esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 2 Related Work
### Enhanced FCM (EnFCM) and Fast Generalized FCM (FGFCM)  
EnFCM and FGFCM are both based on image transformation. They have the same objective function,  
\[J_{\text{EnFCM\&FGFCM}}(\mathbf{U},\mathbf{V})=\sum\limits_{i=1}^{N_{i}}\sum\limits_{ l=1}^{k}\chi_{i}u_{il}^{\lambda}\|\mathbf{X}_{i}^{\star}-\mathbf{V}_{l}\|^{2} \tag{3}\]  
where \(\mathbf{X}^{\star}=\{\mathbf{X}_{i}^{\star}\), \(i=1,\ldots,n\}\) is the data set after image transformation. Assuming \(\mathbf{X}^{\star}\) has N\({}_{i}\) grayscale level, \(\mathbf{X}^{\star}\) also can be rewritten as \(\mathbf{X}^{\star}=\{\mathbf{X}_{i}^{\star}\), \(i=1,\ldots,N_{i}\}\). \(N_{i}\) is the number of gray levels in the new image. \(\mathbf{U}=\{u_{il}\), \(t=1,\ldots,N_{i}\), \(l=1,\ldots,k\}\) is the fuzzy membership about the gray level, and \(u_{il}\) needs to be satisfied in the condition \(\sum\limits_{l=1}^{k}u_{il}=1,t=1,\ldots,N_{i}\).  
In EnFCM, \(\mathbf{X}^{\star}\) is a linear weighted image; it is modeled as,  
\[\mathbf{X}_{i}^{\star}=\frac{1}{1+\alpha}\Bigg{(}\mathbf{X}_{i}+\frac{\alpha}{N_{R}} \sum\limits_{i^{\prime}\in\delta_{i}}\mathbf{X}_{i^{\prime}}\Bigg{)} \tag{4}\]In FGFCM, \(\mathbf{X^{*}}\) is a non-linear weighted image. It is modeled as,  
\[\mathbf{X}_{i}^{*}=\frac{\sum\limits_{i^{\prime}\in\delta_{i}}S_{ii^{\prime}}X_{i^{ \prime}}}{\sum\limits_{i^{\prime}\in\delta_{i}}S_{ii^{\prime}}} \tag{5}\]  
where \(S_{ii^{\prime}}\) is the relationship between central pixel and neighborhood pixels, and it is modeled as,  
\[S_{ii^{\prime}}=\left\{\begin{array}{ll}S_{ii^{\prime}}^{S}\times S_{ii^{ \prime}}^{G}&i\
eq i^{\prime}\\ 0&i=i^{\prime}\end{array}\right. \tag{6}\]  
where \(S_{ii^{\prime}}^{S}\) is the local spatial relationship, and \(S_{ii^{\prime}}^{G}\) is the local spectral relationship.  
\[S_{ii^{\prime}}^{S}=\exp\left(\frac{-\text{max}(|x_{i}-x_{i^{\prime}}|_{\cdot },|y_{i}-y_{i^{\prime}}|)}{\phi^{S}}\right) \tag{7}\]  
\[S_{ii^{\prime}}^{G}=\exp\left(\frac{-\|\mathbf{X}_{i}-\mathbf{X}_{i^{\prime}}\|^{2}}{ \phi^{G}\frac{1}{N_{ii}}\sum\limits_{i^{\prime}\in\delta_{i}}\|\mathbf{X}_{i}-\bm {X}_{i^{\prime}}\|^{2}}\right) \tag{8}\]  
where \((\mathbf{x}_{i},y_{i})\) is the two-dimensional coordinate of pixel \(i\) on the image. \(\varphi^{S}\) and \(\varphi^{G}\) are scale factors of \(S_{ii^{\prime}}^{S}\) and \(S_{ii^{\prime}}^{G}\), respectively.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 3 Material and Methods
### Evaluation Measures  
Performance evaluation of the developed model is carried out using various standard quantitative measures such as accuracy, sensitivity, specificity, F-score, and AUC. Moreover, the ROC of the developed model is plotted. For multi-class problems, a one-vs-all strategy is employed to compute the following evaluation measures:  
**Accuracy**: It is a measure used to evaluate the model effectiveness for identification of correct classifications:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{34}\]  
where \(TP\) and \(TN\) are true positives and negatives. respectively, whereas, \(FP\) and \(FN\) are false positives and negatives, respectively.  
**Sensitivity**: It is used to evaluate the ability of a classifier to identify correct positive samples:  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{35}\]  
**Specificity**: It is used to check the model performance on the identification of negative samples in the dataset:  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{36}\]  
**The F-Score**: It is a weighted harmonic mean of precision and recall having a range between 0 and 1:  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
\[\text{Recall}=\frac{TP}{TP+FN}\]  
\[\text{F-Score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision} \times\text{Recall}}\]  
**ROC Curve and Area Under the Curve (AUC)**: An ROC curve is one of the effective parameters to indicate model performance. It is the ratio between the false and true positive rates. The AUC value is associated with the ROC and is used to assess the overall classification performance [33].
###### Abstract  
The evaluation of aroma-active profiles in dry-cured hams is crucial for determining quality, flavor, consumer acceptance, and economic value. This study characterized the volatile compounds in five varieties of dry-cured hams using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and an electronic nose (E-Nose). In total, 78 volatile compounds were identified across five varieties of dry-cured hams. A total of 29 compounds were recognized as aroma-active compounds. Odor description, intensity, and hedonic assessment were employed to evaluate these compounds. Black Hoof Cured Ham and Special-grade Xuan-Zi Ham contained higher levels of favorable compounds such as nonanal, 5-butyldihydro-2(3H)-furanone, and 2,6-dimethylpyrazine, contributing to sweet and popcorn-like notes. In contrast, Fei-Zhong-Wang Ham and Liang-Tou-Wu Ham exhibited higher proportions of off-odor compounds with lower hedonic scores. A principal component analysis clearly separated the five hams based on their aroma-active profiles, and a correlation analysis between E-Nose sensor responses and GC-MS-O data demonstrated a strong discriminatory ability for specific samples. These findings offer valuable insights into the chemical and sensory differentiation of dry-cured hams and provide a scientific basis for quality control, product development, and future improvements in E-Nose sensor design and intelligent aroma assessment.  
a _Article_  
Characterization of Aroma-Active Compounds in Five Dry-Cured Hams Based on Electronic Nose and GC-MS-Olfactometry Combined with Odor Description, Intensity, and Hedonic Assessment.
## 4 Multi-Source Data Fusion Technology and Algorithm
### Application of Multi-Source Data Fusion Algorithm in Real Estate Management
These threats are dangerous as they can lead to financial losses, legal repercussions, and a loss of public trust in smart city initiatives. To mitigate these risks, it is essential to adopt robust encryption techniques and stringent access control strategies to safeguard data integrity and confidentiality [20]. Compliance with data protection laws and regulations, such as the General Data Protection Regulation (GDPR), is also crucial to ensure that data handling practices are transparent and secure. Furthermore, to address the dynamic nature and real-time requirements of smart city data, we propose the introduction of advanced spatio-temporal data analysis methods. These methods require inputs such as geospatial coordinates, temporal data stamps, and attribute information related to urban features, which enable the algorithms to analyze and model the spatial relationships and temporal dynamics within the city's data. By integrating these inputs, the algorithms can provide insights into urban patterns and trends, supporting decision-making in areas like urban planning, traffic management, and environmental monitoring. Stream data-processing technology can be leveraged for rapid analysis, ensuring that decision-making is informed by the most current data available. For the scalability and adaptability of our algorithms, we advocate for the design of a modular fusion framework [26]. This framework, when integrated with machine-learning technology, can automatically optimize the data fusion process based on the characteristics of the data and specific business needs [28]. This approach not only enhances the efficiency of data processing but also strengthens the resilience of smart city systems against evolving security threats. These technical means will effectively improve data fusion performance and provide strong support for real estate management and urban optimization of smart cities, ensuring that the spatial statistical analysis algorithms are fed with the necessary inputs to deliver actionable insights.
# Single Image Reflection Removal Based on Residual Attention Mechanism
## 4 Experiments
### Quantitative Evaluation  
To evaluate the performance of our method, we compared the model with RmNet [25], Zhang [1], ERRNet [28], and IBLCN [26] in terms of both quality and quantity. As reference standards, we also used the comparison of the hybrid image and the ground truth. We used the code and pre-trained model supplied by the authors in their team for a fair comparison and set the parameters as specified in their original publication. Meanwhile, we used PSNR (Peak Signal to Noise Ratio) [37], SSIM, and LMSE [38] as evaluation metrics. Only PSNR has units (dB) among these three evaluation metrics. PSNR is the most widely used image quality evaluation index, and it is based on the difference in error between corresponding pixels and error-sensitive image quality evaluation. The higher the value, the better the predicted image's visual perception. Real natural images are highly structured, i.e., there are strong correlations between the pixels of the image, and these correlations carry information about the structure of the object. SSIM senses the distortion information of an image by detecting whether the structural information has changed, and measures the similarity of two images in terms of brightness, contrast, and structure. Regarding SSIM, the higher the SSIM value is, the better the overall image quality can be predicted too, and the overall image performance is closer to the ground truth. Meanwhile, LMSE represents the minimum mean square error of the output image and the real image, and a smaller value of LMSE implies a better result.  
The findings of the competing approaches on the four real datasets are summarized in Table 1. The first row of the dataset is from the Nature test dataset of Li [26], and the remaining three rows are from the SIR\({}^{2}\) constructed by [39]; SIR\({}^{2}\) includes three sub-datasets: Object, Postcard and Wild. On the Nature and Wild datasets, our technique is clearly the best, second on the combined dataset of objects, and third on Postcard. In addition, to make the comparison of quantitative data acquired using different methods more intuitive, we visualized the data of PSNR and SSIM, as shown in Figure 6. This has validated that our method can achieve superior performance in various real-world scenarios.  
Figure 5: The method of SCAU-RID dataset collection and a pair of sample images.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
###### Abstract  
Low-power and secure crypto-devices are in crucial demand for the current emerging technology of the Internet of Things (IoT). In nanometer CMOS technology, the static and dynamic power consumptions are in a very critical challenge. Therefore, the FinFETs is an alternative technology due to its superior attributes of non-leakage power, intra-die variability, low-voltage operation, and lower retention voltage of SRAMs. In this study, our previous work on CMOS two-phase docking adiabatic physical unclonable function (TPCA-PUF) is evaluated in a FinFET device with a 4-bits PUF circuit complexity. The TPCA-PUF-based shorted-gate (SG) and independent-gate (IG) modes of FinFETs are investigated under various ambient temperatures, process variations, and \(\pm\)20% of supply voltage variations. To validate the proposed TPCA-PUF circuit, the QUALPFU-based Fin-FETs are compared in terms of cyclical energy dissipation, the security metrics of the uniqueness, the reliability, and the bit-error-rate (BER). The proposed TPCA-PUF is simulated using 45 nm process technology with a supply voltage of 1 V. The uniqueness, reliability, and the BER of the proposed TPCA-PUF are 50.13%, 99.57%, and 0.43%, respectively. In addition, it requires a start-up power of 18.32 nW and consumes energy of 2.3 fJ/bit/cycle at the reference temperature of 27 \({}^{\circ}\)C.  
physical unclonable function (PUF); secure keys; FinFETs; low power; adiabatic; reliable and unique identity; IoT security; SRAM PUF
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Public Blockchains
#### 3.1.2 PoS  
Proof of Stake (PoS) is a distributed consensus mechanism based on the economic interests of token holders [30]. It replaces the traditional proof of work (PoW) model of computational power competition with a probabilistic mechanism for allocating block creation rights, aiming to address the energy efficiency bottleneck in blockchain systems. In the PoS framework, validating nodes participate in the consensus process by taking a certain amount of native tokens. The system dynamically allocates block creation probabilities based on parameters such as the staking ratio and token holding duration, while using Byzantine fault tolerance (BFT) algorithms to validate transaction validity. Compared to the hash power consumption of PoW, PoS ensures network security through economic incentives, as attackers would need to control the majority of tokens in the network to launch an effective attack. However, PoS can lead to some degree of centralization, as individuals or entities with more tokens have a higher chance of becoming validators. To address this, Current PoS variants, such as Delegated Proof of Stake (DPoS), further enhance consensus efficiency and decentralization by introducing mechanisms like validator elections and random seed generation. The development of blockchain consensus mechanisms has witnessed a paradigm shift toward the Proof of Stake (PoS) protocol, driven by the need to overcome the inherent limitations of traditional Proof of Work (PoW) systems. PoW, exemplified by Bitcoin's mining mechanism, has gained widespread attention due to its high energy consumption and environmental impact caused by its computation-intensive operations [31]. In response, PoS has emerged as a sustainable alternative, significantly reducing energy demands by replacing competitive computational challenges with validators' stakes [32]. Ethereum's transition to PoS during its \"Merge\" event is a prime example of this shift, marking a pivotal moment that made Ethereum the largest cryptocurrency by market capitalization to adopt this consensus model [33; 34]. Academic research on PoS has systematically addressed its various challenges and opportunities. Theoretical frameworks have explored reward distribution mechanisms to incentivize honest participation [32], while security analyzes have identified vulnerabilities such as remote attacks and validator collusion [35]. Game theory models have further analyzed the strategic interactions between validators, providing insights into equilibrium states under adversarial conditions [36]. To facilitate systematic progress, [PERSON] et al. [37] categorized PoS improvement proposals based on objectives like scalability and attack resistance, offering guidance for algorithm selection in different applications. Recent innovations have focused on optimizing validator economics
## 6 Result and Discussions
### Evaluation Metrics
#### 6.3.2 BLEU Score  
The machine translation performance is indicated by the BLEU (Bilingual Evaluation Understudy) score. The high BLEU score indicates a better match between predicted and reference sequences. For this calculation, we break the reference and predicted sequences into individual words, extract the n-grams, calculate the overlap between n-grams, calculate the precision of n-grams, and combine the precision of different n-grams using weighted averages. Values of 0.7 and 0.8 are shown in Table 6.The BLEU scores obtained indicate \"Good\" and \"Very Good\" levels of similarity.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 4 Subsystems Analysis  
As reported in Reference [25], the physical architecture of a satellite can be defined as the foundation on which all its functions and performance is built upon. So it comprises the satellite breakdown in physical subsystems and components, their location, and their structural and electrical interfaces. The majority of CubeSat architectures is based on modular stack of printed circuit boards (PCBs), and each of the subsystems is represented by one (or more) PCBs. Beside the classical approach, innovative architectural concepts have been proposed through the years: cellular, panel, plug-and-play, and lean electrical interfaces.  
The following sections are intended to define all the relevant subsystems, presenting the available COTS options, and evaluating their performances and applicability for the mission concept. First, the payload (and the scientific return) is analyzed. Then, one by one, the subsystems composing the spacecraft bus are evaluated, starting from the propulsion system, that due to its large mass and volume, will be one of the driving subsystems.
## 4 Numerical Results
### Time Complexity  
The effectiveness of an optimization method is usually measured by the computational complexity of its solution, which unveils the asymptotic processing time growth as the number of dimensions increases. However, the theoretical analysis of this complexity may pose stringent mathematical obstacles, which arise, for instance, due to the theoretical properties of the method, to the way it is implemented, to the choice of the programming language and to the parameter values [43; 44]. This is particularly true in the case of the optimization problem (4): although it is convex, it has no closed form solution, and in our setup it was not solved using an application-specific code developed under a specific optimization algorithm. Instead, it was solved using the package CVX, which relies on the capabilities of the embedded solvers. Nevertheless, in this final subsection we present the results of an empirical complexity analysis by means of running time tests. The results of this analysis cannot be associated with practical processing times in absolute terms, but can give a rough idea of the relative processing time growths of the proposed lifetime improvement solution.  
For computing the processing times, we have used the tic and toc MATLAB functions, which provide reliable elapsed time measurements. In order to reduce the effects of randomness, the measurements were averaged over 500 solutions of a single instance of the optimization problem (4), for the number of sensor nodes ranging from \(N=50\) to \(N=1000\) in steps of 50 and weighting factors \((w_{1},w_{2})=(1,0)\), \((w_{1},w_{2})=(0,1)\) and \((w_{1},w_{2})=(1,1)\). The measurements were carried out in a Dell computer (Dell, Hortolandia, Brazil) with 2.2-GHz Intel Core i7-3632 QM processors running the Windows 7 Professional operating system with Service Pack 1 and the 64-bit MATLAB R2010b (7.11.0.584) version.  
For an optimization algorithm that exhibits polynomial time complexity in the number of sensor nodes, i.e., \(\mathcal{O}(N^{a})\), \(a\) is the power constant to be determined. We have used a power curve fitting function to find \(a\). The results are summarized in Table 2, from where one can conclude that the largest time growth is \(\mathcal{O}(N^{1.44})\), which shows that our optimization method has a time complexity with a low power constant, meaning that it is tractable enough to be implemented in practice.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Linear Equalizers
#### 4.2.1 MMSE Equalization Scheme  
In order to maximize the equalization Signal-to-Noise Ratio (SNR), the MMSE equalizer can be given as  
\[\mathbf{M}_{MMSE}=(\mathbf{H}^{H}\mathbf{H}+\sigma_{n}^{2}\mathbf{I})^{-1} \mathbf{H}^{H}, \tag{11}\]  
where \(\mathbf{H}^{H}\) describes the Hermitian transpose matrix of the channel and \(\sigma_{n}^{2}\) is the statistical information of noise. Considering the output of the Channel Estimation block in Figure 3 and the received RG in (2), we can obtain the following relationship  
\[\hat{\mathbf{Y}} =\mathbf{M}_{MMSE}\mathbf{Y} \tag{12}\] \[=(\mathbf{H}^{H}\mathbf{H}+\sigma_{N}^{2}\mathbf{I})^{-1}\mathbf{ H}^{H}\mathbf{Y},\] \[=\mathbf{X}+\hat{\mathbf{N}}_{MMSE}\]  
with \(\hat{\mathbf{N}}_{MMSE}=(\mathbf{\hat{H}}^{H}\mathbf{\hat{H}}+\sigma_{N}^{2} \mathbf{I})^{-1}\mathbf{\hat{H}}^{H}\mathbf{N}\). Algorithm 2 explains the MMSE equalizer, considering SISO and MIMO schemes.  
```
Input :Y, \(\mathbf{\hat{H}}\), \(\mathbf{\hat{N}}_{0}\) Output :\(\hat{\mathbf{Y}}\) \(N_{sub}=\mathbf{size}(\mathbf{Y},1)\), \(N_{sym}=\mathbf{size}(\mathbf{Y},2)\), \(N_{Rx}=\mathbf{size}(\mathbf{Y},3)\) & \(N_{Tx}=\mathbf{size}(\mathbf{Y},4)\); if\(N_{Tx}=1\) && \(N_{Rx}==1\)then \(\hat{\mathbf{Y}}=(\mathbf{\hat{H}}^{H}\mathbf{\hat{H}}+\hat{\mathbf{N}}_{0})^{ -1}\mathbf{\hat{H}}^{H}\mathbf{Y}\); else for\(c\gets 1:N_{sub}\)do for\(s\gets 1:N_{sym}\)do \(\hat{\mathbf{Y}}_{(c,s)}^{(:)}=\frac{(\mathbf{H}_{(c,s)}^{H(:)}\mathbf{\hat{H} }_{(c,s)}^{(:)}+\hat{\mathbf{N}}_{0}\mathbf{I})^{-1}}{\mathbf{I}}\hat{\mathbf{ H}}_{(c,s)}^{H(:)}\mathbf{Y}_{(c,s)}^{(:)}\);  end for  end for  end for
```  
**Algorithm 2**MMSE Equalizer Implementation for D&F Relay Node
# Application Ranges of EPB Shield TBM in Weathered Granite Soil: A Laboratory Scale Study
## 2 Materials and Methods
### Weathered Granite Soils and Conditioning Agents Used in Experiments  
#### 2.2.1 Weathered Granite Soils  
Five weathered granite soil samples were prepared, ranging from relatively fine to coarse; the particle-size gradation curves for these five samples are shown in Figure 2 (solid lines); properties of these five samples are shown in Table 1. As shown in Table 1, the percentage of fine particles passing through #200 sieve changed from 17.1% (for Soil 1) to nil (for Soil 5). After the experiments, sieve analysis was conducted again for the oven-dried samples to obtain the particle-size gradation curves in order to find the particle-crushing effect after soil conditioning; these are represented by the dotted lines in Figure 2. The particle-crushing effect is predominantly dependent upon the particle size of the soils. Figure 2 shows that the percentage of fine particles passing through #200 sieve gradually increases with the increase of soil particle sizes (except for Soil 5, which is classified as gravel): increases of 3.7% for Soil 1, 4% for Soil 2, 4.7% for Soil 3, and as much as 5% for Soil 4. As can be seen from regions I, II, and III of Figure 1, fine particle contents in the gradation curve of soils are the most crucial factor when determining the conditioning agent in the EPB Shield TBM method. For this reason, the particle-crushing effect of the weathered granite soil must be taken into consideration when assessing the conditioning agent. This will be further discussed in Section 3.
## 4 Results and Discussion
### Comparison of BET and BTT Agent Performances  
#### 4.2.1 Time and Iteration Efficiencies  
The average readings of time and iteration taken by the multi-agent system to reach consensus in relation to the rendezvous in ten time runs are shown in Table 5. BET was proven to lead BTT by 57.153 s, with 177 iterations. The efficiency of BET and BTT in reaching consensus depended on the effectiveness of the broadcast consensus controller in working with the distributed controller with either event sampling or conventional time sampling.