esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven and Machine-Learning-Based Real-Time Viscosity Measurement Using a Compliant Mechanism
## 5 Results and Discussion
### The REC Curve  
The REC curve plot for predicting viscosity reveals distinct differences in performance among the evaluated ML models, as illustrated in Figure 15.  
Multiple Linear Regression and Decision Tree Regression exhibit a steep initial rise, indicating high accuracy, with a significant fraction of their predictions falling within low error thresholds. Random Forest Regression also performs well; however, it has a lower initial slope as compared to that of the other models. Support Vector Regression demonstrates a slower rise, suggesting a lower precision relative to that of the others. The Random Model, depicted by the dashed line, serves as a baseline, showing a linear increase and highlighting the superior performance of all of the other models. Overall, Multiple  
Figure 14: Scatter plots of actual vs. predicted viscosity values for (**a**) Multiple Linear Regression, (**b**) Decision Tree Regression, (**c**) Support Vector Regression, and (**d**) Random Forest Regression.  
Figure 15: REC curves for Multiple Linear Regression, decision tree, Support Vector, and Random Forest models showing the prediction error rates.  
Linear Regression and Decision Tree Regression emerge as the most effective models for viscosity prediction.  
The MLR model achieved the lowest _MSE_ and _RMSE_ values and the highest \(R^{2}\) value, indicating its high accuracy and minimal prediction error. The scatter plots further reinforced these findings, showing that the predicted values closely aligned with the actual values for Multiple Linear Regression, with minimal deviation from the ideal fit line. The REC curves also highlighted its strong predictive capabilities, with a steep initial rise and a high fraction of its predictions within low error thresholds. Overall, Multiple Linear Regression emerged as the most effective model for viscosity prediction.  
To demonstrate the effectiveness and accuracy of the proposed method, a few common fluids were tested using a standard Ostwald viscometer to determine their dynamic viscosity. Later, these fluids were tested with the proposed viscosity measurement system to obtain the voltage output from the piezoelectric sensor. Later, the voltage signals were evaluated using the MLR ML model to predict the viscosity values. A comparison of the observations is reported in Table 4.  
The comparison of the viscosity values measured using the standard Ostwald viscometer and the proposed system reveals a variation of 0.8 to 2.5%. The close correlation of the results underscores the accuracy and reliability of our proposed method.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 2 Materials and Methods
### Characterization of Bacterial Isolates  
The genomic DNA of the isolated strains was prepared with the Maxwell 16 system (Promega\({}^{\text{\textregistered}}\), Madison, WI, USA), and the 16S rRNA fragments, amplified via polymerase chain reaction (PCR), were analyzed with a DNA sequencer (ABI model 3500 Genetic Analyzer). A SeqMan application (DNASTAR\({}^{\text{\textregistered}}\) v.11.2.1, Madison, WI, USA) from Lasergene (Madison, WI, USA) was employed to modify and assemble the obtained nucleotide sequences. The homology comparison (BLAST analysis) was performed, exploiting the National Center for Biotechnology Information server (NCBI, www.ncbi.nlm.nih.gov/blast/Blast.cgi, accessed on 1 August 2022). A collection of seven isolates belonging to the Comamonadaceae, Microbacteriaceae, Bacillaceae and Micrococaceae families was obtained. These strains were then subjected to a series of in vitro assays to evaluate their plant-growth-promoting (PGP) potential, as reported in [49]. The list of isolates and their PGP properties are shown in Figure 1.  
Figure 1: List of arsenic-tolerant bacterial endophytes isolated from the roots of hemp seedlings grown on an As-contaminated soil and the results of the tests performed on them. Genbank accession numbers and in vitro PGP properties are shown.  
All strains selected for the preparation of the microbial inoculum showed at least four growth-promoting properties; in particular, all of them produced 3-indol acetic acid (IAA), siderophores and ammonia.
## 2 Background and Related Work  
### Service-Oriented Architectures  
Service-oriented Architecture (SOA) describes a collection of design principles for the development of distributed systems. SOA relies on the paradigm of service-orientation, _i.e.,_ the different software components provide their functionalities as loosely-coupled services over a network. This means that services provide functionalities over standardized interfaces independent from the underlying implementation. Web service technologies have emerged as the most prevailing implementation of SOA. XML-based languages like WSDL (Web Service Description Language) are used to describe a service's interface, functionalities and characteristics. The most important activities in a SOA-based system are discovery and orchestration. The UDDI (Universal Description Discovery and Integration) specification depicts a way to build service repositories, in which services can be published by service providers and searched and discovered by service requesters. The combination of existing web services to more complex services or processes is usually performed using BPEL (Business Process Execution Language).  
There are several issues concerning the implementation and usage of systems based on the SOA paradigm using syntactic approaches [7]. Semantic technologies can be used to describe the meaning of service capabilities in a machine-understandable manner by adding a semantic layer to the syntactic service descriptions. In this way, an efficient discovery and dynamic orchestration of services can be realized. In the following sections, semantic technologies and the different approaches for the definition of semantic web services are illustrated.
## 4 Results and Discussion  
### Implementation Details  
In all experiments, the following hyperparameters, hardware, and software are used. For the EAR-Net, the batch size is set to 8, epoch to 120, and learning rate to 0.001, and the AdamW optimizer [30] is used. In addition, a poly learning rate scheduler [14] is used to approach the lowest point. The following data augmentation are applied: a random crop that cuts out an area of 1024 \(\times\) 512 resolution from an input image at a random location, a random horizontal flip that flips the image horizontally with a probability of \(\frac{1}{2}\), and a color jitter that randomly changes the brightness, contrast, and saturation of the image. In addition, we use a model pre-trained with ImageNet in our experiments. The hardware and software used in the experiment are shown in Table 1. Code is available at [[https://github.com/synml/segmentation-pytorch](https://github.com/synml/segmentation-pytorch)]([https://github.com/synml/segmentation-pytorch](https://github.com/synml/segmentation-pytorch)) (accessed on 30 September 2021).  
Experimental results are compared and analyzed, using mean intersection over union (MIoU). MIoU is an evaluation metric for measuring accuracy in semantic segmentation and is defined in Equation (9).  
\[MIoU=\frac{1}{k}\sum_{i=0}^{k}\frac{TP}{TP+FP+FN} \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Descriptions** \\ \hline CPU & AMD Ryzen 3700x \\ GPU & NVIDIA RTX 3090 2\(\times\) \\ RAM & 64 GB \\ OS & Ubuntu 21.04 \\ Framework & PyTorch 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware and software environment.  
Here, \(TP,FP,FN,k\) represent true positive, false positive, false negative, and class number, respectively.
## 4 Implementation of the Proposed Method
The effectiveness of the entire extraction module relies on the careful execution of each phase, ensuring that the secret message is retrieved accurately while the carrier image remains unaltered in appearance. Through these four phases (Conversion, Selection, Modification, and Secret Message Generation), the module efficiently extracts hidden information from images, employing advanced mathematical techniques to ensure the reliability and integrity of the data extraction process. Figure 10 represents the general scenario of the extract data method, which is based on the Hermitian positive definite matrix. The histogram was analyzed for the B channel of the original image and the stego-image with embedding secret data was generated by the Hermitian positive definite matrix. Histograms are shown in Figures 11 and 12. Figure 10: General scenario for data extraction. The histogram (Figure 11) illustrates the distribution of pixel values in the blue (B) channel of an image, with values generally ranging from 0 to 255. Areas of high frequency in the histogram indicate prevalent colors in the B channel. During the comparison with the stego-object (Figure 12), two main factors are crucial: (1) Determining whether the histogram is uniform, bimodal, or skewed, as this can reveal the overall color balance of the image; (2) Identifying any pixel values that are significantly low or high, which may signify extreme colors in the image. Figure 13 represents the result of the histogram comparison of the original and stego-images.
###### Abstract  
Look-up table (LUT) based method is a popular and effective way for inverse halftoning. However, it still has very large development space to improve the reconstructed color image quality for color halftone images, because most of the existing color inverse halftoning methods are the simple extension of LUT methods to each color components separately. To this end, this paper presents a novel color inverse halftoning method by exploiting the correlation of multi-color components. Through considering all existent contone values with the same halftone pattern in three color component tables, we firstly propose a concept of common pattern. Then the extreme learning machine (ELM) is employed to estimate the contone values for nonexistent patterns according to common patterns in color LUT, which can not only improve the fitting precision of nonexistent values but also has fast transformation speed. Experimental results show that the proposed method achieves a better image quality when compared to previously published methods.  
color inverse halftoning; look-up table; correlation of color components; extreme learning machine
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 4 Experiment and Analysis
### Pest Image Preprocessing
#### 4.1.3 Mosaic Image Augmentation  
To enhance the training speed and accuracy of the model, Mosaic image augmentation is applied after images are input. The following transformations are applied to each image in the training set, preserving image categories:  
Figure 4: Original Image of 00197.jpg.  
Figure 5: Image After Adaptive Histogram Equalization.  
Figure 6: Image After NLM Processing.  
1. Random rotations, translations, scaling, cropping, padding, and flipping within specified limits to display varied visual perspectives of the same target; 2. Adding random noise disturbances like salt-and-pepper and Gaussian white noise to the image; 3. Color transformations involving principal component analysis on the RGB color space to derive three principal component vectors, p1, p2, and p3, along with their eigenvalues, followed by incremental adjustments to each channel; 4. Modifying image attributes including brightness, clarity, contrast, and sharpness.  
Mosaic data augmentation enhances the background information of detected objects and compensates for the inability to set larger batch sizes due to hardware limitations, effectively increasing the batch size indirectly.  
Figure 7 shows four images, 00388.jpg, 00418.jpg, 00436.jpg, and 00501.jpg, processed with Mosaic data augmentation. The positions of pests are marked by white boxes in the images.
# Role of Nutribiotics in Skin Care
## 6 Conclusions and Future Perspectives  
Intense research into the gut microbiome has provided tools to explore the role of the microbiota in other physiological systems, including the skin. Despite the scarce knowledge about the physiological role of skin microbiota in cutaneous biology, several strategies have been implemented to modulate the microbiome and improve skin health. The most recent strategy is the use of pre, pro, syn, and post and para-probiotics, which seems to be beneficial on different skin disorders as atopic dermatitis, psoriasis, acne, seborrheic dermatitis, etc., as well as repairing the skin barrier, preventing aging, or promoting wound healing.  
Nevertheless, a deeper understanding of the skin microbiome, the distribution of cutaneous microbial communities, as well as the differences between microbiomes in healthy and altered skin is needed. Thus, metagenomic technologies could facilitate the functional characterization of the microbiome and perhaps even provide a personalized approach to diagnosing and treating conditions underpinned by microbial dysbiosis [44].  
It should also consider the potential adverse effects of probiotics, such as risk of systemic infections, excessive immune stimulation in susceptible individuals, minor gastrointestinal side effects [271], or disturbances in the abundance in the gut _Bifidobacteria_, as shown in subjects who take unregulated probiotics having a significantly lower relative abundance of _Bifidobacteria_, which could potentially have a detrimental impact on health [272].  
New investigations about the use of nanotechnology in the probiotic's formulation are also of great interest as its delivery without any effect on gastrointestinal digestion is one of the most important points for their application [273]. Additionally, the genetic engineering of microbiomes has recently become an area of interest for researchers since it provides solutions to a significant health problem. In this context, strategies such as conjugative plasmids, bacteriophage, mating-assisted genetically integrated cloning (MAGIC), and environmental transformation sequencing (ETSeq) could be effective in the genetic modification of the microbiome [2].  
In conclusion, skin microbiome has emerged as a new field with high potential to develop innovative solutions to manage skin health and disease. Future advances in this field may facilitate the treatment of skin dysbiosis through means that are more sensitive to the physiology of the skin, with nutribiotics being a suitable method for skin care.  
M.L.M.: Conceptualization, Methodology, Writing--original draft preparation, Writing--review and editing. C.P.G.: Methodology, Investigation, Writing--review and editing. J.L.L.: Writing--review and editing. L.P.: Writing--review and editing. Supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### ML Classification
#### 4.3.2 Comparing between Meta-Heuristic Ensembles and Traditional Classifiers  
The superior performance of the meta-ensemble models (VE and SE) on the multi-class prediction of intrinsic compressibility of soils can be further validated by comparing them to other traditional ML multinomial classifiers namely, artificial neural networks (ANN), logistic regressor (LR), boosted decision trees BDT) and random decision forest (RDF). The sensitivity scores of the multiclass classification given in Table 9 indicates that both the stand-alone and tree-ensemble traditional ML classifiers do have the capability of distinguishing between soil plasticity categories just like the meta-heuristic ensemble models. Notwithstanding, among the traditional classifiers, the tree ensembles have clearly outperformed the stand-alone algorithms. ML prediction given by ANN is generally the least accurate as could be observed from Table 9. Some of the elements of the structure and architectural make-up of neural networks that do sometimes contribute to its poor performance, especially when applied in ML prediction problems such as those considered in this study, were stated previously in the regression prediction. Nevertheless, it is quite apt to add that the stand-alone models (ANN and LR) are in many respects quite similar given their common roots in statistics. However, the functional form of expression used as indicated in their mathematical equations stated above is what differentiates these classifiers. That which is used in LR is parametric whereas the ANN operates semi- or non-parametric functions. This is a very important distinction because most of the contributions given by parameters of an LR can be sufficiently interpreted whereas, as stated previously in the discussion of ML regression, those of ANN whose contributions are from the weights and biases may not be easily interpreted. On the other hand, the relatively high prediction accuracy of the tree-based classifiers is partly because their behaviour in learning is often suggested by a prior set of rules and hence, they are referred to as 'white-boxes' as compared to the neural networks. Hence, they tend to predict outcomes of classifications by continually splitting their inputs based on a set of criteria which then leads to a maximisation of the separation between the dataset and a decrease in entropy. The tree-based classifiers do have their setbacks hence, an aggregation of models into multiple classifiers carried out by stacking or by voting does further increase the accuracy of prediction as seen in Table 9 with the dataset trained, tested, and validated by the kFCV method.
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
###### Abstract  
Blockchain technology has been applied in a wide range of domains and has seen major developments in the last years, notably when integrated with other emerging technologies. In this paper, we focus on the integration of blockchain and digital twins with the aim of enhancing the capabilities of both technologies. In particular, we demonstrate how blockchain can improve critical aspects of the security and transparency of digital twins by analyzing a real-world scenario and evaluating produced experimental data. This research identifies and addresses critical vulnerabilities in digital twins, particularly data integrity and transparency, through blockchain-based validation mechanisms and smart-contract integration. Various blockchain-related and digital twin-related technologies are employed to enable the repeatability of the suggested approach. Additionally, an in-depth analysis of such integration is provided to facilitate a symbiotic relationship between these technologies by addressing key challenges, such as scalability, interoperability, and performance, along with viable solutions that could advance their co-evolution in both academic research and industrial applications.  
blockchain; digital twin; surveillance; Ethereum; smart contract Article
## 4 Conclusions  
In this research, a methodological proposal was made for selecting vibration signal characteristics in the time domain for a spur gearbox based on the wrapper method using RF as a classifier. The study employed a complex and realistic dataset, generated under varying motor speeds and load conditions, to better simulate real-world operating scenarios. With the proposed methodology, 7 CIs (Temporal moments higher order, Mean, Skewness, Zero crossing, Slope sign change, Energy operator and Kurtosis) were obtained, which are predominant in the four failure types: breaking, cracking, pitting and scuffing for the feature extraction process and the performance of the classification models.  
By using the 7 CIs obtained in the feature selection stage in the RF and K-NN classification models, the average values of the classification accuracy of the failure severity level as well as the AUC values exceeded 98% across all six accelerometers and four failure types, indicating that the selected CIs are highly effective for analysing vibration signals and accurately determining the severity level of various failure types.  
The comparison of classification accuracy values using factorial ANOVA and Tukey's post-hoc tests revealed statistically significant differences associated with variations in sensor position and inclination. These differences, being less than 1%, are of no practical importance, so when a sensor is installed in a gearbox by moving it either along the drive shaft or the driven shaft and with some degree of inclination, the information obtained from the vibration signal will be minimally affected. The developed methodology is ideal for early detection and assessment of failure severity levels in gearboxes.
_applied sciences_  
_Acticle_  
**Deadbeat Predictive Current Control for Surface-Mounted Permanent-Magnet Synchronous Motor Based on Weakened Integral Sliding Mode Compensation**  
**[PERSON] 1,2, [PERSON] 3, [PERSON] 2, [PERSON] 3,* and [PERSON] 1,*  
1 School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang 212013, China; [EMAIL_ADDRESS]
2 Nantong Academy of Intelligent Sensing, Nantong 226010, China; [EMAIL_ADDRESS]
3 Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China; [EMAIL_ADDRESS]
4 Correspondence: [EMAIL_ADDRESS] (D.S.); [EMAIL_ADDRESS] (Y.X.)  
**Abstract:** Deadbeat predictive current control (DPCC) has excellent dynamics and can achieve current control with less computational effort. However, its control performance relies on the precision of the parameters of the motor. Current static error will be generated and control performance will be decreased when the predictive model parameters do not correspond to the practical parameters of the motor. In this article, a weakened integral sliding mode compensation method is proposed which converts the current error into a voltage compensation term and adds it to the prediction control output to effectively compensate for the steady-state error. In addition, a boundary layer is introduced to weaken the integral so as to solve the problems of integral saturation and overshoot caused by the introduction of the integral term when the error is large. Moreover, weight factors are introduced to optimize the feedback current, which enhances the robustness of the system. In the end, the effectiveness of this method is verified via simulation and experimental results.  
**Keywords:** deadbeat predictive current control (DPCC); weakening integral; error compensation; permanent-magnet synchronous motor (PMSM); robustness