esa-sceva/satcom-chunk-collection huggingface dataset:
# VNF Placement Optimization at the Edge and Cloud +
## 4 Algorithm Design  
During the allocation of an SC, our goal is threefold. First of all, we need to minimize the overall deployment cost as defined in Secion 2 by minimizing the number of used resources both in MEC and Cloud infrastructures. Secondly, we need to minimize the communication delay expressed by the four terms presented in Section 3. Finally, we are trying to achieve the necessary load balancing by appropriately offloading the traffic between the two infrastructures. Two algorithms are proposed: (i) a Mixed Integer Programming (MIP) formulation solved using a CPLEX Branch-and-Cut search algorithm [48] and (ii) a Tabu Search Algorithm.  
Figure 3: VNF Placement at the Edge and Cloud example.
## 3 Results
### Aviation Technical Support as a Service
#### 3.15.1 Tier 1: Aircraft  
At the foundation of this model lies the individual aircraft, equipped with a constellation of IoT devices and sensors. Each aircraft is a node in a broader network, generating a continuous stream of data on its health and performance.  
Sensors collect data on various parameters, from engine health to structural integrity.  
Initial data analysis can occur onboard, utilizing edge computing to provide real-time diagnostics and to identify potential issues.  
The aircraft communicates this data to the next tier for more extensive analysis and integration.  
Figure 10: Cloud-based architecture of HMGT with ATSaaS approach.
## 2 Background
### Mqtt  
MQTT [6] is a lightweight messaging protocol based on the publish-subscribe model. It is designed for constrained devices and low-bandwidth or unreliable networks.  
The MQTT architecture consists of three main components:* Broker: The broker is the central hub that mediates the communication between publishers and subscribers on given topics.
* Publisher: A publisher is a client that sends messages to the broker labeled with given topics.
* Subscriber: A subscriber is a client that receives messages from the broker. Subscribers express interest in one or more topics and receive messages related to those topics.  
The message flow in MQTT is as follows: (1) a publisher sends a message to the broker on a given topic, (2) the broker receives the message and forwards it to all the subscribers interested in that topic, and (3) the subscribers receive the message from the broker.  
Additionally, MQTT allows publishers and subscribers to specify the quality of service (QoS) level they want. There are three QoS levels [17], each guaranteeing message delivery, as follows:  
* QoS 0: At most one delivery.
* QoS 1: At least one delivery.
* QoS 2: Exactly one delivery.
# A Novel Approach to Data Extraction on Hyperlinked Webpages
## 3 Methodology
### HTML Input File  
The table headings and data rows were extracted by identifying appropriate combinations of <table><th><td> tags in the HTML files. It was seen that many page layouts were also designed using <table> tags; such designer tables were referred to as non-real tables. It was a challenging task to extract pattern followed by a table tag that possibly contained a relational structure.  
Identification of the actual structure from HTML code was also a challenging problem because table/div tags were not necessarily used to depict relational structure. Table/div tags were also used for formatting purposes. Different techniques proposed so far have been given for the direct tables as input for further processing; however, our algorithm extracts the pattern by itself. Non-real tables were identified, described in forthcoming sections, and were skipped. Processing was performed on real tables into two chunks as (i) simple structures or (ii) complex structures. The real table had a two-dimensional grid structure in which logical relation existed among cells.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 3 System Model and Problem Description
### Communications System Model
Therefore, the channel estimation and equalization for CPM can be transferred to the three classical problems of the hidden Markov model (HMM) to solve, where channel estimation can be regarded as the parameter learning problem of the HMM and channel equalization can be regarded as the decoding problem of the HMM. (4) Turbo equalization: The receiver can be used in the form of turbo equalization because of the SISO Viterbi-like algorithm. The iterative exchange of information is repeated iteratively between the blind equalizer and the decoder for the same set of the received signal. The received signal \(\mathbf{r}\) and the soft information \(L_{D}(b_{t})\) output from the decoder (initially set to 0) are input into the SISO blind equalizer, and then, the blind equalizer outputs the extrinsic LLR on the symbols \(L_{E}(\alpha_{k}|\mathbf{r})\), which is fed to the decoder after interleaving. The decoder provides an estimation of the information bits \(\hat{m}\) using soft decisions and computes an extrinsic LLR on the coded bits \(L_{D}(b_{t})\). \[s(t,\mathbf{\alpha}_{n})=\sqrt{\frac{E_{s}}{T}}\exp(j\mathbf{\phi}(t,\mathbf{\alpha}_{n}) ),t\in[0,nT]. \tag{1}\]2
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Maintain-Formation  
The Maintain-Formation motor schema behavior mitigates the effects of jamming attacks on autonomous convoy following by generating target goals **T** that are not dependent on a leader's GPS coordinates. Instead, the behavior uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find a target goal nearest to the last known valid goal and to continue convoy operations during jamming attacks.  
DBSCAN is a non-parametric clustering algorithm that, when given a set of points, groups together points that are closely packed together into different clusters [41]. In the Maintain-Formation behavior, the points provided are from the behavioral costmaps generated by the Avoid-Obstacle-Proximity perceptual schema. For every cluster identified, the center of mass for each cluster is calculated for comparison to the last known valid **T** coordinates. To find the center of mass **C\({}_{i}\)**, where **C** are the coordinates of the center of mass for cluster \(I\), and \(i=1,\ldots,n\) clusters, we considered each cluster as a system of weighted particles P\({}_{j}\), where \(j=1,\ldots,n\). The coordinates for each particle were given by the costmap cell coordinates \(c_{j}\), with the mass \(m_{j}\) for each particle being defined as the cost for each given coordinate. The formula for **C\({}_{i}\)** is as follows:  
\[\textbf{C}_{i}=\frac{1}{M_{i}}\sum_{j=1}^{n}\ \ \ m_{j}c_{j}, \tag{2}\]  
where \(M_{i}\) is the total cost of all the cluster points in cluster \(i\), given by:  
\[M_{i}=\sum_{j=1}^{n}\ \ \ m_{j}. \tag{3}\]  
When the autonomous vehicle fails to receive a GPS breadcrumb coordinate **B**, the Behavior Manager determines that a denial-of-service attack is occurring and that communications with the rest of the convoy have been disrupted. The Maintain-Formation behavior will then find the **C\({}_{i}\)** nearest to the last valid target goal **T**, and set that as **T**. This process is repeated until a new valid **B** is provided, as shown in Algorithm 1.  
```
1:if no B received from leader
2:\(\textbf{C}_{i}\) = center of mass of clusters 1,..., n from DBSCAN(costmap)
3:temp_distance = high value placeholder
4:for\(i=1,\ldots,n\)
5:cluster_distance = distance between T and \(\textbf{C}_{i}\)
6:if cluster_distance < temp_distance
7:T = C\({}_{i}\)
8:temp_distance = cluster_distance
9:endif
10:next \(i\)
11:endif
```  
**Algorithm 1** Maintain-Formation
# Performance Analysis of Direct Torque Controllers in Five-Phase Electrical Drives
## 4 Results and Discussion  
To assess the performance of the DTC scheme, different experimental tests were performed on a conventional three-phase induction machine with 30 slots that was redesigned to have five phases and three pole pairs. The electrical and mechanical parameters of the multiphase electrical machine are depicted in Table 2. The reference stator flux (\(\lambda_{s}\)*) is set to its rating value (0.4 Wb) during the experiments, the applied sampling frequency is fixed to 10 kHz and the hysteresis bands of the torque and flux regulators are programmed to be 1% of the rating values (this value was experimentally obtained for our test rig using a trial and error method). The maximum reference torque is set to 3.25 N-m, according to machine limits. The multiphase power converter is based on two conventional three-phase VSIs from SEMIKRON(r) (two SKS-2 EF modules in which five power legs are used). The DC link voltage is set to 300 V using an external DC power supply. The electronic control unit is based on a MSK28335 board and a Texas Instruments(r) TMS320F28335 digital signal processor. A digital encoder (GHMS10296R/2500) and the enhanced quadrature encoder pulse peripheral of the DSP are used to measure the rotor mechanical speed \(\omega_{m}\). The load torque (\(T_{L}\)), which is demanded in the tests, is set by an independently controlled DC machine that is mechanically coupled to the five-phase machine. The experimental test rig is shown in Figure 8, where some photographs of the real system are included.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Stator resistance, \(R_{s}\) & 12.85 & \(\Omega\) \\ \hline Rotor resistance, \(R_{r}\) & 4.80 & \(\Omega\) \\ \hline Stator leakage inductance, \(L_{ls}\) & 79.93 & mH \\ \hline Rotor leakage inductance, \(L_{lr}\) & 79.93 & mH \\ \hline Mutual inductance, \(M\) & 681.7 & mH \\ \hline Rotational inertia, \(J_{m}\) & 0.02 & kg-m\({}^{2}\) \\ \hline Number of pairs of poles, \(p\) & 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrical and mechanical parameters of the five-phase IM.  
Figure 8: Experimental test rig.
# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 1 Introduction
Therefore, these approaches are not well-suited for high resolution multi-spectral satellite image registration. To overcome these limitations with feature-based and area-based methods, we propose a registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In area-based methods, usually one control point is extracted by comparing two patches from the reference and the sensed images. We suggest multi-template matching to extract a larger numbers of control points. In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input images. The results demonstrate its efficacy and accuracy in image registration. In the remainder of this paper, Section 2 discusses the datasets used in the experiments, and the proposed method is explained in Section 3. Then, Section 4 presents the experimental results and comparative analysis. Finally, Section 5 concludes this study.
# Survey of Blockchain-Based Applications for IoT
## 11 Digital Assets and NFTs in the IoT  
### NFTs  
NFTs have revolutionized IoT analytics by providing secure data ownership, management, and monetization frameworks. IoT analytics rely on vast datasets generated by connected devices to derive actionable insights. Tokenization enables these datasets to be represented as digital tokens in the blockchain. Each token signifies ownership or access rights, allowing IoT device owners to monetize their data in decentralized marketplaces while maintaining control and transparency. For instance, a factory's IoT sensors that monitor production metrics can tokenize real-time data, which can be sold to analytics providers for operational insights. NFTs are uniquely suited for representing proprietary datasets, storing metadata, and ownership details to ensure the authenticity of data streams used in analytics. These technologies enhance data liquidity, decentralize data-sharing ecosystems, and reduce reliance on intermediaries, thus enabling more efficient analytics. By integrating Physically Unclonable Functions (PUFs), IoT systems can further enhance security and trust by generating unique hardware-based identifiers for devices, ensuring data authenticity and tamper resistance [77].  
Multiple tokenization standards exist in the blockchain. ERC-20 [78], ERC-721 [79], and ERC-1155 [80] are Ethereum-token standards. ERC-20 defines fungible tokens (e.g., cryptocurrencies), whereas ERC-721 handles NFTs. ERC-1155 supports both fungible and non-fungible tokens within a single contract and offers greater flexibility and efficiency, particularly for gaming, marketplaces, and multi-asset applications in Ethereum. The process of minting an NFT varies depending on the marketplace and the blockchain used. Each platform (e.g., OpenSea, Rapible, or Ocean Protocol) offers different options for defining ownership rights (exclusive, non-exclusive, royalties, etc.), whereas the blockchain (e.g., Ethereum, Polygon, or Solana) determines the transaction speed, costs (gas fees), and scalability. These factors affect how an NFT is created, stored, and traded.  
Figure 16 below shows the general steps of NFT minting and trading processes.  
1. IoT data provider minus the dataset as an NFT;
2. IoT data provider lists the NFT on the marketplace;
3. Data analysts view NFTs available in the NFT marketplace and buy NFTs for the required dataset. It should be noted that this process has several options: * Exclusive: Buyer owns the dataset fully; * Non-exclusive: Seller can sell the dataset multiple times; * Royalties: Seller receives percentage from any future sale; * Time-limited access;
4. Buyer receives the NFT and uses the dataset for analytics.  
Figure 16: General NFT minting process.  
One example of using NFTs to share data is the Ocean Protocol, which offers a blockchain-based data-sharing marketplace where IoT datasets are tokenized and traded securely. Its unique Compute-to-Data feature allows analytics to be performed on datasets without exposing raw data, thereby ensuring privacy and compliance [81].
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Dataset  
As part of this study, a multi-source approach was adopted to integrate various geospatial data to identify and characterize the geological formations of the Western Highlands of Cameroon. Data sources include analog maps (geological, soil, and topographic) and optical and radar satellite images.  
LANDSAT OLI images (30 m resolution) allowed spectral analysis of geological formations. With a spatial resolution of 30 m and 11 spectral bands, these LANDSAT OLI images (OLI2 to OLI 12) capture mineralogical and pedological variations. Visible, near, and mid-infrared bands are particularly useful for analyzing lithological compositions.  
SRTM digital terrain models extracted key topographic attributes, such as slope, aspect, and drainage density.  
Finally, ALOS PALSAR radar images (12.5 m resolution) in the L band were used to detect geological structures, thanks to their sensitivity to surface roughness.  
The processing was carried out using specialized software: ENVI version 5.0 and ERDAS IMAGINE version 11 for image correction and analysis, ArcGIS version 10.0 for GIS integration, and Python version 3.8.8 for implementing machine learning algorithms.  
The raw data were pre-processed to ensure their geometric and radiometric consistency. The analog maps were digitized and georeferenced in the UTM WGS 84 system. The LANDSAT OLI images underwent atmospheric corrections using ENVI's FLAASH module, while the speckle in the ALOS PALSAR radar images was attenuated via adaptive filters ([PERSON], [PERSON]). Mosaicking multi-source images created a homogeneous database. The harmonization of contrasts and blurring junctions between scenes made it possible to optimize the visual and analytical quality of the data.
## 2 Measurement Principles and Methods  
### Wheel Sinkage Calculation Model  
In practical scenarios [21], the ground tends to be rough and uneven. To simulate a realistic driving environment and facilitate calculations and analysis, it is assumed that there is a height difference between the point where the wheel flange contacts the ground (entry point A) and the point where the wheel flange loses contact with the ground (departure point B). Figure 1 illustrates the establishment of a cartesian coordinate system on a surface,with the center of the circle labeled as the origin. The point where the wheel flange makes contact with the ground, known as the entry point, is labeled as point A. Conversely, the point where the wheel flange leaves the ground, known as the departure point, is labeled as point B. The angle \(\theta_{1}\) formed by the line OA and the vertical direction, is referred to as the entry angle. Similarly, the angle \(\theta_{2}\) included between the line OB and the vertical direction is termed the departure angle [22]. The formula for calculating the entry angle and departure angle is presented as follows.  
\[\theta_{1}=\frac{\pi}{2}-arctan\bigg{(}\frac{y_{A}}{x_{A}}\bigg{)} \tag{1}\]  
\[\theta_{2}=\frac{\pi}{2}-arctan\bigg{(}\frac{y_{B}}{x_{B}}\bigg{)} \tag{2}\]  
Since the wheel-terrain boundary is an irregular curve in rugged terrain, a fitted line was used as the wheel-terrain boundary. The wheel sinkage depth is \(z=R-OC\). The wheel-terrain boundary can be expressed as  
\[y=ax+b \tag{3}\]  
where \(a\) and \(b\) are the parameters obtained from the fit. Thus, wheel sinkage can be expressed as Equation (4).  
\[z=R-\frac{|b|}{\sqrt{a^{2}+1}} \tag{4}\]
# Detection of Transmission State of Multiple Wireless Sources: A Statistical Mechanics Approach
## 3 The Belief Propagation Algorithm
}\left(\epsilon_{i}(s_{v\in\mathcal{N}(i)}) -\sum_{v\in\mathcal{N}(i)\setminus\mu}h_{i\to v}\right) \tag{27}\]  
Since we have defined the outgoing messages from sources (\(h_{\mu\to i}\)) and SUs (\(\widehat{h}_{i\to\mu}\)), respectively, it is convenient to provide an expression for the error detection rate in terms of these fields as  
\[\mathbb{P}_{\text{err}}=1-\frac{1}{2M}\sum_{\mu}\tanh\left(\beta\left(\widehat {h}_{i\to\mu}+h_{\mu\to i}\right)\right)\underset{\beta\to\infty}{\to}1- \frac{1}{2M}\sum_{\mu}\operatorname*{sign}\left(\widehat{h}_{i\to\mu}+h_{\mu \to i}\right) \tag{28}\]  
Intuitively, the error occurs when the sign of the incoming messages is opposite to the ones outgoing from the source. The derivation of (28) based on Algorithm 1 is shown in Appendix B, where the replica-symmetry method is used. Note that although the Bethe approximation is exact for the case of loopless graphs, it depends heavily on the graph and, consequently, on the network topology. So instead of having to determine the underlying graph every time, it would be preferable to provide a more general framework that is independent of the specific graph realization. ```
1:Initialization
2:Find the number of sources and sensors from neighborhood matrix \(\mathcal{H}\). 3:Randomly generate the \(\widehat{h}_{i\to\mu}\) and \(h_{\mu\to i}\) fields. 4:for\(k=1\to\max\) number of loops do
5: Generate \(s_{0}\) according to \(\mathbb{P}_{0}\)
6: Generate \(y_{i}\) according to (8)
7:for\(i=1\to M\)do
8: Calculate \(h_{\mu\to i}\) according to
9:\(h_{\mu\to i}=\sum\limits_{j\
eq i}\widehat{gh}_{j\to\mu}\). 10: Calculate \(\widehat{h}_{i\to\mu}\) according to
11:\(\widehat{h}_{i\to\mu}=\tanh^{-1}\left[\epsilon_{\beta}\left(\left\{gh_{\mu \to i}\right\}\right)\right]\). 12:endfor
13:endfor
14:Calculate the error detection rate according to
15:\(1-\mathbb{P}_{\text{err}}=\frac{1}{2M}\sum\limits_{\mu=1}^{M}\operatorname*{ sgn}\left(\widehat{h}_{i\to\mu}+h_{\mu\to i}\right)\). ```  
**Algorithm 1** Message-Passing Neighborhood Matrix \(\mathcal{H}\), Prior Distribution \(\mathbb{P}_{0}\)
## 3 Statistics of the Snr of Equivalent End-to-End Communication Link  
### Cumulative Distribution Function  
Using the DF relaying protocol, the CDF of the end-to-end SNR for the proposed system can be expressed as [9]  
\[F_{\gamma_{e}}(\gamma)=\Pr[\min(\gamma_{1},\gamma_{2})<\gamma]. \tag{6}\]  
By substituting (3) into \(F_{\gamma}(\gamma)=\int_{0}^{\gamma}f_{\gamma}(\gamma)d\gamma\), the CDF of \(\gamma_{1}\) can be expressed as  
\[\Pr(\gamma_{1}\!<\!\gamma)=\Delta_{1}\Big{(}\frac{\varphi}{\Phi}\Big{)}^{ \varphi}\int_{0}^{\gamma}\gamma^{\varphi-1}\exp\Big{(}\!-\!\frac{\gamma\varphi }{\Phi}\Big{)}d\gamma+\Delta_{2}\bigg{(}\frac{\varphi^{\prime}}{\Phi^{\prime} }\bigg{)}^{\varphi^{\prime}}\int_{0}^{\gamma}\gamma^{\varphi^{\prime}-1}\exp \bigg{(}\!-\!\frac{\gamma\varphi^{\prime}}{\Phi^{\prime}}\bigg{)}d\gamma. \tag{7}\]  
Using Equation (8.4.16) from [14] and after some simple manipulations, the CDF of \(\gamma_{1}\) can be written as  
\[\Pr(\gamma_{1}\!<\!\gamma)=\Delta_{1}\times H_{1,2}^{1,1}\bigg{[}\frac{\varphi \gamma}{\Phi}\bigg{|}_{(\varphi,1)\ (0,1)}^{(1,1)}\bigg{]}+\Delta_{2}\times H_{1,2}^{1,1}\bigg{[}\frac{\varphi^{ \prime}\gamma}{\Phi^{\prime}}\bigg{|}_{(\varphi^{\prime},1)\ (0,1)}^{(1,1)}\bigg{]}. \tag{8}\]  
Similarly, by substituting (4) into \(F_{\gamma}(\gamma)=\int_{0}^{\gamma}f_{\gamma}(\gamma)d\gamma\) and using Equation (2.4.5) from [15], the CDF of \(\gamma_{2}\) can be expressed as  
\[\Pr(\gamma_{2}\!<\!\gamma)\!=\!1-\Omega\times H_{2,4}^{4,0}\bigg{[}\frac{ \gamma}{\Psi}\bigg{|}\begin{array}{c}(1,r)\big{(}\xi^{2}+1,r\big{)}\\ \big{(}\xi^{2},r\big{)}\big{(}\alpha,r\big{)}\big{(}\beta,r\big{)}\big{(}0,r \big{)}\end{array}\bigg{]}. \tag{9}\]  
Furthermore, we assume that PLC and FSO links are independent. Hence, the CDF of end-to-end SNR can be further expressed as  
\[F_{\gamma_{e}}(\gamma) =\Pr(\gamma_{1}\!<\!\gamma)+\Pr(\gamma_{2}\!<\!\gamma)-\Pr(\gamma_ {1}\!<\!\gamma)\Pr(\gamma_{2}\!<\!\gamma)\] \[=\!1+\Omega\times H_{2,4}^{4,0}\bigg{[}\frac{\gamma}{\Psi} \bigg{|}\begin{array}{c}(1,r)\big{(}\xi^{2}+1,r\big{)}\\ \big{(}\xi^{2},r\big{)}\big{(}\alpha,r\big{)}\big{(}\beta,r\big{)}\big{(}0,r \big{)}\end{array}\bigg{]}\] \[\times\Bigg{(}1-\Delta_{1}\times H_{1,2}^{1,1}\bigg{[}\frac{ \varphi\gamma}{\Phi}\bigg{|}_{(\varphi,1)\ (0,1)}^{(1,1)}\bigg{]}-\Delta_{2}\times H_{1,2}^{1,1}\bigg{[}\frac{\varphi^{ \prime}\gamma}{\Phi^{\prime}}\bigg{|}_{(\varphi^{\prime},1)\ (0,1)}^{(1,1)}\bigg{]}\bigg{)}. \tag{10}\]
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 2 Literature Review  
Conventionally, the blind node location can be estimated using GPS embedded on the devices [5]. This satellite-based positioning system is accurate, but its reliability is susceptible to the weather, presence of obstacles, position of satellites and the hardware's limitation. Furthermore, the usage of GPS would increase the overall cost due to the requirement of additional hardware. Alternatively,WSN localization uses neighboring nodes, nodes that obtained their location from data shared by their respective neighbors. The usage of neighboring nodes would reduce the dependency on the GPS.  
Two important attributes in WSN localization are wireless communication feature and estimation algorithm. Wireless communication feature is a feature, which contains an informative and non-redundant data extracted from an initial set of measured data using wireless devices. The estimation algorithm, on the other hand, is a method to estimate the position information based on the extracted features. The following section will discuss these two attributes and follow with Monte Carlo-based localization methods.