esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents an all-digital digital background technology for the gain, time skew, and bandwidth mismatch in an \(M\)-channel under-sampling time-interleaved analog-to-digital converters (TI-ADCs) system. First, in the proposed technology, the calibration of the gain, time skew, and bandwidth mismatch is performed simultaneously. Secondly, the proposed approach enables a wide range of time skew mismatch compensation. This is an extraordinary capability that many digital calibration techniques, which use the Taylor series do not possess. Then, the proposed technique can work for ultra-high frequency signals by the bandpass fractional delay filter. Next, fewer computing resources are used to correct the input signal after obtaining the proposed parameter vector than the techniques that use the derivative filter or Hilbert filter. After that, solving a system of linear equations is used to replace matrix inversions because implementing matrix inversions within FPGA is complex. In the end, the measurement result showed that the proposed technique has a good effect on the calibration of time skew.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **[13] Work** & **[16] Work** & **[27] Work** & **[30] Work** & **[31] Work** & **This Work** \\ \hline SNDR improvement (dB) & 15.30 & 22.68 & 22.48 & 11.43 & 16.16 & 26.04 \\ SFDR improvement (dB) & 16.18 & 40.65 & 40.38 & 11.74 & 17.26 & 48.02 \\ The effective number of bits (bit) & 5.10 & 6.33 & 6.29 & 4.51 & 5.24 & 6.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of calibration effects in the ADC12 DL3200 EVM.  
Figure 17: The frequency spectrum of the two-tone signals in the commercial 2-channel TI-ADCs before and after calibration.  
**Author Contributions:** Conceptualization, P.Y. and M.H.; methodology, P.Y.; software, M.H.; validation, P.Y. and M.H.; formal analysis, M.H.; investigation, M.H.; resources, P.Y.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H.; visualization, M.H.; supervision, P.Y.; project administration, P.Y.; funding acquisition, P.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the project under the national key research and development plan, China, grant number [2018 YFB2003303].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** There are no publicly archived datasets analyzed or generated during the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.7 Portland Cement  
Portland cement, specifically a type called \"white Portland cement\", has been explored as a potential treatment for DH. Similarly, to bioactive glasses, Portland cement, primarily comprised of Si, has shown substantial potential in treating DH. Its attributes facilitate the occlusion of dentin tubules through the remineralization of dentin [115]. White Portland cement has fine particles that can infiltrate and occlude these dentin tubules. It contains calcium silicates, which can release calcium and hydroxide ions when they come into contact with moisture, contributing to the remineralization of the dentin, and making the tooth structure stronger and more resilient [129]. It is important to note that while white Portland cement has been investigated as a treatment for DH, it is not a standard or widely accepted treatment in dentistry.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
###### Abstract  
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB-D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state-of-the-art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self-calibrated RGB-D image semantic segmentation neural network model based on an improved residual network without relying on depth sensors, which utilizes multi-modal information from depth maps predicted with depth estimation models and RGB image fusion for image semantic segmentation to enhance the understanding of a scene. First, we designed a novel convolution neural network (CNN) with an encoding and decoding structure as our semantic segmentation model. The encoder was constructed using IResNet to extract the semantic features of the RGB image and the predicted depth map and then effectively fuse them with the self-calibration fusion structure. The decoder restored the resolution of the output features with a series of successive upsampling structures. Second, we presented a feature pyramid attention mechanism to extract the fused information at multiple scales and obtain features with rich semantic information. The experimental results using the publicly available Cityscapes dataset and collected forest scene images show that our model trained with the estimated depth information can achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task and even outperforming some competitive methods.  
s _Applied Sci._**2023**, 13, 9924. [[https://doi.org/10.3390/app13179924](https://doi.org/10.3390/app13179924)]([https://doi.org/10.3390/app13179924](https://doi.org/10.3390/app13179924))
## 3 Design of the Control Scheme
### System Stability Analysis  
**Theorem 1.**_Combined with the system (15), it is assumed that the following adaptive laws designed for carriage i are effective:_  
\[\begin{array}{l}\dot{\xi}_{i}=-\varphi_{\xi}\kappa_{i}\\ \dot{k}_{pi}=-\varphi_{p}[\kappa_{i}\dot{k}_{pi}(\gamma_{1i}-z_{1i})]/\widetilde {k}_{pi}\\ \dot{k}_{di}=-\varphi_{d}[\kappa_{i}\dot{k}_{di}(\gamma_{2i}-z_{2i})]/\widetilde {k}_{di}\end{array} \tag{24}\]  
_where \(\varphi_{\xi}\), \(\varphi_{p}\), and \(\varphi_{d}\) are reasonable positive parameters._  
**Remark 3.**_In order to adjust \(k_{pi}\) and \(k_{di}\) on the basis of IPSO parameter setting, an initial value \(k_{pi}\) and \(k_{pi}\) can be set according to the parameters obtained by IPSO. This can also avoid \(k_{pi}\) and \(k_{di}\) being 0, and the adaptive law can be rewritten:_  
\[\begin{array}{l}\dot{\xi}_{pi}=\int_{0}^{T}\Big{\{}\varphi_{p}[\kappa_{i} \dot{k}_{pi}(\gamma_{1i}-z_{1i})]/\widetilde{k}_{pi}\Big{\}}\ dt+k_{pi}\\ \dot{k}_{di}=\int_{0}^{T}\!\!\Big{\{}\varphi_{d}[\kappa_{i}\dot{k}_{di}(\gamma _{2i}-z_{2i})]/\widetilde{k}_{di}\Big{\}}\ dt+k_{pi}\end{array} \tag{25}\]  
**Theorem 2.**_Considering the HST system (4), under Assumption 1, the designed control laws (14), (20), and adaptive laws (24), then it is proved that all signals of the system are bounded, and the system is in uniform stability._  
**Proof of Theorems 1 and 2.** Referring to [36], a positive definite Lyapunov function is defined as follows:  
\[V(t)=\sum\limits_{i=1}^{n}\biggl{\{}\frac{1}{2}m_{i}\kappa_{i}^{2}+\frac{1}{2 \varphi_{5}}\widetilde{\xi}_{i}^{2}+\frac{1}{2\varphi_{p}}\widetilde{\xi}_{pi}^ {2}+\frac{1}{2\varphi_{d}}\widetilde{\xi}_{di}^{2}\biggr{\}} \tag{26}\]  
Differentiate of the above equation for time:  
\[\dot{V}(t)=\sum\limits_{i=1}^{n}\biggl{\{}m_{i}\dot{\kappa}_{i}\dot{\kappa}_{i} +\frac{1}{\varphi_{5}}\widetilde{\xi}_{i}\dot{\widetilde{\xi}}_{i}+\frac{1}{ \varphi_{p}}\widetilde{\xi}_{pi  
### System Stability Analysis  
It is assumed that the HST operation route passes \"start and traction-cruise-brake-cruise-traction-cruise-brake and stop\". The total operation time of HST is 1000 s, the total operation distance is \(79,074\) m, and the maximum operation speed is 350 km/h. The ideal speed and displacement curve is shown in Figure 4.  
The HST will be affected by additional resistance during operation. It is assumed that the conditions of curves, ramps, and tunnels in the operation route are shown in Table 1.
## 2 Bayesian Functional Mapping  
### Linear Model  
Suppose there is an F\({}_{2}\) population of size \(n\), initiated with two inbred lines. In this F\({}_{2}\) population, many markers are genotyped to construct a genetic linkage map, aimed to identify the QTLs that affect growth curves. For each progeny, a particular growth trait, such as body weight, tail length or cell number, is measured at a series of time points (\(1,\cdots,T\)). Consider a putative QTL with genotypes \(qq\) (denoted as 0), \(Qq\) (denoted as 1) and \(QQ\) (denoted as 2) that affects the shape of growth curves. At a specific time point \(t\), the phenotypic value of the growth trait for progeny \(i\) due to the QTL may be expressed by a linear model, i.e.,  
\[y_{i}(t)=\sum_{j=0}^{2}\xi_{ij}u_{j}(t)+e_{i}(t), \tag{1}\]  
where \(\xi_{ij}\) is an indicator variable for progeny \(i\) carrying a QTL genotype and defined as 1 if a particular QTL genotype \(j\) is indicated and 0 otherwise, \(u_{j}(t)\) is the expected phenotypic value for QTL genotype \(j\) at time \(t\), and \(e_{i}(t)\) is independently and identically distributed as \(N(0,\sigma^{2}(t))\). Note that measurements within an individual are likely to be correlated across times, with covariance \(\sigma(t_{1},t_{2})\) between times \(t_{1}\) and \(t_{2}\) (\(t_{1},t_{2}=1,\ldots,T\)). These variances and covariances form a (\(T\times T\)) matrix \(\mathbf{\Sigma}\).
## 3 Results
### Impact of Sodium Bicarbonate Supplementation on Pigments  
The chlorophyll \(a\) (Chl-_a_) content per cell was determined during 72 h of the experiment in each treatment (Figure 4). While the control did not show any increase in Chl-_a_ for the overall duration of the experiment, the amount of Chl-_a_ in other treatments increased up to 50%-65% after 1 h later from the NaHCO\({}_{3}\) administration, remaining high until 4 h. After 24 h, the Chl-_a_ content was different among the treatments. NaHCO\({}_{3}\) 2 g L\({}^{-1}\) and NaHCO\({}_{3}\) 3 g L\({}^{-1}\) showed the highest values of Chl-_a_, almost two times more than NaHCO\({}_{3}\) 1 g L\({}^{-1}\). At 72 h, in the culture enriched with NaHCO\({}_{3}\) 3 g L\({}^{-1}\), Chl-_a_ content reached 10.7 \(\pm\) 0.3 pg cell\({}^{-1}\), followed by NaHCO\({}_{3}\) 2 g L\({}^{-1}\) (9.3 \(\pm\) 0.3 pg cell\({}^{-1}\)), and 1g L\({}^{-1}\) (3.7 \(\pm\) 0.3 pg cell\({}^{-1}\)). The Phaeo/Chl-_a_ ratio (Table 1), calculated as a proxy of active chlorophyll [51; 52], remained constant (~1) in the control treatment for the entire duration of the experiment (72 h). In bicarbonate treatments, the ratio Phaeo/Chl-_a_ slightly increased after 3 h, then values tended to decrease, showing a weak difference between the control and NaHCO\({}_{3}\) (2 and 3 g L\({}^{-1}\)) supplemented after 72 h. Further details on the effect of bicarbonate addition on the concentration of other photosynthetic pigments have been reported in the Supplementary Materials (Table S2) as pigment:Chl-_a_ ratios. Although additional analyses are required, these preliminary data seem to suggest that the lutein:Chl-_a_ ratio slightly increased in bicarbonate treated cells, particularly those supplemented with 3 g L\({}^{-1}\). A typical chromatogram of photosynthetic pigments extracted in _Chlorella sorokiniana_ cells is shown in the Supplemental Material (Figure S1).  
Figure 3: Average cell diameter of _Chlorella sorokiniana_ control and bicarbonate enriched cultures cells (NaHCO\({}_{3}\) 1 g L\({}^{-1}\), 2 g L\({}^{-1}\), and 3 g L\({}^{-1}\)). Error bars represent SD (\(n\) = 3). The values with the same letter were not significantly different (\(p\)\(\leq\) 0.001, ANOVA, [PERSON]’s multiple comparison).
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 5 System Model
### Phase 1  
The sensed readings are used as an input for the data preparation block that prepares a new observation vector from each new data measurement \(Vt\). \(Vt\) is composed of two humidity measurements, i.e., \(H1\), \(H2\), and two temperature measurements, i.e., \(T1\), \(T2\). Three successive data measurements \(Vt\), \(Vt\)-\(1\), \(Vt\)-\(2\) aggregate to form a new observation vector.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 3 Results
### Maize Kernel Mold Identification and Degree Classification  
3.3.1. ResNet50 Improved Model Training Results  
Based on the two-dimensional sliced grayscale image of a corn kernel obtained by X-ray tomography, mold and mold-free feature images were extracted. The ResNet50 model was enhanced for the rapid detection of corn mold. The training accuracy and validation accuracy of the ResNet50 model varied during training, as depicted in Figure 10a, and the classification loss also varied during training, as shown in Figure 10b. With iterative adjustments, its training accuracy exceeded 99%, and the classification loss was less than 0.1.
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
## 3 Results
### Nutrition Value and Glycemic Index of Bread According to Fermentation Type
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{2}{c}{**With the Addition of**} & \multicolumn{2}{c}{**With the Addition of**} & \multicolumn{2}{c}{**With the Addition of**} \\ \multicolumn{1}{c}{} & \multicolumn{2}{c}{**Sourdough Starter**} & \multicolumn{2}{c}{**Sourdough Starter and**} & \multicolumn{2}{c}{**With the Addition of Yeast**} \\ \multicolumn{1}{c}{} & \multicolumn{2}{c}{**(\(n=11\)’)**} & \multicolumn{2}{c}{**Yeast (\(n=7\))**} & \multicolumn{2}{c}{**(\(n=5\))**} \\ \hline \multirow{4}{*}{GI} & Mean & 47.8 & 57.3 & 65.6 \\ \cline{2-5}  & \(\mathrm{SE}^{2}\) & 1.5 & 3.1 & 3.2 \\ \cline{2-5}  & \(\mathrm{CI}^{2}\) & 44.9; 50.8 & 51.1; 63.5 & 59.2; 72.0 \\ \cline{2-5}  & Median & 47.8 A & 56.7 B & 65.0 B \\ \hline \multirow{4}{*}{IAUC (mg/dL \(\times\) min)} & Mean & 1572.3 & 1784.7 & 1730.1 \\ \cline{2-5}  & \(\mathrm{SE}\) & 79.1 & 131.6 & 84.2 \\ \cline{2-5}  & \(\mathrm{CI}\) & 1415.2; 1729.3 & 1520.8; 2048.7 & 1562.3; 1897.8 \\ \cline{2-5}  & Median & 1331.3 A & 1608.8 A & 1677.9 A \\ \hline \multirow{4}{*}{Delta IAUC (mg/dL \(\times\) min)} & Mean & 1808.2 & 1492.3 & 797.5 \\ \cline{2-5}  & \(\mathrm{SE}\) & 99.4 & 164.2 & 90.3 \\ \cline{2-5}  & \(\mathrm{CI}\) & 1611.4; 2005.0 & 1162.9; 1821.6 & 617.7; 977.4 \\ \cline{2-5}  & Median & 1584.2 B & 1176.9 AB & 659.4 A \\ \hline \multirow{4}{*}{Energy [kcal/100 g]} & Mean & 256.4 & 247.8 & 255.5 \\ \cline{2-5}  & \(\mathrm{SE}\) & 4.6 & 2.3 & 2.9 \\ \cline{2-5}  & \(\mathrm{CI}\) & 247.3; 265.5 & 243.1; 252.4 & 250.0; 261.3 \\ \cline{2-5}  & Median & 256.0 A & 252.0 A & 250.0 A \\ \hline \multirow{4}{*}{Carbohydrates [g/100 g]} & Mean & 35.5 & 36.2 & 37.3 \\ \cline{2-5}  & \(\mathrm{SE}\) & 0.6 & 0.9 & 0.5 \\ \cline{2-5}  & \(\mathrm{CI}\) & 34.2; 36.8 & 34.5; 38.0 & 36.3; 38.2 \\ \cline{2-5}  & Median & 37.0 A & 37.0 A & 35.0 A \\ \hline \multirow{4}{*}{Fat [g/100 g]} & Mean & 7.7 & 6.2 & 5.8 \\ \cline{2-5}  & \(\mathrm{SE}\) & 0.6 & 0.4 & 0.3 \\ \cline{1-1} \cline{2-5}  & \(\mathrm{CI}\) & 6.5; 8.8 & 5.3; 7.0 & 5.1; 6.4 \\ \cline{1-1} \cline{2-5}  & Median & 8.5 A & 5.9 A & 5.9 A \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristics of the glycemic index and average nutritional value for given groups. In the group with the addition of sourdough, a positive relationship between IAUC and carbohydrate content and negative correlations between IAUC and protein and fat content were noted. Interestingly, GI was negatively correlated with fiber content in all three groups. However, no similar correlation was observed for the IAUC.
# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 4 Cryptographic Primitives
### Symmetric Encryption  
Symmetric encryption is an encryption algorithm where the same key needs to be used for encryption and decryption. Because of its fast encryption and decryption speed, symmetric encryption is often used when a large amount of data needs to be encrypted. In our scheme, we use an AES algorithm [28] to encrypt medical data. As a substitute for a DES algorithm [29], this type of algorithm has been deeply analyzed and widely used all over the world. For different application scenarios, the key length is also different. There are three common schemes for an AES algorithm, namely AES-128, AES-192, and AES-256. This section provides an overview of AES-128.  
As a block cipher, this AES algorithm groups pieces of plaintext together, with 128 bits (i.e., 16 bytes) in each group. If a group has less than 128 bits, it will be automatically supplemented. Each group of data is encrypted until the complete plaintext is encrypted.  
The encryption process of this AES algorithm includes ten rounds, each involving four operations:  
SubBytes: use a substitution-box to replace the group byte-to-byte.  
ShiftRows: a simple displacement.  
MixColumns: an alternative to using the arithmetic properties on the field GF (28).  
AddRoundKey: bitwise XOR is performed on the current group and part of the extension key.  
The encryption key of each round is obtained by the expansion of the initial key. In addition, the 16-byte plaintext, ciphertext, and key of each round are represented by a 4 \(\times\) 4 matrix. The approximate encryption process is shown in Figure 2. The specific details of each operation in the encryption process are not repeated here. Interested readers can learn about it in the relevant literature [28].  
The decryption process of the AES algorithm is the inverse operation of encryption process. Since each operation is reversible, decryption in the reverse order can recover the plaintext. However, the need for the receiver of the message to obtain the secret in order to decrypt the ciphertext is an urgent problem. This leads users to incorporate asymmetric encryption.
# Quiescent Optical Solitons with Quadratic-Cubic and Generalized Quadratic-Cubic Nonlinearities
## 2 The Enhanced Kudryashov's Procedure  
Consider a governing model  
\[G(u,u_{x},u_{t},u_{xt},u_{xx},...)=0, \tag{1}\]  
where \(u=u(x,t)\) denotes a wave profile, while \(t\) and \(x\) depict temporal and spatial variables in sequence.  
The relations  
\[u(x,t)=U(\xi),\ \ \ \xi=\mu(x-vt), \tag{2}\]  
condense (1) to  
\[P(U,-\mu vU^{\prime},\mu U^{\prime},\mu^{2}U^{\prime\prime},...)=0, \tag{3}\]  
where \(\mu\) is the inverse wave width, \(\xi\) is the wave variable, and \(v\) is the wave velocity.  
Step-1: The reduced model (3) admits the explicit solution  
\[U(\xi)=\lambda_{0}+\sum_{l=1}^{N}\sum_{i+j=l}\lambda_{ij}Q^{i}(\xi)R^{j}(\xi), \tag{4}\]  
along with the ancillary equations  
\[R^{\prime}(\xi)^{2}=R(\xi)^{2}(1-\chi R(\xi)^{2}), \tag{5}\]  
and  
\[Q^{\prime}(\xi)=Q(\xi)(\eta Q(\xi)-1), \tag{6}\]  
where \(\lambda_{0}\), \(\chi\), \(\eta\), \(\lambda_{ij}(i,j=0,1,...,N)\) are constants, where \(N\) come from the balancing technique in (3).  
Step-2: Equations (5) and (6) also provide the solitons that are of the forms  
\[R(\xi)=\frac{4c}{4c^{2}e^{5}+\chi e^{-\xi}}, \tag{7}\]  
and  
\[Q(\xi)=\frac{1}{\eta+be^{\xi}}, \tag{8}\]  
where \(c\) and \(b\) are constants.  
Step-3: Substituting (4) along with (5) and (6) into (3) provides us with the much-needed constants in (2)-(6). Lastly, plugging in the obtained parametric restrictions along with (7) or (8) into (4), one arrives straddled solitons which can be reduced to dark or singular solitons.
# Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets
## 5 Calculation and Analysis of Defect Characteristic Parameters  
### The Area of Defect Targets  
By calculating the number of pixels inside the edge, we can obtain the area of the defect target detected in the current frame image. The larger the area, the more serious is the defect damage. In the tracking and detection process of a single defect, a defect area value is obtained for each frame of image, and the average value \(A_{r}\) is taken as the final defect area. Table 1 shows the results of defect area calculation of surface defect targets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Defect Target** & **Frame 1** & **Frame 2** & **Frame 3** &... & **Frame 28** & **Frame 29** & **Frame 30** \\ \hline Defect \(a\) & 1956 & 1887 & 1890 &... & 1911 & 1928 & 1931 \\ Defect \(b\) & 2586 & 2507 & 2521 &... & 2544 & 2551 & 2560 \\ Defect \(c\) & 1102 & 1043 & 1055 &... & 1079 & 1081 & 1077 \\ Defect \(d\) & 3199 & 3169 & 3153 &... & 3162 & 3167 & 3166 \\ Defect \(e\) & 2783 & 2770 & 2791 &... & 2790 & 2783 & 2781 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of area calculation of surface defect targets.  
Figure 6: The results of the Sobel operator detection of partial surface defects. The result vacancy represents the loss of target in the tracking process. (**a**) Detection according to the tracking results of the improved kernel correlation filter (KCF) algorithm; (**b**) detection according to the tracking results of the tracking–learning–detection (TLD) algorithm.
# A Survey on Blockchain-Based Search Engines
## 2 Blockchain and Its Applications
### Blockchain Applications
#### 2.2.3 Cloud, Fog, and Edge Computing  
Cloud computing is a computing paradigm that also provides collaboration and facilitates ubiquitous access to computing according to a user's request. This technology offers new data processing and services in various industries while also lowering computing and storage costs for users and improving ease of use [24]. Combining blockchain technology with existing cloud services has great potential for improving functionality, efficiency, and security/privacy [8]. One of the most critical protection technologies for securing confidential data stored in the cloud by businesses and individuals is access control. Since the cloud uses a centralized access control system, personal data in the cloud are easily tampered with or leaked by hackers or cloud internal managers. In [24], AuthPrivacyChain, a blockchain-based access control system with privacy and security, to address this problem was presented. In [25], a new distributed virtual machine agent model was implemented in the cloud using mobile agent technology to solve data protection and trusted computing, which are significant challenges in current cloud computing applications. Multi-tenants will collaborate to ensure data trust verification using the virtual machine agent--the tasks of storing, tracking, and securing secure data. In [26], a new blockchain-based database was proposed to face data integrity threats.  
Edge computing is a new distributed computing paradigm that vows to bring processing and data storage closer to where they are needed, saving network traffic and lowering request latency [27]. Decentralized administration and security are currently the main issues for this technology. The integration of blockchain and edge computing into a single system can provide safe network access, storage, and computation at the edges [9]. In [28], the blockchain was used as a framework for hierarchical and distributed control systems. Hyperledger Fabric was chosen as the blockchain solution in this study, and the interaction with the edge nodes was performed using micro-services architecture. Additionally, [29] provided a mobile edge computing-enabled blockchain system and a revolutionary notion of edge computing for mobile blockchain. IoT devices or mobile users can access and employ resources or computing services from an edge computing service provider in the proposed network to enable their blockchain applications.