esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Noise Robustness Analysis  
For practical implementations, the most important characteristic of MCDDC is its error correction level and density of data encoding.  
Using noise pollution simulation for testing purposes, we determined how the container experimental error correction level depends on the normalized standard deviation (NSTD) value for MCDDC with a different number of gray levels. We used Gaussian noise with gradually increasing variance and \"one module--one pixel\" representation to determine the noise level before the first uncorrected errors occurred. This threshold value is the container experimental error correction level.  
For numerical simulation, we used 3 \(\times\) 30 sets of MCDDCs (with two, four and eight gray levels). Each MCDDC contained a text file in txt format of 5-30 kB size (the size was chosen so that after file encoding, no more than 10 MCDDC messages were received). MCDDC parameters were: message size--255 \(\times\) 255 modules, fuzzy outline width 10% and BCH input error correction level in the range 1-21.7%. For statistics, five noise distributions were generated for each MCDDC.  
The results of the simulation are presented in Figures 5 and 6. For comparison, we presented results for QR code with similar parameters (black circle).  
Figure 5: Dependencies of the experimental error correction level on the NSTD value for MCDDC with different number of gray levels (color online).  
As can be seen, experimental error correction levels lie in vastly different ranges for MCDDC with different number of gray levels. Binary MCDDC can withstand the maximal level of noise pollution but has a lower level of density of data encoding.  
It is important to note that MCDDC can have a range of error correction levels bigger than for input error correction levels. This is caused by the fact that noise errors usually lead to a deviation of a gray level per module by 1. However, each module contains several bits of information (in this case, 2 or 3 bits), and only one of them is damaged; the rest are \"protected\" from damage.
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 4 Discussion
### Effects of Soil Chemical Properties on the Soil Bacterial Diversity and Community Composition
An alternative hypothesis underlying the response of dominant phyla to the N addition is the functional classification model. Proteobacteria and Actinobacteria, that have fast growth rates, were more likely to increase in nutrient-rich conditions, while Acidobacteria and Chloroflexi that have slower growth rates, would likely decline [35; 54]. In general, Acidobacteria are adapted to low pH conditions [55]. However, after 12 months of added N, Proteobacteria were enriched under high N treatment, while interestingly, Acidobacteria declined. N2 and N3 treatments were shown to lower pH but, in comparison to the N0 treatment, Acidobacteria did not increase (Figure 3). The reasons for this behavior could be sought in the fact that effects of pH on the different groups of Acidobacteria can be very different [56]. In this sense, as confirmed by our study, pH might not necessarily be the main driver of changes for the Acidobacteria phyla as a whole. Moreover, correlations between soil pH and bacterial communities following N deposition do not necessarily demonstrate nor imply their direct causative relation. In comparison, soil ammonium nitrogen was an important environmental factor that appeared to explain many of the changes in the soil bacterial community structure observed in the present study. Specifically, N deposition directly affected urban green space soil bacterial communities in relation to the increase in soil ammonium. However, we noticed that also the ammonium in the N0 plot increased along time. Atmospheric N deposition is closely related to precipitation [57]. Previous studies have shown Guangzhou's inorganic N (NH\({}_{4}\)+-N and NO\({}_{3}\)-N) coming as precipitation displays a seasonal fluctuation, and NH\({}_{4}\)+-N was the primary form of N deposition in Guangzhou [58]. In 2011, the NH\({}_{4}\)+-N content in precipitation was 13.876 kg ha\({}^{-1}\), and NO\({}_{3}\)-N was6.562 kg ha\({}^{-1}\). Especially in summer, NH\({}_{4}\)+-N in precipitation reach 951.44 mg.m\({}^{-2}\) 9.514 kg ha\({}^{-1}\), while NO\({}_{3}\)-N was 4.484 kg ha\({}^{-1}\)[59]. Since our experiments were set in open air, and were therefore subjected to natural N deposition, this background can explain the observed rise of N also in the control plot. As regards the drop of total carbon that occurs in time, this can be seen as a consequence of the increase of N which is therefore no longer a limiting factor for carbon consumption and assimilation by soil biota, thus lowering the C/N ratio and pushing more carbon towards its mineralization and volatilization as CO\({}_{2}\).
# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 2 Related Work
To enhance the performance of \"Spray and Wait\". [PERSON] [24] proposed a new metric called Quality of Node (QoN) to measure the ability of a node to forward messages, and then proposed an adaptive Spray and Wait for routing algorithm based on QoN. [PERSON] et al. [25] combined the Spray and Wait strategy with social relations and proposed the SC-SS algorithm, which is an adaptive multi-jet waiting routing algorithm. In the actual network environment, the amount and priority of traffic are different, so the research on priority in routing strategy is valuable. [PERSON] and [PERSON] [26] proposed a new routing algorithm based on an efficient path routing strategy, it aimed to overcome the network congestion which is caused by a large number of traffic with different priorities. [PERSON] and [PERSON] [27] believed that in networks such as mobile ad hoc networks, messages are transmitted from node to node. From node to a base station, dynamic social grouping (DSG) can reduce bandwidth and delivery time. Therefore, the efficiency of the routing algorithm based on the grouping strategy is better. In the study of opportunistic social networks, [PERSON] and [PERSON] [7] proposed a method called the effective data packet iteration and transmission algorithm (EDPIT) to avoid the death of nodes. [PERSON] and [PERSON] [3] found in their research that the information transmission between nodes can be carried out through the broadcast model, so they proposed a low-latency algorithm based on continuous interference cancellation technology for opportunistic networks to improve propagation delay. Based on the symmetry problem in opportunistic social networks, [PERSON] and [PERSON] [6] established a message repetitive adaptive distribution and jet routing strategy (MDASRS) algorithm model, which used social pressure to measure the strength of connections between nodes to achieve the effect of reducing network burden and network overhead. The above research on routing algorithms have discussed how to improve the efficiency of message transmission in opportunistic social networks from various perspectives, but none of them reconstruct the social network according to the actual situation, and the quality of message transmission is not high. In this paper, researchers established a community model in an opportunistic social network, and established a FL distributed training system architecture based on deep reinforcement learning within the community to realize the screening of malicious nodes and the selection of heterogeneous device nodes. In contrast to previous scholars' work, such preparations can ensure the efficiency of message transmission in opportunistic social networks and improve the performance of node selection algorithms. The rest of this paper will mainly introduce the node selection method and FABD algorithm based on federated learning.
# Multivalent Aptamer Approach: Designs, Strategies, and Applications
## 5 Challenges and Outlook  
Multivalency is a strategy inspired by nature. In the last decade, multivalent aptamers have attracted a significant amount of interest as a promising solution to overcome the intrinsic challenges facing aptamer monomers by significantly improving avidity and stability. The rapid growth of research in material science further accelerates the ongoing development of multivalent aptamer-based devices. Encouraged by the significant advantages held by this approach, recent research is now focusing on developing constructs that excel in performance and possess potentials for use in real-life applications. However, there are many challenges that need to be addressed before the potential of the multivalency strategy can be fully realized.  
Although the enhancement of local concentration is one of the advantages of multivalent aptamers, in some cases, it is not optimal to follow the idea of \"the more, the better\", but rather, \"less is more\". When designing an aptamer device, one must ensure the aptamers are properly positioned to expose their binding pockets to the greatest extent. In order to achieve this, different types of structural elements are often used, and the effects of these structural elements are critically important and warrant further investigations.  
Spatial arrangements of aptamers is another important factor to consider in multivalent aptamer design. Proper spatial arrangements, optimal positioning, and correct orientation of aptamers can also significantly enhance overall avidities. As discussed, there has been some work performed to investigate the proper geometries between neighboring aptamers, with significantly higher avidities observed at certain geometric configurations. To properly configure the spatial arrangements of the aptamers in designing multivalent aptamer constructs, a deep understanding of target morphologies, ligand alignments, and aptamer-target interactions is necessary, and more research in this area is needed.
## 3 Results
### Anti-Lipogenic Effect of ARE and Tilianin on mRNA and Protein Expression Level  
Lipogenesis includes fatty acid biosynthesis involving the transcription factor SREBP1c and enzymes, such as ACC and FAS. Results in Figure 5A indicate that ARE treatment downregulated fatty acid synthesis and thus inhibited lipogenesis. As shown in Figure 5B, tilianin dose-dependently caused significant changes in the mRNA levels of the lipogenesis-related genes such as SREBP1, ACC and FAS in dose dependent manner. Figure 5D,E indicated the expression levels of SREBP1 protein regulated by ARE and tilianin, respectively. In addition, SREBP1 protein levels showed the same relationship with ARE and tilianin concentrations. Overall, ARE exhibits both anti-adipogenic and anti-lipogenic effects, which could be influenced by tilianin, a major component of ARE.  
Figure 3: Antiadipogenic effect of ARE and tilianin. 3T3-L1 cells were seeded in 12 well and oil-red-O staining was assessed after treated with differentiation media. With DMSO, oil-red-o was dissolved and the absorption was estimated using spectrophotometer (OD\({}_{675}\)). Effect of (**A**). ARE and (**B**). tilianin on adipogenic differentiation. Lipid accumulation (% control) quantification in the (**C**). ARE-, (**D**). tilianin-treated groups. ND, nondifferentiated; Control, untreated. Data are means of n = 6. Different letters (a–e) in each graph indicate significantly different values in samples (\(p<0.05\), ANOVA Duncan’s test).
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction  
This paper concerns application of the algorithms for detection, assessment and resolution of numerical locking in the \(hpq\)-adaptive finite element elastic analysis of thin-walled structures or complex structures which include thin-walled, solid, and transition parts. We consider all cases when the influence of the locking phenomenon on the problem solution is significant. We focus on the theoretical and methodological aspects such as the idea and justification of the elaborated algorithms for a posteriori detection and assessment of the phenomenon. In addition, the necessary modification of the applied \(hpq\)-adaptive algorithms is of our interest. The employed model- and \(hpq\)-adaptive method allows for different element size \(h\), different element longitudinal and transverse orders of approximation, \(p\) and \(q\), and different element model \(M\) in each finite element. The paper also presents application of the introduced detection, assessment, and resolution algorithms in the \(hpq\)-adaptive analysis of structural elements. These algorithms are investigated in the contexts of their generality, reliability, and effectiveness.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.9 SS MAC  
Slot-sharing MAC was proposed to provide a scalable, reliable, efficient protocol with less delays for safety message broadcasting. Multiple vehicles broadcast alternately on the same time slot using inconsistent coordinations. A circular recording queue is implemented to record the time slot status of the periodic broadcast of safety messages. A distributed time slot sharing (DTSS) mechanism is designed to check on the periodical broadcasting of messages and to share the time slots for reliable transmission. To improve the channel utilization based on heuristic packaging strategy, a random index fit-first (RIFF) scheme is proposed [84]. This assists the vehicles in selecting a suitable time slot for sharing. It employs a traditional TDMA-based mechanism with a slot sharing scheme.  
The major demerit is that the protocol cannot handle dynamic resource environments, and safety and non-safety applications. There is overhead for maintaining the table and broadcasting.
# Influence of Structural Parameters on Deformation Behavior of Metal Rhombic Chain Mesh
## 4 Discussion  
Table 4 shows the reduction magnitude of vertical displacement at various points under the aforementioned conditions. The results indicate that netting solidity significantly influences the deformation of metal diamond chain netting. Overall, the vertical displacement at measurement points notably decreases with increasing netting solidity, showing similar trends under both concentrated and distributed load conditions, with the maximum displacement occurring at the central measurement point. Under identical initial pretension conditions, compared to the displacement at solidity 0.11, the average displacement reduction at measurement points for solidities of 0.17 and 0.29 is 53.07% and 86.38% under concentrated loads and 68.68% and 74.72% under distributed loads, respectively. This indicates an accelerating rate of displacement reduction with increasing netting solidity. Under identical solidity conditions, using the displacement at 200 N initial pretension as a reference, when initial pretension increases to 250 N and 300 N, the average reduction in vertical displacement at measurement points shows specific patterns. The results demonstrate that initial tension significantly affects metal diamond chain netting  
Figure 14: Schematic diagrams of mesh coat deformation under centralized and uniform loads with different initial pretensions.  
deformation, with measurement point displacements clearly decreasing as initial pretension increases. The average reductions in vertical displacement at measurement points are 3.48% and 8.07% under concentrated loads and 3.52% and 8.14% under distributed loads.  
Table 5 presents the reduction magnitude of maximum force at measurement points when solidity increases to 0.17 and 0.29, using the maximum force at 0.11 solidity as a reference, under both concentrated and distributed loads. The results show that solidity significantly influences metal diamond chain netting deformation, with measurement point forces notably decreasing as solidity increases. Under an initial pretension of 200 N, for netting solidities of 0.17 and 0.29, the force reduction at measurement points is 71.89% and 85.03% under concentrated loads and 41.55% and 66.34% under distributed loads, respectively.  
According to the static equilibrium equations considering friction, as netting solidity and initial pretension increase, the total contact area at diamond chain netting nodes increases, leading to higher contact pressure. This results in reduced relative displacement and rotation angles between wires and decreased measurement point displacements and forces. Consequently, the bending resistance of the diamond chain netting increases, with netting deformation decreasing as solidity increases and the bending resistance of metal diamond chain netting increasing with initial pretension.
## 4 Establishment of the Constitutive Model
### Model Validation  
In the past, most of the verification of Mogi-Coulomb criterion has been based on loading conditions, but it has been less focused on the verification of both loading and unloading conditions. In this validation, in order to prevent the over-parameterization of the intrinsic equation in the fitting process and the failure of the fitting, Equation (23) is transformed, and the equivalent constitutive equation is constructed using \(A=\alpha\) and \(B=\beta\cdot\frac{1+\mu}{2}\).  
\[\sigma_{1}=E\varepsilon_{1}\exp[-((A+B\cdot E\varepsilon_{1})/\lambda_{0})^{ m}] \tag{24}\]  
In the process of model validation, the basic physical parameters of the rock are as follows: \(\varphi\) is 45.42\({}^{\circ}\) and \(c\) is 10.02 MPa. When the data fitting effect of Equation (24) is better, the rationality of the intrinsic model can be confirmed. First, according to the cyclic loading test, the loading and unloading test curves under each cycle amplitude were obtained. Then, using the nonlinear fitting function of the Origin software, combined with the model relationship (24), a new custom fitting function was established with \(m\) and \(\lambda_{0}\) as parameters. The applicability of the model's establishment was quickly determined according to the fitting correction coefficient \(R^{2}\) through the rapid iteration of the Levenberg-Marquardt method embedded in the software. The fitting curves of the loading stage amplitudes of 30 MPa and 90 MPa and the unloading stage amplitudes of 30 MPa and 80 MPa were randomly selected and compared with the test curves. The comparison results are shown in Figures 9 and 10.  
As shown in Figures 9-11, except for some deviations in the initial stage, the fitting degree between the theoretical curve and the experimental curve is high. According to the fitting parameters of each cycle level in Table 1, the fitting correction coefficient \(R^{2}\) of the fitting curve of the test data is above 0.99, which also shows that the rock intrinsic relationship has good applicability and rationality. According to the comparison between the loading phase and the unloading phase, the fitting effect of the loading section was better than that of the unloading section. Moreover, the fitting effect of loading and unloading curves with different amplitudes was compared. With the continuous accumulation of rock damage, the fitting effect of the rock test curve is better.  
Figure 9: Curve fitting of loading section: (**a**) 30 MPa. (**b**) 90 MPa.  
to the loading-unloading amplitude, and the parameter \(m\) is inversely proportional to the loading-unloading amplitude. The \(m\) value of the rock increases slightly under the sixth and seventh cyclic loading-unloading in the rock, approaching the failure stage. The relationship between parameters \(\lambda_{0}\), \(m\) and the amplitude are shown in Figure 12.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 1 Introduction
Section 2 provides an overview of private 5G deployment efforts to date along with a literature review of meshed FWA. Then, Section 3 presents the problem we aim to address and our deployment model. Section 4 describes our meshing experiments, and Section 5 presents the evaluation results. Finally, we provide our conclusions and further work in Section 6.
## 4 Results
\begin{tabular}{c} 1-40 Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{ \begin{tabular}{c} 5 Hz, 6 Hz, 7 Hz, 8 Hz, 10 Hz, 12 Hz, 14 Hz, 16 Hz, \\ 15 Hz, 18 Hz, 21 Hz, 24 Hz \\ \end{tabular}  } \\  & & & & & & & & & & & 15 Hz, 18 Hz, 21 Hz, 24 Hz \\ \hline \multirow{2}{*}{ \begin{tabular}{c} Training the classifier on \\ data \\ \end{tabular}  } & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{N} & \multirow{2}{*}{Y} & \multirow{2}{*}{Y} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{N} & \multirow{2}{*}{N} & \multirow{2}{*}{N} & \multirow{2}{*}{N} & \multirow{2}{*}{N} \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{\ \begin{tabular}{c} Feature selection \\ \end{tabular}  } & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{ \begin{tabular}{c} SPS \\ 25 features \\ \end{tabular}  } & SFS & \(t\)-test & \(t\)-test \\  & & & & & & & & 25 features & features & features \\ \hline \multicolumn{11}{c}{Accuracy} \\ \hline User S01 & 0.81 & 0.75 & 0.58 & 0.62 & 0.62 & 0.75 & 0.65 & 0.66 & 0.62 & 0.63 & 0.68 & 0.76 \\ \hline User S02 & 0.88 & 0.54 & 0.51 & 0.61 & 0.61 & 0.59 & 0.56 & 0.40 & 0.48 & 0.47 & 0.61 & 0.50 \\ \hline User S03 & 0.42 &\(p\) = 0.0625. The improved classification accuracy of the CNN network may be due to its ability to automatically generate features. In contrast, other algorithms--whether specialized for SSVEP interfaces (like CCA and sMP) or standard machine learning methods (such as QDA, LDA, QDA-SFS, LDA-SFS, QDA-T, LDA-T)--relied on features derived from frequency analysis. Table 3 presents the calculated F1-scores for various SSVEP potential classification methods. Among these, the CNN method attains the highest average F1-score of 0.63. Other methods, including CCA, MLP, QDA, and LDA, exhibit comparable results, with their average F1-scores ranging approximately from 0.44 to 0.47. This range indicates a moderate level of effectiveness for these techniques in SSVEPs classification. The sMP method recorded the lowest F1-score at 0.38, suggesting its comparatively limited utility. Meanwhile, the QDA and LDA methods, after incorporating SFS feature selection and the \(t\)-test, achieved F1-scores between 0.37 and 0.42. Overall, these findings imply that the CNN method is the most effective for SSVEP classification, whereas the other techniques demonstrate similar yet generally lower levels of effectiveness. Future research should consider expanding the training dataset with EEG recordings from a greater number of individuals and employing different methods of stimulation, as well as various EEG signal acquisition systems.