esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation  
In order to bring to light the accuracy of the results that have been obtained, it is suitable to show that they are in accordance with similar solutions from the existing literature. For that, let us use \(\beta=0\) in Equation (34). It corresponds to \(\alpha=1\), and the solution corresponding to the same unsteady natural convection flow of ordinary nanofluids, has to be obtained. When the Caputo derivative of zero order is the identity operator, Equation (34) becomes:  
\[u(y,t)=\frac{a_{1}a_{2}}{a_{1}-a_{3}}\bigg{\{}\left[t\Psi\bigg{(}2,\frac{-1}{2} ;-y\sqrt{\frac{a_{3}}{t}}\bigg{)}\right]-\left[t\Psi\bigg{(}2,\frac{-1}{2};-y \sqrt{\frac{a_{1}}{t}}\bigg{)}\right]\bigg{\}}. \tag{39}\]On the other hand (see also Equation (A2)):  
\[erfc\left(\frac{\psi\sqrt{a}}{2\sqrt{t}}\right)=\Psi\left(1,\frac{-1}{2};-y\sqrt{ \frac{a}{t}}\right)=\frac{\partial}{\partial t}\left[t\Psi\left(2,\frac{-1}{2};- y\sqrt{\frac{a}{t}}\right)\right] \tag{40}\]  
and then:  
\[t\Psi\left(2,\frac{-1}{2};-y\sqrt{\frac{a}{t}}\right)=\int\limits_{0}^{t}erfc \left(\frac{y\sqrt{a}}{2\sqrt{s}}\right)ds, \tag{41}\]  
where \(erfc(\cdot)\) is the complementary error fucntion of Gauss.  
Now, by introducing Equation (41) in (39), and by using Equation (A4), we get the velocity field as the simple form:  
\[u(y,t)=\frac{a_{1}a_{2}}{a_{1}-a_{3}}\left\{\begin{array}{l}\left(t+\frac{y^{ 2}a_{3}}{2}\right)erfc\left(\frac{y\sqrt{a_{3}}}{2\sqrt{t}}\right)-\left(t+ \frac{y^{2}a_{1}}{2}\right)erfc\left(\frac{y\sqrt{a_{1}}}{2\sqrt{t}}\right)+ \\ \frac{y\sqrt{a_{1}}!}{\sqrt{\pi}}\exp\left(-\frac{y^{2}a_{1}}{4t}\right)-\frac {y\sqrt{a_{3}}!}{\sqrt{\pi}}\exp\left(-\frac{y^{2}a_{3}}{4t}\right)\end{array} \right\}. \tag{42}\]  
Finally, bearing in mind the notations of [PERSON] and [PERSON] [13], as well as their rescaling relation from equality (2.11), it is easy to show that our solution (42) is identical to Equation (3.20), from [13].  
With regards to the Nusselt number \(Nu\) and the skin friction coefficient \(C_{f}\), we use \(\alpha=1\) in Equation (38) and use Equation (A5). The expressions corresponding to the ordinary nanofluid are:  
\[Nu=\frac{k_{nf}}{k_{f}}\frac{\sqrt{a_{3}}}{\sqrt{\pi}t},\ \ C_{f}=\frac{1}{ \left(1-\varphi\right)^{2.5}}\frac{2a_{1}a_{2}}{\sqrt{a_{1}}+\sqrt{a_{3}}} \sqrt{\frac{t}{\pi}} \tag{43}\]  
As expected, by changing \(a_{1},a_{3}\), and \(t\), by \(\frac{1}{a_{1}},\frac{1}{a_{3}}\), and \(\tau_{1}\), respectively, we recover the solutions (3.21), from [13].
## 5 Results and Comparison with Other Models
### Performance of the Custom Model vs. the VGG-16-Based Model  
The comparison of both models was made in terms of the following metrics: P, R and F1-score (Figure 9) and HTER (Figure 10). As shown in Figure 9, in the case of the custom model, its R value does not change between internal and external validation, whereas its P value decreases. This means that most of the colorized images are still classified as colorized in the external validation, but more original images are classified as colorized in the external validation. On the other hand, the VGG-16-based model has better results than the custom model, both for internal and external validation. In addition, for this model, the performance difference between internal validation is very small.  
Additionally, as shown in Figure 10, the best results of HTER correspond to the VGG-16-based model for internal validation (2.6%), with a very close result for external validation (2.9%). In the case of the custom model, the HTER for internal validation is 9% and for external validation it is 16%. The VGG-16-based model not only has lower HTER values, but also less dispersion between its internal and external validation results.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
###### Abstract  
The development of new telecommunication services requires the implementation of advanced technologies and the next generations of networks. Currently, the Long-Term Evolution (LTE) is a widely used standard. On the other hand, more and more mobile network operators (MNOs) are implementing the fifth-generation (5G) New Radio standard in their networks. It allows for increasing throughput, spectral, and energy efficiency and maximizing coverage, while reducing latency. The effectiveness of the introduced changes is measured by assessing the quality of service (QoS) in mobile networks. The paper presents the result evaluation of the QoS measurement campaign carried out using the drive test method in an urban area for four MNOs. We analyze the data transmission scenario, which is the basis of most modern telecommunications services. The result comparison provides an assessment of the 5G service implementation advancement by MNOs. In this analysis, we consider many QoS metrics (e.g., session time, throughput, and round-trip time) and parameters defining the radio signal quality (i.e., reference signal received power, signal-to-interference-plus-noise ratio). Our work also included searching for relationships between these parameters, using a correlation analysis. It allows for the selection of uncorrelated parameters to assess the quality of network, i.e., MNO evaluation, in terms of the provided QoS.  
5G New Radio; mobile network; mobile network operator (MNO); quality of service (QoS); throughput; reference signal received power (RSRP); signal-to-interference-plus-noise ratio (SINR) +
Footnote †: journal: 3 February 2024
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 3 Methodology
### DDoS Dataset
#### 3.1.1 NeTBIOS  
NetBIOS DDoS attacks are reflection-based. NetBIOS is a TCP/IP protocol that runs at Layer 5 of the OSI/ISO model. It connects system applications on multiple computers to interact in a local network. NetBIOS offers three services: Name Service (NS), Datagram Distribution Service (DGM), and Session Service (SSN). By default, NetBIOS-NS operates on port 137. DDoS assaults occur exclusively with the NetBIOS naming service [27].
## 4 Simulation Environment
### Simulation Scenario Setup  
To evaluate MITM attacks in VANET, we used the default map in Veins as depicted in Figure 6a. We introduced 100 vehicles in the network which are enough for many urban scenarios [44]. When vehicles are in the network, an event (accident) is generated at a random location. The vehicles share this information with its neighbors by broadcasting it. Vehicles upon receiving this information waits for sometime and diverts to other routes to avoid congestion as depicted in Figure 6b. We then injected 10%, 20%, 30%, 40% and 50% malicious nodes in the network respectively to study the impact caused by such attackers, where, the attackers delays, drops or tamper the shared messages. To study the attacker pattern, we created two scenarios:  
In scenario 2, we polluted network with '\(N\)' malicious nodes in such a way that they obliged specific pattern in the network, i.e., \(N/3\) MITM nodes exist near the event occurrence, \(N/3\) in the center of the network, while the rest of the MITM nodes (\(N/3\)) are located at the end of the network. Different parameters to perform simulations is described in Table 2.  
Figure 5: Workflow of Veins Simulation Framework.
## 1 Introduction  
### Research Background  
In the automobile industry, exterior parts (e.g., fenders) determine the vehicle appearance and protect the passengers against external impacts. Because the safety of the passengers can be threatened if the exterior parts fail to effectively absorb or disperse energy in the event of a collision, a high level of quality control is required to prevent defects in these parts [1]. They consist of complex curved metal surfaces, and they are mostly manufactured through sheet metal forming. Therefore, it is crucial to minimize defects that may occur in this process, such as wrinkles, cracks, and thickness changes.  
Defects (such as wrinkles and cracks) in sheet metal forming are prevented using a method that employs controlling the flow of materials and providing tension to prevent elastic restoration and increase stiffness [1]. Particularly, drawbacks play an important role in controlling the flow of sheet metal [2]. In other words, drawbacks are placed between the die and sheet metal to control the speed at which the sheet metal enters the die and prevent wrinkling or tearing that may occur during this process. Drawbeads also cause uniform deformation by distributing the tension of the sheet metal appropriately during forming, which improves the forming quality. Optimizing the design and application position of drawbacks is crucial because these parameters significantly affect the forming results [3].  
Drawbead formability prediction, a key element of the manufacturing process, can reduce the defect rate and maximize the production efficiency by accurately predicting the geometry after sheet metal forming. Therefore, such predictions have been employed through finite element analysis (FEA) in recent years. However, this method has difficulty in extrapolating the changes that may occur during production because the prediction depends solely on simulation at the initial design stage. This may deteriorate the forming quality, owing to die wear or material changes, and increase the defect rate and production costs. The time for analysis and optimization increases when further optimizations are performed to include such changes in the production environment. Additionally, if problems occur during production, owing to the nature of press work, modifications depend on the experience of the workers and considerable machining time and trials and errors are required until the design of the designer is actually applied [2].  
However, owing to the recent advances in big data and artificial intelligence (AI) technologies, new approaches to reduce the calculation time have been developed. In particular, machine learning and deep learning can learn the complex nonlinear relationships of the forming process based on large-scale data and perform predictions in real time [4]. They provide faster and more precise results in predicting the formability of complex parts and have been established as innovative tools for process optimization and quality control.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Virulence  
The c-di-GMP signaling pathways can affect various aspects of bacterial virulence. In _V. cholerae_, the first evidence of the role of c-di-GMP in virulence was that low levels of c-di-GMP induce elevated cholera toxin expression in vitro, while high levels of c-di-GMP attenuate virulence in murine cholera [46]. These observations indicate that low levels of c-di-GMP are required for acute infection. Recent studies support this view since pathogenic bacteria have few DGC proteins, and eliminating the DGCs function demonstrates that c-di-GMP signaling is not necessary for virulence in _Yersinia pestis_[21]. The infection of spirochete _B Borrelia burgdorferi_ in mice causing Lyme disease occurs when the bacterium has deleted its single DGC [47]. _Salmonella enterica_ serovar Enteritidis, with the elimination of all GGDEF domain genes, reduces its infectivity in mice [48]. Similar observations are in _Brucella melitensis_[49]. In addition, high levels of c-di-GMP inhibit acute infection of _B. burgdorferi_, _Y. pestis_, and _Francisella novicida_[50].
## 2 Calculation Model and Method
### Space-Time Neutron Diffusion Model
#### 2.2.1 Space-Time Neutron Kinetics Model  
The space and time dependent neutron kinetics model of the ADS reactor is as follows:  
\[\frac{1}{v_{a}}\frac{\partial\phi(z,t)}{\partial t}=D\
abla^{2} \phi(z,t)-\Sigma_{a}\phi(z,t)+(1-\beta)v\Sigma_{f}\phi(z,t)+\sum_{j=1}^{6} \lambda_{j}C_{j}(z,t)+S(z,t) \tag{1}\] \[\frac{\partial C_{j}(z,t)}{\partial t}=\beta_{j}v\Sigma_{f}\phi(z,t)-\lambda_{j}C_{j}(z,t);\quad j=1,2,\ldots,6 \tag{2}\]  
where \(v_{a}\) is the average speed of neutrons, \(\phi\) is the neutron flux, \(z\) is the axial position, \(t\) is the time, \(D\) is the diffusion coefficient, \(\Sigma_{a}\) and \(\Sigma_{f}\) are the macroscopic cross section of absorption and fission, respectively, \(v\) is neutrons yield per fission, \(S\) is the spallation neutron source, \(\beta\) is the total delayed neutron fraction, \(\beta_{j}\) is the delayed neutron fraction of group \(j\), and \(C_{j}\) and \(\lambda_{j}\) are the delayed neutron precursor concentration and decay constant, respectively.  
For the CNES reactor, the Equation (1) can be modified as:  
\[\frac{1}{v_{a}}\frac{\partial\phi(z,t)}{\partial t}=D\
abla^{2} \phi(z,t)-\Sigma_{a}\phi(z,t)+(1-\beta)v\Sigma_{f}\phi(z,t)+\sum_{j=1}^{6} \lambda_{j}C_{j}(z,t) \tag{3}\]  
The difference between Equations (1) and (3) is not only the absence of the source term in the critical core, but also the values of the cross-section parameters in Equations (1) and (3), which could be determined according to the composition and geometric distribution of all materials in the ADS and CNES cores. In the process of the neutron flux calculation, the finite difference method will be used for the discretization in space. This discretization is shown in Figure 3, and the size of each node is \(\Delta_{i}\). The power iteration method is used to calculate the steady-state diffusion equation [28], and the corresponding distribution of neutron flux could be determined by this iterative scheme. After the spatial discretization, neutron kinetics models could be transformed into the normal forms of time-dependent differential equations which could be solved by a backward differentiation formula (BDF) method with a scheme of implicit time discretization.
## 3 Results of Analysis of Ground Subsidence Risk Levels Using Machine Learning
### Results of Applying Machine Learning  
To build a machine learning-based model for the prediction of ground subsidence risk levels in urban areas, we selected a model that exhibited the best performance by applying 24 datasets, which were developed using the attribute information of underground utility lines and the historical information of ground subsidence, to RF, XGB, and LGBM classifiers. To implement machine learning, Python 3.8 was used, and the Scikit-learn library was employed. The model's evaluation indexes, accuracy, F1-score, and AUC were selected. The accuracy was used to determine the presence of overfitting by comparing the results of the training set with those of the test set. If the difference between the training and test scores is equal to or less than 0.1, it was determined that overfitting was avoided. In addition, the model's performance was identified using the F1-score and AUC indices to select the optimal model. The results of an evaluation of the machine learning models derived in this study are presented in Tables 5 and 6. Table 5 shows the model results where SMOTE was not applied, and Table 6 presents the model results where SMOTE was applied. Based on the evaluation results, the optimal model for the prediction of ground subsidence risk levels in the target region was determined. It was SMOTE-applied XGB (No. 3 model) when the number of years used was a five-year unit, the pipeline diameter was 50 mm, and the number of ground subsidence occurrences in the grid of risk level 2 was set to 1 to 3. In this model, the F1-score (0.590) and AUC (0.800) were the best, and the difference between the training (0.744) and test (0.644) scores was equal to or less than 0.1, which meant overfitting was avoided. Thus, this model was selected as the fittest classifier for the prediction model of ground subsidence risk level in the target region. The model results, according to whether or not SMOTE was applied, revealed that when SMOTE was not applied there was an F1-score of 0.310 to 0.570, and when SMOTE was applied there was an F1-score of 0.380 to 0.590. This meant that the imbalance in the number of ground subsidence occurrences, which was the output data, was resolved through SMOTE, thereby obtaining an efficient classification of the model. F1-Score and AUC of the XGB classifier in this study were 0.590 and 0.8 (Figure 2). Thus, XGB was found to not be a very good model from a computer science perspective. This result is due to the deepening of the data imbalance caused by the wide range of target areas and the limited use of influencing factors (underground utility attribute information). As ground subsidence is a phenomenon caused by various causes (underground structures, ground conditions, ground layer, etc.) in addition to the damage to underground utility lines, it is expected that the performance of the model will be improved in the future by obtaining more data on the underground space.
## 4 System Methodology
### Preprocessing, Feature Extraction and Feature Ranking
#### 4.2.2 Statistical Features  
The statistical parameters such as mean, median, and standard deviation, among others, as summarized in Table 1, have been computed at each sub-band of the EEG signal. The details and approximate coefficient at \(d_{1}-d_{10}\) and \(a_{10}\) were extracted, respectively, decreasing the signal dimension for an appropriate input to the classifier.  
\begin{table}
\begin{tabular}{c c c} \hline
**Title 1** & **Name** & **Theory** \\ \hline \multirow{6}{*}{Statistical Domain} & Mean & \(\mu=\frac{1}{M}\sum\limits_{j=1}^{M}\bigl{|}y_{j}\bigr{|}\) \\ \cline{2-3}  & Median & \(M=\left(\frac{\mu+1}{2}\right)^{th}\text{term}\) \\ \cline{2-3}  & Mode & \(M=\left(\frac{\mu+1}{2}\right)^{th}\text{term}\) \\ \cline{2-3}  & Maximum & \(Max=\max(f_{i})\) \\  & Minimum & \(Min=\min(f_{i})\) \\ \cline{2-3}  & Standard Deviation & \(\sigma=\sqrt{\frac{1}{M}\sum\limits_{j=1}^{M}\bigl{(}y_{j}-\mu\bigr{)}^{2}}\) \\  & Skewness & \(\phi=\sqrt{\frac{1}{M}\sum\limits_{j=1}^{M}\bigl{(}\frac{y_{j}-\mu}{\sigma^{3 }}\bigr{)}^{3}}\) \\ \cline{2-3}  & Kurtosis & \(\phi=\sqrt{\frac{1}{M}\sum\limits_{j=1}^{M}\bigl{(}\frac{y_{j}-\mu}{\sigma^{4 }}\bigr{)}^{4}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Summary of Statistical Parameters deployed in this study.  
Figure 8: Feature extraction stage.
## 2 Background Knowledge  
### Geohash  
The geohash algorithm, proposed by [PERSON], is a geographic data coding technique that employs grid partitioning [50]. It converts geographic location data into a string for expressing latitude and longitude coordinates in an abbreviated format. Geohash describes a rectangular cell and uses a binary system of 0s and 1s to represent location data. To convert the latitude and longitude values of a location, a division algorithm truncates them into a series of binary bits. In Figure 1[50], 0s represent smaller latitude or longitude ranges, while 1s represent larger ones. The resulting string forms a geohash string [8; 51].  
The geohash string provides a unique identifier for locations. Each character corresponds to one side of the half-field of the location. Longer geohash strings offer more precise information. For instance, a shorter geohash string like \"sxk3k\" denotes a larger area, whereas a longer geohash string such as \"sxk3k1\" represents a more specific location within a narrower region. Table 1[52], contains the geohash parameters.  
Geohash consists of 18 accuracy levels, ranging in area coverage from the highest level of 1-0.6 \(\mu^{2}\) to the lowest level of 18-25 million km\({}^{2}\). Table 2[52], displays the 12 most commonly used geohash area levels.  
Due to the safety measures taken in the traffic incident, the area affected by the accident can vary from 50 to 1000 square meters. Therefore, in our study, geohash code 6 was chosen to represent a rectangular region of 0.74 km\({}^{2}\) with a cell width of 1.22 km and a cell height of 0.61 km.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline \multirow{2}{*}{Latitude} & Geospatial coordinate is the latitude value in degrees. The valid value is a real number and is in the range [\(-\)90, +90]. \\  & Geospatial coordinate is the longitude value in degrees. The valid value is a real number and is in the range [\(-\)180, +180]. \\ \multirow{2}{*}{Accuracy} & It defines the requested level of accuracy. The supported values fall within the range of (1, 18). If a value is not specified, the default of 5 will be used. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geohash parameters.  
Figure 1: Geohash grid division diagram.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Data Sources  
Drought forecasting relies on various data sources to accurately monitor and predict drought conditions. These data sources provide information on the meteorological, hydrological, and environmental variables that are critical for understanding and forecasting drought events. For this study, we analyzed data from 11 meteorological stations (1985-2022): Chau Doc, Doc Hoa, Cao Lanh, Can Tho, My Tho, Ba Tri, Cang Long, Soc Trang, Bac Lieu, Ca Mau, and Rach Gia (Figure 1 and Table 1). Rainfall and temperature datasets from these meteorological stations were collected from the Southern Regional Hydrometeorological Center (Vietnam Meteorological and Hydrological Administration). Long-term rainfall records help identify trends and anomalies in precipitation patterns. Temperature data are essential for calculating potential evapotranspiration, which is a crucial component of drought assessment. The yearly precipitation in the Mekong Delta typically exceeds 1350-2366 mm. The Ca Mau-Rach Gia region experiences the highest levels of rainfall, with measurements ranging from 2000 to 2366 mm or more. Approximately 30% of the weather stations record a rainfall of 1300 mm or more. In contrast, areas such as My Tho receive a lower amount of precipitation, with levels ranging from approximately 1300 mm. The rainfall distribution exhibits temporal and spatial irregularities. This matter is intricately connected to the equilibrium and utilization of water resources to support the objective of sustainable development of water resources in the Mekong Delta. Figure 2 clearly illustrates that the rainfall was predominantly concentrated between June and October. In addition to monthly rainfall and temperature information, we incorporated four climatic variables, including soil moisture, humidity, Southern Oscillation Index (SOI), and Equatorial Pacific sea surface temperatures (SSTs), to develop machine learning models. Soil moisture and humidity data were acquired via The Enhanced POWER Data Access Viewer (NASA's Prediction of Worldwide Energy Resources (POWER)) website at [[https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)]([https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)) (5 May 2023). SOI and SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA) website at [[https://www.ncei.noaa.gov/access/monitoring/enso](https://www.ncei.noaa.gov/access/monitoring/enso)]([https://www.ncei.noaa.gov/access/monitoring/enso](https://www.ncei.noaa.gov/access/monitoring/enso)) (5 May 2023). The data sources were constantly maintained, and no post-processing was performed.