esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Architecture and Topology
### Network Architecture Classification
#### 3.5.2 Hierarchical Networks  
Unlike flat networks, these networks make use of a hierarchical distribution of tasks among nodes. The network consists of heterogeneous nodes where the most powerful one performs the most demanding tasks and the less powerful nodes are reserved for the less challenging ones, thus saving as much energy as possible. This can be accomplished by using simple schemes. The simplest approach is the one used in many traffic control applications, in which one or few sensor nodes are responsible for traffic detection, while nodes at intersections control traffic lights. However, hierarchical networks are able to offer a more important benefit when they are applied to larger systems.  
A well-known class of hierarchical networks is clustered networks, which stand out thanks to their scalability. They are based on grouping nodes into clusters where one of them is selected as the cluster head, which will present the services offered by all cluster nodes to the external devices. This results, as it was stated, in saving costs and energy, since most of the nodes implement only lightweightfunctions and a few of them, the cluster heads, require a greater investment in more powerful devices and in larger power units and solar panels [11, 15]. Scalability is provided by increasing the number of clusters, which does not increase the complexity of each cluster. Consequently, simple routing algorithms can be applied within the cluster, being single hop star topology a feasible option. This can be useful not only for traffic applications [15] but also in parking applications [30], deploying a cluster star network formed by the composition of different groups of nodes with a star topology which transmit to a central base station.  
If scalability to wider areas is required there is the possibility that the cluster heads self-organize into a multihop network that delivers information to farther points. The result is a two-tiered system where constrained cluster nodes compose the sensing tier and more powerful cluster heads pertain to the distribution tier, as can be seen in the example shown in Figure 4. This permits sharing road information with distant nodes in order to warn drivers timely as well as communicating with a distant DMS since a tiered architecture allows separating delay sensitive operations (speed measurement, detection of dangerous vehicles) from delay insensitive operations (storage at the DMS). In the latter case, when a distant DMS is present in the system, it can be considered as the third tier of it, in charge of the centralized decision making [34].
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements  
On top of all the enhancements that were introduced in Releases 13 and 14, the following improvements were introduced in Release 15 to satisfy the fast adoption of massive deployment with further improved quality of service.
# Dynamic Behavior of an Embedded Foundation under Horizontal Vibration in a Poroelastic Half-Space
## 3 Results and Discussion
### Verification of the Analytical Solutions with Existing Results
#### 3.1.2 Verification of the Side of the Embedded Foundation  
Figure 3 shows a comparison of nondimensional results of dynamic stiffness and damping at the embedded foundation side by varying the embedment from 0 m to 5 m. The dynamic impedance along the foundation side is investigated by comparing the results with [PERSON]'s results [26] (\(f_{\mathrm{ss}}\)P and \(f_{\mathrm{sd}}\)P) by adopting the same material parameters. As indicated in Figure 3, the dynamic impedance side agrees excellently, both in the real and imaginary part of dynamic impedance, as the foundation embedment increases. The real part increases when \(a_{0}<2\) and then decreases throughout the considered frequency range. The imaginary part shows a linear increase with increasing \(a_{0}\). Comparisons of the dynamic impedance of the present solution with [PERSON]'s results (\(f_{\mathrm{ss}}\)P and \(f_{\mathrm{sd}}\)P) proves that the accuracy of the present solution is confirmed.  
Figure 2: Verification of the foundation base.  
In this paper, the soil is considered as drained condition (high permeability) with a soil permeability larger than \(10^{-4}\), that is, 0.1. Selected key parameters, such as the ratio of embedment, the variation of foundation mass, etc., of a rigid, massive, cylindrical foundation embedded under horizontal vibration, are investigated by varying the nondimensional excitation frequency. The effect of Poisson's ratio \(v\) on the horizontal dynamic impedance of embedded foundation can be ignored through verification. The nondimensional frequency of excitation is defined as \(0\leq a_{0}\leq 5\) from lower frequency range to moderate frequency range. The nondimensional horizontal impedance \(K_{\rm h}\) are studied by its real part (\(k_{\rm h}\)) and imaginary part (\(c_{\rm h}\)), respectively. The dynamic response factor is also demonstrated with nondimensional frequency.  
The key parameters are studied, including:  
1. The ratio of embedment over foundation radius, that is, \(L/R\) from 0 to two.
2. The effect of the mass ratio (the variation of foundation mass) from one to four.
3. The effect of poroelastic material property \(M\) from 10 to 300.
4. The relative stiffness of the soil layer around the foundation and under the foundation, that is, \(\ Gs/Gb\) from 0.1 to two.
The criticality evaluations at PSI are performed by means of a computational scheme called BUCSS-R (Burnup Credit System for the Swiss Reactors-Repository case) [3] and complemented by a comprehensive quantification of uncertainties originating from a variety of sources. The information of which spent (or used) nuclear fuel assemblies can be loaded into the final disposal canister, fulfilling the regulatory safety limit from the criticality point of view, is finally provided in the form of loading curves.  
The objective of the present study is to enhance the existing uncertainty quantification (UQ) method with respect to nuclear data (ND) uncertainties. ND uncertainties affect the computation of keff values through the calculation chain of BUCSS-R in three ways: They cause uncertainties in the SNF composition from the transport and depletion calculations, in the decay calculations as well as in the criticality calculations themselves. In a previous study [4], the uncertainties in the final keff values emerging from each of these steps have been quantified independent of each other. This caused an excess in conservatism since a full correlation of the uncertainty contributions had to be assumed between the depletion and criticality calculations. The present work, on the other side, pursues a consistent ND-UQ approach so that ND uncertainties in the criticality results could be determined in a more direct and realistic manner.  
Other UQ methods in the area of criticality safety that are worthwhile mentioning here are, for instance, the Total Monte-Carlo method (TMC) based on reaction model parameters [5], non-intrusive and adaptive polynomial chaos techniques [6] and the linear perturbation method based on sensitivity calculations [7]. The code sequence called \"Sampler\" for uncertainty analysis with SCALE and XSUSA is an example of a statistical sampling method [8]. The direct application of measurements in the criticality models is the approach used in [9]. However, a consistent quantification of ND-related uncertainties in depletion and criticality calculations has not been so far subject of a dedicated analysis to the best of our knowledge and this is presented for the first time in this work.
## 4 Method
### Communication Graph Embedding Learning (CGE)
To better integrate the relational features between nodes, in each graph convolution operation, the feature \(z_{i}\) of node \(v_{i}\) is updated by performing a weighted average with the feature vectors of its neighboring nodes:  
\[h_{i}^{(k)}=\frac{1}{d_{i}+1}h_{i}^{(k-1)}+\sum_{j=1}^{n}\frac{a_{ij}}{\sqrt{ (d_{i}+1)(d_{j}+1)}}h_{j}^{(k-1)} \tag{14}\]  
Figure 5: Learning process and structure of CGE. where \(h_{i}^{(k)}\) represents the hidden state of node \(i\) at the \(k\)-th layer, \(a_{ij}\) is the element in the adjacency matrix, and \(d_{i}\) and \(d_{j}\) denote the degrees of nodes \(i\) and \(j\), respectively, reflecting the contribution of neighboring nodes to the target node's feature update. After CGE learns the neighbor information within the graph structure--i.e., the associations between different IPs--a feature matrix \(F=[f_{1},f_{2},\ldots,f_{n}]^{T}\in\mathbb{R}^{n\times d}\) is extracted, with each node having \(d_{f}\)-dimensional features. By employing the AFF_CGE method, we are able to jointly consider both the internal statistical features of samples and the structural relationships between samples. This comprehensive approach significantly enhances the detection performance of malicious traffic. Not only does this method reinforce the joint learning of both internal and external features of encrypted traffic, generating high-quality, low-dimensional feature representations, but it also enables efficient detection and deployment in real-world applications.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
###### Abstract  
Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the \"quality of service\" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.  
quality of service; smart healthcare; Internet of Things (IoT); artificial intelligence (AI); machine learning; cloud computing +
Footnote †: journal: Sensors
## 3 Image Cryptography
### Asymmetric Encryption  
Asymmetric encryption, also known as public-key cryptography (PKC), uses a pair of keys to perform data encryption. A public key that is known by all nodes of the network is used to encrypt data, and a private key known only by the destination node is used to decrypt that data. Usually, traditional asymmetric cryptography algorithms have high computing overhead, requiring more processing time. Based on this assumption, according to [32], most works seem to claim that public-key cryptography is not feasible for WSN applications. However, over the years, analyses, as conducted in [33], have proven otherwise, where efficient algorithms and reasonable key sizes indicate the feasibility of asymmetric cryptography for wireless sensor networks. Moreover, sensor motes with better resources and affordable prices should be available in the near future.  
Among available public-key algorithms, three of them are widely used in computer networks, and they might be initially considered for WSN applications. Those algorithms are described as follows:  
* Rabin's scheme: Introduced in 1979, this is an algorithm based on the factorization problem, retaining similarities with RSA [34]. The encoding process is faster than the decoding process compared to RSA for the same parameters [32].
* RSA: Based on classical theories of numbers, this was also employed to provide support to the concept of the digital signature, becoming one of the major innovations in public-key cryptography [35].
* Elliptic curve cryptography (ECC): This is a collective term for multiple key exchange algorithms and agreement protocols [36; 37] (e.g., ECDH (Elliptic Curve Diffie-Hellman), ECDSA (Elliptic Curve Digital Signature Algorithm) and ECMV (Elliptic Curve Menezes-Vanstone) [32]. ECC provides security equivalent to RSA, but with much smaller keys, becoming more attractive for WSN. In general, smaller keys generate less memory usage, more bandwidth savings and less computing overhead [38].  
For both RSA and ECC, the encrypted message size is composed of the key size and encrypted data size (cipher text). The size of the RSA cipher text is smaller than the ECC cipher text. However, since ECC generates keys smaller than RSA, the encrypted message size with ECC is also smaller [38]. An energy analysis is performed in [39], comparing RSA and ECC for use in WSN, as presented in Table 1.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 4 System Implementation
### Software
#### 4.2.3 Software for MT3620 as Gateway  
The MT3620 can also be configured as a gateway to allow legacy IoT systems to connect to the Internet and to the Azure IoT Hub. The UART interface of the Azure Sphere device is used to communicate with the ZigBee wireless sensor network, i.e., the legacy IoT system. Meanwhile, the UART peripheral should be included in the application manifest file, in which all the resources that the application requires are listed. Figure 5 shows the application manifest file of the project. It is noted that the application manifest will be accessed by Azure Sphere runtime to determine which capabilities are allowed to use as soon as the application is sideloaded or deployed to the device. Any attempt to access resources that are not described in the manifest will be denied by the runtime. Hence, the UART and Allowed Connections to Azure cloud services are added as essential capabilities.  
The flowchart of the application that is running in the MT3620 as gateway is depicted in Figure 6. Basically, the initialization process for the application is pretty much the same as that in the MT3620 as direct connected device, except that the UART peripheral is configured and opened for communication with ZigBee Coordinator. In the main loop, the UART event handler is called for inbound message receiving, data validity checking and local parameters updating. As soon as the transmission timer expires, the latest parameters of the ZigBee wireless sensor network are sent to the Azure IoT Hub via JSON data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Integrated Development Environment** & **Version** & **Language** \\ \hline CC2530 & IAR Embedded Workbench for 8051 & 8.10 & C \\ MT3620 & Visual Studio 2017 Community & 15.9.5 & C \\ Desktop & Visual Studio 2017 Community & 15.9.5 & C\# \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prerequisites for software design in the project.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 2 Overview  
### Multicast Addresses  
There are one source and a group of destinations in multicast communication protocol. Figure 1 shows the simple multicast communication network, which depicts a set of quantum network nodes. The node of quantum network is a source sub of quantum data that must be delivered to a group \(G_{1}\) of quantum network nodes, \(F_{1}\), \(F_{2}\),..., \(F_{t}\),..., \(F_{M}\), respectively. There is more than one quantum network node, but the group does not contain all possible quantum network nodes. This relationship is one to many.  
The multicast address is a destination address for a group of quantum network nodes that have joined a multicast group, which is a great help to classical communication of quantum network communication protocol. A packet that uses a multicast address as a destination can reach all members of the group unless there are some filtering restrictions by the quantum network node. It only discusses the multicast addresses in the network layer, in particular the multicast addresses used in the \(IPv4\) protocol. Multicast addresses for \(IPv6\) can hardly even be touched. In TCP/IP protocol suites, Class D IP addresses are used as multicast addresses. The range of Class D addresses is 224.0.0.0-239.255.255.255, include 28 variable bits, 2\({}^{28}\) (more than 268 million) multicast groups. Quantum network nodes may be permanent or transient. The former refers to the fact that the group has a permanently assigned address, rather than that members are permanently assigned to the group. The latter refers to the groups which do not have a permanent assignment to unreserved address.
## 3 Numerical Results
### Time Responses  
Figure 3 shows the middle-plane displacement \(w(a/2,b/2)\) (mm) and normal stress \(\sigma_{x}\) (GPa) at center position of outer surface z = 0.5 \(h^{*}\) for the thermal vibration of nonlinear TSDT of FGM thick plates/cylindrical shells during \(t\) = 0.1\(-\)3.0 s, in unsteady supersonic flow \(M_{\infty}\ =\ 2\), \(k_{c}c(t)\) = 0 under \(\theta_{L}\ =\ 60^{\circ}\) with advanced \(k_{a}\), \(L/h^{*}\) = 5, \(L/R\ =\ 1\), \(R_{n}\) = 2, \(T\) = 600 K and \(\overline{T}_{1}\) = 100 K. Figure 3a shows the compared response values of \(w(a/2,b/2)\) versus \(t\) for \(c_{1}\) = 0.925925/mm\({}^{2}\) and \(c_{1}\) = 0.0/mm\({}^{2}\), respectively. The maximum value of center displacement amplitude is 16.588001 mm for \(c_{1}\) = 0.925925/mm\({}^{2}\) and occurs at \(t\) = 0.1 s under \(\theta_{L}\ =\ 60^{\circ}\). The values of center displacement amplitudes have the converging tendency with time in both cases of \(c_{1}\) = 0.925925/mm\({}^{2}\) that is converging to 0.144461 mm and \(c_{1}\) = 0.0/mm\({}^{2}\) that is converging to \(-\)3.63 \(\times\) 10\({}^{-10}\) mm. Figure 3b shows the compared time responses of \(\sigma_{x}\) under \(\theta_{L}\ =\ 60^{\circ}\) for \(c_{1}\) = 0.925925/mm\({}^{2}\) and \(c_{1}\) = 0.0/mm\({}^{2}\), respectively. The maximum absolute value of \(\sigma_{x}\) is \(-\)2.1244 \(\times\) 10\({}^{-3}\) GPa for \(c_{1}\) = 0.925925/mm\({}^{2}\) which occurs at \(t\) = 2.3 s. The values of \(\sigma_{x}\) have the converging tendency firstly with time from \(t\) = 0.1 s to 1.8 s then oscillating with time from \(t\) = 1.9 s to 2.8 s in the case of \(c_{1}\) = 0.925925/mm\({}^{2}\). The \(\sigma_{x}\) values have a constant tendency firstly with time from \(t\) = 0.2 s to 2.5 s then oscillating with time from \(t\) = 2.6 s to 3.0 s in the case \(c_{1}\) = 0.0/mm\({}^{2}\).  
Figure 3: _Cont._  
Figure 2: \(w(a/2,b/2)\) (mm) versus N = M in nonlinear unsteady supersonic flow.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 3 Proposed Approach
### Model Construction  
Often, it has been seen that in a network, communication among the bot nodes is less than the benign nodes. The bot nodes communicate infrequently to evade detection. Thus, if we use network packet passing through a router of a network, our method has to execute a large volume of data to detect bot node communication. This is not feasible in a real-world network scenario. Therefore, we use network flow data in our method to detect bot nodes in a network.  
Network flow data aim to identify botnet flows based on the information extracted from network conversations between hosts. Flow data, as described by [PERSON] et al. [23], encapsulate IP traffic details in the form of features. A flow summarizes the communication between hosts and is characterized primarily by a quintuple attribute set, including the source IP, destination IP, source port, destination port, and transport protocol. In addition to these essential attributes, flow data may encompass various other features like flow duration, flow direction, total bytes transferred, and more. The advantage of using flow data lies in their capability to be analyzed efficiently using machine-learning techniques to detect botnet flows. Notably, the analysis of flow data does not involve the inspection of the payload or content of the data. As a result, detection can occur even when the data are encrypted. Furthermore, flow data analysis remains independent of structure and protocol, enhancing its versatility in botnet detection procedures [6].  
This network flow data are parsed as input into our proposed detection approach. The detection approach starts with feature selection in the input dataset. Here, the differential evolution (DE) algorithm is implemented to select the optimal feature set from the input. Selected feature sets are then applied to six machine-learning models (base learners) which are as follows: Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), decision tree (DT), Random Forest (RF), and Support Vector Machine (SVM). To enhance the results produced by the classification models, we have used the majority voting ensemble learning algorithm. The system workflow of our proposed botnet detection approach is shown in Figure 1.  
Figure 1: System workflow of proposed peer-to-peer botnet detection system.