esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Common Synthesis Methods of ZnFe\({}_{2}\)O\({}_{4}\)
### Wet Chemistry Methods  
Hydrothermal, solvothermal, sol-gel, and co-precipitation are the most exploited wet synthesis methods [51; 52]. They share the use of a liquid solvent and a series of peculiar aspects, such as the easy tuning of the particle size and shape, a high homogeneity of the products, and the easiness to obtain nanoparticles. The main advantage of hydrothermal synthesis, over other conventional wet-chemistry methods, is that it happens under non-standard conditions, so that unconventional crystallization pathways can occur. Together with the numerous advantages, a drawback for the hydrothermal method could be represented by the need that the synthesizing compounds are not sensitive to aqueous atmospheres. In addition, attention should be paid to a proper choice of precipitating agents that can influence the physical and chemical characteristics of spinel; the eventual defects of the obtained spinels can be removed by additional thermal treatments, prolonging the synthesis time. Many factors such as the temperature, reagents' ratio, solvents, and the kind of salts can be varied to customize the size and shape of the ferrite nanoparticles. It is common to start from zinc and iron nitrates, [38] chlorides, or acetates [53] dissolved in an aqueous medium. In some cases, carbon sources are also added to obtain, at the same time, a coating carbon layer [54; 55; 56] (Figure 5).  
During co-precipitation syntheses, the particle growth is controlled only by kinetic factors, such as pH, temperature, ionic strength, salts' nature, or Fe(II)/Fe(III) ratio. The sol-gel technique takes advantage of good stoichiometric control and homogeneity, short preparation time, and inexpensive precursors. Generally, to produce multicomponent oxides, alkoxides (or sometimes acetates or nitrates) are mixed in alcohol at a proper pH: Attention should be paid to salts' concentration and temperature for adequate hydrolysis [57; 58]. The obtained peculiar morphology can be commonly determined with microscopies such as SEM and/or TEM. They are mainly applied to evidence the particle shape (round, needle, hollow sphere, etc.) or size (from micron to nanometers) and the eventual porosity or roughness of samples [59; 60; 61]. SAED images obtained by HR-TEM provide the interplanar distances and a possible phase attribution. In addition, in cases of carbon coating, the identification of the thickness of the amorphous layers is also common. The EDS microanalysis, coupled with microscopies, provides insights into the elemental composition: the visual detection of ions' distribution can also be obtained in the form of colored maps [58]. The particle sizes (nanoparticles) and surface area, two obviously related parameters, are at the base of Li\({}^{+}\) diffusion into the ferrite grains.
## 2 Results
### Computationally Assisted Identification of the Prenylated Product by Using SIRIUS (Version 4.8.2)  
According to the user manual, SIRIUS requires high mass accuracy in which the ppm error is less than 20 ppm before conducting the annotation processes for the most reliable results [55]. The observed molecular ion _m/z_ 365.21 [M - H]\({}^{-}\) was thus estimated by using the mass error calculation tool from the web service ([[https://warwick.ac.uk/fac/sci/chemistry/research/barrow/barrowgroup/calculators/mass_errors/](https://warwick.ac.uk/fac/sci/chemistry/research/barrow/barrowgroup/calculators/mass_errors/)]([https://warwick.ac.uk/fac/sci/chemistry/research/barrow/barrowgroup/calculators/mass_errors/](https://warwick.ac.uk/fac/sci/chemistry/research/barrow/barrowgroup/calculators/mass_errors/)) (accessed on 17 June 2021)), by comparing against its theoretical _m/z_ (365.212218 [M - H]\({}^{-}\)), and showed an error of 8.762029 ppm, permitting the elucidation of the raw mass data by using this tool.  
SIRIUS utilizes the high-resolution isotopic pattern analysis to locally annotate the correct molecular formula based on the experimentally acquired MS/MS data of the query subjects. Among the potential ten elemental formulas retrieved from the PubChem database, our query subject was annotated as C\({}_{24}\)H\({}_{30}\)O\({}_{3}\) with the highest Sirius score of 62.243% (Figure 5A), which was identical to the native elemental formula of ferulenol (2) (C\({}_{24}\)H\({}_{30}\)O\({}_{3}\)) deposited in the PubChem database ([[https://pubchem.ncbi.nlm.nih.gov/compound/Ferulenol](https://pubchem.ncbi.nlm.nih.gov/compound/Ferulenol)]([https://pubchem.ncbi.nlm.nih.gov/compound/Ferulenol](https://pubchem.ncbi.nlm.nih.gov/compound/Ferulenol)) (accessed on 17 June 2021)). SIRIUS also offers a refined search option to explore the query subjects against biological databases, e.g., Natural Products, Collection of Natural Products (COCONUTS), and NORMAN databases to specify and narrow natural molecules at user-defined cut-offs. By searching the possible structure against the aforementioned databases, our query metabolite (compound-1) was annotated as C\({}_{24}\)H\({}_{30}\)O\({}_{3}\) with a score that greatly improved to 100% (Figure 5A). The annotated MS/MS spectra locally computed by the SIRIUS tool revealed that 8 of 11 peaks (indicated in the green spectra) matched with their local database (Figure 5B). The result was also consistent with those fragmentation spectra predicted by MetFrag, where 8 out of 11 peaks matched with the in silico fragmented ions of the candidate ferulenol (2) in the PubChem database (Figure 4).  
Figure 4: Structural annotation of compound-1 (_m/z_ 365.21 [M - H]\({}^{-}\); Rt = 21.3 min) using MetFrag web service, where the query subject was best annotated as ferulenol (2) among 2267 structures retrieved from the PubChem database.
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 9 Evaluation and Analysis
### Limitations
#### 9.5.4 Robustness and Contingency Strategies  
To ensure the robustness and contingency strategy of the proposed system, we implemented several techniques to handle the conditions of incomplete data or failures that may occur. As previously described in the multi-thread implementation, when one of the APIs fails to fetch data, the system will return information about which web APIs failed. However, APIs that successfully fetch data will continue to work on the task until it is finished and display the paper list results to the user. For the mechanism to handle incomplete metadata, the system gives an \"N/A\" mark for incomplete field metadata and applies an alternative method by using fast PDF checking based on _web scraping_ for inaccessible PDF URLs.  
This mechanism may not be able to fully provide all the data that is expected to be complete, especially in cases where the value in the data source field is empty. Even there is a response failure that cannot be fulfilled by the API platform simultaneously. Therefore, future improvements may involve integrating additional open and legally accessible data sources to enhance data completeness and quality.
## 4 Case Study and Applications  
In order to evaluate a Spark-based implementation that can run on both CPUs and GPUs, it is proposed that two cases be considered. This would allow the full advantage to be taken of the performance capabilities that the DSPSO and DAPSO variants of the PSO algorithm implementation can offer. The following is a summary of the advantages that can be realised through the implementation of a PSO distributed approach:  
* Efficiency improvement: The process of parallelisation makes use of the computing power of multiple resources, including graphics processing units (CPUs or GPUs) or central processing units (CPUs), thereby accelerating the training process.
* Faster scanning: The parallelisation of the PSO algorithm permits the evaluation of multiple solutions to be evaluated simultaneously, thereby speeding up the search for optimal solutions in a large parameter space.
* Scalability: The algorithm is capable of handling problems of varying size and complexity, from small tasks to large challenges with massive datasets.
* Increased accuracy: By enabling faster and more efficient training, parallelisation of the PSO algorithm can improve the quality of the neural network models that are designed, resulting in better performance in prediction and classification tasks.
* Reduction in development time: The parallelisation of processes results in a reduction in the time required for the development of machine learning models, due to the acceleration of the training process.  
The prediction of energy consumption (EC) across a range of buildings represents a formidable challenge. This explains why companies and governments around the world are directing their attention to this field of study. One of the most crucial areas to address this issue is the prediction of energy consumption (EC) at the local level. This may be exemplified by buildings associated with an organisation or institution. This enables the foresight of prospective occurrences, thereby facilitating the formulation of more judicious resolutions pertaining to energy conservation. In this context, the analysis of energy consumption data recorded by sensors at an individual level, in specific areas or buildings, has the potential to reduce energy costs and mitigate the environmental impact derived from energy production.  
In addition to its application to regression problems, the PSO algorithm can also be used to train neural networks focused on solving classification problems. In order to further illustrate this point, we propose here a second case study, for which we have taken a dataset from the Kaggle platform corresponding to the Binary Prediction of Smoker Status using Bio-Signals [26] challenge.  
Figure 4: Spark components communicating through the Spark driver.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
### Motivation  
In recent years, the number of LEO satellites is growing rapidly. Almost all the satellites are using RF communication in order to download data to Earth. Therefore, each such satellite is required to have a dedicated frequency coordination, allocated via the International Telecommunication Union (ITU). The demand for RF frequencies grows with every new satellite, which causes a bottleneck on the RF spectrum. Moreover, each country has its own RF channel regulation, which limits the location of ground stations mainly to the country of the satellite's operator. Alternatively, FSO (lasercom) requires no frequency licensing and therefore allows such satellites to communicate with any optical ground station on Earth. Laser optics use very high frequencies and therefore enable significantly faster data rates using very small antennas; such antennas commonly have extremely narrow transmission patterns, thus allowing a high-gain transmission in a particular direction. It should be stated that FSO communication is significantly more sensitive to atmospheric conditions than RF communication. Yet, we conjecture that the superior data rate of the FSO solution and the ability to use many (globally spread) ground stations can be beneficial in a wide range of remote sensing applications (e.g., the Planet [21] use-case).
# Security in Cloud-Native Services: A Survey
## 7 Solution Synergy
### Secure Communications  
ETSI ZSM, ZTA, service mesh, and Shift Left and Static Testing Techniques all play vital roles in enhancing the security of communications within modern network and application ecosystems. ZSM's automation framework ensures that network resources are optimized and security policies are consistently enforced, actively contributing to the establishment of secure communication channels. Meanwhile, ZTA, with its fundamental principle of distrust by default, strengthens secure communications through rigorous identity verification and continuous authentication, ensuring only authorized entities can access and exchange data. Service mesh further reinforces secure communications by applying authentication, authorization, and encryption mechanisms to inter-microservice communication within applications, guaranteeing data confidentiality during transit. Lastly, Shift Left and Static Testing Techniques, when integrated into the development process, proactively identify and rectify security vulnerabilities, thereby establishing secure communication as an inherent aspect of application design and development, fortifying the overall security posture across diverse IT environments.
## 1 Introduction
### Major Contributions  
In this research, we have made a fourfold contribution:  
* We made a comparison of several deep learning algorithms such as CNN, bi-directional long-short memory (Bi-LSTM), deep neural network (DNN) [13], and their combinations, CNN-LSTM and CNN-BiLSTM, for the detection and prediction of diabetes using the static PIMA Indian dataset (PIDD) [14];
* We used the best parameters to train our models. We ran a grid search algorithm that found the best values for parameters such as learning rate, epochs, optimizer, batch sizes, and hidden units;
* We split our dataset into test and training sets by using 10-fold cross-validation, and the precision of each model was improved. On the other hand, CNN-Bi-LSTM outperformed with 98% accuracy, 97% sensitivity, and 98% specificity;
* We proposed a framework to test our optimized models using a real-time dataset.  
The remainder of the paper is structured in the following manner: The second section discusses related work; Section 3 describes the methodology, which defines the PIMA Indian dataset and preprocessing steps to filter data, the models used to diagnose patients with diabetes, and the proposed framework to diagnose diabetes in the real-time environment; the results and discussions are addressed in Section 4; and, finally, Section 5 draws conclusions and discusses future scope.
## 1 Introduction
Remote sensing has recently been applied to updating the boundaries between the Brazilian Cerrado-Amazon biomes, due to land use and land cover changes, by using vegetation indices such as Gross Primary Productivity (GPP) and carbon dioxide fluxes (CO\({}_{2}\)Flux), allowing the development of public policies to curb deforestation and degradation by anthropogenic activities [44]. Remote sensing has also been crucial in helping to uncover the vital role and substantial progress made by IL and PA in conserving the Amazon, and the risks from the recent weakening of forest policies in Brazil [2; 45]. Studies point out that 40% deforestation in the Amazon would lead to a decrease in rainfall and a longer dry season. They also highlight that 20-25% deforestation would be the tipping point for the Amazonian system to shift to a non-forest ecosystem in most of the Amazon [35; 36; 46; 47]. In this regard, it is expected that changes in CO\({}_{2}\) emissions and removals induced by land use and land cover changes in the Brazilian Legal Amazon have intensified in recent years and could be identified and quantified through MODIS images and physically based models. Here, we investigate the relationship between land use and land cover changes, GPP, CO\({}_{2}\)Flux, XCO\({}_{2}\) and precipitation in the Brazilian Legal Amazon from 2009 to 2019. We believe that the investigation into the loss or gain in the carbon absorption capacity of these areas could facilitate more environmentally friendly and sustainable decisions, in order to preserve BLA and carbon pools in the region.
## 4 Result Discussion and Performance Evaluation
The TON_IoT dataset [47] was used for result validation. Figures 11 and 12 reveal the true positive rate (TPR) and false discovery rate (FDR) of the bagged tree and coarse tree, respectively. Although the coarse tree performed best in our field data, bagged tree remains the best ensemble learning method capable of making up for the weaknesses and leveraging the strengths of tree algorithms, as it detected the MiTM attacks. Figure 10: Plot of receiver operator characteristics (ROC) curves. Figure 9: Plot of confusion matrix of the tree algorithms using the facility dataset and public datasets. The TPR and FDR performance of the coarse tree and ensemble tree on other datasets (SCADA pressure, WUSTL-2018, and ORNL Power Grid) are shown in Figures 13 and 14. The results are consistent with the confusion matrix and ROC results. Figure 11: Bagged trees graph showing the (**a**) probability of the positive classification of all attacks (TPR/sensitivity) and (**b**) the proportion of all the identified attacks that may not be attacked (false discovery rates (FDR)) on the TON_IoT datasets. Figure 12: Coarse trees graph illustrating the (**a**) probability of the positive classification of all attacks (TPR/sensitivity) and (**b**) the proportion of all the identified attacks that may not be attacked (false discovery rates (FDR)) on the TON_IoT datasets. Figure 13: Graph illustrating the probability of the positive attack classification (TPR/sensitivity) across all evaluated datasets (**(a)** SCADA Pressure, (**b**) WUSTL-2018, (**c**) ORNL PowerGrid (**d**) TON_IoT).
## 1 Introduction
Due to the scarcity of \(\sim\)2.7-\(\upmu\)m acousto-optic and electric-optic Q-switches, mechanical Q-switching is commonly used in a wavelength range of \(\sim\)2.7-\(\upmu\)m [21; 22], which does not require high voltages or drive power and has avoidable insert losses. Here we report a short-pulse-width repetitively Q-switched Er:Y\({}_{2}\)O\({}_{3}\) ceramic laser at \(\sim\)2.7-\(\upmu\)m using a specially designed mechanical switch, a metal plate carved with slits of both slit-width and duty-cycle optimized. The laser performances with output couplers (OCs) of 5%, 8% and 20% transmissions are compared in both CW and Q-switched operation modes. In the CW operation mode, the 8% transmission OC yields an output power of over 1.8 W. In the Q-switched operation mode, the 20% transmission OC yields pulse trains with durations (FWHM) of 27-38 ns and energies of 80.8-27.5 \(\upmu\)J with repetition rates in the range of 0.26-4 kHz. The corresponding peak power at the 0.26 kHz repetition rate is \(\sim\)3 kW. To the best of our knowledge, the laser offers the shortest pulse durations among \(\sim\)2.7-\(\upmu\)m Q-switched lasers with pulse repetition frequency (PRF) above several Hertz.
## 2 Pentacyclic Triterpenoids (PCTTs)
### Other Triterenoid Skeletons Isolated from Celastraceae
\begin{tabular}{c c c c c c c c c} \hline \hline \(1^{\prime}\) & & 126.5 & & & 170.0 \\ \(2^{\prime}\) & & 104.2 & & & 121.3 \\ \(3^{\prime}\) & & 147.4 & & & 112.2 \\ \(4^{\prime}\) & & 135.8 & & & 146.2 \\ \(5^{\prime}\) & & 147.4 & & & 150.8 \\ \(6^{\prime}\) & & 104.2 & & & 125.2 \\ \(7^{\prime}\) & & 77.2 & & & 114.2 \\ \(8^{\prime}\) & & 75.5 & & & & \\ \(9^{\prime}\) & & 62.8 & & & & \\ \(\mathrm{OCH_{3}}\) & & 56.4 & & & & 56.1 \\ \(\mathrm{OCH_{3}}\) & & 51.4 & & & & & \\ \(\mathrm{OCH_{3}}\) & & 20.6 & & & & & \\ \(\mathrm{C}\)=O & & 170.3 & & & & & \\  Ref & [119] & [124] & [126] & [119] & [124] & [109] & [127] & [127] & [119] & [128] \\ \hline
**C** & **A23** & **A24** & **A25\({}^{\textbf{a}}\)** & **A26\({}^{\textbf{a}}\)** & **A27\({}^{\textbf{d}}\)** & **A28** & **A29** & & \\ \hline  
30 & 14.8 & 14.8 & 33.5 & 33.3 & 30.4 & 31.8 & 31.6 & \\ \(\mathrm{CH_{3}}\) & & & & & & & & \\  CHOH & & & & & & & & \\  Ref & [129] & [39] & [130] & [130] & [127] & [131] & [131] & & \\ \hline \hline \end{tabular}   
Ref. References; \({}^{+}\)13C-NMR data of methylated compound; \({}^{\star}\)**: Values bearing the same superscript are interchangeable; NR: Not reported; Solvent: \(\mathrm{CDCl_{3}}\); \({}^{\textbf{a}}\) Pyridine-\(d_{5}\), \({}^{\textbf{b}}\)\(\mathrm{CDCl_{3}}\)+\(\mathrm{CD_{3}O}\), \({}^{\textbf{c}}\)\(\mathrm{CD_{3}O}\), \({}^{\textbf{d}}\)\(\mathrm{DMSO}\); \({}^{\textbf{d}}\)\(\mathrm{DMSO}\)-\(d_{6}\), \({}^{\textbf{e}}\)\({}^{\textbf{e}}  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**C** & **D1** & **D2** & **D3** & **D6** & **D7** & **D8** & **D9** & **D10** & **D11** & **D12** \\ \hline
1 & 115.5 & 108.2 & 115.5 & 113.0 & 113.0 & 113.1 & 113.0 & 113.1 & 115.5 & 115.0 \\
2 & 191.2 & 144.3 & 191.0 & 191.5 & 191.5 & 191.5 & 191.6 & 191.4 & 190.2 & 189.4 \\
3 & 91.7 & 140.6 & 91.8 & 91.3 & 91.3 & 91.2 & 91.3 & 91.3 & 92.0 & 91.1 \\
4 & 79.9 & 123.2 & 79.0 & 79.5 & 79.5 & 79.5 & 79.4 & 79.5 & 79.4 & 76.9 \\
5 & 130.8 & 124.6 & 130.8 & 134.1 & 134.3 & 134.1 & 134.3 & 134.1 & 130.2 & 132.2 \\
6 & 126.1 & 71.4 & 125.9 & 134.1 & 133.9 & 133.9 & 134.1 & 133.8 & 126.6 & 128.5 \\
7 & 116.2 & 73.7 & 116.1 & 24.2 & 24.2 & 24.2 & 24.2 & 24.2 & 116.3 & 117.2 \\
8 & 160.6 & 44.7 & 160.6 & 41.6 & 41.2 & 41.6 & 41.1 & 41.6 & 160.4 & 163.3 \\
9 & 41.7 & 40.2 & 41.7 & 37.4 & 37.4 & 37.4 & 37.3 & 37.4 & 41.7 & 43.4 \\
10 & 173.8 & 143.7 & 173.5 & 170.2 & 169.8 & 170.0 & 169.8 & 170.0 & 173.8 & 172.9 \\
11 & 32.7 & 34.7 & 32.7 & 30.7 & 30.5 & 30.6 & 30.7 & 32.0 & 33.2 & 33.1 \\
12 & 29.5 & 30.7 & 29.5 & 29.4 & 29.4 & 29.4 & 29.7 & 29.4 & 29.8 [FOOTNOTE:]Footnote : [ENDFOOTNOTE] & 29.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 1 Introduction
### Pathophysiological Mechanisms Connecting Periodontal Disease to Alzheimer's Disease  
The mechanisms underlying the relationship between periodontal disease and Alzheimer's disease are complex and multifactorial. The primary hypotheses include systemic inflammation, direct microbial invasion, and the impact of dysregulated immune responses [112; 113; 114; 115; 116; 117; 118; 119; 120; 121].
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results
### Analysis of the Results
#### 4.3.3 Routing the Information of Offline Nodes  
Routing becomes critical for providing updates to the DT in partially online contexts. In scenarios such as FPOT and DPOT, the lack of Internet access for a subset of nodes necessitates the use of connection-aware routing strategies to transmit network events to the  
Figure 8: Estimated cost of the computational complexity. (**a**) Average energy consumption (mAh) of the nodes involved in the 5G-MANET across the four different evaluation contexts. Node A (”) acts as the gateway device of the device. (**b**) Average CPU usage of the MQTT broker and The ONE Simulator across the four different evaluation contexts.  
DT, utilizing intermediate nodes to reach the gateway. As a result, the DT disregards control information messages and only processes relevant network events, which are included in the reports. However, the routing of local information affects communication latency and introduces additional computational tasks for the devices. To address the relevant contextual and QoS variables of these communications, this section analyzes the impact of routing on the overall communication performance.  
Table 3 presents the corresponding values for multiple QoS and contextual variables related to the routing of information from offline nodes.  
Considering these results, it is evident that node disconnections are the most critical variable affecting the performance of the routing protocol. In the case of the fixed topology, despite only the gateway node being connected to the Internet, control information represents a very low percentage of the communications. In this scenario, a low number of overhead messages are generated, accounting for around 15% of all transmissions. As a result, the number of copies created in the network is minimal, given that routing is only necessary to enable communication between all nodes.  
On the other hand, in the DPOT scenario, routing control transmissions become highly significant relative to the data transferred. This is primarily due to contact interruptions and the additional overhead required when new contacts are established. As a result, control information accounts for more than half of the transmitted messages. In conclusion, the impact of routing becomes substantial in contexts where mobility is high and communication interruptions are frequent.  
Based on the outcomes obtained in the three addressed dimensions, the next subsection discusses the main conclusions drawn from the results.