esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 3 Bayesian Filtering MBPU Method
### Multi-Baseline CKFPU Algorithm  
The CKF filter uses the spherical-radial criterion to approximate the state posterior mean and covariance, and uses \(2n\) cubature points of the same weight to obtain new state points through the system equation. Then, a prediction point of the state at the next moment is obtained. This process does not require the calculation of the complex Jacobian matrix, and it has a good filtering effect. However, the weights of the Cubature points of CKF are equal and appear symmetrically. Therefore, the accuracy of TSPACKF is lower than the other two proposed methods. TSPACKF is more suitable for fast PU with less noise. According to CKF, the cubature points and weights of TSPACKF can be expressed as:  
\[\mathfrak{I}_{j}(k)=\sqrt{\frac{\mathfrak{R}}{2}}[1]_{j},\ b_{j}=\frac{1}{ \mathfrak{R}} \tag{13}\]where \(\mathfrak{I}_{j}(k)\) is the cubature points, \(b_{j}\) is the weight. In TSPACKF method, \(\Re=2\),\([1]=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right]\). We assume that \(S_{i_{\psi\psi}}(k)\) is the factoring error covariance, \(X_{j}(k)\) is the cubature point, and \(Z_{j}(k)\) is the jth propagated cubature point. Therefore, we can obtain the equation as:  
\[\left\{\begin{array}{l}S_{i_{\psi\psi}}(k)=\textit{Chol}[P_{i_{\psi\psi}}^{- }(k)]\\ X_{j}(k)=S_{i_{\psi\psi}}(k)b_{j}+\overset{-}{\psi_{i}}(k)\\ Z_{j}(k)=h(X_{j}(k),v_{j}(k))\end{array}\right. \tag{14}\]  
where \(\textit{Chol}[\cdot]\) is the Cholesky decomposition, and \(v_{j}(k)\) is the observation variance. According to CKF theory, we can obtain the equation as:  
\[\left\{\begin{array}{l}\phi_{i}^{-}(k)=\sum\limits_{i=1}^{m}b_{j}Z_{j}(k)\\ P_{i_{\phi\psi}}^{-}(k)=\sum\limits_{j=1}^{m}b_{j}Z_{j}(k)Z_{j}^{\mathsf{T}}(k )-\phi_{i}^{-}(k)(\phi_{i}^{-}(k))^{\mathsf{T}}+R_{i}(k)\\ P_{i_{\psi\psi}}^{-}(k)=\sum\limits_{j=1}^{m}b_{j}X_{j}(k)Z_{j}^{\mathsf{T}}(k )-\overset{-}{\psi_{i}}^{-}(k)(\overset{-}{\psi_{i}}^{-}(k))^{\mathsf{T}}\end{array}\right. \tag{15}\]  
where \(\phi_{i}^{-}(k)\) represents the predicted unwrapped phase; \(P_{i_{\phi\phi}}^{-}(k)\) represents the prediction error covariance matrix; and \(P_{i_{\psi\psi}}^{-}(k)\) represents the cross-covariance matrix of the predicted measurements. Therefore, we can obtain the unwrapped phase and its corresponding error covariance matrix as the following:  
\[\left\{\begin{array}{l}\ell_{i}(k)=P_{i_{\psi\phi}}^{-}(k)/P_{i_{\phi\psi}}^ {-}(k)\\ \psi_{i}(k)=\overset{-}{\psi_{i}}^{-}(k)+\ell_{i}(k)\cdot[\phi_{i}(k)-\phi_{i} ^{-}(k)]^{\mathsf{T}}\\ P_{i_{\psi\psi}}(k)=P_{i_{\psi\psi}}^{-}(k)-\ell_{i}(k)\cdot P_{i_{\phi\psi}}^ {-}(k)\cdot\ell_{i}(k)^{\mathsf{T}}\end{array}\right. \tag{16}\]  
Equations (10), (11), and (13)-(16) are the equations of the TSPACKF method.
## 3 Experimental Results and Discussion
### Compressive Strength, Tensile Strength, and Modulus of Elasticity  
The test results for compressive strength, split tensile strength, and modulus of elasticity are summarized in Table 4. The results are the average of three samples tested per batch for each test. The corresponding standard deviation is presented in brackets.  
Results revealed that batch FF4 and FF6, with 4 kg/m\({}^{3}\) and 6 kg/m\({}^{3}\) of fibers, respectively, had similar compressive strength and can be classified as high-strength concrete (50 MPa or higher). The major difference between the two mixtures was seen in the 28-day split tensile strength, where FF6 had 28% higher tensile strength compared to the FF4 mixture. The reason for the improvement of concrete tensile and compressive strength in the presence of fibers up to 6 kg/m\({}^{3}\) can be attributed to the ability of the fibers in preventing crack propagation, changing the crack orientation, and reducing the crack growth rate [24].  
On the other hand, FF8 with a higher fiber dosage exhibited lower compressive strength, split tensile strength, and modulus of elasticity among all the concrete batches in this study. The mean compressive strength was 52.7 MPa, which still classifies the concrete as high strength concrete, but represents a 25% reduction in the compressive performance. The poorer results of FF8 are attributed to the poor fresh properties observed during casting. Although the mixture visually presented good fiber dispersion and enough paste, the fresh properties proved the material was not in the range of self-compacting concrete, and since no additional compaction was applied, it likely affected the compaction of the material in the cylinder mold, reducing the compressive and tensile strength. The modulus of elasticity appeared to decrease with the increase in fiber dosage, however, results are not conclusive due to the variations found in the test results.
## 3 System Model and Symbol Definitions  
### RACH Procedure Scenario  
This study considers the contention-based random access procedure of \(N\) mMTC devices (MTDs) in one cell of a B5G network environment. Based on the traffic pattern of the MTC devices, we can divide them into three groups: periodical, on-demand, and event-driven.  
\[N=N_{P}+N_{O}+N_{E} \tag{1}\]  
\(N_{P}\), \(N_{O}\), and \(N_{E}\) represent periodical, on-demand, and event-driven types of MTD, respectively.  
Each device must follow a contention-based network access procedure to obtain authorization and resources for data transmission. Due to their diversity, these devices may be accessed at any time. Hence, there is the possibility of having a massive number of devices trying to access the network simultaneously. Given the very small number of available preambles in each random access opportunity (RAO), which is \(W=54\), the set of preambles is then presented as \(\mathcal{P}a=\{\mathcal{P}a_{0},\dots,\mathcal{P}a_{w-1},\mathcal{P}a_{l}\}\)
# A Single Image Enhancement Technique Using Dark Channel Prior
## 4 Experimental Results And Analysis  
We verify the effectiveness and performance of the improved algorithm through experiments, simulating and analyzing the subjective visual effect and objective quantitative results of the defogged image. Following [11], 5000 images from the dataset are used in our experiments, which are captured from foggy outdoor and urban scenes. All our experiments are simulated in Matlab (2017a), and the operating environment is 3.20 GHz Intel Core (i5-4460) CPU, 4G memory, 64-bit win10 system. In Equation (18), we set \(a=0.001\). In Equation (20), we set \(\beta=0.007\), \(\omega=0.016\). Under normal circumstance, such parameter settings can obtain satisfactory defogged results.
# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 3 Conceptual Model and Research Hypotheses
### Trust Factors
#### 3.2.1 Data Related Factors
Therefore, we can conclude that, in order to gain better blockchain technology that leads to IoT adoption, good data governance is crucial. Hence, we will assume the following:  
**Hypothesis 3C (H3C). **_Data governance has a significant effect on IoT blockchain technology adoption._  
**Data privacy. ** It has been of high significance since it concerns of how the data is controlled, used, and who has access to it. The European Union (EU) issued the General Data Protection Regulation (GDPR), which an organization must fulfill when using others data. In particular, data privacy seeks to empower the users to make their own decisions about who can process their data and for what purpose. Organizations that adopt blockchain in IoT systems expect a massive amount of data generated by IoT sensors and devices, yet this contains sensitive and private user data. Therefore, it is crucial to limit the types of data that can be uploaded to the blockchain functions. Moreover, data privacy laws, for instance the GDPR, are very strict, and following the regulations in terms of ensuring the data is not linked with individuals is a challenging process. A number of studies on data privacy for blockchain and IoT systems have been published [54; 55; 56]; one such study by [PERSON] et al. [54] proposed a decentralized personal data management system that ensures users own and control their data. The system is integrated with a protocol that turns a blockchain into an automated access-controlmanager that does not require trust in a third party. The transactions embedded in the system are able to carry instructions, involving, for example, the storing, querying, and sharing of data. In addition, [PERSON] et al. [55] designed a decentralized and trusted cloud data provenance architecture using blockchain technology called ProvChain. Decentralized systems have dominated in terms of data privacy in IoT Blockchain Platforms; [PERSON] et al. [56] presented a decentralized system to preserve user privacy on IoT devices using the Ethereum platform. Consequently, it is evident that, for any new IoT blockchain technology or service entering a new market, data privacy must be taken into the account. Thus, the following is hypothesized:  
**Hypothesis 3D (H3D). **_Data privacy has a positive influence on IoT blockchain technology adoption._
## 3 Results
### Difference between Young and Older Adults in Transient Characteristics of Postural Control  
The two-way ANOVA showed age \(\times\) task interactions for both DIF_21 and DIF_31 for CoP AP frequency (\(p<\) 0.001; \(\eta^{2}\) = 0.17-0.18). There were no other statistically significant interactions (\(p\) = 0.054-0.852) and no statistically significant main effects of age (\(p=\) 0.074-0.918). The task had a statistically significant effect (\(p\) = 0.001-0.014; \(\eta^{2}\) = 0.11-0.37) on 11 out of 14 outcome measures (7 CoP variables and 2 different indexes of transient behavior). Specifically, only DIF_31 indexes for CoP total velocity (\(p\) = 0.075), CoP AP velocity (\(p\) = 0.771) and CoP AP amplitude (\(p\) = 0.468) were not influenced by task. Pairwise comparisons showed that, regarding CoP frequency, there were statistically significant differences between age groups in parallel EC, tandem and single-leg stance for DIF_21 and tandem and single-leg stance for DIF_31 (Figures 2 and 3). Despite the lack of the main effect of age on the CoP ML velocity according to the ANOVA (\(p\) = 0.056-0.074), post hoc tests indicated a potential difference in DIF_21 and DIF_31 between the age groups regarding CoP ML velocity in the parallel EO task (\(p\) = 0.046-0.048). Namely, both indexes were closer to 100% in young adults (99.1 \(\pm\) 11.0% and 94.1 \(\pm\) 14.7% for DIF_21 and DIF_31, respectively) than in older adults (88.5 \(\pm\) 8.2% and 85.0 \(\pm\) 10.6% for DIF_21 and DIF_31, respectively) (Figures 2 and 3, upper right chart).  
Figure 1: Comparison of whole-trial outcomes between young and older adults across tasks. CoP—center of pressure; AP—anterior–posterior; ML—medial–lateral; EO—eyes open; EC—eyes closed. * Indicates statistically significant difference between the young and older group (\(p\) < 0.05).  
Figure 2: Comparison of DIF_21 index (relative difference between 2 nd and 1 st intervals of the trial) between young and older adults across tasks. CoP—center of pressure; AP—anterior–posterior; ML—medial–lateral; EO—eyes open; EC—eyes closed. The dashed line indicated the value corresponding to no transient response (i.e., the same value of postural sway in 1 st and 2 nd intervals). * Indicates statistically significant difference between the young and older group (\(p<0.05\)).
## 3 Results
### Differences in Environmental and Vegetation Regulations of the LUE Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline Percentage & \multicolumn{2}{c}{**PAR a**} & \multicolumn{2}{c}{**T\({}_{\text{a}}\) b**} & \multicolumn{2}{c}{**Water c**} & \multicolumn{2}{c}{**Vegetation a**} \\ \cline{2-9} (\%) & **N e** & **P f** & **N** & **P** & **N** & **P** & **N** & **P** \\ \hline CASA & 13.02 & 6.94 & 15.07 & 3.66 & 0.45 & 25.70 & 0.75 & 35.42 \\ CFix & 12.66 & 36.26 & 17.37 & 16.06 & 0.65 & 54.85 & 0.05 & 61.78 \\ CFlux & 27.40 & 22.45 & 13.39 & 9.51 & 0.20 & 80.88 & 0.30 & 49.63 \\ EC-LUE & 23.56 & 20.79 & 18.96 & 13.24 & 0.02 & 84.53 & 0.06 & 65.02 \\ MODIS & 18.11 & 12.08 & 13.78 & 13.38 & 11.27 & 23.62 & 0.55 & 30.84 \\ VPM & 6.23 & 19.21 & 7.11 & 11.14 & 0.93 & 35.43 & 0.23 & 56.19 \\ VPRM & 12.77 & 7.99 & 11.68 & 10.56 & 0.85 & 37.95 & 0.23 & 57.63 \\ \hline \end{tabular} a: Annual photosynthetically active radiation; b: annual mean air temperature; c: water availability, indicated by annual evapotranspiration (ET) in the CASA, CFix, CFlux, and EC-LUE models, annual mean inverted Vapor Pressure Deficit (VPD) in the MODIS model, and annual mean Land Surface Water Index (LSWI) in the VPM and VPRM models; d: vegetation conditions, shown by the annual mean Normalized Difference Vegetation Index (NDVI) in the CASA, CFix, and EC-LUE models, annual mean Fraction of absorbed PAR by vegetation canopy (FPAR) in the CFlux and MODIS models, and annual mean Enhance Vegetation Index (EVI) in the VPM and VPRM models; e: significant negative correlation between annual GPP estimates and environmental variables or vegetation indices (_p_ \(<\) 0.05); f: significant positive correlation between annual GPP estimates and environmental variables or vegetation indices (_p_ \(<\) 0.05).  
\end{table} 
Table 3: Percentage of vegetated areas with significant correlations between GPP estimates and environmental variables and vegetation conditions. Figure 6: Spatial patterns on the change trends of environmental variables and vegetation indices: (**a**) Photosynthetically Active Radiation (PAR); (**b**) air temperature (Ta); (**c**) inverted Vapor Pressure Deficit (VPD); (**d**) evapotranspiration (ET); (**e**) Land Surface Water Index (LSWI); (**f**) Normalized Difference Vegetation Index (NDVI); (**g**) Enhance Vegetation Index (EVI); and (**h**) the Fraction of PAR absorbed by the vegetation canopy (FPAR). DES is no vegetation area.
# Degradation Assessment of In-Service Transformer Oil Based on Electrical and Chemical Properties
## 4 Discussion
### Assessment of the DGA Results
#### 4.2.2 \(\rm O_{2}/N_{2}\) Ratio  
The oxygen to nitrogen (\(\rm O_{2}/N_{2}\)) ratio, which was close to 0.5, is used to represent air composition in the oil. With the oxidation of oil and/or degradation of cellulose insulation, the ratio may decrease in the case of an in-service transformer [42]. In the current study, it was observed that the ingression of these gases with air in the transformers was high. The oxygen to nitrogen ratio, which remained below 0.5 across all samples, showed an overall progressive decrease over the time (Table 5). As mentioned earlier, the observed declining trend in the ratio is attributed to the ongoing consumption of oxygen and correlates with the aging of cellulose insulation, a critical factor in transformer longevity.  
The DDF, breakdown voltage, and DGA results indicated that no significant degradation of the insulating oil occurred during the service years; however, the moisture content and DGA analysis revealed the deterioration of the cellulose insulation with each service year.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### Multi-Leadership Structure  
The multi-leader model enhances system throughput through parallel processing, but it introduces the challenge of leader synchronization. Additionally, the presence of multiple leaders increases the risk of multiple nodes being attacked and becoming ineffective.  
To address the synchronization and security challenges of multi-leader consensus models, a decentralized scheduling-based leader synchronization mechanism can be introduced in the future. By leveraging Byzantine fault-tolerant clock synchronization or distributed lock techniques, multiple leaders can efficiently coordinate their actions. Additionally, integrating a dynamic primary node election mechanism that adjusts the number of leaders based on network load and reputation assessment can help reduce synchronization overhead and mitigate security risks. Furthermore, the introduction of a multi-layered primary node architecture can optimize task distribution, enhance system throughput, and alleviate single-point overloads, thereby further strengthening the robustness of consortium blockchains.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 5 Conclusions  
In this paper, we studied the feasibility of using the GAN model to address the channel estimation problem in the scenarios where the channel varies dramatically toward future 6G communication systems. Specifically, the entire estimation process contains two stages, which are the model training stage and the model usage stage. In the model training stage, a pre-designed GAN model is used to learn the channel distribution. In the model usage stage, based on the learned correlation between elements in channel matrix, the data channel coefficients are recovered via matching the generated pilot channel coefficients by the trained GAN model and real pilot channel coefficients by the LS method. Hence, the proposed GAN-based scheme can support the channel estimation for any pilot configuration. Simulation results show that our proposed method can improve the estimation accuracy with a large reduction in the pilots overhead in both medium- and high-speed mobile scenarios. Meanwhile, the corresponding channel matrix is compressed to a low dimension, which is also useful for dramatically reducing the feedback overhead in the CSI feedback service. For future works, the pilot channels' denoised scheme can be considered to combine with the proposed method to further improve the channel estimation performance, especially in the low SNR cases.  
Figure 9: NMSE evaluation of our proposed GAN-based channel estimation scheme.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the research. [PERSON] and [PERSON] implemented the simulations. [PERSON] and [PERSON] drafted the manuscript. [PERSON] helped organize the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Key R&D Program of China (Grant No.2020 YFB1807100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
In this paper, we propose a progressive fruit and vegetable classification technique for supermarket self-checkouts. Fruit and vegetable images are initially grouped based on the average weight of each fruit or vegetable class so as to give a coarse classification. These coarse classes are further processed with AdaBoost-based optimisation of Convolutional Neural Networks (CNNs) for fine classification. The rest of the article is organised as follows. An overview of the state-of-the-art techniques of fruit and vegetables classification along with their applications is presented in Section 2. A prototype design to emulate the placement of fruit or vegetables at a self-checkout for billing with typical supermarket ambient lighting conditions is discussed in Section 3.1. A process of weight and image data acquisition and their organisation for further processing is explained in Section 3.2. A progressive \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. Year** & **Fruit/Vegetable** & **Features** & **ML Technique** & **Accuracy (\%)** \\ \hline
[9] & 2016 & Rice Crop & Morphology, height, length & KNN & 87.9 \\
[10] & 2017 & FoodCast dataset & Colour mean and variance & Naive Bayes & 73.0 \\
[11] & 2017 & Radish & Spectral features & Discriminant Analysis & 74.4 \\
[12] & 2017 & Wheat & Texture approximation & Discriminant Analysis & 77.0 \\
[13] & 2017 & Grapes & Correlation similarity matrix & K-Means & 86.8 \\
[14] & 2018 & Cucumber & Blob centroid & Pixel SVM & 85.6 \\
[15] & 2019 & Date fruit & Deep texture feature & AlexNet & 92.3 \\
[16] & 2020 & Fruit and vegetables & HSV colour transforms & SVM & 92.7 \\
[17] & 2020 & Lettuce & Deep CNN features & DarkNet & 93.0 \\ This paper & Fruit and vegetables & Sample weight & Jenks Natural Breaks & 93.9 \\  & & Deep CNN features & AdaBoost Optimised CNN & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent state-of-the-art features and Machine Learning (ML) techniques for fruit and vegetable classification. coarse to fine classification-based methodology for fruit and vegetables classification is discussed in Section 4. The implementation of the proposed technique and the experimental results are presented in Section 5. A detailed discussion on the results obtained and future applications of the proposed approach for real-world supermarket self-checkouts can be found in Section 6.