esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 2 PPP Model and Evaluation Index  
The traditional PPP function model is a combination of eliminating the ionosphere between the dual-frequency pseudo-distance and the phase observation. When a GNSS precision product is used, the observation equation is [33; 34]:  
\[\left\{\begin{array}{c}P_{IF}^{S}=\rho^{S}+c\widehat{d}\widehat{t}^{S}+T^{S} +\varepsilon(P_{IF}^{S})\\ L_{IF}^{S}=\rho^{S}+c\widehat{d}\widehat{t}^{S}+T^{S}+\lambda^{S}\overline{N}^ {S}+\varepsilon(L_{IF}^{S})\end{array}\right. \tag{1}\]  
where:  
\(S\) = satellite system;  
\(IF\) = ionospheric cancellation combination;  
\(P\) = observation value of pseudo-distance;  
\(L\) = phase observation;  
\(\rho\) = geometric distance;  
\(c\) = speed of light;  
\(T^{S}\) = tropospheric delay;  
\(\widehat{d}\widehat{t}^{S}\) = receiver clock difference;  
\(\lambda^{S}\) = wavelength;  
\(\overline{N}^{S}\) = ambiguity;  
\(\varepsilon(P_{IF}^{S})\) = pseudo-range observation noise;  
\(\varepsilon(L_{IF}^{S})\) = phase observation noise.  
The error caused by the earth's tides, phase entanglement and earth's rotation have been corrected according to the corresponding model and therefore are not listed in the formula.  
Inter-system Bias (ISB) parameter estimation is required when multi-system GNSS combined PPP is carried out. The observation equation of dual-system combined PPP is:  
\[\left\{\begin{array}{c}P_{IF}^{B}=\rho^{B}+c\widehat{d}\widehat{t}^{B}+T^{ B}+\varepsilon(P_{IF}^{B})\\ L_{IF}^{B}=\rho^{B}+c\widehat{d}\widehat{t}^{B}+T^{B}+\lambda^{B}\overline{N} ^{B}+\varepsilon(L_{IF}^{B})\\ P_{IF}^{S_{i}}=\rho^{S_{i}}+c\widehat{d}\widehat{t}^{B}+ISB^{B,S_{i}}+T^{S_{ i}}+\varepsilon(P_{IF}^{S_{i}})\\ L_{IF}^{S_{i}}=\rho^{S_{i}}+c\widehat{d}\widehat{t}^{B}+ISB^{B,S_{i}}+T^{S_{ i}}+\lambda^{S_{i}}\overline{N}^{S_{i}}+\varepsilon(L_{IF}^{S_{i}})\end{array}\right. \tag{2}\]  
where:  
\(B\) = reference satellite system;  
\(S_{i}\) = rest of the constellations except the base system, \(i\) = 1,2,3;  
\(IBS^{B,S_{i}}\) = system deviation between the base system and the rest of the system.  
As the tropospheric error, ionospheric error and multipath error are strongly correlated with the satellite altitude angle, in order to reduce the impact of errors on PPP positioning performance, the altitude angle stochastic model is adopted in this paper. As the noise and multi-path errors of the satellite observations are concentrated in the low altitude satellite, in order to reduce the weight of the low altitude satellite observations, the piecewise
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### PAPR Performances
#### 5.1.1 PC-OFDM PAPR with Variable Smoothness Parameter  
Firstly, we studied the influence of the smoothness parameter \(\beta\) with the variable modulation scheme. The results are presented by the gain in dB at CCDF equal to \(10^{-4}\ dB\).  
The obtained results for the 4-QAM modulation with variable CRs are presented in Figure 9. As we can see in this figure, the gain varied between 6 and 9.4 dB and was in a range between 0.4 and 1 for \(\beta\). When \(\beta\) was higher, the gain was invariable.  
Figure 10 presents the gain in term of PAPR at CCDF = \(10^{-4}\) for the 16-QAM modulation. The obtained gain was between 2 and 9 dB. The best results were obtained when \(\beta\) was between 0.5 and 1 for all CR values. An unvarying gain was obtained when \(\beta\) was higher.  
Figure 11 regroups the obtained gain in PAPR at CCDF = \(10^{-4}\) with PC-OFDM and the 64-QAM modulation. In this case, the maximum gain was less than 9 dB with a minimum equal to 1.98 dB for different CRs. The gain increased with the value of \(\beta\) until it reached a maximum value when \(\beta\) was between 0.4 and 1. After this, the gain decreased while \(\beta\) increased before becoming stable.  
Figure 9: Palm Clipping (PC)-OFDM 4-QAM with variable Clipping Ratio (CR) and smoothness.  
From this study and previous results of the variable smoothness parameter \(\beta\) with different modulation types and CR, we can deduce that the maximum gain in terms of PAPR is given when \(\beta\) is between 0.4 and 1, which is almost the same for the higher values of \(\beta\). Furthermore, increasing the constellation, our method makes it possible to reduce the values of PAPR as we reduce the CR value. In the rest of the study, we set the value of \(\beta\) at 0.5 and 5 and study the variation of CR.
# Activation of Persulfate for Groundwater Remediation:
## 4 Factors Affecting the Application of Persulfate in ISCO
### Persulfate Persistence  
The persistence of the persulfate in porous media after injection into the subsurface is crucial. The persistence of persulfate is frequently determined by tracking the decomposition of persulfate at a specific region over time [26]. In a practical remediation project, the decline of groundwater quality parameters (e.g., ORP) are also sometimes employed as indirect indicators of persulfate persistence [128].  
The persistence of persulfate varies greatly in subsurface and is affected by the activation methods. [PERSON] et al. (2010) evaluated the stability of the inactivated persulfate in the presence of multiple aquifer solids and the result showed that the decomposition of inactivated persulfate remained sluggish in all aquifer, with half-lives ranging from 2 to 600 d [129]. The stability of persulfate will rise with either an increase in ionic strength or the concentration of persulfate per unit mass of aquifer solids [119]. [PERSON] (2008) studied the persulfate persistence under thermal-activated conditions, and observed that the thermolysis half-life of persulfate was over 143.6 days, which was reduced to 4.1 days at 50 \({}^{\circ}\)C and 0.36 day at 70 \({}^{\circ}\)C [26]. [PERSON] (2014) further compared the stability of persulfate under peroxide activation and alkaline activation methods [130]. A rapid decrease in persulfate concentrations was observed at peroxide activation and the consumption rate of persulfate (k\({}_{\text{obs}}\)) was 100 times higher than inactivated persulfate. In contrast, alkaline activation had a much smaller effect on the stability of persulfate since k\({}_{\text{obs}}\) was only a quarter times higher than inactivated persulfate [130].  
For site applications, several pilot and field tests of persulfate persistence were also employed. For persulfate under the alkaline activation, the concentration of persulfate was reduced from 28 g/L to 2 g/L after 40 days [108]. Another report demonstrated that the persulfate can be sustained for nearly one week when heat was applied as an activator [118]. [PERSON](2017) demonstrated that even without activation, the persulfate was almost completely depleted after 14 days of injection [120]. In comparison to the outcomes of laboratory-scale investigations, the persistence of persulfate in practical applications is significantly lower. The main reason is probably due to the groundwater movement and the substantial consumption by soil organic matter in the aquifer sediment.
## 4 Conclusions  
According to the analysis carried out, the following conclusions can be derived:  
Corrosion pitting-like defects have been the cause of the failures found in the piping system. Additionally, the development of the corrosion process has been very fast (5 months).  
* The robotic video-inspection confirmed that generalized pitting corrosion with thorough-wall pitting is present in the whole facility to a higher or lower degree. Pockets of gas associated with some of the pits can be observed, while in others \"mushroom-shaped\" marks show the previous presence of gas bags.
* The chemical composition of the material fits the requirements of AISI 316L grade stainless steel, so material deficiencies are not the cause of the corrosion processes.
* There is also evidence (level marks of corrosion products) of the presence of stagnant water (corrosion products characterized by a reddish hue under the level mark). The culture of the remains on the stagnant water samples from the facility has been positive in terms of the presence of sulfate-reducing bacteria (SRB).
* During the SEM analysis, in the more developed pits, a preferentially radial direction was not observed, as would occur in the case of pitting by chlorides, discarding this type of process; the pits follow an intricate path from the inner face to the outer one. Additionally, it revealed no evidence of sensitization in the steel (roughly constant Cr content in grains and boundaries), but the presence of sulfur was detected in non-negligible quantities in the corrosion products.  
Based on this, it can be concluded that the observed process fits within the so-called microbiologically induced corrosion (MIC), and it can be affirmed that this process is promoted by the presence and proliferation of sulfate-reducing bacteria (SRB). On the other hand, no signs of aggressiveness in the environment different from those typical of port areas were found. Finally, the typology of the detected defects is similar to that of other MIC cases reported in the literature, as well as the rest of the characteristics: type of pits, corrosion products, time to failure, etc., supporting the aforementioned conclusion.  
**Author Contributions:** Conceptualization, B.A., R.L. and J.A.A.; methodology, B.A., R.L., J.A.A. and S.C.; formal analysis, B.A., R.L., J.A.A. and S.C.; investigation, B.A., R.L., J.A.A., S.C. and X.M.-V.; writing--original draft preparation, B.A., R.L., J.A.A. and S.C.; writing--review and editing, B.A., R.L., J.A.A., S.C. and X.M.-V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
### Position-Based Dynamics  
#### 2.2.1 Key Characteristics of Position-Based Dynamics  
The PBD algorithm offers a method for controlling deformable objects by directly manipulating their positions rather than computing acceleration through forces. By bypassing the intermediate step of velocity calculation, this approach ensures computational stability and provides significant advantages in collision handling, particularly in resolving penetration issues between objects. The key characteristics of the PBD algorithm are as follows:  
1. Position-Based Approach: The PBD algorithm enforces physical constraints by directly adjusting the positions of objects, rather than utilizing a force-based approach. This allows for physical simulation to be achieved through direct positional modifications.
2. Stability: PBD prevents excessive energy accumulation due to external forces by directly correcting position values without the need for velocity calculations. This approach effectively reduces instability in the system.
3. Efficiency: The PBD algorithm is notably efficient, especially in collision handling. At each time step, collision constraints are introduced to prevent object penetration. [PERSON] [36] demonstrated that in scenarios involving self-collision, such as cloth movement and overlap, PBD ensures real-time performance, compared to traditional force-based methods.
## 4 Discussion
The enzyme activity of CeAl, a thermostable and pH-stable cellobiohydrolase from _Neocalimastix patriciarum_ J11, was greatly enhanced by Co\({}^{2+}\)[29]. _RuCelA_, encoding a bifunctional xylanase/endoglucanase, was reported in yak rumen microorganisms [30]. The presence of Co\({}^{2+}\) greatly enhanced the endoglucanase activity of RuCelA, but inhibited its xylanase activity. However, further investigation is required to confirm that those activities are dependent on Co\({}^{2+}\). CeAl\({}_{93}\) contains a well-conserved glycosyl hydrolase family 5 cellulase domain (pfam00150) [31], spanning Asn-79 and Ile-316, with an E-value of 1.04 \(\times\) e\({}^{-57}\). Among the proteins sharing high homology with CeAl\({}_{93}\), chain A endoglucanase (5 IHS\({}_{-}\)A) reported in _Cytohuga hutchinsonii_[32] has the highest identity (54%) at the amino acid sequence level. This strain is unique in that it does not have a gene encoding a cellobiohydrolase, and thus, the chain A endoglucanase is thought to be responsible for the initial digestion of cellulose fibers in amorphous regions, facilitating the generation of oligomers to be transported into the periplasm for further hydrolysis. In contrast to chain A endoglucanase, CeAl\({}_{93}\) acts more efficiently on cellulooligosaccharides than CMC, suggesting that it is not the main enzyme responsible for the initial digestion of cellulose fibers. We found two candidate genes for endo-\(\beta\)-1,4-glucanase and two candidate genes for \(\beta\)-D-glucosidase, but none for cellobiohydrolase in the genomic sequence of _Cellulophaga_ sp. J9-3, which is similar to the genome of _C. Intchinsonii_[32]. Therefore, a more precise understanding of the cellulose degradation system of _Cellulophaga_ sp. J9-3 at the molecular level is required to broaden our knowledge to use cellulose. CeAl\({}_{93}\) is the second biochemically characterized enzyme in the cellulase family produced by the genus _Cellulophaga_. It has a unique cleavage sequence for protein maturation and requires Co\({}^{2+}\) as a cofactor. Therefore, this report will be helpful in understanding the molecular and biochemical characteristics of many enzymes listed in GenBank with high homology to CeAl\({}_{93}\). Moreover, CeAl\({}_{93}\) has an optimal temperature of 60 \({}^{\circ}\)C, which will be an advantage for its application in cellulose degradation processes. Conceptualization, W.-J.C. and S.-K.H. ; Methodology, D.S.K. and W.-J.C.; Investigation, D.S.K. ; Resources, W.-J.C.; Data Curation, S.-K.H. ; Writing--Original Draft Preparation, W.-J.C.; Writing--Review & Editing, S.-K.H. ; Funding Acquisition, W.-J.C. This research was funded by a grant from the National Institute of Biological Resources (NIBR), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIBR201920101). The authors declare no conflict of interest.
## 6 Conclusions  
This study investigated the JLAS problem for mobile UNs using sequential one-way TOA measurements. We formulate the JLAS as a CWLS optimization problem, addressing it by projecting the equation into the nullspace of the redundant variables introduced during pseudo-linearization, resulting in a coarse solution. A refinement step follows to enhance accuracy, and this solution is termed CFPS. As CFPS is suboptimal, we propose an RIS from the perspective of FG. Through introducing a damping factor into the iteration, the robustness to measurement noise, position errors of the ANs, and initial guess are significantly enhanced. Theoretical analysis proves that the proposed RIS can attain the CRLB when the noise and errors are mild. Simulations validated the analytical results and demonstrated the superiority of the RIS in terms of the RMSE, bias, and convergence probability. The RIS can maintain CRLB-level RMSEs even when the noise or error is relatively large and exhibits a lower bias than current solutions. It also has a higher probability of convergence than the GN, making it less sensitive to initial guesses. Despite the RIS's better performance compared to existing solutions, its computational complexity does not increase significantly, remaining on the same magnitude as the GN and CFJLAS. This work assumed that ANs are static and that the clock offset of each AN is perfectly known. Future research interests may include JLAS using moving ANs, anchor node placement optimization, and mitigating unknown AN clock offsets. Another challenge is high AN position uncertainty or high measurement noise, which can occur in practical applications. While the proposed RIS significantly improves robustness to AN position uncertainty and measurement noise, further study is needed in this area.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/[PERSON]/JLAS_Resilient_to_Noise_Using_One_Way_TOAs](https://github.com/[PERSON]/JLAS_Resilient_to_Noise_Using_One_Way_TOAs)]([https://github.com/[PERSON]/JLAS_Resilient_to_Noise_Using_One_Way_TOAs](https://github.com/[PERSON]/JLAS_Resilient_to_Noise_Using_One_Way_TOAs)) (accessed on 8 July 2024).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **RIS** & **GN** & **CFPS** & **CFJLAS** \\ \hline Times (s) & 60.2648 & 26.6622 & 1.8245 & 2.7523 \\ Rel. Times & 21.89 & 9.69 & 0.66 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Processing times of compared solutions.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 2 Related Work
### Traditional PPV Prediction Methods  
Ground vibrations originating from blasts are usually recorded as Peak Particle Velocity (PPV). Given its correlation with the extent of potential structural damages, PPV has been deemed to be a crucial metric, especially in environmental considerations within the realm of resource development [6; 11; 12; 14]. Over time, various methods have been devised to predict PPV. These methods can be broadly classified into experimental methods, statistical methods, and Artificial Neural Networks (ANNs). The most basic approach is the experimental method, which has been extensively employed by many researchers [13; 14; 16; 17; 18]. The PPV prediction model using this method can be expressed as  
\[V=kD^{-b} \tag{1}\]  
where \(V\) is the PPV (mm/s), \(D\) is the scaled distance (m/\(\sqrt{\text{kg}}\)), and \(k\), \(b\) are constants determined based on the specific site conditions. The scaled distance, \(D\), is given by  
\[D=\frac{R}{\sqrt{Q}} \tag{2}\]where \(R\) represents the distance from the blasting site (m) and \(Q\) denotes the square root of the amount of explosive (kg). However, given the vast number of parameters involved in real-world blasting-induced ground vibrations, the experimental method shows limitations in its accuracy [2; 20; 24].  
To overcome the limitations of experimental methods, statistical methods were introduced. These take into account a wide array of parameters, from the design of the blasts to the nature of the rock mass [10; 19; 26]. However, their predictive accuracy can be diminished when applied to new locations since they primarily rely on existing datasets [1; 3; 7].  
Emerging from the domain of artificial intelligence (AI) during the 1980s, ANN enabled massive parallel computations, mimicking the human brain's functionality. [PERSON] et al. [1] proposed an ANN-based PPV prediction model using data from 150 blasting sites, illustrating its superior predictive accuracy over traditional experimental methods. [PERSON] et al. [7] and [PERSON] et al. [27] also demonstrated high accuracy with ANN-based models. Recent innovations even incorporated advanced algorithms like particle swarm optimization (PSO) and the imperialism competitive algorithm (ICA) in PPV predictions [8; 28]. Notably, a compilation of research on ANN-based PPV predictions can be seen in Table 1 [2; 3; 7; 8; 9; 27; 28; 29]. However, the major challenge with ANN methods is the requirement for extensive datasets to build high-accuracy models. Urban quarties, due to their proximity to populated areas, face data collection challenges due to the limited opportunities for blasting.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding  
Directly solving the dual problem (10) to obtain the optimal semantic coding represented by the conditional distributions \(P_{Y_{1}}|_{X_{1}}\) and \(P_{Y_{2}}|_{X_{2}}\) is generally difficult or even intractable. Recently, a popular approach is to parameterize the coding scheme using neural networks [31]. However, estimating mutual information and Chernoff information through data sampling but without knowledge of probability distributions remains highly challenging. To address this, we employ variational methods and design discriminator networks for efficient estimation of these information-theoretic terms in the proposed DVSC model. In this section, we first construct the DVSC model and then propose a learning algorithm for efficient training and optimization.
## 3 Results and Discussion
### Carbon Sinks in Bamboo Forest Ecosystems
#### 3.1.2 Factors Affecting the Bamboo Carbon Sinks
Precipitation increases the water availability for carbon sequestration in bamboo forests, directly influencing their productivity [32]. For example, by applying the integrated terrestrial ecosystem carbon-budget model, [PERSON] et al. [46] found that bamboo forests in Zhejiang had a significant positive correlation between precipitation and NPP. [PERSON] et al. [63] also showed that precipitation was the most significant factor influencing the carbon stock of the bamboo forests in Zhejiang. Contrastingly, [PERSON] et al. [71] applied structural equation modeling with the random forest algorithm on inventory data obtained in Zhejiang from 2004 to 2014. They concluded that the mean annual precipitation had adverse effects onMoso bamboo carbon stock. This could be attributed to the exclusion of the soil carbon and atmospheric cycles in the modeling design. Topographic factors, including slope position, slope gradient, aspect, and altitude, can also affect a bamboo ecosystem's carbon pool, especially at the soil level. In Zhejiang, the surface (0-30 cm) soil organic carbon (SOC) content showed a positive correlation with the altitude and slope gradient, while the correlation switched to negative for the deeper layer (60-100 cm) [72]. Comparing the SOC contents between the northern and southern aspects in all soil layers, [PERSON] et al. [72] reported that the northern aspect contained a statistically in significant increase compared with the southern aspect.