esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach
## 3 Data Analysis and Empirical Results
### Modelling the Residual Dependencies Using Vine Copula
#### 3.3.1 Canonical Vine Copula  
Figure 5 represents the structure of the C-vine. The node that maximises the sum of pairwise dependencies is chosen as the root of the tree. In tree 1 all altcoins are connected to Bitcoin. This confirms the leading role Bitcoin plays in the price formations of other cryptoassets, as it accounts for about 44% of the total crypto-market shares.  
A range of 14 bivariate copula functions was available for selection to model the dependences. The following four copula functions were selected based on the AIC criterion (key characteristics are given in Table 9): Survival Joe-Gumbel and Clayton (SBB6, SBB7), survival Gumbel or rotated Gumbel by 180 degree (SG), survival Joe (SJ), student-t and rotated Gumbel by 90 degree (G90): The predominance of survival and mixed copula indicate the presence of lower-tail dependences (Itd) which characterise cryptoasset markets during extremes. The structure in Figure 5 reflects the expected relationship among cryptoassets. The resulting pairs of cryptoassets captured by the appropriate copula are recorded in Table 7 together with their corresponding estimated parameters and Kendall's tau values indicating different strengths of dependencies. We observe from the Kendall's tau column that Bitcoin is positively and moderately correlated with altcoins except with True USD with whom it has a strong negative correlation (with the negative correlation with BTC, whom is positively correlated with other altcoin. True USD can be used for hedging during market turmoil).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Tree** & **Edges** & **Copula** & **par1** & **par2** & **Kendall’s \(\tau\)** & **utd** & **Itd** \\ \hline \multirow{8}{*}{1} & BTC-TUSD & G90 & 0.02 & – & \(-\)0.06 & – & – \\  & BTC-FIL & SG & 0.03 & – & 0.27 & – & 0.35 \\  & BTC-LEO & t & 0.03 & 2.00 & 0.09 & 0.03 & 0.03 \\  & BTC-THETA & SBB7 & 0.07 & 0.07 & 0.38 & 0.04 & 0.57 \\  & BTC-WAVES & SJ & 0.06 & - & 0.33 & - & 0.56 \\  & BTC-LINK & SBB6 & 0.19 & - & 0.45 & - & 0.6 \\  & ONE-BTC & SG & 0.05 & – & 0.41 & – & 0.49 \\ \hline \end{tabular}
\end{table}
Table 7: Parameter estimate of C-vine copula.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Tree** & **Edges** & **Copula** & **par1** & **par2** & **Kendall’s \(\tau\)** & **utd** & **Itd** \\ \hline \multirow{8}{*}{1} & LINK-LEO & t & 0.03 & 2.37 & \(-\)0.09 & 0.02 & 0.02 \\  & BTC-TUSD & G90 & 0.02 & – & \(-\)0.06 & – & – \\  & LINK-FIL & SG & 0.03 & – & 0.27 & – & 0.34 \\  & BTC-THETA & SBB7 & 0.07 & 0.07 & 0.38 & 0.04 & 0.57 \\  & LINK-WAVES & SBB8 & 0.06 & 0.00 & 0.33 & – & – \\  & BTC-LINK & SBB6 & 0.19 & 0.15 & 0.45 & – & 0.60 \\  & ONE-BTC & SG & 0.05 & – & 0.41 & – & 0.49 \\ \hline \end{tabular}
\end{table}
Table 8: Parameter estimate of R-vine copula.
# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 4 Results and Discussions
### Intensity of Concentration, \(I_{c}\)  
Figure 5 reports the simulated values (\(y\)-axis) of \(I_{c}\), graphed towards the field data (\(x\)-axis). For the reason mentioned above, also in this graph the comparative points are reported only for the not-null calculated concentration intensity. The bisector line 1:1, the 1:0.5, and the 1:2 lines are also reported. It is possible to see that 40% of the value is within the range of the _FAC2_. Then, the linear trendline was added, obtaining an angular coefficient of 0.71, an intercept of 1.87 and a \(R^{2}\) = 0.11. The correlation coefficient of 0.33 indicates a moderate linear relationship.  
Remembering that the intensity of concentration is the ratio between the standard deviation of concentration and the mean concentration (see Equation (5)), it is possible to highlight that the biased behavior of overestimation of the standard deviation propagates to intensity of concentration.  
It appears that the model does not perfectly describe the physical phenomenon, but the value of _FAC2_ is not poor considering the simplification of the model.  
Figure 5: Scatter plot of observed against modelled \(I_{c}\). Moreover, 1:1, 1:0.5, and 1:2 and trend lines are reported.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 5 Conclusions  
The protocol presented in this paper ensures causal message ordering in a free scale peer-to-peer overlay network. The protocol uses a combination of the Immediate Dependency Relation protocol and an idea of hierarchical clocks to reduce message overhead. Our protocol is efficient in the overhead attached per message at the communication channels. The overhead sent per message is characterized by being dynamically adapted according to the behavior of the concurrent messages. In addition, the simulations show that the protocol proposed in this work produces less overhead than the IDR and DS protocols, and also requires less storage overhead. As a result, this protocol can be used to ensure causal message ordering, while producing a lower overhead that the IDR and DS protocols. The lower overhead will allow for devices with lower computational capacities to be included in the system. The protocol proposed in this paper can be further extended by reducing an overhead dependency on the number of internal groups, allowing for more super peers and corresponding internal groups to be included in a system. This will allow a further extension of the protocol to be used in hierarchical peer-to-peer networks.  
This research was partially supported by the National Council on Science and Technology (CONACyT, Mexico), Project # C394/2016/271602 and grant number 290817 assigned to [PERSON].  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] contributed to analyzing the results obtained via simulation and writing the paper.  
The authors declare no conflict of interest.
# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
## 3 Experimental Results and Analysis
### Ablation Experiments
#### 3.3.2 Ablation Experiments on the FBDC Module  
To assess the feasibility and effectiveness of the FBDC module in the context of rockiness recognition, the following ablation experiments were conducted. Model 1 comprises only the backbone network without the inclusion of the improved CBAM and FBDC module. Building upon Model 1, Model 2 incorporates the improved CBAM. Subsequently, Model 3 enhances Model 2 by adding the FBDC module. All experiments were conducted and tested using the field collection dataset, and the corresponding experimental results are presented in Table 6. Lithology recognition visualizations are depicted in Figure 6.  
Table 5 showcases the performance metrics, revealing that the highest accuracy achieved by the model in this study is 97.60%. This marks a significant improvement, with a 5.9% increase compared to Model 1 and a 1.61% increase compared to Model 2. The results indicate that both the improved CBAM and the FBDC module effectively enhance the network's performance. Moreover, there is no exclusion or compatibility issue between the two modules. Their combined integration yields the network's optimal performance. Additionally, the lithology identification visualization in Figure 6 illustrates that the FC-Res model proposed in this paper provides superior prediction results.
## 2 Materials and Methods
### Critical Node Identification  
The branch and bound algorithm used to solve \(P1\) and \(P2\) is known to have scalability issues as the fundamental problem is NP-hard. Furthermore, the number of nodes/components in critical infrastructures can be quite large in a city. For example, consider the core (9 km \(\times\) 9 km) area of the Pittsburgh, Pennsylvania metro area, which has a population of 2.3 million. According to the US Department of Homeland Security, the core area contains over 2700 critical infrastructure nodes, which include 80 water infrastructure nodes and 530 communication infrastructure nodes. Hence, optimizing all nodes/components in several infrastructures will be computationally complex. Here, the microgrid location optimization models are scaled by reducing the number of nodes in each infrastructure to only the most critical nodes determined by the TWV values. In effect, this reduces the search space over which the optimization models are solved, significantly speeding up the computation but at the expense of loss of global optimality guarantees. Various approaches targeting the selection of the most critical or essential nodes in each infrastructure have been introduced in the literature. For that, two methods are considered, as follows:
## 2 Material and Method
### Methodology  
Figure 2 depicts the methodology in the form of a flow chart. The two DEMs, namely CartoDEM V3 R1 and TanDEM-X 90, were pre-processed according to the study site, and elevation was extracted using ArcGIS software at the ICESat-2/ ATL08 footprint locations. ICESat-2 footprints are shown as superimposed on both of the DEMs (Figure 3) in the process of elevation extraction and were later used as a reference elevation value. The accuracy of ICSat-2 datasets and openly accessible DEMs was estimated by calculating mean error (_ME_, Equation (1)), mean absolute error (_MAE_, Equation (2)), and root mean square error (_RMSE_) (_RMSE_, Equation (3)). The vertical accuracy is determined by the differential values. \(H_{(i(\textit{DECSat}-2))}\) is the reference height, whereas \(H_{(i(\textit{DEM}))}\) denotes the height of openly accessible DEMs (TanDEM-X 90 and CartoDEM V3 R1) for \(i=1\) to \(n\), as below:  
\[ME=\frac{\sum_{i=0}^{n}H_{i(DEM)}-H_{i(ICESat-2)}}{n} \tag{1}\]  
\[MAE=\frac{\sum_{i=0}^{n}\mid H_{i(DEM)}-H_{i(ICESat-2)}\mid}{n}*100 \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\Bigl{(}H_{i(DEM)}-H_{i(ICESat-2)}\Bigr{)}^{2} }{n}} \tag{3}\]  
Figure 2: Flow chart of the methodology for DEM evaluation using ICESat-2 datasets.
###### Abstract  
Grid-forming converters (GFMCs) have gained significant attention for their functionality in grid voltage formation and grid-supportive services. However, managing harmonic distortions caused by nonlinear loads remains a critical challenge in weak grids. This paper presents a novel grid-forming active power filter (GFMC APF) that integrates voltage and frequency regulation with effective harmonic control. The proposed control method generates harmonic voltage commands by detecting voltage at the point of common coupling. The GFMC APF compensates harmonic voltages by creating a near short-circuit impedance path for harmonics, thereby preventing harmonic currents from propagating into the grid. In addition to improving harmonic performances, the system enhances grid stability by enhancing inertia, damping, and short-circuit capacity while suppressing wide-frequency oscillations. The proposed method avoids complex parameter tuning, ensuring simplicity and scalability. Simulation results validate the effectiveness of the GFMC APF in delivering precise harmonic control, improved power quality, and enhanced grid-forming capabilities.  
active power filter; grid-forming converter; harmonic control; power quality enhancement; renewable energy integration Article
## 7 Experiments
### Simulation Versus Theoretical Value
#### 7.1.2 Statistical Inference  
A statistical inference test was done to prove that the average number of trips performed by the proposed algorithm is different from the theoretical value \(T_{h}\). For this, 50 random samples of flights were taken, as statistical sample, for every DFG. We define the null hypothesis \(H_{0}\) as: _the average number of trips \(\theta\) is equal to \(T_{h}\)_ and the alternative hypothesis \(H_{1}\) as: _the average number of trips \(\theta\) is less than \(T_{h}\)_. Considering the p-value obtained from each set of experiments, the null hypothesis is rejected with a 95% level of confidence, as it can be seen in Table 5.  
A \(t\)-test is applied since we obtain a normal distribution due to the randomness of the movements performed in the experiments. In addition, due to the size of the sample, the variance between trips is homogeneous and there are no significant outliers. Table 6 shows the statistic test. Since the significance level \(\alpha\) is 0.05, for every DFG \(G_{h}\) the test passed.
## 2 Materials and Methods  
### Plant Material  
The fruits of cagaita and \"Cerrado\" cashew were collected in Montes Claros de Goias city (latitude 16\({}^{\circ}\)06\({}^{\prime}\)20, longitude 51\({}^{\circ}\)17\({}^{\prime}\)11, 459 m), washed, sanitized (NaClO, 15 mL/L for 15 min), and rinsed in water. The cashew pseudofruits were homogenized in an industrial blender and filtered (nylon screens) to remove the residue. The cagaita fruits were separated manually from the seed, homogenized, and filtered (nylon screens). Each pulp was combined with 5 g of ascorbic acid and 0.4 g of sodium fluoride [20], stored in 300 mL sterile bottles, and frozen (-6 \({}^{\circ}\)C) for transport.  
The \"Cerrado\" cashew pulp showed moisture, protein, lipid, carbohydrate, and ash contents of 87.99, 1.02, 0.11, 10.23, and 0.64 g/100 g, respectively (Table 1). The cagaita pulp showed moisture, protein, lipid, carbohydrate, and ash contents of 93.65, 2.93, 0.01, 3.06, and 0.32 g/100 g, respectively. While the pH of the pulps was close (3.26 and 3.23 for \"Cerrado\" cashew and cagaita pulps, respectively), the soluble solids in the \"Cerrado\" cashew pulp (13.43 \({}^{\circ}\)Brix) were almost twice that compared to the cagaita pulp (7.40 \({}^{\circ}\)Brix).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cashew** & **Cagaita** \\ \hline Moisture (g-100 g\({}^{-1}\)) & 87.99 \(\pm\) 0.74 & 93.65 \(\pm\) 0.14 \\ Protein (g-100 g\({}^{-1}\)) & 1.02 \(\pm\) 0.18 & 2.93 \(\pm\) 0.11 \\ Lipids (g-100 g\({}^{-1}\)) & 0.11 \(\pm\) 0.01 & 0.01 \(\pm\) 0.00 \\ Carbohydrates (g-100 g\({}^{-1}\)) & 10.23 \(\pm\) 0.85 & 3.06 \(\pm\) 0.04 \\ Ash (g-100 g\({}^{-1}\)) & 0.64 \(\pm\) 0.00 & 0.32 \(\pm\) 0.02 \\ Energy (kcal) & 45.68 \(\pm\) 2.97 & 24.11 \(\pm\) 0.55 \\ pH & 3.26 \(\pm\) 0.08 & 3.23 \(\pm\) 0.08 \\ Total solids soluble (\({}^{\circ}\)Brix) & 13.43 \(\pm\) 0.29 & 7.40 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of “Cerrado” cashew and cagaita pulps.
# Paper and Fiber-Based Bio-Diagnostic Platforms: Current Challenges and Future Needs
## 1 Introduction
High capacity/mass transfer rates, desirable chemistry as well as non-denaturing and re-generable surfaces established fibers as favorable candidates for protein separation with a specific focus on preparative scale separations [43]. In the area of medical diagnostic carbon-based platforms such as single-walled carbon nanotubes (SWCNTs), carbon micro/nano-fibers and composite carbon fibers offered significant advancement in the bio-recognition of a wide range of bio-molecular entities through electrochemical detection [44; 45; 46]. Extensive review articles cover significant improvement of carbon-based biosensors in a great detail [47; 48; 49; 46]. Polymer-based fibers, in turn, have attracted a great deal of attention in medical diagnostics. [PERSON] et al., reported nano-coated nylon fibers, which were surface modified to possess positively charged characteristics for bacterial vaginosis detection and pregnancy diseases monitoring [36]. A triple-blend electrospun fiber mat has been used for the clinical diagnostic of colorectal cancer [50]. [PERSON] et al., reported electrospun poly(\(\epsilon\)-caprolactone) (PCL) fibers for the fluorescent detection of antibody against human serum albumin (anti-HSA) [51]. Polyvinylidene fluoride (PVDF) nano-fiber membrane was used for protein immobilization via Western blotting process [52]. Other examples of fiber platforms are provided in Table 1.  
The current perspective article outlines the challenges in controlled physical and chemical properties of the paper/fiber materials, the shelf life, the multi-functionality of the devices, the readout strategies and the ease of operation for the proposed platforms. In that regard, papers and fibers from different categories have been reviewed for their application as bio-receptive platforms for the detection of a broad range of biomolecular entities. Table 2 summarizes some of the latest applications of paper/fiber materials in bio-recognition, particularly for the detection of antibodies, antigens, whole viruses, bacteria, and different classes of proteins via ELISA. Table 2 also presents the type of applied assay, the approximate duration of the assay, and the important evaluation parameters reported for the developed assay. Table 3 outlines some of the major advantages and disadvantages of each technique. Although the presented information in these three tables provides a general insight on how diverse the bio-analytical applications of the paper/fiber materials have been, it also reveals aspects of performance variation amongst the techniques and the shortages and needs that are yet to be addressed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Paper/Filter** & **Applied Fabrication Method** & **Embedded Platform/Pattern** & **Ref.
## 4 Analysis of Relevant Influencing Parameters
### Effect of Delaminations
#### 4.3.2 Reliability of Frequency Calculations for Delaminated LG Specimens
The interlayer properties were defined as for the PVB\({}_{\mathrm{A}}\) material law reported in Figure 3.  
From Figure 20a, in particular, it is possible to notice that delaminations close to the restraints (i.e., schemes D1 to D3 of Figure 18) can be mostly disregarded. Frequency variations with respect to the vibration frequency of the undamaged (fully bonded) LG members are, in fact, proposed as a function of \(A_{d}/A_{t}\), as obtained from Equations (12)-(14), giving evidence of an expected frequency decrease in the order of \(-0.2\%\). This finding is also in line with literature efforts (for different typologies of composite laminates, see, for example [48; 49; 50]), where it was proven that (artificially imposed) delaminated regions close to restraints have minimum effects. Figure 18: Selected delamination schemes for the parametric study on LG beams. Dashed area are representative of delaminations. Otherwise, as far as the damage location moves along the LG beam span (i.e., scheme D4), a mostly linear frequency decrease was observed from the collected parametric results, see Figure 20b. In this sense, the parametric investigation summarized herein proved that:  
* The analytical method recalled from [51] and extended to the adjusted dynamic thickness for viscoelastic LG beams could be rationally used for preliminary frequency estimates, especially when refined methods of analysis or dedicated experimental investigations are not available;
* The presence of even slight delaminations along the edges of LG beams (i.e., with limited thickness, with respect to the beam width \(b\)) can have marked effects on the bending stiffness of the composite LG sections, thus on the corresponding frequency calculations; and,  
Figure 19: Numerical modelling of LG beams with delaminations: (**a**) delaminated regions, (**b**) end detail, and (**c**) typical deformed shape (ABAQUS). Figure 20: Frequency variation of delaminated LG beams with flexible mechanical restraints (with \(\mathrm{PVB_{A}}\) interlayer, \(K_{r}=150\) kNm/rad and \(K_{s}=\infty\)). (**a**) D1-to-D3 or (**b**) D4 scheme results, for selected LG beams. On the other side, the simplified assumptions of Equations (12)-(14) gave evidence of a certain scatter from the corresponding FE calculations, with respect to a given \(A_{d}/A_{t}\) ratio. Such an effect can be also observed in Figure 20b, for selected LG configurations. As a general trend, the analytical formulation for delaminated LG beams was found to clearly overestimate the FE frequency variations, thus providing even more conservative predictions.
## 4 Numerical Experiments and Applications
### Application in Chemical Spill Emergency Management
\begin{table}
\begin{tabular}{c|c c c|c c} \hline \hline
**Alternatives** & \(\mathbf{DM_{1}}\) & \(\mathbf{DM_{2}}\) & \(\mathbf{DM_{3}}\) & \(\mathbf{DM_{4}}\) \\ \hline \(\mathbf{A_{1}}\) & & 0.1773 & 0.3966 & \\ \(\mathbf{A_{2}}\) & 0.5 & 0.1386 & 0.3046 & 0.3680 \\ \(\mathbf{A_{3}}\) & 0.5 & 0.1250 & 0.2988 & 0.2619 \\ \(\mathbf{A_{4}}\) & & 0.1818 & & 0.2697 \\ \(\mathbf{A_{5}}\) & & 0.2091 & & 0.1349 \\ \(\mathbf{A_{6}}\) & & 0.1682 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Score matrix of the alternatives for each expert. \begin{table}
\begin{tabular}{c|c c|c c|c c} \hline \hline \multirow{2}{*}{**Alternatives**} & \multicolumn{2}{c|}{**Min-Max**} & \multicolumn{2}{c|}{**GSNP**[25]} & \multicolumn{2}{c}{**DBM**[8]} \\ \cline{2-7}  & **Score** & **Rank** & **Score** & **Rank** & **Score** & **Rank** \\ \hline \(\mathbf{A_{1}}\) & 0.1131 & 3 & 0.1455 & 4 & 0.1350 & 4 \\ \(\mathbf{A_{2}}\) & 0.3190 & 1 & 0.1406 & 6 & 0.2670 & 2 \\ \(\mathbf{A_{3}}\) & 0.3123 & 2 & 0.1446 & 5 & 0.3037 & 1 \\ \(\mathbf{A_{4}}\) & 0.0831 & 5 & 0.2263 & 1 & 0.1381 & 3 \\ \(\mathbf{A_{5}}\) & 0.09561 & 4 & 0.1898 & 2 & 0.1070 & 5 \\ \(\mathbf{A_{6}}\) & 0.0769 &affect the final ranking result for the alternatives, the different methods to determine the weights of criteria may result in a different ranking of the alternatives. Actually, by virtue of the optimization Model (3), Table 12 shows that the ranking result is different from those by the other methods. Furthermore, the difference between Min-Max and DBM is less than that between Min-Max and the method in [25]. In other words, construction of optimization models to determine the weights is more promising for offering a believable priority of the alternatives. However, if we partition the ranking results into three groups according to the final scores in Table 12, then there exists a bit of similarity between Min-Max and DBM. The group ranked first is \(\{A_{2},A_{3}\}\); the group ranked second is \(\{A_{1},A_{4},A_{5}\}\); and the third group is \(\{A_{6}\}\). Table 12 demonstrates that the method (Min-Max) in this paper and DBM in [8] have the same partition.
## 3 Results and Discussion
### Statistical Analysis  
#### 3.2.1 The Relative Significance of Input Parameters  
The relative significance of input parameters (A--adjusting nozzle position, B--seat to valve distance), based on their impact, can be effectively illustrated using Pareto charts (Figures 27-29). The charts from Figure 27 indicate that, for fluid velocity, adjusting nozzle position emerges as the most critical factor in case of air, whereas in case of water, the seat-to-valve distance is the most significant factor. Regarding the shear stress and turbulent kinetic energy, for both fluids used in the analysis, the factor with the greatest significance is seat to valve distance, as indicated in Figures 28 and 29.  
The main effect plot graphs, presented in Figures 30-32, reveal a relatively similar evolution of results for both air and water fluids, except for the fluidvelocity parameter when the maximum value corresponds to the S0-H4 configuration for air, while for water, it corresponds to the S0-H3 configuration. As in Figure 28, it can be also highlighted that, in the case of shear stress, adjusting the nozzle position has a very small influence, since the line is almost horizontal (see Figure 31), suggesting that the response mean is the same across all factor levels.  
Figure 30: Main effects plot for fluid velocity: (**a**) air; (**b**) water.  
Figure 29: Pareto Charts for turbulent kinetic energy: (**a**) air; (**b**) water.