esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pathogenic Mechanisms of _P. gingivalis_
### Virulence Factors and Tissue Invasion  
_P. gingivalis_ evades host defense mechanisms primarily through the disruption of epithelial barrier integrity, an early event that triggers inflammatory and immune responses ultimately leading to tissue destruction [28].  
This evasion is mediated by a range of virulence factors that dysregulate innate immune and inflammatory pathways [29].  
Upon adherence to host cells, _P. gingivalis_ is internalized via lipid rafts and incorporated into early phagosomes, where it engages the autophagic machinery to establish a replicative niche while simultaneously inhibiting apoptosis. Autophagy provides essential host-derived proteins that support intracellular survival, whereas its inhibition--via agents such as 3-methyladenine or wortmannin--leads to bacterial trafficking to the phagolysosome and subsequent degradation [30].  
The persistence of _P. gingivalis_ within the periodontal environment is further enhanced by its ability to withstand oxidative stress and by the activity of dipeptidyl peptidase IV (DPPIV), which facilitates nutrient acquisition through dipeptides [31].  
Additionally, the bacterium exploits gingival reticular fluid, an inflammatory exudic rich in peptides and iron (primarily in the form of hemin), as a critical nutrient source [13]. Iron plays a central regulatory role in both bacterial growth and the expression of virulence factors [32].  
Hemin is essential to fulfilling the organism's iron requirements, and _P. gingivalis_ can store endogenous hemin within its membrane, conferring a nutritional advantage under limiting conditions and promoting long-term survival within the periodontal pocket [33].
## 3 Methodology
### Proposed Scheme
#### 3.2.2 Deep Reinforcement Learning-Based Dynamic Threshold
Here \(\epsilon\) was used to prevent zero division, where \(\epsilon\) was set as \(1\). As a result, after agent training, the agent tended to choose a tight, but robust, threshold at each state under varying weather conditions. By setting the \(\beta\) and \(\gamma\) as below, we could provide more weights on what users paid attention to more. \[\beta+\gamma=1 \tag{13}\]  
The reward function can be illustrated as in Figure 9.  
For the training process of the DRL agent,  
1. We randomly selected a day as an episode among the same training dataset that was used for the DNN estimator. 1. The same weather information (irradiance \(D_{t}\), ambient temperature \(T_{t}\), wind speed \(W_{t}\), and humidity \(H_{t}\)) was used as a state to the DRL agent. 2. The DRL agent generated an action that was used as a threshold value. 3. The threshold value was compared with the difference between a measured value and an estimated value. 4. The DRL agent received a reward value, based on the designed reward function. 2. The DRL agent updated its policy to get higher reward values. The (a)-(d) steps were repeated until an episode was done. The 1-2 steps were repeated until the DRL agent's policy converged to an optimal policy. Even though the agent maximized the cumulative rewards, it was not guaranteed that the performance of the agent was practically getting better or not. Therefore, we used metrics, such as recall, precision, and \(F_{1}\) score, to validate the performance of the proposed dynamic threshold scheme. The formulae of the metrics were as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP + FN}} \tag{14}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP + FP}} \tag{15}\]  
Figure 9: Reward function of the proposed DRL-DT with different \(\beta\) and \(\gamma\). \[F_{1}\text{score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision+Recall}} \tag{16}\]  
True positive, false positive, and false positive are denoted as TP, FP, and FN, respectively.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 4 Comparative Analysis
It is because \(S_{12}\), \(S_{18}\), and \(S_{11}\) are located in the same orbital plane, and the relative position relationship between the satellites remains constant if we ignore the influence of perturbation and other factors. Therefore, a link always exists in \(S_{12}\) and \(S_{11}\) with a stable link quality. The same applies with \(S_{18}\) and \(S_{11}\). 2. Since ref. [20] assumed that a single satellite can construct two inter-satellite links and two intra-satellite links at the same time, thus the results of \(\delta_{S_{\rm Si_{1}}S_{\rm 26}}(t)\), \(\delta_{S_{\rm Si_{1}}S_{\rm 27}}(t)\), \(\delta_{S_{\rm Si_{1}}S_{\rm 33}}(t)\  
are not considered in this paper, the expression of the above elements is not 0, but a time-varying function, which means there are time-dependent ISILs existing. It should be noted that \(\delta_{S_{11}S_{36}}(t)=\left\{ \begin{array}{l}\frac{1}{3}\bigg{[}1+\frac{15460 -d_{S_{11}S_{36}}(t)}{10702}+\frac{0.03461-\hat{\theta}_{S_{11}S_{36}}(t)}{0 0922}\bigg{]},t\in(0\sim 221)\\ 0,t\in(221\sim 300)\end{array} \right.\) is a piecewise function portending that the link will be interrupted after 221 s.
3. Both results of \(\delta_{S_{11}S_{28}}(t)\) and \(\delta_{S_{11}S_{41}}(t)\) are always 1, while the corresponding expressions obtained by the method in this paper are two time-varying functions, e.g., \(\delta_{S_{11}S_{28}}(t)=\frac{1}{3}\bigg{[}1+\frac{10,650-d_{S_{11}S_{28}}(t )}{8320}+\frac{0.025-\delta_{S_{11}S_{28}}(t)}{0.05}\bigg{]}\), which indicates that the quality of the two links actually changes with time. Two vector elements, \(\delta_{S_{11}S_{36}}(t)\) and \(\delta_{S_{11}S_{41}}(t)\), are selected for a more intuitive graphical comparison, as shown in Figure 6. Figure 7 shows the value of \(\delta_{S_{11}S_{41}}(t)\) in a finer granularity, which reflects the trend of change. It is obviously that the link quality can be predicted, which means the change trend of link quality in the network topology of space information system can be grasped in real time. For example, if the current time is 50 s, we can predict that the link quality between \(S_{11}\) and \(S_{41}\) will be reduced to the lowest point at 195 s.  
Figure 6: The graphical comparison example of the two results: (**a**) graphical comparison of \(\delta_{S_{11}S_{36}}(t)\); (**b**) graphical comparison of \(\delta_{S_{11}S_{41}}(t)\). In this figure, the result obtained in the method in this paper is indicated by a solid blue line, while the result obtained from ref. [20] is indicated by a red dotted line. Figure 7: The value of \(\delta_{S_{11}S_{41}}(t)\).
## 3 Results
### Simulation Study
The basic standard measures [22] are:  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text {TN}}, \tag{11}\]  
\[\text{sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{12}\]  
\[\text{specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}},\text{and} \tag{13}\]  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{14}\]Using the interpretation given, it can be seen that: (i) accuracy is the ratio of the correctly predicted number of tensors to the total number of tensors (i.e., the ability to select all of the tensors in the CC and reject all the tensors that are not in the CC); (ii) sensitivity (sometimes called recall or true positive rate) is the ratio of the number of correctly predicted tensors in the CC to the number of all tensors in the actual CC (i.e., the ability to select all of the tensors in the CC); (iii) specificity (sometimes called the true negative rate) is the ratio of the number of correctly predicted tensors as being not in the CC to the number of all tensors that are not in the actual CC (i.e., the ability to reject all of the tensors that are not in the CC); and (iv) precision is the ratio of the number of correctly predicted tensors in CC to the number of total predicted tensors in the CC. These basic measures can be combined in pairs to give performance measures [22] called the F-measure (F1 score), which takes into account both the FP and FN values (it is the harmonic mean of precision and sensitivity), and the Gmean (Geometric mean), which combines both the true positive rate and true negative rate. They are defined as follows [22]:  
\[\text{F-measure}=\frac{2(\text{sensitivity}*\text{precision})}{\text{sensitivity}+ \text{precision}},\text{ and} \tag{15}\]  
\[\text{Gmean}=\sqrt{(\text{sensitivity}*\text{specificity})}. \tag{16}\]  
Those two measures are often used to evaluate performance when the dataset used is imbalanced (i.e., the number of objects assigned to each cluster is different). The step-by-step calculations for the following two simulation studies are summarised in a block diagram (Figure A1 in Appendix B).
## 1 Introduction
This, in turn, has some disadvantageous traits, including chemical toxins and some precarious compounds in their products [18]. The size of NPs could be influenced by pH, temperature, and metal concentrations [19]. Otherwise, the biological approaches have the merit of simplicity in NP synthesis, less time of reaction, low toxicity and cost, high yield, and biocompatibility, which are critical and imperative. The biosynthesis of Ag-NPs concerning plant extracts was conducted, in which Ag-NPs were synthesized using different plant extracts e.g., Lucerne, Pine, Persimmon, Magnolia, Platanus, apple, pineapple, and Ginkgo [20; 21; 22; 23]. Likewise, the _Phyllanthus amarus_ leaf extract was found to be efficient in the biosynthesis of Ag-NP silver, as it has antimicrobial and catalytic activities [24]. Moreover, the aqueous extract of _Beta vulgaris_ L. showed efficiency toward Ag-NP biosynthesis, owing to its content of vitamins, manganese, folate, and magnesium in addition to its content of pigments, with all of these factors reducing the metal ions to NPs. The pH value, extract volume, silver nitrate concentrations, and time of incubation play a vital role in Ag-NP biosynthesis [25]. The antibacterial activity of silver nanoparticles has shown potency against reference bacterial strains [17]. Recently, the diverse biological properties of _Beta vulgaris_ L. and betalains have been reported [26]. Metallic/bimetallic nanoparticles have been shown to have antibacterial [27], anticancer [28], anti-inflammatory [29], and antioxidant properties [30]. The small size and large surface area of nanoparticles allow them to interact with cells and tissues [31]. The antibacterial activity of nanoparticles is attributed to their ability to disrupt the cell membranes of bacteria, leading to cell death. The nanoparticles can also generate reactive oxygen species, which can damage DNA and other cellular components [32]. Moreover, the anticancer activity of nanoparticles is related to their aptitude to kill or inhibit the growth of cancer cells or induce apoptosis or programmed cell death [33]. The anti-inflammatory activity is attributed to the aptitude of nanoparticles to inhibit the production of pro-inflammatory cytokines and other molecules and reduce inflammation by scavenging ROS and other reactive molecules [34]. Silver nanoparticles are one of the most widely studied nanoparticles for antibacterial applications. They are effective against a wide range of bacteria, including drug-resistant strains. Herein, our study investigates the following: (i) How to biosynthesisize Ag-mNPs, Ag-bmTiO2, and Ag-bmSeO2 using an aqueous extract of _Beta vulgaris_ L. (ii) The determination of chemical features using phytochemical analyses, UV-visible spectra, FTIR, TEM, and zeta potential analyses. (iii) Assessments of the biological activities of the synthesized metallic/bimetallic nanoparticles, for instance, the antioxidant activity using DPPH and phosphomolybdate assays, and cytotoxic activity against various normal and tumor cell lines using MTT assay, as well as antimicrobial action against human bacterial pathogens.
# Current Context and Research Trends in Linear DC-DC Converters
## 8 Future Prospects for LDO Regulators in Power Supplies
### Concept of SCALDO
Using the SCALDO technique, LDOs can operate with a higher level of difference between input-output voltages without compromising efficiency. The integrated circuit version of SCALDO has been designed as in Figure 12b and is currently in the testing phase.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 6 Results and Discussion  
In this section, we analyze and discuss the BER performance and the detection complexity of the proposed system considering two configurations with different SE and two different scenarios: the uncorrelated and the correlated channels. The results of the proposed system are compared with the conventional MU-MIMO-SMux system [22] and with the recently proposed relay-assisted AF-MU-DQSM system [23] under the same conditions and parameters.
## 4 Results
### Evaluation of Ensemble Flood Forecasting Performance for Four Schemes
After applying GPP correction, the histograms become flatter, indicating a more uniform distribution. Specifically, the uniformity indices for the quantile histograms of schemes (A) through (D) decreased from 1.17 to 0.42, 1.11 to 0.26, 1.13 to 0.35, and 1.09 to 0.31, respectively. This demonstrates that the GPP method effectively mitigates the issue of under-dispersion in ensemble forecasts. Additionally, compared to scheme (A), the other schemes have slightly lower uniformity indices, which indicates that considering the uncertainty in both forecasting models and hydrological models can further enhance the probabilistic skills of ensemble forecasts, to a certain extent. Figure 6: Relative Mean Error (_RME_) plots for flood forecasts driven by RAW and GPP post-processed precipitation forecasts for 4 forecast schemes for 1–7 lead days. In addition, Table 3 presents three uncertainty metrics for evaluating 90% confidence intervals of discharge derived from different schemes. It can be determined that (1) generally, both _ARIL_ and _PCI_ values derived by raw EPFs are smaller than those derived by GPP post-processed EPFs, making it difficult to determine whether post-processing is useful or not; (2) considering the _PUCI_, those derived by GPP post-processed EPFs have larger values than those derived by raw EPFs, indicating that their results are more reliable and exhibit less uncertainty; and (3) the _PUCI_ values of schemes (A) and (C) are generally larger than those of schemes (B) and (D), suggesting that former approaches yield more robust and reliable forecasts. Figure 8: Rank Histograms for flood forecasts driven by RAW and GPP post-processed precipitation forecasts for 1 lead day. Figure 7: _CRPSS_ plots for flood forecasts driven by RAW and GPP post-processed precipitation forecasts for 4 forecast schemes for 1–7 lead days.
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Multi-Channel MAC Protocols
#### 4.7.5 St mac  
The spatio-temporal (ST) coordination-based MAC protocol for VANETs is designed to provide contention-free channel access for safety message exchange. It provides reliable and efficient data transmission for safe driving. Here, transmission occurs by line-of-collision graph based on a set-cover algorithm [93] so that vehicles transmit in the same slot but do not collide due to directional antennas and transmission power control. An optimization-based contention period scheme is proposed for vehicle registration on RSUs to minimize channel utilization. A hybrid MAC is designed by coordination of spatio-temporal characteristics based on PCF for registration of vehicles. This allocates slots and disseminates emergency messages from RSUs to vehicles based on DCF for safety and emergency messages via V2V communication by WPCF [94].  
The proposed work provides better end-to-end delays and packet delivery ratio and less frame access delays. However, it does not handle non-safety-based applications, and the communication efficiency in highway scenarios for safe driving is not discussed. Since it is RSU centric, there is an overhead on RSU.
## 2 Results
### Verification of Aroma Extraction Methodology through HS-SPE
#### 2.2.2 Purge Gas Flow  
The effects of three different purge gas flow levels were evaluated: 80 mL/min, 100 mL/min, and 120 mL/min. A base flow rate of 100 mL/min was initially established, and from this value, the flow rate was varied between 80 and 120 mL/min.  
The optimization of this variable significantly influenced the extraction of the compounds of interest. An excessively fast purge gas flow could reduce the contact time between the sample and the purge gas, decreasing the extracted compounds. In contrast, slow purge gas flow can increase the contact time with the sample, potentially increasing the amount of impurities extracted or weakening the retention of the compounds.  
The results, detailed in Table 2, reveal that despite the levels evaluated in the purge gas flow, there was no significant impact on the areas and number of peaks in the chromatographic results. Although the purge gas flow rate did not significantly influence the chromatographic results, maintaining a flow rate of 100 mL/min was considered the most appropriate. This value balances the efficiency of extracting representative compounds from the matrix and minimizing impurities in the chromatographic results.
## 3 Results
### Evaluation of the Model  
Tables 3 and 4 provide additional details regarding the AUC and F-1 score, respectively. We included the mean of the 100 evaluations from the training folds in the repeated cross-validation, as well as the minimum, mean, maximum, and standard deviation of the validation folds across all 100 evaluations. With a mean AUC of 0.836 and an F1-score of 0.765, the BN with four features was chosen for evaluation on the test data. This selection was based on the highest mean AUC achieved on the validation folds.  
Due to the small size of the dataset (308 observations on training data), a wide range of AUC values is observed for the chosen model (see Table 3) from 0.648 to 0.959, with a standard deviation of 0.056. Regarding the F1-score, we obtained minimum and maximum values of 0.538 and 0.918, respectively, and a standard deviation of 0.053 (Table 4) for the chosen model with four features.  
The selected BN is depicted in Figure 4, where \"Depression group\" (Four PHQ-9 score groups: 5-9, 10-14, 15-19, 20 or above) and \"gad4\" (Trouble relaxing) have a direct arrow pointing to the outcome. Additionally, \"gad6\" (Becoming easily annoyed or irritable) and \"gad2\" (Not being able to stop or control worrying) receive an arrow from the outcome.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Mean F1** & **Min F1** & **Mean F1** & **Max F1** & **Std F1** \\
**BN** & **Train** & **Validation** & **Validation** & **Validation** & **Validation** \\ \hline
1 & 0.766 & 0.633 & 0.766 & 0.903 & 0.045 \\
2 & 0.756 & 0.538 & 0.745 & 0.903 & 0.052 \\
3 & 0.771 & 0.538 & 0.759 & 0.918 & 0.055 \\
4 & 0.784 & 0.538 & **0.765** & 0.918 & 0.053 \\
5 & 0.793 & 0.538 & 0.758 & 0.918 & 0.055 \\
6 & 0.807 & 0.538 & 0.757 & 0.918 & 0.053 \\
7 & 0.818 & 0.538 & 0.759 & 0.918 & 0.053 \\
8 & 0.835 & 0.538 & 0.759 & 0.918 & 0.052 \\
9 & 0.835 & 0.538 & 0.760 & 0.918 & 0.052 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-score metric summary of repeated cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Mean AUC** & **Min AUC** & **Mean AUC** & **Max AUC** & **Std AUC** \\
**BN** & **Train** & **Validation** & **Validation** & **Validation** & **Validation** \\ \hline
1 & 0.790 & 0.648 & 0.783 & 0.913 & 0.051 \\
2 & 0.822 & 0.648 & 0.813 & 0.928 & 0.056 \\
3 & 0.838 & 0.648 & 0.829 & 0.959 & 0.057 \\
4 & 0.852 & 0.648 & **0.836** & 0.959 & 0.056 \\
5 & 0.866 & 0.648 & 0.833 & 0.959 & 0.054 \\
6 & 0.883 & 0.648 & 0.833 & 0.959 & 0.053 \\
7 & 0.896 & 0.648 & 0.834 & 0.959 & 0.052 \\
8 & 0.908 & 0.648 & 0.835 & 0.964 & 0.051 \\
9 & 0.918 & 0.648 & 0.835 & 0.964 & 0.050 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC metric summary of repeated cross-validation.
## 4 Data
### Sentence Level Evaluation
#### 4.3.1 Classification Tasks  
The datasets listed below are intended for the development of a logistic regression classifier utilizing sentence embeddings.  
* CR (Customer Review): This dataset is composed of product reviews from customers. Each piece of feedback is categorized according to the sentiment conveyed, with labels indicating positive or negative. The objective is to predict the sentiment reflected in customer product reviews.
* MR (Movie Review): This dataset features movie reviews. Each review is labeled as having either a positive or negative sentiment. The task involves predicting the sentiment of movie review.
* MPQA (Opinion Polarity): This dataset features annotations of opinion polarity within phrases from newswire texts. The challenge lies in classifying each phrase according to whether it conveys a positive or negative opinion.
* SUBJ (Subjectivity Status): This collection consists of sentences derived from movie reviews and plot summaries. The goal is to determine whether each sentence expresses personal opinions and feelings (subjective) or states factual information (objective)
* SST-2 (Stanford Sentiment Treebank): This sentiment analysis resource provides sentences extracted from movie reviews, with the objective being to determine if the sentiment is positive or negative.
* SST-5 (Stanford Sentiment Treebank): An extension of SST-2, this dataset encompasses a spectrum of sentiment annotations with five distinct levels, from very negative to very positive. It facilitates a more nuanced sentiment analysis task by allowing for fine-grained classification of emotions.
* TREC (Text REtrieval Conference): A dataset curated for the task of question classification, it differentiates between six specific types of questions, including categories like location, numeric value, and person.
* MRPC (Microsoft Research Paraphrase Corpus): is a collection of sentence pairs automatically extracted from online news sources, with annotations to indicate whether each pair captures the same semantic content, thereby serving as a resource for identifying paraphrases in parallel news articles.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Celestial Spectral Velocimetry  
Intelligent celestial spectral velocimetry emerges as a key research focus for enhancing measurement accuracy and real-time performance. Ref. [12] introduces the SRGAN-LSTM, which combined semi-supervised regression generative adversarial networks (SRGANs) with long short-term memory (LSTM) networks. Ref. [13] integrates the Gaussian genetic algorithm (GGA) and empirical mode decomposition (EMD), enhancing celestial spectral reflectivity template construction by optimizing intrinsic mode function (IMF) combinations, thus reducing the effects of asteroid absorption. In a follow-up investigation, the authors proposed the Oryctolagus cuniculus algorithm (OCA) [14] and Anelosimus eximius colony algorithm (AECA) [6], which simulated the behaviors of rabbit and spider populations, respectively. These algorithms provided improved convergence and optimization performance compared to traditional genetic algorithms (GAs) in celestial spectral reflectivity inversion. To correct the velocity error of the SINS, a DVS-aided SINS integrated navigation method is explored, where the Doppler velocities of the Sun and two stars are used as measurements [15].