esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AI-Driven Analysis of Microbiota Functionality
### Machine Learning (ML) and Deep Learning Approaches for Microbiome Data Analysis
Each neuron takes input, performs a computation (usually a mathematical function), and produces an output, which is passed to the next layer of neurons. A deep learning model, such as DeepMicro, can be trained on microbiome sequencing data. After training, it can predict whether a specific microbial composition is associated with conditions like IBD or other CIDs. The model can learn patterns that link the presence or absence of certain microbial species to health outcomes, making it a powerful tool in precision medicine [PERSON] et al. (2020). Gut microbiome research leverages advanced methodologies like sequencing, metabolomics, and transcriptomics, which generate highly complex and diverse datasets. The vast scale of these data requires the precision and flexibility of ML for effective analysis. Omics techniques such as meta-transcriptomics, metabolomics, and metagenomics are frequently used to study the human gut microbiota. These techniques enable a comprehensive and detailed examination of the entire microbial community, providing multifaceted insights into its structure and function. For instance, metagenomics often employs whole-genome shotgun sequencing to analyze the genetic composition of the microbiome, while metabolomics focuses on quantifying the metabolites produced by the microbial community. The intricate and voluminous nature of data from these omics methods necessitates advanced computational tools, with ML playing a crucial role in integrating and interpreting these datasets effectively (Figure 1). Below is an explanation of how AI can be integrated into the diagnosis and understanding of microbiome analysis, along with examples related to metabolomics, transcriptomics, proteomics, and genomics (Table 1). Figure 1: Integration of AI in microbiome analysis. NOTA. This diagram represents how AI integrates into different areas of microbiome analysis, illustrating the flow from data analysis to actionable insights. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Microbiome Analysis** & **Explanation** & **AI Application** & **Example** \\ \hline \multirow{6}{*}{1. Metabolomics} & \multirow{6}{*}{Metabolomics involves the comprehensive analysis of metabolites in a biological system. These metabolites can provide insights into metabolic changes and microbial interactions.} & AI algorithms, particularly machine learning models, can analyze complex metabolomic data to identify patterns and correlations between metabolites and microbial communities. For instance, AI can use clustering techniques to categorize different metabolic profiles associated with specific microbiome compositions or \\  & & & & A study might use AI to analyze urine and blood samples from patients with metabolic disorders to identify specific metabolite patterns \\  & & & & \\ \cline{1-1}  & & & &
## 3 Proposed Methods
### Filters  
Filters are used to achieve specific objectives when processing digital images. These objectives include reducing the number of intensity variations between adjacent pixels, which is known as smoothing the image. Another goal is to eliminate noise, which involves identifying and removing pixels with intensity levels that differ significantly from their neighbors. Such noise can originate from the image acquisition or transmission processes. Filters can also be used to boost edges, highlighting the edges located in an image, or to detect edges, which refer to pixels with sharp changes in intensity. These operations are applied to the pixels of an image to optimize it, emphasize specific information, or achieve a particular effect. The filtering process can be performed in the frequency domain and/or color space [36].  
For better feature extraction (structure versus non-structure), we tested mean, median, variance, convolution, bilateran, Canny, Laplacian, and Sobel filters. However, we selected the Canny, Laplacian, and Sobel filters, because they produced better results than the others, allowing us to improve the detection of archaeological sites in areas with a lot of vegetation.  
* The Canny filter is based on the first derivative of a Gaussian function. However, since raw image data is often affected by noise, the original image is preprocessed with a Gaussian filter to reduce the noise. This results in a slightly blurred version of the original image [37].
* The Laplacian filter on the other hand, is an edge detector that calculates the second derivatives of an image, measuring the rate of change in the first derivative. It is used to determine whether a change in adjacent pixel values represents an edge or a continuous progression [37].
* Finally, the Sobel filter uses a small, separable, integer-valued filter in both the horizontal and vertical directions to convolve the image. This filter is relatively inexpensive in terms of runtime [37].  
The objective of using the different images generated from the RGB image is to include different features (information) in images, to improve the detection of archaeological structures by a classification algorithm.
# Accumulative Roll Bonding--A Review
## 3 Parameters
### Rolling Parameters
#### 3.3.7 Post-Heat Treatment  
The formability of ARBed materials is generally poor due to limited ductility. To address this problem, post ARB heat treatment is a viable choice. For materials deformed more than a certain  
Figure 10: Tensile strength of ARBed pure IF steel, composite and nanocomposite versus number of cycles [60].  
Figure 9: Average peel strength versus the thickness reduction [54].  
level of strain, a transition in annealing behavior occurs from the conventional recrystallization (i.e., nucleation and growth), typically observed for samples deformed to medium/large strains, to a process of extended recovery, where a continuous microstructural coarsening is observed [61; 62]. Strain has a significant effect on the recovery rate of those materials deformed by very large values of strain. A fast recovery rate in the form of structural coarsening may enhance nucleation; nevertheless, the fast structural coarsening in the material around any nuclei reduces the driving force for its growth, i.e., a competitive effects between the growth of nuclei and the structural coarsening that might be influenced by different parameters, e.g., processing conditions, solute content, intermetallics, as well as annealing treatment parameters [63].  
[PERSON] et al. [11] evaluated the influence of annealing treatment on the interface of Cu/Al composite fabricated by one-cycle cold rolling. As shown in Figure 11, it is clear that an appropriate heat treatment may greatly improve bonding quality and high bond strength can be achieved (annealing at 423 K for 20 h). In fact, relatively low annealing temperature and short time heat treatment significantly improves the bond strength. On the other hand, the strength drastically reduces at higher annealing temperature or longer treating time due to the growth of brittle intermetallic compounds that deteriorates the ductility of final products. It was also shown in other research [64] that the formability of ARBed sheets are enhanced by annealing to realize both recrystallization and interlayer diffusion.  
Some studies used post ARB aging process as a way of enhancing the strength of age hardenable sheets. For example, [PERSON] et al. [65] investigated the mechanical properties and microstructural features of ARBed 6061 Al followed by ageing treatment and reported that both the UTS and elongation values increased after an ageing operation at two temperatures (337 K and 437 K) for different rolling cycles (i.e., 0-5 cycles). The results obtained revealed the competing effects of recovery and precipitation involved during the ageing process, i.e., dislocation density decreases due to recovery while precipitation occurs during the ageing contributing to the UTS enhancement and strain hardening. Based on the results obtained, it was shown that the contribution to strength improvement by precipitation is higher than the loss of strength due to recovery as both the UTS and elongation values were enhanced.
# Research Progress of Polysaccharide-Gold Nanocomplexes in Drug Delivery
## 5 Research on polysaccharide-AuNPs as Drug Delivery Carriers
As demonstrated by in vitro experiments, it was found that modification of ticoadian improved the surface properties of the AuNPs and enhanced the binding and delivery of DOX to the polysaccharide-AuNPs. However, the effects of in vivo administration have not been explored and pharmacokinetics have not been studied. Notably, ticoadian AuNPs have also been used in photoacoustic imaging and as contrast agents [131]. [PERSON] et al. conducted a study in which they synthesized gold nanoparticles (AuNPs) using a polysaccharide obtained from pomeranate peels. The inclusion of galactose in the pomeranate polysaccharide-AuNPs serves as a tumor-homing sequence, enabling targeted delivery of the antitumor drug DOX to the tumor tissues. This polysaccharide-AuNPs system enhances the accumulation of DOX in the tumor tissues while reducing potential damage to normal tissues [132]. In the field of research on antitumor drugs, there has been a growing interest in the efficacy of various compounds, including PTX [133], cephalexin (CPT) [133], irinotecan hydrochloride (CPT-11) [134], and topotecan hydrochloride (TPT) [135]. These drugs have gathered attention due to their potential therapeutic benefits, in addition to DOX. The HA-AuNPs prepared by [PERSON] et al. have shown effective delivery of the poorly water-soluble drug salicazosulfapyridine (SSZ). These nanoparticles offer a controlled release effect, improve drug stability, and can be used for oral- and intestinal-targeted drug delivery systems. However their resilience in vivo should be tested later [136]. Gum karaya (GK)-functionalized AuNPs possess remarkable biocompatibility and stability. Incorporating these nanoparticles allows for targeted delivery of the anticancer drug gemcitabine hydrochloride (GEM) specifically to the tumor site. Notably, these nanoparticles have displayed substantial anticancer activity against human lung cancer cells [137]. AuNPs are easily prepared; however, their structural limitations necessitate an improvement in the polysaccharide-AuNPs drug delivery system's structure to enhance the delivery process. Polysaccharide-AuNPs, characterized by a hollow structure, exhibit superior drug delivery capabilities and are employed for the loading and transporting of diverse anticancer drugs. Furthermore, the combination of AuNPs and cyclodextrin-grafted hyaluronic acid constructs results in the formation of innovative polysaccharide-AuNPs supramolecular conjugates (HACD-AuNPs) capable of delivering a wide range of antitumor drugs [138].
# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
## 2 Materials and Methods
### Training
#### 2.3.2 Performance Metrics  
During the analysis of related works (Section 1), it was observed that the most used metrics were accuracy, AUC, F1-score, precision, and recall. In order to be possible and facilitate the comparison of the results with the literature, the authors of this article will also use the following metrics:  
* Accuracy, which measures the proportion of correct classifications over the total number of classifications [36]: \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FN+FP}\] (1)
* Precision (or Specificity), which measures the proportion of true positive predictions over the total number of positive predictions [36]: \[\text{Precision}=\frac{TP}{TP+FP}\] (2)
* Recall (or Sensitivity), which measures the proportion of true positive predictions over the total number of actual positive samples [36]: \[\text{Recall}=\frac{TP}{TP+FN}\] (3)
* F1-score, which measures a weighted mean of Precision and Recall [36]: \[\text{F1}-\text{score}=\frac{2 TP}{2 TP+FP+FN}\] (4)
* AUC, which measures the performance of a binary classifier by calculating the area under the receiver operating characteristic (ROC) curve and plotting the true positive rate against the false positive rate at various classification thresholds [36]:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CNN** & **Number of Parameters** \\ \hline AlexNet & 46,756,609 \\ GoogLeNet & 8,471,347 \\ ResNet-50 & 25,637,713 \\ CancerDetechNN V0 & 929 \\ CancerDetechNN V1 & 18,305 \\ CancerDetechNN V3 & 53,185 \\ CancerDetechNN V4 & 159,809 \\ CancerDetechNN V5 & 307,393 \\ CancerDetechNN V6 & 520,513 \\ CancerDetechNN V7 & 520,513 \\ CancerDetechNN V8 & 700,097 \\ CancerDetechNN V9 & 776,801 \\ CancerDetechNN V10 & 793,121 \\ CancerDetechNN V11 & 454,977 \\ CancerDetechNN V12 & 240,961 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Used convolutional neural networks and their respective number of parameters.  
\[\text{AUC}=1-\frac{1}{2}\bigg{(}\frac{FP}{FP+TN}+\frac{FN}{FN+TP}\bigg{)} \tag{5}\]  
where \(TP\) (True Positives) refers to the number of correct positives; \(TN\) (True Negatives) denotes the number of correct negatives; \(FP\) (False Positives) indicates the number of incorrect positives; \(FN\) (False Negatives) represents the number of incorrect negatives.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Forecasts Accuracy Measures
#### 3.5.2 Prediction Interval Width  
The analysis of the prediction interval width (PIW) at 95 th quantile is presented in this section. Table 6 gives the summary statistics of the generated PIW, which gives the nature of the PI generated.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **No of Used Variables** & **RMSE** & **MAE** & **MAPE** \\ \hline BNN & 14 & 0.2091 & 0.1526 & 2.7437 \\ SGB & 8 & 0.2553 & 0.1397 & 2.3754 \\ GAM & 10 & 0.2468 & 0.1579 & 3.0356 \\ AQRA & 3 & 0.1888 & 0.1167 & 2.0330 \\ LQRA & 3 & 0.1928 & 0.1204 & 2.0986 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy measures for the point forecasting models.  
In Table 6, BNN has the least standard deviation followed by SGB, hence, PIW from these two models are narrower than those from AQRA and GAM, respectively. The skewness measures the distribution of a model and shows in the table that PIWs are not normally distributed, although close to a normal distribution (except SGB), because a normal distribution has a skewness value of 0. All of the models are also negatively skewed, showing that they are left-skewed. The kurtosis value for distribution is expected to be 3 and all kurtosis values for the models show them to be negative and less than 3, and they are therefore said to be platykurtic. The visualisation for PIW is presented in Figure 6. The figure presents PIW from AQRA as the most symmetrical and best model followed by SGB. BNN and GAM are skewed and much narrower than the other two. The PIW visualisation using the density plots in Figure 7, shows model BNN as the model with the narrowest intervals.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Min** & **Max** & **Mean** & **Median** & **St.Dev** & **Skewness** & **Kurtosis** \\ \hline BNN & 0.3281 & 0.9217 & 0.8148 & 0.8613 & 0.1016 & \(-\)0.7914 & \(-\)0.7346 \\ SGB & 0.4378 & 0.8313 & 0.7099 & 0.7487 & 0.1124 & \(-\)1.0610 & \(-\)0.0271 \\ GAM & 0.2461 & 1.1725 & 0.8762 & 0.8744 & 0.1952 & \(-\)0.2188 & \(-\)0.7905 \\ AQRA & 0.3843 & 0.7759 & 0.6277 & 0.6399 & 0.1213 & \(-\)0.5330 & \(-\)0.9302 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary statistics for PIW at 95 th quantile.  
Figure 6: PIW of BNN-AQRA.
## 3 Why GAN Cryptography?
### GAN Cryptography Working Mechanism  
Generative Adversarial Network (GAN) cryptography is a relatively new and evolving field, representing a fusion of machine learning and cryptographic principles. Here is an overview of how GAN Cryptography works, broken down into several key components:
## 3 Results and Discussion
### Characterization of Clinoptiolite Zeolite, Phosphorus Fertilizers, and Soil  
The CZ used in this study (granular form) was obtained from MB Plus Sdn. Bhd. Johore, Malaysia. The pH and CEC of the CZ were relatively high, 8.54 and 75.4 (cmol(+)kg\({}^{-1}\)), respectively (Table 3). The detail information on the surface morphology and elemental composition of the CZ used in this present study is mentioned in one of our published papers [31]. Elemental content of the P fertilizers is shown in Table 3.  
Table 4 shows the selected physico-chemical properties of the Bekenu Series used. The soil was acidic (pH = 4.32) and low in CEC (5.33 cmol(+)kg\({}^{-1}\)). The order of cations abundance was Fe \(>\) Al \(>\) Na \(>\) Ca \(>\) Mg \(>\) K. The soil which was taken from an uncultivated area at UPMKB had slightly higher soil total carbon, exchangeable K, Ca, and Mg than the standard range noted by [PERSON] [32]. It could be due to litter decomposition with time on the soil surface. Dominated by more than 70% sand,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Property** & **TSP** & **ERP** & **CIRP** & **CZ** \\ \hline pH (water) & 2.46 & 7.42 & 7.93 & 8.54 \\ CEC (cmol(+)kg\({}^{-1}\)) & ND & ND & ND & 75.4 \\ Total P (\%) & 18.09 & 11.96 & 10.62 & 0.01 \\ Total P\({}_{2}\)O\({}_{5}\) (\%) & 41.12 & 27.19 & 24.15 & ND \\ Total K (\%) & 0.42 & 0.25 & 0.31 & 0.37 \\ Total Ca (\%) & 4.88 & 47.55 & 51.73 & 0.67 \\ Total Mg (\%) & 0.35 & 0.17 & 0.24 & 0.10 \\ Total Fe (\%) & 0.38 & 0.61 & 0.52 & 0.11 \\ \hline \hline \end{tabular} Note: CEC is cation exchange capacity, TSP is triple superphosphate, ERP is Egypt rock phosphate, CIRP is Christmas Island rock phosphate, and ND is not determined.  
\end{table}
Table 3: Selected phosphorus fertilizers and Clinoptiolite zeolite chemical properties.  
this sandy loam soil is consistent with its inherent low fertility probably due to leaching of cations. Very low P availability (approximately 5% of soil total P) is similar to that of [PERSON] et al. (2018) who also reported that 85-90% of inorganic P added to tropical soils were unavailable to plants within the year of application. The initial inorganic P speciation of the soil was in the order of Fe-P (68%) \(>\) Al-P (13%) \(>\) Red-P (10%) \(>\) Occl-P (7%) \(>\) Ca-P (2%) \(>\) Sol-P. Most of the P was immobilized within the crystalline and secondary minerals. The largest Pi fractions are associated with a high content of Fe-oxides, acidity, and advanced pedogenesis of the soil.
# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 2 Methods
### The Montreal Cognitive Assessment  
MoCA is a cognitive health evaluation tool, primarily used for detecting mild cognitive impairment [13]. This test covers a range of cognitive domains, including short-term memory recall, visuospatial abilities, and executive functions. This ability to evaluate various cognitive aspects in a short (around 15 min) time makes it a practical and comprehensive screening tool.  
MoCA has proven to be more effective than some other cognitive tests, such as the Mini-Mental State Examination (MMSE), especially in the context of [PERSON]'s disease [14; 15]. Here, research indicates that MoCA is more sensitive in detecting cognitive impairments in PD patients.  
Furthermore, it is helpful in predicting cognitive decline in the early stages of PD, with scores of 26 or lower marking a significant risk for progressive cognitive deterioration [16]. This sensitivity to early-stage cognitive issues in PD highlights MoCA's clinical importance.  
Additionally, MoCA's adaptability for remote administration is particularly beneficial for patients with movement disorders [17]. This enhances its accessibility in varied clinical settings. MoCA's effectiveness extends to reflecting cognitive reserve, with emphasis on the influence of education and work activity [18].  
It is important to acknowledge reported limitations, as for example, cultural and educational biases present a significant challenge. The MoCA's performance can vary across diverse cultural and educational backgrounds, potentially leading to misinterpretation of results. Moreover, individuals with lower education levels may score lower on the MoCA, not necessarily due to cognitive impairment but due to the test's design, which might favor those with higher educational and cultural backgrounds [19]. However, [PERSON] (2013) cautioned that adjusting the MoCA for education could decrease its sensitivity, leading to an increased number of false negatives [20].  
Another noteworthy concern is its susceptibility to practice effects, especially between the first and second administrations. This could potentially skew results and requires consideration in clinical interpretation [21].  
In summary, while the MoCA is a valuable tool for detecting cognitive impairment, it is essential to be aware of its limitations. These include cultural and educational biases, the potential for overdiagnosis, difficulty in distinguishing between different cognitive disorders, language barriers, and time constraints in clinical administration. Awareness and consideration of these factors are crucial for ensuring appropriate use of the MoCA in nuanced contexts.
## 4 Implementation
### Data Preprocessing  
Since we were interested in evaluating CTI for threat classification, we deleted the normal traffic from the datasets. Additionally, in the BoT-IoT dataset, we omitted the pKSeqID feature since it represented an identifier for the traffic records. The datasets contains some categorical features that could not be processed by the neural network. Thus, we converted the nominal values into numeric using sklearn LabelEncoder. LabelEncoder converts categorical values into numerical values [22]. We implemented sklearn StandardScaler to scale the data. For training and evaluation, several papers have split the dataset into training and testing, with a ratio of 20% for testing s in [19] and 30% for testing in [21]. However, due to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack** & **Attack Subcategory** & **Number of Instances** \\ \hline \multirow{2}{*}{Reconnaissance} & Service scan & 73,168 \\ \cline{2-3}  & OS fingerprinting & 17,914 \\ \hline \multirow{4}{*}{DoS} & TCP & 615,800 \\ \cline{2-3}  & UDP & 1,032,975 \\ \cline{2-3}  & HTTP & 1485 \\ \hline \multirow{4}{*}{DDoS} & TCP & 977,380 \\ \cline{2-3}  & UDP & 948,255 \\ \cline{2-3}  & HTTP & 989 \\ \hline \multirow{2}{*}{Information theft} & Keylogging & 73 \\ \cline{2-3}  & Data theft & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attack categories in BoT-IoT dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack** & **Number of Instances** \\ \hline DoS & 20,000 \\ \hline DDoS & 20,000 \\ \hline Scanning & 20,000 \\ \hline Ransomware & 20,000 \\ \hline Backdoor & 20,000 \\ \hline Injection & 20,000 \\ \hline Cross-Site Scripting (XSS) & 20,000 \\ \hline Password & 20,000 \\ \hline Man-In-The-Middle (MITM) & 1043 \\ \hline \end{tabular}
\end{table}
Table 3: Attack categories in TON_IoT dataset.  
the size of the BoT-IoT dataset and the resource constraints of our device, we divided the data into training and testing sets, with a ratio of 35% for testing, while having the same ratio of classes in both parts by using the stratify parameter.