esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy Optimization in MHD Nanofluid Flow over an Exponential Stretching Sheet
## 6 Results and Discussion
The Sherwood number increases with higher values of \(\xi\) and \(Nt\) and decreases with an increase in the Brownian motion (\(Nb\)) or any thermophoresis parameter. The force generated by the increased temperature gradient in the fluid causes an increase in mass energy transfer through convection at the vibrating sheets, resulting in a higher mass transfer coefficient. Similar results were obtained by [32, 33]. Higher emitted thermal radiation increases the boundary layer temperature, as clearly depicted in Figure 5a. Heat energy in the form of electromagnetic radiation is emitted by the vibrating sheets and transferred to the fluid, causing the temperature to increase. The concentration profiles decrease with higher values of \(Nr\), as displayed in Figure 5b. Figure 4: (**a**) Velocity; (**b**) temperature, and (**c**) concentration profiles for different values of the viscosity parameter. The Soret effect is observed in Figure 6a-c. Higher values of the thermophoresis parameter lead to increases in all three flow profiles. This can be explained by the fact that the exponentially stretching sheet causes an upward shift in the temperature gradient. The movement of fluid particles in the hot region and high energy levels displace the fluid particles in the cold region, making the three flow variables increase. Similar results were obtained by [34]. The impacts of the local Reynolds (\(Re\)) and Brinkman numbers (\(Br\)) on entropy generation are displayed in Figure 7a,b, respectively. Entropy generation increases with higher values of the Reynolds number. The force due to the momentum of the fluid flow causes the inertial forces to increase, and as a result, the irreversibility associated with the flow  
Figure 5: The impacts of thermal radiation on (**a**) temperature and (**b**) concentration profiles. Figure 6: Impacts of the thermophoresis parameter on (**a**) velocity, (**b**) temperature, and (**c**) concentration profiles. increases. A similar trend can be observed for the Brinkman number. With higher values of \(Br\), more thermal energy is produced by viscous dissipation than thermal energy produced by molecular dissipation. This will thus mean slower heat conduction, and the temperature rises, causing more entropy generation. The influences of the temperature difference parameter (\(\Omega\)) and the temperature Grashof number (\(Gr_{T}\)) on entropy generation are depicted in Figure 8a,b, respectively. Entropy generation can be observed to reduce with higher values of \(\Omega\); the opposite can be observed with increasing values of \(Gr_{T}\). The rate of entropy generation increases as a result of increased buoyancy force due to spatial variation in the density of the fluid caused by the temperature difference.
## 4 The Developed Context Model
### Sensing Contexts  
These are the representations used to generate the knowledge about the context in which the data are being captured. They describe the sensing conditions, the sensing operations, and help to evaluate and understand the sensing data [27]. In order to infer the knowledge about these contexts some metadata elements are needed. The metadata elements would contain (a) the spatial information, such as the sensor and data location and spatial reference system; (b) the temporal information, such as instant time or interval of observation; and (c) thematic information, such as feature of interest and phenomena [9]. Other descriptive metadata elements are related to the data capture and observation processes, data collection characteristics (periodic, continuing, or reactive). The inferred knowledge could be related with \"when\" the data are sensing (day, night, season), \"where\" (sea, mountain, forest), \"how\" (sensing process, sensors) and \"what\" (phenomena, feature of interest). For instance, a WSN node is attached to a bike and it must monitor only when the bike is moving. When it infers from the variation of GPS or accelerometer data that the bike is moving, the monitoring system is started. In our model, this WSN status in which the movement could be inferred from sensing data is represented by the _mobile phenomena context_.
## 2 Related Work
In contrast to most researchers that focused on modeling the latent factors, fewer researchers found that the baseline estimation could also consider more factors to enhance the global deviation predictions. In [8], [PERSON] that users' preferences for items drifted over time, and items' popularity also constantly change. He modeled the dynamics over time and proposed a time factor-based CF recommendation (timeSVD++), where the baseline estimations were modeled as \(\hat{b}_{ui}=\mu+b_{u}(t)+b_{i}(t)\). [PERSON] et al. [14] proposed a proportion-based baseline estimation model for CF recommendation by considering personalized rating segments, and deviations \(\text{b}_{i}\) were weighted with factor \((b_{u}+\mu-m)/(\mu-m)\) when \(\text{b}_{u}\) is negative, and with \((n-b_{u}-\mu)/(n-\mu)\) when \(b_{u}\) is positive, where \([m,n]\) is the rating segment. The Gaussian process is usually applied to improve the accuracy of recommendations. [PERSON] et al. [16] proposed a modified EM model by Bayesian hierarchical linear models assuming user models are sampled randomly from a Gaussian distribution. [PERSON] and [PERSON] [30] proposed a Bayesian procedure based on the Gaussian process using a nonstationary covariance function for time series prediction. [PERSON] et al. [31] integrated Gaussian processes into different forms of Bayes filters; both considered the global mean of previous states and related uncertainty. Inspired by the above, our motivation was to find an improved and unified baseline estimation model for latent factor-based CF recommendation, with consideration of both global deviations and local deviations from users' personalized normal distributions. A preliminary version of this report appeared in the Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME) [32].
## 2 Literature Selection Criteria  
A systematic review of peer-reviewed studies published in Pubmed (www.ncbi.nlm.nih.gov; last accessed on 10 January 2021), ISI Web of Science (www.webofknowledge.com; last accessed on 10 January 2021), and ScienceDirect (www.sciencedirect.com; last accessed on 10 January 2021) databases was performed covering a time-span of 20 years (January 2000 to December 2020). The keywords used for the search were: plant extract, plant by-product, essential oil, natural vitamins, synthetic vitamins, swine, pig, rabbit, ruminant, small ruminant, beef, cattle, cow, sheep, goat, livestock, vitamin E, vitamin C, tocopherols, tocopherols, antioxidant, natural antioxidant, and organic farming. First, documents were selected based on the title and then on the abstract. If the objective of the work and the tested parameters were in agreement with the selection criteria and consistent with the aim of the present review, the articles were downloaded and summarized in the tables that were then described and discussed in the text. The following selection criteria were established: (i) comparisons of the effects either of plant extract, EO, or plant by-products from different agro-industries, with a specific dose of synthetic antioxidant vitamins -or antioxidants in general- in livestock nutrition and (ii) the use of additives of plant origin (excluding propolis and algae). The final database consisted of a total of 22 papers. Most of the research has been conducted in swine (\(n=12\)), followed by ruminants (\(n=8\)) and rabbits (\(n=2\)). Essential oils and extracts of rosemary and oregano (\(n=5\)) and grape by-products (\(n=2\)) were the most investigated PFA. In fact, a consistent variability of PFAtopology across the papers selected was found. Based on the selected available literature, the present review was divided into two different sections according to the animal species we found (monogastrics, including pigs and rabbits; and ruminants). Then, based on the tested parameters, a further division was created concerning the impact of PFA on feed intake, growth, other productive performance parameters, metabolic parameters, and oxidative status for the two categories of animal species. Since data were also available on the impact of PFA on hematological parameters and stress-related gene expression in pigs and rabbits only, it was decided to include this information together with the metabolic and oxidative status parameters.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 2 Related Works
### The Legacy Contention-Based Random Access Procedure
The authors of [24] proposed a novel grant-free RA method based on non-orthogonal multiple access (NOMA) to fulfill the massive connection requirements of 5G mMTC. The proposed method achieved a higher RA throughput than the NOMA-based RA method in various user-density environments. However, in previous studies on contention-based RA, it was difficult to reduce the collision rate owing to the lack of reference preamble sequences. To support massive connectivity, it is necessary to consider the preamble extension.
## 3 Results
### LRNTS Self-Calibration Model  
There are \(k\) transceivers in an LRNTS configuration setup, and they are distributed along the crater. Their approximate coordinates \(T_{i}(X,Y,Z)_{0}\), \(i=1,2,\ldots k\) are obtained from [48]. All transceivers are mutually connected with code ranges and carrier phase measurements:  
\[P_{ij}=\sqrt{\left(X_{i}-X_{j}\right)^{2}+\left(Y_{i}-Y_{j}\right)^{2}+\left( Z_{i}-Z_{j}\right)^{2}} \tag{8}\]  
\[\Delta\varphi_{ij}=MOD\left(\frac{\sqrt{\left(X_{i}-X_{j}\right)^{2}+\left(Y_ {i}-Y_{j}\right)^{2}+\left(Z_{i}-Z_{j}\right)^{2}}}{\lambda},2\pi\right) \tag{9}\]  
where \(i,j=1,2,\ldots,n\). The total number of independent measurements per epoch is, therefore,  
\(n=k\cdot(k-1)\), while the number of unknown transceiver coordinates is \(u=3k\). Since all measurements are uncorrelated, the weight matrix \(\mathbf{P}\) is diagonal, with the elements \(p_{i}=\left(\sigma_{i}^{2}\right)^{-1}\), where \(\sigma_{i}^{2}\) is the measurement dispersion. The design matrix \(\mathbf{A}\) is a Jacobian with elements \(a_{qm}=\frac{\partial O_{ij}}{\partial C_{m}}\), where \(O_{ij}\) is the observation function (8) or (9), and \(C_{m}\) is an unknown coordinate.  
Index \(q=1,2,\ldots,n\) indicates a measurement, while \(i,j,m=1,2,\ldots u\) are related to the unknowns.  
The adjustment model used here is developed as a free 3D network. Therefore, the normal equation matrix \(\mathbf{N}=\mathbf{A^{T}}\mathbf{PA}\) is singular, so Moore-Penrose pseudoinversion is used for calculating a cofactor matrix \(\mathbf{Q}_{\mathbf{x}}=\mathbf{N}^{+}\). Coordinate increments are calculated as: \(\mathbf{x}=\mathbf{Q}_{\mathbf{x}}\mathbf{A^{T}}\mathbf{PI}\), where l is the vector of the measurements. After calculating the new coordinate values \(\mathbf{X}=\mathbf{X}_{0}+\mathbf{x}\), an iterative adjustment procedure is repeated, with coordinatesX from the previous iteration assigned as approximate ones for the next epoch. Standard deviations of the unknown parameters are calculated as: \[\delta_{ii}=\delta_{0}\sqrt{Q_{ii}}\] (10) With: \[\bullet\quad\delta_{0}=\sqrt{\frac{\sqrt{\mathbf{Y}\mathbf{Y}_{\mathbf{v}}}}{n- u}}\text{--a posteriori dispersion factor, where vector of measurement increments }\mathbf{v}=\mathbf{A}\mathbf{x}-\mathbf{1}\text{;}\] \[\bullet\quad Q_{ii}\text{--corresponding element of the cofactor matrix }\mathbf{Q}_{\mathbf{x}}\text{.}\] Self-calibration is a process that establishes but also regularly checks the LRNTS coordinate system. The measurements needed for self-calibration can be completed in a short time (in 1-2 min), due to the ease of solving the integer ambiguities, as stated earlier. Therefore, periodical checks of the coordinate frame may be performed on a monthly or even weekly basis. During the periodical checks, the system still broadcasts navigation messages, so the users do not need to pause their work.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 6 Conclusions  
The continuous functionality of a SCADA system enables smooth operation of cri-tical infrastructure systems. DDoS attacks against SCADA systems may interrupt the whole system causing functionality lost. Interruption in the operation of the SCADA system can be costly from both financial and time aspects. The methods proposed in the study will reinforce SCADA systems against cyberattacks. Thus, early DDoS attack detection on the system will be possible, and it will be easier to prevent disaster scenarios.  
In this study, DDoS attacks were performed against the prepared testbed using the SCADA system. The obtained data both under the attacks and without attacks were recorded. LSMT, CNN, LSTM-CNN hybrid, and machine learning-based models were tested on the preprocessed dataset. After modifying the parameters of the models, various versions were obtained and used. The deep learning-based LSTM-CNN hybrid model achieved a classification accuracy of 95.00%, and the machine learning-based decision tree model achieved a classification accuracy of 99%. For a further evaluation of the success of the hybrid model, tests were conducted on a commonly used dataset in the literature which resulted in a high success rate of 98%. A higher success rate was achieved compared to the study in the literature using this dataset.  
In addition to DDoS attack detection, DDoS attack type detection was also performed. With deep learning-based and machine learning-based models, all TCP, SYN, and ICMP flooding attacks were correctly detected. These models will provide high success and efficiency in the detection of such attacks. In this respect, it is aimed to contribute to the literature and provide guidance for future studies.  
More detection studies should be carried out to reduce the effects of DDoS attacks on SCADA systems. Since SCADA systems are used in many different sectors, studies should be diversified by using different and new technologies and environments. In future studies, it should be an aim to prepare the SCADA system testbed environment more comprehensively and effectively. It should be an aim to apply different type of attacks other than DDoS attacks to this environment and to diversify the models used for their detection.  
The first author (E.S.) conducted experiments, wrote the manuscript, and executed software process. The second author (O.A.E.) was responsible for supervision and correcting the direction of the work. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, or publication of this paper.  
Not applicable.
## 2 Materials and Methods
### Study Area
#### 2.1.2 Vegetation Types in the Yellow River Basin  
The vegetation types in the YRB include forests, shrublands, and grasslands [32]. Natural forests can be divided into deciduous broadleaf forests, evergreen needleleaf forests, evergreen broadleaf forests, and deciduous needleleaf forests according to the plant functional type. Among them, the deciduous broadleaf forest is the largest forest type in the YRB, with an area of 108,055 km\({}^{2}\). The deciduous broadleaf forests are mainly concentrated in Gansu, the southern region of Shaanxi, the south-central region of Shanxi, and the northwestern region of Henan. In addition, deciduous broadleaf forests have a fragmented distribution in Shandong and Inner Mongolia. Evergreen needleleaf forest is the second largest forest type in the YRB, with an area of 54,835 km\({}^{2}\), mainly represented in Sichuan, Shanxi, and the southwestern region of Gansu. Evergreen broadleaf forests and deciduous needleleaf forests have a smaller distribution area in the YRB. Evergreen broadleaf forests and deciduous needleleaf forests are scattered in the southern region of Sichuan and Shandong, respectively. The natural shrublands in the YRB are mainly deciduous broadleaf shrublands with an area of 221,115 km\({}^{2}\). The deciduous broadleaf shrublands are distributed in the central and southern regions of the YRB, concentrated in Gansu and Shanxi. The area of natural grasslands in the YRB is 864,352 km\({}^{2}\), which is much larger than the area of forests in the YRB. The main types of grassland are temperate meadow, temperate steppe, alpine steppe, alpine meadow, and tussock. The temperate steppe in the YRB has the largest area of 403,498 km\({}^{2}\), which is mainly distributed in Gansu, Ningxia, the southeastern region of Qinghai, the northern region of Shaanxi, and the southeastern region of Inner Mongolia. The area of alpine meadows and alpine steppe is relatively similar, with 159,960 km\({}^{2}\) and 157,888 km\({}^{2}\), respectively. Alpine meadows are mainly distributed in the southeastern region of Qinghai, while alpine steppe are mainly distributed in Sichuan, as well as the northern region of Qinghai. The area of tussock and temperate meadow is smaller, with 100,316 km\({}^{2}\) and 42,690 km\({}^{2}\), respectively. The tussocks are mainly gathered in Shanxi and Shaanxi, with less distribution in Gansu and Shandong. The temperate meadows are mainly distributed in the west-central and south-central region of Qinghai.  
Figure 1: Schematic diagram of the geographical location of the Yellow River Basin. **A**, **B**, and **C** are the areas facing ecological problems in the upstream, middle and upper reaches, and downstream, respectively. The right panel indicates the location of this study area in China.
## 5 Novel Treatment Strategies Using Phyto-Nanomedicines  
Green nanotechnology aims to synthesize nanomaterials using the principles of green chemistry. Aside from the fact that green nanotechnology is beneficial to the environment, green-synthesized nanomaterials are more biocompatible and, therefore, more suitable for applications in nanomedicine [36; 37]. The impact of nanomaterials on the environment and human health is relatively unknown, and the immense diversity of nanomaterials and contradictory reports on their safety further exacerbate this problem. NP synthesis methods using biological or organic reducing agents of metal cations have been well developed. Microorganisms [38] and plants [39; 40; 41] have been used in green synthesis methods to produce MNPs. Many studies have shown that microorganisms such as actinomyces, fungi, and yeast possess the ability to synthesize MNPs [38]. These organisms have acquired the ability to accumulate and detoxify heavy metals through biochemical processes using various reductase enzymes to reduce metal ions [42]. Phyto-nanotechnology, defined as the use of plant extracts to synthesize MNPs, has become the common method to synthesize biogenic NPs [36; 37; 39; 40; 41]. The BRN and collaborators have shown that a number of African plant species, some directly from the UWC Nature Reserve shown in Table 1, can be used to synthesize bioactive AuNPs and AgNPs [39; 43]. The use of plant extracts in the production of MNPs has drawn much attention because these methods are rapid, eco-friendly, economical, and mostly single-step processes [39; 44]. These synthesis processes involve the reduction and stabilization of metal ions by biomolecules such as proteins, enzymes, polysaccharides, polyphenols, alkaloids, tannins, phenolics, terpenoids, and vitamins [45]. Thus, Africa's rich plant biodiversity thus offers a huge opportunity for the synthesis of a diverse range of nanomaterials with various bioapplications through phyto-nanotechnology.  
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline \multirow{2}{*}{**Plant Name**} & **Traditional or Medicinal Use** & \multirow{2}{*}{**MNPs Type**} & **Properties of the NPs** & **NP Activity** & **Ref** \\ \hline Acai berry (_Euterpe olerne
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.1 Dataset Composition  
Following the steps provided in the previous subsections, we created 754 images and cropped them to an area of the _ship sector_. The _ship sector_ images were enhanced by [PERSON]'s \(I\) operator and manually labeled so that they can be used for training machine learning models. Not all _ship sector_ images contained a visually identifiable NO\({}_{2}\) plume. Moreover, due to the dispersion and chemical transformation of a ship plume, some parts of the plume will always be under the detection limit of the satellite and, therefore, indistinguishable. Thus, labeling errors are possible. To minimize the chance of mistakes, the labeler was supported with several representations of the area of interest: the original not enhanced NO\({}_{2}\) tropospheric vertical columns for the area of a _ship plume image_, the enhanced with the [PERSON]'s \(I\) area of a _ship plume image_, and NO\({}_{2}\) tropospheric vertical columns for the full studied area in Mediterranean Sea with the positions of the neighboring ships. The descriptive statistics of the resulting dataset are provided in Figure 8. In Table 2, the information on the data distribution within the two classes of the dataset is shown. All mentioned numbers correspond to the full dataset before the training/test set division. The dataset used in this study, along with the code used for the experiments can be found under the following link: [[https://github.com/SolaK24/ShipPlumeSegmentation_Supervised](https://github.com/SolaK24/ShipPlumeSegmentation_Supervised)]([https://github.com/SolaK24/ShipPlumeSegmentation_Supervised](https://github.com/SolaK24/ShipPlumeSegmentation_Supervised)) 28 September 2022.