esa-sceva/satcom-chunk-collection huggingface dataset:
# Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy
###### Abstract  
As viruses evolve rapidly, variations in their DNA may arise due to environmental factors. This study examines the classification of COVID-19 DNA sequences based on their country of origin and analyzes their primary correlation with the country's international travel policy. Focusing on DNA sequences from nine ASEAN countries, we conducted a two-class classification to distinguish sequences from individual countries and mixed sequences from others. The sequences were initially dissected into 200 base pair units, and a deep-learning method was employed to construct a model. Our results showcase the capacity to differentiate DNA sequences with varying accuracy for each country. Additionally, the index of international travel policy, which reflects how countries implemented varying levels of restrictions regarding inbound travel, several months before the sequence collection date, moderately correlated with the classification accuracy within each country. This finding suggests a preliminary insight that a country's pandemic management might influence the variation in the DNA virus, determining whether these sequences will evolve distinctly from those of other countries or exhibit similarities.  
Keywords:ASEAN; COVID-19; DNA sequences classification; international travel policy; pandemic management +
Footnote †: journal: Applied sciences
## 1 Introduction
Prior work has explored various optimization strategies for rainbow table generation, including GPU-accelerated, FPGA-based, and MPI-based methods, each with different trade-offs in performance, scalability, and implementation complexity. Our proposed method's contribution is a modified and improved implementation for parallelization of rainbow tables using the MPI standard based on the structure of the master-slave approach, which is more efficient and convenient for this task. The approach differs from the traditional master-slave model by introducing a modification in the task distribution: instead of the master distributing starting points to each slave process, each process generates them independently, which is more efficient. This design eliminates the need for the master to coordinate task distribution, which is a well-known performance bottleneck in traditional MPI-based master-slave parallelization models [13; 14]. As a result, our method significantly reduces inter-process communication, improves scalability and maintains high parallel efficiency even with limited computational resources. MPI relies on communication between processes for parallelization, and most MPI-based algorithms require efficient communication to minimize latency and maximize computational performance. However, MPI communications require time due to factors such as latency, bandwidth limitations, network topology and communication overhead. Communication overhead in particular requires thoughtful consideration, and reducing it is the key to improving the performance of MPI-based algorithms. In the master-slave parallelization model, the communication overhead can be significant due to the way tasks and data are distributed between the master (which controls task assignment) and the slave processes (which perform computations). The overhead in this model comes from several key factors; the most relevant is the sending of messages and the use of message buffers. In this paper, we modified the master-slave model to reduce the number of point-to-point communications and improve the overall performance of the parallel algorithm. The paper consists of five additional sections. Section 2 provides a comprehensive literature review on cryptographic hash functions and cryptanalytic time-memory tradeoffs leading to parallel computing methods for rainbow tables generation. Section 3 presents the proposed method for parallel rainbow tables generation using MPI. The experimental results are presented in Section 4. Section 5 addresses the limitations of the research and suggests areas for future work. Finally, Section 6 concludes the paper.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 2 Discretisation
### Characterisation of Attributes by Discretisation  
One of the goals of subjecting a continuous-valued attribute to a discretisation process is to prepare the input data for a specific data mining algorithm and its operation mode [3]. The research carried out in this area shows that the results of discretisation may also constitute features' characteristics. It can be expressed as the number of bins assigned to the values of a given attribute by a specific discretisation algorithm [22]. On the basis of that, features can be evaluated from the point of view of the possibility of distinguishing decision classes and constructing classifiers, since the obtained bin number reflects the relationship between the attribute values and the class label. It leads to reason that higher numbers of bins indicate a higher importance of some variables. In the case of discretisation approaches based on entropy, it means that with increased numbers of intervals and lower numbers of ranges, the class entropy can be too degraded, or there may exist more complex relationships between these attributes and decision classes [23].  
The attributes for which only one interval has been indicated by supervised discretisation often turn out to be the ones occupying the last places in feature rankings, i.e., their values do not affect the distinguishability of objects relative to decision classes. From a classification point of view, such attributes appear to be irrelevant. However, a deeper analysis and additional research on the \"1-bin\" features show that the application of the next stage of processing for such attributes causes them to no longer be considered irrelevant, and the validity of their use in the classifier construction process exists [24].  
When the data are discretised, the division of samples into the training and testing parts should also be considered. When these sets are discretised independently, there is a risk that not only the cut points but the obtained numbers of bins for specific variables in the test and training sets may differ. Local variations in values and their irregularities expressed through discretisation can result in a deterioration of classification quality [25], especially in the case of attributes that by discretisation were assigned one interval. With this approach for separate sets, separate discretisation models for variables are also constructed and become a part of a learning problem, thereby adding a degree of complexity to a task.
## 2 Materials and Methods
### Statistical Analysis  
Data preprocessing, descriptive statistics, and model evaluations were carried out using NumPy 1.18.0, Pandas 0.25.3, and SciPy 1.4.0. Univariate comparisons were performed using built-in functions in SciPy and statsmodels 0.10.2. Machine learning models, including Random Forest, Support Vector Machine, and XGBoost, as well as Recursive Feature Elimination (RFE) for feature selection, were implemented using scikit-learn 0.22 and XGBoost 0.90. Neural network modelling, including the multi-layer perceptron (MLP), was conducted using the Keras 2.3.1 API with a TensorFlow 2.1.0 backend. Model performance was evaluated using mean absolute error (MAE) and, where applicable, 95% prediction intervals (PI). Figures were created using Matplotlib 3.1.2 and Plotly 4.4.1. A significance level of \(p<0.05\) was applied for all hypothesis testing.
## 5 MIMO Diversity Analysis
### ECC and DG  
The ECC is a very essential metric to analyze the performance of a smartphone in the presence of a rich scattering environment as the performance of the smartphone is significantly affected by different hand postures. The ECC values should be \(<\)0.5 in the presence of a scattering environment to quantify the uncorrelated operation of each antenna element in the multiple antenna system. The ECC is computed from either S-parameters or from 3D far-field radiation patterns. By considering Equation (1), the ECC values are calculated as [26]:  
\[\text{ECC}=\frac{\left|\int\int\left[\overset{\rightarrow}{F_{i}}(\theta,\varphi )*\overset{\rightarrow}{F_{j}}(\theta,\varphi)\right]d\Omega^{2}\right|}{\int \int\left|\overset{\rightarrow}{F_{i}}(\theta,\varphi)\right|^{2}d\Omega\int \left|\overset{\rightarrow}{F_{j}}(\theta,\varphi)\right|^{2}d\Omega} \tag{1}\]  
where, in Equation (1), \(\overset{\rightarrow}{F_{i}}(\theta,\varphi)\) and \(\overset{\rightarrow}{F_{j}}(\theta,\varphi)\) are the 3D radiation patterns of the antenna when port \(i\) is excited and port \(j\) is excited, respectively. \(\Omega\) represents the solid angle, \(\theta\), \(\varphi\) represents spherical angles (elevation, azimuth), and \({}^{\star}\) denotes the Hermitian product.  
The calculated ECC values for the proposed flexible MIMO antenna are well below 0.05, as depicted in Figure 11.  
The diversity gain is calculated by using Equation (2) [26]:  
\[\text{DG}=10\sqrt{1-\left|\text{ECC}\right|^{2}} \tag{2}\]  
Figure 11 depicts the value of DG obtained using Equation (2) from far-field radiation patterns. It can be noted that the DG value is greater than 9.8 dB throughout the functioning band. This confirms the good diversity performance of the proposed flexible MIMO antenna.  
Figure 10: Gain and efficiency of 4-element mobile MIMO antenna.
## 1 Introduction
The mean SNR for COSMIC is equal to 700 \(\
icefrac{{\eta}}{{\var b}}\). The mean L1 SNRs for both KOMPSAT-5 and MetOp-A, -B, -C GRAS are also equal to about 700 \(\
icefrac{{\eta}}{{\var b}}\) (not shown). Here the COSMIC data are from the global, and the COSMIC-2 data are within 45\({}^{\circ}\) N and 45\({}^{\circ}\) S (see Section 2.1). The distribution of the SNR is not dependent on the latitude and longitude. To see whether the high SNR RO signals will lead to better retrieval results, we separate the COSMIC-2 neutral atmosphere retrievals into different SNR groups and quantify their quality for individual SNR groups. Figure 1: The histograms of the normalized accumulation (in %) L1 SNR for COSMIC (in green line), COSMIC-2 with GPS emitter (in orange line), and COSMIC-2 with GLONASS emitter (in red). The SNR is computed from the average SNR values from 60 km to 80 km geometric height range of the L1 signal. We normalized the lines to the maximum of the sample number of the SNR bin. The mean L1 SNR for COSMIC is equal to 700 \(\
icefrac{{\eta}}{{\var b}}\), where that for COSMIC-2/GLONASS samples and COSMIC-2/GPS samples are equal to 1100 \(\
icefrac{{\eta}}{{\var b}}\) and 1250 \(\
icefrac{{\eta}}{{\var b}}\), respectively. All the data are collected from June to October 2019. In Section 2, we describe the COSMIC-2 data, data from other RO missions, and RS41 observations. We introduce the comparison approaches in Section 3. We quantify the COSMIC-2 penetration, precision, and stability in Section 4. We quantify the accuracy and uncertainty of COSMIC-2 refractivity, temperature, and water vapor profiles in Section 5. We conclude this paper in Section 6.
## 2 Principle
### GS-Based CD Post-Compensation Algorithm at the Receiver  
Figure 3 illustrates the principle of iterative GS-based CD post-compensation algorithm. Firstly, the amplitude information of the received signal is obtained through a square root operation, where I(t) represents the power signal after PD detection. The phase is initialized to linearly increase from 0 to \(2\pi\). Secondly, the inverse CD transfer function, which means CD compensation, is applied following square and match filter operation. At the transmitter of the algorithm, the signal is down-sampled to 1 sps, and a pilot sequence is inserted to increase convergence speeds. Then, up-sampling and pulse-shaping operations are applied. After passing through square root and CD transfer function, the phase information is updated. Finally, the received signal is reconstructed for next iteration. As the precondition, the GS-based CD post-compensation algorithm is sensitive to ISI. As a result, it is important to add a pre-distortion process at the transmitter.
# Innovative Sensor Approach to Follow _Campylobacter jejuni_ Development
###### Abstract  
_Campylobacter spp_ infection affects more than 200,000 people every year in Europe and in the last four years a trend shows an increase in campylobacteriosis. The main vehicle for transmission of the bacterium is contaminated food like meat, milk, fruit and vegetables. In this study, the aim was to find characteristic volatile organic compounds (VOCs) of C. _jejuni_ in order to detect its presence with an array of metal oxide (MOX) gas sensors. Using a starting concentration of \(10^{3}\) CFU/mL, VOCs were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS) with a Solid-Phase Micro Extraction (SPME) technique at the initial time (T0) and after 20 h (T20). It has been found that a Campylobacter sample at T20 is characterized by a higher number of alcohol compounds that the one at T0 and this is due to sugar fermentation. Sensor results showed the ability of the system to follow bacteria curve growth from T0 to T20 using Principal Component Analysis (PCA). In particular, this results in a decrease of \(\Delta\)R/R\({}_{0}\) value over time. For this reason, MOX sensors are a promising technology for the development of a rapid and sensitive system for C. _jejuni_.  
_Campylobacter jejuni_; VOCs; GC-MS SPME; nanowire sensors; PCA
## 5 Conclusions  
In this paper, we have proposed a new energy efficient and scalable data dissemination method, _i.e._, ASST, based on DHT and TBF. ASST is a type of data-centric storage system for sensor networks. ASST provides fast access to the data stored in a sensor network by using the distributed hash function. In ASST, the source node stores data at the nodes forming a trajectory around the target position computed by the hash function. A client sends a query packet to the position computed by the hash function that is the same as the source node. By storing data at several repository nodes, ASST distributes the network load caused by query packets.  
ASST also provides scalability for a large sensor network. When the query frequency is increased, the range of the trajectory is also increased to reduce the network load at the repository node. By adjusting the range of the trajectory in proportion to the query frequency, ASST can ensure a fast response time for multiple clients and reduce the network load assigned to a sensor node. Such capabilities result in an increased life span for the sensor network.  
The main focus of ASST is providing load scalability with constant response time by adjusting the width of trajectory in proportion to the query frequency under uniform distribution of query, however the query distribution might not be uniform over network and thus increasing or decreasing four edges with same distance \(\mathrm{H}\) can cause load unbalance under non-uniform distribution of query. In the future  
Figure 11: Energy consumption for storage construction.  
work, we will consider the adjustment of distance H of four edges in trajectory zone in separated manner under with under non-uniform distribution of query.
# Analysis of Six Active Power Control Strategies of Interconnected Grids with VSC-HVDC
## 5 Transient Stability Simulation Results with Six Different Active Power Control Schemes
### Angle Spread Evaluation with PGs
#### 5.1.1 Step Control  
The results in Figure 11 show the angle spread of two interconnected grids with the control scheme as (a) and (b). Both control schemes are step control strategies, and we aimed to determine whether the active power should be transmitted at \(t_{1}\) or \(t_{2}\). In area 1, the only difference between the two schemes is the power release time, so we may see only the result of area 2. From the angle spread results of area 2, it can be seen that sending power at \(t_{2}\) can further improve the angle stability. More specifically, the first damping of angle spread as \(x_{F}\) after the contingency event is mitigated at about 1.45 s, and the recovery characteristic is more improved from 2.1 s to 3 s. This is because the power from area 1 contributes to the frequency recovery characteristic of area 2. However, in (a), the power is transmitted immediately after the fault, so it contributes less to the angle stability of area 2. The reason for this is that the initial frequency drop driven by fault is momentarily increased due to the immediate active power support. Therefore, the PGs of area 2 generate a relatively small amount  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \
\begin{tabular}{c} **Generator** \\ **Number** \\ \end{tabular} & **Generator List** & **Initial Power (MW)** & **GVIF** &
\begin{tabular}{c} **PGs** \\ **(O: PG, X: Not PG)** \\ \end{tabular} \\ \hline
11 &
12 &
\begin{tabular}{c} Inchen \#GT1 \\ \end{tabular} & 164.065 & 0.004 & O \\
13 &
\begin{tabular}{c} Inchen \#GT3 \\ \end{tabular} & 180.69 & 0.003 & O \\
14 &
\begin{tabular}{c} Inchen \#GT5 \\ \end{tabular} & 168 & 0.005 & O \\
15 &
\begin{tabular}{c} Inchen \#ST1 \\ \end{tabular} & 163.02 & 0 & X \\
16 &
\begin{tabular}{c} Inchen \#ST2 \\ \end{tabular} & 178.12 & 0 & X \\
17 &
\begin{tabular}{c} Inchen \#ST3 \\ \end{tabular} & 167.1 & 0 & X \\
18 &
\begin{tabular}{c} POS \#GT1 \\ \end{tabular} & 210.52 & 0.005 & O \\
19 &
\begin{tabular}{c} POS\#GT2 \\ \end{tabular} & 210.52 & 0.005 & O \\
20 &
\begin{tabular}{c} POS6 \#GT3 \\ \end{tabular} & 217.17 & 0.005 & O \\
21 &
\begin{tabular}{c} POS6 \#GT4 \\ \end{tabular} & 224.4 & 0.005 & O \\
22 &
\begin{tabular}{c} POS6 \#ST2 \\ \end{tabular} & 232.65 & 0 & X \\
23 & & \(\vdots\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._of power according to the small frequency drop. However, this process finally makes a slow frequency recovery, and the angle stability of the PGs becomes worse than that in (b). Therefore, we can conclude that sending power during the frequency recovery stage is more beneficial to both TSOs.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 2 Related Work
### Supervised Machine Learning Algorithms  
Supervised machine learning offers powerful tools for building input-output relationships of observed systems and have shown their efficacy in a range of problems, such as image classification and control [19; 20].  
The most prominent supervised machine learning methods are Artificial Neural Networks, which are very powerful for building classifications models of high dimensional data, and solving problems such as object classification in images. These advances were made possible by the wide availability of large amounts of annotated data, reducing overfitting [19]. This makes them however unreliable in the application at hand, where preexisting data is sparse or expensive to obtain due to computationally expensive simulations or costly tests involving physical hardware.  
A supervised machine learning method that can build surrogate models based on small data sets is the Support Vector Machine (SVM) [21; 22]. They were shown to be effective in scenario prediction of human driving as shown in Reference [23]. The disadvantage of SVMs compared to GPs is that they do not provide an inherent estimate of the confidence of a prediction. Furthermore, GPs can offer better adjustment in nonlinear modelling by allowing custom kernel functions [24].  
The k-Nearest Neighbour (k-NN) algorithm can also be used to model a relationship between input and observed output of an unknown function [25]. The method predicts new data points by relating known data in the vicinity of the predicted point. As the predicted point moves closer to the decision boundary between classes, the reliability of the method declines, however. This makes it less suited for the evaluation of automated vehicles, where it is critical to find corner case scenarios on the boundary between two performance modes.
## 3 Indoor Positioning Systems
### Systems Based on Radio Frequency  
Many different wireless networks can be deployed in an indoor environment, from traditional Wi-Fi networks, which are used in millions of buildings around the world to provide Internet access, to models with more specific purposes. Among the various models available are WSN models, which are  
Figure 1: Classification of indoor positioning systems (IPSs).  
designed to connect the Internet of Things (IoT) devices, popular RFID devices, and Bluetooth beacons, among other alternatives. A more simplified way to classify network-based indoor positioning systems is to consider how information is obtained. In this classification model, there are two groups: (i) methods based on range and (ii) methods without range.  
Range-based methods extract geometric information (distance or angle) from signals from different wireless nodes and then combine the geometric constraints of each anchor to obtain the user's position [43]. Free-range methods are based on the connectivity information between nodes or the identification of signal resource patterns that depend on location [1].
## 2 Ground Motion Record Selection  
### Spectral Matching-Based Record Selection  
The GM records that should be utilized for the linear and/or nonlinear dynamic analyses must align with the designated level of the target spectrum, which is typically defined by the uniform hazard spectrum as per seismic codes, the conditional mean spectrum, and similar criteria. Different approaches can be employed, whether in the frequency domain or the time domain, to match real ground motion records with the corresponding target spectrum. Frequency-domain methods to adjust the frequency spectrum of the records or modify the original GM records by incorporating wavelets were studied by different re searchers [56; 57]. On the other hand, scaling the amplitude of the spectrum in time-domain methods are also used in the literature [24; 58; 59]. Several studies in the literature have assessed the alignment of the chosen GM spectra with the target spectra [58; 59]. [PERSON] et al. [59] used the \(\delta\) parameter to assess the alignment of the selected GM spectra with the target spectra. It was observed that the \(\delta\) parameter was between 4% and 17%. Another study conducted by [PERSON] et al. [58] used the \(MSE\) (mean squared error) parameter. In the study, the \(MSE\) was calculated as 3.1% and 4.3% for the considered period ranges. In this study, the \(MSE\) parameter is used to evaluate the alignment of the selected GM spectra with the target spectra. The equation of the \(MSE\) is given in Equation (1). In Equation (1), \(k\) denotes the number of equal segments for the specified period range, and \(S_{a}(T_{i})\) indicates the spectral acceleration of the \(i\)th for the target spectra. Additionally, \(S_{M}\) shows the spectral acceleration of the mean spectrum of selected GMs.  
\[MSE=\frac{1}{k}\sum_{i=1}^{k}(S_{M}(T_{i})-S_{a}(T_{i}))^{2} \tag{1}\]
## 3 Results and Discussion  
### Operational Parameters  
Figure 3 summarizes the operational parameters of the bioreactor.  
The OLR applied to the bioreactor is defined as the ratio between the mass flow rate of biodegradable solids fed and the working volume of the bioreactor, expressed in g VS.L\({}^{-1}\)bioreactor.d\({}^{-1}\). Considering the fact that the volume of the bioreactor is constant, this parameter is dependent on the HRT and the concentration of VS present in the substrate, influencing the fermentation pathway, the substrate conversion efficiency, the composition of the microbial population, and consequently the production of bioH\({}_{2}\).  
The change in HRT has a direct impact on OLR variation, namely as the decrease in HRT leads to an increase in OLR due to the increase in the daily mass of VS applied to the bioreactor. Increasing the VS load applied to the bioreactor is preferred for the energy efficiency of fermentation processes. However, excessively high VS loads can result in an unstable fermentation process due to the accumulation of VFAs and can lead to the inhibition in bioH\({}_{2}\) production.