esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Metal Complexes  
Metal complexes containing a transition metal and an amine or ammonium component are good solvents of cellulose. One of the important examples is [PERSON]'s reagent ([Cu(NH\({}_{3}\))\({}_{4}\)(H\({}_{2}\)O)\({}_{2}\)](OH)\({}_{2}\)), which was originally used in the production of artificial silk. Today, many alternatives exhibiting good solution properties are known, e.g., [Cu(NH\({}_{2}\)(CH\({}_{2}\))\({}_{2}\)NH\({}_{2}\))\({}_{2}\)](OH)\({}_{2}\) (Cuoxen), [Ni(NH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\))\({}_{3}\)N](OH)\({}_{2}\) (Nitren), [Pd(NH\({}_{2}\)(CH\({}_{2}\))\({}_{2}\)NH\({}_{2}\))](OH)\({}_{2}\) (Pden), and [Cd(NH\({}_{2}\)(CH\({}_{2}\))\({}_{2}\)NH\({}_{2}\))\({}_{3}\)](OH)\({}_{2}\) (Cadoxen). The last one was very efficient in dissolving cellulose with high DP. The mechanism involves deprotonation and coordinative binding of hydroxyl groups in the C2 and C3 positions of the anhydroglucose units [59; 65; 66].
## 5 Simulation Study
### Impact of Varying Node Speed  
Network performance is closely related to node moving speed, so this group of experiments depicts the performance of the two protocols by varying the maximum moving speed of subscriber nodes. The experimental results can be seen from Figure 7.  
We obtain that the CED protocol outperforms DG in terms of delivery ratio. With the increase of node movement speed, the delivery ratios of both CED and DG rise. This is because the subscriber node with a higher speed has a higher frequency of meeting sensors in a community. Thus, events have a higher chance to be delivered before they are dropped. We also notice that the transmission overhead of the proposed CED increases with the increase of node moving speed, as shown in Figure 7(b). This  
Figure 6: Delivery ratio vs value of \(\alpha\).  
Figure 7: Impact of subscriber moving speed.  
is because more event copies are produced when more sensors are encountered with higher node moving speed then. Figure 7(b) also depicts that the moving speed of subscribers has little impact on the average event copies in DG protocol. Figure 7(c) demonstrates that with the increase of node moving speed, the delivery delay of both CED and DG decreases. This is reasonable since events can be forwarded to interested subscribers quicker with a higher node moving speed.
## 3 Case Study: Structural Factor Analysis for Public Transportation Bus
### Discussion
#### 3.6.2 Managerial Insights  
Analyzing failure-causing factors has multifaceted practical contributions which benefit various stakeholders, including bus company owners, engineers, and citizens.  
From the perspective of bus company owners, the insights gained from this analysis can significantly improve operational efficiency and financial performance. The insights derived from the analysis and the recommendations of this work can be seamlessly integrated and optimize the real-world maintenance processes in the bus depots. By prioritizing maintenance efforts on the most significant failure-causing factors, companies can reduce the frequency of breakdowns and extend the lifespan of their fleets. Such a maintenance approach can lower overall maintenance costs and minimize service disruptions. Moreover, the ability to better manage resources and inventory based on empirical evidence helps bus companies optimize their budgets and improve cost effectiveness. One of the significant advantages of the proposed maintenance optimization is that it does not incur extra costs or require additional resources. This work does not require the installation of sensors, data collectors, or other IoT devices which make better use of collected maintenance records and expert judgments.  
Engineers, who are responsible for the implementation of maintenance at bus depots, can allocate maintenance resources more effectively. Focusing on the most significant factors allows maintenance efforts to be prioritized efficiently. Understanding the interrelationships between failure-causing factors enables the development of optimized maintenance plans to prevent minor issues from escalating into major problems. Therefore, the in sights from this analysis support resource allocation, maintenance schedule optimization, and inventory management. Additionally, the strategic approach proposed can lead to continuous improvements in maintenance practices with the updated empirical evidence, which results in a more resilient public transport system capable of maintaining high service quality even under challenging conditions. Furthermore, the outcomes of this work can also facilitate the training of new employees as clear guidelines.  
The practical benefits are substantial for citizens, who are the ultimate users of public transport. A more reliable and safe bus service means fewer delays and a more pleasant commuting experience. A well-maintained bus fleet ensures that passengers can travel safely and comfortably. Additionally, improved public transport reliability encourages more people to use buses, reducing traffic congestion and environmental pollution. Enhanced public transport bus services contribute to social equity by providing all citizens with dependable and affordable mobility options.
# Polymer-Stabilized Silver (Gold)-Zinc Oxide Nanoheterodimer Structures as Antimicrobials
###### Abstract  
A simple one-pot method is proposed for obtaining the colloidal nanohybrid structures of silver (gold) and zinc oxide as well as nanostructures doped with zinc ions. The copolymers of maleic acid were used for the stabilization of nanoheterostructures. To characterize the preparation, UV-Vis spectroscopy, TEM, FTIR, XPS, and XRD were used. The bactericidal properties of the nanoheterostructures were studied in relation to the fungus _C. albicans_ and the bacteria _E. coli_ and _S. aureus_, used in planktonic form. In general, the samples containing nanosilver were the most active, and the preparations containing gold nanoparticles were the least active. The minimum inhibitory concentrations (MICs) of the Ag/ZnO samples, based on all copolymers, were in the ranges of 1.4-1.7 \(\mu\)g/mL for _C. albicans_, 2-6.8 \(\mu\)g/mL for _E. coli_, and 23-27 \(\mu\)g/mL for _S. aureus_; the MIC values of Au/ZnO samples were 472 \(\mu\)g/mL for _S. aureus_ and 945 \(\mu\)g/mL for C. _ albicans_ and _E. coli_. The additional introduction of zinc cations into heterodimers had practically no effect on the antimicrobial properties of the composites. For all prepared composites and all tested microorganisms, the fractional inhibitory concentration indexes were in the range of 0.5-2.2, which indicates a close-to-additive contribution of the bioactive components in the samples used in the bactericidal process.  
copolymers of maleic acid; one-pot synthesis; Ag/(Au)ZnO nanoheterostructures; antibacterial activity +
Footnote †: journal: Applied Sciences
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
## 3 Results
### Physicochemical and Thermal Characterization of Sediment Samples Treated with Phosphogypsum
#### 3.1.5 XRF Analysis  
The chemical compositions of the sediment and both mixtures are primarily comprised of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), which indicates that clay minerals are significantly present (Table 6). Apart from these dominant oxides, there are also MgO, K\({}_{2}\)O and CaO, while SO\({}_{3}\) is present in a higher percentage in the F-3 and F-5 mixtures, which is understandable. Oxides Na\({}_{2}\)O and P\({}_{2}\)O\({}_{5}\) are presented in trace amounts. The assumption is that \"other\" includes a significant part of the organic substance. The mineral composition of phosphogypsum, which includes gypsum (CaSO\({}_{4}\) 2H\({}_{2}\)O) and other impurities, may provide reactive sites for heavy metal ions. This interaction can lead to the formation of less soluble complexes, thereby sequestering heavy metals and minimizing their concentration in the bulk sediment.  
The concentration of seven heavy metals (Cr, Ni, Cu, Zn, As, Cd and Pb) in sediment and mixtures is also determined by XRF and is shown in Table 6. As can be seen, the amount of all heavy metals is lower in the mixture with sediment and phosphogypsum compared to just sediment. The structural properties of phosphogypsum may also enhance the physical encapsulation of heavy metals within the sediment matrix, limiting their mobility and reducing leaching potential, as explained in the next section.
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Accuracy**} & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & \(1.000\) & 0.248 & **0.000** & 0.232 & 1.000 & **0.000** & **0.000** \\ \hline ANN & 0.248 & 1.000 & **0.000** & 1.000 & 0.356 & 0.107 & 0.375 \\ \hline LR & **0.000** & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline SVM & 0.232 & 1.000 & **0.000** & 1.000 & 0.336 & 0.116 & 0.396 \\ \hline LGBM & 1.000 & 0.356 & **0.000** & 0.336 & 1.000 & **0.000** & **0.001** \\ \hline CB & **0.000** & 0.107 & **0.000** & 0.116 & **0.000** & 1.000 & 0.997 \\ \hline XGB & **0.000** & 0.375 & **0.000** & 0.396 & **0.001** & 0.997 & 1.000 \\ \hline
**Precision** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & \(1.000\) & **0.000** & **0.005** & 1.000 & 0.225 & **0.000** & **0.000** \\ \hline ANN & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & 0.996 & 1.000 \\ \hline LR & **0.005** & **0.000** & 1.000 & **0.002** & **0.000** & **0.000** & **0.000** \\ \hline SVM & \(1.000\) & **0.000** & **0.002** & 1.000 & 0.365 & **0.000** & **0.000** \\ \hline LGBM & 0.225 & **0.000** & **0.000** & 0.365 & 1.000 & **0.000** & **0.000** \\ \hline CB & **0.000** & 0.996 & **0.000** & **0.000** & **0.000** & 1.000 & 1.000 \\ \hline XGB & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & 1.000 & 1.000 \\ \hline
**Recall** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & \(1.000\) & **0.000** & **0.000** & **0.004** & 0.170 & **0.002** & **0.000** \\ \hline ANN & **0.000** & 1.000 & **0.000** & **0.000** & **0.005** & 0.273 & 0.771 \\ \hline LR & **0.000** & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline SVM & **0.004** & **0.000** & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** \\ \hline LGBM & 0.170 & 0.005 & **0.000** & **0.000** & 1.000 & 0.798 & 0.299 \\ \hline CB & **0.002** & 0.273 & **0.000** & **0.000** & 0.798 & 1.000 & 0.986 \\ \hline XGB & **0.000** & 0.771 & **0.000** & **0.000** & 0.299 & 0.986 & 1.000 \\ \hline
**f1** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & \(1.000\) & 0.995 & **0.000** & 0.055 & 0.996 & 0.135 & 0.592 \\ \hline ANN & 0.995 & \(1.000\) & **0.000** & **0.007** & 1.000 & **0.022** & 0.203 \\ \hline LR & **0.000** & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline LR & **0.000** & **0.000** & 1.000 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline SVM & \(0.055\) & **0.007** & **0.000** & 1.000 & **0.007** & 1.000 & 0.902 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: [PERSON] post-hoc analysis across multiple metrics (\(p\)-values).
# A Survey on Fault Diagnosis of Rolling Bearings
## 6 Conclusions  
The rolling bearing is an indispensable part of rotating machinery, and its running status typically affects the operation of the whole equipment. The research into rolling bearing fault diagnosis technology is of great significance to ensure the safe and stable operations of rotating machinery. This paper comprehensively reviewed existing fault diagnosis methods of the rolling bearing in terms of fault detection and fault type recognition. For fault detection, the methods, i.e., morphological transformation-based methods, filter-based methods, decomposition-based methods, and deconvolution-based methods, were discussed. For fault type recognition, traditional methods and deep learning-based methods were discussed. The commonly used datasets of fault diagnosis were presented for better practices. In addition, we summarized the limitations of existing methods and pointed out future research directions, which provides helpful guidance for researchers who are interested in this field. Overall, this field of fault diagnosis has potential for future study. Given the current limitations, it is still needed to develop automatic, intelligent, effective, and efficient methods for rolling bearing fault diagnosis under real-world scenarios. In addition, some topics such as transfer learning, few-shot learning, and evolutionary deep learning can also be further investigated to enrich this field.  
**Author Contributions:** B.P.: writing--original draft preparation; Y.B.: writing--review and editing; B.X., M.Z. and S.W.: supervision and revision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Scientific Research Foundation for the Talents of Hebei Agricultural University (No: YJ2021056) and Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention Fund (No.: KF2021-01).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Illustrations of the proposed GPAFEC method in [183]. (**a**) Flowchart of GPAFEC, (**b**) Solution evolved by GP.
# Bounce: A High Performance Satellite-Based Blockchain System
## 5 Implementation
### Prototype Public Key Infrastructure  
In our implementation, each agent's (whether Sending Station, Broadcast Ground Station, and satellite Bounce Unit) private key and corresponding public key are initialized using the BLS algorithm. Mission Control has all agents' public keys. At the very beginning of the protocol, Mission Control, acting as a Certificate authority, sends a start message, which contains the public keys of all the agents to all agents, so that each agent can authenticate messages from other agents.
## 1 Introduction
Leveraging natural language models [11; 12] becomes advantageous as we can extract and analyze a scripting code from document-type malware. Previous research on document-based malware needs to be expanded to develop a comprehensive model applicable to various document types, as current efforts focus on extracting features from malicious files in HWP and PDF documents. To effectively detect malware and its variants, it is imperative to employ generic methods that are not reliant on specific samples. Deep models are good at extracting features without human intervention in feature definition, making them ideal candidates for constructing a generic model. Therefore, using Transformer, we propose a language model known for its versatility across multiple languages for developing the document-type malware detection and family classification model. Transformer offers several key advantages over traditional machine learning models, including an attention mechanism focusing on relevant segments of the input sequence during prediction. This mechanism proves particularly useful for malware analysis, enabling the identification of key patterns and dependencies within the scriptingcode of malware samples. Furthermore, the context-aware nature of Transformer helps it to capture relationships between different segments of the malware code, leading to more accurate detection. We require a model capable of handling long-range dependencies to comprehend extensive relationships effectively. To convert script codes into meaningful numerical vectors (as a deep learning model exclusively accepts numerical input), we propose employing a Word2 Vec model [13] on the malware script codes. Furthermore, to solve the class imbalance issue within the malware families and improve detection performance, we use a generative adversarial network (GAN) operating at the script code level. Our first focus is on HWP files. Our detection framework can be applied to PDF or RTF file formats; an HWP document shares the same object linking and embedding (OLE) structure as MS documents and rich text format (RTF) files. A malicious code is often embedded within objects, and extracting a malicious code from document objects is feasible. The main contributions are listed as follows:  
* We propose a Transformer model tailored for document-based malware detection and classification. Our method is designed to perform detection at both the stream and file levels. * We propose using a word-embedding model, called MalCode2 Vec, which we pre-train on malware script codes. * We propose employing a generative adversarial network to make malicious fake samples. These generated samples are used to solve the class imbalance problem within the malware family. Our manuscript is organized as follows: In Section 2, we review related research and explore different approaches. Section 3 describes the proposed malware detection and classification model. Section 4 presents the experiment results. In Section 5, we address the discussion from the experiment results. Finally, Section 6 provides conclusions to this paper.
# An Alternative Globalization Barometer for Investigating the Trend of Globalization
## 3 Results
### Driving Forces
#### 3.3.1 Driving Forces of de Facto Globalization Barometer  
In 2020, the _de facto_ economic Globalization Barometer was in trend (99.26). The _de facto_ social Globalization Barometer was also in trend (100.86), while the _de facto_ political Globalization Barometer was below trend (97.44).  
First, the _de facto_ economic globalization displayed remarkable buoyancy in the face of the significant shocks caused by the COVID-19 outbreak. In response to the virus, economies around the world implemented lockdowns and restrictions, generally disrupting international trade. However, after a brief negative depression, global trade quickly turned positive in the second half of 2020, as suggested by the DHL connectedness index. Mirroring this trend was the global financial markets, which responded negatively with increased  
Figure 5: Three-dimensional forces of globalization barometer. (**a**) Three-dimensional forces of _de facto_ globalization barometer. (**b**) Three-dimensional forces of _de jure_ globalization barometer.  
uncertainty and systematic risks during the pandemic, likely exacerbated by the loss of confidence from investors and other actors responding to more pessimistic forecasts. Yet, a general recovery in the global trade was thought to have contained the panic in the global financial markets, contributing to more stability and positive prospects regarding international capital flows.  
Second, _de facto_ social globalization is characterized by structural differences. Restrictions on international travel greatly disrupted cross-border human exchange, leading to a fall in interpersonal globalization. The main means of international transportation, air transport services, including the airline industry, suffered a great blow. However, it is also worth mentioning that informational globalization boomed during the pandemic, to some extent mitigating social impacts. Limitations on public gatherings and activities led to a significant increase in online activities and far more frequent internet use compared with pre-pandemic times, which fostered thriving online communities. The exchanges of information became considerably more efficient thanks to the internet, and informational globalization acted as a major counter-weight against the negative impact of interpersonal globalization. Therefore, _de facto_ social globalization maintained a mildly steady trend in 2020.  
Third, as for political globalization, both international cooperation and governance displayed a downward trend in the post-pandemic period. A relatively lower speech contribution in UN conferences, which brings more uncertainty to political globalization, may provide one of the reasonable explanations for the trend.  
Overall, there are well-grounded reasons to expect the optimistic development of economic and social globalization in the short-term, whereas more attention needs to be paid to the dynamics of _de facto_ political globalization.
# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 4 The Proposed Method
### Image Preprocessing Stage
#### 4.1.1 Noise Reduction
All results of accuracy are presented in Table 4. The best results of the accuracy were obtained using the \"tansig\" activation function, the learning rate was 1.00 and accuracy was 80.04% with training time was 17.687415 s.

### The Results of Scenario 5

The accuracy of training and testing of the different activation functions are evaluated and compared. The summary of the results of this scenario is presented in Table 5. The best results (accuracy of the trained and tested data) were obtained using the \"tribas\" activation function and it was 99



is the best in terms of the accuracy, specificity, and sensitivity. In addition, our results have obtained from the largest dataset of all the compared work. This means that our results would be much reliable in terms of the scale-ability. In addition to the above comparison, we compared our method with the CNN based method [26] which reported classification results (TPR = 100% and PPV = 100% using a dataset containing 73 breast images). It is obvious that the CNN-based method achieved a slightly better results. However, our results are concluded from using a large dataset (1345 images) while CNN-based one obtained from 73 breast images. ## 7 Conclusions

This paper introduced a comparative study between two machine learning techniques (MLP and ELM) for the early detection of breast cancer through thermograms. Before applying any of these techniques, the entered images were firstly pre-processed, ROI was then segmented and finally, features were extracted. Under different scenarios, experiments were conducted using public dataset (DMR-IR). Different activation functions of both MLP and ELM were tested and investigated too. The experimental results showed that ELM-based breast cancer detection gave the best accuracy (100%) while the MLP classifiers gave only 82.20%. The ELM results were obtained using its TRIBAS function and only one hidden layer. Also, it was found that ELM is much faster than MLP. These promising detection results would be an important step toward an automatic detection of breast cancer using thermal images. The limitation of the proposed method depends on the quality of the protocol used to capture the thermograms. As the thermal images depends mainly on the temperature, the room and patient's temperature could affect the predication techniques. In future work, we think there are two points which could further improve/confirm the results of this study. Firstly, a thorough comparison and analysis between ELM and CNN could be conduct. Secondly, to advance the performance of the proposed segmentation technique, it is suggested to use more extracted features of different types to evaluate the classifier performance.