esa-sceva/satcom-chunk-collection huggingface dataset:
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods
### Support Vector Machine  
For SVM, the contemplated technique was SMOTE-temek [50], using as hyperparameters \(RBF\) (Radial Basis Function), a value of \(C\) = 464.1588, and a scale gamma value where the kernel function \(RBF\) uses these last 2 values. The parameter \(C\) trades  
Figure 11: Logistic Regression Hardware Architecture.  
Figure 10: Neurons of the Feed-Forward Neural Network. (**a**) Neuron of the First Layer, 15-bit input and _ReLU_ activation function; (**b**) Neuron of the Second Layer, 5-bit input and _ReLU_ activation function; (**c**) Neuron of the Third Layer, 5-bit input and _Sigmoid_ activation function.  
off misclassification of training examples against the simplicity of the decision surface, whereas gamma defines how much influence a single training can have.  
SVM Hardware Architecture is shown in Figure 12. The architecture is formed by a Multiplier-Adder 1 and comparators that act like Hard Margins. The 15 inputs were entered into Multiplier-Adder 1. Then, they were multiplied by the weights and added until a single value was obtained, compared with the Hard Margin, which determines if CLASS0 or CLASS1 is activated.  
A hyperplane can be written as \((\sum_{i=0}^{k-1}x_{i}w_{i})-b=0\) for \(k\) dimensions. For Hard Margin, two hyper-planes were selected for separating two classes of data for this particular problem (high and low CQI classification), such that the distance between these two hyperplanes was calculated by \(2/|w|\), where \(|w|\) is the norm of \(w\). The output was determined by Equation (7):  
\[output=\left\{\begin{array}{ll}CLASS\_0&\text{if }(\sum_{i=0}^{14}x_{i}w_{i})-b \geq 1\\ CLASS\_1&\text{if }(\sum_{i=0}^{14}x_{i}w_{i})-b\leq-1\end{array}\right. \tag{7}\]  
The module called _Comparators_ is formed by two comparators in parallel, such that it is determined if the output of the Multiplier-Adder 1 is equal or greater than one, or if it is equal or less than minus one.
## 3 Deployment Overview
### Overview  
The network was deployed in a warehouse located in Guadalajara (Spain), as depicted in Figure 1. The warehouse is built of steel and concrete, and measures 451 m \(\times\) 244 m (area 110,044 m\({}^{2}\)). As shown in Figure 2, a total of 11 nodes (orange) and one gateway (green) were placed in the warehouse. The gateway was located near the warehouse entrance (bottom, center) at a height of 2 m, whereas the nodes were placed at different distances and heights from the gateway. Table 1 shows the node identifiers (i.e., last two bytes of the EUI-64 address) and the distance to the gateway, which ranges between 34.0 m and 273.5 m.  
Figure 1: Warehouse environment overview. Notice how the warehouse is cluttered with large metal objects and tall metal structures, creating an environment subject to multi-path propagation conditions. (**a**) General overview of the warehouse. Notice the concrete floor and the steel structures. (**b**) Depth view of the warehouse. Notice the construction materials laying on the ground. (**c**) Tall construction where four nodes have been deployed at different floor levels.  
Figure 2: Map of the nodes (orange) and gateway (green) deployed inside the warehouse, as depicted in Figure 1. Notice that four nodes are deployed at the same position in the middle of the warehouse, but at four different floor levels (i.e., labels P0, P1, P2, and P3 inside the grey rectangle).
## 5 DDoS Attacks Emulation on SDN-Based IoT Architecture
### IoT Traffic Emulation  
We use iPerf synthetic traffic for network measurements in order to create TCP and UDP traffic between IoT users and the IoT-DS. The UDP traffic is generally preferred in wireless communication, such as wireless sensors, because it is connectionless and has less overhead, which means lower power consumption and a longer lifespan. Another reason is that UDP is faster than TCP, thus making it an ideal carrier for real-time streaming for IoT applications, whereas most of the applications, especially for interaction with the website or other people, such as email, adopt TCP as the solution. Because the transmission is connection-oriented and guaranteed, which ensures the reliability of message you received, TCP traffic is not the preferred for majority of IoT applications. Thus, we divide traffic into four categories:  
Figure 5: The SDN-based IoT architecture of the testbed used for the emulation of DDoS attacks.  
* **Periodical UDP Traffic:** IoT-DAG transmits 100 bytes UDP packets every 10 s (\(\alpha=10\) s). It is usually employed in smart city applications to collect information from sensors every period.
* **Periodical TCP Traffic:** IoT-DAG transmits TCP traffic with maximum available bandwidth every 60 s (\(\beta=60\) s). It is usually employed in the smart grid to read meters remotely every period.
* **Random UDP Traffic:** IoT-DAG transmits 100 UDP bytes every 60-120 s (\(\gamma=60\) s, \(\delta=120\) s). It usually happens on the wireless sensor devices when an event is triggered.
* **Random TCP Traffic:** IoT-DAG transmits TCP traffic with maximum available bandwidth every 60-120 s (\(\epsilon=60\) s, \(\zeta=120\) s). This usually happens on the personal device trying to connect to IoT applications, such as remote control of smart home.  
iPerf is running on both the sender (IoT-DAG), as well as the receiver (IoT-DS) to test network performance by generating TCP/UDP traffic. For UDP traffic, packet loss is the metric used to measure the performance, and available bandwidth is the metric for TCP traffic. These four types of traffic are implemented at the same time under different environments to emulate a real IoT traffic model, while showcasing the impact of DDoS attack and effect of SECOD.
# An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks
## 2 Related Works
### Graph Convolutional Networks  
Recently, graph-based deep learning algorithms have been increasingly studied. Much research is devoted to extending the traditional convolutional neural network (CNN) that works in the Euclidean domain to arbitrary graphs [19]. [PERSON] et al. [20] proposed a graph convolutional network (GCN), which constructs graph convolution networks by local first-order approximation of spectral convolution. The features of nodes are aggregated from local neighbors. Since then, a large number of researchers have set foot in this field. [PERSON] et al. [21] proposed an inductive framework, GraphSAGE. As a node embedding algorithm, the framework effectively generates node embedding for invisible data using node sampling and feature aggregation technology, which solves the limitation that GCN needs to calculate the full graph Laplacian. The output embedding of each layer of nodes depends on all the neighbors of the previous layer' neighbors and shows how to extract nodes from the neighborhood of nodes aggregate information. [PERSON] et al. [22] proposed an attention-based architecture to classify graph structure data.  
At present, a graph convolution neural network has been applied in many fields, such as in a recommendation system [23; 24; 25; 26], in malicious account detection [27], etc. [PERSON] et al. [28] proposed a spam bot detection model based on a graph convolutional neural network (GCNN) by using the features of nodes and aggregating the features of node neighborhoods. In addition to the feature set, they also considered the social graph, which can detect spam bots better.
# 5G NPN Performance Evaluation for I4.0 Environments
## 3 Hybrid 5G and I4.0 Concept Architecture
### 5G and I4.0 Fns  
The management of 5G on the factory floor requires resource isolation and flexible turnkey physical/virtualized entity management. Therefore, a Non-Public Network (NPN) is the preferred solution, which enables the deployment of a 5G system for private use, simplifying on-the-fly configuration and ensuring resource allocation exclusivity. In the framework of the proposed FNMS, the NPN can be deployed as a Standalone Non-Public Network (SNPN), which is operated by an NPN operator and does not rely on network functions provided by a PLMN. An SNPN is identified by a combination of PLMN ID and NID (Network Identifier); however, 5G NPNs are not limited to the core and RAN parts--they also need to address the routing, onboarding, and commissioning of various industrial devices that are not fully integrated with the 5G specifications. A 5G FNMS can address this open challenge by integrating a 5G LAN-type service, which provides services with similar functionalities to Local Area Networks (LANs) and VPNs, but with improved 5G capabilities (e.g., high-performance and long-distance access, mobility, and security). The 5G LAN-type service enables management of 5G Virtual Network (VN) group identification, membership, and group data. The Network Exposure Function (NEF) also exposes services to dynamically manage 5G VN group data. Furthermore, 5G supports optimized routing by enabling support for local switching at the UPF, without having to traverse the data network for UE-UE communication when two UEs are served by the same User Plane Function. In the framework of the proposed architecture, the OPC UA specification for interfaces and add-ons (OPC 10001-7) can be used as a basis to interlink the 3 GPP NEFs. This can achieve full I4.0 integration of 5G exposure primitives, and automated management and configuration through an OPC UA-compliant interface.
# Inactivation of the Plant Pathogen _Pythium ultimum_ by Plasma-Processed Air (PPA)
## 2 Materials and Methods
### Generation of Plasma Processed Air (PPA) and Agar Block Treatment  
PPA was generated using a microwave driven discharge at frequency of 2.45 GHz, as well as the supplied power of 4 kW. Plasma was operated at a gas temperature of \(\sim\)4000 K. For further details on the construction and composition of the device see [PERSON] et. al. [35]. The flow rate of the compressed air was 63 and 73 slm (standard liter per minute). The exhaust PPA was cooled down to room temperature (about 23 \({}^{\circ}\)C) before entering the one-liter glass incubation bottles. After completing the flushing of incubation bottles with PPA, gas flow was stopped and PPA was exposed to the sealed agar blocks for different time intervals. The following combinations of process parameter were applied: 63 slm and 25 min, 73 slm and 15 min, 73 slm and 10 min. The two gas controls using compressed air only were applied with 63 slm gas flow and 25 min and with 73 slm gas flow and 15 min. To stop the exposure to PPA, the incubation bottles were refilled twice with untreated compressed air. The experimental set-up encompassed four independent runs on four different days with seven replicates for each run.
## 2 Nutritional Properties of Honey Corresponding to Its General and Specific Chemical Composition
### General Chemical Composition of Honey  
#### 2.1.1 Carbohydrates  
The main carbohydrates found in honey are represented by sugars, containing 95% of the dry matter [53], and are responsible for its nutritional value. They are produced by honey bees through the enzyme invertase process, when sucrose is transformed from the collected nectar in different sugar elements [53; 54] in order to bring the honey to maturity. The carbohydrates are divided into three important groups that are found in all types of honey, namely: monosaccharides, disaccharides and trisaccharides [53; 55].  
Monosaccharides, specifically fructose and glucose, represent 75% of the total sugars found in honey structure [32]. Disaccharides are composed by maltose, sucrose, turnanose, maltulose, laminaribiose, isomaltose, nigerose, kojibiose, gentiobiose, and B-trehalose, whereas trisaccharides comprises: maltotriose, enclose, melezitose, centrose 3-a5, l-kestose, isomaltosylglucose, isomaltotriose, panose, isopanose, and theanedose [56]. Furthermore, when further reviewing the composition of honey's sugars, we can also find fructologosaccharides, which are an important source of prebiotics with great importance for the digestive system [57]. The most notable disaccharides are maltose and sucrose, while in terms of trisaccharides, melezitose is most commonly found in the composition of Honeydew honeys. [PERSON] et al. (2019) [58] analysed samples of Honeydew honeys from Spain, Turkey, Poland, and Romania, and found they presented a melezitose concentration ranging from 0.1 to 3.2 g/100 g\({}^{-1}\). Studies have revealed that the type and the concentration of sugars are the most efficient to indicate the difference between monofloral honeys [59]. For most honey types, a higher concentration of sugars is represented by fructose, (in this case we can mention Acacia honey, which has the highest amount of fructose) [60], however there are other honey varieties such as Rape, Dandelion or Blue Curls honeys, where glucose is the carbohydrate found in the greatest proportion [53]. Moreover, in their study [PERSON] et al. (2020a) [21] identified some Romanian honey samples of which the highest fructose content was identified in thyme honey (36.77%), while the highest glucose content was related to Rape honey (31.78%). Honey sugar's concentration is influenced principally by the type of flowers used by honeybees when gathering nectar, and can be affected by several external factors, with storage time and conditions being the most significant [61]. Therefore, the sugar content is considered one of the main indicators of honey's adulteration, as we later highlight.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology
### Turbulence Models Equations
#### 2.3.2 SST K-\(\omega\) Model  
The SST k-omega turbulence model is a mathematical model used in CFD to simulate turbulent flows. It is a two-equation model that solves two turbulence-related variables, \(k\) (turbulent kinetic energy) and omega (specific dissipation rate) (e.g., [PERSON] et al. [27] and [PERSON] et al. [28]).  
Turbulent kinetic energy (\(k\)):  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho kU_{j})}{\partial x_{i} }=\frac{\partial}{\partial x_{j}}\Bigg{[}\tau_{k}\frac{\partial\omega}{ \partial x_{j}}\Bigg{]}+G_{k}-Y_{k}+S_{k} \tag{11}\]  
Specific dissipation rate (\(\omega\)):  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial(\rho\omega U_{j})}{ \partial x_{i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\tau_{\omega}\frac{ \partial\omega}{\partial x_{j}}\Bigg{]}+G_{\omega}-Y_{\omega}+D_{\omega}+S_{\omega} \tag{12}\]  
where:  
\(G_{\omega}\) represents the generation of \(\omega\), \(G_{k}\) represents the generation of k due to mean velocity gradients.  
\(\tau_{k}\) and \(\tau_{\omega}\) represent the effective diffusivity of k and \(\omega\), respectively.  
\(Y_{\omega}\) and \(Y_{k}\) represent the dissipation of \(\omega\) and k in the turbulence, while \(\delta\omega\) represents the cross-diffusion term.  
\(S_{k}\) represents user-defined source terms.  
The \(\omega\) model addresses two transport equations for k and \(\omega\), along with the continuity, momentum, and energy equations. The transport equation for k covers the production, diffusion, and dissipation of turbulent kinetic energy, while the transport equation for omega deals with the production and destruction of the specific dissipation rate.
## 3 Proposed Architecture for NR-IQA  
Figure 2 depicts the suggested architecture for predicting the quality score of distorted images. With larger datasets, it is viable to use transfer learning, which involves fine-tuning pre-trained models using IQA datasets. The suggested architecture comprises a pre-trained network, namely Inception-RenNet-V2, which holds an adaptive receptive field that helps to generate better feature maps. This enables the removal of the greatest number of outliers from the feature vector that will be used by the unified learning mechanism, namely the spinal-net. The deformed pictures are influenced by noise, which mostly affects high-frequency areas because low-level characteristics are more useful for dealing with local distortions while elevated features are more representative of global abnormalities. Therefore, the selection of Inception-ResNet-V2 is a better choice.  
The Inception-ResNet-V2 and the spinal network employed in the proposed framework are described in depth below.  
Figure 1: Distorted Images from the KADID-10K dataset.
## 3 Materials and Methods
### Proposed Model
#### 3.2.3 Explainability  
The outputs of classification models created based on DL or ML techniques are not transparent enough to clearly show the reasons for model decisions. Therefore, the comprehensibility and interpretation of the obtained results becomes difficult. In this case, it is not reliable for experts to act according to a decision without knowing the reason for this decision [30]. XAI methods that make the internal workings and decision process of the model understandable in a way that increases this level of confidence have recently been integrated into models and more transparent outputs are offered [31]. XAI explanations are performed at different levels: global, local and semi-local. Global explanations provide an explanation of the general behavior of the model. They provide an understanding  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Value** \\ \hline Optimizer algorithm & Adam \\ Batch\_size & 32 \\ Epoch & 50 \\ Loss function & Cross-Entropy \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparameter values of CNN model.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Value** \\ \hline n\_estimators & 100 \\ learning\_rate & 0.01 \\ max\_depth & 3 \\ \hline \end{tabular}
\end{table}
Table 3: Hyperparameter values of XGBoost.  
of the model's output distribution and help one to understand how it works in general. They do not provide an explanation based on single samples. Local explanations provide explanations for specific input samples. The reason for the decision made for a sample is explained. Semi-local explanations provide local explanations for a group of data consisting of similar samples.  
Grad-CAM is an XAI method that visualizes the results of deep learning models and makes model decisions explainable [31]. A more transparent result is provided by utilizing the power of visualization techniques to determine which regions of medical images, such as X-rays and MRI, the model focuses on [32]. In a classification problem, the regions that are effective in assigning the class of the image are visualized using heat maps. As the class output of a model is obtained from the last layers, Grad-CAM is applied to these last layers. Gradients containing information about the model's class predictions are obtained. These gradients are multiplied with the feature maps in each layer. This weighted average shows which regions affect the model decision more for each class. The resulting weighted feature maps are converted into heat maps. On these maps, cold colors show regions that are less considered by the model, while warm colors show regions that are more considered. Thus, it is clearly shown which regions on the image are effective in creating the class label. This enhances understanding of how the model makes decisions.
## 3 System Architecture of the DSS
### Model Management
#### 3.3.3 Final Solution Procedure for the Product Mix  
The final decision process operates in a twofold manner. The first is a scenario where the product mix solution derived by the AHP-TOC heuristic is in compliance with the results garnered from product portfolio analysis; the DSS algorithm results in final strategies for the correct product mix and then terminates. This compliance is characterized by the number of products and production quantity, as well as the importance order.  
The other scenario is one where the mentioned compliance is disrupted, on the grounds that the process of product portfolio analysis prescribes a norm strategy that necessitates the elimination of any SBU. In such instances, the DSS algorithm retraces its steps, completely removing the SBUs in question from the product range and isolating pertinent data from the PMP dataset. Subsequently, the algorithm initiates the entire process anew. This iterative cycle persists as long as non-compliance prevails within the final decision process. Ultimately, this iterative mechanism yields non-conflicting sets of product mix and product portfolio strategies.
## 5 Results
### Detailed Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{\(\Delta\pi\) (mm)} & \multicolumn{4}{c}{\(\text{SMR}(\mathbf{I}_{\phi^{\rm F}})\) (dB)} & \multicolumn{4}{c}{\(\text{SCR}(\mathbf{I}_{\phi^{\rm F}})\) (dB)} \\ \cline{2-10}  & \(\phi^{\rm G}_{\rm GSS}\) & \(\phi^{\rm L}_{\rm M}\) & \(\phi^{\rm S}_{\rm ACM}\) & \(\phi^{\rm G}_{\rm GSS}\) & \(\phi^{\rm L}_{\rm M}\) & \(\phi^{\rm S}_{\rm ACM}\) & \(\phi^{\rm G}_{\rm GSS}\) & \(\phi^{\rm L}_{\rm M}\) & \(\phi^{\rm S}_{\rm ACM}\) \\ \hline
**10\% het.** & & & & & & & & & & \\ d = 5.3 & 12 & 11 & 12 & 1.9 & 1.7 & 1.9 & 15.6 & 15.7 & 15.6 \\ d = 7.8 & 14 & 14 & 14 & 3.9 & 3.6 & 3.9 & 17.8 & 17.8 & 17.8 \\ d = 10.9 & 1 & 1 & 1 & 1.5 & 1.9 & 1.9 & 16.6 & 16.6 & 16.6 \\ d = 13.1 & 2 & 63 & 2 & 0.5 & 2.9 & 0.5 & 14.3 & 14 & 14.3 \\ d = 20.2 & 8 & 8 & 8 & 2.3 & 1.9 & 2.3 & 13.7 & 13.7 & 13.7 \\ \hline
**20\% het.** & & & & & & & & & \\ d = 5.3 & 83 & 83 & 83 & 3.5 & 3.5 & 3.5 & 14.9 & 14.9 & 14.9 \\ d = 7.8 & 21 & 76 & 21 & 1.1 & 0.7 & 1.1 & 14.6 & 14 & 14.6 \\ d = 10.9 & 45 & 52 & 45 & 0.9 & 0.8 & 0.9 & 12.8 & 12.8 & 12.8 \\ d = 13.1 & 3 & 4 & 3 & 3.8 & 3.6 & 3.8 & 17.9 & 17.9 & 17.9 \\ d = 20.2 & 7 & 8 & 7 & 2.4 & 2.5 & 2.4 & 15.5 & 15.6 & 15.5 \\ \hline
**30\% het.** & & & & & & & & & \\ d = 5.3 & 36 & 16 & 36 & 0.1 & 0.6 & 0.1 & 11.8 & 10.4 & 11.8 \\ d = 7.8 & 21 & 21 & 21 & 0.2 & 0.2 & 0.2 & 12.1 & 12.1 & 12.1 \\ d = 10.9 & 29 & 29 & 29 & 0.5 & 0.9 & 0.5 & 11.9 & 12.1 & 11.9 \\ d = 13.1 & 39 & 28 & 39 & 0.6 & 1 & 0.6 & 13 & 11.1 & 13 \\ d = 20.2 & 13 & 9 & 13 & 1.2 & 1.3 & 1.2 & 16.5 & 15.6 & 16.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(\Delta\pi\), \(\text{SMR}(\mathbf{I}_{\phi^{\rm F}})\) and \(\text{SCR}(\mathbf{I}_{\phi^{\rm F}})\) evaluated for spherical targets of increasing diameter in phantoms of increasing heterogeneity. * have a well-understood method of action as differentiation is analogous to high-pass filtering;
* and identify the optimal image in heterogeneous phantoms with different tumour sizes. Additionally, the Gaussian Energy, \(\phi^{\mathrm{G}}_{\mathrm{GSS}}\), is shown in this work to be a suitable cost function in three-dimensional images with realistic artefact removal. Figure 5: Shown are coronal, sagittal and transverse slices of images of the tumour model \(T_{4}\). (**a**,**b**) are in a phantom with 10% glandular structures by volume and (**c**,**d**) are in a phantom with 30% glandular structures by volume. (**a**,**c**) are the images selected by the Gaussian Energy, \(\phi^{\mathrm{G}}_{\mathrm{GSS}}\); and (**b**,**d**) are the images selected by the Modified Laplacian, \(\phi^{\mathrm{L}}_{\mathrm{M}}\). The actual target location is marked by the dotted, red ellipse in each slice.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 3 The Proposed EEMIP Method
### Control Messages Exchange  
The coordinator and an end device use a simple model for exchanging messages. For offering available priorities in a network, the coordinator sends the Offer message to the end device, which contains priorities it can use. The end device receives the Offer message and replies using the Selection message with the selected priority for its data. The coordinator receives the reply and stores the information. This process is illustrated in Figure 3.