esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 2 Review on Chaotic Time Series Forecasting
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref, Year** & **Country** & **Journal/Conference** & \begin{tabular}{c} **Forecasting** \\ **Approach** \\ \end{tabular} & **Application** & \begin{tabular}{c} **Forecasting** \\ **Parameter(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Comparison** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Performance** \\ **Measures** \\ \end{tabular} & **Data** \\ \hline
[25], 1992 & China & IEEE Conference & Complex weighted & Music formula & Arrival direction & – & – & – \\
[26], 1996 & South Korea & IEEE Conference & ANN & Power system & Daily peak load & – & MAPE & South Korea \\  & & & & & & & electric power \\
[27], 1998 & China & IEEE Conference & Embedding phase space using RNN & Mackey-Glass model & Time series & – & MSE & – \\
[28], 1998 & Norway & Physica D: Nonlinear Phenomena & Ordinary least square method & Sunspot, R-R intervals & Time series & PCR, PLS, RS & NRMSE & – \\
[29], 1999 & China & IEEE Transactions on Temporal difference & Heen map, & External & – & Prediction error & – \\  & & Neural Networks & GA based & Logistic map & reinforcement signal & & \\  & & & & neutral network & & & \\
[30], 2000 & China & IEEE Conference & Novel noise reduction & Chaotic interference & Frequency & – & Residual error & – \\
[31], 2001 & Australia & IEEE Conference & Standard Gaussian & Asynchronous & Accuracy & Improved GA & – & – \\  & & & & & & & \\  & & & & & & & \\
[32], 2001 & Spain & IEEE Conference & ANN & Hot wire anemometer & Turbulent flow & – & MSE & – \\  & & & & & & temporal signals & \\
[33], 2002 & UK & IEEE Conference & Gaussian processes & Henon map & Time series & SVM & NMSE & Far infrared-laser \\
[34], 2004 & Iran & Chaos, Solitons & RBFNN & Logistic map, Henon map, Mackey-Glass & Time series & - & MSE, NMSE & – \\  & & and Fractals & & map, Mackey-Glass & & model & & \\
[35], 2004 & Canada & IEEE Transactions on & ANN & Silbo model & Onset of & – & – & – \\  & & Biomedical Engineering & & & state transitions & & \\
[36], 2004 & China & IEEE Transactions on & Recurrent predictor & Sunspot number & Time series & Kalman filter, & RMSE, PE & – \\  & & Signal Processing & neural network & & ULN & & \\
[37], 2004 & China & Chemical Engineering & Chaotic forecasting & Evaporator with & Heat-transfer & – & ARE & – \\  & & Science & & two-phase flow & coefficient & & \\
[38], 2004 & China & IEEE Conference & WNN & Electricity & Spot market prices & – & MSE, APE & South China \\
[39], 2004 & China & IEEE Conference & KII-chaotic & IJCNN CATS & Time series & N-based method & MSE & IJCNN’Q4 CATS \\  & & & neural network & benchmark test data & & & & benchmark set \\
[40], 2005 & China & IEEE Conference & RNN & Power system & Price & – & Mean and & New England \\  & & & & & & maximum & electricity market, \\  & & & & & & percentage errors & USA 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of works focused on chaos forecasting using machine-learning-based approaches.
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 4 Polycystic Ovary Syndrome and Soy Isoflavones
### Metabolic Mechanisms
#### 4.2.1 Central Obesity and Adipocyte Dysfunction in PCOS  
In PCOS, the presence of central obesity is associated with an increased risk of metabolic abnormalities, even when body weight is normal [142; 143]. Adipose tissue, especially visceral adipose tissue, has high metabolic and endocrine activity. It is a source of pro-inflammatory cytokines (such as IL-6 and TNF-\(\alpha\)) and adipocytes that promote the development of low-grade chronic inflammation [144; 145]. Adipose tissue dysfunction contributes to the development of IR, secondary hyperinsulinemia, and endocrine disruption. This includes increased production of androgens and their conversion to oestrogens as a result of increased aromatase activity [146]. In addition, PCOS shows increased oxidative stress, markers of which are elevated levels of malondialdehydedehyde (MDA) and reduced levels of glutathione (GSH), with impaired activity of antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GPx) [147; 148]. Adipocyte dysfunction is also associated with the activation of the NF-\(\kappa\)B pathway (which regulates the inflammatory response) and dysregulation of PPAR\(\gamma\)-dependent gene expression, which can lead to increased inflammatory and metabolic processes [149]. Excess and dysfunctional adipose tissue adversely affects the HPO axis, folliculogenesis, SHBG levels, and androgen concentrations, exacerbating the clinical symptoms of PCOS [150; 151]. In this context, a meta-analysis of 30 clinical trials showed that soy and isoflavone intake may favourably affect anthropometric parameters in overweight individuals, including women, leading to reductions in weight, waist circumference, and BMI, particularly with moderate doses and short intervention times [152].
## 5 Simulation Results and Discussions
### Simulation Results and Analysis  
#### 5.2.1 Performance Comparison of Unsupervised NTC Methods  
In this section, we first compare the accuracy of our DC-CAAE method with PCA, CAE, CVAE, and the corresponding deep clustering method when the potential spatial feature dimension is 100. Then, we compare the simulation results of DC-CAAE under different clustering methods.  
It can be seen from the results in Table 4 that the performance of the traditional PCA dimension reduction algorithm on the USTC-TFC2016 dataset is not good, and the accuracy of the clustering is only 49.4%. However, the performance significantly improves after using the deep learning method. The clustering accuracy of CAE is 33.0% higher than that of PCA, and its silhouette coefficient is 20.0% higher. This shows that in complex network traffic datasets, the deep learning method will achieve better results than traditional methods. The combination of the CAAE method and DC proposed in this paper further optimizes the feature extractor. After adding the deep clustering module, the performance of the CAE and CVAE models improves. Of course, the CAAE has the greatest improvement. The clustering accuracy of the DC-CAAE method is 92.2%, which is the highest among all methods in Table 4. At the same time, considering NMI, silhouette coefficients, ARI, and V-measure, the DC-CAAE method proposed in this paper still has good advantages.  
According to the results in Table 4, in order to avoid the dependence of the results on clustering methods, we used five different clustering algorithms to analyze the stability of the above feature extractors.  
It can be seen from Table 5 that the performance of k-means, minibatch k-means, spectral clustering, BIRCH, and GMM clustering algorithms on DC-CAAE was similar. The accuracy of the five clustering algorithms was about 90%, and the error was less than 2%. It can be seen that the DC-CAAE method proposed in this paper was stable  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**NMI**} & \multirow{2}{*}{**AC**} & \multicolumn{2}{c}{**Silhouette**} & \multirow{2}{*}{**ARI**} & \multirow{2}{*}{**V-Measure**} \\  & & & & **Coefficient** & \\ \hline PCA & 0.685 & 0.494 & 0.326 & 0.345 & 0.685 \\ \hline CAE & 0.887 & 0.824 & 0.526 & 0.740 & 0.887 \\ \hline CVAE & 0.888 & 0.830 & 0.492 & 0.758 & 0.889 \\ \hline CAAE & 0.882 & 0.888 & 0.408 & 0.796 & 0.890 \\ \hline DC-CAE & 0.873 & 0.844 & 0.518 & 0.736 & 0.873 \\ \hline DC-CVAE & 0.890 & 0.857 & 0.481 & 0.753 & 0.891 \\ \hline DC-CAAE (proposed) & 0.911 & 0.922 & 0.422 & 0.844 & 0.917 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between DC-CAAE and other methods.  
on the USTC-TFC2016 dataset, and its performance did not change due to the different clustering methods.
## Materials and Methods
### Pulsed Electric Field Treatment  
For the microbial test, autoclaved orange juice inoculated with each microorganism was exposed to PEF treatments. For the other tests, freshly prepared orange juice was exposed to PEF. The orange juice was stored in a service tank and transferred by a peristaltic pump (323 Du, Watson Marlow, Wilmington, MA, USA) at a flow rate of 30 L/h. The inlet temperature of the orange juice was adjusted to 35 \({}^{\circ}\)C by flow-through stainless-steel coils immersed in a water bath. PEF treatment was performed using a 5 kW pulse generator (HVP-5, DIL, Quakenbruck, Germany) equipped with a continuous treatment chamber, and the shape of the pulse is bipolar square type. The treatment chamber had an inner diameter of 1.0 cm, with a gap distance of 1.0 cm between the electrodes. The PEF treatment was applied as a continuous flow using bipolar pulses of 25 \(\upmu\)s, and we conducted PEF treatment at 10, 15, and 20 kV/cm with three different energy inputs (50, 100, 150 kJ/L). It means we had 9 different PEF conditions, as shown in Figure 1. From the result of Figure 1, we selected the electric field strength of 20 kV/cm for further experiments, since the field strength of 20 kV/cm resulted in the most effective microbial inactivation at the same specific energy inputs. Two different levels of specific energy input, 100 kJ/L (PEF-1) and 150 kJ/L (PEF-2), were applied for the volatile composition and sensory properties.
## 2 Materials and Methods
### In Situ Hydrogen Permeation Monitoring Test
#### 2.2.3 Measurement of Corrosion Thickness Loss  
The hydrogen permeation sensor was also used as a corrosion coupon. The parameter of corrosion thickness loss was adopted to quantify the corrosion degree of sensors after outdoor exposure. The hydrogen permeation sensor was cut into two sections from the middle with a hacksaw. One section of the sensor was sealed with epoxy resin, and then the cross-section of the sensor was grinded with SiC paper up to 1000 grade. Subsequently, it was polished with a 3 \(\upmu\)m diamond abrasive and then rinsed with distilled water and alcohol to obtain a smooth surface for observation. Measurement of the residual thickness of the sensor wall was performed with a Jangnan MR-2000 optical metallographic microscope and accompanying software of ScopeImage 9.0.
# Second-Order Text Matching Algorithm for Agricultural Text
## 3 Related Concepts and Algorithms
### BERT Model Architecture  
BERT model is a pre-trained language representation model based on Transformer architecture. It consists of multiple layers of Transformer encoders, including a self-attention mechanism. A key innovation of BERT is the pre-training of the model with bidirectional context, allowing it to better understand the contextual relationships between words in a sentence. The basic architecture of BERT model includes the following key components. Input embeddings convert words in the input text into embeddings, typically using techniques like WordPiece. The transformer encoder consists of multiple layers, each containing multi-head self-attention mechanisms and feedforward neural networks. This allows the model to capture complex relationships between words. During pre-training, BERT employs two objectives to learn general language representations: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) [37]. In MLM, some words in the input text are randomly masked, and the model's task is to predict the original words that were masked. NSP, on the other hand, teaches the model to understand the relationship between two sentences by predicting whether they are consecutive or not. After pre-training, BERT model can be fine-tuned for a wide range of natural language processing tasks, including text classification, named entity recognition, and so on, to adapt to specific application domains.  
Each layer of BERT model contains a multi-head self-attention module, a feed-forward neural network, a residual connection and a layer normalization module. The input to BERT model usually consists of three types, the token embedding, the segment embedding and the position embedding. The token embedding encodes information about the content of the input text, which is formatted with respect to the task to be fine-tuned. The segment embedding encodes information about the class of the sentence. If the input consists of two sentences, it is common to mask the first sentence with all zeros and the second sentence with all ones. The position embedding encodes the positional and sequential information of the input, and the position embedding of BERT model is automatically learned by the model. To improve the utilization of BERT model outputs, a softmax layer is superimposed on the original BERT model in this paper, characterizing the probability that two sentences are similar as a value ranging from 0 to 1. This paper uses the parameter settings of BERTBASE. BERTBASE model is constructed with 12 Transformer encoder layers, each layer utilizing 12 self-attention heads in its multi-head attention mechanism. The dimension of the encoding embeddings is 768, and the model comprises approximately 110 M (million) parameters in total [37]. The model uses word as the input unit and the size of the word list is 21,128. The maximum length of the model's input embeddings is 512 [38]. Smaller embeddings will be padded and larger embeddings will be truncated. The architecture of BERT model is shown in Figure 1[37].
## Materials and Methods
### Data Analysis  
Adapters and low-quality ends of sequenced reads were removed using Trimmomatic (version v0.36) [27], based on quality control statistics generated by FastQC (v0.11.5) [28]. After trimming, fragments without sufficient length of both reads (>35 bp) were removed from the data set. All trimmed paired reads with sequence overlaps were merged using PEAR (v0.9.10) [29] and passed to QIIME 2 (v2019.10.0) for microbial analyses [30]. There, fastq files were dereplicated using built-in tool vsearch. Then, chimeras and \"borderline chimeras\" [31] were removed from the dataset using de novo chimera filtering with default parameters [30]. Operational taxonomic units (OTUs) with 99% similarity were created with de novo clustering of features.  
Taxonomy of OTUs was assigned by QIIME's pre-fitted sklearn-based taxonomy classifier [32]. The pre-fitted classifier was trained by the Naive-Bayes classifier with default parameters on Silva 16S database (Silva release 132) [33] and ITS database (UNITE 8.2) [34]. Only taxons with <0.1% of all reads per sample were used for bacteria and <0.01% for fungi. Finally, Faith's phylogenetic diversity for all samples was calculated using faith-pd plugin [35]. All computational analyses were written and executed using the SnakeLines framework (v0.9.2) [36; 37].
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 3 The Function of Probiotics
### Immunomodulatory Function  
The intestinal tract is the largest immune organ in the human body, where approximately 70-80% of immune cells are concentrated [51]. Probiotics not only modulate the immune system locally in the gut but also influence the systemic immune response through various mechanisms. [PERSON] et al. [52] described two ways in which probiotics activate the immune system. The first involves probiotics passing through the intestinal wall as living cells or undergoing limited proliferation within the intestinal tract; the second involves dead probiotics releasing antigens absorbed by the intestinal tract and directly stimulating the host's immune system. Probiotics enhance host immunity by activating immune cells, including dendritic cells, macrophages, and lymphocytes, while modulating the production of anti-inflammatory and pro-inflammatory cytokines, thereby influencing both innate and adaptive immune responses [53]. On the one hand, LPS on the surface of probiotics activates toll-like receptor 2 (TLR2) negative regulatory proteins, thereby blocking the recruitment of MyD88 signal transduction proteins and inhibiting the activation of pro-inflammatory signaling pathways [54]. On the other hand, the metabolic products SCFAsstabilize inhibitors of \(\kappa\)B alpha (I\(\kappa\)B\(\alpha\)) via HDAC, thereby blocking the phosphorylation of I\(\kappa\)B\(\alpha\) by i\(\kappa\)B kinase and effectively preventing the nuclear translocation and activation of NF-\(\kappa\)B. Additionally, SCFAs induce inhibitory histone methylation in the promoter regions of pro-inflammatory genes and upregulate peroxisome proliferatorator-activated receptor gamma expression, which competitively binds to NF-\(\kappa\)B, thereby comprehensively reducing the secretion of pro-inflammatory factors and maintaining immune homeostasis [55]. Additionally, probiotics inhibit tumor necrosis factor-alpha-mediated (TNF-\(\alpha\)) apoptosis by activating anti-apoptotic factors and Akt [56]. [PERSON] et al. [57] induced colitis in mice using dextran sodium sulfate (DSS), resulting in significantly elevated TNF-\(\alpha\) levels in colon tissue. The subsequent administration of _Bifidobacterium bifidum_ BB12 effectively alleviated colitis symptoms. This intervention alleviated TNF-\(\alpha\)-mediated intestinal epithelial cell apoptosis, enhanced the survival rate of intestinal epithelial cells, and promoted the balance of the intestinal immune environment. The metabolic products of probiotics, specifically SCFAs, promote the differentiation of regulatory T cells via multiple mechanisms. They inhibit Th2 overreactions, such as allergen sensitization, and enhance Th1 proinflammatory capabilities, thereby reducing intestinal inflammation and promoting immune homeostasis [58].
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 2 Materials and Methods
### Experiments  
We conduct three experiments. First, we configure our iCTU-Net in an ablation study. Then, we investigate the impact of different sinogram input data for training in an input study. Finally, we compare our best network configuration with state-of-the-art MAR algorithms.  
In the ablation study, we investigate different post-processing layers and loss functions. The purpose of the ablation study is to find settings for the iCTU-Net that yield the best reconstructions. The resulting network configuration will be used in following studies.  
Figure 3: Generation of the metal training dataset. The input and labels for the three used deep learning networks are indicated by the colored words “Input” and “Label” beneath the corresponding images. The color coding corresponds to the colors used for the network architectures in Figure 1 (green: iCTU-Net, blue: U-Net Sino, and red: U-Net Image).  
We train three networks with different post-processing layers after backprojection: no post-processing, three convolution layers, and a U-Net. All of these networks are trained with the SSIM loss and with pre-corrected NMAR sinograms as input. To investigate the influence of the loss function, we additionally train the U-Net post-processing network with the MSE loss. Both SSIM and MSE are commonly used loss functions in CT artifact reduction and CT reconstruction [28].  
In the input study, we train the network with different sets of training input data in addition to the previously used pre-corrected NMAR sinograms. The idea behind the input study is to find out how the network behaves for different kinds of sinogram input data. We use sinograms without metal (ground truth sinogram in Figure 3 but with additional noise added via Equation (2)) to investigate the network's performance if no metal is present. In this way, the reconstruction performance and the ability to mitigate metal artifacts can be evaluated separately. We calculate the evaluation metrics for different categories of artifact severity, even though none of the test data contain any metal. Nevertheless, the categories are used to allow fair comparisons to the other networks. We also train a network with uncorrected metal sinograms (noisy metal sinogram in Figure 3), to see if an NMAR pre-correction is necessary.  
Finally, in the comparison study, we compare our iCTU-Net with the NMAR sinogram inpainting algorithm and the U-Net Sino and U-Net Image networks described earlier.
## 4 Experiment  
### Experimental Setup  
We set up an experiment in the nighttime traffic scenario, as shown in Figure 24. Two cameras, which were called left and right cameras, were kept static on a fixed height with a fixed horizontal inter-distance to capture front vehicles moving along the four-lane road. The model of both cameras used was a Sony Cybershot DSC-RX100, with a CMOS image sensor. Besides, these cameras were configured to maintain sufficient details of the candidate taillight regions in the images.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Input layers & 3 \\ Hidden layers & 6 \\ Output layers & 1 \\ Learning rate & 0.001 \\ Batch size & 32 \\ Epoch & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setup for training the pairing classifier.  
All system parameters for the experiment are listed in Table 3. It should be noted that when setting a camera system on a vehicle for real-time applications, the horizontal distance and camera height could be changed due to the variations in size of different vehicle types in real scenarios.  
After the experiment, we obtained the raw dataset including 2312 image pairs, with each image pair being obtained by the two cameras capturing at the same time. Each image was processed by the proposed detection method to determine the pixel coordinates of the taillights that appeared inside. Then, the detection results were used for the stereo taillight matching and taillight pairing methods independently.  
The proposed methods were implemented using Python programming language version 3.6.5. The computer system that ran the program has an 8 GB RAM and an Intel Core i3-3230 3.30 GHz CPU. The stereo taillight matching classifier matchingClassifier and the taillight pairing classifier pairingClassifier were designed and trained using Google Colab, which provides two-core 2.30 GHz CPU and 13 GB RAM. The average processing time of the methods was about 0.39 s per each image pair, which could be improved by doing some algorithm optimizations in future work.