esa-sceva/satcom-chunk-collection huggingface dataset:
# MAISTRO: Towards an Agile Methodology for AI System Development Projects
## 3 Materials and Methods
### Phase IV: Project Execution
#### 3.4.2 Algorithm Selection  
The appropriate selection of algorithms is a decisive step in the development of AI projects. This choice directly impacts the success of the project and should be guided by the characteristics of the data and the specific requirements of the problem to be solved. The \"there is no free lunch\" theorem, discussed by [PERSON] [89], emphasizes that there is no universal algorithm for all scenarios. Therefore, it is essential to evaluate and compare different approaches to determine which offers the best performance in each specific case.  
Decision trees are widely used due to their simplicity and interpretability. These algorithms divide data into subsets based on specific criteria, facilitating the understanding of the decisions made by the model. They are effective in classification and regression problems, especially when interpretability is an important requirement [90]. However, it is important to consider that the decision trees may be subject to overfitting, especially when applied to noisy or complex data.  
Neural networks have revolutionized areas such as computer vision and natural language processing. These models are capable of learning hierarchical representations of data, capturing complex relationships. However, training neural networks can be computationally intensive and requires large volumes of data to achieve optimal performance [91].  
Support Vector Machines (SVMs) are robust and efficient algorithms in high-dimensional spaces. These algorithms perform well when there is a clear separation between classes. In addition, SVMs are less susceptible to overfitting due to the use of maximum separation margins [92]. However, they may be less efficient on large datasets or those with many irrelevant features.  
Ensemble algorithms, such as Random Forests and Gradient Boosting, combine multiple models to improve accuracy and robustness. The Random Forests algorithm combines several decision trees, reduces the risk of overfitting, and improves model generalization [93]. The Gradient Boosting algorithm builds models sequentially, correcting the errors of previous models, resulting in high accuracy, although it requires adequate regularization to avoid overfitting [94].  
The selection of algorithms must be judicious, considering the specific characteristics of each problem and the advantages and limitations of each approach. The appropriate combination of these methods can lead to more robust and accurate results in AI projects.
## 2 Materials and Methods
### Methodology  
This section introduces the specific steps of the research method in this paper. The flow chart of the research scheme is shown in Figure 2, which mainly consists of the following two parts: downscaling and calibration. In the first step, based on the cloud optical attributes and the physical properties, namely CER, COT, and CWP, three satellite precipitation datasets, namely IMERG, CMORPH, and ERA5, were used to construct GWR models and were downscaled to a 1 km resolution, respectively. In the second step, based on the GDA and GRA methods, the downscaled satellite precipitation data were calibrated using the calibration group rain gauge data, and the inverse distance weight (IDW), ordinary kriging interpolation (OK) and radial basis function (RBF) were employed to interpolate the calibration residual results to a 1 km resolution and fused with the downscaled results to obtain three final satellite precipitation downscaling results. Finally, the validation group rain gauge data were used to evaluate the final precipitation downscaling result, and combined with the verification data. The AHP and EW method were employed to determine the weights to obtain the merged satellite precipitation (MSP) data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station ID** & **Longitude (\({}^{\circ}\)E)** & **Latitude (\({}^{\circ}\)N)** & **Elevation (m)** &
\begin{tabular}{c} **Average Annual** \\ **Precipitation (mm/Year)** \\ \end{tabular} \\ \hline
56074 & 102.08 & 34.00 & 3471.6 & 612.4 \\
56018 & 95.30 & 32.90 & 4067.5 & 567.7 \\
56029 & 97.02 & 33.02 & 3681.2 & 528.8 \\
56038 & 98.10 & 32.98 & 4200.0 & 631.2 \\
56151 & 100.75 & 32.93 & 3750.0 & 708.4 \\
56152 & 100.33 & 32.28 & 3893.9 & 694.0 \\
56125 & 96.48 & 32.20 & 3643.7 & 570.4 \\
56106 & 93.78 & 31.88 & 4022.8 & 668.0 \\
56116 & 95.60 & 31.42 & 3873.1 & 684.1 \\
56137 & 97.17 & 31.15 & 3306.0 & 491.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Comprehensive flow chart of a geographically weighted regression (GWR) downscaling and rain gauge calibration method based on cloud attributes in this study.
## 2 Data Source and Surveying Methodology  
The methodology used to analyze this study is a bibliometric analysis, a scientific method that is widely accepted and used worldwide. Bibliometrics is a scientific method that uses mathematical techniques and statistics to evaluate a given scientific output [51]. Numerous databases, including PubMed, Web of Science, Scopus, Web of Science, Google Scholar, and Baidu Scholar can be searched for articles containing strategy in research [52; 53; 54]. The statistical analysis for this study was conducted using the Scopus database. Scopus was chosen rather than other databases because it contains a broader range of publications throughout almost all research disciplines [55]. Therefore, it was an easy choice for this analysis. The first publication of the yttrium iron garnet was recorded in the year 1957. Our study examined research publications on yttrium iron garnet (YIG) thin films that were published solely between 2012 and 2022. The data for yttrium iron garnet were collected from the Scopus database in the third week of July 2022.  
The primary objective of this research is to update the summary section of the publications in the Scopus database and to evaluate the development advancements in the fields of yttrium iron garnet thin film over the last ten years. This is to shed light on the state of the research in this area of publication. This study analyzes the most cited research in YIG as well as the top citation journals, publishers, authors, and nations. The methodologies, limitations/research gaps, keywords, challenges, difficulties, and solutions, as well as suggestions for future YIG research, were highlighted in the evaluation of the publications with the highest citation counts.
# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 3 Fact-Checking
### Fact-Checking Tools  
Table 2 lists the most popular fact-checking tools. For every tool, it reports whether it uses manual or automatic fact-checking techniques, the themes they are covering, and the rating scale they use for the credibility assessment of the news. Below we discuss the most prominent ones.  
Snopes [75] is one of the oldest fact-checking websites, which has been used as a source for validating and debunking urban legends and stories in American culture. It covers politics, health, and social issues. In 2020, Snopes was very active regarding the COVID-19 pandemic misinformation and had around 237 COVID-related fact-checked articles. Its rating scale consists of values like _True, Mostly True, Mixture, Mostly False, Unproven_, etc.  
Media Bias/Fact-Check [37] examines bias in the media from all points of the political spectrum. It includes a \"Daily Source Bias Check\" that examines the truthfulness and bias of various news sources. It also includes a conspiracy/pseudoscience category that includes COVID-19-related news.  
Another well-known fact-checker is Politifact [36]. This service is devoted to fact-checking claims made by political pundits. Specifically, everyday journalists explore transcripts, speeches, news stories, press releases, and campaign brochures to identify statements that should be fact-checked. In order to decide which statements to check, they consider questions like \"Is the statement rooted in a fact that is verifiable?\" or \"Is the statement likely to be passed on and repeated by others?\" They cover politics and health topics, and they provide a scorecard that gives information about the statistics on the authenticity distribution of all statements related to a specific topic.  
The Washington Post [76] also provides a column discussing the factual accuracy of statements with zero to four Pinocchios. Full-fact [77] is another fact-checker that uses a three-stage review fact-checking process that may also involve external academics (soon it will also apply automatic approaches). FactCheck [78] consists of features like \"Ask FactCheck\", with which users can ask questions that are usually based on rumors; \"Viral Spiral\", which makes a list of the most popular myths that the site has debunked; and \"Mailbag\", which includes readers' opinions on the site's claims.  
Finally, Google Fact-Check [79] is a search engine for fact-checks. It consists of the Fact-Check Markup tool, which provides structured markup, and the Fact-Check Explorer. The first one allows publishers to add claim-related markup into their web content, which will then be crawled and indexed by Google. The explorer allows users to query claimsand returns whether they are true or false based on the indexed structured markup added by publishers and Google's algorithms that evaluate the credibility of the sources.
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 6 SaaS Application Case Study
### Process Discovery, Mining, and Analysis  
After preprocessing the event logs by using the CPA-PM method, the resulting dataset was imported to two PM tools, namely Disco and ProM, for process discovery (the specific type of process mining of interest in this case study) and analysis. The preprocessing steps can go through several iterations before getting to the final answers to the analysis questions. Even after importing the dataset to PM tools, a process model is generated (e.g., Figure 5), and the analyst can iterate again through filtering, aggregations, and pattern substitutions before importing the resulting dataset again to PM tools (see Figure 2).  
Figure 5: Process map for users who converted to customers within 5 min, generated with Disco from an event log with 96 cases and 30 variants.  
Different variants of our R script were produced to answer different stakeholder questions, such as \"what are the steps followed by trial users who give up before registering (non-converters)?\", \"what is the main process for converters?\", and \"how different are the processes when non-converters give up within 5 min from when they use the application for between 1 to 14 days?\". Figure 5 shows one of these answers about converters in the form of a Disco process model, whereas Figure 6 shows the process used by non-converters.  
Figure 6: Process map for users who did not convert to customers within 5 min, generated with Disco from an event log with 2301 cases.  
These two models are interesting because in the original log, 76% of the cases had a duration shorter than 5 min, so they are quite representative. In addition, there are more reasons (alternatives) why people do not convert and 24 times more cases than for converters. The most frequent path for non-converters is \(<\)Trial Sign Up Complete, Help Guide Start, Help Guide Exit\(>\), which suggest that non-converters quickly abandon without really exploring the service. Other such models can be found in [23].
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 7 Case Study
### Short-Term Prediction Example

Table 2: The dependence of LPG consumption on outside temperature values expressed by means of the Pearson correlation coefficient (PCC) and the determination coefficient (R\({}^{2}\)). The baseline approaches were based on consumption values only and are collectively named as trivial because they calculated the predicted value as one of the following:  
* the last registered consumption value,
* the mean consumption value from the last seven days,
* the median consumption value from the last seven days. These approaches are referred to as trivial last, mean, and median, respectively. They can be applied just after one or seven days (respectively) of measurements, and they were re-calculated after each day. The quality of the applied models is expressed by means of the root mean squared error (RMSE):  
\[E_{RMSE}=\sqrt{\frac{1}{T}\Sigma_{t=1}^{T}\big{(}\hat{y}_{t}-y_{t}\big{)}^{2}}, \tag{1}\]  
where \(T\) is the time of analysis in days corresponding to the number of examples, \(\hat{y}_{t}\) is a predicted consumption value on day \(t\), and \(y_{t}\) is an actual consumption value on day \(t\). The results obtained for each model are presented in Tables 11-14 in the Appendix A, whereas the summary containing aggregations of the calculated error values is presented in Table 3.  
The results presented in Table 3 show that the performance of the proposed temperature-based model was better than all the trivial approaches. Looking at the source error values, it can be seen that none of the error values presented in Tables 11-14 and characterizing trivial models were lower than the respective values in Table 11. In addition, among the trivial models, the mean and median were better then the trivial last approach, whereas there was no clear difference between the mean and median methods. Analyzing the results from the perspective of the models generated for different prediction horizon values, it can be seen that the error values increased slightly for most tanks with the increasing prediction horizon, apart from the model type.
# Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios
## 4 Results And Discussion
### Comparison of Outlier Detection Algorithms
#### 4.4.1 Baselines and Training Configurations  
We employed careful hyperparameter tuning for both the one-class SVM and the autoencoder models to achieve optimal performance.  
For the one-class SVM, we fine-tuned hyperparameters using grid search with k-fold cross-validation. This optimized parameters like \(\
u\) (outlier fraction) and kernel type, ensuring model generalizability and prioritizing a balance between precision and recall (F1-score).  
We employed a similar approach for hyperparameter tuning in the autoencoder model. Grid search was again utilized to optimize various settings, including the optimizer and the activation functions, and the number of neurons within the hidden layers. During this search, we prioritized minimizing the reconstruction error. A lower reconstruction error signifies the model's better ability to capture the underlying structure of the data, which can be crucial for identifying anomalies that deviate from this structure.
## 2 Establishment of the Seasonally Frozen Ground-Pipeline System Model
### Basic Assumptions and Fundamental Governing Equations of the Physical Fields
#### 2.2.2 Temperature Field Governing Equations  
Due to seasonal climate changes, frozen soil undergoes freezing and thawing each year. At the same time, the buried water supply pipelines continuously release heat to the surrounding soil, leading to the thawing of the frozen soil. The soil parameters also change  
Figure 2: Hydro–thermo–mechanical coupling.  
with the thawing and freezing of the frozen soil. Based on the fundamental theories of heat transfer and frozen soil mechanics, a heat transfer equation that considers the latent heat of phase change is established:  
\[\rho C(\theta)\partial T/\partial t=\lambda(\theta)\
abla^{2}T+L\rho_{I}\partial \theta_{I}/\partial t \tag{1}\]  
where  
\[\frac{\partial\theta_{I}}{\partial t}=(\theta_{s}-\theta_{r})\cdot\left(\frac {\partial B(T)}{\partial t}\cdot S+B(T)\cdot\frac{\partial S}{\partial t} \right)+\theta_{r}\frac{\partial B(T)}{\partial t} \tag{2}\]  
in which \(\lambda\) is the thermal conductivity (W/cm\({}^{\circ}\)C); \(C\) is the heat capacity (J/kg\({}^{\circ}\)C); \(\rho\) is the density of the soil (kg/m\({}^{3}\)); \(\rho_{I}\) is the density of ice (kg/m\({}^{3}\)); \(T\) is the temperature of the soil (\({}^{\circ}\)C); \(t\) is time (s); \(L\) is the latent heat of phase change, typically taken as 334.56 kJ/kg; \(B\) is the solid-liquid ratio; \(S\) is the saturation degree; and \(\theta_{I}\) is the volumetric ice content.  
When studying hydrothermal coupling, it is essential to consider not only the pore water in the soil but also the water content in the analysis of soil moisture. To calculate the thermal conductivity and specific heat capacity of the soil, the volumetric water content is defined as follows:  
\[\theta=\theta_{u}+\rho_{I}/\rho_{w}\cdot\theta_{I} \tag{3}\]  
where \(\theta_{u}\) is the volumetric unfrozen water content, and \(\rho_{w}\) is the density of water.  
The heat conduction equation for the water supply pipeline made of steel is expressed as follows:  
\[\rho_{s}C_{s}\partial T/\partial t=\lambda_{s}\
abla^{2}T \tag{4}\]  
In the equation, \(\rho_{s}\) is the density of steel; \(C_{s}\) is the heat capacity of concrete; and \(\lambda_{s}\) is the thermal conductivity of steel.
## 2 Materials and Methods
### Processing  
#### 2.5.1 Multiple Endmember Spectral Mixture Analysis (MESMA)  
The Multiple Endmember Spectral Mixture Analysis (hereafter MESMA) has been applied on a single S2 image to obtain a preliminary crop classification. To capture the greatest spatial variability in the phenology of agricultural patches, the S2 image of the  
Figure 5: Crop class distribution in the AOI. Pie chart with the % distribution of each class and crop relative areas (in hectares).  
maize and soybeans in the growing season, freshly harvested winter cereals, and freshly flooded rice was used. This algorithm consists of a spectral mixture analysis that considers a larger number of pure spectra, called Endmembers (EMs), for each pixel, as an extension of the most used Linear Spectral Mixture Analysis (LSMA) that does not consider the contrast between the materials within the pixel [22; 37; 38]. Thus, the MESMA overcomes the limitations of LSMA by requiring a model to meet the minimum fit, fraction, and residual constraints while testing multiple models for each image pixel. Therefore, this approach allows mapping more than four materials across an image [39].  
The MESMA application procedure consists of four steps: (1) the selection of the EMs starting from a random sampling of the training dataset, (2) the creation of a spectral library, (3) the application of the spectral unmixing to obtain Fractional Abundance Maps (FAMs), and (4) shade normalization. At the end of the process, the FAMs were used to obtain an intermediate crop classification map. A majority analysis was carried out using a kernel size of 3 \(\times\) 3 pixels to remove the noise and the spurious pixels within agricultural parcels. This analysis assigned the central pixel to the class to which most kernel pixels belonged [38; 40]. The classification map was validated with a confusion matrix, using the validation dataset to calculate its accuracy.
## 3 Key Technologies of UAV-Assisted SAGINs
### Routing Protocols
#### 3.1.3 Reactive Routing Protocols  
A reactive routing protocol is a routing protocol designed specifically for mobile ad hoc networks (MANETs). This type of protocol does not preset routes but dynamically discovers and maintains routes when data transmission needs arise. Reactive routing protocols typically consist of two main stages: route discovery and route maintenance. In reactive routing protocols, common routing protocols include AODV [49], DSR [50], ABR [51], etc.  
In the route discovery phase, when the source node needs to send data to the destination node but lacks an effective route, it initiates the route discovery process by broadcasting a route request to explore the path to the destination node. After the routing request reaches the destination node, the destination node sends a routing response (RREP) to the source node to establish the route. In our previous research [38], we discussed in detail the special requirements of flying ad hoc networks in SAGINs, including the mobility of drone nodes themselves, vulnerability to interference, and limited channel capacity. At the same time, we also need to consider interaction and integration with other networks such as ground stations and satellite networks to achieve better network performance.  
In the routing maintenance phase, in order to reduce future routing discovery overhead, nodes can store discovered routes in the routing cache for subsequent transmission. When the network topology changes, only the routing paths that affect data transmission need to be updated. This on-demand update mechanism reduces unnecessary control messages and adapts to the dynamic characteristics of drone networks.  
In UAV-assisted SAGINs, reactive routing protocols can quickly respond to changes in drone location and dynamic adjustments in network topology, providing flexible routing solutions for communication between drones and data transmission between drones and ground stations. [PERSON] et al. [58] proposed many routing algorithms in their research, including their performance, efficiency, and applicability, particularly in the SAGIN environment.
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Experimental Progress and Result Analysis
#### 4.2.3 Result Comparison with Other Detection Models  
To prove each algorithm's generalization performance, we compare and analyze different evaluation indexes for different algorithms on the test set, which are shown as shown in Table 4. From Table 4, we can draw the following conclusions. On the test set, compared with the YOLOv3-tiny, SAS-YOLOv3-tiny improves R from 75.4% to 80.9% improves mAP from 74.6% to 80.3%, improves F1 from 72.9% to 75.2% and reduces the size of weight file from 69.5 MB to 46.9 MB, which mainly benefits from the use of SR module based on channel attention and SPP module, the application of three-scale prediction method, and the introduction of CIoU loss. Compared with the latest YOLOv4-tiny, SAS-YOLOv3-tiny improves R from 80.0% to 80.9% and improves mAP from 78.9% to 80.3%, but P and F1 decreases to some extent. The main reason is that the idea of the Cross Stage Partial network (CSPNet) [30] is applied into the YOLOv4-tiny, which strengthen the network feature representation. Compared with the YOLOv3 and the YOLOv4, SAS-YOLOv3-tiny has tremendous advantages in terms of the number of parameters and speed, although its accuracy is inferior to the YOLOv3 and the YOLOv4, and the main reason for the decrease of accuracy lies in less parameters and small computational burden.  
Figure 6: The curves of F1 and mAP in the different models.
# Variance-Triggered Two-Step GPS Acquisition
## 5 Experiments and Results
### Success Rate  
The success rates of all algorithms for different C/N0 rates are presented in Figure 8. For VTTS and TSA-RD algorithms, which features the proposed two-step approach and different adaptive threshold detection, the results are presented for 1, 5, 10 and 16 rounds of the CS step. Since the signal being acquired has 16 samples per chip, using 16 rounds of the CS step is the maximum processing that the two-step algorithms can perform although that does not mean all samples were processed. Since the CS step only processes a limited number of samples, some sample will only be processed by the FS step if a promising region is identified in the CS step.  
Figure 7: Comparison of memory usage of SA versus VTTS. The depicted values represent the amount of memory savings obtained by VTTS over the SA.  
Figure 8 shows that after 16 rounds, the success rate of the VTTS acquisition suppresses those of all other approaches. Moreover, to reach a success rate equivalent to those of the three SA-based approaches, VTTS needs only 10 rounds of the CS step.