25% of the dry weight of green tea [173] to which antioxidant, immunomodulatory [174] and antiviral activities have been ascribed [175]. In addition, green tea has beneficial effects against degenerative diseases, oxidative stress and chronic diseases [176], and lipid profile [177]. A 2017 double-blind placebo-controlled study into the efficacy of green tea in ameliorating dry eye and meibomian gland dysfunction due to its anti-oxidative, anti-bacterial, anti-androgen and immunomodulatory anti-inflammatory properties [174] reported significant improvement in TBUT, meibomian gland health and ocular comfort scores after one month. The authors suggest efficacy might be due to Epigallocatechin Gallate which exerts inhibitory effects on inflammation, through the suppression of IL-1, IL-6, MCP-1 and TNF-\(\alpha\) and t NF-kB.  
Contrary to this finding is a recent study reporting a single dose of green tea reduced tear quality as assessed by tear framing patterns and phenol red thread test [173] postulated as being due to high polypphenolic content leading to lipid oxidation and reduction in electrolyte concentration.
## 3 Results  
The authors of this paper have conducted pixel analysis over the images, and based on the images' information, each pixel in the sample images was 20 \(\upmu\)m or 0.02 mm. The algorithm's accuracy in detecting a gap is about 360 square \(\upmu\)m or 0.36 square mm.  
Figure 6: The binary output of the sample’s sub-image.  
Figure 7: The output of the defect detection algorithm highlighting the disconnected resistance lines (yellow circle).  
Anything below that will not be detected by the algorithm as a defect, i.e., there should be a minimum defective gap of 3 \(\times\) 3 pixels. These findings were then cross-verified using precision measurement tools and confirmed that the algorithm's detected pixel size deviation is very close to the gold-standard measurements of the pixel sizes, which is 19.5 \(\upmu\)m. Moreover, caused by the limited number of defects present in the sample images, the authors of this paper also introduced artificial defects (disconnections) using an image editing tool to evaluate the algorithm with more defect types using data augmentation to ensure the accuracy of the algorithm remains high. Table 1 demonstrates a sample output of the ADR system consisting of each resistance line's quality and health condition saved as a JavaScript Object Notation (JASON) file.  
It was observed that from the 59 sample images and their 2478 sub-images, there were 2000 defects in which the ADR algorithm managed to detect 98% of the true positive defects.
## 2 Materials and Methods
### High Performance Liquid Chromatography (HPLC) Analysis  
The Waters 600 HPLC system (Waters, Milford, MA, USA) with Empower 2 Pro Software (Waters, Milford, MA, USA) had an integrated controller, a quaternary pump, a column temperature controller, an autoinjector, and a photodiode array detector. The mobile phase consisted of a mixture of A (0.085% phosphoric acid), B (acetonitrile), and C (water) [18]. The percentage of the mobile phase was as follows: 0-30 min with the ratio of 98-80% A and 2-20% B; 30-40 min with the ratio of 80-65% A and 20-35% B; 40-55 min with the ratio of 65-0% A, 35-75% B, and 0-25% C; 55-60 min with the ratio of 75-0% B, 25-100% C; and 60-65 min with the ratio of 0-98% A, 0-2% B, and 100-0% C. The flow rate was 1.0 mL/min, the column temperature was controlled at 35 \({}^{\circ}\)C, the post-run time of chromatography was 15 min, the analytical column was a Cosmosil 5C18-MS-II column (5 \(\upmu\)m, 4.6 \(\times\) 250 mm, Nacalai tesque), while a Lichrospher RP-18 endcapped column (5 \(\upmu\)m, 4.0 \(\times\) 10 mm, Merck) was used as a guard column, and absorbance was measured at 270 nm.  
Standard solutions of caffeine, (-)-epigallocatechin, (-)-epicatechin-3-gallate, (-)-epigallocatechin-3-gallate and (-)-gallocatedin-3-gallate were prepared at 1.0 mg/mL in 70% methanol, and a series of diluted standards were prepared for HPLC calibration curves (caffeine: 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 mg/mL; (-)-epigallocatechin: 2.5, 5.0, 10.0, 15.0, 20.0, and 25.0 \(\upmu\)g/mL; (-)-epicatechin-3-gallate: 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 \(\upmu\)g/mL; (-)-epigallocatechin-3-gallate: 0.5, 1.0, 2.0, 4.0, 5.0, 6.0 \(\upmu\)g/mL; (-)-gallocatedin-3-gallate: 0.4, 0.5, 1.0, 1.5, 2.0, and 2.5 \(\upmu\)g/mL). Calibration graphs (peak area, y, versus concentration, x) were constructed to obtain the regression equations and their correlation coefficients (r) as follows: caffeine, y = 55772937x + 64886 (r = 0.9999); (-)-epigallocatechin, y = 3474x - 3320 (r = 0.9961); (-)-epicatechin-3-gallate, y = 25471x + 118 (r = 0.9987); (-)-epigallocatechin-3-gallate, y = 19181x - 6239 (r = 0.9995); and (-)-gallocatechin-3-gallate, y = 15542x - 2883 (r = 0.9963).
## 7 Results and Analysis
### Sensitivity Analysis
#### 7.2.1 The Proportion of Buses  
A total of 178 sets of examples were classified according to the proportion of buses and cars on the arterial road, and the average values of per capita delay, per bus delay, and per car delay of each type of examples were calculated, respectively. Based on the average value of the first type of examples, the reduced value of per bus delay, the increased value of per car delay, and the difference between the two were calculated. The results are shown in Figure 17.  
Within the range of 10% to 18%, as the proportion of buses increases, the per capita delay and bus average delay decreased significantly, with a decrease of 18.5% and 24.8%,  
Figure 16: Comparison of the average vehicle delay after the optimization of the two models.  
Figure 17: Optimization results with different bus ratios.  
respectively. The average delay of cars changed little and had no evident pattern. The difference between the decrease in the average delay of buses and the increase in the average delay of cars was greater than 0 and showed an increasing trend, indicating that the decrease in the average delay of buses was greater than the increase in the average delay of small cars, and, as the proportion of buses increases, the model in this paper was more effective in reducing the average delay of buses.