esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 4 Design
### Network Topology Improvement
#### 4.1.2 Domain Gateway Node Election
The specific \(n\times n\) evaluation matrix is illustrated in Figure 3. \[ \begin{bmatrix}s_{1,1}&s_{1,2}&\cdots&s_{1,n}\\ s_{2,1}&s_{2,2}&\cdots&s_{2,n}\\ \vdots&\vdots&\ddots&\vdots\\ s_{n,1}&s_{n,2}&\cdots&s_{n,n}\end{bmatrix} \] (3) where \(s_{i,j}\) represents the data flow forwarding dependency between node \(i\) and node \(j\), taking a value of \(1\) if node \(j\) is an upstream node of \(i\), and 0 otherwise. The weight of each node's score is denoted by \(\omega\). After normalization, the local betweenness centrality score of each node is computed as follows: \[ \begin{bmatrix}s_{1}\\ \vdots\\ s_{n}\end{bmatrix} =\frac{1}{n}\cdot \begin{bmatrix}s_{11}&\cdots&s_{1n}\\ \vdots&\ddots&\vdots\\ s_{n1}&\cdots&s_{nn}\end{bmatrix}\begin{bmatrix}\omega_{1}\\ \vdots\\ \omega_{n}\end{bmatrix} \] (4)\[s_{i}=\frac{1}{n}\sum_{j=1}^{n}s_{i,j}\cdot\omega_{j}=\frac{1}{n}\sum_{j=1}^{n}s_{ i,j} \tag{5}\]  
The candidate node with the highest local betweenness centrality score is selected as the domain gateway node based on the node local betweenness centrality evaluation table \([s_{1},s_{2},\ldots,s_{n}]\). For a single round of elections, the specific algorithm is illustrated in Algorithm 1, where \(f\) represents the maximum number of Byzantine nodes, satisfying \(f\leq\frac{n}{3}\), with \(n\) being the total number of nodes. The election process consists of the following phases:  
* Proposal phase: The candidate node \(p\) broadcasts a proposal message PROPOSAL along with its local betweenness centrality evaluation table \(\text{Score}_{p}\) to other intra-domain ICN nodes. * Voting phase: Upon receiving the proposal, each node verifies the proposal and multicasts the vote message PREVOTE and its own local betweenness centrality evaluation table \(\text{Score}_{r}\) to other nodes. * Proposal confirmation phase: After receiving at least \(2f\) PREVOTE messages, each node constructs the local betweenness centrality evaluation table \([s_{1},s_{2},\ldots,s_{n}]\) based on the local betweenness centrality evaluation matrices of other nodes. The candidate node with the highest local betweenness centrality score is then selected and a new voting message PRECOMMIT, along with the updated local betweenness centrality evaluation table, is multicast to other nodes. * Election submission phase: After receiving at least \(2f\) PRECOMMIT messages, the candidate node in the proposal is confirmed as the domain gateway node. The new domain gateway node information COMMIT is then multicast to all nodes.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 2 Hybrid TOA/RSS Localization: Formulation and Resolution Approaches
### TOA Model  
For the purposes of this paper, we assume that anchors and blind nodes are not perfectly synchronized to the same clock, as is typical in real scenarios. More precisely, the TOA information measured by the blind node based on the signal transmitted by the \(i\)-th anchor can be modeled as:  
\[t_{i,b}=\frac{\|\mathbf{x}_{i}-\mathbf{x}_{b}\|}{c}+t_{b}-t_{i}+w_{i,b}, \tag{3}\]  
with \(c\) denoting the speed of light, while \(t_{i}\) and \(t_{b}\) are the offsets of the \(i\)-th anchor and target clocks with respect to an ideal one, respectively. The additive term \(w_{i,b}\) represents the TOA measurement error and is modeled as a zero-mean Gaussian random variable with variance \(\sigma_{t}^{2}\), the latter reflecting the specific receiver precision [52]. It is worth noting that Equation (3) implicitly assumes the transmit time is known. We recall that the proposed approach exploits cooperation between anchors and blind nodes.  
Therefore, such an information can be actually provided to the blind node by the \(i\)-th transmitting anchor. Although the TOA Model (3) neglects the presence of multipath and the drift of the clocks, it is widely-adopted in different WSN applications and several experiments have shown that it is a reasonable approximation in many cases [40; 41; 42; 52; 53].  
According to Model (3), it thus follows that:  
\[t_{i,b}\sim\mathcal{N}\left(\frac{\|\mathbf{x}_{i}-\mathbf{x}_{b}\|}{c}+t_{b}-t_{i}, \sigma_{t}^{2}\right). \tag{4}\]  
As aforementioned, state-of-the-art approaches typically assume that the model parameters are known. In the specific case of the TOA Model (3), it is also customary to assume that all the nodes in the network are perfectly synchronized to the same clock, that is, the offsets \(t_{i}\) and \(t_{b}\) are often even set to zero.
## 2 Theory
### Split Bregman Based Group Sparse Reconstruction
Iterating the \(u\) and \(z\) sub-problems and Bregman update over \(k\) in Equation (12) solves the unconstrained problem from Equation (10), where the Lagrange multiplier, \(\mu\), was introduced; however, to solve the constrained problem in Equation (9) and reduce the dependence of the reconstruction result on the initial value of \(\mu\), an outer iteration is completed over \(i\) that updates \(f\) based on changes to the reconstruction, \(u^{k+1}\):  
\[f^{i+1}=f^{i}+\left(f-R\mathcal{F}u^{k+1}\right) \tag{17}\]  
where \(f\) is the initial value of the sampled data. By replacing \(f\) with \(f^{i}\) in Equation (12) then applying Equation (17) during each outer iteration, \(f^{i+1}\to f^{i}\) and \(R\mathcal{F}u^{k+1}\to f\) to within the standard deviation of the noise, as the algorithm converges to the solution of the constrained problem [29]. The pseudocode for the Split Bregman GS MRSI reconstruction algorithm is shown in Algorithm 1 and incorporates Equations (12)-(17). The inner loop iterates over \(k\) and updates \(u\) and \(z\), and the outer loop iterates over \(i\) and updates the sampled data \(f^{i}\) as a Bregman parameter. ```
Initialize:\(f^{0}=f_{0},K=(\mu R^{\prime}R+\lambda G^{\prime}G)\), and \(u^{0}=z^{0}=b_{z}^{0}=0\) repeat For\(k=1\) to M \(rhs^{k+1}=\mu\mathcal{F}^{\prime}R^{\prime}f^{i}+\lambda G^{\prime}\left(z^{k}- b_{z}^{k}\right)\) \(u^{k+1}=\mathcal{F}^{\prime}K^{-1}\mathcal{F}rhs^{k+1}\) \(z^{k+1}=gshrink(Gu^{k+1}+b_{z}^{k},\frac{1}{\lambda},G)\) \(b_{z}^{k+1}=b_{z}^{k}+\left(Gu^{k+1}-z^{k+1}\right)\) end for \(f^{i+1}=f^{i}+\left(f_{0}-R\mathcal{F}u^{k+1}\right)\) until stopping condition met (loop over \(i\)) return\(\mathcal{F}u^{k+1}\)
```  
**Algorithm 1**SBGS Reconstruction \(f_{0}\), \(G\), \(R\)  
The pseudocode for the Split Bregman GS MRSI reconstruction algorithm is shown in Algorithm 1 and incorporates Equations (12)-(17). The inner loop iterates over \(k\) and updates \(u\) and \(z\), and the outer loop iterates over \(i\) and updates the sampled data \(f^{i}\) as a Bregman parameter. ```
Initialize:\(f^{0}=f_{0},K=(\mu R^{\prime}R+\lambda G^{\prime}G)\), and \(u^{0}=z^{0}=b_{z}^{0}=0\) repeat For\(k=1\) to M \(rhs^{k+1}=\mu\mathcal{F}^{\prime}R^{\prime}f^{i}+\lambda G^{\prime}\left(z^{k}- b_{z}^{k}\right)\) \(u^{k+1}=\mathcal{F}^{\prime}K^{-1}\mathcal{F}rhs^{k+1}\) \(z^{k+1}=gshrink(Gu^{k+1}+b_{z}^{k},\frac{1}{\lambda},G)\) \(b_{z}^{k+1}=b_{z}^{k}+\left(Gu^{k+1}-z^{k+1}\right)\) end for \(f^{i+1}=f^{i}+\left(f_{0}-R\mathcal{F}u^{k+1}\right)\) until stopping condition met (loop over \(i\)) return\(\mathcal{F}u^{k+1}\)
```  
**Algorithm 2**SBGS Reconstruction \(f_{0}\), \(G\), \(R\)  
The number of iterations over \(k\) is application-dependent, but for the 4D MRSI GS problem, we use \(M=15\) iterations over \(k\) for each iteration over \(i\) and iterate over \(i\) until the normalized residual error between \(u^{k+1}\) and \(f^{i}\) is less than \(10^{-6}\).
## 3 Results
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) was applied to the data set consisting of treatments as cases (a) and data for total values of aromatic (Table 1) and phenolic (Table 2) groups as variables (b). The first three principal components (PC) had an eigenvalue greater than 1, cumulatively explaining 87.08% of the variability among the data. The analysis of eigenvectors showed that the greatest effect on the first main component (PC 1) had anthocyanins, flavones, flavonoids, flavan-3-ol, dihydroronols, hydroxybenzoic acids, hydroxycinnamic acids, hydroxyphenylpropanoic acids, stilbenes, hydroxybenzaldehyde, tyrosols from the phenolic group, and other aromas as a representative of compounds from the aromatic group. Terpenes, esters, and alcohols from the aromatic group and flavones and dihydroronols from the phenolic group had the greatest effect on the second main component (PC 2), while hydroxyphenylpropanoic acids and stilbenes showed the greatest effect on the third main component (PC 3). Figure 3 shows the results for the first two principal components, which together explain 76.63% of the total variability (PC1 53.04%, PC2 23.59%). The first principal component (PC 1) showed the separation of two groups: group 1 (elderflower) and group 2 (rosemary, thyme, and control). Group 1 is located at the negative end of PC 1 and is characterized by a higher content of anthocyanins; flavonols; flavan-3-ol; hydroxybenzoic acids; hydroxycinnamic acids; and miscellaneous aromatic components: anisole, benzaldehyde, and benzeneacetaldehyde. Within group 2, the separation by the second principal component (PC 2) is noticeable. The rosemary and thyme treatments were grouped on the positive sides of PC 1 and PC2 and were characterized by a higher content of alcohol and terpenes within the aromatic components, as well as flavones and dihydroronols. The control treatment is located on the positive side of PC 1 and the negative side of PC 2 and is defined by a higher content of esters.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## Appendix A Built-In Function in Mathcad  
\(\Gamma(\alpha)=\int_{0}^{\infty}\mathrm{t}^{\alpha-1}\mathrm{\cdot}\mathrm{e} ^{-\mathrm{t}}\mathrm{dt}\) the complete gamma function;  
\(\Gamma(\alpha,\mathrm{x})=\int_{\mathrm{x}}^{\infty}\mathrm{t}^{\alpha-1} \mathrm{\cdot}\mathrm{e}^{-\mathrm{t}}\mathrm{dt}\) returns the value of the upper incomplete gamma function of x with parameter \(\alpha\);  
\(\mathrm{dgamma}(\mathrm{x},\alpha)=\frac{\mathrm{x}^{\alpha-1}\mathrm{\cdot} \mathrm{e}^{-\mathrm{x}}}{\Gamma(\alpha)}\) returns the probability density for value x, for the Gamma distribution;  
\(\mathrm{dgamma}(\mathrm{x},\alpha)=\int_{0}^{\mathrm{x}}\frac{\mathrm{x}^{ \frac{\alpha-1}{2}\mathrm{\cdot}\mathrm{e}^{-\mathrm{x}}}}{2^{\frac{\alpha}{2 }\mathrm{\cdot}\mathrm{T}}(\frac{\alpha}{2})}\mathrm{d}\mathrm{x}=1-\Gamma \Big{(}\frac{\mathrm{d}}{2},\frac{\mathrm{x}}{2}\Big{)}/\Gamma\Big{(}\frac{ \mathrm{d}}{2}\Big{)}\) returns the cumulative probability distribution for value x, for the Chi-Squared distribution;  
\(\mathrm{dgnisq}(\mathrm{p},\mathrm{d})\) returns the inverse cumulative probability distribution for probability p, for the Chi-Squared distribution;  
\(\mathrm{dweight}(\mathrm{x},\mathrm{s})=\mathrm{s}\mathrm{\cdot}\mathrm{x}^{ -1}\mathrm{\cdot}\mathrm{e}^{-\mathrm{x}^{\mathrm{s}}}\) returns the probability density for value x, for the Weibull distribution;  
\(\mathrm{pwiebull}(\mathrm{x},\mathrm{s})=1-\mathrm{e}^{-\mathrm{x}^{\mathrm{s}}}\) returns the cumulative probability distribution for value x, for the Weibull distribution;  
\(\mathrm{qweight}(\mathrm{p},\mathrm{s})=(-\ln(1-\mathrm{p}))^{\frac{1}{\mathrm{ s}}}\) returns the inverse cumulative probability distribution for probability p, for the Weibull distribution;  
\(\mathrm{ibeta}(\mathrm{a},\mathrm{x},\mathrm{y})=\mathrm{B}(\mathrm{a};\mathrm{x}, \mathrm{y})=\frac{\Gamma(\mathrm{x}+\mathrm{y})}{\Gamma(\mathrm{x}+\mathrm{y} )}\int_{0}^{\mathrm{x}}\mathrm{t}^{\mathrm{x}-1}(1-\mathrm{t})^{\mathrm{y}-1} \mathrm{dt}\), the incomplete Beta function, returns the value of the incomplete beta function of x and y with parameter a;  
\(\mathrm{Psi}(\mathrm{z})=\psi(\mathrm{z})=\frac{\mathrm{d}}{\mathrm{d}z}\ln( \Gamma(\mathrm{z}))=\frac{\mathrm{d}_{z}\Gamma(\mathrm{z})}{\Gamma(\mathrm{z})}\), the digamma function.  
\(\mathrm{Psi}(\mathrm{n},\mathrm{z})=\frac{\mathrm{d}^{n}}{\mathrm{d}\mathrm{z }^{n}}\psi(\mathrm{z})=\frac{\mathrm{d}^{n+1}}{\mathrm{d}\mathrm{z}^{n+1}}\ln( \Gamma(\mathrm{z}))\), the polygamma function of order n.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Trend Analysis and Significance Tests  
To analyze the interannual spatial and temporal trends of drought, greening, and GPP, we used the Theil-Sen Median trend analysis and the Mann-Kendall (M-K) trend test. Where SPEI was calculated using SPEI_12 for December. SSMI was calculated by synthesizing the monthly scale SM data into annual-scale SM data according to the method of averaging before performing the trend calculation and then calculating it as SSMI by using the formula. The combination of Sen trend analysis and M-K test has been applied in many studies and is a well-established method in time series analysis. Sen trend analysis is a very robust nonparametric trend calculation method, compared with ordinary linear regression analysis, is insensitive to measurement error and outlier data and can reduce  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SPEI** & **SSMI** & **Class** \\ \hline \(>\)\(-\)0.5 & \(>\)0 & No drought \\ \(-\)1.0\(--\)0.5 & \(-\)1.0–0 & Mild drought \\ \(-\)1.5\(--\)1.0 & \(-\)1.5\(--\)1 & Moderate drought \\ \(-\)2.0\(--\)1.5 & \(-\)2.0\(--\)1.5 & Severe drought \\ \(\leq\)\(-\)2 & \(\leq\)\(-\)2 & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 1: SPEI and SSMI drought classification.  
the impact of outliers [54; 55]. The M-K test is a non-parametric statistical test based on rank [56]. It does not require the data to conform to a particular distributional state and is not affected by outliers.
## 3 Materials and Methods
### Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4, Collection 6  
For the same period as the Landsat data, daily, 500 m MODIS nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance datasets (MCD43A4) have been obtained in the product collection 6 for the tiles h17v07 and h17v08 (Figure 1). A major improvement of MCD43A4 collection 6 is that the production frequency has been increased from 8 days to daily, providing maximum flexibility in the definition of dates and time steps for generation of high temporal resolution time series with ESTARFM. MCD43A4 is a 16-day rolling composite, representing the best BRDF possible during this period of time. Unlike in the previous collection 5, the date associated with each daily dataset is the centre of the moving 16-day input window (and not its first day). More information on collection 6 of the MCD43A4 product can be found in the MODIS User Guide [25].  
The red and NIR bands of the MODIS datasets have been resampled using the nearest neighbour method to a resolution of 30 m, automatically co-registered to Landsat, and reprojected to UTM zone 30\({}^{\circ}\)N using the MODIS Reprojection Tool (MRT) [26]. As recommended in the MODIS User Guide [25], only data with a full BRDF inversion (labelled with the best quality flag) have been processed. From these, NDVI was calculated and remaining outliers in the time series were removed (defined as observations which considerably differ from the median in a moving window with a length of 20 days). Similar to what was suggested by [PERSON] and [PERSON] [27], the threshold for outlier detection was defined as the standard deviation of the entire time series times a factor of 0.3 for the outliers above the median and times a factor of 0.1 for the outliers below the median. The temporal gaps in the result have been linearly interpolated and, subsequently, a Savitzky-Golay filter [28] with a total window length of 16 days and a quadratic polynomial has been applied to reduce residual noise. The factors and window lengths for outlier removal and Savitzky-Golay filtering were determined based on a visual analysis of their effect on temporal plots similar to those implemented in the software package TIMESAT [27].
## 2 Materials and Methods
### Model Improvement
#### 2.2.5 Wise-IoU  
This approach may result in a distorted evaluation of the outcomes [34]. However, the Weighted Intersection over Union function (WIoU) mitigates this issue by incorporating the areas between the predicted and ground truth bounding boxes into the IoU computation, thus reducing potential biases present in conventional IoU assessments [35]. To calculate the IoU score between the predicted and ground truth bounding boxes, the spatial interaction between them is examined. More specifically, the distance between the centers of the predicted and ground truth bounding boxes is measured and used as a benchmark for the maximum feasible distance between the two. Based on the regions between the two boxes, a weighting coefficient is calculated, measuring the relationship between the two boxes, and this is used to weight the IoU score. By introducing the regions between the boxes and the weighting coefficient, WIoU can more accurately evaluate object detection results, avoiding biases inherent in traditional IoU.  
\[L_{\text{{{CIoU}}}}=1-\text{IoU}+\frac{{{\rho}^{2}\left({{b}^{A}},{{b}^{B}} \right)}}{{{c}^{2}}}+\alpha\
u \tag{3}\]  
\[L_{\text{{WIoU}}}=rR_{\text{{WIoU}}}L_{\text{{{IoU}}}},r=\frac{{\beta}}{{{ \delta\alpha}^{{\beta-\alpha}}}} \tag{4}\]  
\[\beta=\frac{{L_{\text{{IoU}}}^{*}}}{{L_{\text{{{IoU}}}}}}\in\left[{0,+\infty}\right) \tag{5}\]  
\[R_{\text{{WIoU}}}=\exp\left({\frac{{{\left({x-{x_{gt}}}\right)}^{2}+\left({y- {y_{gt}}}\right)^{2}}}{{{\left({{c}_{w}^{2}+{c}_{h}^{2}}\right)}^{*}}}}\right) \tag{6}\]  
where \(L_{\text{{{CIoU}}}}\) is an enhanced Intersection over Union (IoU) loss function that accounts for the impact of both the distance between bounding box centers and their aspect ratios. \(L_{\text{{WIoU}}}\) represents a weighted IoU loss function, which modifies the traditional IoU loss by employing a weighting coefficient \(r\) alongside the region weight \(R_{\text{{WIoU}}}\). Additionally, parameters \(\alpha\) and \(\
u\) serve to balance considerations related to center distances and aspect ratios.  
The \(R_{\text{{WIoU}}}\) is the region weight based on the distance between the centers of the predicted and ground truth bounding boxes, and \(\beta\) is a dynamically adjusted weighting coefficient that balances the IoU loss across different samples. By introducing region weights and a dynamic adjustment mechanism, WIoU can precisely evaluate the object detection results, enhancing the model's detection performance.
_Applied_  
_sciences_  
_Impact of Soil Aeration on the Environmental Fate of Pre-Emergent Herbicide Metolachlor_  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/10/](https://www.mdpi.com/journals/article/pdf/10/)]([https://www.mdpi.com/journals/article/pdf/10/](https://www.mdpi.com/journals/article/pdf/10/))  
species are reported to have developed resistance to post-emergence herbicides including glyphosate, the use of pre-emergent or residual herbicides like metolachlor is encouraged to promote the management of resistant biotypes. Unlike post-emergence herbicides, residual herbicides must remain active in the soil to stop weed populations from emergence. Metolachlor is used widely for weed control in the humid regions of the United States, primarily in the midwestern states [3], much of which are prone to flooding, which consequentially leads to transient anaerobic situations within the soil profile. While terrestrial soil environments are typically considered to be dominated by aerobic conditions, there are also situations where anaerbiosis prevails, such as high soil moisture resulting from a high water table or heavy rain [4]. Such variances in soil conditions are also expected to affect herbicide degradation [5], and thus it is essential to understand the fate and transformation of a soil-applied herbicide such as metolachlor in aerobic and anaerobic soil environmental conditions.  
Metolachlor is a neutral non-ionizable herbicide dominated by apolar groups, and the adsorption mechanism has been frequently described by the herbicide interactions with soil organic matter [6]. The relatively high soil persistence, high water solubility, and significant toxicological implications of this universally used herbicide have attracted the interest of researchers from various fields [7]. However, existing works on the environmental behavior of metolachlor have primarily focused on its fate and degradation in an aerobic soil environment [8]. Like several other homocyclic herbicides containing halogens, metolachlor tends to undergo slower degradation kinetics under aerobic conditions than anaerobic conditions [9; 10]. Although many studies have been published on the adsorption of metolachlor to soils [11; 12; 13], few attempts have been made to elucidate the models to predict this herbicide's soil binding affinities in an anaerobic environment.  
Treatment of different soil types under anaerobic and aerobic conditions would allow further exploration of the bioavailability and metabolism of a popular herbicide like metolachlor. This knowledge could be applied to enforce more efficient and judicious usage of this herbicide in cropping systems. The present study was undertaken to investigate the environmental fate determinant factors like the adsorption, desorption, degradation, and mineralization of metolachlor in different soils treated under aerobic and anaerobic conditions.