esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 2 RSS-AOA Distributed Localization Problem
### Main Sources of Errors  
All terrestrial localization systems based on pair-wise measurements closely rely on the quality of the measurements. Therefore, the key objective in establishing reliable localization systems is to accurately model the main degrading effects of the channel through which the radio signals propagate. In many practical applications, the environment in which the propagation of the radio signals occurs consists of various stationary or mobile objects/people (obstructions) that impede line of sight between the transmitter and the receiver, and cause reflections of the signal. These factors severely aggravate the task of propagation modeling [41].  
In general, range and direction measurements involved in the process of localization are affected by both time-varying errors and static, environment-dependent errors [59]. The former ones might occur due to additive noise and interference, and can be alleviated by averaging over several measurements in time. The latter ones are the product of physical arrangement of objects (such as walls and furniture) in a given surrounding in which the network is operating. Owing to unpredictability of the surrounding, both types of errors are unmanageable and are treated as random. Nevertheless, in a given environment in which objects are mostly stationary, environment-dependent errors will be largely constant over time.  
In practice, multipath signals and shadowing are the main causes of errors in RSS measurements [42; 60]. The former sources of error occurs because of reflections, causing multiple copies of the signal (of various amplitudes and phases) to arrive at the receiving end. These signals can cause destructive and constructive interference, which might result in weak correlation of RSS with distance. The latter source of error are environment-dependent and are caused by attenuation of the received signal due to obstructions, since the signal penetrates or diffracts around. This type of errors are often referred to as medium-scale fading.  
Similar with RSS measurements, the main source of errors in AOA measurements is the multipath, although additive noise can also cause inaccuracies [41]. Essentially, at the receiving end, we desire to determine the direction of arrival of the signal coming from a direct path from the transmitting end. This can be challenging to achieve in multipath channels, since instead of finding the highest peak of the cross correlation, the receiver has to determine the first-arriving peak, because there is no guarantee that the direct-path signal will be the strongest of the arriving signals. Many multipath signals arrive instants after the direct-path signal, and they can obscure the location of the peak from the direct-path signal. Furthermore, in some cases the direct-path signal can be considerably attenuated compared with its late-arriving multipath components, which might cause the receiver to overlook it completely [41].
## 4 Case Study
### Operations
#### 4.2.2 Building the Link Table  
Periodically, each QuadBox sends a \"Hello\" message that contains its identity (ID), called its node ID, over all its ports. Two access QuadBoxes of the same QuadBox pair have the same node ID, which is the node ID of the QuadBox pairs. Based on the \"Hello\" messages received from neighboring QuadBoxes, each QuadBox then builds its neighbor list. Two access QuadBoxes of the same QuadBox pair share the same neighbor list.  
The DSP algorithm finds dual paths based on the network link-state information, called the link table. To build the link table, each QuadBox broadcasts a _link_ (_LINK_) message that contains its neighbor list. Based on _LINK_ messages received from trunk QuadBoxes and other QuadBox pairs, each QuadBox pair builds its link table. All QuadBoxes in an HSR network have the same link table for the network.
## 3 Mathematical Modeling of ProposedDC Architecture
### Mathematical Modeling of the Battery  
The mathematical models of the battery focus mainly on \(V\), \(I\) parameters. The current is determined by a change in the terminal voltage of the battery [36]. The transfer of electrons from one electrode to another leads to the generation of current. The open circuit voltage at the battery is determined from the potential difference between the positive and negative electrodes [37; 38; 39; 40]. The charging/discharging of battery is expressed as  
\[V_{discharge}=E_{o}-V_{op}{}^{+}-V_{op}-IR_{pol} \tag{33}\]  
\[V_{charge}=E_{o}+V_{op}{}^{+}+V_{op}+IR_{pol} \tag{34}\]  
\[V_{battery}=E_{o}\;-\;K\bigg{[}\frac{Q}{Q\;-\;it}\bigg{]}i\;-\;R_{o}i \tag{35}\]  
\[V_{battery}=E_{o}\;-\;\bigg{(}\frac{K}{Soc}\bigg{)}i\;-\;R_{o}i \tag{36}\]  
\[V_{discharge}=E_{o}\;-\;Kd_{r}\frac{Q}{Q\;-\;it}i_{1}\;-\;R_{o}i\;-\;Kd_{v} \frac{Q}{Q\;-\;it}it+e(t) \tag{37}\]  
\[V_{charge}=E_{o}\;-\;Kc_{r}\frac{Q}{it+\lambda Q}i_{1}\;-\;R_{o}i\;-\;Kc_{v} \frac{Q}{Q\;-\;it}it+e(t) \tag{38}\]  
\[e(t)\;=\;Bi[(e(t)\;+\;Au(t)] \tag{39}\]  
\[V_{discharge}=E_{o}\;-\;K_{dr}\frac{1}{Soc}i\;-\;R_{o}i\;-\;K_{V}(\frac{1}{Soc }-\;1)+e(t) \tag{40}\]  
Equation (35) can be rewritten using state of charge (\(SoC\)) due to the polarization ohmic voltage. Equations (37) and (38) are modified by the shepherd relation model. \(E_{o}\) = open circuit voltage of a battery (V), \(K\) = polarization coefficient (\(\Omega\)), \(Q\) = battery capacity (A/h), and \(R\) = internal resistance. Some of the limitations associated with Equations (37) and (38) are (i) ageing of battery and self-discharge, (ii) the battery capacity does not depend upon the amplitude of the current, and (iii) the temperature coefficient is not considered [35]. These limitations can be overcome by considering the  
Figure 7: Single fuel cell characteristics curve (stack current vs. cell voltage and power).  
factors affecting the lifetime of the battery. The \(SoC\) condition is analyzed at every instant of time and is calculated with threshold capacity using  
\[Soc=Soc_{in}-\int\limits_{0}^{t}{(i-\max(i_{g},i_{d}))\frac{d\tau}{Q}}. \tag{41}\]  
The net power of the DC microgrid architecture is calculated by the summing of all the power of the energy sources.  
\[P_{net}=P_{PV}+P_{Wind}+P_{fuelcell}+P_{diesel} \tag{42}\]
# The Condition Evaluation of Bridges Based on Fuzzy BWM and Fuzzy Comprehensive Evaluation
## 4 Case Study
### Case Study 2
#### 4.2.2 Condition Rating of Jigongling Bridge
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Criteria** & **Primary Indicator** & **Secondary Indicator** & \multicolumn{6}{c}{**Membership Matrix**} \\ \cline{4-9}
**Layer** & **Layer** & **Layer** & **Excellent** & **Good** & **Fail** & **Poor** & **Critical** \\ \hline \multirow{8}{*}{B1} & & & \(D1\) & 0 & 0.3 & 0.6 & 0 & 0 \\  & & \(D2\) & 0 & 0.82 & 0.5 & 0 & 0 \\  & C1 & \(D3\) & 0 & 0.12 & 0.7 & 0 & 0 \\  & & \(D4\) & 0 & 0.5 & 0.4 & 0 & 0 \\  & & \(D5\) & 0 & 0 & 0.7 & 0.35 & 0 \\ \cline{2-9}  & & \(D7\) & 0 & 0.2 & 0.5 & 0 & 0 \\  & C2 & \(D7\) & 0 & 0.16 & 0.6 & 0 & 0 \\  & & \(D8\) & 0 & 0.6 & 0.4 & 0 & 0 \\ \cline{2-9}  & C3 & \(D9\) & 0 & 0.7 & 0.6 & 0 & 0 \\ \cline{2-9}  & C4 & \(D10\) & 0 & 0.1 & 0.7 & 0 & 0 \\ \hline \multirow{8}{*}{C5} & & & \(D11\) & 0 & 0 & 0.4 & 0.25 & 0 \\  & & \(D12\) & 0 & 0.7 & 0.5 & 0 & 0 \\  & C5 & \(D13\) & 0 & 0.5 & 0.7 & 0 & 0 \\  & & \(D14\) & 0.8 & 0.2 & 0 & 0 & 0 \\  & & \(D15\) & 0 & 0.7 & 0.3 & 0 & 0 \\  & & \(D16\) & 0 & 0.45 & 0.6 & 0 & 0 \\ \cline{2-9}  & & \(D13\) & 0 & 0.4 & 0.7 & 0 & 0 \\ \cline{2-9}  & & \(D14\) & 0 & 0.8 & 0 & 0 & 0 \\  & C6 & \(D15\) & 0 & 0.5 & 0.3 & 0 & 0 \\  & & \(D16\) & 0 & 0.6 & 0.6 & 0 & 0 \\  & & \(D17\) & 0 & 0 & 0.6 & 0.3 & 0 \\  & & \(D18\) & 0 & 0.1 & 0.5 & 0 & 0 \\ \cline{2-9}  & & \(D19\) & 0 & 0.7 & 0.3 & 0 & 0 \\  & C7 & \(D20\) & 0 & 0.6 & 0.5 & 0 & 0 \\  & & \(D21\) & 0 & 0.7 & 0.4 & 0 & 0 \\ \hline \multirow{8}{*}{\(B3\)} & & & \(D22\) & 0 & 0.34 & 0 & 0 & 0 \\  & & \(D23\) & 0 & 0.25 & 0.6 & 0 & 0 \\ \cline{2-9}  & C8 & \(D24\) & 0 & 0.4 & 0.3 & 0 & 0 \\  & & \(D25\) & 0.11 & 0.85 & 0.4 & 0 & 0 \\  & & \(D26\) & 0.15 & 0.80 & 0.3 & 0 & 0 \\ \hline \multirow{8}{*}{B4} & & C9 & \(D27\) & 0 & 0.4 & 0.5 & 0 & 0 \\ \cline{2-9}  & & \(D28\) & 0 & 0.5 & 0.3 & 0 & 0 \\ \cline{1-1}  & & \(D29\) & 0 & 0.8 & 0 & 0 & 0 \\ \cline{1-1} \cline{2-9}  & & C11 & \(D30\) & 0 & 0.3 & 0.2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The membership matrices for the primary indicator and secondary indicator layers. As demonstrated in Table 13, the standard method produces outcomes consistent with the FBWM. However, the standard method exhibits procedural complexity and operational inefficiency, whereas the FBWM demonstrates superior applicability in bridge condition evaluation through effectively balancing computational efficiency and rating accuracy.
## 4 The COASYM-MAC Protocol
### Tree Formation
#### 4.2.1 Identifying First Hop and Distance for Each Node  
During this stage, the BS sends a \"\(HELLO\)\" message to each node. Every node calculates how far it is from the base station (\(d_{i,BS}\)) [53]. To make our protocol more realistic, we did not consider GPS availability. Each node sends a \(firsthopcount\) request within its range \(r_{i}\). The nodes that receive the acknowledgement from the Base Station are considered as first hop nodes.
## 4 Conversion Method for CCA-Secure IBKEM
### IBKEM
#### 4.1.2 Security Model of IBKEM
Definition 5: The IBKEM scheme is \((t,\epsilon,q_{id})\)-IND-ID-CPA-secure if for any polynomial time adversary \(\mathcal{A}\) that runs in time at most \(t\) and issues at most \(q_{id}\) key generation queries, then we have \(\mathbf{Adv}_{\textsc{IND-ID-CPA}}^{\mathsf{IBKEM},\_4}<\epsilon\). Now, the security experiment of IND-ID-CCA, where the additional decapsulation oracle is given to an adversary \(\mathcal{A}\), is described as follows. \[ \begin{array}{l}\mathbf{Experiment}\ \mathbf{Exp}_{\textsc{IND-ID-CCA}, \lambda}^{\mathsf{IBKEM},\_4}(\lambda)\\ (\mathsf{PP},\mathsf{MSK})\stackrel{{\$}}{{\leftarrow}}\mathsf{IBKEM.Setup}(\lambda);\\ \mathsf{ID}^{*}\leftarrow\mathcal{A}^{\mathsf{KeyGen}(\cdot),\mathsf{Dec}( \cdot)}\ (\mathsf{PP});\\ \mathsf{KS}_{0}^{*}\stackrel{{\$}}{{\leftarrow}}\mathcal{K}_{i} ;(\mathsf{KS}_{1}^{*},\mathsf{CT}^{*})\stackrel{{\$}}{{\leftarrow}} \mathsf{IBKEM.Encap}(\mathsf{PP},\mathsf{ID}^{*});\\ b^{\prime}\leftarrow\mathcal{A}^{\mathsf{KeyGen}(\cdot),\mathsf{Dec}( \cdot)}(\mathsf{PP},\mathsf{CT}^{*},\mathsf{KS}_{0}^{*});\\ \mathsf{Output}\ b^{\prime}.\end{array} \]  
The oracle \(\mathsf{KeyGen}(\cdot)\) is the same as that in the IND-ID-CPA security game. The additional oracle \(\mathsf{DecO}(\cdot)\) takes as input ID and CT and returns a key \(\mathsf{K}\leftarrow\mathsf{IBKEM.Decap}\) (PP, CT, \(\mathsf{SK}_{\mathsf{ID}}\)), where \(\mathsf{SK}_{\mathsf{ID}}\stackrel{{\$}}{{\leftarrow}}\mathsf{IBKEM.KeyGen}(\mathsf{PP},\mathsf{MSK},\mathsf{ID})\). The restriction on the oracle \(\mathsf{DecO}(\cdot)\) is that \(\mathcal{A}\) is not able to query the pair \((\mathsf{ID}^{*},\mathsf{CT}^{*})\). The advantage of \(\mathcal{A}\) for breaking the IND-ID-CCA-security of IBKEM is defined as \(\mathbf{Adv}_{\textsc{IND-ID-CCA}}^{\mathsf{IBKEM},\_4}=\Big{|}\Pr\left[1 \leftarrow\mathbf{Exp}_{\textsc{IND-ID-CCA},1}^{\mathsf{IBKEM},\_4}(\lambda) \right]\)\(-\Pr\left[1\leftarrow\mathbf{Exp}_{\textsc{IND-ID-CCA},0}^{\mathsf{IBKEM}, \_4}(\lambda)\right]\Big{|}\). Definition 6: The IBKEM scheme is \((t,\epsilon,q_{id},q_{d})\)-IND-ID-CCA-secure if for any polynomial time adversary \(\mathcal{A}\) that runs in time at most \(t\) and issues at most \(q_{id}\) key generation queries and \(q_{d}\) decapsulation queries, then we have \(\mathbf{Adv}_{\textsc{IND-ID-CCA}}^{\mathsf{IBKEM},\_4}<\epsilon\).
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
A new hybrid loss function is designed that includes an inner-dot-based cross-entropy (ICE) loss, center loss (CL) and radius loss, to make the features of the known class signals at the periphery of the feature space;
* We propose a model that is used for the OSR of interference signals and called hybrid attention and feature reuse net (HAFRNet), which contains a feature reuse structure and hybrid domain attention module (HDAM);
* The influence of the hyperparameter in the hybrid loss function of HCPL is analyzed, and the role of HDAM and the feature reuse structure in the OSR of interference signals is proven in a simulation. The OSR experimental results of nine common interference types show that the proposed method has a better performance and less parameters than other methods under different openness. The rest of this paper is organized as follows. Section 2 presents related works. Section 3 models the OSR problem of the interference signals. Section 4 discusses the proposed method in detail. Section 5 gives the simulation results and analyzes the performances of different methods in detail. Lastly, Section 6 concludes this paper and discusses our future work.
## 1 Introduction
The following notations have been used in this article: \(\omega_{r}\): frequency of polarization rotation, or frequency of modulation (rad/s); \(f_{r}\): frequency of modulation (Hz); \(\omega_{c}\): carrier frequency (rad/s); \(f_{c}\): carrier frequency (Hz); \(T_{r}\): symbol period (s); \(h_{t}(t)\): baseband signal to be transmitted through horizontal polarized antenna; \(v_{t}(t)\): baseband signal to be transmitted through vertically polarized antenna; \(E_{b}\): energy per bit; \(E_{s}\): energy per symbol; \(M\): order of modulation; \(k\): number of bits in a symbol, \(k=\log_{2}M\); \(m\): an integer that limits the number of symbols in \(M\)th order modulation (\(m=0,1,2,\ldots,M-1\)); \(s_{m}\): \(m\)th symbol of \(M\)th order modulation; \(x_{c}\): even bit stream; \(x_{o}\): odd bit stream; \(\mathbf{a}_{x}\): unit vector of \(x\)-axis; \(\mathbf{a}_{y}\): unit vector of \(y\)-axis; \(\beta\): phase constant; \(z\): displacement in the direction of \(z\)-axis; \(m\): number referring a multipath component; \(\phi_{n}\): polarization angle of the \(n\)th multipath component; \(\theta_{n}\): phase of the nth multipath component; \(a_{n}\): magnitude of the \(n\)th multipath component received on horizontal polarized antenna; \(b_{n}\): magnitude of the nth multipath component received on vertical polarized antenna; \(r_{h}(t)\): signal received on horizontally polarized antenna; \(r_{o}(t)\): signal received on vertically polarized antenna; \(n_{h}\): AWGN component of \(r_{h}(t)\); \(n_{v}\): AWGN component of \(r_{v}(t)\); \(h_{r}(t)\): baseband signal obtained from \(r_{h}(t)\); \(v_{r}(t)\): baseband signal obtained from \(r_{v}(t)\); \(f_{s}\): sampling frequency;\(T_{s}\): sampling interval; \(N_{p}\): number of samples per \(T_{r}\); \(p\): an integer that limits the number of samples (\(p=0,1,2,3,\ldots,N_{p}\)); \(h_{p}\): demodulated sequence obtained from \(h_{r}(t)\); \(v_{p}\): demodulated sequency obtained from \(v_{r}(t)\); \(r_{p}\): sequence obtained after combining; \(s_{m}\): estimated \(s_{m}\); \(\hat{s}_{m,p}\): estimated \(p\)th replica of \(s_{m}\): \(q_{m}\): quaternion symbol corresponding to \(s_{m}\); \(H\): quaternion channel; \(q_{m,\ell}\): \(\ell\)th replica of \(q_{m}\) (\(\ell=p\), \(N_{p}=N_{\ell}\)); \(\hat{q}_{m}\): estimated \(q_{m}\); \(\hat{q}_{m,\ell}\): estimated \(q_{m,\ell}\); \(w\): quaternion AWGN.
# Study of Image Classification Accuracy with Fourier Ptychography
## 2 Methodology
### Fourier Ptychography
Based on Equation (2), the higher resolution image spectrum was updated until the converging condition specified was reached as shown in Equation (3). For the purpose of comparison between the FPM reconstructed image (the higher resolution) and the lower resolution image (the raw lower resolution image, without going through the FPM reconstruction process), the middle LED of the 15 by 15 LED array was chosen to be used. The system numerical aperture (NA) chosen here was varied from 0.05 to 0.5.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Channel Coding
#### 5.1.3 Rate Matching  
The three output bit streams of the turbo encoder are further forwarded to the rate-matching block. The applied procedures are presented in Figure 4:  
Initially, the parallel data streams are interleaved according to [21], resulting in product \(v^{(0)}_{k}\), \(v^{(1)}_{k}\), \(v^{(2)}_{k}\) vectors, where \(k=0,...K_{\Pi}-1\) and \(K_{\Pi}=(R^{\TC}_{\text{sublblock}}\times C^{\TC}_{\text{sublblock}})\). The number  
Figure 4: Rate-matching procedures.  
Figure 3: Turbo encoder with a rate of 1/3.  
of columns \(C_{subblock}^{TC}=32\) and number of rows \(R_{subblock}^{TC}\) may be defined as the minimum integer fulfilling the formula  
\[D_{in}\leq(R_{subblock}^{TC}\times C_{subblock}^{TC}), \tag{3}\]  
where \(D_{in}\) represents the number of sub-block interleaver's input bits. Subsequently, the \(v_{k}\) streams are distributed within an \((R_{subblock}^{TC}\times C_{subblock}^{TC})\) matrix, and an intercolumn permutation is applied. The permutation pattern is presented in Table 1.  
Finally, the virtual circular buffer of length \(K_{\omega}=3K_{\Pi}\) is formulated as follows:  
\[w_{k}=v_{k}^{(0)} \tag{4}\]  
\[w_{K_{\Pi}+2k}=v_{k}^{(1)} \tag{5}\]  
\[w_{K_{\Pi}+2k+1}=v_{k}^{(2)}. \tag{6}\]  
The length of the output vector \(e_{k}\) equals \(E=Q_{m}\cdot G^{\prime}\), where \(Q_{m}\) denotes the modulation order and \(G^{\prime}=G/Q_{m}\) depends on the total number of bits available for the transmission \(G\). The procedure of arranging bits within output vector \(e_{k}\) is specified in detail in [21].
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 2 LSM-UVCN
### Neighbor Discovery Algorithm
Each node broadcasts the generated second type of control frame to the network. By collecting the neighbor broadcast frames of other nodes, a network connection matrix is generated, as shown in Figure 2.  
Figure 1: Distance determination: (**a**) example of network topology; (**b**) example of the distance determination process; (**c**) the second-type control frame of Node 2 features an added “neighbors segment” compared to the first-type control frame. Figure 2: Generation of the network connection matrix. The connection matrix comprehensively encapsulates the inter-nodal connectivity relationships throughout the network infrastructure. Specifically, when the element \(A^{\overline{i}}\) within the matrix assumes a value of \"1\", it denotes that nodes \(i\) and \(j\) are mutually situated within a one-hop communication range. Through systematic traversal of this matrix, any node is empowered to enumerate all viable data transmission routes between arbitrary pairs of nodes. Upon obtaining the identifiers of the source and destination nodes, a node can utilize a unified routing algorithm to ascertain the requisite relay nodes for data conveyance, as well as those nodes along the transmission path that may induce interference. This process enables the node to accurately discern the available channel resources that remain unencumbered. Moreover, the proposed protocol uses \"0\" and \"1\" to denote the connectivity status between nodes. In a 100-node network, each node can establish the connection matrix by constructing 100 registers with 100 bits each, thus significantly reducing both computational complexity and storage difficulty. It is imperative to note that the connection matrix established via the neighbor discovery algorithm merely represents a snapshot of the network topology at a given instant. As nodes undergo mobility, the dynamic alterations in inter-nodal connectivity render the existing matrix obsolete. The rate of matrix obsolescence is directly proportional to the velocity of nodal movement. Hence, during the course of data transmission, periodic execution of neighbor discovery procedures is indispensable to maintain an up-to-date and accurate representation of the network connection matrix.
# Seismic Responses of GRS Walls with Secondary Reinforcements Subjected to Earthquake Loading
## 4 Results
### Effects of the Vertical Spacing of the Primary Reinforcement Layer  
Figure 18a-d show the effect of vertical spacing on the relative lateral displacement, connection load, maximum reinforcement load, and acceleration amplification of the wall with/without secondary reinforcement, respectively. The walls with a VS of the primary reinforcement layer of 0.6 and 0.4 m were compared. As shown in Figure 18a, a small difference was observed between the walls with and without secondary reinforcement and between the walls with a VS of 0.4 and 0.6 m. This result also proved, as above, that decreasing the VS of the primary reinforcement layer has a slight influence on the overall displacement.  
Figure 18: The effect of vertical spacing on (**a**) the relative lateral displacement; (**b**) connection load; (**c**) maximum reinforcement load; (**d**) and acceleration amplification of the wall with and without secondary reinforcement.  
From Figure 18b,c, it is shown that the walls with secondary reinforcement have a smaller connection load and maximum reinforcement load. The maximum reinforcement loads in each layer were summed up, as shown in Table 7. For the walls with a VS of 0.4 m, the reinforcement load decreased by 9.4% when a secondary reinforcement was installed, while for the walls with a vertical spacing of 0.6 m, the corresponding decrease was 12.9%. This indicates that the effect of secondary reinforcement on reducing the reinforcement load is diminished with reduced VS of the primary reinforcement layer.  
As shown in Figure 18d, the effect of the vertical spacing on acceleration amplification can be negligible. Similar findings were also reported by [PERSON] et al. (2020).
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Trustworthiness  
#### 4.2.1 Knowledge Sharing  
In knowledge-defined networks, knowledge sharing (dissemination) is a key component for making knowledge-driven decisions and updating application policies based on inferences from knowledge. Even though knowledge retrieval and manipulation languages such as SPARQL/SQWRL can be used for efficient knowledge dissemination, these protocols do not guarantee the trustworthiness of the knowledge. Therefore, in the recent past, there have been numerous attempts to improve the trustworthiness of knowledge sharing by employing blockchain technology.  
A various medium-dispersed blockchain network named MKShareNet has been applied for the cooperative sharing of knowledge in service-minded repair choice-making [245]. Moreover, given the necessity to use computationally intensive consensus procedures like PoW, PoS, etc. due to resource constraints in network gadgets and the risk of knowledge spying, work in [246] proposes employing a user-centric blockchain for edge knowledge sharing with the aid of a speedy and low-energy-consuming proof of popularity consensus approach. Furthermore, the practice of exchanging knowledge has evolved as a collective leadership and collaborative game in the trade market that effectively reduces the malicious attacks on knowledge in an Internet of Vehicles network, where the machine learning-generated knowledge is distributed using the Hierarchical Blockchain-Enabled Federated Learning (HBEFL) framework, enabling feasible trustworthy knowledge sharing in large vehicular networks [247]. Additionally, a framework called CKShare utilizes a trustworthy blockchain network to record knowledge and its transactions, where a K-nearest neighbor-based retrieval mechanism is proposed for knowledge retrieval [248]. Likewise, for intelligent connected vehicular networks, an Asynchronous Distributed Learning (ADL) algorithm has been used for knowledge generation, while Directed Acyclic Graphs have been used to reduce the operation latency and ensure fast consensus for the blockchain, which has been used for knowledge sharing [249].