esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 4 Implementation of SD6 WSN on the Contiki Platform
### Hardware Architecture  
Devices with IEEE 802.15.4 transceivers, also called motes, are composed of an integrated circuit with a power amplifier and a micro-controller that includes the implementation of the MAC layer. Some manufacturers aggregate a radio to the micro-controller into a single integrated circuit, called SoC (system on a chip). The main features of motes are low cost and low power consumption, associated with low processing capacities, and small flash memory and RAM. Because of these limiting characteristics, they are classified as limited resource devices. In 2010, an IETF working group called Constrained RESTful Environments (CoRE) was created to propose a framework for applications in IP networks using devices with limited resources. In [32], motes are classified according to the size of RAM and flash (see Table 4). Class 0 devices do not have sufficient resources to run an IP stack, requiring proprietary protocol gateways to be connected to IPv6 networks. Class 1 devices are necessary to implement an IPv6 stack with security features such as DTLS. SD6 WSN nodes can be implemented either in Class 2 motes or in Class 1 motes with at least 32 kbytes of RAM and 100 kbytes of flash memory.  
Another relevant point for the hardware architecture is the configuration of the node that runs the border router function. In the following, we present the four main options for constructing a Contiki-based 6 LBR, each with a different organization of the communication layers.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 3 System Model  
Diverse applications of a smart city are based on IoT nodes. These nodes are designed to meet the application requirements for necessary automation. Due to the limited processing and computing capacity of these nodes, they are unable to perform most of their tasks and have to offload these tasks to other nodes. The fog nodes are placed in different areas and the IoT nodes are supposed to offload their tasks to the fog node placed in their close vicinity. All fog nodes are backwardly connected with the cloud server. A complete system model is shown in Figure 2.  
IoT nodes of different applications are subdivided into small clusters and multiple clusters are connected with one fog node. Nodes in all these clusters are supposed to offload their tasks to their attached fog node directly or through intermediate nodes. There are \(N\) number of legitimate nodes and \(M\) number of malicious nodes in each of the \(C\) clusters. The maximum time interval required in transferring the offloaded task data of any of the nodes placed in the connected cluster to the fog node is \(T_{max}\).  
After each \(T_{max}\), the fog node computes the total number of offloaded tasks received within this time duration. If a node \(b\) offloads \(T_{b}\) tasks from \(C_{a}\) clusters to fog node and there are \(J\) number of nodes in each of the \(K\) numbers of clusters, the total number of offloaded tasks (\(\sigma_{OL}\)) that are required to be executed by fog node are calculated as:  
\[\sigma_{OL}=\sum_{a=1}^{K}\sum_{b=1}^{J}C_{a}\times T_{b} \tag{1}\]  
There are \(m\) malicious nodes present in each of the \(K\) number of clusters and \(m_{a}\) malicious node \(m_{a}\) offload \(m_{t}\) tasks then a total number of tasks offloaded by legitimate nodes (\(\sigma_{LN}\)) is calculated as:  
\[\sigma_{LN}=\sum_{a=1}^{K}\sum_{b=1}^{J}(C_{a}\times T_{b})-m_{a} \tag{2}\]  
All the offloaded tasks are different in nature and require different execution times. If the average task execution time of a task is \(T_{d}\), then the total execution time \(\zeta_{OL}\) for all offloaded tasks with a uniform processing capacity of fog node is calculated as:  
\[\zeta_{OL}=\sum_{a=1}^{K}\sum_{b=1}^{J}(C_{a}\times T_{b})\times T_{d} \tag{3}\]  
The task execution time of offloaded tasks by all legitimate nodes \(\zeta_{LN}\) is calculated as:  
\[\zeta_{LN}=\sum_{a=1}^{K}\sum_{b=1}^{J}[(C_{a}\times T_{b})-m_{a}]\times T_{d} \tag{4}\]
## 2 RPC Model and Generative Adversarial Network  
As an approximation of the satellite imaging model, the RPC model is an important input for the 3D reconstruction of satellite images. In addition, the method proposed in this paper is based on GAN image enhancement. Therefore, this section will briefly introduce the principles of the RPC model and the GAN model.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 6 Conclusions  
In conclusion, routing attacks pose a significant threat to the security and reliability of 6 LoWPAN-based IoT networks. Various types of routing attacks, such as Blackhole, Selective Forwarding, and Sybil Attacks, can compromise the network's integrity and disrupt communication between the nodes. An attack on the Routing Protocol for Low-Power and Lossy Networks (RPL) can have a significant impact on the power usage of the nodes in the network. The RPL protocol is designed to ensure efficient communication to conserve the battery life of these resource-constrained nodes. If the RPL routing protocol is attacked, nodes may take longer routes, resulting in increased power consumption due to greater data transmission and reception. Additionally, nodes may be forced to re-transmit data packets, which can further deplete their batteries. This can ultimately lead to a reduction in the network lifetime as nodes run out of power and fail. In addition, the presence of a harmful node in the network can lead to increased power consumption due to its involvement in various attacks, including Selective Forwarding and Blackhole Attacks. As a result, the overall network efficiency may decrease, and power consumption issues may become more severe.  
Therefore, it is crucial to develop effective defense mechanisms to detect and prevent routing attacks in IoT networks. This article has introduced a new Intrusion Detection System (IDS) for IoT infrastructure that utilizes a feature set specifically designed to detect various types of routing attacks. The proposed system is based on a hybrid IDS approach that combines two stages of detection using a stacking ensemble method, resulting in improved detection accuracy. The experimental results demonstrate that the hybrid IDS, which uses an ensemble of C4.5 and one-class SVM in two cascaded stages, outperforms other individual techniques and approaches reported in previous studies in terms of accuracy and false alarm rate. This implies that the proposed technique can be valuable for use in designing modern IDSs. Future work will involve extending the proposed IDS to detect other types of attacks against IoT systems.  
Conceptualization, A.A. and A.K.; methodology, A.K.; software, S.S.; validation, A.A., A.K. and S.S.; formal analysis, A.A.; investigation, A.K.; resources, A.A.; data curation, A.K. writing--original draft preparation, A.A.; writing--review and editing, O.A.M.; visualization, S.S.; supervision, A.A.; project administration, S.B.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Accuracy in detection when using different algorithms.
## 2 Cable Model and Effective Length in 3D Twisted Conductors  
### Overview of the Baseline Cable  
A five-segment cable configuration, based on a previous work by [15], is established by subdividing a large conductor cross-section (\(\approx\)2711 mm\({}^{2}\) total) into five bundles, each containing 85 hexagonally packed (under full compression assumption) conductor strands with no electrical insulation between the strands in each bundle, and therefore, each segment can be treated as a solid bundle from a perspective of electromagnetic behaviour. Figure 1 illustrates detailed schematic of the cable's cross-section with all measurements in meter. The outer air boundary extends 30 cm radially from the cable's center for all modelsin this study. In the parallel (untwisted) scenario, the cable is extruded along the \(z\)-axis with no helical pitch, effectively forming a purely longitudinal arrangement of sub-conductors.  
The 3D parallel model extends 4 m in the longitudinal direction, capturing the same cross-section along its entire length (see Figure 2). Under AC current, the skin and proximity effects within each bundle drive the current toward the outer periphery and near inter-bundle boundaries.  
Figure 1: Five-segment cable cross-section (2D view) used for extruding 3D cables. Each segment is treated as a solid bundle of hexagonally packed strands. All dimensions are in _meters_.  
Figure 2: Three-dimensional extruded model of the five-segment cable with parallel (no twisting) sub-conductors.
## 4 The Security of QOT
### Privacy for [PERSON]
#### 4.1.3 Contrastive Analysis and Determination of the Parameters in Practical Protocols  
If a malicious [PERSON] wants to obtain both \(b_{0}\) and \(b_{1}\) in Protocol 2, he must get at least \(2k\) conclusive bits in Protocol 1. The difference between an honest [PERSON]'s probability of obtaining a conclusive bit and half of a malicious [PERSON]'s probability of obtaining a conclusive bit is \(p_{diff}(\mu)=p_{con}(\mu)-\frac{1}{2}p_{con}^{\prime\prime}(\mu)\), which can be seen in Figure 4.  
When \(\mu=4.85\), the difference \(p_{diff}(\mu)\) takes a maximum value 0.0732. The probability of obtaining \(i\) conclusive bits is \(p_{obt}\), which is referred to the binomial distribution and shown in Figure 5.  
Figure 4: The difference between an honest [PERSON]’s probability of obtaining a conclusive bit and half of a malicious [PERSON]’s probability of obtaining a conclusive bit changing with \(\mu\).  
Figure 5: The solid line denotes the probability of an honest [PERSON] obtains \(i\) conclusive bits when \(N=800\), \(\mu=5\). It can be seen that an honest [PERSON] can obtain more than 259 conclusive bits with a great probability. The dashed line denotes the probability of a malicious [PERSON] obtains \(i+259\) conclusive bits.  
Figure 3: The probability that a malicious [PERSON] gets a conclusive bit changing with \(\mu\).  
Suppose the probability of the case where the number of conclusive bits obtained by an honest [PERSON] is no more than \(l_{obt}\) is \(p_{1}\), and the probability of the case where the number of conclusive bits obtained by a malicious [PERSON] is no less than \(2l_{obt}\) is \(p_{2}\). Then,  
\[p_{1}=\sum_{i=0}^{l_{obt}}C_{N}^{i}[p_{con}(\mu)]^{i}[1-p_{con}( \mu)]^{N-i} \tag{9}\] \[p_{2}=\sum_{i=2l_{obt}}^{N}C_{N}^{i}[p_{con}^{\prime}(\mu)]^{i}[1 -p_{con}^{\prime}(\mu)]^{N-i} \tag{10}\]  
To ensure that the honest [PERSON] obtains one correct message in Protocol 2 and the malicious [PERSON] cannot obtain both \(b_{0}\) and \(b_{1}\), \(p_{1}\) and \(p_{2}\) should be small enough.  
The probability that an honest [PERSON] cannot execute Protocol 2 successfully is \(p\),  
\[p=1-(1-\varepsilon_{2})(1-p_{1}). \tag{11}\]  
To detect a cheating [PERSON], \(p\) should be less than \(20\%\). Given an error rate \(\varepsilon_{2}\), \(p_{1}\) has an upper bound \(p_{1t}\) to ensure \(p\leq 20\%\). To ensure the concealing of the BC protocol, \(p_{2}\) is set up with a magnitude of \(10^{-6}\).  
When \(\mu\) is too low, the difference between the probability of obtaining a conclusive bit by an honest and a malicious [PERSON] is not large enough to select the proper parameters. When \(\mu\) is too large, the proper \(k\) is large, which will lead to a large \(\varepsilon_{2}\). Then, there is no proper parameters either. It can be seen from Table 1 that when \(2\leq\mu\leq 6\), we can always find the proper parameters to execute the protocols successfully.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 3 Energy-Based RPL Routing for IoT-Fog-Enabled Forest Region  
For routing the data in IoT-Fog-enabled forest fire network, the most important criteria is energy-efficient routing with minimal delay and timely delivery. The reason being forest fire monitoring requires timely information for real-time analysis and quick action. This requires the need to propose an energy-efficient routing for IoT-Fog-based environment for forest fire networks. So, before going into the details of the proposed work, we talk in brief about Contiki RPL, which is the most predominant routing protocol in the IoT environment. Following that, we talk about energy-efficient RPL, which is the modification of Contiki RPL and compression-aware aggregation-based RPL. These details are explained below.
# A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
## 3 Results
### Offensive Machine Learning Techniques  
Figure 4 shows the popular techniques used in offensive cybersecurity. Neural networks were the more commonly used technique, while unsupervised ML techniques such as association rule mining [21], frequent pattern mining [11], and clustering [22] were used in a few studies. Combinations of supervised and unsupervised methods as semi-supervised learning were also used [23].  
Many authors have conducted research on bigraph-based ML algorithms and a subset of anonymized data learning methods function as offensive filter-based methods for network defense as well as moving target defense techniques that change the view of the network from the attacker through spatio-temporal randomization. These studies contributed significantly to a paradigm shift from defensive to offensive cybersecurity. One of the selected reviewed papers of interest discussed a framework created by the authors with the purpose of predicting adversarial movement with progressing threats. The authors employed the Clopper 12-stage intrusion-chain model [24] and used various ML and DM models for predicting threats with time series data: Auto Regressive Integrated Moving Average (ARIMA), Nonlinear Auto Regressive (NAR) neural network, NAR neural network with eXogenous input (NARX), and NAR neural network for multi-steps ahead prediction. The authors were successful in predicting adversarial movement with the proposed innovative mixed-methods approach [25]. Similarly, to solve the challenge of predicting potentially malicious actions in execution files, Recurrent Neural Network (RNN) models have been used, as RNNs have been proven to be effective in time series data processing, providing high accuracy with minimal execution time for the case of dynamic malware, with appropriate feature selection and optimally configured hyperparameters [26].
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 3 Experimental Results
### Results  
#### 3.3.1 Results on TextRS and Merced Datasets  
Table 2 shows the results obtained with our model using m-R50x1 as a pre-trained CNN for the image branch. In the case of text-to-image retrieval, the scores (R@1, R@5, and R@10) are 19.02%, 55.25%, and 71.72%, respectively, on the TextRS dataset and they are 21.86%, 60.00%, and 75.58%, respectively, for the Merced dataset. On the other hand, the image-to-text matching scores are 22.95%, 59.52%, and 77.23%, respectively, on the TextRS dataset and 25.47%, 59.76%, and 72.61%, respectively, on the Merced dataset. We observe that retrieval accuracy increases significantly when going from R@1 to R@10, which indicates the difficulty of getting the exact match for the first retrieved query.  
In Figure 6, we show two successful scenarios for query sentences with their corresponding ground-truth images. The retrieval results show that the output images almost have the same objects. On the other hand, Figure 7 shows two unsuccessful scenarios, where the true matched image was not retrieved correctly. It is worth recalling that during training, we learn an embedding loss that aims to learn close representations of images with the same descriptions. However, this task is very challenging as the dataset is not very large (each image is associated with a textual description).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pretrained**} & \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**TextRS**} & \multicolumn{3}{c}{**Merced**} \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline Text-to-image & m-R50x1 & 19.02 & 55.25 & 71.72 & 21.86 & 60.00 & 75.58 \\ Image-to-text & m-R50x1 & 22.95 & 59.52 & 77.23 & 25.47 & 59.76 & 72.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bidirectional retrieval results on the TextRS and Merced datasets.  
Figure 6: Successful scenarios of text-to-image retrieval.
## Proposed System
### Applicability
#### 2.3.1 Photovoltaic Generator's Storage System  
PV power generation converting solar energy directly into electricity has attracted attention due to its renewability and sustainability. PV generation is very positive for daytime applications at a high-power demand; however, it cannot generate at night. Also, its generation depends on the weather and topography, and its power is reduced by shading, etc. To resolve such inconveniences, it needs the usage of rechargeable batteries for storing the generated power during daytime, also for smoothing power production. Below is explained the operational characteristics of an independent PV power station of 2 kW capacity. Figure 12 shows an example of the generated power in winter, including the fluctuations generated due to cloudy weather.  
The BSS of 5 kWh capacity was assumed as a storage facility in this simulation and was modeled by two 18650 type Li-Ion cells. The rated voltage of the Li-Ion battery cell was 3.7 V, with rated capacity 2250 mAh (8.325 Wh) and 3250 mAh (12.025 Wh), respectively. The reason for using two different battery capacities was for confirming the generalization ability of the system. The charged or discharged power for the test using the hardware simulation was reduced to 1/625 for 2250 mAh cell and 1/416 for 3250 mAh cell. The characteristics from 9:30 AM to 10:30 AM in Figure 12 were used mainly for the purpose of the hardware simulator test done by the MTC.  
Figure 11: Dynamic arbitrary waveform: (**a**) during discharging of Li-Ion battery pack 3S6P configuration connected with BMS, (**b**) magnified current waveform.  
Figure 12: Generated power by a solar panel array in February 2016.  
was approximately 1.8 kW, and the generated power less than 400 W was within 10 min in the period. Figure 13 shows the voltage and current characteristics of the selected battery cell using the MTC.  
Its initial SoC of 50% and with the scaled current waveform assuming a constant load of 1 kW. The test was carried out using the measured data sampled at every 1 s.  
Figure 13 shows the result for the simulated case when the generated power was larger than the load power of 1 kW, where the waveform fluctuation comes due to the dynamic situation of the charging and discharging process at the same time. Especially during the moments when the generated power was smaller, the insufficient required amount of power was supplied from the battery to the load.
## 3 Aerodynamics
### Numerical Computation
#### 3.2.1 Physical and Mathematical Models  
The full Navier-Stokes model is defined for fluid that conducts heat in unsteady and compressible flow. The Euler model corresponds to the Navier-Stokes equations with zero viscosity and zero thermal conductivity. Neglecting mass forces, it can be presented in the following form:  
- continuity equation  
\[\frac{\partial p}{\partial t}+\text{div}(\rho V)=0 \tag{1}\]  
- Euler equation:  
\[\frac{\partial V}{\partial t}+\left(V\text{grad}\right)V=\frac{1}{\rho}\text{ grad}p \tag{2}\]  
- state equation:  
\[p=p_{\infty}(\frac{\rho}{\rho_{\infty}})^{\kappa} \tag{3}\]  
where:  
\(V\)--flow velocity;  
\(\rho\)--air density;  
\(p\)--air pressure;  
\(\kappa\)--adiabatic exponent.  
Summarizing the Euler model is represented by the set of quasilinear partial differential equations that describe adiabatic and inviscid flow. To obtain potential model the irrotational flow is assumed (rot\(V=0\)) and the scalar function, called velocity potential (\(\Phi\)), can be defined that satisfies the following condition:  
\[\text{grad}\Phi(x,y,z,t)=V \tag{4}\]  
Figure 3: Model no. 1211 (**left**) and the test stand (**right**).  
Additionally, assuming small disturbances (\(\Phi=\Phi_{\infty}+\varphi\), \(\mathrm{mod}\
abla\varphi\ll U_{\infty}\)) and steady incompressible flow, the potential model in its simplest form can be obtained.  
The compressibility effect can be taken into account using the Prandtl-Glauert (PG) or Karman-Tsien (KT) corrections [32]. Another approach is the PG transformation, which uses a coefficient (\(\beta=\sqrt{1-M^{2}}\)) to scale the geometry, which, however, causes a loss of simplicity of the potential model and, consequently, significantly increases the computation time. The use of the corrections mentioned above or PG transformation is limited by the condition that there is no transonic flow, approximately stated by the requirement that the local Mach number does not exceed unity.
# Machine Learning Techniques for Non-Terrestrial Networks
## 1 Introduction
Satellite communications have to tolerate long delays, while aerial networks suffer from limited capacity and link instability, and both of them have to deal with high mobility patterns. On the other hand, traditional terrestrial networks have higher throughput and reliability. ML comes into play with the aim to take the best from these technologies in order to efficiently complement each other, thus improving the overall quality at each altitude from the users' perspective. More specifically, ML can be applied from the physical layer, link and medium access control layers, network and eventually in the application layer. In general, ML proved to be a valid alternative to complex traditional algorithms, offering a superior outcome in terms of accuracy and response time. Moreover, in highly dynamic scenarios, self-adaptable models can evolve and therefore tackle unseen issues. The authors' contributions introduced in this paper are reported in the following. * Presentation of the innovative services that novel NTN may provide. They are provided in terms of the most suited NTE and required data rate as well as being linked with the 5G categories. * Description of the NTN architecture envisaged in the 3 GPP Release 17 and in the forthcoming Release 18. Involved NTEs and functionalities within 5G system are detailed in terms of architectures and protocol stacks over the 5G network. It is also proposed how multi-connectivity (both in hybrid terrestrial/non-terrestrial and non-terrestrial/non-terrestrial modes) and the deployment of multi-access edge computing (MEC) should be implemented in NTN. * Adoption of the most suited ML techniques in order to guarantee the novel services in NTN by supporting their requirements. Proposed ML techniques are also provided in order to enhance the UE connectivity or to optimize the system performance in NTN. Furthermore, some insights are also reported in order to properly investigate the adoption of the ML techniques in selected situations typical of NTN. This paper is organized as follows. In Section 2, a brief review of the literature is proposed in terms of communication provided by satellites or aerial elements as well as the adoption of ML to support the connectivity. In Section 3, it is reported the general aspects of a NTN network and the typical parameters of any NTE type. In Section 4, we describe the services and the network architectures of NTNs. Some details on 3 GPP standardization are also given. In Section 5, the general aspect of ML is introduced, while in Section 6, ML techniques are reported both for NTN services and NTN performance improvements in each protocol stack layer. In Section 7, conclusions and open research issues are reported.