esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Cohesive Subgraph Search
#### 4.3.2 Biclique Model  
**Definition 10** (Biclique).: _Given a bipartite graph \(G=(U\cup V,E)\) where \(U\),\(V\) is vertices on different sides of \(G\), and \(E\) is edges between \(U\) and \(V\), a biclique \(\mathcal{C}\) is a maximal subgraph in \(G\), s.t., each vertex in \(C\cap U\) is connected to each vertex in \(C\cap V\)._  
Figure 4 shows a bipartite graph \(G\) with \(U(G)=\{U_{1},U_{2},\ldots,U_{7}\}\), \(V(G)=\{V_{1},V_{2},\ldots,V_{6}\}\). As the maximum biclique \(\mathcal{C}\), we have \(U(C)=\{U_{3},U_{4},U_{5},U_{6}\}\), \(V(C)=\{V_{2},V_{3},V_{4},V_{5}\}\).  
On dynamic bipartite graph with adding batch edges over time, Ref. [66] solves the problem of incremental maintenance for maximal bicliques(MBE). Following the key idea for clique maintenance of [63; 65], Ref. [66] proposes a change-sensitive incremental maintenance algorithm i.e., its time complexity is proportional to the magnitude of change in the set of maximal bicliques. In other words, all bicliques enumerated are exactly the newly added bicliques or removed bicliques. Ref. [67] proposes a novel algorithm for enumerating maximal bicliques in a dynamically changed graph, focusing only on newly created or reduced maximal bicliques rather than re-searching the entire graph. This approach leverages previously enumerated results to efficiently update the biclique enumeration after each graph modification.  
**Algorithm analysis.** We compare the maximal clique enumeration (MCE) and maximal biclique enumeration (MBE) algorithms over dynamic graphs, respectively. As shown in Table 4, most algorithms can handle incremental dynamic graphs with edge/vertex insertion. For the time complexity, [PERSON]. [62; 65] propose algorithms whose worst-time time complexity is bounded by a polynomial in \(W\) and \(n\) where \(W\) represents the number of cliques and \(n\) represents the number of vertices. In [63; 66], the time complexity depends on several factors including the maximum degree, the size of update edge set and the new formed clique or biclique on the updated graph. In addition, existing works demonstrate limitations when applied to fully dynamic graphs and streaming graphs, as most algorithms are memory-based and rely on single-threaded execution, restricting their scalability and efficiency in more complex and dynamic scenarios.  
Figure 4: Example of biclique.
## Appendix B NRMSE
### Correlation Plot  
Figure 15 explains the correlation plot for the NRMSE metric, the ensemble model with multiple annotations on the x-axis, and the remaining models on the y-axis. When comparing the MA model to other models for the Knee dataset, the plot shows a positive correlation, indicating strong agreement. However, this outcome is not robust due to thenumber of samples in the Knee dataset. For the remaining dataset, when the MA and SA models were compared, the plot for the SJI dataset shows a vertical line on the plot's left side, which clearly reveals that the MA model outperforms the SA model. The Bland-Altman plot shows the same conclusion when the MA model scored \(-21\%\) average bias compared to the SA model. For the Lung dataset, the SA model scored better results, yet the points are evenly spread around the line, showing a positive correlation between the models. Also, the plot shows multiple points in the [0.5-1] x-axis range, revealing the MA low scoring. Both models perform well for the Heart dataset, yet for HHS, there is a broader spread of points interpreted in the box plot as a bigger IQR for the MA model. Furthermore, when the MA model was compared with MC versions, like the GDC metric in the Lung dataset, a cluster of points in the bottom left indicates that all MC models outperform the MA model. The Bland-Altman plot draws the same conclusion, in which all MC models score above +20% average bias, which also suggests that the MC dropout rate has a limited effect on the model's performance. For the HHS dataset, the MA scored better NRMSE; however, MC, with 40% and 60% dropout rates, scored better for segmenting the Lungs. Moreover, the points are more spread out when the dropout rate increases, suggesting correlation decreases when the dropout rate increases. For the SJI dataset, the MA model and MC models perform equally, with strong agreement between the models. Finally, comparing the MA and regression models for the Heart dataset revealed that the regression model performed better (opposite of NMI), yet there is a positive correlation indicating consistency between the models (similar to NMI). For the Lung dataset, the points are spread all around the plot
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
###### Abstract
[7] used finite state machines to describe the system information, but only used the information in the state machines manually and therefore could not acquire the dynamic process of loss scenario rigorously. State machine is an effective, sufficient and convenient way to construct system behaviors. State machine is also supported by many modeling languages, such as SysML, AADL and AltaRica, and could be analyzed by either simulation or model checking. Therefore, state machine is ideal method to build the system behaviors for the identifications of UCAs and loss scenarios. [PERSON] et al. [8] applied the \"four-variable model\" to identify the variables that are used in the analytical model. Although the truth table was used to describe the relationships between some of the variables, the system behavior was not adequately described. [PERSON] et al. [9] agreed that STPA lacks formalism and proposed a method that combines STPA and SysML modeling activities, but did not change the STPA method itself. [PERSON] et al. [10] constructed the analytical model with Petri Net. Despite its mathematical formality, Petri Net is not as popular as SysML, which is the de facto standard for model-based systems engineering. [PERSON] et al. [11] put forth ISTPA (Improved STPA), having contributed the following improvements to the formalization of STPA:  
* Using CA-PR (control action that controlled process receives) to update the definition of UCA. * Defining the loss scenario as the process in which the system emerges UCA and system-level hazard with the involvement of causal factors. * Using the finite state machine to describe the system behavior and using model checking [12] to identify the loss scenario. Nevertheless, ISTPA still needs to be instantiated for its application. Based on ISTPA, STPA-SN (STPA based on SysML/MARTE and NuSMV) is put forth to demonstrate how to solve the aforementioned two drawbacks of HSTPA. Herein, NuSMV [13] is an open-source model checker and MARTE (Modeling and Analysis of Real-Time and Embedded systems) [14] is specification of a UML profile that adds capabilities to UML for the model-driven development of real-time and embedded systems. In the following sections, STPA-SN is first introduced, then an application example of STPA-SN is presented; next, the differences against HSTPA are discussed; and finally, both the advantages and disadvantages are summarized. The readers should be familiar with HSTPA, NuSMV, SysML and MARTE, otherwise the following contents might be impenetrable.
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
## 2 Numerical Modelling
### Description of the Modeling Method
#### 2.1.1 Constitutive Model for Recycled Aggregate Concrete (RAC)  
The \"con_ma\" model [52; 53; 54] available in SeismoStruct was used to represent the uniaxial constitutive behavior of RAC. This model can be completely determined by the following three key properties: the elastic modulus (\(E_{\mathrm{c}}\)), the cylindrical compressive strength (\(f_{\mathrm{c}}\)), and the peak strain (\(\epsilon_{\mathrm{co}}^{\mathrm{r}}\)). Based on a thorough review and extensive experimental database of RAC, [PERSON] et al. [55] have proposed an accurate stress-strain relationship for obtaining\(f_{\mathrm{c}}\) and \(E_{\mathrm{c}}\) of RAC:  
\[f_{\mathrm{c}}(\mathrm{MPa})=\frac{23.5\times 0.998^{\prime}\times(w_{\mathrm{ eff}}/c+0.09)}{w_{\mathrm{eff}}/c^{1.7}}, \tag{1}\]  
\[E_{\mathrm{c}}(\mathrm{GPa})=0.016\times(6.1-0.015r)\times(5.3-1.7w_{\mathrm{ eff}}/c)^{3.9}, \tag{2}\]  
where \(r\) = the replacement ratio of RCAs (\(0\%\leq r\leq 100\%\)), and \(w_{\mathrm{eff}}/c\) = the effective water-to-cement ratio (\(0.3\leq w_{\mathrm{eff}}/c\leq 0.8\)).  
[PERSON] et al. [56] suggested an equation to determine the peak strain of RAC (\(\epsilon_{\mathrm{co}}^{\mathrm{r}}\)) as a function of \(r\):  
\[\epsilon_{\mathrm{co}}^{\mathrm{r}}=\epsilon_{\mathrm{co}}^{\mathrm{n}}\bigg{(} 1+\frac{r}{65.715r^{2}-109.43r+48.989}\bigg{)}. \tag{3}\]  
Figure 1: Loading scheme, boundary conditions, and fiber discretization for reinforced recycled concrete (RRC) columns.  
The value of \(\epsilon_{\rm co}^{\rm n}\) in the above equation was determined based on the work by [PERSON] and [PERSON] [57]:  
\[\varepsilon_{\rm co}^{\rm n}=\frac{f_{\rm c}^{0.225w_{\rm d}}}{1000}w_{\rm s}w_{ \rm a};\,w_{\rm d}=\left(\frac{2400}{\rho_{\rm c,f}}\right)^{0.45};\,w_{\rm s}= \left(\frac{152}{D_{\rm c}}\right)^{0.1};\,w_{\rm a}=\left(\frac{2D_{\rm c}}{H_ {\rm c}}\right)^{0.13}, \tag{4}\]  
where \(\rho_{\rm c,f}=\) the bulk density of concrete (2250 kg/m\({}^{3}\leq\rho_{\rm c,f}\leq\) 2550 kg/m\({}^{3}\)); \(D_{\rm c}\) and \(H_{\rm c}\) = the diameter and height of cylinder concrete samples, respectively (50 mm \(\leq D_{\rm c}\leq\) 400 mm, 100 mm \(\leq H_{\rm c}\leq\) 850 mm). \(w_{\rm d}\), \(w_{\rm s}\), and \(w_{\rm a}\) are, respectively, the coefficients accounting for the concrete density and the samples' aspect ratio. It should be highlighted that \(w_{\rm d}=w_{\rm s}=w_{\rm a}=1.0\) for the common NAC samples. Consequently \(\rho_{\rm c,f}=\) 2400 kg/m\({}^{3}\), \(D_{\rm c}\) = 152 mm, and \(H_{\rm c}/D_{\rm c}\) = 2.0.
# Blockchain Architecture for Lightweight Storage
## 5 Evaluation
### Application Scenario  
Blockchain technology provides a framework for establishing reliable message communication and privacy protection within the Internet of Vehicles. However, as data volume increases, many devices, such as mobile phones and in-vehicle systems, encounter difficulties in storing high-capacity blockchain copies. LSB retains the advantages of traditional blockchain technology while mitigating over-reliance on storage resources.  
For instance, in the emerging in-vehicle social network, drivers utilize social software to access a range of messages released daily, including traffic updates, social information, and news. However, users have diverse needs for different types of information; for example, taxi drivers prioritize traffic conditions, while tourists are more interested in local attractions. As the volume and variety of data grow, it becomes increasingly important to classify and selectively store the information.  
Figure 12: Malicious node removal.  
Figure 13: Number of blocks generated.  
In LSB, messages can be categorized by wallet addresses. For example, in the in-vehicle network, messages sent by Newspaper A can be stored in light node A corresponding to wallet_address A. Similarly, the news from Newspaper B is stored in light node B. Drivers who prefer reading Newspaper A can quickly search for the news associated with Newspaper A using wallet_address A and download it to the in-vehicle system, rather than backing up the entire blockchain.
## 3 Results and Discussion  
Influence of Non-Pre-Treated and Extruded/Fermented Wheat Bran on Dough Color Characteristics and Hardnessuntreated wheat bran, hardness was increased by increasing the quantity of untreated wheat bran in the dough formulation. However, samples prepared with 5% untreated wheat bran showed the same hardness as the control group samples (0.2 mJ). Comparing doughs prepared with extruded/fermented wheat bran with those of the control group, no significant differences in hardness were found between groups (for both groups, hardness was, on average, 0.2 mJ). A first characteristic of wheat bran that should be pointed out is its water absorption, which is different to that of endosperm flour [2]. However, after extrusion, as well as after fermentation, wheat bran characteristics change, and in this study, the hardness of dough prepared with extruded/fermented wheat bran was the same as that of the control dough. In addition, it should be mentioned that in this study, by increasing the wheat bran content in the main bread formula, water content, also, was additionally added. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [13] reported that untreated bran had a more negative influence on dough properties in comparison with extruded bran. Pre-treatment of wheat bran using extrusion leads to the solubilization of dietary fiber compounds [14], and extruded bran has a smaller particle size than non-extruded bran, which also has an influence on dough properties [15]. The negative influence on dough quality is attributed to the pre-treated bran disrupting the gluten network more, due to increased flour-bran contact for small particles [15; 16]. However, in this study, extruded/fermented wheat bran was used, and such a type of product could be used as an alternative sourdough, because of the high number of viable LAB and low pH, as well as high total titratable acidity values [6]. Finally, no differences in dough hardness were established by including extruded/fermented wheat bran in the main bread formula compared with control doughs. The results of the ANOVA test indicated that there is a significant effect of adding untreated or extruded/fermented wheat bran (\(p<0.05\)) and in different quantities (\(p<0.05\)) on dough color characteristics (L*, a*, b*) and dough hardness. Influence of Non-Pre-Treated and Extruded/Fermented Wheat Bran on Bread Quality Parameters and Changes in Bread Firmness during Storage  
The main bread quality characteristics (specific volume, porosity, shape coefficient, mass loss after baking, and color characteristics of the bread crust and crumb) are shown in Table 2. In comparing the specific volume of the different bread groups, it was found that by increasing the quantity of untreated wheat bran in the main bread formula, the specific volume was reduced by 2.2%, 12.1%, and 23.2% compared with the control for bread prepared with 5%, 10%, and 15% bran, respectively. Opposite to these findings, the addition of extruded/fermented wheat bran to the main bread formula did not cause any undesirable  
\begin{table}
## 2 Survey of Commercial Small Platforms
### Passive Optical
#### 3.2.1 Radiometer: Multispectral and Hyperspectral  
Table 14 presents the features of the available multispectral and hyperspectral radiometers instruments, in terms of wavelength, spatial resolution, aperture size, swath, mass, power consumption, and data rate. The variables of interest that can be measured with optical sensors for the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress use cases are the Sea Surface Temperature (SST), atmospheric pressure over the sea surface, ocean chlorophyll concentration, ocean imagery and weather leaving radiance, Color Dissolved Organic Matter (CDOM), detection of water in crops, estimation of crop evapotranspiration and the sea ice cover.  
A good example of multispectral radiometer on micro-platform is AVHRR/3 [61] and also has good performance, and it could support the measurements with detected gaps, such as SST, ocean chlorophyll concentration, ocean imagery and weather leaving radiance, CDOM, detection of water in crops, estimation of crop evapotranspiration, sea ice cover, and atmospheric pressure over the sea surface (it can be inferred through measurements in the infrared band).
# Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review
## 4 Passive and Active Suppression Techniques
### Passive Chatter Suppression Techniques  
Passive chatter suppression methods focus on the implementation of external devices with the aim of modifying the stiffness or damping to the machine tool or the tool holder. Such methods are based on structure modifications or parameter optimization, which can be limited to physical restrictions.  
[PERSON] et al. [101] tuned a holder for turning by varying the axial position of a sleeve. The modification of the tool-dynamics using dynamic vibration absorbers (DVA), tunable vibration absorbers (TVA) or tuned mass damper (TMD) have the purpose of increase the chatter boundaries. The DVA is a passive device with a spring-mass-damper attached to the tool holder (Figure 6), when tuned properly, it reduces the peak magnitude of the frequency response function. [PERSON] et al. [102] and [PERSON] [103] applied a passive absorber to enhance the stability of a boring process. [PERSON] and [PERSON] [104] optimized the mass and stiffness distribution yielding better chatter-resistance for turning bars. The scope of their study was to reduce chatter vibrations when the cutting tool is the most flexible part of the system. However, they did not present any experimental evidence to support their theoretical calculations.  
[PERSON] et al. [105] modified the cutting system's FRF (Frequency Response Function) and selecting proper dimensions for the assembly tool-toolholder-spindle. The methodology was proposed to increase and predict allowable stability for system where tool length can be varied or not. However, this technique was applied to milling systems.
## 3 Underlying Terminology and Concepts
### Validation Stage and Maturity Matrix
While the previous definitions are mainly focusing on bio-geophysical variables, the EDAP quality matrix is a cross-cutting concept, which can be equally applied to any satellite-based product level. This matrix contributes to the effort of standardisation in the quality assessment procedures for EO satellite data. The EDAP matrix is therefore pivotal in moving towards enhanced interoperability in the EO domain.
# An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans
## 3 Results and Discussion
### Aerodynamic Optimization with IMOGWO
#### 3.2.2 Optimization Results  
Figure 16 gives the convergence history of the normalized pressure rise (\(\overline{p_{r}}\)) and total pressure efficiency (\(\overline{\eta}\)). For the convenience of optimization, it should be noted that the normalized pressure rise and total pressure efficiency are defined as shown in Equations (31) and (32) and minimized in the optimization process. Conversely, the pressure rise and total pressure efficiency can be improved.  
\[\overline{p}_{r}=\frac{420-P_{r}}{420} \tag{31}\]  
\[\overline{\eta}=\frac{1-Eff_{tp}}{0.22} \tag{32}\]  
It can be found that after eight iterations, the iteration loop ends. The improvement of total pressure efficiency is more obvious than pressure rise. In addition, Figure 17 shows the Pareto-optimal front regarding the aerodynamic performance of the cooling fan obtained by the IMOGWO. The non-dominant solutions are clustering in the lower right corner. Therefore, the proposed IMOGWO algorithm, by generating a diverse set of high-quality non-dominated solutions, enables more flexible and informed decision-making. Rather than providing a single optimal design, it offers a spectrum of viable solutions across the Pareto front, allowing decision-makers to choose configurations that best meet industrial priorities such as cost constraints, performance targets, or manufacturability.  
Figure 15: Single-passage fluid computation domain.  
The aerodynamic performance of the baseline and optimized design is presented in Figure 18. In the stable region (0.12-0.16 m\({}^{3}\)/s), the total pressure efficiency and pressure rise have been improved. At the design point (black dot), the optimized total pressure efficiency is increased by 3.2%, and the pressure rise is increased by 2.75%.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 3 Methods
### Improved Dung Beetle Optimization Algorithm
#### 3.2.3 OGolden-DBO-XGBoost Chl-a Concentration Inversion Model  
The flowchart of the OGolden-DBO-XGBoost Chl-a concentration inversion model is displayed in Figure 5. The XGBoost model's parameters and complex tuning were significantly improved by adjusting the model's parameters using OGolden-DBO.  
The specific steps of the OGolden-DBO-XGBoost Chl-a concentration inversion model are as follows:  
1. Input of the remotely sensed image data of Wuliangsu Lake and the measured data of Chl-a concentration.
2. Building of the XGBoost inversion model.  
Figure 5: Flowchart of the OGolden-DBO-XGBoost Chl-a concentration inversion model.  
3. Initialization of the dung beetle population and parameters.
4. According to Figure 3, calculation of the reverse population of the dung beetle population, and selection of the better individuals from the dung beetle population and its reverse population to form new population.
5. Updating of position of dung beetle population based on improved rolling (Equation (10)), spawning, foraging and stealing behavioural strategies. Then calculation of fitness of population.
6. Updating of the location and fitness of the best dung beetle.
7. Determination of whether the stopping condition is satisfied; if the condition is satisfied execute step (8), otherwise execute step (5).
8. Outputting of the location of the best dung beetle (the best parameters of the XGBoost Chl-a concentration inversion model).
9. Training of the XGBoost Chl-a concentration inversion model based on the best parameters obtained by the OGolden-DBO algorithm.
10. Obtaining of the Chl-a concentration inversion results for Wuliangsu Lake and calculation of the model rating index R\({}^{2}\), RMSE, and MAE to evaluate the model performance.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 7 Conclusions  
This paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. In this paper, it is proven that the proposed channel estimation method can provide good SER performance whenever under the static multipath channels or dynamic multipath channels. When the statistical frame is five, the confidence level channel estimation method has the best SER performance compared to the SER performance with the statistical frame equal to seven, nine and eleven.  
Compared with the conventional block-type pilot-based LS, IMMSE, threshold value method and Chi-square distribution method, the proposed confidence level channel estimation method has the best SER performance. The proposed confidence level channel estimation method is easy to implement in hardware and has broad market prospects that can be applied in underwater acoustic communication  
Figure 10: SER performance of the 16 QAM-modulated CP-OFDM system over Brazil D under \(T_{1}=5\).  
with multiple-input multiple-output (MIMO) technology [19], RAKE receiving technology [20] and frequency hopping technology [21; 22].  
[PERSON] and [PERSON] conceived of the algorithm and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the results. [PERSON] and [PERSON] drafted the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] revised the manuscript. All authors read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61702303) and the Shandong Provincial Natural Science Foundation, China (Grant Nos. ZR2017 MF020 and ZR2015 PF004).  
The authors declare no conflict of interest.