esa-sceva/satcom-chunk-collection huggingface dataset:
# Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy
## 2 Data and Method
### Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Country Name**} & \multicolumn{6}{c}{**The Distribution Percentage of Variant:**} \\ \cline{2-5}  & **Delta** & **Omicron** & **Alpha** & **Beta** & **Gamma** & **Others** \\ \hline Brunei & 100.00\% & & & & & \\ Cambodia & 72.02\% & & 27.98\% & & & 2.21\% \\ Indonesia & 97.70\% & 0.07\% & 0.02\% & & & 2.21\% \\ Laos & 99.65\% & & 0.23\% & & 0.12\% & \\ Malaysia & 98.67\% & 0.09\% & & 0.31\% & & 0.93\% \\ Philippines & 70.47\% & & 13.61\% & 12.52\% & & 3.40\% \\ Singapore & 99.78\% & 0.13\% & 0.03\% & 0.01\% & & 0.06\% \\ Thailand & 94.32\% & 0.05\% & 5.10\% & 0.38\% & & 0.15\% \\ Vietnam & 100.00\% & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variant distribution of SARS-CoV-2 DNA sequence contributed by ASEAN countries in GISAID during the second half of 2021. The third dataset consists of data accessed from The Oxford COVID-19 Government Response Tracker (OxCGRT) project. This comprehensive initiative has collected information on policy measures adopted to combat COVID-19 throughout the years 2020, 2021, and 2022 [27]. We downloaded an Excel file containing countries from around the world and their corresponding restriction indicators, as shown in Figure 2. Subsequently, we extracted data covering only the nine ASEAN countries for the year 2021. Given that the study focuses on classifying DNA sequences between countries, our primary concern was restrictions related to policies impacting individual movement between countries, specifically restrictions on international travel (c8), or what we later refer to as international travel policy. This indicator is used to record policies related to incoming foreign travelers to a certain country. It does not report restrictions on outbound travel and does not count citizen repatriation as a case of open borders (if all other inbound travel is restricted). The level score of the international travel policy is recorded daily, indicating 'no restrictions' (scored 0),'screening arrivals' (scored 1), 'quarantining arrivals from some or all regions' (scored 2), 'banning arrivals from some regions' (scored 3), and 'complete ban on all regions or total border closure' (scored 4). For ease of interpretation, the score is multiplied by a scalar of 25 to transform it into an index ranging from 0 to 100. Consequently, we obtain a daily index of the international travel policy, appearing in the last column of every row of the dataset. Next, by averaging the index per week or month, we can derive weekly and monthly indices, respectively. This process allows us to create graphical representations of the weekly or monthly international travel policy index for each ASEAN country throughout 2021. Figure 1: (a) Weekly confirmed cases per million people and (b) weekly confirmed deaths per million people in ASEAN countries over three years. Figure 2: Processing international travel policy data into daily, weekly, and monthly index.
## 3 Results and Discussions
### Characterization of Y-CNC and Yc Films  
The thickness evaluation of yeast films indicates a uniform thickness between samples of approx. 0.20 mm, without significant differences (\(p>0.05\)) (Table 1). Dry matter for Y-CNC films is significantly higher (\(p<0.05\)) than that of control yeast films without CNC. The water activity of grafted CNC yeast films was significantly lower (\(p<0.05\)) than those for control Yc films without bacterial cellulose, which could be explained by cellulose' hydrophilicity, as other groups reported [37]. All CNC grafted yeast films presented visible differences in color when compared to the control samples, without CNC, according to the classification used by other researchers [30]. The film's opacity evaluated by colorimetry was calculated based on luminosity measured on a white or black background. The grafted bacterial cellulose significantly increased the opacity of yeast films (\(p<0.05\)) compared to the control samples, as other studies indicated [38], but the functionalized CNC films do not differ significantly (\(p>0.05\)) of each other. Nevertheless, all Y-CNC films were see-through (Figure 3).
## 1 Introduction
Available larger-scale species richness datasets offer a valuable database for the assessment of these remote sensing metrics. Instituto de Pesquisas Ecologicas (Brasil) offers a suite of large-scale species richness data [49], which has been validated in previous studies and exhibits high accuracy [50; 51]. Machine learning provides a great opportunity for the study of species richness, while construction of mechanistic models between species richness and remote sensing metrics is still challenging [52; 53]. Compared with traditional mechanistic approaches, machine learning avoids the over-simplification during modelling. Machine learning models are as complex as real ecosystems, therefore, in most cases the results that come from machine learning are more valid for drawing any conclusions for real situations [54; 55]. Moreover, machine learning has been widely used for species assessment and prediction [56; 57; 58]. In this study, to assess remote sensing metrics on a global scale, we selected in situ species richness data on three animal classes (mammals, birds, and amphibians) and 21 remote sensing metrics that all had global coverage and long-term availability (Table 1). We analyzed the correlations between animal species richness and remote sensing metrics, and developed a multivariate integration index based on a machine learning model. This study aimed to address the following questions:  
1. What are the differences between the distributions of various animal classes (mammals, birds, and amphibians) on a global scale? 2. What are the correlations between remote sensing metrics and species richness distributions on a global scale? 3. Given the major driving metrics, can we develop a multivariate integration index to map the global species richness continuously?
###### Abstract  
Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing complexity and ensuring efficient performance. While SLAM solutions aim at ensuring accurate and timely localization and mapping, one of their main limitations is their computational complexity. In this scenario, particle filter-based approaches such as FastSLAM 2.0 can significantly benefit from parallel programming due to their modular construction. The parallelization process involves identifying the parameters affecting the computational complexity in order to distribute the computation among single multiprocessors as efficiently as possible. However, the computational complexity of methodologies such as FastSLAM 2.0 can depend on multiple parameters whose values may, in turn, depend on each specific use case scenario ( inge., the context), leading to multiple possible parallelization designs. Furthermore, the features of the hardware architecture in use can significantly influence the performance in terms of latency. Therefore, the selection of the optimal parallelization modality still needs to be empirically determined. This may involve redesigning the parallel algorithm depending on the context and the hardware architecture. In this paper, we propose a CUDA-based adaptable design for FastSLAM 2.0 on GPU, in combination with an evaluation methodology that enables the assessment of the optimal parallelization modality based on the context and the hardware architecture without the need for the creation of separate designs. The proposed implementation includes the parallelization of all the functional blocks of the FastSLAM 2.0 pipeline. Additionally, we contribute a parallelized design of the data association step through the Joint Compatibility Branch and Bound (JCBB) method. Multiple resampling algorithms are also included to accommodate the needs of a wide variety of navigation scenarios.  
FastSLAM2.0; CUDA; GPGPU; JCBB +
Footnote †: journal: Applied sciences
# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
## 2 Background and Related Work
### Threat Modeling Techniques  
#### 2.2.1 PASTA Threat Modeling  
[PERSON] introduced PASTA [16] in 2012 as a framework for risk-centric threat modeling that encompasses various abstraction layers and takes into account both high-level layers, such as the business logic, and lower layers, such as the concrete attack vectors. Table 2 summarizes important PASTA terms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Term** & **Meaning in PASTA** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Asset} & & A resource that has an intrinsic value to the business. This could include, among others: \\  & & – & Information the business uses, trades, or needs \\  & & – & Hardware, software, frameworks, libraries the business relies on for a subject \\  & & – & The reputation of the business \\ \hline
2 & Threat & Anything that can unfavorably impact an asset. \\ \hline \hline \end{tabular}
\end{table}
Table 2: PASTA terms.  
PASTA, which stands for Process for Attack Simulation and Threat Analysis, describes a structured approach to threat modeling that includes seven major stages:  
1. Define Objectives and Scope: This stage identifies the specific legacy ICS system(s) under evaluation and outlines the assessment's goals.
2. Define Technical Scope: This stage maps out the technical components and data flows within the legacy ICS, resulting in a detailed image of the attack surface.
3. Decompose the Application: At this stage, the legacy ICS is divided into smaller, more manageable components to enable a more thorough threat analysis.
4. Identify Threats: This stage involves brainstorming potential threats and attack vectors that target the legacy ICS's specified functionality. Regarding the data, since data is both in storage and transit, it is important to map these threats not just to the data storage, such as databases, but also to the data in transit across the different components of the application, which includes the different servers allocated in the different tiers of the application architecture. During the threat modeling phase, UcedaVelez suggests utilizing STRIDE [14]. This process involves enumerating threats from a list of STRIDE acronyms that impact specific components like users/browsers and authentication credentials, which the attackers consider high-value targets.
5. Analyze Vulnerabilities: Based on the identified threats, specific weaknesses, and exploitable faults in the legacy ICS are identified.
6. Attack Analysis: This stage entails creating attack trees to visualize how attackers could exploit vulnerabilities and achieve their objectives within the legacy ICS.
7. Risk and Impact Analysis: at this stage, the potential repercussions of successful attacks are identified, focusing on corporate operations, safety, and regulatory compliance. This helps to prioritize detected vulnerabilities based on their severity.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 1 Introduction
Starting from the infrared spectrum absorption characteristics of the gas, considering the known target gas's influence on the background radiation measurement, the background was imaged through the non-target gas absorption spectrum band. After twice mapping the gray level and temperature, the background gray level image was extracted and the gas column concentration was calculated via combination with the gas column concentration inversion method, and the inversion of gas column concentration by using multispectral imaging technology was achieved. The experimental results showed that the average relative error of this method was 4.84%, which solved the problem whereby existing multispectral infrared gas imaging detection technology faces difficulty in achieving the quantitative measurement of gas column concentrations in complex environments.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 3 Communication Optimization
### Dynamic Synchronous Parallel Strategy
#### 3.2.2 Improvements of SSP  
We propose the Stale Synchronous Parallel Strategy, named DSP, based on the DEFT. This strategy can effectively solve the problems of SSP in distributed machine learning model training. The block diagram of out optimization strategy procedure is shown in Figure 7. Here we describe the solutions in DSP for the two problems mentioned previously.  
In order to achieve a finite fault tolerance and solve the low efficiency problem when the cluster is composed of similar performance worker nodes we increase the conditions for entering the synchronization barrier to two: (1) the worker node that trains a smallest number of iterations (which means the worst performance node) completes the iterations w times (we call w the weak threshold); (2) the worker node with the largest number of iterations is faster than the worker node with the smallest number of iterations s times, where \(s\) is the stale threshold. All worker nodes will enter the synchronization barrier when meeting any of the two conditions, they will update the global model parameters once when they finish their current iteration. As shown in Figure 8, if the computing performance of all worker nodes is similar, the stale threshold \(s\) is disabled, and the worker node 3 with relatively weak computing performance reaches the weak threshold \(w\). Therefore, all worker nodes will still enter the synchronization barrier which avoids any serious decrease of accuracy of the machine learning model.  
In order to achieve the fault tolerance dynamics and solve the problem that the stale threshold \(s\) will be disabled when the computing performance of the worker node has changed in the distributed machine learning training, we implemented a performance monitoring model that calls third-party open source software to monitor the current index, such as CPU, memory, network and I/O, for each worker node and decides if it is necessary to change the stale threshold \(s\) based on the monitored data.  
Figure 7: We select a worker node and the PS to show the flow diagram of our optimization strategy procedure.  
As shown in Figure 9, when all worker nodes finish a synchronization barrier and start a new iteration, the performance model finds the change in the index of the worker node 3, and estimates that the computing performance of the worker node 3 is increased and the performance difference of each worker node is reduced, so the performance model reduces the stale threshold \(s\) to 2 to avoid the excessive iteration situation and solves the problem of the stale threshold failure.
## 4 Experimental Results  
Using machine learning in a BMS may significantly improve the efficiency, performance, and lifespan of batteries. A battery management system is a crucial component in electric vehicles, renewable energy systems, and portable electronic devices. Its primary function is to monitor, control, and optimize the various parameters of a battery pack. Integrating machine learning methods may enhance the capabilities of a BMS in terms of the SoH. Predicting the state of health of a battery involves estimating its remaining capacity and overall condition. Machine learning models may analyze historical data, usage patterns, and environmental factors to make more accurate predictions about a battery's health. In this study, attempts were undertaken to predict the discharge capacities of Li-ion batteries through the development of various models. The results obtained from tests conducted at four different temperatures, two varied discharge capacities, and two different charge cut-off current values might be employed to train machine learning models in recognizing data attributes, such as the capacity and voltage, for alternative charge/discharge currents and temperature values. Therefore, this study aims to reduce the number of required measurements.
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 3 Proposed Reliability-Based Optimization Method
### Failure Probability Function Approximation Using First-Order Exponential Function
The first-order exponential function is chosen for its computational efficiency in decoupling reliability analysis from optimization. This form requires only one reliability analysis to approximate the relationship between design parameters and failure probability, making it particularly suitable for problems with low-to-moderate nonlinearity. However, for systems with highly nonlinear limit states (e.g., threshold effects) or strong interactions between variables, higher-order terms or adaptive approximation functions may be necessary. The sequential approximation framework in the next section mitigates these limitations by iteratively refining the local region of validity. In reliability-based optimization, it is crucial to estimate both the failure probability and the sensitivity of failure probability with respect to various design parameters. These quantities are essential for solving the undetermined coefficients of a polynomial response surface model, which is used to approximate the system's behavior. In the approach discussed here, the failure probability at an interpolation point and the reliability sensitivity values for each design parameter are computed in order to solve for the coefficients \(\mathrm{C_{0}},\mathrm{C_{i}}(i=1,2,\ldots,\mathrm{n_{\theta}})\)[30]. The calculation process is facilitated using importance sampling (IS) [31], a well-established method that significantly improves computational efficiency, especially for rare-event probability estimations like failure probability. Importance sampling (IS) is a statistical technique used to estimate the failure probability by generating samples from a different, auxiliary distribution \(q(x)\), which is chosen to better capture the tail behavior of the distribution, where failures are more likely to occur. The method is particularly useful when failure events are rare, which is typically the case in reliability analysis. Instead of sampling directly from the original distribution \(f_{X}(x)\), which would require a prohibitively large number of samples to observe failures, IS  
In summary, this method offers an efficient way to calculate the failure probability and the sensitivity of the failure probability with respect to design parameters. By leveraging the power of importance sampling and the score function method, it provides a reliable and computationally efficient approach for reliability analysis in optimization problems. This approach is particularly valuable in situations where the failure event is rare and traditional Monte Carlo methods would require an impractically large number of samples. Additionally, the ability to compute sensitivities without the need for additional sampling or differentiation of the limit state function further enhances its efficiency.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges
### Challenge 1  
When improving healthcare apps, the cost must be considered. Cost is the amount of money spent on providing healthcare services while maintaining a standard of care given to patients. One solution for cost optimization in QoS in smart healthcare applications is to use data analytics and ML practices to detect patterns and trends in patient data and use this knowledge to make more informed decisions regarding resource allocation. Designing more efficient treatment protocols, identifying high-risk patients who may require more frequent monitoring, and optimizing session timing are some examples of how to achieve this. Another option to reduce the need for in-person visits and save healthcare costs is to use telemedicine and remote monitoring technologies [73]. Additionally, by reducing the number of people in need of medical care, the introduction of preventative measures and health awareness campaigns can help reduce costs.
# A High Power Factor LED Driver with Intrinsic Current Balancing Capability
## 4 Circuit Design and Experimental Results
### Parameters Design
#### 4.1.2 Parameters of the Resonant Converter  
The component parameters are calculated from the following steps.  
Step 1 Calculate \(I_{r,rms}\).  
The root-mean-square value of \(i_{r}\) is calculated by using Equation (33).  
\[I_{r,rms}=\sqrt{2}\pi\times 0.78=3.46\text{ A}.\]  
Step 2 Calculate \(V_{1,rms}\) and \(V_{o1,rms}\).  
The root-mean-square value of \(V_{1}\) is calculated by using Equation (25).  
\[V_{1,\text{ }rms}=\frac{\sqrt{2}\times 100}{\pi}=45\text{ V}.\]  
As shown in Figure 1, each LED string is connected in series with a diode, and the conduction voltage of the diode is considered when designing the parameters. Referring to Figure 5b, the root-mean-square value of \(V_{o1}\) can be calculated from Equation (29).  
\[V_{o1,\text{ }rms}=\frac{2\sqrt{2}(0.7+23.1)}{\pi}=21.4\text{ V}.\]  
Step 3 Calculate \(R_{o,qu}\) and \(X_{s}\).  
By using Equations (30) and (36), the equivalent load resistance and impedance of the series resonant circuit are calculated, respectively.  
\[R_{o,equ}=\frac{V_{o1,rms}}{I_{r,rms}}=\frac{21.4}{3.46}=6.18\text{ }\Omega\]  
\[X_{s}=\frac{\sqrt{\left(V_{1,rms}\right)^{2}-\left(V_{o1,rms}\right)^{2}}}{I_ {r,rms}}=\frac{\sqrt{\left(45\right)^{2}-\left(21.4\right)^{2}}}{3.46}=11.44 \text{ }\Omega.\]  
Step 4 Determine \(Q_{L}\) and Calculate \(L_{r}\) and \(C_{r}\).  
Typically, when \(Q_{L}\) is higher than 2, \(i_{r}\) will approach a sinusoid [27]. Here, \(Q_{L}\) is chosen to be 3. Substituting \(Q_{L}\) = 3 and \(X_{s}\) = 11.44 \(\Omega\) into Equations (31) and (35), respectively, the values of \(L_{r}\) and \(C_{r}\) are calculated.  
\[C_{r}=232\text{ nF},\,L_{r}=0.08\text{ mH}.\]  
In order to use standard capacitance value close to 232 nF, \(C_{r}\) is chosen to be 220 nF, and then \(L_{r}\) is recalculated.  
\[C_{r}=220\text{ nF},\,L_{r}=0.0825\text{ mH}.\]