esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Simulation Results
#### 4.2.2 PER for LTE-V2X and NR-V2X Protocols  
Figure 5 compares the packet error rate of the NR-V2X and LTE-V2X protocols for the transmission of 375 bytes.  
Figure 4: Comparison of (**A**) PER vs. SNR and (**B**) PER vs. distance for 802.11p and 802.11 bd with payload of 375 bytes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **802.11p/bd** & **LTE-V2X/NR-V2X** \\ \hline Symbol duration & 6.4 μs & 16.7 μs \\ \hline Cyclic prefix & 1.6 μs & 1.17 μs \\ \hline Modulation and coding & MCS2 (QPSK), MCS4 & MCS2 (QPSK), MCS7 (QPSK), \\ schemes & (16-QAM), MCS7 (64-QAM) & MCS11 (16-QAM) \\ \hline Carrier spacing & 156.25 kHz & 15 kHz, 30 kHz, 60 kHz \\ \hline n\({}^{\circ}\) subcarriers & 48 & 132 \\ \hline \end{tabular}
\end{table}
Table 8: _Cont._Figure 5A analyzes the PER as a function of the SNR. In general, for all the MCS options analyzed, NR-V2X tends to perform better than LTE-V2X for NSR values higher than 5 dB. NR-V2X offers a better PER than LTE-V2X due to the use of advanced interference cancellation techniques, such as beamforming or advanced MIMO systems, and the use of LDPC codes.  
Figure 5B analyzes the PER as a function of the distance. In general, for all the MCS options analyzed and even with increasing distanced, NR-V2X shows lower PER values when compared to LTE-V2X. Once again, this is due to the use of advanced interference cancellation techniques, such as beamforming or advanced MIMO systems, and the use of LDPC codes.  
Tables 5-7 detail the values of each element of the equations of the mathematical method used (\(n_{sym}\), \(n_{dc}\), \(n_{psps}\), etc.), and Figures 2-5 of the simulations highlight the performance of the protocols studied. The procedures and parameters for obtaining the data shown in this manuscript will make it easier for other authors to replicate the data and will serve as a solid foundation for new studies into V2X communications.  
The methodology used in this study, based on official documents from IEEE, 3 GPP and relevant scientific articles, guarantees the reliability of the results obtained. The combination of mathematical methods and detailed computer simulations enabled a comprehensive and accurate assessment of the subject in question.
## 2 Materials and Methods
### Delineation of Drought Region
#### 2.3.2 Model Input Data  
**Meteorology data**: Regional daily meteorology data (maximum air temperature, minimum air temperature, relative humidity, total precipitation, and sunshine duration) for the whole China at a resolution of 1 km \(\times\) 1 km were obtained by using the ANUSPLIN algorithm to interpolate the 674 meteorology stations' data (provided by the Chinese National Meteorological Information Centre) to 1-km resolution pixels. Daily incoming solar radiation data was only observed in 122 stations, and missing data was generated by the Angstrom equation and inputs from the empirical relationship  
Figure 2: The area and extent of droughts in 2009 and 2010 based on 3-month standard precipitation index (SPI) from September 2009 to March 2010. (**a**) The mean 3-month SPI from September 2009 to March 2010; (**b**) minimum 3-month SPI from September 2009 to March 2010, which calculated by the minimum SPI during drought period based on pixel by pixel; (**c**) the corresponding month for minimum SPI during the drought period.  
between radiation and sunshine duration in the rest of the meteorological stations according to the method of [PERSON] et al., (2010) [PERSON] et al. (2010).  
**Remote-sensing data**: In the BEPS model, land-cover information is used to specify plant physiological parameters that differ among land cover types. The land-cover map for the study area was derived from a nation-wide land-use map at a 30-m resolution that was interpreted from Landsat TM images and a 1:2,500,000 vegetation map [47]. The vegetation map was resampled with the same resolution as the land-use map, and the each forest pixel in the land-use map was assigned a forest type based on the vegetation map using the cell-to-cell function in ArcGIS [47]. The land-cover map has been validated with the site data and other satellite data (i.e., GLC2000) [47]. The 8-day leaf area index (LAI) dataset at a 1 km resolution from 2001 to 2010 were inverted using the MODIS reflectance product (MOD09A1 V05) and the 4-scale geometric optical model [49; 55]. The locally adjusted cubic-spline capping (LACC) method [56] was used to smooth abrupt fluctuations of inversed 8-day LAI caused by residual cloud contamination [49]. In the BEPS model, we assumed the LAI was unchanged in the 8 day interval. The quality of retrieved LAI has been validated with the observation data in 6 regions in China [49]. The remote-sensing data that were of high quality can be used to drive the simulation of carbon and water cycle of forests in south-western China.  
**Soil data**: The soil spatial database of physical soil properties for sand, silt, and clay fractions at a 1 km resolution was generated using the soil map and soil profiles from the Second National Soil Survey of China [57]. In this study, the soil texture datasets were used to determine hydrological parameters including the wilting point (water potential at 1500 kPa), field capacity (water potential at 33 kPa), porosity, saturated hydrological conductivity, air entry water potential and suction at saturation [49].
## 4 Discussion  
The results of this study are consistent with previous research on phosphogypsum PG treatments, demonstrating the effectiveness of zeolite powder and CaO in reducing soluble phosphorus and fluorine impurities. The observed reductions in impurity concentrations support the hypothesis that physical adsorption and chemical modification can work synergistically to mitigate the environmental risks associated with PG. However, the findings also highlight areas requiring further investigation. For instance, while the study successfully reduced the impurity concentrations, it did not assess the long-term stability of these reductions under variable environmental conditions. Additionally, the minimal impact on the PG's structural properties suggests that although the treatment preserves  
Figure 5: FT-IR spectra of PG under different treatment conditions.  
Figure 6: (**a**) Specific surface area and (**b**) pore size analyses of PG samples under different treatment conditions.  
its porosity, further studies are needed to evaluate how these changes might affect the mechanical strength of PG, particularly in construction or soil applications. PG-based cementitious materials are becoming increasingly important for large-scale PG utilization. This technology typically involves using PG as an aggregate, mixed with cementitious materials and water to prepare a paste, which is then used for backfilling in underground mines or open pits. However, phosphorus and fluoride can retard the hardening of PG and weaken its structure. The soluble phosphorus also significantly reduces the hydration rate. Fluoride, in particular, impacts the strength development by interfering with the pore structure and formation of hydration products, which results in the need for increased amounts of cementitious materials to compensate for the strength loss, thereby raising costs. Future research should focus on optimizing the treatment process for large-scale applications, evaluating the long-term leaching behavior of treated PG, and assessing the economic viability and environmental impact of using zeolite and CaO on an industrial scale. Furthermore, exploring alternative, more sustainable treatment agents could further enhance the effectiveness and cost-efficiency of PG resource recovery efforts.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 9 Numerical Results  
We consider a two tier WSN covering a circular area with radius \(R_{WSN}\) with several nodes or motes equal to \(N_{m}=364\). Six topologies have been studied, respectively labeled according to the maximum number of rings, \(r_{\text{max}}=0,1,2,3,4,5\). Hence, the number of CHs in each ring are, respectively, \(1,6,12,18,24\) and \(30\) and the total number of CHs in the WSN is \(N_{c}(r_{\text{max}})=1+3r_{\text{max}}(r_{\text{max}}+1)\), respectively \(1,7,19,37,61\) and \(91\). Although some random distribution of the nodes could be considered, such as a 2-D Poisson, for the sake of illustration we assume spatial uniform distribution where the total number of nodes are equally distributed among the entire WSN area. Nodes within a given hexagon are clustered. In a given cluster one of the nodes is elected as CH and at the same time exempted from data sensing tasks. Keeping in mind this fact, and accounting for an integer number of nodes, the number of sensors per clustering, \(M_{c}(r_{\text{max}})\) is \(363\), \(51\), \(18\), \(9\), \(5\) and \(3\), see the second column of Table 7. Then, accounting the number of nodes that are finally considered, it comes to result as \(N_{m}^{{}^{\prime}}(r_{\text{max}})=N_{c}(r_{\text{max}})\times(1+\#M_{c}(r_{ \text{max}}))\), i.e., \(1\times(1+363)=364\), \(7\times(1+51)=364\), \(19\times(1+18)=361\), \(37\times(1+9)=370\), \(61\times(1+5)=366\) and \(91\times(1+3)=364\) which show a small difference of, respectively \(0,0,-3,6,2\) and \(0\) nodes, differences that we believe are not significant for our numerical evaluation.  
For the traffic model, the probability of activity per each sensor has been set to \(p_{act}=0.001\) and, adopting the FSA protocol during the CONT sub-frame, the permission probability equal to \(r=1\)[33]. For each one of the six scenarios we did an exhaustive search to find the most suitable parameters\(N_{msc}\), \(N_{intra}\), \(N_{mst}\) and \(N_{inter}\); they are given in Table 7. Since each mode or member of the group can hold at most one packet at a time not all the offered traffic is carried. According to the set of parameters given in the first three columns of Table 7, the ratio between the carried and the offered traffic equals to 0.91900, 0.95164, 0.98941, 0.99200, 0.99283 and 0.99319 for \(r_{\text{max}}\) equals to \(0,1,2,3,4\) and \(5\), respectively. From an engineering point of view we can assume that the carried traffic is roughly the same in the six scenarios. Then, from the traffic load balance principle, see Figure 4 and Equation (10), the traffic carried by each CH (normalized to 1) is shown in Table 7. As expected, the traffic load supported by the CHs near the sink is much greater than that supported by the CHs that are far from the sink.
## 4 Rate-Distortion Analysis of Using CS Encoding in WSN
### Rate--Distortion Results
One can observe that the RD performance improves as \(N\) increases. However, for the envisioned scenario the trade-off between the length of the signal block and the energy consumption of sensor nodes should be considered instead.
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
###### Abstract  
This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters' uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional-integral-derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.  
ANFIS; flyback converter (FC); robust; fuzzy logic control (FLC); PID +
Footnote †: journal: Applied Software and Energy
## 3 Dynamic Response Analysis of RPC Columns Under Coupled Fire and Explosion
### Reinforcement Rate  
The reinforcement rates of 0.7%, 1.5%, and 2% were selected to analyze the effect of reinforcement rate on the blast resistance of RPC columns. The time course curves of the mid-span displacement of RPC columns with different reinforcement rates are shown in Figure 12, which shows that the slope of the curves decreases significantly with the increase in reinforcement rate, indicating that increasing the reinforcement rate effectively slows down the deformation of RPC columns.  
The maximum deformation and residual deformation of RPC columns based on the reinforcement rate are shown in Figure 13, from which it can be seen that the maximum  
Figure 11: Plot of horizontal displacement with axial pressure ratio in RPC column span.  
Figure 12: Horizontal displacement–time course curves of columns in RPC columns span under columns with different reinforcement ratios.  
deformation of RPC columns with a reinforcement rate of 2% is 6.13 mm, and the residual deformation is 2.36 mm, representing decreases of 8.5% and 35.5% compared to the maximum deformation of RPC columns with a reinforcement rate of 1.5%, which is 6.70 mm, and the residual deformation of these RPC columns, with residual deformation values of 3.66 mm and 8.22 mm, and a decrease of 35.5% compared to the maximum deformation of RPC columns with a reinforcement rate of 0.7%, which is 8.22 mm. The maximum deformation of 8.22 mm and the residual deformation of 6.36 mm of RPC columns with a 0.7% reinforcement rate decreased by 25.4% and 62.8%, respectively. The above results indicate that increasing the reinforcement rate can enhance the displacement restraint ability of the longitudinal reinforcement on RPC columns, thus reducing the maximum deformation and residual deformation of RPC columns within the span.
# Unique Features of Nonlocally Nonlinear Systems with Oscillatory Responses
## 4 Characteristics of Solitons  
The MI of the nonlocally nonlinear system (7) always exists for cases of \(s=\pm 1\)[31]. In consequence, due to the MI [36], the dark solitons [27; 37] in self-defocusing sides of such a nonlocally nonlinear system are unstable [27], while the bright solitons existing in self-focusing sides can propagate stably. Concretely, in the case of \(s=-1\), self-focusing and self-defocusing nonlinearities are, respectively, exhibited in high (\(\sigma_{0}>\sigma_{c}\)) and low (\(\sigma_{0}<\sigma_{c}\)) ranges of the degree of nonlocality, while in the case of \(s=1\), the self-focusing and self-defocusing properties are on the contrary. In the following, we will review our works on the bright solitons in the self-focusing sides of such a system described by Equation (7) in cases of \(s=\pm 1\). In addition to fundamental solitons [27; 28], there are new kinds of solitons including the in-phase and out-of-phase bound-state solitons [29] and multi-peak (more than four peaks) solitons [30] in the nonlocally nonlinear system with oscillatory responses.  
The variational approach is used to obtain the approximate solutions of bright solitons, including the fundamental solitons [28] and and Hermite-Gaussian-type multi-peak solitons [30]. The variational results have been confirmed by the numerical ones. We used the imaginary time evolution method to iterate the fundamental solitons [27; 28] and the in-phase and out-of-phase bound-state solitons [29]. To iterate the multi-peak solitons with arbitrary peak numbers [30], we specifically developed a perturbation-iteration method [38].  
Figure 7: Evolution of stable solution when \(w_{m}=1\) at different modulation frequencies: (**a**) \(k_{x}=1.6\); (**b**) \(k_{x}=1.4\) (After Ref. [32]).
## 3 Results
### GPP Increased the mRNA Expression of INOS, TNF-\(\alpha\) and IL-10  
To further explore the effect of GPP on NO release function, the mRNA expression of iNOS at the concentrations of 100 \(\mu\)g/mL of GLP, PUP and GPP were determined by RT-qPCR, respectively. As shown in Figure 2a, LPS significantly increased the mRNA expression of iNOS. Compared with the control group, the mRNA expression of iNOS in experimental groups was also significantly enhanced, and the mRNA expression of iNOS in GPP group was significantly higher than that in GLP group and PUP group. The results indicated that the increased release of NO in GPP might be related to the increased mRNA expression of iNOS.  
We also detected the mRNA expression of related factors such as TNF-\(\alpha\), IL-6 and IL-10 in each group at the concentration of 100 \(\mu\)g/mL. LPS significantly stimulated the mRNA expression of TNF-\(\alpha\) and IL-10 (Figure 2b,c). Moreover, the TNF-\(\alpha\) mRNA level was significantly stimulated in each experimental group compared with the control group (Figure 2b). Among the three groups, GPP had the strongest effect on stimulating macrophages of TNF-\(\alpha\) mRNA expression, although there was no  
Figure 1: The effect of _Ganoderma lucium_ polysaccharides (GLP), _Polyporus unbellatus_ polysaccharides (PUP) and a polysaccharide composition consisting of GLP on phagocytosis and NO production in RAW264.7 cells, respectively. (**a**) Phagocytosis of macrophages was detected by neutral red assay. (**b**) The release of nitric oxide (NO) was detected by Griess assay in macrophages. Data were expressed as mean \(\pm\) SEM, \({}^{\star}\)\(p\)\(<\) 0.05, \({}^{\star\star}\)\(p\)\(<\) 0.01, \({}^{\star\star}\)\(p\)\(<\) 0.001 vs. control group, \({}^{\blacktriangle}\)\(p\)\(<\) 0.05, \({}^{\blacktriangle}\)\(p\)\(<\) 0.01 vs. GPP group.  
Figure 2: The effect of GLP, PUP and a polysaccharide composition consisting of GLP and PUP in a ratio of 3:1 (GPP) on iNOS, TNF-\(\alpha\), IL-10 and IL-6 mRNA expression, respectively. The mRNA expression level of iNOS (**a**), TNF-\(\alpha\) (**b**), IL-10 (**c**) and IL-6 (**d**). Data were expressed as mean \(\pm\) SEM, \({}^{\star}\)\(p\)\(<\) 0.05, \({}^{\star\star}\)\(p\)\(<\) 0.01, \({}^{\star\star}\)\(p\)\(<\) 0.001 vs. control group, \({}^{\blacktriangle}\)\(p\)\(<\) 0.05, \({}^{\blacktriangle}\)\(p\)\(<\) 0.01 vs. GPP group.  
significant difference. Figure 2c showed that three experimental groups also significantly increased the mRNA expression of IL-10, and the PPS group had the strongest effect on stimulating macrophages of IL-10 mRNA expression. There were no significant changes in the expression of IL-6 mRNA (Figure 2d). The results indicated that the immunomodulatory effect of GPP on macrophages included stimulation of some cytokine secretion.
## 4 Suggested Modifications of RQD
### RQD Derived from Discrete Fracture Network (DFN) Modeling  
Discrete fracture networks (DFNs) are synthetic stochastic models used to represent naturally fractured rock masses. Data from multiple sources, including 2D rock exposures and 1D boreholes, are used to build these models. Relatively undisturbed rock core samples from high-quality drill coring provide information about subsurface conditions. Nonetheless, there is a special benefit to observations made from exposed rock faces at or close to the project site. They make it possible to quantify important fracture parameters directly, such as fracture length, spacing, and discontinuity orientation--all of which are essential for precise modeling and analysis.  
A strong quantitative classification system that incorporates the discrete fracture network (DFN) approach was presented by [PERSON] et al. [25]. This method significantly enhances the ability to account for the complex scale and anisotropic effects present in rock masses. By leveraging DFNs, the system provides a more comprehensive understanding of fracture patterns, improving the accuracy of modeling the connectivity within natural fracture networks. This leads to a better reflection of the real behavior of fractured rock masses in engineering and geological applications, offering improved predictions for stability and performance.  
Generating a discrete fracture network (DFN) model typically involves defining four key fracture properties (as outlined in Table 8). For a comprehensive explanation of the methodology used to create calibrated DFN models, readers are encouraged to consult the detailed work by [26].  
Figure 11: Plots for the tested 3 m long core sections with varying values of n [23].  
Using the parameters outlined in Table 8, a conceptual rock mass model was created to represent a homogeneous geotechnical domain, free of faults and shear zones. An example of the DFN model is depicted in Figure 12. Also, in this method, the cubic model, with dimensions of 30 m in height, length, and width, includes three orthogonal boreholes centered within the model to condition fractures to a specified P\({}_{10}\) value [26].  
A novel approach to quantitatively ascertain the ideal threshold for the quantity of virtual boreholes and models in rock mass characterization was put out by [PERSON] et al. [26]. Their strategy combines class ratio analysis with the Confidence Neutrosophic Number Cubic Value (CNNCV) method. By defining this threshold, their method effectively produced a representative rock quality designation (RQD) for a given model size. Additionally, recognizing the influence of model size on RQD simulations, they established the proper model size barrier by introducing a strategy based on class ratio analysis. Afterward, the representative RQD for the whole rock mass was considered to be the one that corresponded to this threshold.
## 4 The Propose Transmission Optimization Algorithm
### The Complexity of the Proposed Algorithm  
Here, we analyze the complexity of the proposed algorithm. According to the description of implementation for the proposed algorithm, each sensor node needs to execute the following operations. First, the transition probability is required to be calculated. In this procedure, we consider the complexity of the calculation is \(U\). Moreover, the main calculation in Algorithm has the calculation of utility in Equation (23) and regret value in Equation (24). The corresponding complexity is denoted by \(C_{u}\) and \(C_{r}\). We know the iteration number is \(T\). Thus, the total computational complexity is \(U+T(C_{u}+C_{r})\). In terms of energy consumption, we assume the consumed energy of the computation is \(E_{c}\). In procedure of implementation, the messages mainly have Information and State Collection Messages, Node and Action Selection Messages, State and Strategy Information Messages, Utility Information Message, Sensing Information Message, and Confirmation Message. We assume the energy consumptions of these messages are denoted by \(E_{IS}\), \(E_{NA}\), \(E_{SS}\), \(E_{UII}\), \(E_{SI}\), and \(E_{CM}\). The total energy consumption is \(E_{c}+E_{IS}+E_{NA}+E_{SS}+E_{UII}+E_{SI}+E_{CM}\). We know from that the CB itself is a technique of improving energy efficiency and the consumed energy of each node can be reduced by an order of \(\frac{1}{M}\) for the consumed energy without using CB. From the implementation scheme, the State and Strategy Information Messages and Sensing Information Message are transmitted to sink node by CB. Therefore, the proposed algorithm can reduce the energy consumption of \(\frac{M-1}{M}(E_{SS}+E_{SI})\).
## 1 Introduction
Taking the satellite attitude determination system as an example, the scientificity and effectiveness of the proposed method, as well as its superiority in analyzing nonlinear faults, are verified. The fault information and properties contained in the fault manifold geodesics are deeply excavated, and information geometry theory is used to analyze some essential problems in the process of fault development and evolution and to lay the foundation for future research on efficient fault detection, diagnosis, design, and optimization methods.