esa-sceva/satcom-chunk-collection huggingface dataset:
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
## 3 System Model
### Node Cross Reconfiguration Sharding Scheme (NCRS)
#### 3.2.1 Limited Number of Shards  
The number of shards is the premise to ensure the effectiveness of sharding, and an improper number of shards will lead to the failure to achieve the safety and effectiveness of each sharding no matter how the nodes are divided. It is assumed that there are N nodes in the blockchain, where the proportion of malicious nodes is \(\delta(\delta\leq 1/3)\), the number of  
Figure 3: Node elimination consensus process.  
shards is k and Ni is the number of the ith shard nodes. The number of shards must meet Lemma 1.  
**Lemma 1.**_The number of shards must meet \(k\leq min\{N(1-3\delta),\ N/4\}\) to ensure the safety of all shards._  
**Proof:** The minimum consensus size in PBFT requires the existence of four nodes, so the maximum number of shards cannot exceed \(N/4\). After sharding, each shard runs separately PBFT internal consensus, so the internal shard can tolerate the maximum number of malicious nodes is \(\lfloor(N_{i}-1)/3\rfloor\), and the sum of the number of malicious nodes that can be tolerated by each shard must not be less than the number of malicious nodes that can be tolerated before the sharding, so there will be \(\sum_{i=1}^{k}\lfloor(N_{i}-1)/3\rfloor\geq\lfloor N\delta\rfloor\), through the zooming method can get \(\sum_{i=1}^{k}\left(N_{i}-1\right)/3\geq N\delta\), Substitute \(\sum_{i=1}^{k}N_{i}=N\) into \(\sum_{i=1}^{k}\left(N_{i}-1\right)/3\geq\)\(N\delta\) and you obtain \(k\leq N(1-3\delta)\). Therefore, to ensure the reliability of sharding, the number of shards must meet \(k\leq min\{N(1-3\delta),N/4\}\). \(\square\)  
Although the reliable sharding scheme must satisfy Lemma 1, the reliable sharding scheme may not be obtained by satisfying Lemma 1. Lemma 1 is only a necessary and insufficient condition for the reliability of the sharding scheme.
## 2 Materials and Methods  
### Study Area  
The study area included the central and western parts of the Black Sea. The discussed data were obtained during the research period from 2017 to 2021. Data provided by the MODIS (Aqua) satellite and a network of ground-based photometers AERONET were analyzed. MODIS (or Medium-Resolution Spectroradiometer) is the key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is calculated in such a way that in the morning, it passes from north to south through the equator, and Aqua passes through the equator from south to north in the afternoon. MODIS Terra and MODIS Aqua scan the entire surface of the Earth every 2 days, collecting data in 36 spectral bands. These data improve the understanding of global dynamics and processes occurring on land, in the oceans, and in the lower atmosphere. MODIS plays a vital role in the development of proven global interactive models of the Earth system capable of predicting global climate change trends with sufficient accuracy. Since 2005, MODIS data have regularly been used in the Ocean Color algorithms [25].
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 4 The a Priori SRP Models for BeiDou-3 I2-S Satellite
### Empirical SRP Model  
In addition, the purely empirical parameter fitting approach was used to develop an a priori SRP model for BeiDou-3 I2-S satellite based on the reconstructed SRP acceleration from ABW solution.  
The SRP accelerations for BeiDou-3 I2-S satellite were reconstructed by orbit integration with the determined orbital elements and SRP parameters from the ABW solution, and were decomposed in DYB frame. Figure 4 illustrates the amplitude spectra of the reconstructed SRP acceleration for \(\beta\) angle about \(10^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\) in DYB frame. The strength of spectra line for 0-cpr signal in \(D\) reaches to about \(-100\) nm/s\({}^{2}\), where it is about \(1\) nm/s\({}^{2}\) in \(B\) direction. For other periodic signals, the sizeable spectral lines exist for even orders in \(D\). However, for \(B\) axis the sizeable spectral lines exist for odd orders in \(B\). Similar results were obtained for GPS and GLONASS [10]. A significant 1-cpr signal in \(B\) with amplitudes of about \(2\) nm/s\({}^{2}\) can be identified. The accelerations along Y are not presented here as the order of that is as small as \(0.1\) nm/s\({}^{2}\).  
Based on the above spectrum analysis, the periodic signals with amplitudes of over \(0.6\) nm/s\({}^{2}\) are selected to present the SRP perturbation acting on BeiDou-3 I2-S satellite. These are 2-cpr as well as 4-cpr signal in \(D\), and 1-cpr, as well as 3-cpr signal in \(B\). Hence, the a priori model can be expressed as,  
\[\begin{split} a_{D,ap}&=D_{0,ap}+D_{2c}\cos(2\epsilon )+D_{4c}\cos(4\epsilon)\\ a_{B,ap}&=B_{0,ap}+B_{1c}\cos(\epsilon)+B_{3c}\cos(3 \epsilon)\end{split} \tag{14}\]  
where \(D_{0,ap}\), \(D_{2c}\), \(D_{4c}\), \(B_{0,ap}\), \(B_{1c}\), and \(B_{3c}\) are the model coefficients. Here, and after, we called this empirical reconstructed a priori SRP model as FFT model.  
Afterwards, the coefficients in Equation (14) were estimated by fitting with the daily reconstructed accelerations in each axes. Clear \(\beta\)-angle-dependent variation for \(D_{0,ap}\) and \(D_{2c}\) could be observed, as shown in Figure 5. The estimations of \(D_{0,ap}\) and \(D_{2c}\) show parabola variations against \(\beta\) angle with \(0^{\circ}\) vertices. These indicate that the model needs to be improved further with accounting for such  
Figure 4: Power spectra of the reconstructed SRP accelerations of ABW solutions in (**a**) \(D\) and (**b**) \(B\) directions, respectively. (The dashed line in the above figures refers to \(1\) nm/s\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Para.** & **Value** \\ \hline \(a_{Cs}^{a,s}\) & 11.9 \\ \(a_{S}^{a,s}\) & 2.1 \\ \hline \end{tabular}
\end{table}
Table 3: Values for cuboid box model parameters (unit: nm/s\({}^{2}\)).  
variations. To model them, we fitted these parameters with the 2-order polynomial against the \(\beta\) angle for \(D_{0,ap}\) and \(D_{2c}\). The derived coefficients for the model are listed in Table 4.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 4 Localization Algorithm
### ML-Based Localization Algorithm  
After acquiring the measurement information, we can obtain the ranging and phase information of all agents. Then we use the observed phase to estimate the AOA of the agent relative to the anchor and calculate the position by using the propagation delay.  
For the agent \(i\), we estimate the propagation delay \(\hat{\tau}_{i}\) and the arrival phase \(\boldsymbol{\psi}_{i}\) from the signals. We calculate the AOA by solving the optimization problem as follows  
\[\{\hat{\theta}_{i},\hat{\phi}_{r}\}=\underset{\theta_{i},\hat{\phi}_{r}}{\arg \min}\sum_{j=1}^{M}[\phi_{ji}-\phi_{r}+\frac{2\pi f_{c}d_{r}}{c}\cos(\theta_{i }-\theta_{j})]^{2} \tag{10}\]  
where \(\hat{\theta}_{i}\) and \(\hat{\phi}_{r}\) are the AOA of agent \(i\) and reference phase estimations, respectively.  
Finally, we use the geometric relationship to calculate the coordinates of agent \(i\) by \((\hat{x}_{i},\hat{y}_{i})=(c\hat{\tau}_{i}\cos\theta_{i},c\hat{\tau}_{i}\sin \hat{\theta}_{i})\). In the 3D scene, we introduce the estimation of the elevation angle \(\beta_{i}\)[43]. Accordingly, the expression of agent coordinates becomes \((\hat{x}_{i},\hat{y}_{i},\hat{z}_{i})=(c\hat{\tau}_{i}\cos\hat{\theta}_{i}\cos \hat{\beta}_{i},c\hat{\eta}_{i}\sin\hat{\theta}_{i}\cos\hat{\beta}_{i},c\hat{ \eta}_{i}\sin\hat{\beta}_{i})\).
## 4 Discussion
The simulation was performed for the presence of two defects with the location \(E_{d1}\) = 1.51 eV and \(E_{d2}\) = 1.55 eV, thermal diffusivity of 0.05 cm\({}^{2}\)/s, thermal conductivity of 0.07 W/(cm\(\cdot\)K) for the sample, and conductivity of the layer, ten times smaller than the bulk: \(A_{d1}\) = 15 cm\({}^{-1}\), \(A_{d2}\) = 7 cm\({}^{-1}\), \(\beta_{1}\) = 0.2 cm\({}^{-1}\), \(\beta_{2}\) = 0.06 cm\({}^{-1}\). The thickness of the defective layer was 0.01 mm. The simulations qualitatively confirm the presence of defects on the surface, but they are not entirely consistent with the experiment data. Figure 9: Amplitude (**a**) and phase (**b**) spectra of the ground Cd\({}_{0.95}\)Be\({}_{0.05}\)Te mixed crystal for 126 Hz modulation frequency. Simulated spectra are presented by black lines, experimental amplitude by red lines, and experimental phase by blue lines. Figure 10 presents amplitude (a) and phase (b) spectra of etched Cd\({}_{0.95}\)Be\({}_{0.05}\)Te mixed crystal. Experimental data are shown together with simulations. The simulation was performed for the presence of two defects with a location different than the previous case: \(E_{d1}\) = 1.6 eV and \(E_{d2}\) = 1.62 eV, thermal diffusivity of 0.05 cm\({}^{2}\)/s, thermal conductivity of 0.07 W/(cm\(\cdot\)K) for the sample, and conductivity of the layer, 12 times smaller than the bulk: \(A_{d1}\) = 110 cm\({}^{-1}\), \(A_{d2}\) = 80 cm\({}^{-1}\), \(\beta_{1}\) = 0.045 cm\({}^{-1}\), \(\beta_{2}\) = 0.06 cm\({}^{-1}\). The thickness of the defective layer was 0.001 mm, ten times smaller than in the previous case. In this case, lower compatibility in experiment and simulation was achieved. The applied theory assumes the generation of a photothermal signal only due to the thermoelastic effect. As shown before, the fall of the signal above the gap may be due to free carrier contribution. Their participation should also be considered when generating the signal in the defective subsurface layer. This assumption can be supported by the most recent results obtained by [PERSON] et al. [31]. In their article, the dependence of the photoacoustic signal on the frequency of the excitation beam was used to analyze the influence of thin transparent foil on the thermal and elastic properties of a two-layer sample consisting of a silicon substrate and a thin TiO\({}_{2}\) foil. A thin layer can significantly affect the substrate's thermal state and increase the sample's bending. This is related to a slight change in the number of photogenerated carriers in the transparent layer, which strongly influences the temperature differences between the illuminated and non-illuminated sides of the sample [31]. The influence of the carriers in the defective layer probably extinguished the signal for the first sample etching procedure.
## 6 Conclusions  
The applicability of several existing methods to siltstone and granite is investigated, based on step cycle loading and unloading experiments. A novel volumetric strain decomposition method is proposed based on the deformation characteristics of siltstone and granite. Moreover, a novel approach for quantifying crack stress thresholds is proposed and compared with existing methods. The main findings of this study are as follows:  
1. Based on the volumetric strain evolution of granite and siltstone under step loading and unloading, the volumetric strain of rock is decomposed into solid-, plastic-, and gas-phase elastic volumetric strains. The solid-phase volumetric strain is calculated by hydrostatic pressure and bulk modulus;
2. The proposed method for calculating crack stress thresholds determines \(\sigma_{\text{cc}}\) and \(\sigma_{\text{ci}}\) from the initiation and termination points of the stationary stage of the gas-phase volumetric strain and \(\sigma_{\text{cd}}\) from the point at which the gas-phase strain changes from positive to negative;
3. The proposed method provides reasonable characteristic stresses of siltstone and granite and is applicable to rocks with similar stress-strain evolution. However, it may not be applicable to rocks that exhibit minimal dilatancy before peak and maximum volumetric strain near the peak, such as coal and marble;
4. The proposed method offers the advantages of the independence from other methods, suitability across high and low confining pressures, and the capability for quantitative calculation and processing of numerous samples. This contributes to the assessment of long-term strength, fatigue strength, in situ spalling strength of the tunnel surrounding rock, and the long-term stability analysis of engineering projects.  
M.L.: methodology, writing--original draft preparation. S.M.: project administration, funding acquisition, writing--review and editing. M.C.: supervision. F.L.: validation. Z.L.: resources. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant Nos. U2034206 and 52374077) and the National Key Research and Development Program of China (Grant No. 2022 YFC3004601).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
## 3 Results and Discussion
### Change in Amount of Volatile Compounds Associated with Desirable Arona Descriptors
#### 3.4.1 Pyrazines  
The group of pyrazines, which are fundamental to chocolate's nutty and roasted flavor notes, is considered one of the most relevant volatile compounds in chocolate, and they could be classified as high-quality key aroma markers. In the present study, the total amounts of pyrazines increased during fermentation, as shown in Figure S4. This  
Figure 5: Heatmap of mean relative amount of volatile compounds (ratio of the compound’s area to the area of the butanol) with VIP \(\geq\) 1 (rows) of chocolate samples produced from beans fermented for 48, 72, 96, and 120 h from nine regions of Arauca (columns). The dark-red color indicates higher relative amount, while the light-yellow color indicates lower relative amount.  
study aligns with findings from previous studies suggesting that pyrazines form not only during the roasting process but also as a result of microbiological activities. Specifically, tetramethylpyrazine and trimethylpyrazine are synthesized by _Bacillus subtilis_ and _Bacillus megaterium_ toward the end of fermentation [16; 17; 27; 42]. Notably, the amounts of 2,3,5,6-tetramethylpyrazine and 2,6-dimethylpyrazine exhibited significant variations throughout the fermentation process. Amounts increased between 96 and 120 h of fermentation across most regions, as shown in Figure S4. However, exceptions were observed in agroclimatic regions C2 and C5, where the pattern did not hold.
## 3 Results and Analysis
### SVM-Based Prediction Model of Improved Loess Permeability  
From the above analysis, it can be seen that many factors affect the permeability of improved loess, and it is not easy to establish a predictive model in the form of a mathematical analysis because of a complex nonlinear mapping relationship between these influencing factors and the permeability coefficient. For this reason, this paper establishes a prediction model based on support vector machine (SVM) regression directly from the experimental data. It transforms the analysis of permeability coefficients influenced by multiple factors into the problem of regression model parameter identification.
## 3 Results
### Filter Press Backwash Process
#### 3.2.3 The Effect of the pH Value of Backwash Water  
The leaching toxicity results of backwashed cyanide tailings under different pH conditions of the backwash water are shown in Figure 4a, and the corresponding elution water components are given in Figure 4b. A set of runs was conducted under the following conditions: 0.7 times the backwash water, 1.17 mg/L of total cyanide in the backwash water, 1.5 min of primary air-drying, and 10 min of secondary air-drying. Referring to Figure 4, when the pH value of backwash water was 1 and 1.5, the leaching toxicity of backwashed cyanide tailings exceeded the standard, while when the pH value of backwash water was greater than 2, the leaching toxicity of backwashed tailings met the expected requirements. It is seen that when the pH was 1 and 1.5 (see Figure 4b), the total cyanide and copper concentrations of the corresponding elution water were lower, and as the pH of the backwash water continued to increase, total cyanide and copper concentrations in the eluent remained stable after increasing to a certain extent. When the pH of the backwash water was less than 2, the acidity of the backwash water was too high, and it reacted directly with the copper-cyanide complex in the cyanide tailings during backwashing, resulting in a low copper content in the elution water and obvious turbidity of the effluent [17]. There was an off-white precipitate, which was a mixture of CaSO4, CuCN, etc. The lower content of copper ions in the elution water indicates that part of copper sediment was left in the backwashed cyanide tailings; therefore, the leaching toxicity of backwashed cyanide tailings could not meet the technical requirements for the disposal of cyanide tailings in the TSPC. The copper content in the elution water increased with the increase in the pH value of the backwash water, and the leaching toxicity of the backwashed cyanide tailings decreased. Therefore, the pH value of the backwash water should not be lower than 2.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## 3 Proposed Regularized Least Squares Ensemble Model  
### Basic Formulation of the Least Squares Method  
A general linear regression model can be represented as follows [47]:  
\[y=w_{0}+\sum\limits_{i=1}^{M}w_{i}p_{i}(x)+\varepsilon \tag{9}\]  
where \(p_{i}(x)\) represents any function about the variable \(x\) or simply the variable \(x\), \(M\) is the number of regression terms, and \(\varepsilon\) is the approximation error. For convenience, \(p_{i}(x)\) is characterized with \(X_{i}\), for \(N\) samples with \(M\) dimensions, \(\mathbf{X}=[p_{1}(x_{i})\), \(p_{2}(x_{i})\),..., \(p_{M}(x_{i})]\), and the corresponding responses \(\mathbf{Y}=[y_{1},y_{2},\ldots,y_{N}]^{\text{T}}\). Then, the matrix form of linear regression is represented as:  
\[\mathbf{Y}=\mathbf{X}\mathbf{w}+\varepsilon \tag{10}\]  
where \(\mathbf{w}=[w_{1},\,w_{2},\,\ldots,\,w_{M}]^{\text{T}}\), and the error term \(\varepsilon=[\varepsilon_{1},\,\varepsilon_{2},\,\ldots,\,\varepsilon_{N}]^{ \text{T}}\), supposing that the errors are normally and independently distributed, with zero mean and finite variance, that is \(\varepsilon_{i}\sim N(0,\sigma^{2})\). Based on the Gauss-Markov theorem [47], the weight factors \(\hat{\mathbf{w}}=[\hat{w}_{1},\hat{w}_{2},\cdots,\hat{w}_{M}]\) calculated by the OLS method form the best linear unbiased estimator, which can be represented as:  
\[\hat{\mathbf{w}}=\left(X^{\text{T}}X\right)^{-1}X^{\text{T}}Y \tag{11}\]and \(\hat{\mathbf{w}}\) satisfies the following equations:  
\[\begin{split}& E(\hat{\mathbf{w}})=E(\mathbf{w})\\ & Var(\hat{\mathbf{w}})=\sigma^{2}{\left({\rm X}^{\rm T}{\rm X}\right)}^ {-1}\\ &\sigma^{2}=Var(\varepsilon_{1})=Var(\varepsilon_{2})=\cdots Var( \varepsilon_{N})\end{split} \tag{12}\]
## 3 Results and Discussion
### Juice
#### 3.2.3 Color in Juice  
Table 3 presents the \(L^{*}a^{*}b^{*}\) color parameters of the beetroot juices, beetroot powders, and freeze-dried samples. Generally, samples of all the tested beetroot juices were characterized by dark color following low values of the \(L^{*}\) parameter at the level of 2.71-4.48. The \(a^{*}\) parameter values decreased from 15.46 to 9.13, while the \(b^{*}\) parameter decreased from 3.04 to 1.63, with the lowest average values observed for the variant pretreated with the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Code**} & \multirow{2}{*}{**Water Content [\%]**} & **S** & **G** & **F** & & & \\  & & **[g/100 g** & **[g/100 g** & **[g/100 g** & **\(L^{*}\)** & \(a^{*}\)** & \(b^{*}\)** \\  & & **Solids]** & **Solids]** & & & & \\ \hline J0 & 91.3 \(\pm\) 0.5 \({}^{\rm a}\) & 2.22 \(\pm\) 0.04 \({}^{\rm b}\) & 1.74 \(\pm\) 0.02 \({}^{\rm b}\) & 1.42 \(\pm\) 0.02 \({}^{\rm a}\) & 4.48 \(\pm\) 0.12 \({}^{\rm c}\) & 15.46 \(\pm\) 0.18 \({}^{\rm c}\) & 3.04 \(\pm\) 0.12 \({}^{\rm b}\) \\ J1 & 90.9 \(\pm\) 0.3 \({}^{\rm a}\) & 1.81 \(\pm\) 0.03 \({}^{\rm a}\) & 1.49 \(\pm\) 0.05 \({}^{\rm a}\) & 1.12 \(\pm\) 0.16 \({}^{\rm a}\) & 3.74 \(\pm\) 0.07 \({}^{\rm b}\) & 10.53 \(\pm\) 0.42 \({}^{\rm b}\) & 1.63 \(\pm\) 0.19 \({}^{\rm a}\) \\ J2 & 91.0 \(\pm\) 0.4 \({}^{\rm a}\) & 1.69 \(\pm\) 0.04 \({}^{\rm a}\) & 1.39 \(\pm\) 0.03 \({}^{\rm a}\) & 1.13 \(\pm\) 0.01 \({}^{\rm a}\) & 2.71 \(\pm\) 0.08 \({}^{\rm a}\) & 9.13 \(\pm\) 0.28 \({}^{\rm a}\) & 1.65 \(\pm\) 0.16 \({}^{\rm a}\) \\ \hline \hline \end{tabular} Note: \({}^{*}\)=–the same symbols in the columns indicate homogeneous groups (for each group of materials separately).  
\end{table}
Table 3: Sugar content: sucrose (S), glucose (G), fructose (F), and \(L^{*}a^{*}b^{*}\) color parameters in the beetroot juices.  
highest number of PEF pulses (J2) and the highest values observed for the raw, untreated beetroot juice (J0). The color parameters of the beetroot juices differed statistically. It was observed that increasing the number of electric pulses caused a decrease in the \(a^{*}\) and \(b^{*}\) color parameters (indicating the shares of the red and yellow colors, respectively) of the beetroot juices. The obtained results contradict previous studies. [PERSON] et al. [35], who conducted research on blended orange and carrot juice treated with a PEF, observed no differences in lightness between samples with higher and lower intensities of the PEF. [PERSON] et al. [36] reported that apple juice was lighter with a higher PEF intensity, and at the same time, there was no influence on the \(a^{*}\) parameter.